From 0e2ae46d3fb3ca3a903dfad0fe4a809d353a0851 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:10:55 +0200 Subject: [PATCH 001/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 140 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/es/config.yaml diff --git a/content/es/config.yaml b/content/es/config.yaml new file mode 100644 index 0000000000..4aaf75b2e6 --- /dev/null +++ b/content/es/config.yaml @@ -0,0 +1,140 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 1.26. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + - + link: https://twitter.com/numpy_team + icon: twitter + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From 949cef98482bb9646d6834960690b934d0fe8d70 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:10:56 +0200 Subject: [PATCH 002/586] New translations config.yaml (Arabic) --- content/ar/config.yaml | 140 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/ar/config.yaml diff --git a/content/ar/config.yaml b/content/ar/config.yaml new file mode 100644 index 0000000000..4aaf75b2e6 --- /dev/null +++ b/content/ar/config.yaml @@ -0,0 +1,140 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 1.26. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + - + link: https://twitter.com/numpy_team + icon: twitter + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From 68b2c68ea110e37cbc7a9f1395cfa89b7c0801fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:10:57 +0200 Subject: [PATCH 003/586] New translations config.yaml (Japanese) --- content/ja/config.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index bb63338184..6b2d8b59a3 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -138,4 +138,3 @@ params: - text: プレス用資料 link: /ja/press-kit - From 6cbcf36c841f1b1bec31222c2de75b864bba50fe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:10:59 +0200 Subject: [PATCH 004/586] New translations config.yaml (Korean) --- content/ko/config.yaml | 140 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/ko/config.yaml diff --git a/content/ko/config.yaml b/content/ko/config.yaml new file mode 100644 index 0000000000..4aaf75b2e6 --- /dev/null +++ b/content/ko/config.yaml @@ -0,0 +1,140 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 1.26. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + - + link: https://twitter.com/numpy_team + icon: twitter + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From da94e41491a48d616d8fdb7d0bb0ad9eab60a96c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:00 +0200 Subject: [PATCH 005/586] New translations config.yaml (Russian) --- content/ru/config.yaml | 140 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/ru/config.yaml diff --git a/content/ru/config.yaml b/content/ru/config.yaml new file mode 100644 index 0000000000..4aaf75b2e6 --- /dev/null +++ b/content/ru/config.yaml @@ -0,0 +1,140 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 1.26. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + - + link: https://twitter.com/numpy_team + icon: twitter + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From 3f37c5b6240353821d9bcc3a5e79d5a57d506e47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:01 +0200 Subject: [PATCH 006/586] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 140 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/zh/config.yaml diff --git a/content/zh/config.yaml b/content/zh/config.yaml new file mode 100644 index 0000000000..4aaf75b2e6 --- /dev/null +++ b/content/zh/config.yaml @@ -0,0 +1,140 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 1.26. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + - + link: https://twitter.com/numpy_team + icon: twitter + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From 083489473ba31eb8dd5d6dff90ef5ab69a648db4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:02 +0200 Subject: [PATCH 007/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/es/tabcontents.yaml diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/es/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From 53c8bc43e54c572a0c63cf4d08f940e208d4ab24 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:03 +0200 Subject: [PATCH 008/586] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/ar/tabcontents.yaml diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/ar/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From 8a638ce6c16fb1110eaca504af714be77662302a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:04 +0200 Subject: [PATCH 009/586] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 250 +++++++++++++++++++++++++++++------- 1 file changed, 202 insertions(+), 48 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index e3dc2ba4ed..de3a75a312 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -86,76 +86,230 @@ params: img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: Numpy、MXNet、PyTorch、TensorFlowまたはCupyをシームレスに使用するための、テンソル学習、テンソル代数、およびそれらのテンソル計算のためのバックエンド - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Pythonを使って働くほとんどの科学者はNumPyの力を利用しています。 - text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" - librariesrow1: + libraries: - - title: 量子コンピューティング - alttext: コンピューターチップ + title: Quantum Computing + alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: 統計コンピューティング - alttext: 線グラフで、グラフが上に移動します。 + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: 信号処理 - alttext: 正と負の値を持つ棒グラフ。 + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: 画像処理 - alttext: 山々の写真 + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg - - - title: グラフとネットワーク - alttext: シンプルなグラフ + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - - title: 天文学 - alttext: 望遠鏡 + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: 認知心理学 - alttext: ギアをつけた人間の頭部 + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: + links: + - + url: https://www.psychopy.org/ + label: PsychoPy - - title: 生命情報科学 - alttext: DNAの鎖 + title: Bioinformatics + alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: ベイズ推論 - alttext: 鐘形の曲線のグラフ + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: 数学的分析 - alttext: ４つの数学記号 + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: 化学 - alttext: 試験管 + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg - - - title: 地球科学 - alttext: 地球 + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg - - - title: 地理情報処理 - alttext: 地図 + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg - - - title: アーキテクチャとエンジニアリング - alttext: マイクロプロセッサ開発ボード + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok datascience: intro: "Numpy は豊富なデータサイエンスライブラリのエコシステムの中核にあります。一般的なデータサイエンスのワークフローは次のようになります。" image1: From 3bfe6a414ec55657109632d6e82c97ace97a6dec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:06 +0200 Subject: [PATCH 010/586] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/ko/tabcontents.yaml diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/ko/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From 7963dd9500b26b99bd251cee080a53ffda58dcff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:07 +0200 Subject: [PATCH 011/586] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/ru/tabcontents.yaml diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/ru/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From 2b8edf19f27df8ac6df339d03fd0c334fcb92a89 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:08 +0200 Subject: [PATCH 012/586] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/zh/tabcontents.yaml diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/zh/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From 5704e4098163c48950466acb912d009daf07e959 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:09 +0200 Subject: [PATCH 013/586] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 250 +++++++++++++++++++++++++++++------- 1 file changed, 202 insertions(+), 48 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index a2d2b0df44..e5903d0a68 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -86,76 +86,230 @@ params: img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: Ferramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço. - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." - librariesrow1: + libraries: - - title: Computação quântica - alttext: Um chip de computador. + title: Quantum Computing + alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Computação estatística - alttext: Um gráfico com uma linha em movimento para cima. + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Processamento de sinais - alttext: Um gráfico de barras com valores positivos e negativos. + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Processamento de imagens - alttext: Uma fotografia das montanhas. + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Gráficos e Redes - alttext: Um grafo simples. + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - - title: Processos de Astronomia - alttext: Um telescópio. + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Psicologia Cognitiva - alttext: Uma cabeça humana com engrenagens. + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: + links: + - + url: https://www.psychopy.org/ + label: PsychoPy - - title: Bioinformática - alttext: Um pedaço de DNA. + title: Bioinformatics + alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Inferência Bayesiana - alttext: Um gráfico com uma curva em forma de sino. + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Análise Matemática - alttext: Quatro símbolos matemáticos. + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Química - alttext: Um tubo de ensaio. + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geociências - alttext: A Terra. + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Processamento Geográfico - alttext: Um mapa. + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg - - - title: Arquitetura e Engenharia - alttext: Uma placa de desenvolvimento de microprocessador. + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok datascience: intro: "NumPy está no centro de um rico ecossistema de bibliotecas de ciência de dados. Um fluxo de trabalho típico de ciência de dados exploratório pode parecer assim:" image1: From 788b25873c1899e5b4c2565b1ad21a0d347d9df9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:10 +0200 Subject: [PATCH 014/586] New translations 404.md (Spanish) --- content/es/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/es/404.md diff --git a/content/es/404.md b/content/es/404.md new file mode 100644 index 0000000000..7fe5d79a2c --- /dev/null +++ b/content/es/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 9bfd1ac6ef9e990f4c3a2daa14ef5121ccb84ebd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:11 +0200 Subject: [PATCH 015/586] New translations 404.md (Arabic) --- content/ar/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ar/404.md diff --git a/content/ar/404.md b/content/ar/404.md new file mode 100644 index 0000000000..7fe5d79a2c --- /dev/null +++ b/content/ar/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 67fc1a51dfb71600ebdc140314500e9f1ed3f32c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:12 +0200 Subject: [PATCH 016/586] New translations 404.md (Japanese) --- content/ja/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/404.md b/content/ja/404.md index 8e4db85255..8af76ac163 100644 --- a/content/ja/404.md +++ b/content/ja/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -おっとっと！ 間違った所にアクセスしているようです。 +Oops! You've reached a dead end. -何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 +何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 From ebb9f75189101dd6ff3feb04a3328439cc645b27 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:13 +0200 Subject: [PATCH 017/586] New translations 404.md (Korean) --- content/ko/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ko/404.md diff --git a/content/ko/404.md b/content/ko/404.md new file mode 100644 index 0000000000..7fe5d79a2c --- /dev/null +++ b/content/ko/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From a313b835d72edab37fcbcac402a4ef39e069afe7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:14 +0200 Subject: [PATCH 018/586] New translations 404.md (Russian) --- content/ru/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ru/404.md diff --git a/content/ru/404.md b/content/ru/404.md new file mode 100644 index 0000000000..7fe5d79a2c --- /dev/null +++ b/content/ru/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From b75f990e8ea7f19a3566ba93ce54eb533d3da295 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:15 +0200 Subject: [PATCH 019/586] New translations 404.md (Chinese Simplified) --- content/zh/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/zh/404.md diff --git a/content/zh/404.md b/content/zh/404.md new file mode 100644 index 0000000000..7fe5d79a2c --- /dev/null +++ b/content/zh/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From c2cda526cd46073e399c831ed6e297cffd6c818f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:17 +0200 Subject: [PATCH 020/586] New translations 404.md (Portuguese, Brazilian) --- content/pt/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/404.md b/content/pt/404.md index 627cde96d0..ac3a516791 100644 --- a/content/pt/404.md +++ b/content/pt/404.md @@ -5,4 +5,4 @@ sidebar: false Oops! Você atingiu um beco sem saída. -Se você acha que algo deveria estar aqui, você pode [abrir uma issue](https://github.com/numpy/numpy.org/issues) no GitHub. +Se você acha que algo deveria estar aqui, você pode [abrir uma issue](https://github.com/numpy/numpy.org/issues) no GitHub. From 1f899406e7b7ec186af7b20221429ccb7ed59bbe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:18 +0200 Subject: [PATCH 021/586] New translations _index.md (Spanish) --- content/es/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/es/_index.md diff --git a/content/es/_index.md b/content/es/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/es/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From 1c0eeaa11500ddd536ad729bc08d96cc37acf548 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:19 +0200 Subject: [PATCH 022/586] New translations _index.md (Arabic) --- content/ar/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ar/_index.md diff --git a/content/ar/_index.md b/content/ar/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/ar/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From 5809ce4c2ddb619c0b7f9f46e9486fe510e4e581 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:20 +0200 Subject: [PATCH 023/586] New translations _index.md (Japanese) --- content/ja/_index.md | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/content/ja/_index.md b/content/ja/_index.md index 1109f91332..29646d6db4 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -1,5 +1,5 @@ --- -title: +title: null --- {{< grid columns="1 2 2 3" >}} @@ -10,6 +10,7 @@ title = '強力な多次元配列' body = ''' NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ>ンダードです。 ''' +''' [[item]] type = 'card' @@ -17,6 +18,15 @@ title = '数値計算ツール群' body = ''' NumPyは、様々な数学関数、乱数生成器、線形代数ルーチン、フーリエ変換などを提供しています。 ''' +''' + +[[item]] +type = 'card' +title = 'オープンソース' +body = ''' +NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt)で公開されています。NumPyは活発で、互>いを尊重し、多様性を認め合う[コミュニティ](/ja/community)によって、 [GitHub](https://github.com/numpy/numpy)上でオープンに開発されていま +す. +''' [[item]] type = 'card' @@ -25,6 +35,7 @@ body = ''' NumPyは、幅広いハードウェアとコンピューティング・プラットフォームをサポートしており、分散処理、GPU、疎行列ライブラリにも対 応しています。 ''' +''' [[item]] type = 'card' @@ -32,6 +43,7 @@ title = '高パフォーマンス' body = ''' NumPyの大部分は最適化されたC言語のコードで構成されています。これによりPythonの柔軟性とコンパイルされたコードの高速性の両方 を享受できます。 +''' Enjoy the flexibility of Python with the speed of compiled code. ''' [[item]] @@ -40,13 +52,6 @@ title = '使いやすさ' body = ''' NumPyの高水準なシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高め>ることができます。 ''' - -[[item]] -type = 'card' -title = 'オープンソース' -body = ''' -NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt)で公開されています。NumPyは活発で、互>いを尊重し、多様性を認め合う[コミュニティ](/ja/community)によって、 [GitHub](https://github.com/numpy/numpy)上でオープンに開発されていま -す. ''' {{< /grid >}} From 0be3c4ec0314d67d0b702d412d59390c6012584b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:21 +0200 Subject: [PATCH 024/586] New translations _index.md (Korean) --- content/ko/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ko/_index.md diff --git a/content/ko/_index.md b/content/ko/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/ko/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From 92d0e8347ec8ccc6c050110c9c81754d1ee3adf4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:23 +0200 Subject: [PATCH 025/586] New translations _index.md (Russian) --- content/ru/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ru/_index.md diff --git a/content/ru/_index.md b/content/ru/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/ru/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From b5879d568e1092b168344eeeec489ae4f18e50fb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:24 +0200 Subject: [PATCH 026/586] New translations _index.md (Chinese Simplified) --- content/zh/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/zh/_index.md diff --git a/content/zh/_index.md b/content/zh/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/zh/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From d692284a3260f8fd0ff2e23dce4e68848c21b114 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:25 +0200 Subject: [PATCH 027/586] New translations _index.md (Portuguese, Brazilian) --- content/pt/_index.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/content/pt/_index.md b/content/pt/_index.md index 0a39687659..deb9baacd4 100644 --- a/content/pt/_index.md +++ b/content/pt/_index.md @@ -1,5 +1,5 @@ --- -title: +title: null --- {{< grid columns="1 2 2 3" >}} @@ -18,6 +18,13 @@ body = ''' O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. ''' +[[item]] +type = 'card' +title = 'Código aberto' +body = ''' +Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt) liberal, o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/pt/community) vibrante, responsiva, e diversa. +''' + [[item]] type = 'card' title = 'Interoperabilidade' @@ -29,7 +36,7 @@ O NumPy suporta um grande número de plataformas de hardware e computação, e p type = 'card' title = 'Alto desempenho' body = ''' -O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. +O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. Enjoy the flexibility of Python with the speed of compiled code. ''' [[item]] @@ -38,12 +45,6 @@ title = 'Fácil de usar' body = ''' A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. ''' - -[[item]] -type = 'card' -title = 'Código aberto' -body = ''' -Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt) liberal, o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/pt/community) vibrante, responsiva, e diversa. ''' {{< /grid >}} From 4a39b62ad7711902d1606a92c203c04f59711ba3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:26 +0200 Subject: [PATCH 028/586] New translations about.md (Spanish) --- content/es/about.md | 87 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 content/es/about.md diff --git a/content/es/about.md b/content/es/about.md new file mode 100644 index 0000000000..357bd15fe1 --- /dev/null +++ b/content/es/about.md @@ -0,0 +1,87 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
+NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} From 13d3fd3a1b0c0f03b63f3d1b62aea9297920aa0d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:27 +0200 Subject: [PATCH 029/586] New translations about.md (Arabic) --- content/ar/about.md | 87 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 content/ar/about.md diff --git a/content/ar/about.md b/content/ar/about.md new file mode 100644 index 0000000000..357bd15fe1 --- /dev/null +++ b/content/ar/about.md @@ -0,0 +1,87 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
+NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} From 06dc7eb83b309aad5d78520bb9342fa2c3d0dddb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:29 +0200 Subject: [PATCH 030/586] New translations about.md (Japanese) --- content/ja/about.md | 24 ++++++++++-------------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index d153a89b33..626a3ff721 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -3,14 +3,13 @@ title: 私たちについて sidebar: false --- -NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは[修正BSD ライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt) の条項の下で、すべての人が利用可能です。 - -NumPy は 、NumPyコミュニティやより広範な科学計算用Python コミュニティとの合意のもと、GitHub 上でオープンに開発されています。 NumPyのガバナンス方法の詳細については、 [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html) をご覧ください。 +NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは[修正BSD ライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt) の条項の下で、すべての人が利用可能です。 It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy は 、NumPyコミュニティやより広範な科学計算用Python コミュニティとの合意のもと、GitHub 上でオープンに開発されています。 NumPyのガバナンス方法の詳細については、 [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html) をご覧ください。 For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). ## 運営委員会 -Numpy運営委員会はこのプロジェクトの管理組織です。 その役割は、Numpy コミュニティと協力し、Numpyのソフトウェアサービスを確実にユーザに提供することです。 ソフトウェアパッケージとコミュニティの両方において、プロジェクトの長期的な持続可能性を保っていきます。 NumPy運営委員会は現在以下のメンバーで構成されています (姓のアルファベット順): +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): - Sebastian Berg - Ralf Gommers @@ -22,7 +21,7 @@ Numpy運営委員会はこのプロジェクトの管理組織です。 その - Melissa Weber Mendonça - Eric Wieser -過去のメンバー +Emeritus: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -41,11 +40,11 @@ Numpy プロジェクトのコアメンバーは、プロジェクトへの貢 - 開発 - ドキュメント -- トリアージ +- triage - ウェブサイト - 調査 - 翻訳 -- スプリントのメンター +- sprint mentors - 最適化 - 資金と助成金 @@ -64,10 +63,9 @@ Numpy プロジェクトのコアメンバーは、プロジェクトへの貢 NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} - ## パートナー団体 -パートナー団体は、NumPyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 +パートナー団体は、NumPyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 Current Institutional Partners include: - カルフォルニア大学 バークレー校 (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) @@ -75,16 +73,14 @@ NumPyは以下の団体から直接資金援助を受けています。 {{< partners >}} - ## 寄付 -NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 +NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. -NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. -NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 +NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 As with any donation, you should consult with your tax advisor about your particular tax situation. NumPyの運営委員会は、受け取った資金をどのように使えば良いかを検討し、使用する方法について決定します. NumPyに関する技術とインフラの投資の優先順位に関しては、[NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap) に記載されています。 {{}} - From 2541ff2a30379979664d60e2cee2a9076fd792a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:30 +0200 Subject: [PATCH 031/586] New translations about.md (Korean) --- content/ko/about.md | 87 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 content/ko/about.md diff --git a/content/ko/about.md b/content/ko/about.md new file mode 100644 index 0000000000..357bd15fe1 --- /dev/null +++ b/content/ko/about.md @@ -0,0 +1,87 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
+NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} From 63bcefd4197584cc52fda005b40c6054e6d38494 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:31 +0200 Subject: [PATCH 032/586] New translations about.md (Russian) --- content/ru/about.md | 87 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 content/ru/about.md diff --git a/content/ru/about.md b/content/ru/about.md new file mode 100644 index 0000000000..357bd15fe1 --- /dev/null +++ b/content/ru/about.md @@ -0,0 +1,87 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
+NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} From d7bbb332a86e3bd1b6540410e8ff5782b86e36cf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:33 +0200 Subject: [PATCH 033/586] New translations about.md (Chinese Simplified) --- content/zh/about.md | 87 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 content/zh/about.md diff --git a/content/zh/about.md b/content/zh/about.md new file mode 100644 index 0000000000..357bd15fe1 --- /dev/null +++ b/content/zh/about.md @@ -0,0 +1,87 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
+NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} From 7125ba40a2a35549f2a754cc019924431f203e02 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:34 +0200 Subject: [PATCH 034/586] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index 8461f68e0e..9b0796be05 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -7,10 +7,9 @@ NumPy é um projeto de código aberto que visa possibilitar a computação numé O NumPy é desenvolvido no GitHub, através do consenso da comunidade NumPy e de uma comunidade mais ampla de Python científico. Para obter mais informações sobre nossa abordagem de governança, por favor, consulte nosso [Documento de Governança](https://www.numpy.org/devdocs/dev/governance/index.html). - ## Conselho Diretor (Steering Council) -O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. Isso é feito através do trabalho com e para a comunidade NumPy em geral. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética, pelo sobrenome): +The NumPy Steering Council is the project's governing body. O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética, pelo sobrenome): - Sebastian Berg - Ralf Gommers @@ -22,7 +21,7 @@ O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo p - Melissa Weber Mendonça - Eric Wieser -Membros Eméritos: +Emeritus: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -35,7 +34,7 @@ Membros Eméritos: Para entrar em contato com o conselho diretor do NumPy, por favor envie um email para numpy-team@googlegroups.com. -## Times +## Teams A liderança do projeto NumPy trabalha ativamente na diversificação dos caminhos possíveis para contribuições.
Atualmente, o NumPy conta com os seguintes times: @@ -64,7 +63,6 @@ Veja a página sobre os [Times](/teams) para mais informações. O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} - ## Parceiros Institucionais Os Parceiros Institucionais são organizações que apoiam o projeto, empregando pessoas que contribuem para a NumPy como parte de seu trabalho. Os parceiros institucionais atuais incluem: @@ -75,7 +73,6 @@ Os Parceiros Institucionais são organizações que apoiam o projeto, empregando {{< partners >}} - ## Doações Se você achou o NumPy útil no seu trabalho, pesquisa ou empresa, por favor considere fazer uma doação para o projeto que seja compatível com seus recursos. Qualquer quantidade ajuda! Todas as doações serão utilizadas estritamente para financiar o desenvolvimento do software de código aberto da NumPy, documentação e comunidade. @@ -87,4 +84,3 @@ Doações para o NumPy são gerenciadas pela [NumFOCUS](https://numfocus.org). P O Conselho Diretor da NumPy tomará as decisões sobre a melhor forma de utilizar os fundos recebidos. Prioridades técnicas e de infraestrutura estão documentadas no [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{}} - From 8a0aa5d8b7e2b6b0664a89db66ff6570fe91e8e0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:35 +0200 Subject: [PATCH 035/586] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 39 ++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 content/es/arraycomputing.md diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md new file mode 100644 index 0000000000..0771101eff --- /dev/null +++ b/content/es/arraycomputing.md @@ -0,0 +1,39 @@ +--- +title: Array Computing +sidebar: false +--- + +_Array computing is the foundation of statistical, mathematical, scientific computing +in various contemporary data science and analytics applications such as data +visualization, digital signal processing, image processing, bioinformatics, +machine learning, AI, and several others._ + +Large scale data manipulation and transformation depends on efficient, +high-performance array computing. The language of choice for data analytics, +machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming +language library that supports large, multi-dimensional arrays and matrices, +and comes with a vast collection of high-level mathematical functions to +operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, +and it was not until a couple of years ago that several array computing +libraries showed up in succession, crowding the array computing landscape. +Many of these newer libraries mimic NumPy-like features and capabilities, and +pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + + + +**Array computing** is based on **arrays** data structures. _Arrays_ are used +to organize vast amounts of data such that a related set of values can be easily +sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is _unique_ as it involves operating on the data array _at +once_. What this means is that any array operation applies to an entire set of +values in one shot. This vectorized approach provides speed and simplicity by +enabling programmers to code and operate on aggregates of data, without having +to use loops of individual scalar operations. From 8bc360aafb7430e142c7ac705a14410d3977064b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:36 +0200 Subject: [PATCH 036/586] New translations arraycomputing.md (Arabic) --- content/ar/arraycomputing.md | 39 ++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 content/ar/arraycomputing.md diff --git a/content/ar/arraycomputing.md b/content/ar/arraycomputing.md new file mode 100644 index 0000000000..0771101eff --- /dev/null +++ b/content/ar/arraycomputing.md @@ -0,0 +1,39 @@ +--- +title: Array Computing +sidebar: false +--- + +_Array computing is the foundation of statistical, mathematical, scientific computing +in various contemporary data science and analytics applications such as data +visualization, digital signal processing, image processing, bioinformatics, +machine learning, AI, and several others._ + +Large scale data manipulation and transformation depends on efficient, +high-performance array computing. The language of choice for data analytics, +machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming +language library that supports large, multi-dimensional arrays and matrices, +and comes with a vast collection of high-level mathematical functions to +operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, +and it was not until a couple of years ago that several array computing +libraries showed up in succession, crowding the array computing landscape. +Many of these newer libraries mimic NumPy-like features and capabilities, and +pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + + + +**Array computing** is based on **arrays** data structures. _Arrays_ are used +to organize vast amounts of data such that a related set of values can be easily +sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is _unique_ as it involves operating on the data array _at +once_. What this means is that any array operation applies to an entire set of +values in one shot. This vectorized approach provides speed and simplicity by +enabling programmers to code and operate on aggregates of data, without having +to use loops of individual scalar operations. From cc292e2653670de114bed865290e8fd7eae3b75e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:38 +0200 Subject: [PATCH 037/586] New translations arraycomputing.md (Japanese) --- content/ja/arraycomputing.md | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/content/ja/arraycomputing.md b/content/ja/arraycomputing.md index 7713e7e0f2..7b60583a4b 100644 --- a/content/ja/arraycomputing.md +++ b/content/ja/arraycomputing.md @@ -3,19 +3,27 @@ title: 配列演算 sidebar: false --- -*配列演算は統計、数学、科学計算の基礎です。可視化、信号処理、画像処理、生命情報学、機械学習、人工知能など、現代のデータサイエンスやデータ分析の様々な分野で配列演算は中核を担っています。* +_Array computing is the foundation of statistical, mathematical, scientific computing +in various contemporary data science and analytics applications such as data +visualization, digital signal processing, image processing, bioinformatics, +machine learning, AI, and several others._ -大規模なデータ処理やデータ変換には、効率的な配列演算が重要です。 データ分析や、機械学習、効率的な数値計算に最適な言語のひとつは **Python** です。 +Large scale data manipulation and transformation depends on efficient, +high-performance array computing. The language of choice for data analytics, +machine learning, and productive numerical computing is **Python.** **Num**erical **Py**thon: NumPyは、Pythonにおけるデファクトスタンダードなライブラリであり、大規模な多次元配列や行列、そして、それらの配列を処理する様々な分野の数学ルーチンをサポートしています。 2006年にNumPyが発表されてから、2008年にPandasが登場し、その後、数年間にいくつかの配列演算関連のライブラリが次々と現れるようになりました。 これらの新しい配列演算ライブラリの多くは、NumPyの機能や能力を模倣しており、機械学習や人工知能向けの新しいアルゴリズムや機能を持っています。 +Many of these newer libraries mimic NumPy-like features and capabilities, and +pack newer algorithms and features geared towards machine learning and artificial intelligence applications. arraycl +src="/images/content_images/array_c_landscape.png" +alt="arraycl" +title="配列演算の概略" /> -**配列演算** は **配列** のデータ構造に基づいています。 *配列* は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 +**配列演算** は **配列** のデータ構造に基づいています。 _配列_ は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 大規模なデータ処理やデータ変換には、効率的な配列演算が重要です。 データ分析や、機械学習、効率的な数値計算に最適な言語のひとつは **Python** です。 -配列演算は *一度に* 配列のデータの複数の要素を操作するため、 * ユニーク* な処理と言えます。 これは、配列操作が一回の処理で、配列内の 全ての値に適用されることを意味しています。 このベクトル化手法は、速さと単純さという恩恵をもたらします。 プログラマーはループを回して個々の要素のスカラー演算を行うことなく、データの集合を操作しコーディングすることができるのです。 +_配列演算は統計、数学、科学計算の基礎です。可視化、信号処理、画像処理、生命情報学、機械学習、人工知能など、現代のデータサイエンスやデータ分析の様々な分野で配列演算は中核を担っています。_ What this means is that any array operation applies to an entire set of +values in one shot. 配列演算は _一度に_ 配列のデータの複数の要素を操作するため、 \* ユニーク\* な処理と言えます。 これは、配列操作が一回の処理で、配列内の 全ての値に適用されることを意味しています。 このベクトル化手法は、速さと単純さという恩恵をもたらします。 プログラマーはループを回して個々の要素のスカラー演算を行うことなく、データの集合を操作しコーディングすることができるのです。 From 35564f33a5ff589c6fd984691020e600a49c1047 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:39 +0200 Subject: [PATCH 038/586] New translations arraycomputing.md (Korean) --- content/ko/arraycomputing.md | 39 ++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 content/ko/arraycomputing.md diff --git a/content/ko/arraycomputing.md b/content/ko/arraycomputing.md new file mode 100644 index 0000000000..0771101eff --- /dev/null +++ b/content/ko/arraycomputing.md @@ -0,0 +1,39 @@ +--- +title: Array Computing +sidebar: false +--- + +_Array computing is the foundation of statistical, mathematical, scientific computing +in various contemporary data science and analytics applications such as data +visualization, digital signal processing, image processing, bioinformatics, +machine learning, AI, and several others._ + +Large scale data manipulation and transformation depends on efficient, +high-performance array computing. The language of choice for data analytics, +machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming +language library that supports large, multi-dimensional arrays and matrices, +and comes with a vast collection of high-level mathematical functions to +operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, +and it was not until a couple of years ago that several array computing +libraries showed up in succession, crowding the array computing landscape. +Many of these newer libraries mimic NumPy-like features and capabilities, and +pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + + + +**Array computing** is based on **arrays** data structures. _Arrays_ are used +to organize vast amounts of data such that a related set of values can be easily +sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is _unique_ as it involves operating on the data array _at +once_. What this means is that any array operation applies to an entire set of +values in one shot. This vectorized approach provides speed and simplicity by +enabling programmers to code and operate on aggregates of data, without having +to use loops of individual scalar operations. From 4de592818571d5bbc817d6d7ee29401ce8503c3a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:40 +0200 Subject: [PATCH 039/586] New translations arraycomputing.md (Russian) --- content/ru/arraycomputing.md | 39 ++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 content/ru/arraycomputing.md diff --git a/content/ru/arraycomputing.md b/content/ru/arraycomputing.md new file mode 100644 index 0000000000..0771101eff --- /dev/null +++ b/content/ru/arraycomputing.md @@ -0,0 +1,39 @@ +--- +title: Array Computing +sidebar: false +--- + +_Array computing is the foundation of statistical, mathematical, scientific computing +in various contemporary data science and analytics applications such as data +visualization, digital signal processing, image processing, bioinformatics, +machine learning, AI, and several others._ + +Large scale data manipulation and transformation depends on efficient, +high-performance array computing. The language of choice for data analytics, +machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming +language library that supports large, multi-dimensional arrays and matrices, +and comes with a vast collection of high-level mathematical functions to +operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, +and it was not until a couple of years ago that several array computing +libraries showed up in succession, crowding the array computing landscape. +Many of these newer libraries mimic NumPy-like features and capabilities, and +pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + + + +**Array computing** is based on **arrays** data structures. _Arrays_ are used +to organize vast amounts of data such that a related set of values can be easily +sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is _unique_ as it involves operating on the data array _at +once_. What this means is that any array operation applies to an entire set of +values in one shot. This vectorized approach provides speed and simplicity by +enabling programmers to code and operate on aggregates of data, without having +to use loops of individual scalar operations. From 894b7f37892bc395dc55baf2d1fb9ddc399ef608 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:41 +0200 Subject: [PATCH 040/586] New translations arraycomputing.md (Chinese Simplified) --- content/zh/arraycomputing.md | 39 ++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 content/zh/arraycomputing.md diff --git a/content/zh/arraycomputing.md b/content/zh/arraycomputing.md new file mode 100644 index 0000000000..0771101eff --- /dev/null +++ b/content/zh/arraycomputing.md @@ -0,0 +1,39 @@ +--- +title: Array Computing +sidebar: false +--- + +_Array computing is the foundation of statistical, mathematical, scientific computing +in various contemporary data science and analytics applications such as data +visualization, digital signal processing, image processing, bioinformatics, +machine learning, AI, and several others._ + +Large scale data manipulation and transformation depends on efficient, +high-performance array computing. The language of choice for data analytics, +machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming +language library that supports large, multi-dimensional arrays and matrices, +and comes with a vast collection of high-level mathematical functions to +operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, +and it was not until a couple of years ago that several array computing +libraries showed up in succession, crowding the array computing landscape. +Many of these newer libraries mimic NumPy-like features and capabilities, and +pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + + + +**Array computing** is based on **arrays** data structures. _Arrays_ are used +to organize vast amounts of data such that a related set of values can be easily +sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is _unique_ as it involves operating on the data array _at +once_. What this means is that any array operation applies to an entire set of +values in one shot. This vectorized approach provides speed and simplicity by +enabling programmers to code and operate on aggregates of data, without having +to use loops of individual scalar operations. From 52ebd2378f2b5d63671185fca9623155e5a83d93 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:42 +0200 Subject: [PATCH 041/586] New translations arraycomputing.md (Portuguese, Brazilian) --- content/pt/arraycomputing.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/content/pt/arraycomputing.md b/content/pt/arraycomputing.md index 941f69fe42..b43b5d58b4 100644 --- a/content/pt/arraycomputing.md +++ b/content/pt/arraycomputing.md @@ -3,19 +3,20 @@ title: Computação com Arrays sidebar: false --- -*A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras.* +_A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras._ A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alta performance com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é **Python.** **Num**erical **Py**thon (Python Numérico) ou NumPy é a biblioteca em Python padrão para o suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays. -Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. +Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. +Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. arraycl +src="/images/content_images/array_c_landscape.png" +alt="arraycl" +title="Panorama de Computação com Arrays" /> -A **computação com arrays** é baseada em estruturas de dados chamadas **arrays**. *Arrays* são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente. +A **computação com arrays** é baseada em estruturas de dados chamadas **arrays**. _Arrays_ são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente. -A computação com arrays é *única* pois envolve operar nos valores de um array de dados *de uma vez*. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais. +A computação com arrays é _única_ pois envolve operar nos valores de um array de dados _de uma vez_. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais. From 9b880b8b994ded96c7a7d52d75c1a87bc529e849 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:43 +0200 Subject: [PATCH 042/586] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/es/citing-numpy.md diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md new file mode 100644 index 0000000000..93a1708556 --- /dev/null +++ b/content/es/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + +``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From b7d7a8dcf9d3b6dd5c13943a180fef4a38312e1a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:44 +0200 Subject: [PATCH 043/586] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ar/citing-numpy.md diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md new file mode 100644 index 0000000000..93a1708556 --- /dev/null +++ b/content/ar/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + +``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 3061a64e91fcaf77b48eed212240a58f906fcbdc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:46 +0200 Subject: [PATCH 044/586] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 42 +++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index 397ca192ab..fddd4696dc 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -5,30 +5,30 @@ sidebar: false もしあなたの研究においてNumPyが重要な役割を果たし、論文でこのプロジェクトについて言及したい場合は、こちらの論文を引用して下さい。 -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). _BibTeX形式:_ - ``` +``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{'{a}}ndez del - R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{'{a}}ndez del + R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From 813ae80285b03dbb667f53b425f11c2cfbb8f0e6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:47 +0200 Subject: [PATCH 045/586] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ko/citing-numpy.md diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md new file mode 100644 index 0000000000..93a1708556 --- /dev/null +++ b/content/ko/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + +``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 5cdc984bc3621a80463ebaf1e8507b6fa2247f7f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:48 +0200 Subject: [PATCH 046/586] New translations citing-numpy.md (Russian) --- content/ru/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ru/citing-numpy.md diff --git a/content/ru/citing-numpy.md b/content/ru/citing-numpy.md new file mode 100644 index 0000000000..93a1708556 --- /dev/null +++ b/content/ru/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + +``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 41bec7232c9af299e8b137e05a734d23185e258d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:49 +0200 Subject: [PATCH 047/586] New translations citing-numpy.md (Chinese Simplified) --- content/zh/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/zh/citing-numpy.md diff --git a/content/zh/citing-numpy.md b/content/zh/citing-numpy.md new file mode 100644 index 0000000000..93a1708556 --- /dev/null +++ b/content/zh/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + +``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 88d7bc5059600fb64dca3fddb4afb2eb78063741 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:50 +0200 Subject: [PATCH 048/586] New translations citing-numpy.md (Portuguese, Brazilian) --- content/pt/citing-numpy.md | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/pt/citing-numpy.md b/content/pt/citing-numpy.md index f947689548..f73f541896 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -5,31 +5,31 @@ sidebar: false Se a NumPy é importante na sua pesquisa, e você gostaria de dar reconhecimento ao projeto na sua publicação acadêmica, sugerimos citar os seguintes documentos: -* Harris, C.R., Millman, K.J., van der Walt, S.J. Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Link da editora](https://www.nature.com/articles/s41586-020-2649-2)). +- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Link da editora](https://www.nature.com/articles/s41586-020-2649-2)). _Em formato BibTeX:_ - ``` +``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{'{a}}ndez del - R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{'{a}}ndez del + R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From a835bd18197c6b687654b108ef448bf9525ae853 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:51 +0200 Subject: [PATCH 049/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/es/code-of-conduct.md diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md new file mode 100644 index 0000000000..bba5d56bf1 --- /dev/null +++ b/content/es/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + - Violent threats or language directed against another person. + - Sexist, racist, or otherwise discriminatory jokes and language. + - Posting sexually explicit or violent material. + - Posting (or threatening to post) other people’s personally identifying information (“doxing”). + - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + - Personal insults, especially those using racist or sexist terms. + - Unwelcome sexual attention. + - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + - Repeated harassment of others. In general, if someone asks you to stop, then stop. + - Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 6512e010d4ab9fe00ff351422f41c6c21d37e7c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:52 +0200 Subject: [PATCH 050/586] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ar/code-of-conduct.md diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md new file mode 100644 index 0000000000..bba5d56bf1 --- /dev/null +++ b/content/ar/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + - Violent threats or language directed against another person. + - Sexist, racist, or otherwise discriminatory jokes and language. + - Posting sexually explicit or violent material. + - Posting (or threatening to post) other people’s personally identifying information (“doxing”). + - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + - Personal insults, especially those using racist or sexist terms. + - Unwelcome sexual attention. + - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + - Repeated harassment of others. In general, if someone asks you to stop, then stop. + - Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 54304be86327a414a5dfc3b0596d61abc76f32d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:54 +0200 Subject: [PATCH 051/586] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 66 +++++++++++++++++------------------ 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 70ca4d8b6e..a26d0fd172 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -5,69 +5,69 @@ aliases: - /ja/conduct/ --- -### はじめに +### Introduction -この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、Twitter、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。 +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. この行動規範は、NumPy コミュニティに正式または非公式に参加するすべての人が順守する必要があります。 その他にも、NumPyとの提携・関連するプロジェクト活動においては、特にそれらのプロジェクトを代表する場合、同様の行動規範に従う必要があります。 -この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。 +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. ### ガイドラインの概要 -私たちは下記の内容に真摯に取り組みます。 - -1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、公にすべきではない内容を議論する場合以外、プロジェクトに関連するメッセージを公の場で告知することを選びます。 これは、NumPyに関するヘルプやプロジェクトサポートにも適用されます。公式なサポートだけでなく、NumPyに関する質問に答える場合もです。 これにより、質問に答えた際の意図しない間違いを、より簡単に検出し、訂正できるようになります。 -2. 共感し、歓迎し、友好的で、そして我慢強くありましょう。 私たちは互いに争いを解決し合い、互いの善意を信じ合います。 私たちは時折り不満を感じるかもしれません。 しかしそのような場合も、不満を個人的な攻撃に変えることは許容されません。 人々が不快や脅威を感じるコミュニティは、生産的ではないからです。 -3. 互いに協力し合おう。 私たちの開発成果は他の人々によって利用され、一方で、たちは他の人々の開発成果に依存しているのです。 私たちがプロジェクトために何かを作るとき、私たちはそれがどのように動作するかを他の人に説明する必要があります。 しかし、この作業により、より良いものを作り上げることができるのです。 私たちが下す全ての決断は、ユーザと開発コミュニティに影響を与えうるし、その決断がもたらす結果を私たちは真摯に受け止めます。 -4. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 -5. 使う言葉に注意しましょう。 私たちは、コミュニティにおけるコミュニケーションに注意と敬意を払います。 そして、私たちは自分の言葉に責任を持ちます。 他人に優しくしましょう。 他のコミュニティの参加者を侮辱しないでください。 私たちは、以下のようなハラスメントやその他の排斥行為を許しません。 : - * 他の人に向けられた暴力的な行為や言葉。 - * 性差別や人種差別、その他の差別的なジョークや言動。 - * 性的または暴力的な内容の投稿。 - * 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 - * 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 - * 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 - * 不快な思いをさせる性的な言動。 - * 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。 - * 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。 - * 上記のいずれかの行動を擁護すること、または奨励すること。 +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + - 他の人に向けられた暴力的な行為や言葉。 + - 性差別や人種差別、その他の差別的なジョークや言動。 + - 性的または暴力的な内容の投稿。 + - 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 + - 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 + - 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 + - Unwelcome sexual attention. + - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + - Repeated harassment of others. In general, if someone asks you to stop, then stop. + - 上記のいずれかの行動を擁護すること、または奨励すること。 ### 多様性に関する声明 -NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 +NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. -あなたの自己認識や、他者のあなたへの認識は関係ありません。 私たちはあなたを歓迎します。 民族、遺伝、性同一性あるいは関連する表現、言語、国籍、神経学的な差異、生物学的な差異、 政治的信条、職業、人種、宗教、性的指向、社会経済的地位、文化的な差異、技術的な能力。 +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. 私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。 -NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。 +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). ### 報告ガイドライン -私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 +私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 Please keep this in mind when deciding on how to respond to a breach of this Code. -意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。 +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. NumPy行動規範委員会に問題を報告する場合は、こちらにご連絡下さい: numpy-conduct@googlegroups.com。 現在、行動規範委員会は以下のメンバーで構成されています: -* Stefan van der Walt -* Melissa Weber Mendonça -* Rohit Goswami +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami -もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 もしくは、行動規範委員会に報告するのが躊躇われる場合は、こちらからNumFOCUSのシニアスタッフに連絡することも可能です:[conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible) 。 +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). ### インシデント報告の解決 & 行動規範の実施 本節では、_最も重要な点のみをまとめます。 _詳細については、[NumPy Code of Conduct - How to follow up on a report](report-handling-manual) をご覧ください。 -私たちはすべての訴えを調査し、対応するようにします。 NumPy行動規範委員会およびNumPy運営委員会(もし関係する場合) は、報告者の身元を保護します。 また(報告者が同意しない限り) 苦情の内容を機密として扱うこととします。 +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). -もし深刻で明らかな違反の場合、例えば、 個人的な脅し、または暴力的、性差別的または人種差別的な発言などの場合、我々は直ちにNumPyのコミュニケーションの場から発言者を退場させます。詳細についてはマニュアルを参照してください。 +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. -もし、行動規範に対して明白な違反がみられない場合、受領された行動規範違反報告に対するプロセスは以下の通りです。 +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: 1. 報告書の受領を確認 2. 建設的な議論/フィードバック @@ -76,7 +76,7 @@ NumPy行動規範委員会に問題を報告する場合は、こちらにご連 行動規範委員会は、可能な限り速やかに対応し、最大で72時間以内に対応する様にします。 -### 文末脚注: +### Endnotes 私たちは下記のドキュメントを作成したグループに感謝します。 内容・発想ともに大いに影響されています。 From f8a8caff9a3689b8606c8dbab6edaf0e8eeda74b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:55 +0200 Subject: [PATCH 052/586] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ko/code-of-conduct.md diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md new file mode 100644 index 0000000000..bba5d56bf1 --- /dev/null +++ b/content/ko/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + - Violent threats or language directed against another person. + - Sexist, racist, or otherwise discriminatory jokes and language. + - Posting sexually explicit or violent material. + - Posting (or threatening to post) other people’s personally identifying information (“doxing”). + - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + - Personal insults, especially those using racist or sexist terms. + - Unwelcome sexual attention. + - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + - Repeated harassment of others. In general, if someone asks you to stop, then stop. + - Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From d795bebd2b90c1392ece08ae410546ae42045cea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:56 +0200 Subject: [PATCH 053/586] New translations code-of-conduct.md (Russian) --- content/ru/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ru/code-of-conduct.md diff --git a/content/ru/code-of-conduct.md b/content/ru/code-of-conduct.md new file mode 100644 index 0000000000..bba5d56bf1 --- /dev/null +++ b/content/ru/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + - Violent threats or language directed against another person. + - Sexist, racist, or otherwise discriminatory jokes and language. + - Posting sexually explicit or violent material. + - Posting (or threatening to post) other people’s personally identifying information (“doxing”). + - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + - Personal insults, especially those using racist or sexist terms. + - Unwelcome sexual attention. + - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + - Repeated harassment of others. In general, if someone asks you to stop, then stop. + - Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From ef939bfbecdb142921796f4ff2e70bae47111c3c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:57 +0200 Subject: [PATCH 054/586] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/zh/code-of-conduct.md diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md new file mode 100644 index 0000000000..bba5d56bf1 --- /dev/null +++ b/content/zh/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + - Violent threats or language directed against another person. + - Sexist, racist, or otherwise discriminatory jokes and language. + - Posting sexually explicit or violent material. + - Posting (or threatening to post) other people’s personally identifying information (“doxing”). + - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + - Personal insults, especially those using racist or sexist terms. + - Unwelcome sexual attention. + - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + - Repeated harassment of others. In general, if someone asks you to stop, then stop. + - Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 644ce8a75b811b7324c6d8c3017ce621b6c11d10 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:11:59 +0200 Subject: [PATCH 055/586] New translations code-of-conduct.md (Portuguese, Brazilian) --- content/pt/code-of-conduct.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md index 1e2c9e53bb..4b9515189f 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -7,7 +7,7 @@ aliases: ### Introdução -Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, *issue tracker*, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. O projeto NumPy não organiza eventos presenciais. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. +Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, _issue tracker_, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. Este Código de Conduta deve ser honrado por todas as pessoas que participam da comunidade NumPy formal ou informalmente, ou que reivindicam qualquer afiliação com o projeto, em qualquer atividade relacionada ao projeto, especialmente ao representar o projeto, em qualquer função. @@ -22,16 +22,16 @@ Nós nos esforçamos para: 3. Sermos colaborativos. O nosso trabalho será utilizado por outras pessoas e, por sua vez, dependeremos do trabalho dos outros. Quando fazemos algo em benefício do projeto, estamos dispostos a explicar aos outros como esse algo funciona, para que outros possam desenvolver o trabalho e torná-lo ainda melhor. Qualquer decisão que tomemos afetará nossos usuários e os colegas, e levamos essas consequências a sério quando tomamos decisões. 4. Sermos inquisitivos. Ninguém sabe tudo! Fazer perguntas antecipadamente evita muitos problemas mais tarde, por isso encorajamos as perguntas, embora possamos encaminhá-las para um fórum adequado. Vamos nos esforçar para sermos sensíveis e úteis. 5. Termos cuidado com as palavras que escolhemos. Somos cuidadosos e respeitosos na nossa comunicação e assumimos a responsabilidade pelo nosso próprio discurso. Seja gentil com os outros. Não insulte ou deprecie outros participantes. Nós não aceitaremos assédio ou outros comportamentos exclusivos, como: - * Ameaças ou linguagem violenta direcionadas contra outra pessoa. - * Piadas e linguagem sexista, racista ou discriminatória. - * Postagem de material sexualmente explícito ou violento. - * Postar (ou ameaçar postar) informações pessoais de outras pessoas (“doxing”). - * Compartilhar conteúdo privado, como e-mails enviados de maneira privada ou não-pública, ou fóruns não registrados, como histórico de canais IRC, sem o consentimento do remetente. - * Insultos pessoais, especialmente aqueles que utilizam termos racistas ou sexistas. - * Atenção sexual não consentida. - * Profanidade excessiva. Por favor, evite palavrões; as pessoas diferem muito na sua sensibilidade à linguagem. - * Assédio reiterado. Em geral, se alguém pedir que você pare, então pare. - * Advogar em favor ou encorajar qualquer um dos comportamentos acima. + - Ameaças ou linguagem violenta direcionadas contra outra pessoa. + - Piadas e linguagem sexista, racista ou discriminatória. + - Postagem de material sexualmente explícito ou violento. + - Postar (ou ameaçar postar) informações pessoais de outras pessoas (“doxing”). + - Compartilhar conteúdo privado, como e-mails enviados de maneira privada ou não-pública, ou fóruns não registrados, como histórico de canais IRC, sem o consentimento do remetente. + - Insultos pessoais, especialmente aqueles que utilizam termos racistas ou sexistas. + - Atenção sexual não consentida. + - Profanidade excessiva. Por favor, evite palavrões; as pessoas diferem muito na sua sensibilidade à linguagem. + - Repeated harassment of others. Em geral, se alguém pedir que você pare, então pare. + - Advogar em favor ou encorajar qualquer um dos comportamentos acima. ### Declaração de diversidade @@ -43,7 +43,7 @@ Embora sejamos receptivos às pessoas fluentes em todas as línguas, o desenvolv Padrões de comportamento na comunidade NumPy estão detalhados no Código de Conduta acima. Os participantes da nossa comunidade devem se comportar de acordo com esses padrões em todas as suas interações e ajudar os outros a fazê-lo também (veja a próxima seção). -### Diretrizes de resposta a incidentes +### Reporting Guidelines Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Reconhecemos também que, por vezes, as pessoas podem ter um dia ruim, ou não conhecer algumas das orientações deste Código de Conduta. Tenha isto em mente ao decidir como responder a uma violação deste Código. @@ -53,9 +53,9 @@ Você pode relatar problemas ao Comitê do Código de Conduta NumPy em numpy-con Atualmente, o comitê é formato por: -* Stefan van der Walt -* Melissa Weber Mendonça -* Rohit Goswami +- Stefan van der Walt +- Melissa Weber Mendonça +- Rohit Goswami Se o seu relatório envolve algum membro da comissão, ou se você sentir que existe um conflito de interesses em tratá-lo, então os membros abster-se-ão de considerar o seu relatório. Como alternativa, se por qualquer razão você se sentir desconfortável em fazer um relatório à comissão, então você também pode entrar em contato com a equipe sênior da NumFOCUS em [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). From 12c0604bf1ad8c46551935528c6b599c9b444e68 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:00 +0200 Subject: [PATCH 056/586] New translations community.md (Spanish) --- content/es/community.md | 72 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/es/community.md diff --git a/content/es/community.md b/content/es/community.md new file mode 100644 index 0000000000..d4f188d6ce --- /dev/null +++ b/content/es/community.md @@ -0,0 +1,72 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + +## Participate online + +The following are ways to engage directly with the NumPy project and community. +_Please note that we encourage users and community members to support each other +for usage questions - see [Get Help](/gethelp)._ + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. +Announcements about NumPy, such as for releases, developer meetings, sprints or +conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to +another sender), and don't reply to digests. A searchable archive of this list +is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. +This is a private space, specifically meant for people who are hesitant to +bring up their questions or ideas on a large public mailing list or GitHub. +Please see +[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more +details and how to get an invite. + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From 08fe22ba1a3e04cc77a2557e72ba8b17337c9510 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:01 +0200 Subject: [PATCH 057/586] New translations community.md (Arabic) --- content/ar/community.md | 72 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/ar/community.md diff --git a/content/ar/community.md b/content/ar/community.md new file mode 100644 index 0000000000..d4f188d6ce --- /dev/null +++ b/content/ar/community.md @@ -0,0 +1,72 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + +## Participate online + +The following are ways to engage directly with the NumPy project and community. +_Please note that we encourage users and community members to support each other +for usage questions - see [Get Help](/gethelp)._ + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. +Announcements about NumPy, such as for releases, developer meetings, sprints or +conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to +another sender), and don't reply to digests. A searchable archive of this list +is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. +This is a private space, specifically meant for people who are hesitant to +bring up their questions or ideas on a large public mailing list or GitHub. +Please see +[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more +details and how to get an invite. + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From dd5b54af14595d9a1708eb1f0cad2e7b215b6776 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:02 +0200 Subject: [PATCH 058/586] New translations community.md (Japanese) --- content/ja/community.md | 35 ++++++++++++++++++++--------------- 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/content/ja/community.md b/content/ja/community.md index 2629f72358..5178b60aa3 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,21 +3,24 @@ title: コミュニティ sidebar: false --- -NumPy は 常に多様な[コントリビュータ](/ja/teams/) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 NumPy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/ja/code-of-conduct) をご覧ください。 +NumPy は 常に多様な[コントリビュータ](/ja/teams/) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 NumPy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/ja/code-of-conduct) をご覧ください。 The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 - ## オンラインで参加する方法 -NumPy プロジェクトやコミュニティと直接交流する方法は次の通りです。 _重要: 私たちはユーザとコミュニティメンバーに互いにNumPyの使い方の質問に関して助言し合って欲しいと思っています。 - 参照[サポート](/gethelp)._ - +The following are ways to engage directly with the NumPy project and community. +_Please note that we encourage users and community members to support each other +for usage questions - see [Get Help](/gethelp)._ ### [NumPyメーリングリスト：](https://mail.python.org/mailman/listinfo/numpy-discussion) このメーリングリストは、NumPy に新しい機能を追加するなど、より長い期間の議論のための主なコミュニケーションの場です。 NumPyのRoadmapに変更を加えたり、プロジェクト全体での意思決定を行います。 このメーリングリストでは、リリース、開発者会議、スプリント、カンファレンストークなど、NumPy についてのアナウンスなどにも利用されます。 +Announcements about NumPy, such as for releases, developer meetings, sprints or +conference talks are also made on this list. -このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 このメーリングリストの検索可能なアーカイブは [こちら](https://mail.python.org/archives/list/numpy-discussion@python.org/) にあります。 +On this list please use bottom posting, reply to the list (rather than to +another sender), and don't reply to digests. このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 このメーリングリストの検索可能なアーカイブは [こちら](https://mail.python.org/archives/list/numpy-discussion@python.org/) にあります。 *** @@ -27,25 +30,28 @@ NumPy プロジェクトやコミュニティと直接交流する方法は次 - ドキュメントの問題 (例: "I find this section unclear"); - 機能追加リクエスト (例: "I would like to have a new interpolation method in `np.percentile`"). -_ちなみに、セキュリティの脆弱性を報告するには、GitHubのイシュートラッカーは適切な場所ではないことに注意してください。 NumPy でセキュリティ上の脆弱性を発見したと思われる場合は、 [こちら](https://tidelift.com/docs/security) から報告してください。_ +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ *** ### [Slack](https://numpy-team.slack.com) -SlackはNumpyに_ 貢献するための質問をするための_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 - +A real-time chat room to ask questions about _contributing_ to NumPy. +This is a private space, specifically meant for people who are hesitant to +bring up their questions or ideas on a large public mailing list or GitHub. +Please see +[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more +details and how to get an invite. ## 勉強会とミートアップ NumPyや、データサイエンス、科学技術計算などのより広いエコシステムのためのPythonパッケージついて、もっと学ぶためのローカルミートアップや勉強会を見つけたい場合、 [PyData ミートアップ](https://www.meetup.com/pro/pydata/) (150人以上のミートアップ、10万人以上のメンバーをまとめたもの) を調べてみることをお勧めします。 -加えて、NumPy では開発チームと参加に興味があるコントリビュータのために、対面でのスプリントを時折開催しています。 この開発スプリントは通常数ヶ月に一度に開催されており、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) と [Twitter](https://twitter.com/numpy_team) で開催連絡されます。 - +NumPy also organizes in-person sprints for its team and interested contributors occasionally. 加えて、NumPy では開発チームと参加に興味があるコントリビュータのために、対面でのスプリントを時折開催しています。 この開発スプリントは通常数ヶ月に一度に開催されており、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) と [Twitter](https://twitter.com/numpy_team) で開催連絡されます。 ## カンファレンス -NumPy プロジェクトは独自のカンファレンスは開催していません。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 +The NumPy project doesn't organize its own conferences. NumPy プロジェクトは独自のカンファレンスは開催していません。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) @@ -56,11 +62,10 @@ NumPy プロジェクトは独自のカンファレンスは開催していま これらのカンファレンスの多くは、NumPyの使い方や関連するオープンソースプロジェクトに貢献する方法を学ぶことができるチュートリアルを開催しています。 - ## NumPy コミュニティに参加する -NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 プログラマーじゃないから参加できない？ そんなことはありません！ NumPy に貢献する様々な方法があります。 +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. -もし、NumPyに貢献したい場合は、 [コントリビュート](/ja/contribute) ページをご覧いただくことをお勧めします。 +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. -また、私たちのコミュニティミーティングにもぜひ参加してみてください。 コミュニティミーティングの活動を確認するには、[こちら](https://scientific-python.org/calendars/)のイベントカレンダーを確認ください。 +Also, feel free to stop by and say hi at one of our community meetings. また、私たちのコミュニティミーティングにもぜひ参加してみてください。 コミュニティミーティングの活動を確認するには、[こちら](https://scientific-python.org/calendars/)のイベントカレンダーを確認ください。 From aaf609f32803536b1e11bc28a418eed5208ad8a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:03 +0200 Subject: [PATCH 059/586] New translations community.md (Korean) --- content/ko/community.md | 72 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/ko/community.md diff --git a/content/ko/community.md b/content/ko/community.md new file mode 100644 index 0000000000..d4f188d6ce --- /dev/null +++ b/content/ko/community.md @@ -0,0 +1,72 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + +## Participate online + +The following are ways to engage directly with the NumPy project and community. +_Please note that we encourage users and community members to support each other +for usage questions - see [Get Help](/gethelp)._ + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. +Announcements about NumPy, such as for releases, developer meetings, sprints or +conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to +another sender), and don't reply to digests. A searchable archive of this list +is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. +This is a private space, specifically meant for people who are hesitant to +bring up their questions or ideas on a large public mailing list or GitHub. +Please see +[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more +details and how to get an invite. + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From 56458ee24c5d1f10cd841dbe7f7534abbd6e90e2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:05 +0200 Subject: [PATCH 060/586] New translations community.md (Russian) --- content/ru/community.md | 72 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/ru/community.md diff --git a/content/ru/community.md b/content/ru/community.md new file mode 100644 index 0000000000..d4f188d6ce --- /dev/null +++ b/content/ru/community.md @@ -0,0 +1,72 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + +## Participate online + +The following are ways to engage directly with the NumPy project and community. +_Please note that we encourage users and community members to support each other +for usage questions - see [Get Help](/gethelp)._ + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. +Announcements about NumPy, such as for releases, developer meetings, sprints or +conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to +another sender), and don't reply to digests. A searchable archive of this list +is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. +This is a private space, specifically meant for people who are hesitant to +bring up their questions or ideas on a large public mailing list or GitHub. +Please see +[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more +details and how to get an invite. + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From 27e7966b140ba47f4f42fc92e009f6bb390f8594 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:06 +0200 Subject: [PATCH 061/586] New translations community.md (Chinese Simplified) --- content/zh/community.md | 72 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/zh/community.md diff --git a/content/zh/community.md b/content/zh/community.md new file mode 100644 index 0000000000..d4f188d6ce --- /dev/null +++ b/content/zh/community.md @@ -0,0 +1,72 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + +## Participate online + +The following are ways to engage directly with the NumPy project and community. +_Please note that we encourage users and community members to support each other +for usage questions - see [Get Help](/gethelp)._ + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. +Announcements about NumPy, such as for releases, developer meetings, sprints or +conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to +another sender), and don't reply to digests. A searchable archive of this list +is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. +This is a private space, specifically meant for people who are hesitant to +bring up their questions or ideas on a large public mailing list or GitHub. +Please see +[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more +details and how to get an invite. + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From b78494f61b4de0806a61e957e26c3a943fade09f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:07 +0200 Subject: [PATCH 062/586] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/content/pt/community.md b/content/pt/community.md index 7992ff2fd6..23e7fe3aef 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -7,17 +7,17 @@ NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros dentro da comunidade NumPy. - ## Participar online -Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade do NumPy. _Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja [Obter Ajuda](/gethelp)._ - +Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade do NumPy. +_Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja [Obter Ajuda](/gethelp)._ ### [Lista de discussões NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) -Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. +Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. +Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. -Nesta lista, por favor, use *bottom posting*, responda à lista (em vez de a outro remetente), e não responda aos *digests*. Um arquivo pesquisável desta lista está disponível [aqui](https://mail.python.org/archives/list/numpy-discussion@python.org/). +Nesta lista, por favor, use _bottom posting_, responda à lista (em vez de a outro remetente), e não responda aos _digests_. Um arquivo pesquisável desta lista está disponível [aqui](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** @@ -33,8 +33,9 @@ _Por favor, note que o GitHub não é o lugar certo para relatar uma vulnerabili ### [Slack](https://numpy-team.slack.com) -Uma sala de bate-papo em tempo real para fazer perguntas sobre _contribuir_ para o NumPy. Este é um fórum privado, especificamente para pessoas hesitantes em levantar suas perguntas ou idéias em uma grande lista de e-mails públicos ou no GitHub. Por favor, clique [aqui](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para mais detalhes e como obter um convite. - +Uma sala de bate-papo em tempo real para fazer perguntas sobre _contribuir_ para o NumPy. +Este é um fórum privado, especificamente para pessoas hesitantes em levantar suas perguntas ou idéias em uma grande lista de e-mails públicos ou no GitHub. +Por favor, clique [aqui](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para mais detalhes e como obter um convite. ## Grupos de Estudo e Meetups @@ -42,7 +43,6 @@ Se você gostaria de encontrar um encontro ou grupo de estudo local para aprende O NumPy também organiza sprints presenciais para sua equipe e colaboradores interessados ocasionalmente. Estes eventos são normalmente planejados com vários meses de antecedência e serão anunciados na [lista de discussão](https://mail.python.org/mailman/listinfo/numpy-discussion) e no [Twitter](https://twitter.com/numpy_team). - ## Conferências O projeto NumPy não organiza suas próprias conferências. As conferências que tradicionalmente têm sido mais populares com mantenedores, colaboradores e usuários são as conferências SciPy e PyData: @@ -56,7 +56,6 @@ O projeto NumPy não organiza suas próprias conferências. As conferências que Muitas dessas conferências incluem dias de tutorial sobre o NumPy e/ou sprints onde você pode aprender como contribuir com o NumPy ou projetos de código aberto relacionados. - ## Junte-se à comunidade NumPy Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não é uma pessoa programadora? Sem problemas! Existem muitas maneiras de contribuir com o NumPy. From b0dd30d87a69a47903b0b2199b2c5536c405bd3f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:08 +0200 Subject: [PATCH 063/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 108 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 content/es/contribute.md diff --git a/content/es/contribute.md b/content/es/contribute.md new file mode 100644 index 0000000000..3e9565acf4 --- /dev/null +++ b/content/es/contribute.md @@ -0,0 +1,108 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You +can ask on the mailing +list or +[GitHub](http://github.com/numpy/numpy) (open an +[issue](https://github.com/numpy/numpy/issues) or comment on a relevant +issue). + +Those are our preferred channels (open source is open by nature), but +if you prefer to talk privately, contact our community coordinators at +numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) +(write numpy-team@googlegroups.com for an invite). + +We also have a biweekly _community call_, details of which are announced on +the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). +You are very welcome to join. +If you are new to contributing to open source, we also highly recommend reading +[this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all +contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open +and welcoming environment. + +### Writing code + +Programmers, this +[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) +explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + +### Reviewing pull requests + +The project has more than 250 open pull requests -- meaning many potential +improvements and many open-source contributors waiting for feedback. If you're +a developer who knows NumPy, you can help even if you're not familiar with the +codebase. You can: + +- summarize a long-running discussion +- triage documentation PRs +- test proposed changes + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. +We're in need of new tutorials, how-to's, and deep-dive explanations, and the +site needs restructuring. Opportunities aren't limited to writers. We'd also +welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the +NumPyDocumentation +lays out our ideas -- and you may have others. + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ +of open issues. Some are no longer valid, some should be prioritized, and some +would make good issues for new contributors. You can: + +- check if older bugs are still present +- find duplicate issues and link related ones +- add good self-contained reproducers to issues +- label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + +### Website development + +We've just revamped our website, but we're far from done. If you love web +development, these +[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) +list some of our unmet needs -- and feel free to share your own ideas. + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. +Our docs are parched for illustration; our growing website craves images -- +opportunities abound. + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy +accessible to users in their native language. Volunteer translators are at the heart +of this effort. See +[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) +for background; comment on this GitHub +issue to sign up. + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're +falling short. We're eager to get more people involved in efforts like our +[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code +sprints, a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became +clear that to ensure stability and growth we'd need financial support. This +SciPy'19 talk explains how much +difference that support has made. Like all the nonprofit world, we're +constantly searching for grants, sponsorships, and other kinds of support. We +have a number of ideas and of course we welcome more. Fundraising is a scarce +skill here -- we'd appreciate your help. From d7c7c49a514b4899f53b4962600c06d1bf4f0534 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:10 +0200 Subject: [PATCH 064/586] New translations contribute.md (Arabic) --- content/ar/contribute.md | 108 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 content/ar/contribute.md diff --git a/content/ar/contribute.md b/content/ar/contribute.md new file mode 100644 index 0000000000..3e9565acf4 --- /dev/null +++ b/content/ar/contribute.md @@ -0,0 +1,108 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You +can ask on the mailing +list or +[GitHub](http://github.com/numpy/numpy) (open an +[issue](https://github.com/numpy/numpy/issues) or comment on a relevant +issue). + +Those are our preferred channels (open source is open by nature), but +if you prefer to talk privately, contact our community coordinators at +numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) +(write numpy-team@googlegroups.com for an invite). + +We also have a biweekly _community call_, details of which are announced on +the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). +You are very welcome to join. +If you are new to contributing to open source, we also highly recommend reading +[this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all +contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open +and welcoming environment. + +### Writing code + +Programmers, this +[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) +explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + +### Reviewing pull requests + +The project has more than 250 open pull requests -- meaning many potential +improvements and many open-source contributors waiting for feedback. If you're +a developer who knows NumPy, you can help even if you're not familiar with the +codebase. You can: + +- summarize a long-running discussion +- triage documentation PRs +- test proposed changes + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. +We're in need of new tutorials, how-to's, and deep-dive explanations, and the +site needs restructuring. Opportunities aren't limited to writers. We'd also +welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the +NumPyDocumentation +lays out our ideas -- and you may have others. + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ +of open issues. Some are no longer valid, some should be prioritized, and some +would make good issues for new contributors. You can: + +- check if older bugs are still present +- find duplicate issues and link related ones +- add good self-contained reproducers to issues +- label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + +### Website development + +We've just revamped our website, but we're far from done. If you love web +development, these +[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) +list some of our unmet needs -- and feel free to share your own ideas. + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. +Our docs are parched for illustration; our growing website craves images -- +opportunities abound. + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy +accessible to users in their native language. Volunteer translators are at the heart +of this effort. See +[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) +for background; comment on this GitHub +issue to sign up. + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're +falling short. We're eager to get more people involved in efforts like our +[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code +sprints, a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became +clear that to ensure stability and growth we'd need financial support. This +SciPy'19 talk explains how much +difference that support has made. Like all the nonprofit world, we're +constantly searching for grants, sponsorships, and other kinds of support. We +have a number of ideas and of course we welcome more. Fundraising is a scarce +skill here -- we'd appreciate your help. From 9e81c4dcb7fda498731b29d89891740c78c3db39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:11 +0200 Subject: [PATCH 065/586] New translations contribute.md (Japanese) --- content/ja/contribute.md | 70 +++++++++++++++++++++++++--------------- 1 file changed, 44 insertions(+), 26 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 90db608852..644e7eaa61 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -3,64 +3,82 @@ title: NumPy に貢献する sidebar: false --- -NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページには**あなたができる** 様々な種類の貢献方法が示されています。 +The NumPy project welcomes your expertise and enthusiasm! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. もしどこから始めればいいか、あなたのスキルをどう生かせばいいかがわからない場合は、 _是非ご連絡下さい。 _ 連絡の方法としては、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) 、 [GitHub](http://github.com/numpy/numpy)、 [イシューの作成](https://github.com/numpy/numpy/issues) 、関連するイシューへのコメントがあります。 -連絡先としては、 または、[Slack](https://numpy-team.slack.com) (グループに招待するためにこちらに連絡お願いします: )があります。 +連絡先としては、 numpy-team@googlegroups.com または、[Slack](https://numpy-team.slack.com) (グループに招待するためにこちらに連絡お願いします: numpy-team@googlegroups.com)があります。 また、隔週の _コミュニティミーティング_もあり、詳細は [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) で発表されています。 あなたの参加を大いに歓迎します。 オープンソースプロジェクトに貢献するのが初めての方は、是非、 [このガイド](https://opensource.guide/how-to-contribute/) を読んでみて下さい。 +You are very welcome to join. +If you are new to contributing to open source, we also highly recommend reading +[this guide](https://opensource.guide/how-to-contribute/). -私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために [行動基準](/ja/code-of-conduct) を制定しています。 +私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために [行動基準](/ja/code-of-conduct) を制定しています。 We have a [Code of Conduct](/code-of-conduct) to foster an open +and welcoming environment. ### コードを書く -プログラマーの方には、こちらの [ガイド](https://numpy.org/devdocs/dev/index.html#development-process-summary)でNumPyのコードに貢献する方法を説明しています。
追加情報に関しては、 こちらの[YouTube チャンネル](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) もご覧ください。 - +プログラマーの方には、こちらの [ガイド](https://numpy.org/devdocs/dev/index.html#development-process-summary)でNumPyのコードに貢献する方法を説明しています。
追加情報に関しては、 こちらの[YouTube チャンネル](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) もご覧ください。
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. ### プルリクエストのレビュー -NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 -* 長期にわたる議論をまとめる -* ドキュメントのPRをトリアージする -* 提案された変更をテストする +The project has more than 250 open pull requests -- meaning many potential +improvements and many open-source contributors waiting for feedback. If you're +a developer who knows NumPy, you can help even if you're not familiar with the +codebase. You can: + +- 長期にわたる議論をまとめる +- ドキュメントのPRをトリアージする +- 提案された変更をテストする ### 教育用の資料を作成する NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模な再設計中です。 新しいNumPyのWebページは、新しいチュートリアルや、NumPyの使い方、NumPy内部の深い説明など必要としており、サイト全体にも再設計と再構築が必要です。 このウェブサイトの再構築の作業は、ドキュメントを書くだけではありません。 コード例や、ノートブック、ビデオなどの作成も歓迎しています。 [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)に、ウェブサイトの再構築についての詳細が説明されています。 +We're in need of new tutorials, how-to's, and deep-dive explanations, and the +site needs restructuring. Opportunities aren't limited to writers. We'd also +welcome worked examples, notebooks, and videos. NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページには**あなたができる** 様々な種類の貢献方法が示されています。 +### Issue triaging -### イシューのトリアージ - -[NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: - -* 古いバグがまだ残っているか確認する -* 重複したイシューを見つけ、お互いに関連づける -* 問題を再現するコードを作成する -* イシューに正しいラベル付けをする (トリアージ権が必要なので、連絡下さい) +[NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: Some are no longer valid, some should be prioritized, and some +would make good issues for new contributors. You can: -ぜひ、やってみて下さい。 +- 古いバグがまだ残っているか確認する +- find duplicate issues and link related ones +- add good self-contained reproducers to issues +- 問題を再現するコードを作成する イシューに正しいラベル付けをする (トリアージ権が必要なので、連絡下さい) +Please just dive in. ### ウェブサイトの開発 -私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、この[イシュー](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。 - +We've just revamped our website, but we're far from done. 私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、この[イシュー](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。 ### グラフィックデザイン -グラフィックデザイナーの方が可能な貢献は、枚挙にいとまがありません。 しかし、私たちのドキュメントは説明のために可視化が重要であり、私たちの拡大しているウェブサイトは良い画像を求めていることから、 貢献する機会が沢山あると言えます。 - +We can barely begin to list the contributions a graphic designer can make here. +Our docs are parched for illustration; our growing website craves images -- +opportunities abound. ### ウェブサイトの翻訳 -私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 - +NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 Volunteer translators are at the heart +of this effort. 私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 ### コミュニティとの連携とアウトリーチ -コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 +Through community contact we share our work more widely and learn where we're +falling short. コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 ### 資金調達 -NumPyは何年にも渡ってボランティアだけ活動していましたが、その重要性が高まるにつれ、安定性と成長のためには資金面での支援が必要であることがわかってきました。 こちらの[SciPy'19のプレゼン](https://www.youtube.com/watch?v=dBTJD_FDVjU) では、資金的なサポートを受けたことで、どれだけ違いが出たかを説明しています。 他の非営利団体のように、私たちは助成金や、スポンサーシップ、その他の資金支援を常に探しています。 私たちはすでにいくつかの資金調達のアイデアを持っていますが、他にもより多くを資金調達を受けたいと思っています。 資金調達に関する知識は、我々には不足しているスキルです。 是非、あなたのサポートをお待ちしています。 +NumPy was all-volunteer for many years, but as its importance grew it became +clear that to ensure stability and growth we'd need financial support. This +SciPy'19 talk explains how much +difference that support has made. Like all the nonprofit world, we're +constantly searching for grants, sponsorships, and other kinds of support. We +have a number of ideas and of course we welcome more. Fundraising is a scarce +skill here -- we'd appreciate your help. From dff9d2fe9fd25120723c04d0193307023b9387ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:13 +0200 Subject: [PATCH 066/586] New translations contribute.md (Korean) --- content/ko/contribute.md | 108 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 content/ko/contribute.md diff --git a/content/ko/contribute.md b/content/ko/contribute.md new file mode 100644 index 0000000000..3e9565acf4 --- /dev/null +++ b/content/ko/contribute.md @@ -0,0 +1,108 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You +can ask on the mailing +list or +[GitHub](http://github.com/numpy/numpy) (open an +[issue](https://github.com/numpy/numpy/issues) or comment on a relevant +issue). + +Those are our preferred channels (open source is open by nature), but +if you prefer to talk privately, contact our community coordinators at +numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) +(write numpy-team@googlegroups.com for an invite). + +We also have a biweekly _community call_, details of which are announced on +the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). +You are very welcome to join. +If you are new to contributing to open source, we also highly recommend reading +[this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all +contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open +and welcoming environment. + +### Writing code + +Programmers, this +[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) +explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + +### Reviewing pull requests + +The project has more than 250 open pull requests -- meaning many potential +improvements and many open-source contributors waiting for feedback. If you're +a developer who knows NumPy, you can help even if you're not familiar with the +codebase. You can: + +- summarize a long-running discussion +- triage documentation PRs +- test proposed changes + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. +We're in need of new tutorials, how-to's, and deep-dive explanations, and the +site needs restructuring. Opportunities aren't limited to writers. We'd also +welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the +NumPyDocumentation +lays out our ideas -- and you may have others. + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ +of open issues. Some are no longer valid, some should be prioritized, and some +would make good issues for new contributors. You can: + +- check if older bugs are still present +- find duplicate issues and link related ones +- add good self-contained reproducers to issues +- label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + +### Website development + +We've just revamped our website, but we're far from done. If you love web +development, these +[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) +list some of our unmet needs -- and feel free to share your own ideas. + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. +Our docs are parched for illustration; our growing website craves images -- +opportunities abound. + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy +accessible to users in their native language. Volunteer translators are at the heart +of this effort. See +[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) +for background; comment on this GitHub +issue to sign up. + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're +falling short. We're eager to get more people involved in efforts like our +[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code +sprints, a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became +clear that to ensure stability and growth we'd need financial support. This +SciPy'19 talk explains how much +difference that support has made. Like all the nonprofit world, we're +constantly searching for grants, sponsorships, and other kinds of support. We +have a number of ideas and of course we welcome more. Fundraising is a scarce +skill here -- we'd appreciate your help. From e6da2a1cef49f1c0748985f0d9099dcd51b4ffa4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:14 +0200 Subject: [PATCH 067/586] New translations contribute.md (Russian) --- content/ru/contribute.md | 108 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 content/ru/contribute.md diff --git a/content/ru/contribute.md b/content/ru/contribute.md new file mode 100644 index 0000000000..3e9565acf4 --- /dev/null +++ b/content/ru/contribute.md @@ -0,0 +1,108 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You +can ask on the mailing +list or +[GitHub](http://github.com/numpy/numpy) (open an +[issue](https://github.com/numpy/numpy/issues) or comment on a relevant +issue). + +Those are our preferred channels (open source is open by nature), but +if you prefer to talk privately, contact our community coordinators at +numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) +(write numpy-team@googlegroups.com for an invite). + +We also have a biweekly _community call_, details of which are announced on +the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). +You are very welcome to join. +If you are new to contributing to open source, we also highly recommend reading +[this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all +contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open +and welcoming environment. + +### Writing code + +Programmers, this +[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) +explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + +### Reviewing pull requests + +The project has more than 250 open pull requests -- meaning many potential +improvements and many open-source contributors waiting for feedback. If you're +a developer who knows NumPy, you can help even if you're not familiar with the +codebase. You can: + +- summarize a long-running discussion +- triage documentation PRs +- test proposed changes + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. +We're in need of new tutorials, how-to's, and deep-dive explanations, and the +site needs restructuring. Opportunities aren't limited to writers. We'd also +welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the +NumPyDocumentation +lays out our ideas -- and you may have others. + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ +of open issues. Some are no longer valid, some should be prioritized, and some +would make good issues for new contributors. You can: + +- check if older bugs are still present +- find duplicate issues and link related ones +- add good self-contained reproducers to issues +- label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + +### Website development + +We've just revamped our website, but we're far from done. If you love web +development, these +[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) +list some of our unmet needs -- and feel free to share your own ideas. + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. +Our docs are parched for illustration; our growing website craves images -- +opportunities abound. + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy +accessible to users in their native language. Volunteer translators are at the heart +of this effort. See +[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) +for background; comment on this GitHub +issue to sign up. + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're +falling short. We're eager to get more people involved in efforts like our +[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code +sprints, a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became +clear that to ensure stability and growth we'd need financial support. This +SciPy'19 talk explains how much +difference that support has made. Like all the nonprofit world, we're +constantly searching for grants, sponsorships, and other kinds of support. We +have a number of ideas and of course we welcome more. Fundraising is a scarce +skill here -- we'd appreciate your help. From 5e168839a5dc034c0ca47d984187c281d69af146 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:15 +0200 Subject: [PATCH 068/586] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 108 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 content/zh/contribute.md diff --git a/content/zh/contribute.md b/content/zh/contribute.md new file mode 100644 index 0000000000..3e9565acf4 --- /dev/null +++ b/content/zh/contribute.md @@ -0,0 +1,108 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You +can ask on the mailing +list or +[GitHub](http://github.com/numpy/numpy) (open an +[issue](https://github.com/numpy/numpy/issues) or comment on a relevant +issue). + +Those are our preferred channels (open source is open by nature), but +if you prefer to talk privately, contact our community coordinators at +numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) +(write numpy-team@googlegroups.com for an invite). + +We also have a biweekly _community call_, details of which are announced on +the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). +You are very welcome to join. +If you are new to contributing to open source, we also highly recommend reading +[this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all +contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open +and welcoming environment. + +### Writing code + +Programmers, this +[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) +explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + +### Reviewing pull requests + +The project has more than 250 open pull requests -- meaning many potential +improvements and many open-source contributors waiting for feedback. If you're +a developer who knows NumPy, you can help even if you're not familiar with the +codebase. You can: + +- summarize a long-running discussion +- triage documentation PRs +- test proposed changes + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. +We're in need of new tutorials, how-to's, and deep-dive explanations, and the +site needs restructuring. Opportunities aren't limited to writers. We'd also +welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the +NumPyDocumentation +lays out our ideas -- and you may have others. + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ +of open issues. Some are no longer valid, some should be prioritized, and some +would make good issues for new contributors. You can: + +- check if older bugs are still present +- find duplicate issues and link related ones +- add good self-contained reproducers to issues +- label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + +### Website development + +We've just revamped our website, but we're far from done. If you love web +development, these +[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) +list some of our unmet needs -- and feel free to share your own ideas. + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. +Our docs are parched for illustration; our growing website craves images -- +opportunities abound. + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy +accessible to users in their native language. Volunteer translators are at the heart +of this effort. See +[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) +for background; comment on this GitHub +issue to sign up. + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're +falling short. We're eager to get more people involved in efforts like our +[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code +sprints, a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became +clear that to ensure stability and growth we'd need financial support. This +SciPy'19 talk explains how much +difference that support has made. Like all the nonprofit world, we're +constantly searching for grants, sponsorships, and other kinds of support. We +have a number of ideas and of course we welcome more. Fundraising is a scarce +skill here -- we'd appreciate your help. From fa95e5b7f64765893d87b970004064b987802fc1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:16 +0200 Subject: [PATCH 069/586] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 44 +++++++++++++++++++++------------------- 1 file changed, 23 insertions(+), 21 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 65b82636b8..fe039ce94c 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -3,13 +3,17 @@ title: Contribua com o NumPy sidebar: false --- -O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação -- além de +O projeto NumPy precisa de sua experiência e entusiasmo! +Your choices aren't limited to programming, as you can +see below there are many areas where we need **your** help. Se você não sabe por onde começar ou como suas habilidades podem ajudar, _fale conosco!_ Você pode perguntar na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion) ou [GitHub](http://github.com/numpy/numpy) (abrindo uma [issue](https://github.com/numpy/numpy/issues) ou comentando em uma issue relevante). -Estes são os nossos canais de comunicação preferidos (projetos de código aberto são abertos por natureza!). No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para para obter um convite antes de entrar). +No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em numpy-team@googlegroups.com ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para numpy-team@googlegroups.com para obter um convite antes de entrar). -Nós também temos uma _reunião aberta da comunidade_ a cada duas semanas. Os detalhes são anunciados na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion). Convidamos você a participar. Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). +Os detalhes são anunciados na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion). +You are very welcome to join. +Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contribuições. Temos um [Código de Conduta](/pt/code-of-conduct) para promover um ambiente aberto e acolhedor. @@ -17,45 +21,43 @@ Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contrib Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código.
Confira também nosso [canal do YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) para obter informações adicionais. - ### Revisar pull requests + O projeto tem mais de 250 pull requests abertos -- o que significa que muitas potenciais melhorias e muitos contribuidores de código aberto estão aguardando feedback. Se você é uma pessoa programadora que conhece o NumPy, você pode ajudar, mesmo que não tenha familiaridade com o código. Você pode: -* resumir uma discussão longa -* fazer triagem de PRs de documentação -* testar alterações propostas +- resumir uma discussão longa +- fazer triagem de PRs de documentação +- testar alterações propostas ### Desenvolvimento de materiais educacionais -O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. - +O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. +Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. ### Triagem de Issues -O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: - -* verificar se erros mais antigos ainda estão presentes -* encontrar issues duplicadas e criar links entre issues relacionadas -* adicionar bons exemplos autocontidos que reproduzam issues -* rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) +O [_issue tracker_ do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: Sinta-se à vontade! -Sinta-se à vontade! +- verificar se erros mais antigos ainda estão presentes +- encontrar issues duplicadas e criar links entre issues relacionadas +- adicionar bons exemplos autocontidos que reproduzam issues +- rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) +Please just dive in. ### Desenvolvimento do site Acabamos de renovar o nosso site, mas estamos longe de terminar. Se você adora o desenvolvimento web, estas [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) listam algumas de nossas necessidades não atendidas -- e sinta-se livre para compartilhar suas próprias ideias. - ### Design gráfico -Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. - +Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. +Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. ### Traduzir conteúdo do site -Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. - +Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Volunteer translators are at the heart +of this effort. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. ### Coordenação e promoção na comunidade From a6c628c650ea6dfad11532ef0b690629fe03de43 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:17 +0200 Subject: [PATCH 070/586] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/es/gethelp.md diff --git a/content/es/gethelp.md b/content/es/gethelp.md new file mode 100644 index 0000000000..cce276b98a --- /dev/null +++ b/content/es/gethelp.md @@ -0,0 +1,25 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please +see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site +like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or +[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From c8a8fc5195ade55ccac3cf2f1fec05b9942a744b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:18 +0200 Subject: [PATCH 071/586] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/ar/gethelp.md diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md new file mode 100644 index 0000000000..cce276b98a --- /dev/null +++ b/content/ar/gethelp.md @@ -0,0 +1,25 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please +see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site +like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or +[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From 1cada99be3401636233a9e2ac909927d61821395 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:20 +0200 Subject: [PATCH 072/586] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 22 +++++----------------- 1 file changed, 5 insertions(+), 17 deletions(-) diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md index 0a77e294c0..7bde760dc4 100644 --- a/content/ja/gethelp.md +++ b/content/ja/gethelp.md @@ -3,32 +3,20 @@ title: サポートを得る方法 sidebar: false --- -**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大です。 - **開発関連の問題:** NumPyの開発関連の問題 (例: バグレポート) については、[コミュニティ](/community) のページを参照してください。 - +**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大です。 We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、[ `#numpy` タグ](https://stackoverflow.com/help/tagging) を使用してください。 +NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、[ `#numpy` タグ](https://stackoverflow.com/help/tagging) を使用してください。 [Gitter](https://gitter.im/numpy/numpy) *** ### [Reddit](https://www.reddit.com/r/Numpy/) -もう一つの使い方に関する質問の場です。 - -*** - -### [Gitter](https://gitter.im/numpy/numpy) - -ユーザーとコミュニティメンバーがお互いに助け合うリアルタイムのチャットルームです。 - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -ユーザーとコミュニティメンバーがお互いを助け合うもう一つのリアルタイムチャットルームです。 +Another forum for usage questions. *** From 7658d716208d539ca887201f781423db22d15784 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:21 +0200 Subject: [PATCH 073/586] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/ko/gethelp.md diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md new file mode 100644 index 0000000000..cce276b98a --- /dev/null +++ b/content/ko/gethelp.md @@ -0,0 +1,25 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please +see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site +like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or +[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From 29fe95220a3d021ac5dc7bce891a575a88d726a1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:22 +0200 Subject: [PATCH 074/586] New translations gethelp.md (Russian) --- content/ru/gethelp.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/ru/gethelp.md diff --git a/content/ru/gethelp.md b/content/ru/gethelp.md new file mode 100644 index 0000000000..cce276b98a --- /dev/null +++ b/content/ru/gethelp.md @@ -0,0 +1,25 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please +see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site +like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or +[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From 4e0f0d9d4f5658ff788662ba5d51f43ba0b7dd4d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:23 +0200 Subject: [PATCH 075/586] New translations gethelp.md (Chinese Simplified) --- content/zh/gethelp.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/zh/gethelp.md diff --git a/content/zh/gethelp.md b/content/zh/gethelp.md new file mode 100644 index 0000000000..cce276b98a --- /dev/null +++ b/content/zh/gethelp.md @@ -0,0 +1,25 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please +see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site +like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or +[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From 9fa5d5c48e4a8e9b95c688f86922a12767b346a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:24 +0200 Subject: [PATCH 076/586] New translations gethelp.md (Portuguese, Brazilian) --- content/pt/gethelp.md | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/content/pt/gethelp.md b/content/pt/gethelp.md index bba586e7f2..047be9368d 100644 --- a/content/pt/gethelp.md +++ b/content/pt/gethelp.md @@ -3,11 +3,9 @@ title: Obter ajuda sidebar: false --- -**Perguntas de usuários:** A melhor maneira de obter ajuda é postar sua pergunta em um site como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), com milhares de usuários disponíveis para responder. Outras alternativas incluem [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy)e [Reddit](https://www.reddit.com/r/Numpy/). Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso! - **Issues sobre desenvolvimento:** Para assuntos relacionados ao desenvolvimento do NumPy (por exemplo, relatórios de bugs), veja a [Comunidade](/community). - +**Perguntas de usuários:** A melhor maneira de obter ajuda é postar sua pergunta em um site como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), com milhares de usuários disponíveis para responder. Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ Um fórum para fazer perguntas sobre a utilização da biblioteca, por exemplo: Outro fórum para perguntas de utilização. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -Uma sala de bate-papo em tempo real onde usuários e membros da comunidade se ajudam uns aos outros. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -Outra sala de bate-papo em tempo real onde usuários e membros da comunidade se ajudam uns aos outros. - -*** From ee5b9672052c000e919b3dd38916bdd5abccdbda Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:25 +0200 Subject: [PATCH 077/586] New translations history.md (Spanish) --- content/es/history.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/es/history.md diff --git a/content/es/history.md b/content/es/history.md new file mode 100644 index 0000000000..eafe550ab0 --- /dev/null +++ b/content/es/history.md @@ -0,0 +1,20 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* + +[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* + +\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From d5feaba038ab7c68355a6ba0f4fc731292aca490 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:26 +0200 Subject: [PATCH 078/586] New translations history.md (Arabic) --- content/ar/history.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/ar/history.md diff --git a/content/ar/history.md b/content/ar/history.md new file mode 100644 index 0000000000..eafe550ab0 --- /dev/null +++ b/content/ar/history.md @@ -0,0 +1,20 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* + +[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* + +\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From 0fd7d36da20db0c87efe3e3ded8d3be0d53070e8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:27 +0200 Subject: [PATCH 079/586] New translations history.md (Japanese) --- content/ja/history.md | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/content/ja/history.md b/content/ja/history.md index 04a5eb6432..66ae3a1475 100644 --- a/content/ja/history.md +++ b/content/ja/history.md @@ -3,23 +3,18 @@ title: NumPyの歴史 sidebar: false --- -NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 開始当初は資金も少なく、主に大学院生により開発されていました。その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。少数の "野良"学生プログラマーのグループが、すでに確立されていた商用研究ソフトウェアのエコシステムをひっくり返すなんて、想像することすら馬鹿げていました。 商用ソフトは、何百万もの資金と何百人もの優秀なエンジニアに支えられていましたから。それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。現在では、NumPyは科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 このライブラリの開発開始当初は資金も少なく、主に大学院生が開発していましたが、その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。 何百万もの資金調達と何百人もの優秀なエンジニアに支えられている当時の商用研究ソフトウェアのエコシステムを、少数の "野良"学生プログラマーのグループがひっくり返すことができると想像することさえ、当時は馬鹿げていると考えられていました。 それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 現在では、Numpy は科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. NumPy および関連ライブラリの開発におけるマイルストーンの詳細については、 [arxiv.org](arxiv.org/abs/1907.10121) を参照してください。 NumPyのベースとなったNumericとNumarrayライブラリのコピーを入手したい場合は、以下のリンクを参照してください。 -[ *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/) のダウンロード** +[ _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/) のダウンロード\*\* -[*Numarray *](https://sourceforge.net/projects/numpy/files/Old%20Numarray/) のダウンロード** +[\*Numarray \*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/) のダウンロード\*\* -*これらの古いパッケージはもはや保守されていないことに注意してください。 配列関連の処理をしたい場合は、NumPyを使用するか、NumPyライブラリを利用するために既存のコードをリファクタリングすることを強くお勧めします。

+\*これらの古いパッケージはもはや保守されていないことに注意してください。 配列関連の処理をしたい場合は、NumPyを使用するか、NumPyライブラリを利用するために既存のコードをリファクタリングすることを強くお勧めします。

-

- 過去の資料 -

- -

- Numericマニュアルのダウンロード -

+### 過去の資料 +Numericマニュアルのダウンロード From 2c31150fff32f9c8a9bf4d1211f266ec1c063ea0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:28 +0200 Subject: [PATCH 080/586] New translations history.md (Korean) --- content/ko/history.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/ko/history.md diff --git a/content/ko/history.md b/content/ko/history.md new file mode 100644 index 0000000000..eafe550ab0 --- /dev/null +++ b/content/ko/history.md @@ -0,0 +1,20 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* + +[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* + +\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From e9411ae947997b46916f79a3324c618c99ac2762 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:29 +0200 Subject: [PATCH 081/586] New translations history.md (Russian) --- content/ru/history.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/ru/history.md diff --git a/content/ru/history.md b/content/ru/history.md new file mode 100644 index 0000000000..eafe550ab0 --- /dev/null +++ b/content/ru/history.md @@ -0,0 +1,20 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* + +[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* + +\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From d9df0268c6558346beff9de0b141b30bd80f5ee0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:30 +0200 Subject: [PATCH 082/586] New translations history.md (Chinese Simplified) --- content/zh/history.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/zh/history.md diff --git a/content/zh/history.md b/content/zh/history.md new file mode 100644 index 0000000000..eafe550ab0 --- /dev/null +++ b/content/zh/history.md @@ -0,0 +1,20 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* + +[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* + +\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From 51bfa739d2ad93fc6badba0e8083041aa4344471 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:31 +0200 Subject: [PATCH 083/586] New translations history.md (Portuguese, Brazilian) --- content/pt/history.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/content/pt/history.md b/content/pt/history.md index 2ddc33eb57..f62ae9b663 100644 --- a/content/pt/history.md +++ b/content/pt/history.md @@ -3,19 +3,18 @@ title: Histórico do NumPy sidebar: false --- -NumPy é uma biblioteca Python fundamental que fornece estruturas de *arrays* de dados e rotinas numéricas rápidas relacionadas a estas arrays. Quando começou, a biblioteca tinha pouco financiamento e foi escrita principalmente por estudantes de pós-graduação—muitos deles sem educação em ciência da computação e, muitas vezes, sem autorização dos seus orientadores. Imaginar que um pequeno grupo de programadores estudantis "desobedientes" poderiam subverter o já bem estabelecido ecossistema de software de pesquisa - apoiado por milhões em financiamento e muitas centenas de engenheiros altamente qualificados - era absurdo. No entanto, as motivações filosóficas por trás de uma ferramenta totalmente aberta, em combinação com a vibrante, amigável comunidade com foco singular, provaram ser auspiciosas a longo prazo. Hoje em dia, cientistas, engenheiros e muitos outros profissionais ao redor do mundo confiam no NumPy. Por exemplo, os scripts usados e publicados na análise de ondas gravitacionais importam o NumPy, e o projeto de imagem para buraco negro M87 cita diretamente o NumPy. +NumPy é uma biblioteca Python fundamental que fornece estruturas de _arrays_ de dados e rotinas numéricas rápidas relacionadas a estas arrays. Quando começou, a biblioteca tinha pouco financiamento e foi escrita principalmente por estudantes de pós-graduação—muitos deles sem educação em ciência da computação e, muitas vezes, sem autorização dos seus orientadores. Imaginar que um pequeno grupo de programadores estudantis "desobedientes" poderiam subverter o já bem estabelecido ecossistema de software de pesquisa - apoiado por milhões em financiamento e muitas centenas de engenheiros altamente qualificados - era absurdo. No entanto, as motivações filosóficas por trás de uma ferramenta totalmente aberta, em combinação com a vibrante, amigável comunidade com foco singular, provaram ser auspiciosas a longo prazo. Hoje em dia, cientistas, engenheiros e muitos outros profissionais ao redor do mundo confiam no NumPy. Por exemplo, os scripts usados e publicados na análise de ondas gravitacionais importam o NumPy, e o projeto de imagem para buraco negro M87 cita diretamente o NumPy. Para um histórico aprofundado dos marcos no desenvolvimento do NumPy e bibliotecas relacionadas, por favor veja [arxiv.org](arxiv.org/abs/1907.10121). Se você quiser obter uma cópia das bibliotecas Numeric e Numarray, siga os links abaixo: -[Página de download para *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* +[Página de download para _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* -[Página de download para *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* +[Página de download para _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* -*Por favor, note que esses pacotes antigos não são mais mantidos, e os usuários são fortemente aconselhados a usar o NumPy para quaisquer propósitos relacionados a arrays e matrizes ou refatorar qualquer código pré-existente para utilizar a biblioteca do NumPy. +\*Por favor, note que esses pacotes antigos não são mais mantidos, e os usuários são fortemente aconselhados a usar o NumPy para quaisquer propósitos relacionados a arrays e matrizes ou refatorar qualquer código pré-existente para utilizar a biblioteca do NumPy. ### Documentação Histórica -[Baixe o manual do *`Numeric'*](static/numeric-manual.pdf) - +[Baixe o manual do _\`Numeric'_](static/numeric-manual.pdf) From 645fcd148b7a093bf3943dd9be9b71ee4c544d65 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:33 +0200 Subject: [PATCH 084/586] New translations install.md (Spanish) --- content/es/install.md | 201 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 content/es/install.md diff --git a/content/es/install.md b/content/es/install.md new file mode 100644 index 0000000000..236d5dd53a --- /dev/null +++ b/content/es/install.md @@ -0,0 +1,201 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have +Python yet and want the simplest way to get started, we recommend you use the +[Anaconda Distribution](https://www.anaconda.com/download) - it includes +Python, NumPy, and many other commonly used packages for scientific computing +and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on +macOS and Linux, or [from source](https://numpy.org/devdocs/building). +For more detailed instructions, consult our Python and NumPy +installation guide below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` +channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` + +Also when using pip, it's good practice to use a virtual environment - +see [Reproducible Installs](#reproducible-installs) below for why, and +[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) +for details on using virtual environments. + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a +number of alternative solutions for most tasks. This guide tries to give the +reader a sense of the best (or most popular) solutions, and give clear +recommendations. It focuses on users of Python, NumPy, and the PyData (or +numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and +operating system of interest. If you're in between "beginning" and "advanced", +please go with "beginning" if you want to keep things simple, and with +"advanced" if you want to work according to best practices that go a longer way +in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all + packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in + [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for + exploratory and interactive computing, and + [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) + for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to + manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more + [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main +differences between conda and pip below, they prefer a pip/PyPI-based solution, +we recommend: + +- Install Python from [python.org](https://www.python.org/downloads/), + [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool + that provides a dependency resolver and environment management capabilities + in a similar fashion as conda does. + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of +tools. For web and general purpose Python development there's a whole +[host of tools](https://packaging.python.org/guides/tool-recommendations/) +complementary with pip. For high-performance computing (HPC), +[Spack](https://github.com/spack/spack) is worth considering. For most NumPy +users though, [conda](https://conda.io/en/latest/) and +[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their +functionality partially overlaps (e.g. both can install `numpy`), however, they +can also work together. We'll discuss the major differences between pip and +conda here - this is important to understand if you want to manage packages +effectively. + +The first difference is that conda is cross-language and it can install Python, +while pip is installed for a particular Python on your system and installs other +packages to that same Python install only. This also means conda can install +non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while +pip can't. + +The second difference is that pip installs from the Python Packaging Index +(PyPI), while conda installs from its own channels (typically "defaults" or +"conda-forge"). PyPI is the largest collection of packages by far, however, all +popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing +packages, dependencies and environments, while with pip you may need another +tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your +code can break completely. It's important to be able to reconstruct the set +of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; + each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and + [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an +accelerated linear algebra library - typically +[Intel MKL](https://software.intel.com/en-us/mkl) or +[OpenBLAS](https://www.openblas.net/). Users don't have to worry about +installing those (they're automatically included in all NumPy install methods). +Power users may still want to know the details, because the used BLAS can +affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. + The OpenBLAS libraries are included in the wheel. This makes the wheel + larger, and if a user installs (for example) SciPy as well, they will now + have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a + separate package that will be installed in the users' environment when they + install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When + a user installs NumPy from conda-forge, that BLAS package then gets installed + together with the actual library - this defaults to OpenBLAS, but it can also + be MKL (from the defaults channel), or even + [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk + while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to +consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if + a user needs to redistribute an application built with NumPy, this could be + an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like + `np.dot`, with the number of threads being determined by both a build-time + option and an environment variable. Often all CPU cores will be used. This is + sometimes unexpected for users; NumPy itself doesn't auto-parallelize any + function calls. It typically yields better performance, but can also be + harmful - for example when using another level of parallelization with Dask, + scikit-learn or multiprocessing. + +## Troubleshooting + +If your installation fails with the message below, see Troubleshooting +ImportError. + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` From e0c2c4cc826f02cb4c3c582653f261fed7e11dfc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:34 +0200 Subject: [PATCH 085/586] New translations install.md (Arabic) --- content/ar/install.md | 201 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 content/ar/install.md diff --git a/content/ar/install.md b/content/ar/install.md new file mode 100644 index 0000000000..236d5dd53a --- /dev/null +++ b/content/ar/install.md @@ -0,0 +1,201 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have +Python yet and want the simplest way to get started, we recommend you use the +[Anaconda Distribution](https://www.anaconda.com/download) - it includes +Python, NumPy, and many other commonly used packages for scientific computing +and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on +macOS and Linux, or [from source](https://numpy.org/devdocs/building). +For more detailed instructions, consult our Python and NumPy +installation guide below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` +channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` + +Also when using pip, it's good practice to use a virtual environment - +see [Reproducible Installs](#reproducible-installs) below for why, and +[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) +for details on using virtual environments. + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a +number of alternative solutions for most tasks. This guide tries to give the +reader a sense of the best (or most popular) solutions, and give clear +recommendations. It focuses on users of Python, NumPy, and the PyData (or +numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and +operating system of interest. If you're in between "beginning" and "advanced", +please go with "beginning" if you want to keep things simple, and with +"advanced" if you want to work according to best practices that go a longer way +in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all + packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in + [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for + exploratory and interactive computing, and + [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) + for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to + manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more + [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main +differences between conda and pip below, they prefer a pip/PyPI-based solution, +we recommend: + +- Install Python from [python.org](https://www.python.org/downloads/), + [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool + that provides a dependency resolver and environment management capabilities + in a similar fashion as conda does. + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of +tools. For web and general purpose Python development there's a whole +[host of tools](https://packaging.python.org/guides/tool-recommendations/) +complementary with pip. For high-performance computing (HPC), +[Spack](https://github.com/spack/spack) is worth considering. For most NumPy +users though, [conda](https://conda.io/en/latest/) and +[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their +functionality partially overlaps (e.g. both can install `numpy`), however, they +can also work together. We'll discuss the major differences between pip and +conda here - this is important to understand if you want to manage packages +effectively. + +The first difference is that conda is cross-language and it can install Python, +while pip is installed for a particular Python on your system and installs other +packages to that same Python install only. This also means conda can install +non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while +pip can't. + +The second difference is that pip installs from the Python Packaging Index +(PyPI), while conda installs from its own channels (typically "defaults" or +"conda-forge"). PyPI is the largest collection of packages by far, however, all +popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing +packages, dependencies and environments, while with pip you may need another +tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your +code can break completely. It's important to be able to reconstruct the set +of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; + each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and + [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an +accelerated linear algebra library - typically +[Intel MKL](https://software.intel.com/en-us/mkl) or +[OpenBLAS](https://www.openblas.net/). Users don't have to worry about +installing those (they're automatically included in all NumPy install methods). +Power users may still want to know the details, because the used BLAS can +affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. + The OpenBLAS libraries are included in the wheel. This makes the wheel + larger, and if a user installs (for example) SciPy as well, they will now + have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a + separate package that will be installed in the users' environment when they + install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When + a user installs NumPy from conda-forge, that BLAS package then gets installed + together with the actual library - this defaults to OpenBLAS, but it can also + be MKL (from the defaults channel), or even + [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk + while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to +consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if + a user needs to redistribute an application built with NumPy, this could be + an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like + `np.dot`, with the number of threads being determined by both a build-time + option and an environment variable. Often all CPU cores will be used. This is + sometimes unexpected for users; NumPy itself doesn't auto-parallelize any + function calls. It typically yields better performance, but can also be + harmful - for example when using another level of parallelization with Dask, + scikit-learn or multiprocessing. + +## Troubleshooting + +If your installation fails with the message below, see Troubleshooting +ImportError. + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` From a0975353021858910a5890d8d11289ad7761400e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:36 +0200 Subject: [PATCH 086/586] New translations install.md (Japanese) --- content/ja/install.md | 73 ++++++++++++++++++++++++++++++------------- 1 file changed, 51 insertions(+), 22 deletions(-) diff --git a/content/ja/install.md b/content/ja/install.md index ba4c568ec2..b503f38663 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -3,9 +3,11 @@ title: NumPyのインストール sidebar: false --- -NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、[Anaconda Distribution](https://www.anaconda.com/distribution)の使用をおすすめします。これにはPython、NumPy、および科学計算やデータサイエンスでよく使われる様々な多くのパッケージが含まれています。 +NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、[Anaconda Distribution](https://www.anaconda.com/distribution)の使用をおすすめします。これにはPython、NumPy、および科学計算やデータサイエンスでよく使われる様々な多くのパッケージが含まれています。 まずはユーザの経験レベルと、関心のあるOSに基づいた推奨方法から説明していきたいと思います。 PythonやNumPyの経験が「初級」と「上級」の間の方は、シンプルにインストールしたい場合は「初級」を、より長い視点にたったベストプラクティスに沿ってインストールしたい方は「上級」を参照ください。 NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/building)からインストールすることが出来ます。 詳細な手順については、以下の [Python と Numpyの インストールガイド](#python-numpy-install-guide) を参照してください。 +For more detailed instructions, consult our Python and NumPy +installation guide below. **CONDA** @@ -28,18 +30,25 @@ conda install numpy ```bash pip install numpy ``` -またpipを使う場合、仮想環境を使うことをおすすめします。 [再現可能なインストール](#reproducible-installs)を参照ください。 [こちらの記事](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)では仮想環境を使う詳細について説明されています。 +またpipを使う場合、仮想環境を使うことをおすすめします。 [再現可能なインストール](#reproducible-installs)を参照ください。 [こちらの記事](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)では仮想環境を使う詳細について説明されています。 # PythonとNumPyの インストールガイド -Pythonパッケージのインストールと管理は複雑なので、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 +Pythonパッケージのインストールと管理は複雑なので、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 This guide tries to give the +reader a sense of the best (or most popular) solutions, and give clear +recommendations. It focuses on users of Python, NumPy, and the PyData (or +numerical computing) stack on common operating systems and hardware. ## 推奨方法 -まずはユーザの経験レベルと、関心のあるOSに基づいた推奨方法から説明していきたいと思います。 PythonやNumPyの経験が「初級」と「上級」の間の方は、シンプルにインストールしたい場合は「初級」を、より長い視点にたったベストプラクティスに沿ってインストールしたい方は「上級」を参照ください。 +We'll start with recommendations based on the user's experience level and +operating system of interest. If you're in between "beginning" and "advanced", +please go with "beginning" if you want to keep things simple, and with +"advanced" if you want to work according to best practices that go a longer way +in the future. ### 初級ユーザ @@ -49,7 +58,6 @@ Windows、macOS、Linuxのすべてのユーザー向けには: - コードを書いたり、実行してみましょう。 探索的・対話的コンピューティングには[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html)のノートブックが便利です。 スクリプトやパッケージの作成には[Spyder](https://www.spyder-ide.org/)や[Visual Studio Code](https://code.visualstudio.com/)を利用できます。 - [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) を使ってパッケージを管理し、JupyterLab、Spyder、Visual Studio Codeを使い始められます。 - ### 上級ユーザー #### Conda @@ -60,30 +68,41 @@ Windows、macOS、Linuxのすべてのユーザー向けには: #### pip/PyPI を利用したい場合 個人的な好みや、下記のcondaとpipの違いを理解した上で、pip/PyPIベースの方法を使いたいユーザーには、下記をお勧めします: + - [python.org](https://www.python.org/downloads/)からや、Macを使っている場合は[Homebrew](https://brew.sh/)、 Linuxを使っている場合は、Linuxのパッケージマネージャーを使ってPythonをインストールします。 - 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry.org/) をconda と同様な方法で使用することができます。 - ## Pythonにおけるパッケージ管理 -パッケージの管理は難しいため、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 - +Managing packages is a challenging problem, and, as a result, there are lots of +tools. パッケージの管理は難しいため、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 For high-performance computing (HPC), +[Spack](https://github.com/spack/spack) is worth considering. For most NumPy +users though, [conda](https://conda.io/en/latest/) and +[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. ### Pipとconda -`pip` と `conda` がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも `numpy`をインストールできます)、一緒に動作することもできます。 ここでは、pip とcond の主要な違いについて説明します。 これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。 +`pip` と `conda` がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも `numpy`をインストールできます)、一緒に動作することもできます。 ここでは、pip とcond の主要な違いについて説明します。 これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。 Their +functionality partially overlaps (e.g. both can install `numpy`), however, they +can also work together. We'll discuss the major differences between pip and +conda here - this is important to understand if you want to manage packages +effectively. -2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には "defaults "や "conda-forge "など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。 +最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPIは、最大のパッケージ管理システムですが、すべての代表的なパッケージは、condaにも利用可能です。 This also means conda can install +non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while +pip can't. -最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPIは、最大のパッケージ管理システムですが、すべての代表的なパッケージは、condaにも利用可能です。 +2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には "defaults "や "conda-forge "など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。 PyPI is the largest collection of packages by far, however, all +popular packages are available for conda as well. -3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ +3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ for dealing with environments or complex dependencies. ### 再現可能なインストール -ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: +ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: It's important to be able to reconstruct the set +of packages and versions you're using. Best practice is to: 1. プロジェクトごとに異なる仮想環境を使用して下さい。 2. パッケージインストーラを使用してパッケージ名とバージョンを記録するようにして下さい。 それぞれ、独自のメタデータフォーマットがあります: @@ -91,17 +110,23 @@ Windows、macOS、Linuxのすべてのユーザー向けには: - pipの場合: [仮想環境](https://docs.python.org/3/tutorial/venv.html) と [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - poetryの場合: [仮想環境とpyproject.toml](https://python-poetry.org/docs/basic-usage/) - - ## NumPyパッケージと高速線形代数ライブラリ -NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。 典型的には、[インテル® MKL](https://software.intel.com/en-us/mkl)や[OpenBLAS](https://www.openblas.net/)がこれにあたります。 ユーザーは、これらの線形代数ライブラリのインストールを心配する必要はありません (NumPyのインストール方法に、あらかじめ含まれているためです)。 高度なユーザーは、使用されているBLASがパフォーマンスや、動作、ディスク上のサイズに影響を与えるため、より詳細を知りたがるかもしれません。 +NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。 典型的には、[インテル® MKL](https://software.intel.com/en-us/mkl)や[OpenBLAS](https://www.openblas.net/)がこれにあたります。 ユーザーは、これらの線形代数ライブラリのインストールを心配する必要はありません (NumPyのインストール方法に、あらかじめ含まれているためです)。 高度なユーザーは、使用されているBLASがパフォーマンスや、動作、ディスク上のサイズに影響を与えるため、より詳細を知りたがるかもしれません。 Users don't have to worry about +installing those (they're automatically included in all NumPy install methods). +Power users may still want to know the details, because the used BLAS can +affect performance, behavior and size on disk: - pipでインストールされるPyPI上の NumPy wheelは、OpenBLASを使ってビルドされます。 つまりwheelにはOpenBLASライブラリが含まれています。 そのため、ユーザが（例えば）SciPyも同じようにインストールした場合、ディスク上にOpenBLASのコピーをNumPyのものと2つ持つことになります + The OpenBLAS libraries are included in the wheel. This makes the wheel + larger, and if a user installs (for example) SciPy as well, they will now + have two copies of OpenBLAS on disk. -- condaのデフォルトチャンネルでは、NumPy はインテル® MKLを使ってビルドされます。 MKLはNumPyのインストール時に、独立したパッケージとしてユーザー環境にインストールされます。 +- condaのデフォルトチャンネルでは、NumPy はインテル® MKLを使ってビルドされます。 MKLはNumPyのインストール時に、独立したパッケージとしてユーザー環境にインストールされます。 MKL is a + separate package that will be installed in the users' environment when they + install NumPy. -- conda-forgeのチャンネルでは、NumPyはダミーの「BLAS」パッケージを使ってビルドされています。 ユーザーがconda-forgeからNumPyをインストールすると、BLASパッケージが実際のライブラリと一緒にインストールされます。 デフォルトはOpenBLASですが、MKL(default チャンネルの場合)や [BLIS](https://github.com/flame/blis)、またはBLASを利用することもできます。 +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. conda-forgeのチャンネルでは、NumPyはダミーの「BLAS」パッケージを使ってビルドされています。 ユーザーがconda-forgeからNumPyをインストールすると、BLASパッケージが実際のライブラリと一緒にインストールされます。 デフォルトはOpenBLASですが、MKL(default チャンネルの場合)や [BLIS](https://github.com/flame/blis)、またはBLASを利用することもできます。 - OpenBLASは約30MBですが、MKLパッケージはOpenBLASよりもはるかに大きく、ディスク上の約700MBです。 @@ -109,9 +134,14 @@ NumPy は他の Python パッケージに依存していませんが、高速な インストールサイズ、パフォーマンスとロバスト性に加えて、考慮すべき2つの点があります: -- インテル® MKL はオープンソースではありません。 通常の使用では問題ではありませんが、 ユーザーが NumPy で構築されたアプリケーションを再配布する必要がある場合、これは 問題が発生する可能性があります。 -- MKLとOpenBLASの両方とも、 np.dotのような関数呼び出しにマルチスレッドを使用し、スレッド数はビルド時オプションと環境変数の両方で決定されます。 多くの場合、すべての CPU コアが使用されます。 これにユーザーにとっては予想外のことかもしれません。 NumPy 自体は、関数呼び出しを自動的に並列化しないからです。 自動並列化により、一般にはパフォーマンスが向上しますが、逆にパフォーマンスが悪化する場合もあります。 例えば、Daskやscikit-learn、multiprocessingなど別のレベルの並列化を使用している場合です。 - +- インテル® MKL はオープンソースではありません。 通常の使用では問題ではありませんが、 ユーザーが NumPy で構築されたアプリケーションを再配布する必要がある場合、これは 問題が発生する可能性があります。 For normal use this is not a problem, but if + a user needs to redistribute an application built with NumPy, this could be + an issue. +- MKLとOpenBLASの両方とも、 np.dotのような関数呼び出しにマルチスレッドを使用し、スレッド数はビルド時オプションと環境変数の両方で決定されます。 多くの場合、すべての CPU コアが使用されます。 これにユーザーにとっては予想外のことかもしれません。 NumPy 自体は、関数呼び出しを自動的に並列化しないからです。 自動並列化により、一般にはパフォーマンスが向上しますが、逆にパフォーマンスが悪化する場合もあります。 例えば、Daskやscikit-learn、multiprocessingなど別のレベルの並列化を使用している場合です。 Often all CPU cores will be used. This is + sometimes unexpected for users; NumPy itself doesn't auto-parallelize any + function calls. It typically yields better performance, but can also be + harmful - for example when using another level of parallelization with Dask, + scikit-learn or multiprocessing. ## トラブルシューティング @@ -122,4 +152,3 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` - From 3c25a4b21076f70032d053346393b6c162424267 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:37 +0200 Subject: [PATCH 087/586] New translations install.md (Korean) --- content/ko/install.md | 201 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 content/ko/install.md diff --git a/content/ko/install.md b/content/ko/install.md new file mode 100644 index 0000000000..236d5dd53a --- /dev/null +++ b/content/ko/install.md @@ -0,0 +1,201 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have +Python yet and want the simplest way to get started, we recommend you use the +[Anaconda Distribution](https://www.anaconda.com/download) - it includes +Python, NumPy, and many other commonly used packages for scientific computing +and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on +macOS and Linux, or [from source](https://numpy.org/devdocs/building). +For more detailed instructions, consult our Python and NumPy +installation guide below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` +channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` + +Also when using pip, it's good practice to use a virtual environment - +see [Reproducible Installs](#reproducible-installs) below for why, and +[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) +for details on using virtual environments. + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a +number of alternative solutions for most tasks. This guide tries to give the +reader a sense of the best (or most popular) solutions, and give clear +recommendations. It focuses on users of Python, NumPy, and the PyData (or +numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and +operating system of interest. If you're in between "beginning" and "advanced", +please go with "beginning" if you want to keep things simple, and with +"advanced" if you want to work according to best practices that go a longer way +in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all + packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in + [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for + exploratory and interactive computing, and + [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) + for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to + manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more + [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main +differences between conda and pip below, they prefer a pip/PyPI-based solution, +we recommend: + +- Install Python from [python.org](https://www.python.org/downloads/), + [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool + that provides a dependency resolver and environment management capabilities + in a similar fashion as conda does. + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of +tools. For web and general purpose Python development there's a whole +[host of tools](https://packaging.python.org/guides/tool-recommendations/) +complementary with pip. For high-performance computing (HPC), +[Spack](https://github.com/spack/spack) is worth considering. For most NumPy +users though, [conda](https://conda.io/en/latest/) and +[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their +functionality partially overlaps (e.g. both can install `numpy`), however, they +can also work together. We'll discuss the major differences between pip and +conda here - this is important to understand if you want to manage packages +effectively. + +The first difference is that conda is cross-language and it can install Python, +while pip is installed for a particular Python on your system and installs other +packages to that same Python install only. This also means conda can install +non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while +pip can't. + +The second difference is that pip installs from the Python Packaging Index +(PyPI), while conda installs from its own channels (typically "defaults" or +"conda-forge"). PyPI is the largest collection of packages by far, however, all +popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing +packages, dependencies and environments, while with pip you may need another +tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your +code can break completely. It's important to be able to reconstruct the set +of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; + each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and + [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an +accelerated linear algebra library - typically +[Intel MKL](https://software.intel.com/en-us/mkl) or +[OpenBLAS](https://www.openblas.net/). Users don't have to worry about +installing those (they're automatically included in all NumPy install methods). +Power users may still want to know the details, because the used BLAS can +affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. + The OpenBLAS libraries are included in the wheel. This makes the wheel + larger, and if a user installs (for example) SciPy as well, they will now + have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a + separate package that will be installed in the users' environment when they + install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When + a user installs NumPy from conda-forge, that BLAS package then gets installed + together with the actual library - this defaults to OpenBLAS, but it can also + be MKL (from the defaults channel), or even + [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk + while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to +consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if + a user needs to redistribute an application built with NumPy, this could be + an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like + `np.dot`, with the number of threads being determined by both a build-time + option and an environment variable. Often all CPU cores will be used. This is + sometimes unexpected for users; NumPy itself doesn't auto-parallelize any + function calls. It typically yields better performance, but can also be + harmful - for example when using another level of parallelization with Dask, + scikit-learn or multiprocessing. + +## Troubleshooting + +If your installation fails with the message below, see Troubleshooting +ImportError. + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` From 72653896cbf5a2290cdf0c757f81304eeea0ffbe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:38 +0200 Subject: [PATCH 088/586] New translations install.md (Russian) --- content/ru/install.md | 201 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 content/ru/install.md diff --git a/content/ru/install.md b/content/ru/install.md new file mode 100644 index 0000000000..236d5dd53a --- /dev/null +++ b/content/ru/install.md @@ -0,0 +1,201 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have +Python yet and want the simplest way to get started, we recommend you use the +[Anaconda Distribution](https://www.anaconda.com/download) - it includes +Python, NumPy, and many other commonly used packages for scientific computing +and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on +macOS and Linux, or [from source](https://numpy.org/devdocs/building). +For more detailed instructions, consult our Python and NumPy +installation guide below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` +channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` + +Also when using pip, it's good practice to use a virtual environment - +see [Reproducible Installs](#reproducible-installs) below for why, and +[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) +for details on using virtual environments. + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a +number of alternative solutions for most tasks. This guide tries to give the +reader a sense of the best (or most popular) solutions, and give clear +recommendations. It focuses on users of Python, NumPy, and the PyData (or +numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and +operating system of interest. If you're in between "beginning" and "advanced", +please go with "beginning" if you want to keep things simple, and with +"advanced" if you want to work according to best practices that go a longer way +in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all + packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in + [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for + exploratory and interactive computing, and + [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) + for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to + manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more + [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main +differences between conda and pip below, they prefer a pip/PyPI-based solution, +we recommend: + +- Install Python from [python.org](https://www.python.org/downloads/), + [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool + that provides a dependency resolver and environment management capabilities + in a similar fashion as conda does. + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of +tools. For web and general purpose Python development there's a whole +[host of tools](https://packaging.python.org/guides/tool-recommendations/) +complementary with pip. For high-performance computing (HPC), +[Spack](https://github.com/spack/spack) is worth considering. For most NumPy +users though, [conda](https://conda.io/en/latest/) and +[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their +functionality partially overlaps (e.g. both can install `numpy`), however, they +can also work together. We'll discuss the major differences between pip and +conda here - this is important to understand if you want to manage packages +effectively. + +The first difference is that conda is cross-language and it can install Python, +while pip is installed for a particular Python on your system and installs other +packages to that same Python install only. This also means conda can install +non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while +pip can't. + +The second difference is that pip installs from the Python Packaging Index +(PyPI), while conda installs from its own channels (typically "defaults" or +"conda-forge"). PyPI is the largest collection of packages by far, however, all +popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing +packages, dependencies and environments, while with pip you may need another +tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your +code can break completely. It's important to be able to reconstruct the set +of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; + each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and + [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an +accelerated linear algebra library - typically +[Intel MKL](https://software.intel.com/en-us/mkl) or +[OpenBLAS](https://www.openblas.net/). Users don't have to worry about +installing those (they're automatically included in all NumPy install methods). +Power users may still want to know the details, because the used BLAS can +affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. + The OpenBLAS libraries are included in the wheel. This makes the wheel + larger, and if a user installs (for example) SciPy as well, they will now + have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a + separate package that will be installed in the users' environment when they + install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When + a user installs NumPy from conda-forge, that BLAS package then gets installed + together with the actual library - this defaults to OpenBLAS, but it can also + be MKL (from the defaults channel), or even + [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk + while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to +consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if + a user needs to redistribute an application built with NumPy, this could be + an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like + `np.dot`, with the number of threads being determined by both a build-time + option and an environment variable. Often all CPU cores will be used. This is + sometimes unexpected for users; NumPy itself doesn't auto-parallelize any + function calls. It typically yields better performance, but can also be + harmful - for example when using another level of parallelization with Dask, + scikit-learn or multiprocessing. + +## Troubleshooting + +If your installation fails with the message below, see Troubleshooting +ImportError. + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` From 65a8793288ddffc75211a19038b81d9042ae0e4c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:39 +0200 Subject: [PATCH 089/586] New translations install.md (Chinese Simplified) --- content/zh/install.md | 201 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 content/zh/install.md diff --git a/content/zh/install.md b/content/zh/install.md new file mode 100644 index 0000000000..236d5dd53a --- /dev/null +++ b/content/zh/install.md @@ -0,0 +1,201 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have +Python yet and want the simplest way to get started, we recommend you use the +[Anaconda Distribution](https://www.anaconda.com/download) - it includes +Python, NumPy, and many other commonly used packages for scientific computing +and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on +macOS and Linux, or [from source](https://numpy.org/devdocs/building). +For more detailed instructions, consult our Python and NumPy +installation guide below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` +channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` + +Also when using pip, it's good practice to use a virtual environment - +see [Reproducible Installs](#reproducible-installs) below for why, and +[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) +for details on using virtual environments. + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a +number of alternative solutions for most tasks. This guide tries to give the +reader a sense of the best (or most popular) solutions, and give clear +recommendations. It focuses on users of Python, NumPy, and the PyData (or +numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and +operating system of interest. If you're in between "beginning" and "advanced", +please go with "beginning" if you want to keep things simple, and with +"advanced" if you want to work according to best practices that go a longer way +in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all + packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in + [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for + exploratory and interactive computing, and + [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) + for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to + manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more + [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main +differences between conda and pip below, they prefer a pip/PyPI-based solution, +we recommend: + +- Install Python from [python.org](https://www.python.org/downloads/), + [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool + that provides a dependency resolver and environment management capabilities + in a similar fashion as conda does. + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of +tools. For web and general purpose Python development there's a whole +[host of tools](https://packaging.python.org/guides/tool-recommendations/) +complementary with pip. For high-performance computing (HPC), +[Spack](https://github.com/spack/spack) is worth considering. For most NumPy +users though, [conda](https://conda.io/en/latest/) and +[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their +functionality partially overlaps (e.g. both can install `numpy`), however, they +can also work together. We'll discuss the major differences between pip and +conda here - this is important to understand if you want to manage packages +effectively. + +The first difference is that conda is cross-language and it can install Python, +while pip is installed for a particular Python on your system and installs other +packages to that same Python install only. This also means conda can install +non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while +pip can't. + +The second difference is that pip installs from the Python Packaging Index +(PyPI), while conda installs from its own channels (typically "defaults" or +"conda-forge"). PyPI is the largest collection of packages by far, however, all +popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing +packages, dependencies and environments, while with pip you may need another +tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your +code can break completely. It's important to be able to reconstruct the set +of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; + each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and + [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an +accelerated linear algebra library - typically +[Intel MKL](https://software.intel.com/en-us/mkl) or +[OpenBLAS](https://www.openblas.net/). Users don't have to worry about +installing those (they're automatically included in all NumPy install methods). +Power users may still want to know the details, because the used BLAS can +affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. + The OpenBLAS libraries are included in the wheel. This makes the wheel + larger, and if a user installs (for example) SciPy as well, they will now + have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a + separate package that will be installed in the users' environment when they + install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When + a user installs NumPy from conda-forge, that BLAS package then gets installed + together with the actual library - this defaults to OpenBLAS, but it can also + be MKL (from the defaults channel), or even + [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk + while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to +consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if + a user needs to redistribute an application built with NumPy, this could be + an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like + `np.dot`, with the number of threads being determined by both a build-time + option and an environment variable. Often all CPU cores will be used. This is + sometimes unexpected for users; NumPy itself doesn't auto-parallelize any + function calls. It typically yields better performance, but can also be + harmful - for example when using another level of parallelization with Dask, + scikit-learn or multiprocessing. + +## Troubleshooting + +If your installation fails with the message below, see Troubleshooting +ImportError. + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` From a62d4d47cae779cc14b9d98dccc1e32d42468753 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:41 +0200 Subject: [PATCH 090/586] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 29 +++++++++++++---------------- 1 file changed, 13 insertions(+), 16 deletions(-) diff --git a/content/pt/install.md b/content/pt/install.md index 371bb833af..cf3adf3a9a 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -5,7 +5,8 @@ sidebar: false O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem o Python e quer começar do jeito mais simples, nós recomendamos que você use a [Distribuição Anaconda](https://www.anaconda.com/distribution) - inclui Python, NumPy e outros pacotes comumente usados para computação científica e ciência de dados. -O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [da fonte](https://numpy.org/devdocs/building). Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. +O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [da fonte](https://numpy.org/devdocs/building). +Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. **CONDA** @@ -28,14 +29,14 @@ Se você usa o `pip`, você pode instalar o NumPy com: ```bash pip install numpy ``` -Também ao usar o pip, é uma boa prática usar um ambiente virtual - veja em [Instalações Reprodutíveis](#reproducible-installs) abaixo por quê, e [esse guia](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) para detalhes sobre o uso de ambientes virtuais. +Também ao usar o pip, é uma boa prática usar um ambiente virtual - veja em [Instalações Reprodutíveis](#reproducible-installs) abaixo por quê, e [esse guia](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) para detalhes sobre o uso de ambientes virtuais. # Guia de instalação do Python e do NumPy -Instalar e gerenciar pacotes no Python pode ser complicado. Há várias soluções alternativas para a maioria das tarefas. Este guia tenta dar ao leitor um resumo das melhores (ou mais populares) soluções e dar recomendações claras. Ele se concentra em usuários do Python, NumPy e do PyData (ou computação numérica) em sistemas operacionais e hardware comuns. +Instalar e gerenciar pacotes no Python pode ser complicado. Este guia tenta dar ao leitor um resumo das melhores (ou mais populares) soluções e dar recomendações claras. Ele se concentra em usuários do Python, NumPy e do PyData (ou computação numérica) em sistemas operacionais e hardware comuns. ## Recomendações @@ -49,7 +50,6 @@ Em Windows, macOS e Linux: - Para escrever e executar código, use notebooks no [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) para a computação exploratória e interativa, e o [Spyder](https://www.spyder-ide.org/) ou [Visual Studio Code](https://code.visualstudio.com/) para escrever scripts e pacotes. - Use o [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) para gerenciar seus pacotes e iniciar o JupyterLab, Spyder ou o Visual Studio Code. - ### Usuários avançados #### Conda @@ -60,18 +60,17 @@ Em Windows, macOS e Linux: #### Alternativa se você preferir pip/PyPI Para usuários que preferem uma solução baseada em pip/PyPI, por preferência pessoal ou leitura sobre as principais diferenças entre o conda e o pip, nós recomendamos: + - Instale o Python a partir de, por exemplo, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), ou seu gerenciador de pacotes Linux. - Use [Poetry](https://python-poetry.org/) como a ferramenta mais bem mantida que fornece um resolvedor de dependências e recursos de gerenciamento de ambiente de forma semelhante ao que o conda faz. - ## Gerenciamento de pacotes Python -Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. - +Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. ### Pip & conda -As duas principais ferramentas que instalam pacotes do Python são `pip` e `conda`. Algumas de suas funcionalidades são redundantes (por exemplo, ambos podem instalar o `numpy`). No entanto, elas também podem trabalhar juntas. Vamos discutir as principais diferenças entre o pip e o conda aqui - é importante entender isso se você deseja gerenciar pacotes de forma efetiva. +As duas principais ferramentas que instalam pacotes do Python são `pip` e `conda`. Algumas de suas funcionalidades são redundantes (por exemplo, ambos podem instalar o `numpy`). Vamos discutir as principais diferenças entre o pip e o conda aqui - é importante entender isso se você deseja gerenciar pacotes de forma efetiva. A primeira diferença é que "conda" é multilinguagens e pode instalar o Python, enquanto o pip é instalado em um determinado Python em seu sistema e instala outros pacotes apenas para essa mesma instalação de Python. Isto também significa que o conda pode instalar bibliotecas e ferramentas não-Python das quais você pode precisar (por exemplo, compiladores, CUDA, HDF5), enquanto pip não pode. @@ -83,7 +82,7 @@ A terceira diferença é que o conda é uma solução integrada para gerenciar p ### Instalações reprodutíveis -À medida que as bibliotecas são atualizadas, os resultados obtidos ao executar seu código podem mudar, ou o seu código pode parar de funcionar. É importante poder reconstruir o conjunto de pacotes e versões que você está usando. A recomendação é: +À medida que as bibliotecas são atualizadas, os resultados obtidos ao executar seu código podem mudar, ou o seu código pode parar de funcionar. É importante poder reconstruir o conjunto de pacotes e versões que você está usando. Best practice is to: 1. usar um ambiente diferente para cada projeto em que você trabalha, 2. gravar nomes de pacotes e versões usando seu instalador de pacotes; cada um tem seu próprio formato de metadados para essa tarefa: @@ -91,17 +90,17 @@ A terceira diferença é que o conda é uma solução integrada para gerenciar p - Pip: [ambientes virtuais](https://docs.python.org/3/tutorial/venv.html) e [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [ambientes virtuais e pyproject.toml](https://python-poetry.org/docs/basic-usage/) - - ## Pacotes NumPy & bibliotecas de álgebra linear aceleradas -O NumPy não depende de quaisquer outros pacotes Python. No entanto, depende de uma biblioteca de álgebra linear acelerada - tipicamente [Intel MKL](https://software.intel.com/en-us/mkl) ou [OpenBLAS](https://www.openblas.net/). Os usuários não precisam se preocupar com a instalação desses pacotes (eles são incluídos automaticamente em todos os métodos de instalação do NumPy). No entanto, usuários experientes podem querer saber os detalhes, porque o BLAS usado pode afetar o desempenho, o comportamento e o tamanho em disco: +No entanto, depende de uma biblioteca de álgebra linear acelerada - tipicamente [Intel MKL](https://software.intel.com/en-us/mkl) ou [OpenBLAS](https://www.openblas.net/). Os usuários não precisam se preocupar com a instalação desses pacotes (eles são incluídos automaticamente em todos os métodos de instalação do NumPy). +No entanto, usuários experientes podem querer saber os detalhes, porque o BLAS usado pode afetar o desempenho, o comportamento e o tamanho em disco: -- As wheels da NumPy no PyPI, que é o que o pip instala, são compiladas com OpenBLAS. As bibliotecas da OpenBLAS são empacotadas dentro da wheel. Isso faz com que a wheel fique maior, e se um usário também instalar (por exemplo) a SciPy, terá agora duas cópias da OpenBLAS no disco. +- As wheels da NumPy no PyPI, que é o que o pip instala, são compiladas com OpenBLAS. + As bibliotecas da OpenBLAS são empacotadas dentro da wheel. Isso faz com que a wheel fique maior, e se um usário também instalar (por exemplo) a SciPy, terá agora duas cópias da OpenBLAS no disco. - No canal defaults do conda, a NumPy é compilada com a Intel MKL. MKL é um pacote separado que será instalado no ambiente do usuário quando instalar a NumPy. -- No canal do conda-Forge, a NumPy é compilada com um pacote "BLAS" fictício. Quando um usuário instala o NumPy do conda-forge, esse pacote BLAS então é instalado juntamente com a biblioteca real - o padrão é OpenBLAS, mas também pode ser MKL (do canal defaults), ou até mesmo [BLIS](https://github.com/flame/blis) ou *reference BLAS*. +- No canal do conda-Forge, a NumPy é compilada com um pacote "BLAS" fictício. Quando um usuário instala o NumPy do conda-forge, esse pacote BLAS então é instalado juntamente com a biblioteca real - o padrão é OpenBLAS, mas também pode ser MKL (do canal defaults), ou até mesmo [BLIS](https://github.com/flame/blis) ou _reference BLAS_. - O pacote MKL é muito maior que o OpenBLAS, ocupa cerca de 700 MB no disco enquanto OpenBLAS ocupa cerca de 30 MB. @@ -112,7 +111,6 @@ Além do tamanho instalado, desempenho e robustez, há mais duas coisas a se con - A Intel MKL não é de código aberto. Para uso normal isto não é um problema, mas se um usuário precisa redistribuir uma aplicação compilada com a NumPy, isso pode ser um problema. - Tanto MKL quanto OpenBLAS usarão multi-threading para chamadas de função como `np.dot`, com o número de threads sendo determinado tanto por uma opção no momento da compilação quanto por uma variável de ambiente. Muitas vezes, todos os núcleos de CPU serão usados. Isto é, às vezes, inesperado para usuários; o NumPy em si não paraleliza automaticamente nenhuma chamada de função. Normalmente, isso produz melhor desempenho, mas também pode ser prejudicial - por exemplo, ao usar outro nível de paralelização com Dask, scikit-learn ou multiprocessamento. - ## Solução de problemas Se sua instalação falhar com a mensagem abaixo, consulte [Solucionando ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). @@ -123,4 +121,3 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` - From 2d51cfa1fe6fe663804d70b3835a28ae9eee0099 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:42 +0200 Subject: [PATCH 091/586] New translations learn.md (Spanish) --- content/es/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/es/learn.md diff --git a/content/es/learn.md b/content/es/learn.md new file mode 100644 index 0000000000..0da095930c --- /dev/null +++ b/content/es/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ +- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ +- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ +- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ +- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ + + **Videos** + +- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ + +*** + +## NumPy Talks + +- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) +- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) +- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) +- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) +- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From 15218eaf2e27fa9af2e9814c74bfe075cca94457 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:43 +0200 Subject: [PATCH 092/586] New translations learn.md (Arabic) --- content/ar/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ar/learn.md diff --git a/content/ar/learn.md b/content/ar/learn.md new file mode 100644 index 0000000000..0da095930c --- /dev/null +++ b/content/ar/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ +- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ +- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ +- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ +- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ + + **Videos** + +- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ + +*** + +## NumPy Talks + +- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) +- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) +- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) +- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) +- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From 5eba4eaaa55e8a9bf10329747775c58810b058c3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:45 +0200 Subject: [PATCH 093/586] New translations learn.md (Japanese) --- content/ja/learn.md | 64 ++++++++++++++++++++++----------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 867861fd9e..8b95019964 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -1,5 +1,5 @@ --- -title: NumPyの学び方 +title: Learn sidebar: false --- @@ -11,30 +11,30 @@ sidebar: false ## 初心者向け -NumPyについての資料は多数存在しています。 初心者の方にはこちらの資料を強くお勧めします： +NumPyについての資料は多数存在しています。 初心者の方にはこちらの資料を強くお勧めします： If you are just starting, we'd strongly recommend the following: - **動画** + **書籍** -* [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) -* [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 -* [イラストで学ぶNumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [Scientific Pythonレクチャー](https://lectures.scientific-python.org/) NumPyだけでなく、科学的なPythonソフトウェアエコシステムを広く紹介しています。 -* [NumPy: 初心者のための基本](https://numpy.org/devdocs/user/absolute_beginners.html) -* [NumPy チュートリアル *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [スタンフォード大学 CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) -* [NumPyユーザーガイド](https://numpy.org/devdocs) +- [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) +- [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm +- [イラストで学ぶNumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Pythonレクチャー](https://lectures.scientific-python.org/) NumPyだけでなく、科学的なPythonソフトウェアエコシステムを広く紹介しています。 +- [NumPy: 初心者のための基本](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy チュートリアル _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [スタンフォード大学 CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPyユーザーガイド](https://numpy.org/devdocs) **チュートリアル** -* [NumPガイド *Travelis E. Oliphant著*](http://web.mit.edu/dvp/Public/numpybook.pdf) これは2006年の無料版の初版です 最新版(2015年)については、こちら [を参照ください](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -* [PythonにおけるNumPy (発展編)](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -* [エレガントなSciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著* +- [NumPガイド _Travelis E. Oliphant著_](http://web.mit.edu/dvp/Public/numpybook.pdf) これは2006年の無料版の初版です 最新版(2015年)については、こちら [を参照ください](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +- [PythonにおけるNumPy (発展編)](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [エレガントなSciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著_ -また、「Python+SciPy」を題材にした[推薦本リスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの本にはNumPyを核とした「SciPyエコシステム」が説明されています。 +また、「Python+SciPy」を題材にした[推薦本リスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの本にはNumPyを核とした「SciPyエコシステム」が説明されています。 Most books there are about the "SciPy ecosystem," which has NumPy at its core. - **動画** + **書籍** -* [NumPy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +- [NumPy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ *** @@ -42,32 +42,32 @@ NumPyについての資料は多数存在しています。 初心者の方に 高度なインデックス指定、分割、スタッキング、線形代数など、NumPyの概念をより深く理解するためには、これらの上級者向け資料を試してみてください。 - **書籍** + **動画** -* https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm -* [NumPyとSciPyへのイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *M. Scott Shell著* -* [NumPy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) *Stéfan van der Walt著* -* [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 +- NumPyの学び方 +- [NumPyとSciPyへのイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _M. Scott Shell著_ +- [NumPy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) _Stéfan van der Walt著_ +- [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). **チュートリアル** -* [Pythonデータサイエンスハンドブック](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *Jake Vanderplas著* -* [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *Wes McKinney著* -* [数値解析Python: NumPy, SciPy, Matplotlibによる数値計算とデータサイエンスアプリケーション](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *Robert Johansson著* +- [Pythonデータサイエンスハンドブック](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) _Jake Vanderplas著_ +- [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) _Wes McKinney著_ +- [数値解析Python: NumPy, SciPy, Matplotlibによる数値計算とデータサイエンスアプリケーション](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _Robert Johansson著_ - **書籍** + **動画** -* [アドバンスドNumPy - ブロードキャストルール・ストライド・高度なインデックス指定](https://www.youtube.com/watch?v=cYugp9IN1-Q) *Fan Nunuz-Iglesias著* +- [アドバンスドNumPy - ブロードキャストルール・ストライド・高度なインデックス指定](https://www.youtube.com/watch?v=cYugp9IN1-Q) _Fan Nunuz-Iglesias著_ *** ## NumPyに関する講演 -* [NumPyにおけるインデックス指定の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *Jaime Fernadezによる* (2016) -* [Pythonにおける配列計算の進化](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *Ralf Gommersによる* (2019) -* [NumPy: 今までどう変わってきて、今後どう変わっていくのか？ ](https://www.youtube.com/watch?v=YFLVQFjRmPY) *Matti Picusによる* (2019) -* [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) *Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harrisによる* (2019) -* [Pythonにおける配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) *Travis Oliphantによる* (2019) +- [NumPyにおけるインデックス指定の未来](https://www.youtube.com/watch?v=o0EacbIbf58) _Jaime Fernadezによる_ (2016) +- [Pythonにおける配列計算の進化](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _Ralf Gommersによる_ (2019) +- [NumPy: 今までどう変わってきて、今後どう変わっていくのか？ ](https://www.youtube.com/watch?v=YFLVQFjRmPY) _Matti Picusによる_ (2019) +- [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) _Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harrisによる_ (2019) +- [Pythonにおける配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) _Travis Oliphantによる_ (2019) *** From eba2c0aaa448bc32b9fa89bb70414c2abe14ad73 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:46 +0200 Subject: [PATCH 094/586] New translations learn.md (Korean) --- content/ko/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ko/learn.md diff --git a/content/ko/learn.md b/content/ko/learn.md new file mode 100644 index 0000000000..0da095930c --- /dev/null +++ b/content/ko/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ +- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ +- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ +- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ +- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ + + **Videos** + +- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ + +*** + +## NumPy Talks + +- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) +- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) +- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) +- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) +- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From cc70b25926a2fc694cae99d794de54591e83df2b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:47 +0200 Subject: [PATCH 095/586] New translations learn.md (Russian) --- content/ru/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ru/learn.md diff --git a/content/ru/learn.md b/content/ru/learn.md new file mode 100644 index 0000000000..0da095930c --- /dev/null +++ b/content/ru/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ +- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ +- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ +- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ +- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ + + **Videos** + +- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ + +*** + +## NumPy Talks + +- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) +- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) +- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) +- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) +- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From 1481cc7812d52ab31e4803eff77ca0eefe182963 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:48 +0200 Subject: [PATCH 096/586] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/zh/learn.md diff --git a/content/zh/learn.md b/content/zh/learn.md new file mode 100644 index 0000000000..0da095930c --- /dev/null +++ b/content/zh/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ +- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ +- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ +- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ +- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ + + **Videos** + +- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ + +*** + +## NumPy Talks + +- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) +- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) +- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) +- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) +- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From 08aefe3a9a10980444c2d923b8c486e265750e76 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:49 +0200 Subject: [PATCH 097/586] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index f49c82dff1..c5290c33d6 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -15,26 +15,26 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa **Tutoriais** -* [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) -* [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). -* [NumPy Illustrated: The Visual Guide to NumPy *por Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [Scientific Python Lectures](https://lectures.scientific-python.org/) Além de incluir conteúdo sobre a NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python. -* [NumPy: the absolute basics for beginners ("o básico absoluto para inciantes")](https://numpy.org/devdocs/user/absolute_beginners.html) -* [NumPy tutorial *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [Stanford CS231 *por Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) -* [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) +- [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). +- [NumPy Illustrated: The Visual Guide to NumPy _por Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +- [Scientific Python Lectures](https://lectures.scientific-python.org/) Além de incluir conteúdo sobre a NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python. +- [NumPy: the absolute basics for beginners ("o básico absoluto para inciantes")](https://numpy.org/devdocs/user/absolute_beginners.html) +- [NumPy tutorial _por Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) +- [Stanford CS231 _por Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) +- [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) **Livros** -* [Guide to NumPy *de Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -* [From Python to NumPy *por Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow* +- [Guide to NumPy _de Travis E. Oliphant_](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +- [From Python to NumPy _por Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow_ Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. **Vídeos** -* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* +- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _por Alex Chabot-Leclerc_ *** @@ -44,30 +44,30 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos **Tutoriais** -* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* -* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* -* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *por Stéfan van der Walt* -* [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). +- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _por Nicolas P. Rougier_ +- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _por M. Scott Shell_ +- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _por Stéfan van der Walt_ +- [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). **Livros** -* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* -* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por Wes McKinney* -* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *por Robert Johansson* +- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) _por Jake Vanderplas_ +- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) _por Wes McKinney_ +- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _por Robert Johansson_ **Vídeos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunuz-Iglesias* +- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _por Juan Nunuz-Iglesias_ *** ## Palestras sobre NumPy -* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) -* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) -* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *por Matti Picus* (2019) -* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) -* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *por Travis Oliphant* (2019) +- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _por Jaime Fernández_ (2016) +- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _por Ralf Gommers_ (2019) +- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _por Matti Picus_ (2019) +- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) +- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _por Travis Oliphant_ (2019) *** From c0b0b43b14ad96ef75daf184034cd820f0bdffc4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:51 +0200 Subject: [PATCH 098/586] New translations news.md (Spanish) --- content/es/news.md | 425 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 425 insertions(+) create mode 100644 content/es/news.md diff --git a/content/es/news.md b/content/es/news.md new file mode 100644 index 0000000000..76b4f46cc3 --- /dev/null +++ b/content/es/news.md @@ -0,0 +1,425 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 +--- + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) +is now available. The highlights of the release are: + +- Python 3.12.0 support. +- Cython 3.0.0 compatibility. +- Use of the Meson build system +- Updated SIMD support +- f2py fixes, meson and bind(x) support +- Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the +transition to the Meson build system and provision of support for Cython 3.0.0. +A total of 20 people contributed to this release and 59 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: +Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) + +_Japanese:_ + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. +If you’d like to help, please connect with the NumPy Translations Team on Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Look for the #translations channel.) We are also building a Translations Team who will be +working on localizing documentation and educational content across the Scientific Python +ecosystem. If this piqued your interest, join us on the Scientific Python +Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) +is now available. The highlights of the release are: + +- Support for MUSL, there are now MUSL wheels. +- Support for the Fujitsu C/C++ compiler. +- Object arrays are now supported in einsum. +- Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, and clarify the +documentation. There has also been preparatory work for the future NumPy 2.0.0, +resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? +Read the report and find out how to get involved +[here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy +documentation team leads replacing Melissa Mendonça. We thank Melissa for all her +contributions to the NumPy official documentation and educational materials, +and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) +is now available. The highlights of the release are: + +- New "dtype" and "casting" keywords for stacking functions. +- New F2PY features and fixes. +- Many new deprecations, check them out. +- Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase execution speed, and clarify the documentation. +There are a large number of new and expired deprecations due to changes in +dtype promotion and cleanups. It is the work of 177 contributors spread over +444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) +is now available. The highlights of the release are: + +- Implementation of `loadtxt` in C, greatly improving its performance. +- Exposure of DLPack at the Python level for easy data exchange. +- Changes to the promotion and comparisons of structured dtypes. +- Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, clarify the documentation, +and expire old deprecations. It is the work of 151 contributors spread over +494 pull requests. The Python versions supported by this release 3.8-3.10. +Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a +[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) +funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to +understand the barriers to participation that contributors, particularly those +from historically underrepresented groups, face in the open-source software +community. The research team would like to talk to new contributors, project +developers and maintainers, and those who have contributed in the past about +their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) +which contains additional information on the research goals, privacy, and +confidentiality considerations. Your participation will be valuable to the +growth and sustainability of diverse and inclusive open-source software +communities. Accepted participants will participate in a 30-minute interview +with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) +is now available. The highlights of the release are: + +- Type annotations of the main namespace are essentially complete. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +- A preliminary version of the proposed + [array API Standard](https://data-apis.org/array-api/latest/) is provided + (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). + This is a step in creating a standard collection of functions that can be + used across libraries such as CuPy and JAX. +- NumPy now has a DLPack backend. DLPack provides a common interchange format + for array (tensor) data. +- New methods for `quantile`, `percentile`, and related functions. The new + methods provide a complete set of the methods commonly found in the + literature. +- The universal functions have been refactored to implement most of + [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). + This also unlocks the ability to experiment with the future DType API. +- A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread +over 609 pull requests. The Python versions supported by this release are +3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has +[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) +to support the onboarding, inclusion, and retention of people from historically +marginalized groups on scientific Python projects, and to structurally improve +the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), +this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) +will support the creation of dedicated Contributor Experience Lead positions to +identify, document, and implement practices to foster inclusive open-source +communities. This project will be led by Melissa Mendonça (NumPy), with +additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), +Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and +Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that +should structurally improve the community dynamics of our projects. By +establishing these new cross-project roles, we hope to introduce a new +collaboration model to the Scientific Python communities, allowing +community-building work within the ecosystem to be done more efficiently and +with greater outcomes. We also expect to develop a clearer picture of what +works and what doesn't in our projects to engage and retain new contributors, +especially from historically underrepresented groups. Finally, we plan on +producing detailed reports on the actions executed, explaining how they have +impacted our projects in terms of representation and interaction with our +communities. + +The two-year project is expected to start by November 2021, and we are excited +to see the results from this work! +[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 +NumPy users from 75 countries participated in our inaugural survey last year. +The survey findings gave us a very good understanding of what we should focus +on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will +take about 15 minutes of your time. Besides English, the survey questionnaire +is available in 8 additional languages: Bangla, French, Hindi, Japanese, +Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) +is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 +people. The Python versions supported for this release are 3.7-3.9, support +for Python 3.10 will be added after Python 3.10 is released. + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students +and faculty from the University of Michigan and the University of Maryland +conducted the first official NumPy community survey. Find the survey results +here: https://numpy.org/user-survey-2020/. + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) +is now available. This is the largest NumPy release to date, thanks to 180+ +contributors. The two most exciting new features are: + +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule + containing `ArrayLike` and `DtypeLike` aliases that users and downstream + libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, + AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant + performance improvements for many functions (examples: + [sin/cos](https://github.com/numpy/numpy/pull/17587), + [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of +[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) +as a review article in Nature. This comes 14 years after the release of NumPy 1.0. +The paper covers applications and fundamental concepts of array programming, +the rich scientific Python ecosystem built on top of NumPy, and the recently added +array protocols to facilitate interoperability with external array and tensor +libraries like CuPy, Dask, and JAX. + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an +early adopter of Python versions, you may be dissapointed to find that NumPy +(and other binary packages like SciPy) will not have binary wheels ready on the +day of the release. It is a major effort to adapt the build infrastructure to a +new Python version and it typically takes a few weeks for the packages to appear +on PyPI and conda-forge. In preparation for this event, please make sure to + +- update your `pip` to version 20.1 at least to support `manylinux2010` and + `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from + trying to build from source. + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- NumPy +1.19.2 is now available. +This latest release in the 1.19 series fixes several bugs, prepares for the +upcoming Cython 3.x +release and pins +setuptools to keep distutils working while upstream modifications are ongoing. +The aarch64 wheels are built with the latest manylinux2014 release that fixes +the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for +decision-making about the development of NumPy as software and as a community. +The survey is available in 8 additional languages besides English: +Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey +[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + + + +The logo is a modern take on the old one, with a cleaner design. Thanks to +Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught +for the old logo that served us well for 15+ years. + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release +without Python 2 support, hence it was a "clean-up release". The minimum +supported Python version is now Python 3.6. An important new feature is that +the random number generation infrastructure that was introduced in NumPy 1.17.0 +is now accessible from Cython. + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for +the Google Season of Docs program. We are excited about the opportunity to +work with a technical writer to improve NumPy's documentation once again! For more +details, please see +[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our +[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in +1.17.0, this is a consolidation release. It is the last minor release that will +support Python 3.5. Highlights of the release includes the addition of basic +infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix +releases (only the `z` changes in the `x.y.z` version number) have no new +features; minor releases (the `y` increases) do. + +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 12669e4c95b1f3aaff63d6886048d258191931ab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:53 +0200 Subject: [PATCH 099/586] New translations news.md (Arabic) --- content/ar/news.md | 425 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 425 insertions(+) create mode 100644 content/ar/news.md diff --git a/content/ar/news.md b/content/ar/news.md new file mode 100644 index 0000000000..76b4f46cc3 --- /dev/null +++ b/content/ar/news.md @@ -0,0 +1,425 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 +--- + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) +is now available. The highlights of the release are: + +- Python 3.12.0 support. +- Cython 3.0.0 compatibility. +- Use of the Meson build system +- Updated SIMD support +- f2py fixes, meson and bind(x) support +- Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the +transition to the Meson build system and provision of support for Cython 3.0.0. +A total of 20 people contributed to this release and 59 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: +Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) + +_Japanese:_ + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. +If you’d like to help, please connect with the NumPy Translations Team on Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Look for the #translations channel.) We are also building a Translations Team who will be +working on localizing documentation and educational content across the Scientific Python +ecosystem. If this piqued your interest, join us on the Scientific Python +Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) +is now available. The highlights of the release are: + +- Support for MUSL, there are now MUSL wheels. +- Support for the Fujitsu C/C++ compiler. +- Object arrays are now supported in einsum. +- Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, and clarify the +documentation. There has also been preparatory work for the future NumPy 2.0.0, +resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? +Read the report and find out how to get involved +[here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy +documentation team leads replacing Melissa Mendonça. We thank Melissa for all her +contributions to the NumPy official documentation and educational materials, +and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) +is now available. The highlights of the release are: + +- New "dtype" and "casting" keywords for stacking functions. +- New F2PY features and fixes. +- Many new deprecations, check them out. +- Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase execution speed, and clarify the documentation. +There are a large number of new and expired deprecations due to changes in +dtype promotion and cleanups. It is the work of 177 contributors spread over +444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) +is now available. The highlights of the release are: + +- Implementation of `loadtxt` in C, greatly improving its performance. +- Exposure of DLPack at the Python level for easy data exchange. +- Changes to the promotion and comparisons of structured dtypes. +- Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, clarify the documentation, +and expire old deprecations. It is the work of 151 contributors spread over +494 pull requests. The Python versions supported by this release 3.8-3.10. +Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a +[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) +funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to +understand the barriers to participation that contributors, particularly those +from historically underrepresented groups, face in the open-source software +community. The research team would like to talk to new contributors, project +developers and maintainers, and those who have contributed in the past about +their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) +which contains additional information on the research goals, privacy, and +confidentiality considerations. Your participation will be valuable to the +growth and sustainability of diverse and inclusive open-source software +communities. Accepted participants will participate in a 30-minute interview +with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) +is now available. The highlights of the release are: + +- Type annotations of the main namespace are essentially complete. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +- A preliminary version of the proposed + [array API Standard](https://data-apis.org/array-api/latest/) is provided + (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). + This is a step in creating a standard collection of functions that can be + used across libraries such as CuPy and JAX. +- NumPy now has a DLPack backend. DLPack provides a common interchange format + for array (tensor) data. +- New methods for `quantile`, `percentile`, and related functions. The new + methods provide a complete set of the methods commonly found in the + literature. +- The universal functions have been refactored to implement most of + [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). + This also unlocks the ability to experiment with the future DType API. +- A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread +over 609 pull requests. The Python versions supported by this release are +3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has +[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) +to support the onboarding, inclusion, and retention of people from historically +marginalized groups on scientific Python projects, and to structurally improve +the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), +this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) +will support the creation of dedicated Contributor Experience Lead positions to +identify, document, and implement practices to foster inclusive open-source +communities. This project will be led by Melissa Mendonça (NumPy), with +additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), +Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and +Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that +should structurally improve the community dynamics of our projects. By +establishing these new cross-project roles, we hope to introduce a new +collaboration model to the Scientific Python communities, allowing +community-building work within the ecosystem to be done more efficiently and +with greater outcomes. We also expect to develop a clearer picture of what +works and what doesn't in our projects to engage and retain new contributors, +especially from historically underrepresented groups. Finally, we plan on +producing detailed reports on the actions executed, explaining how they have +impacted our projects in terms of representation and interaction with our +communities. + +The two-year project is expected to start by November 2021, and we are excited +to see the results from this work! +[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 +NumPy users from 75 countries participated in our inaugural survey last year. +The survey findings gave us a very good understanding of what we should focus +on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will +take about 15 minutes of your time. Besides English, the survey questionnaire +is available in 8 additional languages: Bangla, French, Hindi, Japanese, +Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) +is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 +people. The Python versions supported for this release are 3.7-3.9, support +for Python 3.10 will be added after Python 3.10 is released. + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students +and faculty from the University of Michigan and the University of Maryland +conducted the first official NumPy community survey. Find the survey results +here: https://numpy.org/user-survey-2020/. + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) +is now available. This is the largest NumPy release to date, thanks to 180+ +contributors. The two most exciting new features are: + +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule + containing `ArrayLike` and `DtypeLike` aliases that users and downstream + libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, + AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant + performance improvements for many functions (examples: + [sin/cos](https://github.com/numpy/numpy/pull/17587), + [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of +[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) +as a review article in Nature. This comes 14 years after the release of NumPy 1.0. +The paper covers applications and fundamental concepts of array programming, +the rich scientific Python ecosystem built on top of NumPy, and the recently added +array protocols to facilitate interoperability with external array and tensor +libraries like CuPy, Dask, and JAX. + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an +early adopter of Python versions, you may be dissapointed to find that NumPy +(and other binary packages like SciPy) will not have binary wheels ready on the +day of the release. It is a major effort to adapt the build infrastructure to a +new Python version and it typically takes a few weeks for the packages to appear +on PyPI and conda-forge. In preparation for this event, please make sure to + +- update your `pip` to version 20.1 at least to support `manylinux2010` and + `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from + trying to build from source. + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- NumPy +1.19.2 is now available. +This latest release in the 1.19 series fixes several bugs, prepares for the +upcoming Cython 3.x +release and pins +setuptools to keep distutils working while upstream modifications are ongoing. +The aarch64 wheels are built with the latest manylinux2014 release that fixes +the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for +decision-making about the development of NumPy as software and as a community. +The survey is available in 8 additional languages besides English: +Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey +[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + + + +The logo is a modern take on the old one, with a cleaner design. Thanks to +Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught +for the old logo that served us well for 15+ years. + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release +without Python 2 support, hence it was a "clean-up release". The minimum +supported Python version is now Python 3.6. An important new feature is that +the random number generation infrastructure that was introduced in NumPy 1.17.0 +is now accessible from Cython. + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for +the Google Season of Docs program. We are excited about the opportunity to +work with a technical writer to improve NumPy's documentation once again! For more +details, please see +[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our +[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in +1.17.0, this is a consolidation release. It is the last minor release that will +support Python 3.5. Highlights of the release includes the addition of basic +infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix +releases (only the `z` changes in the `x.y.z` version number) have no new +features; minor releases (the `y` increases) do. + +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 8def5f6f039fa05e6feacab5f4185c620be49dcf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:55 +0200 Subject: [PATCH 100/586] New translations news.md (Japanese) --- content/ja/news.md | 292 +++++++++++++++++++++++++++++---------------- 1 file changed, 191 insertions(+), 101 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 9621531110..157333976a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,139 +1,216 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.26.0 がリリースされました。" -date: 2023-09-16 +newsHeader: NumPy 1.26.0 がリリースされました。 +date: 2024-05-23 --- +### NumPy 1.19.2 リリース + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Numpy 1.25. +- 多くの新しい非推奨(Deprecation)の追加 + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + ### NumPy 1.26.0 がリリースされました。 -_2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 +_2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 The highlights of the release are: -* Python 3.12.0 のサポート -* Cython 3.0.0 との互換性 -* Mesonビルドシステムの利用 -* SIMD サポートの改善 -* f2py のバグ修正, meson と bind(x) のサポート -* 更新された BLAS/LAPACK の高速化ライブラリのサポート +- Python 3.12.0 のサポート +- Cython 3.0.0 との互換性 +- Mesonビルドシステムの利用 +- SIMD サポートの改善 +- f2py のバグ修正, meson と bind(x) のサポート +- 更新された BLAS/LAPACK の高速化ライブラリのサポート Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0のサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。 +A total of 20 people contributed to this release and 59 pull requests were +merged. このリリースでサポートされている Python のバージョンは3.9から 3.12 です。 ### numpy.orgが日本語とポルトガル語で利用可能になりました -_2023年4月2日_ -- numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： +_2023年4月2日_ -- numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： This wouldn’t be possible without our dedicated volunteers: _ポルトガル語_ -* Melissa Weber Mendonça (melissawm) -* Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) -* Julio Batista Silva (jbsilva) -* Alexandre de Siqueira (alexdesiqueira) -* Alexandre B A Villares (villares) -* Vini Salazar (vinisalazar) + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) _日本語：_ -* Atsushi Sakai (AtsushiSakai) -* KKunai -* Tom Kelly (TomKellyGenetics) -* Yuji Kanagawa (kngwyu) -* Tetsuo Koyama (tkoyama010) + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) 翻訳インフラストラクチャに関するプロジェクトは、CZIからの資金援助でサポートされています。 -今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. (#translation チャンネルを探してください) +Looking ahead, we’d love to translate the website into more languages. +今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(#translation チャンネルを探してください) (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な[参加者の興味](https://numfocus.typeform.com/to/WBWVJSqe)フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。 umd. ### NumPy 1.25.0 リリース -_2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 +_2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 The highlights of the release are: -* MUSLのサポート。MUSLのWheelが準備されました。 -* 富士通のC/C++コンパイラサポート -* einsum でオブジェクト配列がサポートされるようになりました. -* 行列の置き換え(inplace)掛け算のサポート (`@=`). +- MUSLのサポート。MUSLのWheelが準備されました。 +- 富士通のC/C++コンパイラサポート +- einsum でオブジェクト配列がサポートされるようになりました. +- 行列の置き換え(inplace)掛け算のサポート (`@=`). -Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。 +The NumPy 1.25.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, and clarify the +documentation. There has also been preparatory work for the future NumPy 2.0.0, +resulting in a large number of new and expired deprecations. 合計148人がこのリリースに貢献し、530個のプルリクエストが マージされました。 -このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。 +この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。 ### インクルーシブな文化の育成: 参加の募集 _2023年5月10日_ -- インクルーシブ・カルチャーの育成: 参加募集 -NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの [レポート](https://contributor-experience.org/docs/posts/dei-report/) を読んで参加する方法を確認してください。 +How can we be better when it comes to diversity and inclusion? +Read the report and find out how to get involved +[here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy ドキュメンテーションチームのリーダーの変更 -_2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。 +_2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。 We thank Melissa for all her +contributions to the NumPy official documentation and educational materials, +and Mukulika and Ross for stepping up. ### NumPy 1.24.0 リリース -_2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 +_2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 The highlights of the release are: -* スタッキング関数のための新しい"dtype"と"casting"キーワードの追加 -* F2PYの新機能追加とバグ修正 -* 多くの新しい非推奨(Deprecation)の追加 -* 多くの期限切れの非推奨(Deprecation)の削除 +- スタッキング関数のための新しい"dtype"と"casting"キーワードの追加 +- F2PYの新機能追加とバグ修正 +- Many new deprecations, check them out. +- Many expired deprecations, -Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。 +The NumPy 1.24.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase execution speed, and clarify the documentation. +There are a large number of new and expired deprecations due to changes in +dtype promotion and cleanups. It is the work of 177 contributors spread over +444 pull requests. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。 ### Numpy 1.23.0 リリース -_2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 +_2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 The highlights of the release are: -* `loadtxt` がCで実装されたことによる、大幅なパフォーマンス向上 -* より簡単なデータ交換のためのPythonレベルでのDLPackの公開 -* 構造化されたdtypesのプロモーションと比較方法の変更 -* f2pyの改善 +- `loadtxt` がCで実装されたことによる、大幅なパフォーマンス向上 +- より簡単なデータ交換のためのPythonレベルでのDLPackの公開 +- 構造化されたdtypesのプロモーションと比較方法の変更 +- f2pyの改善 -Numpy 1.23. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。 +リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。 It is the work of 151 contributors spread over +494 pull requests. このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。 +edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。 ### NumFOCUS DEI研究への参加募集 -_2022年4月13日_ -- NumPyは、[NumFOCUS](http://numfocus.org/)と協力して、[ある研究プロジェクト](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)を進めており、これは[Gordon & Betty Moore Foundation](https://www.moore.org/)によって資金提供されています。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。 +_2022年4月13日_ -- NumPyは、[NumFOCUS](http://numfocus.org/)と協力して、[ある研究プロジェクト](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)を進めており、これは[Gordon & Betty Moore Foundation](https://www.moore.org/)によって資金提供されています。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。 The research team would like to talk to new contributors, project +developers and maintainers, and those who have contributed in the past about +their experiences joining and contributing to NumPy. **あなたの経験を共有することに興味がありますか?** -もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な[参加者の興味](https://numfocus.typeform.com/to/WBWVJSqe)フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。 - -### NumPy 1.19.2 リリース - -_2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 - -* メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。 -* 以前から提案されていた [array API 標準](https://data-apis.org/array-api/latest/) のベータ版が提供されています ( [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。 -* NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。 -* `quantile`, `percentile`, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。 -* ユニバーサル関数は、[NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。 -* ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。 - -NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) +which contains additional information on the research goals, privacy, and +confidentiality considerations. Your participation will be valuable to the +growth and sustainability of diverse and inclusive open-source software +communities. Accepted participants will participate in a 30-minute interview +with a research team member. + +### Numpy 1.25. + +_2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 The highlights of the release are: + +- Type annotations of the main namespace are essentially complete. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +- 以前から提案されていた [array API 標準](https://data-apis.org/array-api/latest/) のベータ版が提供されています ( [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。 + This is a step in creating a standard collection of functions that can be + used across libraries such as CuPy and JAX. +- NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。 DLPack provides a common interchange format + for array (tensor) data. +- `quantile`, `percentile`, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。 The new + methods provide a complete set of the methods commonly found in the + literature. +- ユニバーサル関数は、[NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。 + This also unlocks the ability to experiment with the future DType API. +- ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。 + +NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。 ### 科学的なPythonエコシステムにおける包括的な文化の前進 _ 2021年8月31日_ -- この度、Chan Zuckerberg Initiativeより、科学的なPythonプロジェクトにおいて、歴史的に疎外されてきたグループの人々のオンボーディング、インクルージョン、リテンションを支援し、NumPy、SciPy、Matplotlib、Pandasのコミュニティダイナミクスを構造的に改善するための [ 助成金を授与されました ](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) ことをお知らせします。 -[ CZIのEssential Open Source Software for Scienceプログラム ](https://chanzuckerberg.com/eoss/)の一環として、この[ Diversity & Inclusion補助金 ](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。 - -このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。 - -2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! このプロジェクトの提案書に関しては、[こちら](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) から全文を読むことができます. +[ CZIのEssential Open Source Software for Scienceプログラム ](https://chanzuckerberg.com/eoss/)の一環として、この[ Diversity & Inclusion補助金 ](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。 This project will be led by Melissa Mendonça (NumPy), with +additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), +Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and +Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that +should structurally improve the community dynamics of our projects. By +establishing these new cross-project roles, we hope to introduce a new +collaboration model to the Scientific Python communities, allowing +community-building work within the ecosystem to be done more efficiently and +with greater outcomes. We also expect to develop a clearer picture of what +works and what doesn't in our projects to engage and retain new contributors, +especially from historically underrepresented groups. Finally, we plan on +producing detailed reports on the actions executed, explaining how they have +impacted our projects in terms of representation and interaction with our +communities. + +2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! +このプロジェクトの提案書に関しては、[こちら](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) から全文を読むことができます. ### 2021年度NumPyアンケート -_2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 +NumPy users from 75 countries participated in our inaugural survey last year. +The survey findings gave us a very good understanding of what we should focus +on for the next 12 months. -今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。 +It’s time for another survey, and we are counting on you once again. It will +take about 15 minutes of your time. Besides English, the survey questionnaire +is available in 8 additional languages: Bangla, French, Hindi, Japanese, +Mandarin, Portuguese, Russian, and Spanish. こちらのリンク先から、アンケートを始めることができます: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSL4q. +### Numpy 1.18.0 リリース -### NumPy 1.19.0 リリース - -_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 +_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 The highlights of the release are: - より多くの機能やプラットフォームをカバーするためのSIMD関連の改善が実施されました。 - dtypeのための新しいインフラとキャストの準備 @@ -142,90 +219,103 @@ _2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0 - アノテーションの改善 - 乱数生成用の新しい `PCG64DXSM` ビット生成機 -今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。 - +This NumPy release is the result of 581 merged pull requests contributed by 175 +people. 今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。 ### 2020年度 NumPy アンケート結果 -_2021年6月22日_ -- NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/ +_2021年6月22日_ -- NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/ 今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。 +### NumPy 1.20.0 リリース -### NumPy 1.18.0 リリース +_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。 This is the largest NumPy release to date, thanks to 180+ +contributors. The two most exciting new features are: -_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。 - NumPyの大部分のコードに型注釈が追加されました。 そして新しいサブモジュールである`numpy.typing`が追加されました。 このサブモジュールは`ArrayLike` や`DtypeLike`という型注釈のエイリアスが定義されており、これによりユーザーやダウンストリームのライブラリはこの型注釈を使うことができます。 -- X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +- X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). This yielded significant + performance improvements for many functions (examples: + [sin/cos](https://github.com/numpy/numpy/pull/17587), + [einsum](https://github.com/numpy/numpy/pull/18194)). ### NumPyプロジェクトの多様性 _2020年9月20日に_ 、私たちは[ NumPyプロジェクトにおけるダイバーシティやインクルージョンの状況や、ソーシャルメディア上での議論についての宣言 ](/diversity_sep2020)について書きました。 - ### Natureに初の公式NumPy論文が掲載されました! -_2020年9月16日_ -- NumPyに関する [ 最初の公式の論文 ](https://www.nature.com/articles/s41586-020-2649-2)がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。 - +提案されたイニシアチブとその成果の詳細については、 [フルグラントプロポーザル](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。 This comes 14 years after the release of NumPy 1.0. +The paper covers applications and fundamental concepts of array programming, +the rich scientific Python ecosystem built on top of NumPy, and the recently added +array protocols to facilitate interoperability with external array and tensor +libraries like CuPy, Dask, and JAX. ### Python 3.9のリリースに伴い、いつNumPyのバイナリwheelがリリースされるのですか？ -_2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。 +_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) がリリースされました。 この 1.19 シリーズの最新リリースでは、いくつかのバグが修正され、[ 来るべき Cython 3.xリリース ](http:/docs.cython.orgenlatestsrcchanges.html)への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。 _2020年7月2日_ -- このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語. _2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。 In preparation for this event, please make sure to + - `pip` が`manylinux2010` と `manylinux2014` をサポートするためにpipを少なくともバージョン 20.1 に更新する。 - [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) または `--only-binary=:all:` を`pip`がソースからビルドしようとするのを防ぐために使用します。 - ### NumPy 1.19.2 リリース -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) がリリースされました。 この 1.19 シリーズの最新リリースでは、いくつかのバグが修正され、[ 来るべき Cython 3.xリリース ](http:/docs.cython.orgenlatestsrcchanges.html)への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。 +_2020年9月16日_ -- NumPyに関する [ 最初の公式の論文 ](https://www.nature.com/articles/s41586-020-2649-2)がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。 +メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。 +The aarch64 wheels are built with the latest manylinux2014 release that fixes +the problem of differing page sizes used by different linux distros. ### 初めてのNumPyの調査が公開されました!! -_2020年7月2日_ -- このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語. - -NumPy をより良くするために、こちらの [アンケート](https://umdsurvey. umd. edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。 +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for +decision-making about the development of NumPy as software and as a community. +The survey is available in 8 additional languages besides English: +Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +NumPy をより良くするために、こちらの [アンケート](https://umdsurvey. ### NumPy に新しいロゴができました! _2020年6月24日_ -- NumPyのロゴが新しくなりました: -NumPyのロゴ - -新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。 + +The logo is a modern take on the old one, with a cleaner design. 新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。 -### NumPy 1.20.0 リリース - -_2020年6月20日_ -- NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、"クリーンアップ用のリリース" です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。 +### NumPy 1.19.0 リリース +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. 多くの期限切れの非推奨(Deprecation)の削除 _2019年12月22日_ -- NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、`numpy.random`のための新しいC-APIの追加などが行われました。 _2020年6月20日_ -- NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、"クリーンアップ用のリリース" です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。 ### ドキュメント受諾期間 -_2020年5月11日_ -- NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! 詳細については、 [シーズンオブドキュメント公式サイト](https://developers.google.com/season-of-docs/) と [アイデアページ](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) をご覧ください。 - +_2020年5月11日_ -- NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの [レポート](https://contributor-experience.org/docs/posts/dei-report/) を読んで参加する方法を確認してください。 詳細については、 [シーズンオブドキュメント公式サイト](https://developers.google.com/season-of-docs/) と [アイデアページ](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) をご覧ください。 -### Numpy 1.18.0 リリース +### NumPy 1.18.0 リリース -_2019年12月22日_ -- NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、`numpy.random`のための新しいC-APIの追加などが行われました。 +2023-09-16 After the major changes in +1.17.0, this is a consolidation release. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。 Highlights of the release includes the addition of basic +infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. 詳細については、 [リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.0) を参照してください。 - ### NumPyはChan Zuckerberg財団から助成金を受けました。 _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つであるOpenBLASが、Chan Zuckerberg財団の[Essential Open Source Software for Scienceプログラム](https:/chanzuckerberg.comeoss)を通じて、科学に不可欠なオープンソースツールのソフトウェアのメンテナンス、成長、開発、コミュニティへの参加などを支援する195,000ドルの共同助成金を獲得したことを発表しました。 -この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。 - -提案されたイニシアチブとその成果の詳細については、 [フルグラントプロポーザル](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。 +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. ## 過去のリリース -こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - +Here is a list of NumPy releases, with links to release notes. こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. +- _2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. @@ -244,7 +334,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ - NumPy 1.23.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022年7月8日_. - NumPy 1.23.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022年6月22日_. - NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022年5月20日_. -- NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. +- Numpy 1.23. - NumPy 1.22.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2022年3月7日_. - NumPy 1.22.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022年2月3日_. - NumPy 1.22.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022年1月14日_. From 71b73e351883411bd2e3d8b0e727acd8514cabf4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:57 +0200 Subject: [PATCH 101/586] New translations news.md (Korean) --- content/ko/news.md | 425 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 425 insertions(+) create mode 100644 content/ko/news.md diff --git a/content/ko/news.md b/content/ko/news.md new file mode 100644 index 0000000000..76b4f46cc3 --- /dev/null +++ b/content/ko/news.md @@ -0,0 +1,425 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 +--- + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) +is now available. The highlights of the release are: + +- Python 3.12.0 support. +- Cython 3.0.0 compatibility. +- Use of the Meson build system +- Updated SIMD support +- f2py fixes, meson and bind(x) support +- Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the +transition to the Meson build system and provision of support for Cython 3.0.0. +A total of 20 people contributed to this release and 59 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: +Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) + +_Japanese:_ + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. +If you’d like to help, please connect with the NumPy Translations Team on Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Look for the #translations channel.) We are also building a Translations Team who will be +working on localizing documentation and educational content across the Scientific Python +ecosystem. If this piqued your interest, join us on the Scientific Python +Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) +is now available. The highlights of the release are: + +- Support for MUSL, there are now MUSL wheels. +- Support for the Fujitsu C/C++ compiler. +- Object arrays are now supported in einsum. +- Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, and clarify the +documentation. There has also been preparatory work for the future NumPy 2.0.0, +resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? +Read the report and find out how to get involved +[here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy +documentation team leads replacing Melissa Mendonça. We thank Melissa for all her +contributions to the NumPy official documentation and educational materials, +and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) +is now available. The highlights of the release are: + +- New "dtype" and "casting" keywords for stacking functions. +- New F2PY features and fixes. +- Many new deprecations, check them out. +- Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase execution speed, and clarify the documentation. +There are a large number of new and expired deprecations due to changes in +dtype promotion and cleanups. It is the work of 177 contributors spread over +444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) +is now available. The highlights of the release are: + +- Implementation of `loadtxt` in C, greatly improving its performance. +- Exposure of DLPack at the Python level for easy data exchange. +- Changes to the promotion and comparisons of structured dtypes. +- Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, clarify the documentation, +and expire old deprecations. It is the work of 151 contributors spread over +494 pull requests. The Python versions supported by this release 3.8-3.10. +Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a +[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) +funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to +understand the barriers to participation that contributors, particularly those +from historically underrepresented groups, face in the open-source software +community. The research team would like to talk to new contributors, project +developers and maintainers, and those who have contributed in the past about +their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) +which contains additional information on the research goals, privacy, and +confidentiality considerations. Your participation will be valuable to the +growth and sustainability of diverse and inclusive open-source software +communities. Accepted participants will participate in a 30-minute interview +with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) +is now available. The highlights of the release are: + +- Type annotations of the main namespace are essentially complete. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +- A preliminary version of the proposed + [array API Standard](https://data-apis.org/array-api/latest/) is provided + (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). + This is a step in creating a standard collection of functions that can be + used across libraries such as CuPy and JAX. +- NumPy now has a DLPack backend. DLPack provides a common interchange format + for array (tensor) data. +- New methods for `quantile`, `percentile`, and related functions. The new + methods provide a complete set of the methods commonly found in the + literature. +- The universal functions have been refactored to implement most of + [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). + This also unlocks the ability to experiment with the future DType API. +- A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread +over 609 pull requests. The Python versions supported by this release are +3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has +[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) +to support the onboarding, inclusion, and retention of people from historically +marginalized groups on scientific Python projects, and to structurally improve +the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), +this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) +will support the creation of dedicated Contributor Experience Lead positions to +identify, document, and implement practices to foster inclusive open-source +communities. This project will be led by Melissa Mendonça (NumPy), with +additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), +Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and +Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that +should structurally improve the community dynamics of our projects. By +establishing these new cross-project roles, we hope to introduce a new +collaboration model to the Scientific Python communities, allowing +community-building work within the ecosystem to be done more efficiently and +with greater outcomes. We also expect to develop a clearer picture of what +works and what doesn't in our projects to engage and retain new contributors, +especially from historically underrepresented groups. Finally, we plan on +producing detailed reports on the actions executed, explaining how they have +impacted our projects in terms of representation and interaction with our +communities. + +The two-year project is expected to start by November 2021, and we are excited +to see the results from this work! +[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 +NumPy users from 75 countries participated in our inaugural survey last year. +The survey findings gave us a very good understanding of what we should focus +on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will +take about 15 minutes of your time. Besides English, the survey questionnaire +is available in 8 additional languages: Bangla, French, Hindi, Japanese, +Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) +is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 +people. The Python versions supported for this release are 3.7-3.9, support +for Python 3.10 will be added after Python 3.10 is released. + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students +and faculty from the University of Michigan and the University of Maryland +conducted the first official NumPy community survey. Find the survey results +here: https://numpy.org/user-survey-2020/. + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) +is now available. This is the largest NumPy release to date, thanks to 180+ +contributors. The two most exciting new features are: + +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule + containing `ArrayLike` and `DtypeLike` aliases that users and downstream + libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, + AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant + performance improvements for many functions (examples: + [sin/cos](https://github.com/numpy/numpy/pull/17587), + [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of +[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) +as a review article in Nature. This comes 14 years after the release of NumPy 1.0. +The paper covers applications and fundamental concepts of array programming, +the rich scientific Python ecosystem built on top of NumPy, and the recently added +array protocols to facilitate interoperability with external array and tensor +libraries like CuPy, Dask, and JAX. + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an +early adopter of Python versions, you may be dissapointed to find that NumPy +(and other binary packages like SciPy) will not have binary wheels ready on the +day of the release. It is a major effort to adapt the build infrastructure to a +new Python version and it typically takes a few weeks for the packages to appear +on PyPI and conda-forge. In preparation for this event, please make sure to + +- update your `pip` to version 20.1 at least to support `manylinux2010` and + `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from + trying to build from source. + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- NumPy +1.19.2 is now available. +This latest release in the 1.19 series fixes several bugs, prepares for the +upcoming Cython 3.x +release and pins +setuptools to keep distutils working while upstream modifications are ongoing. +The aarch64 wheels are built with the latest manylinux2014 release that fixes +the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for +decision-making about the development of NumPy as software and as a community. +The survey is available in 8 additional languages besides English: +Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey +[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + + + +The logo is a modern take on the old one, with a cleaner design. Thanks to +Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught +for the old logo that served us well for 15+ years. + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release +without Python 2 support, hence it was a "clean-up release". The minimum +supported Python version is now Python 3.6. An important new feature is that +the random number generation infrastructure that was introduced in NumPy 1.17.0 +is now accessible from Cython. + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for +the Google Season of Docs program. We are excited about the opportunity to +work with a technical writer to improve NumPy's documentation once again! For more +details, please see +[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our +[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in +1.17.0, this is a consolidation release. It is the last minor release that will +support Python 3.5. Highlights of the release includes the addition of basic +infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix +releases (only the `z` changes in the `x.y.z` version number) have no new +features; minor releases (the `y` increases) do. + +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 0bff369bec43cf8f56a4d5339b803ac7a2b3461c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:12:58 +0200 Subject: [PATCH 102/586] New translations news.md (Russian) --- content/ru/news.md | 425 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 425 insertions(+) create mode 100644 content/ru/news.md diff --git a/content/ru/news.md b/content/ru/news.md new file mode 100644 index 0000000000..76b4f46cc3 --- /dev/null +++ b/content/ru/news.md @@ -0,0 +1,425 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 +--- + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) +is now available. The highlights of the release are: + +- Python 3.12.0 support. +- Cython 3.0.0 compatibility. +- Use of the Meson build system +- Updated SIMD support +- f2py fixes, meson and bind(x) support +- Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the +transition to the Meson build system and provision of support for Cython 3.0.0. +A total of 20 people contributed to this release and 59 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: +Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) + +_Japanese:_ + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. +If you’d like to help, please connect with the NumPy Translations Team on Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Look for the #translations channel.) We are also building a Translations Team who will be +working on localizing documentation and educational content across the Scientific Python +ecosystem. If this piqued your interest, join us on the Scientific Python +Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) +is now available. The highlights of the release are: + +- Support for MUSL, there are now MUSL wheels. +- Support for the Fujitsu C/C++ compiler. +- Object arrays are now supported in einsum. +- Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, and clarify the +documentation. There has also been preparatory work for the future NumPy 2.0.0, +resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? +Read the report and find out how to get involved +[here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy +documentation team leads replacing Melissa Mendonça. We thank Melissa for all her +contributions to the NumPy official documentation and educational materials, +and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) +is now available. The highlights of the release are: + +- New "dtype" and "casting" keywords for stacking functions. +- New F2PY features and fixes. +- Many new deprecations, check them out. +- Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase execution speed, and clarify the documentation. +There are a large number of new and expired deprecations due to changes in +dtype promotion and cleanups. It is the work of 177 contributors spread over +444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) +is now available. The highlights of the release are: + +- Implementation of `loadtxt` in C, greatly improving its performance. +- Exposure of DLPack at the Python level for easy data exchange. +- Changes to the promotion and comparisons of structured dtypes. +- Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, clarify the documentation, +and expire old deprecations. It is the work of 151 contributors spread over +494 pull requests. The Python versions supported by this release 3.8-3.10. +Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a +[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) +funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to +understand the barriers to participation that contributors, particularly those +from historically underrepresented groups, face in the open-source software +community. The research team would like to talk to new contributors, project +developers and maintainers, and those who have contributed in the past about +their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) +which contains additional information on the research goals, privacy, and +confidentiality considerations. Your participation will be valuable to the +growth and sustainability of diverse and inclusive open-source software +communities. Accepted participants will participate in a 30-minute interview +with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) +is now available. The highlights of the release are: + +- Type annotations of the main namespace are essentially complete. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +- A preliminary version of the proposed + [array API Standard](https://data-apis.org/array-api/latest/) is provided + (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). + This is a step in creating a standard collection of functions that can be + used across libraries such as CuPy and JAX. +- NumPy now has a DLPack backend. DLPack provides a common interchange format + for array (tensor) data. +- New methods for `quantile`, `percentile`, and related functions. The new + methods provide a complete set of the methods commonly found in the + literature. +- The universal functions have been refactored to implement most of + [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). + This also unlocks the ability to experiment with the future DType API. +- A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread +over 609 pull requests. The Python versions supported by this release are +3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has +[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) +to support the onboarding, inclusion, and retention of people from historically +marginalized groups on scientific Python projects, and to structurally improve +the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), +this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) +will support the creation of dedicated Contributor Experience Lead positions to +identify, document, and implement practices to foster inclusive open-source +communities. This project will be led by Melissa Mendonça (NumPy), with +additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), +Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and +Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that +should structurally improve the community dynamics of our projects. By +establishing these new cross-project roles, we hope to introduce a new +collaboration model to the Scientific Python communities, allowing +community-building work within the ecosystem to be done more efficiently and +with greater outcomes. We also expect to develop a clearer picture of what +works and what doesn't in our projects to engage and retain new contributors, +especially from historically underrepresented groups. Finally, we plan on +producing detailed reports on the actions executed, explaining how they have +impacted our projects in terms of representation and interaction with our +communities. + +The two-year project is expected to start by November 2021, and we are excited +to see the results from this work! +[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 +NumPy users from 75 countries participated in our inaugural survey last year. +The survey findings gave us a very good understanding of what we should focus +on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will +take about 15 minutes of your time. Besides English, the survey questionnaire +is available in 8 additional languages: Bangla, French, Hindi, Japanese, +Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) +is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 +people. The Python versions supported for this release are 3.7-3.9, support +for Python 3.10 will be added after Python 3.10 is released. + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students +and faculty from the University of Michigan and the University of Maryland +conducted the first official NumPy community survey. Find the survey results +here: https://numpy.org/user-survey-2020/. + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) +is now available. This is the largest NumPy release to date, thanks to 180+ +contributors. The two most exciting new features are: + +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule + containing `ArrayLike` and `DtypeLike` aliases that users and downstream + libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, + AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant + performance improvements for many functions (examples: + [sin/cos](https://github.com/numpy/numpy/pull/17587), + [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of +[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) +as a review article in Nature. This comes 14 years after the release of NumPy 1.0. +The paper covers applications and fundamental concepts of array programming, +the rich scientific Python ecosystem built on top of NumPy, and the recently added +array protocols to facilitate interoperability with external array and tensor +libraries like CuPy, Dask, and JAX. + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an +early adopter of Python versions, you may be dissapointed to find that NumPy +(and other binary packages like SciPy) will not have binary wheels ready on the +day of the release. It is a major effort to adapt the build infrastructure to a +new Python version and it typically takes a few weeks for the packages to appear +on PyPI and conda-forge. In preparation for this event, please make sure to + +- update your `pip` to version 20.1 at least to support `manylinux2010` and + `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from + trying to build from source. + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- NumPy +1.19.2 is now available. +This latest release in the 1.19 series fixes several bugs, prepares for the +upcoming Cython 3.x +release and pins +setuptools to keep distutils working while upstream modifications are ongoing. +The aarch64 wheels are built with the latest manylinux2014 release that fixes +the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for +decision-making about the development of NumPy as software and as a community. +The survey is available in 8 additional languages besides English: +Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey +[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + + + +The logo is a modern take on the old one, with a cleaner design. Thanks to +Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught +for the old logo that served us well for 15+ years. + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release +without Python 2 support, hence it was a "clean-up release". The minimum +supported Python version is now Python 3.6. An important new feature is that +the random number generation infrastructure that was introduced in NumPy 1.17.0 +is now accessible from Cython. + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for +the Google Season of Docs program. We are excited about the opportunity to +work with a technical writer to improve NumPy's documentation once again! For more +details, please see +[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our +[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in +1.17.0, this is a consolidation release. It is the last minor release that will +support Python 3.5. Highlights of the release includes the addition of basic +infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix +releases (only the `z` changes in the `x.y.z` version number) have no new +features; minor releases (the `y` increases) do. + +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 12e59264c267a9c8cf70a22a903325371e386404 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:00 +0200 Subject: [PATCH 103/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 425 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 425 insertions(+) create mode 100644 content/zh/news.md diff --git a/content/zh/news.md b/content/zh/news.md new file mode 100644 index 0000000000..76b4f46cc3 --- /dev/null +++ b/content/zh/news.md @@ -0,0 +1,425 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 +--- + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) +is now available. The highlights of the release are: + +- Python 3.12.0 support. +- Cython 3.0.0 compatibility. +- Use of the Meson build system +- Updated SIMD support +- f2py fixes, meson and bind(x) support +- Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the +transition to the Meson build system and provision of support for Cython 3.0.0. +A total of 20 people contributed to this release and 59 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: +Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) + +_Japanese:_ + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. +If you’d like to help, please connect with the NumPy Translations Team on Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Look for the #translations channel.) We are also building a Translations Team who will be +working on localizing documentation and educational content across the Scientific Python +ecosystem. If this piqued your interest, join us on the Scientific Python +Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) +is now available. The highlights of the release are: + +- Support for MUSL, there are now MUSL wheels. +- Support for the Fujitsu C/C++ compiler. +- Object arrays are now supported in einsum. +- Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, and clarify the +documentation. There has also been preparatory work for the future NumPy 2.0.0, +resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were +merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? +Read the report and find out how to get involved +[here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy +documentation team leads replacing Melissa Mendonça. We thank Melissa for all her +contributions to the NumPy official documentation and educational materials, +and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) +is now available. The highlights of the release are: + +- New "dtype" and "casting" keywords for stacking functions. +- New F2PY features and fixes. +- Many new deprecations, check them out. +- Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase execution speed, and clarify the documentation. +There are a large number of new and expired deprecations due to changes in +dtype promotion and cleanups. It is the work of 177 contributors spread over +444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) +is now available. The highlights of the release are: + +- Implementation of `loadtxt` in C, greatly improving its performance. +- Exposure of DLPack at the Python level for easy data exchange. +- Changes to the promotion and comparisons of structured dtypes. +- Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and +promotion of dtypes, increase the execution speed, clarify the documentation, +and expire old deprecations. It is the work of 151 contributors spread over +494 pull requests. The Python versions supported by this release 3.8-3.10. +Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a +[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) +funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to +understand the barriers to participation that contributors, particularly those +from historically underrepresented groups, face in the open-source software +community. The research team would like to talk to new contributors, project +developers and maintainers, and those who have contributed in the past about +their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) +which contains additional information on the research goals, privacy, and +confidentiality considerations. Your participation will be valuable to the +growth and sustainability of diverse and inclusive open-source software +communities. Accepted participants will participate in a 30-minute interview +with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) +is now available. The highlights of the release are: + +- Type annotations of the main namespace are essentially complete. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +- A preliminary version of the proposed + [array API Standard](https://data-apis.org/array-api/latest/) is provided + (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). + This is a step in creating a standard collection of functions that can be + used across libraries such as CuPy and JAX. +- NumPy now has a DLPack backend. DLPack provides a common interchange format + for array (tensor) data. +- New methods for `quantile`, `percentile`, and related functions. The new + methods provide a complete set of the methods commonly found in the + literature. +- The universal functions have been refactored to implement most of + [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). + This also unlocks the ability to experiment with the future DType API. +- A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread +over 609 pull requests. The Python versions supported by this release are +3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has +[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) +to support the onboarding, inclusion, and retention of people from historically +marginalized groups on scientific Python projects, and to structurally improve +the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), +this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) +will support the creation of dedicated Contributor Experience Lead positions to +identify, document, and implement practices to foster inclusive open-source +communities. This project will be led by Melissa Mendonça (NumPy), with +additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), +Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and +Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that +should structurally improve the community dynamics of our projects. By +establishing these new cross-project roles, we hope to introduce a new +collaboration model to the Scientific Python communities, allowing +community-building work within the ecosystem to be done more efficiently and +with greater outcomes. We also expect to develop a clearer picture of what +works and what doesn't in our projects to engage and retain new contributors, +especially from historically underrepresented groups. Finally, we plan on +producing detailed reports on the actions executed, explaining how they have +impacted our projects in terms of representation and interaction with our +communities. + +The two-year project is expected to start by November 2021, and we are excited +to see the results from this work! +[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 +NumPy users from 75 countries participated in our inaugural survey last year. +The survey findings gave us a very good understanding of what we should focus +on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will +take about 15 minutes of your time. Besides English, the survey questionnaire +is available in 8 additional languages: Bangla, French, Hindi, Japanese, +Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) +is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 +people. The Python versions supported for this release are 3.7-3.9, support +for Python 3.10 will be added after Python 3.10 is released. + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students +and faculty from the University of Michigan and the University of Maryland +conducted the first official NumPy community survey. Find the survey results +here: https://numpy.org/user-survey-2020/. + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) +is now available. This is the largest NumPy release to date, thanks to 180+ +contributors. The two most exciting new features are: + +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule + containing `ArrayLike` and `DtypeLike` aliases that users and downstream + libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, + AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant + performance improvements for many functions (examples: + [sin/cos](https://github.com/numpy/numpy/pull/17587), + [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of +[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) +as a review article in Nature. This comes 14 years after the release of NumPy 1.0. +The paper covers applications and fundamental concepts of array programming, +the rich scientific Python ecosystem built on top of NumPy, and the recently added +array protocols to facilitate interoperability with external array and tensor +libraries like CuPy, Dask, and JAX. + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an +early adopter of Python versions, you may be dissapointed to find that NumPy +(and other binary packages like SciPy) will not have binary wheels ready on the +day of the release. It is a major effort to adapt the build infrastructure to a +new Python version and it typically takes a few weeks for the packages to appear +on PyPI and conda-forge. In preparation for this event, please make sure to + +- update your `pip` to version 20.1 at least to support `manylinux2010` and + `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from + trying to build from source. + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- NumPy +1.19.2 is now available. +This latest release in the 1.19 series fixes several bugs, prepares for the +upcoming Cython 3.x +release and pins +setuptools to keep distutils working while upstream modifications are ongoing. +The aarch64 wheels are built with the latest manylinux2014 release that fixes +the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for +decision-making about the development of NumPy as software and as a community. +The survey is available in 8 additional languages besides English: +Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey +[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + + + +The logo is a modern take on the old one, with a cleaner design. Thanks to +Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught +for the old logo that served us well for 15+ years. + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release +without Python 2 support, hence it was a "clean-up release". The minimum +supported Python version is now Python 3.6. An important new feature is that +the random number generation infrastructure that was introduced in NumPy 1.17.0 +is now accessible from Cython. + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for +the Google Season of Docs program. We are excited about the opportunity to +work with a technical writer to improve NumPy's documentation once again! For more +details, please see +[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our +[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in +1.17.0, this is a consolidation release. It is the last minor release that will +support Python 3.5. Highlights of the release includes the addition of basic +infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix +releases (only the `z` changes in the `x.y.z` version number) have no new +features; minor releases (the `y` increases) do. + +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From d6f1c436010737a0a026768739ab611e383d8a05 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:02 +0200 Subject: [PATCH 104/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 170 +++++++++++++++++++++++++++------------------ 1 file changed, 104 insertions(+), 66 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index e972a74130..62db8878f4 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,22 +1,45 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy versão 1.26.0" -date: 2023-09-16 +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 --- -### Lançado o NumPy versão 1.26.0 +### NumPy 2.0 release date: June 16 + +2023-09-16 This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +### NumFOCUS end of the year fundraiser + +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount +on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now +until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ +or a coupon code ISUPPORTDATASCIENCE  + +### NumPy versão 1.26.0 _16 de setembro de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) está disponível. Os destaques desta versão são: -* Suporte ao Python 3.12.0. -* Compatibilidade com Cython 3.0.0. -* Utilização do sistema Meson para compilação -* Suport a SIMD atualizado -* Melhorias para f2py, suporte a meson e bind(x) -* Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK +- Suporte ao Python 3.12.0. +- Compatibilidade com Cython 3.0.0. +- Utilização do sistema Meson para compilação +- Suport a SIMD atualizado +- Melhorias para f2py, suporte a meson e bind(x) +- Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK -A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.0.0. Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas. +A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.0.0. +Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas. As versões do Python suportadas por esta versão são 3.9-3.12. @@ -25,33 +48,39 @@ As versões do Python suportadas por esta versão são 3.9-3.12. _2 de agosto de 2023_ -- numpy.org agora está disponível em 2 idiomas adicionais: japonês e português. Isto não seria possível sem nossos voluntários dedicados: _Português:_ -* Melissa Weber Mendonça (melissawm) -* Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) -* Julio Batista Silva (jbsilva) -* Alexandre de Siqueira (alexdesiqueira) -* Alexandre B A Villares (villares) -* Vini Salazar (vinisalazar) + +- Melissa Weber Mendonça (melissawm) +- Ricardo Prins (ricardoprins) +- Getúlio Silva (getuliosilva) +- Julio Batista Silva (jbsilva) +- Alexandre de Siqueira (alexdesiqueira) +- Alexandre B A Villares (villares) +- Vini Salazar (vinisalazar) Japonês: -* Atsushi Sakai (AtsushiSakai) -* KKunai -* Tom Kelly (TomKellyGenetics) -* Yuji Kanagawa (kngwyu) -* Tetsuo Koyama (tkoyama010) + +- Atsushi Sakai (AtsushiSakai) +- KKunai +- Tom Kelly (TomKellyGenetics) +- Yuji Kanagawa (kngwyu) +- Tetsuo Koyama (tkoyama010) O trabalho na infraestrutura de traduções é financiado pela CZI. -No futuro, adoraríamos traduzir o site para mais línguas. Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Procure pelo canal #translations) +No futuro, adoraríamos traduzir o site para mais línguas. +Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Look for the #translations channel.) (Procure pelo canal #translations) Também estamos organizando um time de tradutores que serão responsáveis por trabalhar na localização da documentação e conteúdo educacional para o ecossistema Scientific Python. Se esse trabalho te interessa, junte-se a nós no Discord do projeto Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation) +### Lançado o NumPy versão 1.26.0 + _17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Os destaques desta versão são: -* Suporte para MUSL, agora existem rodas MUSL. -* Suporte para o compilador Fujitsu C/C++. -* Arrays de objetos agora são suportados em einsum. -* Suporte para a multiplicação da matriz inplace (`@=`). +- Suporte para MUSL, agora existem rodas MUSL. +- Suporte para o compilador Fujitsu C/C++. +- Arrays de objetos agora são suportados em einsum. +- Suporte para a multiplicação da matriz inplace (`@=`). A versão 1.25.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Também tem havido trabalho preparatório para a futura versão 2.0.0, resultando em um grande número de depreciações novas e expiradas. @@ -63,7 +92,8 @@ As versões do Python suportadas por esta versão são 3.9-3.11. _10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação -Como podemos ser melhores quando se trata de diversidade e de inclusão? Leia o relatório e descubra como colaborar [aqui](https://contributor-experience.org/docs/posts/dei-report/). +Como podemos ser melhores quando se trata de diversidade e de inclusão? +Leia o relatório e descubra como colaborar [aqui](https://contributor-experience.org/docs/posts/dei-report/). ### Transição de liderança do time de documentação do NumPy @@ -73,27 +103,29 @@ _6 de janeiro de 2023_ –- Mukulika Pahari e Ross Barnowski são nomeados como _18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. Os destaques desta versão são: -* Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. -* Novas funcionalidades e correções do F2PY. -* Muitas depreciações novas, confira. -* Muitas depreciações expiradas. +- Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. +- Novas funcionalidades e correções do F2PY. +- Muitas depreciações novas, confira. +- Muitas depreciações expiradas. -A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11. +A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. +Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11. ### NumPy versão 1.23.0 _22 de junho de 2022_ -- O [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) está disponível. Os destaques desta versão são: -* Implementação de `loadtxt` em C, melhorando muito seu desempenho. -* Exposição do DLPack ao nível de Python para facilitar a troca de dados. -* Mudanças na promoção e comparações de dtypes estruturados. -* Melhorias no f2py. +- Implementação de `loadtxt` em C, melhorando muito seu desempenho. +- Exposição do DLPack ao nível de Python para facilitar a troca de dados. +- Mudanças na promoção e comparações de dtypes estruturados. +- Melhorias no f2py. -A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão 3.8-3.10. Python 3.11 será suportado quando chegar na etapa rc. +A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão 3.8-3.10. +Python 3.11 será suportado quando chegar na etapa rc. ### Pesquisa NumFOCUS DEI: chamada para participação -_13 de abril de 2022_ -- O NumPy está trabalhando com a [NumFOCUS](http://numfocus.org/) em um [projeto de pesquisa](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado pela [Gordon & Betty Moore Foundation](https://www.moore.org/) para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy. +_13 de abril de 2022_ -- O NumPy está trabalhando com a [NumFOCUS](http://numfocus.org/) em um [projeto de pesquisa](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado pela [Gordon & Betty Moore Foundation](https://www.moore.org/) para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy. **Quer compartilhar suas experiências?** @@ -103,12 +135,16 @@ Por favor, preencha este breve formulário: ["Participant Interest form"](https: _31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) está agora disponível. Os destaques desta versão são: -* Anotações de tipo do namespace principal estão praticamente completas. Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. Esta é provavelmente a melhoria mais visível para os usuários nesta versão. -* Uma versão preliminar da proposta do [array API Standard](https://data-apis.org/array-api/latest/) está disponível (veja [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX. -* NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores). -* Novos métodos para `quantile`, `percentile`, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura. -* As funções universais foram refatoradas para implementar a maior parte da [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Isso também desbloqueia a capacidade de experimentar a futura API DType. -* Um novo alocador de memória configurável para uso pelos projetos downstream. +- Anotações de tipo do namespace principal estão praticamente completas. Upstream is + a moving target, so there will likely be further improvements, but the major + work is done. Esta é provavelmente a melhoria mais visível para os usuários nesta versão. +- Uma versão preliminar da proposta do [array API Standard](https://data-apis.org/array-api/latest/) está disponível (veja [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). + Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX. +- NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores). +- Novos métodos para `quantile`, `percentile`, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura. +- As funções universais foram refatoradas para implementar a maior parte da [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). + Isso também desbloqueia a capacidade de experimentar a futura API DType. +- Um novo alocador de memória configurável para uso pelos projetos downstream. NumPy 1.22.0 é uma versão importante com o trabalho de 153 contribuidores espalhados por mais de 609 pull requests. As versões do Python suportadas por esta versão são 3.8-3.10. @@ -120,17 +156,18 @@ Como parte do programa [CZI's Essential Open Source Software for Science](https: Esse é um projeto ambicioso que visa descobrir e implementar atividades que devem estruturalmente melhorar a dinâmica da comunidade de nossos projetos. Ao criar essas novas funções entre projetos, esperamos introduzir um novo modelo de colaboração às comunidades de Python científico, permitir que o trabalho de construção da comunidade no ecossistema seja feito de forma mais eficiente e com maiores resultados. Também esperamos desenvolver uma imagem mais clara do que funciona e o que não funciona em nossos projetos para engajar e reter novos colaboradores, especialmente de grupos historicamente sub-representados. Finalmente, planejamos produzir relatórios detalhados sobre as ações executadas, explicando como eles afetaram nossos projetos em termos de representação e interação com nossas comunidades. -O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! [Você pode ler a proposta completa aqui](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! +[Você pode ler a proposta completa aqui](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### Pesquisa NumPy 2021 -_12 de julho de 2021_ -- Nós do NumPy acreditamos no poder da nossa comunidade. 1,236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes. +_12 de julho de 2021_ -- Nós do NumPy acreditamos no poder da nossa comunidade. 1,236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. +Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes. Chegou a hora de fazer outra pesquisa e estamos contando com você novamente. Vai levar cerca de 15 minutos do seu tempo. Além de Inglês, o questionário de pesquisa está disponível em 8 idiomas adicionais: Bangla, Francês, Hindi, Japonês, Mandarim, Português, Russo e Espanhol. Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. - ### NumPy versão 1.19.0 _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) está disponível. Os destaques desta versão são: @@ -144,15 +181,14 @@ _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1 Esta versão do NumPy é o resultado de 581 pull requests aceitos, a partir das contribuições de 175 pessoas. As versões do Python suportadas por esta versão são 3.7-3.9; o suporte para o Python 3.10 será adicionado após o lançamento do Python 3.10. - ### Resultados da pesquisa NumPy 2020 _22 de junho de 2021_ -- Em 2020, o time de pesquisas NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, realizou a primeira pesquisa oficial sobre a comunidade NumPy. Encontre os resultados da pesquisa aqui: https://numpy.org/user-survey-2020/. - ### NumPy versão 1.20.0 _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) está disponível. Este é o maior lançamento do NumPy até hoje, graças a mais de 180 colaboradores. As duas novidades mais emocionantes são: + - Anotações de tipos para grandes partes do NumPy, e um novo submódulo `numpy.typing` contendo aliases `ArrayLike` e `DtypeLike` que usuários e bibliotecas downstream podem usar quando quiserem adicionar anotações de tipos em seu próprio código. - Otimizações de compilação SIMD multi-plataforma, com suporte para instruções x86 (SSE, AVX), ARM64 (Neon) e PowerPC (VSX). Isso rendeu melhorias significativas de desempenho para muitas funções (exemplos: [sen/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). @@ -160,48 +196,50 @@ _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release _20 de setembro de 2020_ -- Escrevemos uma [declaração sobre o estado da diversidade e inclusão no projeto NumPy e discussões em redes sociais sobre isso.](/diversity_sep2020). - ### Primeiro artigo oficial do NumPy publicado na Nature! -_16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. - +_16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. +O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. ### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se você for quiser usar imediatamente a nova versão do Python, você pode ficar desapontado ao descobrir que o NumPy (e outros pacotes binários como SciPy) não terão wheels no dia do lançamento. É um grande esforço adaptar a infraestrutura de compilação a uma nova versão de Python e normalmente leva algumas semanas para que os pacotes apareçam no PyPI e no conda-forge. Em preparação para este evento, por favor, certifique-se de + - atualizar seu `pip` para a versão 20.1 pelo menos para suportar `manylinux2010` e `manylinux2014` - usar [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) ou `--only-binary=:all:` para impedir `pip` de tentar compilar a partir do código fonte. - ### NumPy versão 1.19.2 -_10 de setembro de 2020_ -- O [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) está disponível. Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento [do Cython 3](http://docs.cython.org/en/latest/src/changes.html) e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. As wheels para aarch64 são compiladas com manylinux2014 mais recente que conserta um problema com distribuições linux diferentes. +_10 de setembro de 2020_ -- O [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) está disponível. +Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento [do Cython 3](http://docs.cython.org/en/latest/src/changes.html) e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. +As wheels para aarch64 são compiladas com manylinux2014 mais recente que conserta um problema com distribuições linux diferentes. ### A primeira pesquisa NumPy está aqui! -_2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. +_2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. +A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). - ### O NumPy tem um novo logo! _24 de junho de 2020_ -- NumPy agora tem um novo logo: -NumPy logo + O logotipo é uma versão moderna do antigo, com um design mais limpo. Obrigado à Isabela Presedo-Floyd por projetar o novo logotipo, bem como ao Travis Vaught pelo o logotipo antigo que nos serviu bem durante mais de 15 anos. - ### NumPy versão 1.19.0 _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira versão sem suporte ao Python 2, portanto foi uma "versão de limpeza". A versão mínima de Python suportada agora é Python 3.6. Uma característica nova importante é que a infraestrutura de geração de números aleatórios que foi introduzida na NumPy 1.17.0 agora está acessível a partir do Cython. - ### Aceitação no programa Season of Docs -_11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um *technical writer* para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). - +_11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um _technical writer_ para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy versão 1.18.0 @@ -209,22 +247,22 @@ _22 de dezembro de 2019_ -- O NumPy 1.18.0 está disponível. Após as principai Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. - ### O NumPy recebe financiamento da Chan Zuckerberg Initiative _15 de novembro de 2019_ -- Estamos felizes em anunciar que o NumPy e a OpenBLAS, uma das dependências-chave do NumPy, receberam um auxílio conjunto de $195,000 da Chan Zuckerberg Initiative através do seu programa [Essential Open Source Software for Science](https://chanzuckerberg.com/eoss/) que apoia a manutenção, crescimento, desenvolvimento e envolvimento da comunidade em ferramentas de código aberto fundamentais para a ciência. -Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a *thread-safety*, AVX-512, e *thread-local storage* (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende. +Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a _thread-safety_, AVX-512, e _thread-local storage_ (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende. Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrados na [proposta completa de concessão de auxílio](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). O trabalho está agendado para começar no dia 1 de dezembro de 2019 e continuar pelos próximos 12 meses. - ## Lançamentos Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 de novembro de 2023_. - NumPy 1.26.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de outubro de 2023_. - NumPy 1.26.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de setembro de 2023_. From 561617ea363d645624554550d085fd0ba25d45da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:03 +0200 Subject: [PATCH 105/586] New translations press-kit.md (Spanish) --- content/es/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/es/press-kit.md diff --git a/content/es/press-kit.md b/content/es/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/es/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 6cec6f6ed5ed9df4d48fc329775b48c6d83dc567 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:04 +0200 Subject: [PATCH 106/586] New translations press-kit.md (Arabic) --- content/ar/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ar/press-kit.md diff --git a/content/ar/press-kit.md b/content/ar/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ar/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 3cdacc022678fb263cb7a700f1d601e8f913a107 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:05 +0200 Subject: [PATCH 107/586] New translations press-kit.md (Japanese) --- content/ja/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/press-kit.md b/content/ja/press-kit.md index 6d28214989..39f88f2388 100644 --- a/content/ja/press-kit.md +++ b/content/ja/press-kit.md @@ -5,4 +5,4 @@ sidebar: false 私たちはユーザーの皆さんが次に書く学術論文や、コース教材、プレゼンテーションなどに、NumPyプロジェクトのロゴを簡単に盛り込めるようにしたいと考えています。 -こちらから、様々な解像度のNumPyロゴのファイルをダウンロードできます: [ロゴリンク](https://github.com/numpy/numpy/tree/main/branding/logo)。 numpy.orgのリソースを使用することで、[NumPy行動規範](/code-of-conduct) を受け入れたことになることに注意してください。 +こちらから、様々な解像度のNumPyロゴのファイルをダウンロードできます: [ロゴリンク](https://github.com/numpy/numpy/tree/main/branding/logo)。 numpy.orgのリソースを使用することで、[NumPy行動規範](/code-of-conduct) を受け入れたことになることに注意してください。 Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From ecc1406a42194721b800249bfcfb3e2f5de45136 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:06 +0200 Subject: [PATCH 108/586] New translations press-kit.md (Korean) --- content/ko/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ko/press-kit.md diff --git a/content/ko/press-kit.md b/content/ko/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ko/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 8b4382d06179464a26fcf7a84ffc562e2a023bff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:07 +0200 Subject: [PATCH 109/586] New translations press-kit.md (Russian) --- content/ru/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ru/press-kit.md diff --git a/content/ru/press-kit.md b/content/ru/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ru/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From a681be4b5c1caf2564b0347f9cbfc81114c476b8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:08 +0200 Subject: [PATCH 110/586] New translations press-kit.md (Chinese Simplified) --- content/zh/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/zh/press-kit.md diff --git a/content/zh/press-kit.md b/content/zh/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/zh/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From b5116da8818f51053ee13dce4fd9844129c6e710 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:10 +0200 Subject: [PATCH 111/586] New translations privacy.md (Spanish) --- content/es/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/es/privacy.md diff --git a/content/es/privacy.md b/content/es/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/es/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 13a52b92a854316a1aed974f468a1f1ad63844c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:11 +0200 Subject: [PATCH 112/586] New translations privacy.md (Arabic) --- content/ar/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ar/privacy.md diff --git a/content/ar/privacy.md b/content/ar/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ar/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From b57a436e778d232810a655b2e27ef9475399e4a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:12 +0200 Subject: [PATCH 113/586] New translations privacy.md (Japanese) --- content/ja/privacy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/privacy.md b/content/ja/privacy.md index 8cd76d43e4..2d4d2fba91 100644 --- a/content/ja/privacy.md +++ b/content/ja/privacy.md @@ -3,6 +3,6 @@ title: プライバシーポリシー sidebar: false --- -**numpy.org** は、NumPyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policy を参照してください。 +**numpy.org** は、NumPyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policy を参照してください。 For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. ポリシーまたはNumFOCUSのデータ収集、使用、および開示方法についてご質問がある場合は、privacy@numfocus.orgのNumFOCUSスタッフにお問い合わせください。 From faf8784d0eb52cb91d1f3582a6d2b4baf9895c7f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:14 +0200 Subject: [PATCH 114/586] New translations privacy.md (Korean) --- content/ko/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ko/privacy.md diff --git a/content/ko/privacy.md b/content/ko/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ko/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From bdac3cd5372d365939cdda403b7d7a578b27360b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:15 +0200 Subject: [PATCH 115/586] New translations privacy.md (Russian) --- content/ru/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ru/privacy.md diff --git a/content/ru/privacy.md b/content/ru/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ru/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 142ddeef19b2519a6358dc2096305c1d2bd6789e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:16 +0200 Subject: [PATCH 116/586] New translations privacy.md (Chinese Simplified) --- content/zh/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/zh/privacy.md diff --git a/content/zh/privacy.md b/content/zh/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/zh/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From f3f0d6a42468b0578ef825b3f23abcaab3651dbc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:18 +0200 Subject: [PATCH 117/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 89 ++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/es/report-handling-manual.md diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md new file mode 100644 index 0000000000..161757fe41 --- /dev/null +++ b/content/es/report-handling-manual.md @@ -0,0 +1,89 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +- Find a candidate who can serve as a mediator. +- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +- Obtain the agreement of the reported person(s). +- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +- Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +- Immediately disconnect the originator from all NumPy communication channels. +- Reply to the reporter that their report has been received and that the originator has been disconnected. +- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +- What happened. +- Whether this event constitutes a Code of Conduct violation. +- Who are the responsible party(ies). +- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +- Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From e9f884a41ba567f4351ba0b4851bfc935252e798 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:19 +0200 Subject: [PATCH 118/586] New translations report-handling-manual.md (Arabic) --- content/ar/report-handling-manual.md | 89 ++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/ar/report-handling-manual.md diff --git a/content/ar/report-handling-manual.md b/content/ar/report-handling-manual.md new file mode 100644 index 0000000000..161757fe41 --- /dev/null +++ b/content/ar/report-handling-manual.md @@ -0,0 +1,89 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +- Find a candidate who can serve as a mediator. +- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +- Obtain the agreement of the reported person(s). +- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +- Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +- Immediately disconnect the originator from all NumPy communication channels. +- Reply to the reporter that their report has been received and that the originator has been disconnected. +- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +- What happened. +- Whether this event constitutes a Code of Conduct violation. +- Who are the responsible party(ies). +- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +- Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 2ff6d3344dcbe74828e6415db33ee303a86389e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:21 +0200 Subject: [PATCH 119/586] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 96 +++++++++++++--------------- 1 file changed, 45 insertions(+), 51 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index b200124145..62363d58cb 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -3,93 +3,87 @@ title: NumPy行動規範 - 報告書のフォローアップ方法 sidebar: false --- -NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 +NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 It’s used when we respond to an issue to make sure we’re consistent and fair. -[行動規範](/ja/code-of-conduct) を施行することは、私たちのコミュニティの現在のため、未来のために重要です。 この施行は、軽いものではありません。 施行の基準を見直す際、行動規範委員会は以下の考え方とガイドラインに留意するようにします。 +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: -* 機械的ではなく、人間的に行動します。 委員会は、当事者のプライバシーと報告者の必要なだけの機密性を尊重しながら、状況を理解するように働きかけることができます. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。 -* 行動を判断するのではなく、個人への共感を強調し、「良い」と「悪い」の二値評価を避けます。 明確な攻撃性とハラスメントが存在した場合、私たちはそれらに対処します。 しかし、解決が困難なシナリオの多くは、通常の意見の相違が、複数の当事者による無益または有害な行動に発展した場合です。 完全に文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、コミュニティにとって最終的に最も有益な方法です。 -* 私たちは、電子メールが判断に困難な媒体であり、独立して利用できることを理解しています。 個人の情報なしに電子メール上で批判を受けることは、特に苦痛である場合もあります。 そこで、他者の見解に対して、開放的で、敬意を持った雰囲気を保つことが重要になります。 それはまた、私たちの行動が透明でなければならないことを意味します。 全てのメンバーが公平かつ同情をもって扱われるようにするため、私たちは全力を尽くします。 -* 差別の境界は時に曖昧で、また無意識に行われている場合もあります。 これにより、普通の人との関わりの中で、不公平感や敵意として現れてくるのです。 私達は、このようなことが起こることはわかっているので、気をつけて見ていきたいと思います。 不当な扱いを受けたと思われる方は、ぜひご連絡ください。 -* 良い議論を実践することで、エンゲージメントの向上に取り組みます。例えば議論がどこで止まっているのかを特定したり、 実践的な情報、指針、資源を提供することで、これらの問題を前向きな方向に変えていきます。 -* 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。 -* 一人一人の文化的背景や母国語は異なります。 ネイティブでない人が起こした悪気のない誤解を確認し、問題を理解してもらい、不快感を与えないために何を変えればよいかを教えてあげてください。 外国語での複雑な議論はとても難しいものであり、国籍や文化を超えて多様性を育てていきたいと考えています。 +- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。 +- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +- 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。 +- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +## Mediation -## 仲介 +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: -自主的な非公式の調停は、私たちの重要な役割です。 2つのグループ以上の当事者が不適切な行動をエスカレートした場合(人類の紛争では悲しいことに一般的なものですが)、調停プロセスを促進するは非常に重要です。 ちなみに、これは一例に過ぎません。委員会は、どのようなケースでも調停を検討することができますが、このプロセスはあくまでも自発的なものであり、当事者に参加を迫ることはできないことを念頭に置いて下さい。 委員会が調停を提案する場合は、次のようにすべきです。 - -* 調停者として役立つ候補者を見つけます。 -* 報告者の合意を取得します。 報告者は、調停のアイデアを拒否したり、代替の調停者を提案する権利を持ちます。 -* 報告者の同意を取得します。 -* 調停人を決定します。当事者は、提案された候補者とは別の調停人を提案することができます。すべての条件で共通の合意に達した場合のみ、プロセスを進めることができます。 -* 調停が完了するまでのタイムラインを設定し、理想的には2週間以内に完了させます。 - -調停者は、すべての当事者と関わり、すべての人に満足のいく決議を求めていきます。 終了後、調停人は（プロセスの全当事者によって吟味された）報告書を委員会に提出し、今後のステップに関する推奨事項を提示します。 委員会は、これらの結果(満足のいく決議が達成されたか否か) を評価し、必要と判断される追加的な措置を決定します。 +- Find a candidate who can serve as a mediator. +- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +- Obtain the agreement of the reported person(s). +- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +- Establish a timeline for mediation to complete, ideally within two weeks. +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. ## 報告に対する委員会の対応 -委員会(または委員) が行動規範違反報告を受けた時、その報告が明確で深刻な違反であるかどうかは判断されます(以下に違反項目を定義します)。 違反判定された場合は、通常のレポート処理プロセスに加えて、即時の対応が必要になります。 - +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. ## 明確かつ深刻な違反行為の解決 -私たちは、インターネットでの会話が簡単にひどい誹謗中傷になってしまうことを、痛いほど知っています。 個人的な脅迫、暴力的、性差別的、人種差別的な言葉など、明らかで深刻な違反に対しては、迅速に対処します。 +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. 行動規範委員会のメンバーは、明確かつ深刻な違反に気づいた場合、以下のように行動します。 -* 直ちにすべてのNumPyのオンラインコミュニティから違反者を排除します。 -* 報告が受信され、違反者が排除されたことを報告者に連絡します。 -* どのような場合でも、モデレーターは違反者に連絡するための合理的な努力を行い、違反者の言葉や行動がどのように「明確かつ重大な違反」に該当するのかを具体的に伝えるべきです。 モデレーターは、違反者がこれは不当だと思う場合、あるいはNumPyチャンネルとの再接続を望む場合には、行動規範委員会による以下のような審査を求める権利があることも述べるべきです。 モデレータは、この説明を行動規範委員会に転送する必要があります。 -* 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし署名することで、よくある盛り上がりすぎた論争を諫めるためこのプロセスが使用されたのでないことを確認します。 - +- Immediately disconnect the originator from all NumPy communication channels. +- Reply to the reporter that their report has been received and that the originator has been disconnected. +- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. ## 報告の処理 -報告が委員会に送られると、直ちに報告者に返信して報告を受領したことを確認します。 この返信は72時間以内に送信される必要があり、委員会はそれよりもはるかに迅速に対応するよう努める必要があります。 +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. -レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。 +レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。 The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. -その後、委員会は今回の問題を見直し、効果を最大限に発揮する対策を決定します。 +The Committee will then review the incident and determine, to the best of their ability: -* 問題の種類 -* 今回の事情が行動規範違反であるかどうか。 -* 責任者が誰であるか -* これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。 +- What happened. +- 今回の事情が行動規範違反であるかどうか。 +- 責任者が誰であるか +- これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。 これらの情報は書面で収集され、可能な限りグループの審議が記録され、保持されます (例えば、チャットの記録、Eメールのディスカッション、会議通話の記録、音声会話の概要など)。 -行動の一貫性を確保し、プロジェクトのために記録を残すために、委員会のすべての活動のアーカイブを保持することが重要です。 この活動支援するために、委員会のデフォルトの議論チャネルは、正当化された要求に応じて、委員会の現在および将来のメンバー、および運営委員会のメンバーがアクセスできるプライベートメーリングリストにします。 委員会がリストにはない連絡方法を使用する必要がある場合(例: 早期/迅速な対応を求める電話など)、そのプロセスの良い記録となるように、これらをリストにまとめて戻すべきです。 - -行動規範委員会は、2週間以内に決議の合意を目指すべきです。 その期間内に決議が確定できない場合。 委員会は、レポーターに対して現状の更新と今後のタイムラインを連絡します。 +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. ## 解決方法 -委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 +The Committee must agree on a resolution by consensus. 委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 ありうる返答は次のとおりです: -* これ以上アクションを取らない。 - - 違反が起きていないと判断された - - 検討中に問題が明らかに解決された -* 調停の調整。すべての関係者が合意した場合、委員会は上記のように調停プロセスを促進することができます。 -* 公の場における説明。 どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。 -* 委員会から関係者(複数可) への非公開処分の実施。 この場合、委員会は、電子メールを介して、グループにccを入れながら、対象者に問題の指摘を連絡します。 -* 公の場での指摘。 この場合、委員会の議長は、違反が発生したのと同じ場所で、実用性の範囲内で叱責を行います。 例えば、メールルールの違反の元のメーリングリストなどです。しかし、人や状況がかわるかもしれないチャットルームなどの場合、他の手段を利用する可能性もあります。 文書化のため、この問題のメッセージを他の場所で公開することを対策グループが選択する場合もあります。 -* 報告者がこの考えに同意することを前提とした、公的または私的な謝罪の要求。 報告者は自分の裁量で、違反者とのさらなる接触を拒否することもできます。 委員会がこの要求をお届けします。 委員会は、必要に応じてこの要求に「条件」を付けることができます。例えば、メーリングリストの会員資格を維持するために、違反者に謝罪を求めることができます。 -* 「相互に合意した休止」の要求。 これは、委員会から個人への、コミュニティへの参加を一時的に控えるような要請です。 対象者が自発的に一時的な休みを取らないことを選択した場合、委員会は「冷却期限」を準備することがあります。 -* これは、一部またはすべてのNumPyオンラインコミュニティ (メーリングリスト、gitter.im など) からの永続的または一時的な出入り禁止。 将来的に禁止が見直されるのか、維持されるか決定できるよう、対策グループは出入り禁止の記録を全て保持します。 +- これ以上アクションを取らない。 + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +- 公の場における説明。 どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。 +- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. 「相互に合意した休止」の要求。 これは、委員会から個人への、コミュニティへの参加を一時的に控えるような要請です。 対象者が自発的に一時的な休みを取らないことを選択した場合、委員会は「冷却期限」を準備することがあります。 +- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. -決議が合意されると制定される前に、委員会は、元の報告者およびその他の影響を受けた当事者に連絡し、提案された決議を説明します。 委員会は、この決議が受け入れられるかどうかを尋ねます。 そして、記録のためのフィードバックに注意を払います。 +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. 最後に 委員会は、NumPy Steering Councilに報告を行います(NumPy Coreチームにも、出入り禁止など進行中の出来事については報告します)。 委員会はこの問題について公に議論することはありません。 すべての公開声明は、行動規範委員会またはNumPy Steering Councilの議長によって行われます。 - ## 利益相反 -利益相反が発生した場合、委員会メンバーは直ちに他のメンバーに通知し、必要に応じて対応を辞退しなければなりません。 +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 1edbaf649faf32d1a3c81e98c72d8e50b98bc7c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:22 +0200 Subject: [PATCH 120/586] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 89 ++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/ko/report-handling-manual.md diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md new file mode 100644 index 0000000000..161757fe41 --- /dev/null +++ b/content/ko/report-handling-manual.md @@ -0,0 +1,89 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +- Find a candidate who can serve as a mediator. +- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +- Obtain the agreement of the reported person(s). +- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +- Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +- Immediately disconnect the originator from all NumPy communication channels. +- Reply to the reporter that their report has been received and that the originator has been disconnected. +- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +- What happened. +- Whether this event constitutes a Code of Conduct violation. +- Who are the responsible party(ies). +- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +- Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From df4217b949ddd9d0898641ec9d461b8da72dafea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:23 +0200 Subject: [PATCH 121/586] New translations report-handling-manual.md (Russian) --- content/ru/report-handling-manual.md | 89 ++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/ru/report-handling-manual.md diff --git a/content/ru/report-handling-manual.md b/content/ru/report-handling-manual.md new file mode 100644 index 0000000000..161757fe41 --- /dev/null +++ b/content/ru/report-handling-manual.md @@ -0,0 +1,89 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +- Find a candidate who can serve as a mediator. +- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +- Obtain the agreement of the reported person(s). +- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +- Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +- Immediately disconnect the originator from all NumPy communication channels. +- Reply to the reporter that their report has been received and that the originator has been disconnected. +- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +- What happened. +- Whether this event constitutes a Code of Conduct violation. +- Who are the responsible party(ies). +- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +- Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From ed2df2b81431114ef6096e659922e90ac4ee9964 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:25 +0200 Subject: [PATCH 122/586] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 89 ++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/zh/report-handling-manual.md diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md new file mode 100644 index 0000000000..161757fe41 --- /dev/null +++ b/content/zh/report-handling-manual.md @@ -0,0 +1,89 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +- Find a candidate who can serve as a mediator. +- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +- Obtain the agreement of the reported person(s). +- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +- Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +- Immediately disconnect the originator from all NumPy communication channels. +- Reply to the reporter that their report has been received and that the originator has been disconnected. +- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +- What happened. +- Whether this event constitutes a Code of Conduct violation. +- Who are the responsible party(ies). +- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +- Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From ffb98fede1c22fadc228a2b8a28b81be0e121414 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:26 +0200 Subject: [PATCH 123/586] New translations report-handling-manual.md (Portuguese, Brazilian) --- content/pt/report-handling-manual.md | 70 +++++++++++++--------------- 1 file changed, 32 insertions(+), 38 deletions(-) diff --git a/content/pt/report-handling-manual.md b/content/pt/report-handling-manual.md index 14418d0e11..cedb1d4c5a 100644 --- a/content/pt/report-handling-manual.md +++ b/content/pt/report-handling-manual.md @@ -7,44 +7,40 @@ Este é o manual seguido pelo Comitê do Código de Conduta do NumPy. É usado q Garantir que o [Código de Conduta](/code-of-conduct) seja respeitado afeta nossa comunidade hoje e no futuro. É uma ação que levamos muito a sério. Ao analisar medidas de aplicação do Código de Conduta, o Comitê terá em mente os seguintes valores e orientações: -* Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. -* Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. -* Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. -* A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. -* Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. -* Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. -* As pessoas vêm de meios culturais e linguísticos diferentes. Tentar identificar quaisquer mal-entendidos honestos causados por falantes não-nativos e ajudá-los a entender a questão e o que pode ser modificado para evitar causar ofensa. Uma discussão complexa numa língua estrangeira pode ser muito intimidante, e queremos aumentar a nossa diversidade também entre nacionalidades e culturas. - +- Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. +- Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. +- Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. +- A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. +- Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. +- Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. +- As pessoas vêm de meios culturais e linguísticos diferentes. Tentar identificar quaisquer mal-entendidos honestos causados por falantes não-nativos e ajudá-los a entender a questão e o que pode ser modificado para evitar causar ofensa. Uma discussão complexa numa língua estrangeira pode ser muito intimidante, e queremos aumentar a nossa diversidade também entre nacionalidades e culturas. ## Mediação -A mediação informal voluntária é um instrumento à nossa disposição. Em contextos em que duas ou mais partes escalaram ao ponto de demonstrarem comportamento inapropriado (algo tristemente comum no conflito humano), poderá ser útil facilitar um processo de mediação. Isto é apenas um exemplo: em todo caso, o Comitê pode considerar a mediação, tendo em conta que o processo se destina a ser estritamente voluntário e que nenhuma das partes pode ser pressionada a participar. Se o Comitê sugerir mediação, deve: +A mediação informal voluntária é um instrumento à nossa disposição. Em contextos em que duas ou mais partes escalaram ao ponto de demonstrarem comportamento inapropriado (algo tristemente comum no conflito humano), poderá ser útil facilitar um processo de mediação. Isto é apenas um exemplo: em todo caso, o Comitê pode considerar a mediação, tendo em conta que o processo se destina a ser estritamente voluntário e que nenhuma das partes pode ser pressionada a participar. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. Se o Comitê sugerir mediação, deve: -* Encontrar uma pessoa candidata que possa servir de mediadora. -* Obter o acordo da(s) pessoa(s) relatante(s). A(s) pessoa(s) relatante(s) têm total liberdade para recusar a ideia de mediação ou propor um mediador alternativo. -* Obter o acordo da(s) pessoa(s) relatada(s). -* Estabelecer uma pessoa mediadora: enquanto as partes podem propor um mediador diferente da pessoa sugerida, o processo só poderá avançar se for alcançado um acordo comum em todos os termos. -* Estabelecer um cronograma para a mediação ser concluida, idealmente dentro de duas semanas. +- Encontrar uma pessoa candidata que possa servir de mediadora. +- Obter o acordo da(s) pessoa(s) relatada(s). A(s) pessoa(s) relatante(s) têm total liberdade para recusar a ideia de mediação ou propor um mediador alternativo. +- Obter o acordo da(s) pessoa(s) relatante(s). +- Estabelecer uma pessoa mediadora: enquanto as partes podem propor um mediador diferente da pessoa sugerida, o processo só poderá avançar se for alcançado um acordo comum em todos os termos. +- Estabelecer um cronograma para a mediação ser concluida, idealmente dentro de duas semanas. A pessoa mediadora entrará em contato com todas as partes e procurará uma resolução satisfatória para todos. Após a sua conclusão, a pessoa mediadora apresentará ao Comitê um relatório (examinado por todas as partes envolvidas no processo) com recomendações sobre outras medidas. O Comitê avaliará então esses resultados (em caso de resolução satisfatória ou não) e decidirá sobre quaisquer medidas adicionais consideradas necessárias. - ## Como o Comitê responderá aos relatórios Quando o Comitê (ou um membro do Comitê) recebe um relatório, será inicialmente determinado se o relatório é sobre uma violação clara e severa (como definido abaixo). Em caso afirmativo, medidas imediatas serão tomadas para além do processo regular de tratamento dos relatórios. - ## Ações claras e severas de violação Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Trataremos rapidamente de violações claras e severas como ameaças pessoais, linguagem violenta, sexista ou racista. Quando um membro do Comitê do Código de Conduta tomar conhecimento de uma violação clara e grave, fará o seguinte: -* Desligará imediatamente a pessoa originadora de todos os canais de comunicação do NumPy. -* Responderá à pessoa relatante para informá-la que seu relatório foi recebido e que a pessoa originadora foi desligada. -* Em todos os casos, a pessoa moderadora deve fazer um esforço razoável para entrar em contato com a pessoa originadora, e dizer-lhes especificamente como sua linguagem ou ações se qualificam como uma "violação clara e severa". A pessoa moderadora deve também dizer que, se a pessoa originadora considerar que isso é injusto ou quiser ser reconectada ao NumPy, tem o direito de solicitar uma revisão, de acordo com as disposições do Comitê do Código de Conduta. A pessoa moderadora deve copiar esta explicação para o Comitê do Código de Conduta. -* O Comitê do Código de Conduta procederá formalmente à análise e decisão em todos os casos em que este mecanismo tenha sido aplicado para garantir que não seja utilizado para controlar desentendimentos acalorados comuns. - +- Desligará imediatamente a pessoa originadora de todos os canais de comunicação do NumPy. +- Responderá à pessoa relatante para informá-la que seu relatório foi recebido e que a pessoa originadora foi desligada. +- Em todos os casos, a pessoa moderadora deve fazer um esforço razoável para entrar em contato com a pessoa originadora, e dizer-lhes especificamente como sua linguagem ou ações se qualificam como uma "violação clara e severa". A pessoa moderadora deve também dizer que, se a pessoa originadora considerar que isso é injusto ou quiser ser reconectada ao NumPy, tem o direito de solicitar uma revisão, de acordo com as disposições do Comitê do Código de Conduta. A pessoa moderadora deve copiar esta explicação para o Comitê do Código de Conduta. +- O Comitê do Código de Conduta procederá formalmente à análise e decisão em todos os casos em que este mecanismo tenha sido aplicado para garantir que não seja utilizado para controlar desentendimentos acalorados comuns. ## Tratamento de relatórios @@ -54,10 +50,10 @@ Se um relatório não contiver informações suficientes, o Comitê obterá todo O Comitê analisará então o incidente e determinará, do melhor jeito possível: -* O que aconteceu. -* Se este evento constitui ou não uma violação do Código de Conduta. -* Quem são as pessoas responsáveis. -* Se se trata de uma situação contínua, e existe uma ameaça para a segurança física de alguém. +- O que aconteceu. +- Se este evento constitui ou não uma violação do Código de Conduta. +- Quem são as pessoas responsáveis. +- Se se trata de uma situação contínua, e existe uma ameaça para a segurança física de alguém. Estas informações serão recolhidas por escrito e, sempre que possível, as deliberações do grupo serão gravadas e armazenadas (por exemplo, transcrições de conversas, discussões por e-mail, chamadas gravadas de videoconferência, resumos de conversas por voz, etc). @@ -65,31 +61,29 @@ Estas informações serão recolhidas por escrito e, sempre que possível, as de O Comitê do Código de Conduta deve ter por objetivo chegar a um acordo sobre uma resolução no prazo de duas semanas. Caso uma resolução não possa ser determinada nesse período, o Comitê responderá à(s) pessoa(s) relatante(s) com uma atualização e cronograma previsto para a resolução. - ## Resoluções O Comitê tem de chegar a um acordo sobre uma resolução por consenso. Se o grupo não conseguir chegar a um consenso e permanece bloqueado durante mais de uma semana, o grupo encaminhará o assunto para o Conselho Diretor para resolução. Possíveis respostas podem incluir: -* Não tomar nenhuma outra ação: - - se determinarmos que não ocorreram violações; - - se a questão tiver sido resolvida publicamente enquanto o Comitê estava considerando uma resposta. -* Coordenação de mediação voluntária: se todas as partes envolvidas concordarem, o Comitê poderá facilitar um processo de mediação, conforme detalhado acima. -* Salientar publicamente que alguns comportamentos, ações ou linguagem foram julgados inapropriados ou podem ser considerados danosos para algumas pessoas, explicando por que no contexto atual e solicitando que a comunidade se auto-ajuste. -* Uma advertência privada do Comitê para a(s) pessoa(s) envolvida(s). Neste caso, a pessoa presidente do Comitê irá entregar essa advertência à(s) pessoa(s) por e-mail, em cópia (CC) ao grupo. -* Uma advertência pública. Neste caso, a pessoa presidente do Comitê vai apresentar essa advertência no mesmo fórum em que ocorreu a violação, dentro dos limites da viabilidade. Exemplo: a lista original para uma violação de e-mail, mas para uma discussão em sala de bate-papo onde a pessoa/contexto pode sumir, isto pode ser feito por outros meios. O grupo pode optar por publicar esta mensagem em outro local para fins de documentação. -* Um pedido de desculpas públicas ou privadas, supondo que a(s) pessoa(s) relatante(s) concorde(m) com esta ideia: a(s) pessoa(s) pode(m), a seu critério, recusar contatos adicionais com a pessoa relatada. A Presidência dará seguimento a este pedido. O Comitê, se escolher, pode anexar condições adicionais a este pedido inicial: por exemplo, o grupo pode pedir à pessoa relatada que se desculpe para que tenha o direito de manter a sua adesão a uma lista de e-mails. -* Um “acordo mútuo de trégua” onde o Comitê solicita à pessoa que se abstenha temporariamente da participação na comunidade. Se a pessoa optar por não fazer uma pausa temporária voluntariamente, o Comitê pode aplicar um “período de afastamento obrigatório”. -* Um banimento permanente ou temporário de alguns ou todos os espaços do NumPy (listas de e-mails, gitter.im, etc.). O grupo manterá registro de todas essas proibições, para que elas possam ser revistas no futuro ou mantidas. +- Não tomar nenhuma outra ação: + - se determinarmos que não ocorreram violações; + - se a questão tiver sido resolvida publicamente enquanto o Comitê estava considerando uma resposta. +- Coordenação de mediação voluntária: se todas as partes envolvidas concordarem, o Comitê poderá facilitar um processo de mediação, conforme detalhado acima. +- Salientar publicamente que alguns comportamentos, ações ou linguagem foram julgados inapropriados ou podem ser considerados danosos para algumas pessoas, explicando por que no contexto atual e solicitando que a comunidade se auto-ajuste. +- Uma advertência privada do Comitê para a(s) pessoa(s) envolvida(s). Neste caso, a pessoa presidente do Comitê irá entregar essa advertência à(s) pessoa(s) por e-mail, em cópia (CC) ao grupo. +- Uma advertência pública. Neste caso, a pessoa presidente do Comitê vai apresentar essa advertência no mesmo fórum em que ocorreu a violação, dentro dos limites da viabilidade. Exemplo: a lista original para uma violação de e-mail, mas para uma discussão em sala de bate-papo onde a pessoa/contexto pode sumir, isto pode ser feito por outros meios. O grupo pode optar por publicar esta mensagem em outro local para fins de documentação. +- Um pedido de desculpas públicas ou privadas, supondo que a(s) pessoa(s) relatante(s) concorde(m) com esta ideia: a(s) pessoa(s) pode(m), a seu critério, recusar contatos adicionais com a pessoa relatada. A Presidência dará seguimento a este pedido. O Comitê, se escolher, pode anexar condições adicionais a este pedido inicial: por exemplo, o grupo pode pedir à pessoa relatada que se desculpe para que tenha o direito de manter a sua adesão a uma lista de e-mails. +- Um “acordo mútuo de trégua” onde o Comitê solicita à pessoa que se abstenha temporariamente da participação na comunidade. Se a pessoa optar por não fazer uma pausa temporária voluntariamente, o Comitê pode aplicar um “período de afastamento obrigatório”. +- Um banimento permanente ou temporário de alguns ou todos os espaços do NumPy (listas de e-mails, gitter.im, etc.). O grupo manterá registro de todas essas proibições, para que elas possam ser revistas no futuro ou mantidas. Uma vez aprovada uma resolução, mas antes de ser efetivamente aplicada, o Comitê entrará em contato com a pessoa relatante original e quaisquer outras partes afetadas e explicará a resolução proposta. O Comitê perguntará se esta resolução é aceitável e terá de tomar nota da sua resposta para registro futuro. -Finalmente, o Comitê apresentará um relatório ao Conselho Diretor do NumPy (bem como ao time *core* do NumPy no caso de uma resolução em curso, como um banimento). +Finalmente, o Comitê apresentará um relatório ao Conselho Diretor do NumPy (bem como ao time _core_ do NumPy no caso de uma resolução em curso, como um banimento). O Comitê nunca discutirá publicamente a questão; todas as declarações públicas serão feitas pela pessoa presidente do Comitê do Código de Conduta ou pelo Conselho Diretor do NumPy. - ## Conflitos de Interesse Em caso de conflito de interesses, um membro do Comitê deve notificar imediatamente os outros membros e abdicar de sua participação no processo caso seja necessário. From eea99add97a653914d63d080bb76a441a26db39a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:27 +0200 Subject: [PATCH 124/586] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/es/user-survey-2020.md diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md new file mode 100644 index 0000000000..99182de7a5 --- /dev/null +++ b/content/es/user-survey-2020.md @@ -0,0 +1,25 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a +Master’s course in Survey Methodology jointly hosted by the University of +Michigan and the University of Maryland conducted the first official NumPy +community survey. Over 1,200 users from 75 countries participated to help us +map out a landscape of the NumPy community and voiced their thoughts about the +future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** +to take a closer look at the survey findings. + +For the highlights, check out +**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. From 46b2e80c4402ead0df4524430d2afe7cf2720762 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:28 +0200 Subject: [PATCH 125/586] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/ar/user-survey-2020.md diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md new file mode 100644 index 0000000000..99182de7a5 --- /dev/null +++ b/content/ar/user-survey-2020.md @@ -0,0 +1,25 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a +Master’s course in Survey Methodology jointly hosted by the University of +Michigan and the University of Maryland conducted the first official NumPy +community survey. Over 1,200 users from 75 countries participated to help us +map out a landscape of the NumPy community and voiced their thoughts about the +future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** +to take a closer look at the survey findings. + +For the highlights, check out +**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. From a0c698978bddca916a3376e7479abb35dae4d2f1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:29 +0200 Subject: [PATCH 126/586] New translations user-survey-2020.md (Japanese) --- content/ja/user-survey-2020.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index b79cc13ed8..46e47e5073 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -3,7 +3,12 @@ title: 2020年 NumPyコミュニティ調査 sidebar: false --- -2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。 +In 2020, the NumPy survey team in partnership with students and faculty from a +Master’s course in Survey Methodology jointly hosted by the University of +Michigan and the University of Maryland conducted the first official NumPy +community survey. Over 1,200 users from 75 countries participated to help us +map out a landscape of the NumPy community and voiced their thoughts about the +future of the project. {{< figure >}} src = '/surveys/NumPy_usersurvey_2020_report_cover.png' @@ -13,8 +18,6 @@ width = '250' 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。 - 結果の概要については、 **[こちらの図](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)** をチェックしてください。 -より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 - +Ready for a deep dive? より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 From e8667ab5d434b12f2c7c8174bd5b621d40f41d90 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:30 +0200 Subject: [PATCH 127/586] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/ko/user-survey-2020.md diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md new file mode 100644 index 0000000000..99182de7a5 --- /dev/null +++ b/content/ko/user-survey-2020.md @@ -0,0 +1,25 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a +Master’s course in Survey Methodology jointly hosted by the University of +Michigan and the University of Maryland conducted the first official NumPy +community survey. Over 1,200 users from 75 countries participated to help us +map out a landscape of the NumPy community and voiced their thoughts about the +future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** +to take a closer look at the survey findings. + +For the highlights, check out +**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. From 4669842528e1d1a96fb450d2ba8dc4815506f33b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:31 +0200 Subject: [PATCH 128/586] New translations user-survey-2020.md (Russian) --- content/ru/user-survey-2020.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/ru/user-survey-2020.md diff --git a/content/ru/user-survey-2020.md b/content/ru/user-survey-2020.md new file mode 100644 index 0000000000..99182de7a5 --- /dev/null +++ b/content/ru/user-survey-2020.md @@ -0,0 +1,25 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a +Master’s course in Survey Methodology jointly hosted by the University of +Michigan and the University of Maryland conducted the first official NumPy +community survey. Over 1,200 users from 75 countries participated to help us +map out a landscape of the NumPy community and voiced their thoughts about the +future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** +to take a closer look at the survey findings. + +For the highlights, check out +**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. From bfab0bab16bb0a31e88339f60f933295119ae70a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:33 +0200 Subject: [PATCH 129/586] New translations user-survey-2020.md (Chinese Simplified) --- content/zh/user-survey-2020.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 content/zh/user-survey-2020.md diff --git a/content/zh/user-survey-2020.md b/content/zh/user-survey-2020.md new file mode 100644 index 0000000000..99182de7a5 --- /dev/null +++ b/content/zh/user-survey-2020.md @@ -0,0 +1,25 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a +Master’s course in Survey Methodology jointly hosted by the University of +Michigan and the University of Maryland conducted the first official NumPy +community survey. Over 1,200 users from 75 countries participated to help us +map out a landscape of the NumPy community and voiced their thoughts about the +future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** +to take a closer look at the survey findings. + +For the highlights, check out +**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. From 36cb61f82636e46a472cdbd454dd96cd1058b0b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:34 +0200 Subject: [PATCH 130/586] New translations user-survey-2020.md (Portuguese, Brazilian) --- content/pt/user-survey-2020.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index 8747efca88..5f2a397baf 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -13,8 +13,6 @@ width = '250' **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. - Para os destaques, confira **[este infográfico](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. -Quer saber mais? Visite **https://numpy.org/user-survey-2020-details/**. - +Ready for a deep dive? Visite **https://numpy.org/user-survey-2020-details/**. From 7bc7db32138c1983d84429542523ed841a12ebec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:35 +0200 Subject: [PATCH 131/586] New translations user-surveys.md (Spanish) --- content/es/user-surveys.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 content/es/user-surveys.md diff --git a/content/es/user-surveys.md b/content/es/user-surveys.md new file mode 100644 index 0000000000..529a6c1ea7 --- /dev/null +++ b/content/es/user-surveys.md @@ -0,0 +1,11 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** +The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From ae7f8c69ee27af197708e290ea06e843fb59410e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:36 +0200 Subject: [PATCH 132/586] New translations user-surveys.md (Arabic) --- content/ar/user-surveys.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 content/ar/user-surveys.md diff --git a/content/ar/user-surveys.md b/content/ar/user-surveys.md new file mode 100644 index 0000000000..529a6c1ea7 --- /dev/null +++ b/content/ar/user-surveys.md @@ -0,0 +1,11 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** +The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 0b6ac86389ecd6427f49368f356ffcab03b4ad6e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:37 +0200 Subject: [PATCH 133/586] New translations user-surveys.md (Japanese) --- content/ja/user-surveys.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/user-surveys.md b/content/ja/user-surveys.md index 7be9979c3a..bc952ab2a9 100644 --- a/content/ja/user-surveys.md +++ b/content/ja/user-surveys.md @@ -1,10 +1,10 @@ --- -title: NumPyユーザアンケート +title: NUMPY USER SURVEYS sidebar: false --- -**2020** NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果は[こちら](https://numpy.org/user-survey-2020/)をご覧ください。 +**2020** NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果は[こちら](https://numpy.org/user-survey-2020/)をご覧ください。 Find the survey results [here](https://numpy.org/user-survey-2020/). **2021** 収集された調査データは現在解析中です。 -過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、[こちら](https://github.com/numpy/numpy-surveys/issues)にイシューを作成してください。 +過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、[こちら](https://github.com/numpy/numpy-surveys/issues)にイシューを作成してください。 From 8c8ad9b5f991fc47baa10eb521e11086a7a97b63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:38 +0200 Subject: [PATCH 134/586] New translations user-surveys.md (Korean) --- content/ko/user-surveys.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 content/ko/user-surveys.md diff --git a/content/ko/user-surveys.md b/content/ko/user-surveys.md new file mode 100644 index 0000000000..529a6c1ea7 --- /dev/null +++ b/content/ko/user-surveys.md @@ -0,0 +1,11 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** +The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 75ee6fe4d5c82fb0fb0da83841c344dcaf415303 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:39 +0200 Subject: [PATCH 135/586] New translations user-surveys.md (Russian) --- content/ru/user-surveys.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 content/ru/user-surveys.md diff --git a/content/ru/user-surveys.md b/content/ru/user-surveys.md new file mode 100644 index 0000000000..529a6c1ea7 --- /dev/null +++ b/content/ru/user-surveys.md @@ -0,0 +1,11 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** +The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 048ca42d38e2f3386c9101c5a6da4f8bd33e9242 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:40 +0200 Subject: [PATCH 136/586] New translations user-surveys.md (Chinese Simplified) --- content/zh/user-surveys.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 content/zh/user-surveys.md diff --git a/content/zh/user-surveys.md b/content/zh/user-surveys.md new file mode 100644 index 0000000000..529a6c1ea7 --- /dev/null +++ b/content/zh/user-surveys.md @@ -0,0 +1,11 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** +The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 54ec1e52f1cf5da043293a8877cc4ebfbee85dae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:41 +0200 Subject: [PATCH 137/586] New translations user-surveys.md (Portuguese, Brazilian) --- content/pt/user-surveys.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/user-surveys.md b/content/pt/user-surveys.md index 4f60686926..1c0e4f62af 100644 --- a/content/pt/user-surveys.md +++ b/content/pt/user-surveys.md @@ -7,4 +7,4 @@ sidebar: false **2021** Os dados coletados estão em análise. -Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue [aqui](https://github.com/numpy/numpy-surveys/issues). +Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue [aqui](https://github.com/numpy/numpy-surveys/issues). From 2c4180f710b328f6c667d37299375f4313208124 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:42 +0200 Subject: [PATCH 138/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 144 +++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 content/es/case-studies/blackhole-image.md diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md new file mode 100644 index 0000000000..bfa12875ff --- /dev/null +++ b/content/es/case-studies/blackhole-image.md @@ -0,0 +1,144 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" + +> }} +> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studing the universe with unprecedented +sensitivity and resolution. The huge virtual telescope, which uses a technique +called very-long-baseline interferometry (VLBI), has an angular resolution of +[20 micro-arcseconds][resolution] — enough to read a newspaper in New York +from a sidewalk café in Paris! + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +### Key Goals and Results + +- **A New View of the Universe:** + The groundwork for the EHT's groundbreaking image had been laid 100 years + earlier when [Sir Arthur Eddington][eddington] yielded the first + observational support of Einstein's theory of general relativity. + +- **The Black Hole:** EHT was trained on a supermassive black hole + approximately 55 million light-years from Earth, lying at the center + of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is + 6.5 billion times the Sun's. It had been studied for + [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before + had a black hole been visually observed. + +- **Comparing Observations to Theory:** From Einstein’s general theory of + relativity, scientists expected to find a shadow-like region caused by + gravitational bending and capture of light. Scientists could + use it to measure the black hole's enormous mass. + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +### The Challenges + +- **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric + phase fluctuations, large recording bandwidth, and telescopes that are + widely dissimilar and geographically dispersed. + +- **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on + helium-filled hard drives. Reducing the volume and complexity of this much + data is enormously difficult. + +- **Into the unknown** + + When the goal is to see something never before seen, how can scientists be + confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHT Data Processing Pipeline' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too +heavily on a particular assumption. Will the image change drastically if a +single parameter is changed? + +The EHT collaboration met these challenges by having independent teams +evaluate the data, using both established and cutting-edge image reconstruction +techniques. When results proved consistent, they were combined to yield the +first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in +advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for +simulating and performing image reconstruction on VLBI data. +NumPy is at the core of array data processing used +in this package, as illustrated by the partial software +dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +[ehtim]: https://github.com/achael/eht-imaging + +Besides NumPy, many other packages, such as +[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +data processing pipeline for imaging the black hole. +The standard astronomical file formats and time/coordinate transformations +were handled by [Astropy][astropy], while [Matplotlib][mpl] was used +in visualizing data throughout the analysis pipeline, including the generation +of the final image of the black hole. + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature +enabled researchers to manipulate large numerical datasets, providing a +foundation for the first-ever image of a black hole. A landmark moment in +science, it gives stunning visual evidence of Einstein’s theory. The +achievement encompasses not only technological breakthroughs but also +international collaboration among over 200 scientists and some of the world's +best radio observatories. Innovative algorithms and data processing +techniques, improving upon existing astronomical models, helped unfold a +mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From fc244095e3c1cfece18c7dff8e0fc849c716d176 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:44 +0200 Subject: [PATCH 139/586] New translations blackhole-image.md (Arabic) --- content/ar/case-studies/blackhole-image.md | 144 +++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 content/ar/case-studies/blackhole-image.md diff --git a/content/ar/case-studies/blackhole-image.md b/content/ar/case-studies/blackhole-image.md new file mode 100644 index 0000000000..bfa12875ff --- /dev/null +++ b/content/ar/case-studies/blackhole-image.md @@ -0,0 +1,144 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" + +> }} +> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studing the universe with unprecedented +sensitivity and resolution. The huge virtual telescope, which uses a technique +called very-long-baseline interferometry (VLBI), has an angular resolution of +[20 micro-arcseconds][resolution] — enough to read a newspaper in New York +from a sidewalk café in Paris! + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +### Key Goals and Results + +- **A New View of the Universe:** + The groundwork for the EHT's groundbreaking image had been laid 100 years + earlier when [Sir Arthur Eddington][eddington] yielded the first + observational support of Einstein's theory of general relativity. + +- **The Black Hole:** EHT was trained on a supermassive black hole + approximately 55 million light-years from Earth, lying at the center + of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is + 6.5 billion times the Sun's. It had been studied for + [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before + had a black hole been visually observed. + +- **Comparing Observations to Theory:** From Einstein’s general theory of + relativity, scientists expected to find a shadow-like region caused by + gravitational bending and capture of light. Scientists could + use it to measure the black hole's enormous mass. + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +### The Challenges + +- **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric + phase fluctuations, large recording bandwidth, and telescopes that are + widely dissimilar and geographically dispersed. + +- **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on + helium-filled hard drives. Reducing the volume and complexity of this much + data is enormously difficult. + +- **Into the unknown** + + When the goal is to see something never before seen, how can scientists be + confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHT Data Processing Pipeline' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too +heavily on a particular assumption. Will the image change drastically if a +single parameter is changed? + +The EHT collaboration met these challenges by having independent teams +evaluate the data, using both established and cutting-edge image reconstruction +techniques. When results proved consistent, they were combined to yield the +first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in +advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for +simulating and performing image reconstruction on VLBI data. +NumPy is at the core of array data processing used +in this package, as illustrated by the partial software +dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +[ehtim]: https://github.com/achael/eht-imaging + +Besides NumPy, many other packages, such as +[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +data processing pipeline for imaging the black hole. +The standard astronomical file formats and time/coordinate transformations +were handled by [Astropy][astropy], while [Matplotlib][mpl] was used +in visualizing data throughout the analysis pipeline, including the generation +of the final image of the black hole. + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature +enabled researchers to manipulate large numerical datasets, providing a +foundation for the first-ever image of a black hole. A landmark moment in +science, it gives stunning visual evidence of Einstein’s theory. The +achievement encompasses not only technological breakthroughs but also +international collaboration among over 200 scientists and some of the world's +best radio observatories. Innovative algorithms and data processing +techniques, improving upon existing astronomical models, helped unfold a +mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 353f9df3133ae2528dec5c12aa2b5efe73ce05df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:45 +0200 Subject: [PATCH 140/586] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 81 ++++++++++++++-------- 1 file changed, 53 insertions(+), 28 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 7d7dfb2505..163d6347e0 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -1,5 +1,5 @@ --- -title: "ケーススタディ：世界初のブラックホール画像" +title: ケーススタディ：世界初のブラックホール画像 sidebar: false --- @@ -12,37 +12,50 @@ attrk = 'https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg' {{< /figure >}} {{< blockquote - cite="https://www.youtube.com/watch?v=BIvezCVcsYs" - by="*カリフォルニア工科大学 計算・数理学部*のKatie Bouman助教授" ->}} -M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 -{{< /blockquote >}} +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="_カリフォルニア工科大学 計算・数理学部_のKatie Bouman助教授" + +> }} +> M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 +> {{< /blockquote >}} +> {{< /blockquote >}} ## 地球大の望遠鏡 -[Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です! +[Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です! The huge virtual telescope, which uses a technique +called very-long-baseline interferometry (VLBI), has an angular resolution of +[20 micro-arcseconds][resolution] — enough to read a newspaper in New York +from a sidewalk café in Paris! + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole ### 主な目標と結果 -* **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington]がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 +- **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington]がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 -* **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 [100年以上](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 +- **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 [100年以上](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 Its mass is + 6.5 billion times the Sun's. It had been studied for + [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before + had a black hole been visually observed. -* **観測と理論の比較:** 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 +- **観測と理論の比較:** 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment ### 課題 -* **大規模な計算** +- **大規模な計算** - EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 + EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 -* **大量のデータ** +- **大量のデータ** - EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 + EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 Reducing the volume and complexity of this much + data is enormously difficult. -* **よくわからないものを観測する** +- **よくわからないものを観測する** - 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? + 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' @@ -55,9 +68,13 @@ attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' ## NumPyが果たした役割 -データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ +What if there's a problem with the data? Or perhaps an algorithm relies too +heavily on a particular assumption. データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ -EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 +The EHT collaboration met these challenges by having independent teams +evaluate the data, using both established and cutting-edge image reconstruction +techniques. When results proved consistent, they were combined to yield the +first-of-a-kind image of the black hole. 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 @@ -68,6 +85,9 @@ title = 'ブラックホール画像化でNumPyが果たした役割' {{< /figure >}} 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 +NumPy is at the core of array data processing used +in this package, as illustrated by the partial software +dependency chart below. {{< figure >}} src = '/images/content_images/cs/ehtim_numpy.png' @@ -75,23 +95,28 @@ alt = 'ehtim dependency map highlighting numpy' title = 'NumPyの中心としたehtimのソフトウェア依存図' {{< /figure >}} +[ehtim]: https://github.com/achael/eht-imaging + +Besides NumPy, many other packages, such as +[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +data processing pipeline for imaging the black hole. NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ + ## まとめ -NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 +NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 A landmark moment in +science, it gives stunning visual evidence of Einstein’s theory. The +achievement encompasses not only technological breakthroughs but also +international collaboration among over 200 scientists and some of the world's +best radio observatories. Innovative algorithms and data processing +techniques, improving upon existing astronomical models, helped unfold a +mystery of the universe. {{< figure >}} src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}} - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole - -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment - -[ehtim]: https://github.com/achael/eht-imaging - -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ From 35ccbf18d14131a70f920dc48620c6e56d55fbcf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:46 +0200 Subject: [PATCH 141/586] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 144 +++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 content/ko/case-studies/blackhole-image.md diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md new file mode 100644 index 0000000000..bfa12875ff --- /dev/null +++ b/content/ko/case-studies/blackhole-image.md @@ -0,0 +1,144 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" + +> }} +> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studing the universe with unprecedented +sensitivity and resolution. The huge virtual telescope, which uses a technique +called very-long-baseline interferometry (VLBI), has an angular resolution of +[20 micro-arcseconds][resolution] — enough to read a newspaper in New York +from a sidewalk café in Paris! + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +### Key Goals and Results + +- **A New View of the Universe:** + The groundwork for the EHT's groundbreaking image had been laid 100 years + earlier when [Sir Arthur Eddington][eddington] yielded the first + observational support of Einstein's theory of general relativity. + +- **The Black Hole:** EHT was trained on a supermassive black hole + approximately 55 million light-years from Earth, lying at the center + of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is + 6.5 billion times the Sun's. It had been studied for + [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before + had a black hole been visually observed. + +- **Comparing Observations to Theory:** From Einstein’s general theory of + relativity, scientists expected to find a shadow-like region caused by + gravitational bending and capture of light. Scientists could + use it to measure the black hole's enormous mass. + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +### The Challenges + +- **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric + phase fluctuations, large recording bandwidth, and telescopes that are + widely dissimilar and geographically dispersed. + +- **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on + helium-filled hard drives. Reducing the volume and complexity of this much + data is enormously difficult. + +- **Into the unknown** + + When the goal is to see something never before seen, how can scientists be + confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHT Data Processing Pipeline' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too +heavily on a particular assumption. Will the image change drastically if a +single parameter is changed? + +The EHT collaboration met these challenges by having independent teams +evaluate the data, using both established and cutting-edge image reconstruction +techniques. When results proved consistent, they were combined to yield the +first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in +advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for +simulating and performing image reconstruction on VLBI data. +NumPy is at the core of array data processing used +in this package, as illustrated by the partial software +dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +[ehtim]: https://github.com/achael/eht-imaging + +Besides NumPy, many other packages, such as +[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +data processing pipeline for imaging the black hole. +The standard astronomical file formats and time/coordinate transformations +were handled by [Astropy][astropy], while [Matplotlib][mpl] was used +in visualizing data throughout the analysis pipeline, including the generation +of the final image of the black hole. + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature +enabled researchers to manipulate large numerical datasets, providing a +foundation for the first-ever image of a black hole. A landmark moment in +science, it gives stunning visual evidence of Einstein’s theory. The +achievement encompasses not only technological breakthroughs but also +international collaboration among over 200 scientists and some of the world's +best radio observatories. Innovative algorithms and data processing +techniques, improving upon existing astronomical models, helped unfold a +mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 758efd2d846b3922e832f016367a79085adc6746 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:47 +0200 Subject: [PATCH 142/586] New translations blackhole-image.md (Russian) --- content/ru/case-studies/blackhole-image.md | 144 +++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 content/ru/case-studies/blackhole-image.md diff --git a/content/ru/case-studies/blackhole-image.md b/content/ru/case-studies/blackhole-image.md new file mode 100644 index 0000000000..bfa12875ff --- /dev/null +++ b/content/ru/case-studies/blackhole-image.md @@ -0,0 +1,144 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" + +> }} +> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studing the universe with unprecedented +sensitivity and resolution. The huge virtual telescope, which uses a technique +called very-long-baseline interferometry (VLBI), has an angular resolution of +[20 micro-arcseconds][resolution] — enough to read a newspaper in New York +from a sidewalk café in Paris! + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +### Key Goals and Results + +- **A New View of the Universe:** + The groundwork for the EHT's groundbreaking image had been laid 100 years + earlier when [Sir Arthur Eddington][eddington] yielded the first + observational support of Einstein's theory of general relativity. + +- **The Black Hole:** EHT was trained on a supermassive black hole + approximately 55 million light-years from Earth, lying at the center + of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is + 6.5 billion times the Sun's. It had been studied for + [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before + had a black hole been visually observed. + +- **Comparing Observations to Theory:** From Einstein’s general theory of + relativity, scientists expected to find a shadow-like region caused by + gravitational bending and capture of light. Scientists could + use it to measure the black hole's enormous mass. + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +### The Challenges + +- **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric + phase fluctuations, large recording bandwidth, and telescopes that are + widely dissimilar and geographically dispersed. + +- **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on + helium-filled hard drives. Reducing the volume and complexity of this much + data is enormously difficult. + +- **Into the unknown** + + When the goal is to see something never before seen, how can scientists be + confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHT Data Processing Pipeline' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too +heavily on a particular assumption. Will the image change drastically if a +single parameter is changed? + +The EHT collaboration met these challenges by having independent teams +evaluate the data, using both established and cutting-edge image reconstruction +techniques. When results proved consistent, they were combined to yield the +first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in +advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for +simulating and performing image reconstruction on VLBI data. +NumPy is at the core of array data processing used +in this package, as illustrated by the partial software +dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +[ehtim]: https://github.com/achael/eht-imaging + +Besides NumPy, many other packages, such as +[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +data processing pipeline for imaging the black hole. +The standard astronomical file formats and time/coordinate transformations +were handled by [Astropy][astropy], while [Matplotlib][mpl] was used +in visualizing data throughout the analysis pipeline, including the generation +of the final image of the black hole. + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature +enabled researchers to manipulate large numerical datasets, providing a +foundation for the first-ever image of a black hole. A landmark moment in +science, it gives stunning visual evidence of Einstein’s theory. The +achievement encompasses not only technological breakthroughs but also +international collaboration among over 200 scientists and some of the world's +best radio observatories. Innovative algorithms and data processing +techniques, improving upon existing astronomical models, helped unfold a +mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From c0ce84be9a660fe26f3ad2995b315f711046bca7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:50 +0200 Subject: [PATCH 143/586] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 144 +++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 content/zh/case-studies/blackhole-image.md diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md new file mode 100644 index 0000000000..bfa12875ff --- /dev/null +++ b/content/zh/case-studies/blackhole-image.md @@ -0,0 +1,144 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" + +> }} +> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studing the universe with unprecedented +sensitivity and resolution. The huge virtual telescope, which uses a technique +called very-long-baseline interferometry (VLBI), has an angular resolution of +[20 micro-arcseconds][resolution] — enough to read a newspaper in New York +from a sidewalk café in Paris! + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +### Key Goals and Results + +- **A New View of the Universe:** + The groundwork for the EHT's groundbreaking image had been laid 100 years + earlier when [Sir Arthur Eddington][eddington] yielded the first + observational support of Einstein's theory of general relativity. + +- **The Black Hole:** EHT was trained on a supermassive black hole + approximately 55 million light-years from Earth, lying at the center + of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is + 6.5 billion times the Sun's. It had been studied for + [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before + had a black hole been visually observed. + +- **Comparing Observations to Theory:** From Einstein’s general theory of + relativity, scientists expected to find a shadow-like region caused by + gravitational bending and capture of light. Scientists could + use it to measure the black hole's enormous mass. + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +### The Challenges + +- **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric + phase fluctuations, large recording bandwidth, and telescopes that are + widely dissimilar and geographically dispersed. + +- **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on + helium-filled hard drives. Reducing the volume and complexity of this much + data is enormously difficult. + +- **Into the unknown** + + When the goal is to see something never before seen, how can scientists be + confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHT Data Processing Pipeline' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too +heavily on a particular assumption. Will the image change drastically if a +single parameter is changed? + +The EHT collaboration met these challenges by having independent teams +evaluate the data, using both established and cutting-edge image reconstruction +techniques. When results proved consistent, they were combined to yield the +first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in +advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for +simulating and performing image reconstruction on VLBI data. +NumPy is at the core of array data processing used +in this package, as illustrated by the partial software +dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +[ehtim]: https://github.com/achael/eht-imaging + +Besides NumPy, many other packages, such as +[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +data processing pipeline for imaging the black hole. +The standard astronomical file formats and time/coordinate transformations +were handled by [Astropy][astropy], while [Matplotlib][mpl] was used +in visualizing data throughout the analysis pipeline, including the generation +of the final image of the black hole. + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature +enabled researchers to manipulate large numerical datasets, providing a +foundation for the first-ever image of a black hole. A landmark moment in +science, it gives stunning visual evidence of Einstein’s theory. The +achievement encompasses not only technological breakthroughs but also +international collaboration among over 200 scientists and some of the world's +best radio observatories. Innovative algorithms and data processing +techniques, improving upon existing astronomical models, helped unfold a +mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 032b3f104cfff41d5ab0e66ce65bcf6429bf03ff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:51 +0200 Subject: [PATCH 144/586] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 53 ++++++++++++---------- 1 file changed, 28 insertions(+), 25 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index d8429b35cc..cb3b12bd4c 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -12,37 +12,42 @@ attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20 {{< /figure >}} {{< blockquote - cite="https://www.youtube.com/watch?v=BIvezCVcsYs" - by="Katie Bouman, *Professora Assistente, Ciências da Computação e Matemática, Caltech*" ->}} -Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. -{{< /blockquote >}} +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="Katie Bouman, _Professora Assistente, Ciências da Computação e Matemática, Caltech_" + +> }} +> Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. +> {{< /blockquote >}} ## Um telescópio do tamanho da Terra O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um conjunto de oito telescópios em solo formando um telescópio computacional do tamanho da Terra, projetado para estudar o universo com sensibilidade e resolução sem precedentes. O enorme telescópio virtual, que usa uma técnica chamada interferometria de longa linha de base (VLBI), tem uma resolução angular de [20 micro-arcossegundos][resolution] — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris! +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + ### Principais Objetivos e Resultados -* **Uma nova visão do universo:** A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. +- **Uma nova visão do universo:** A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. -* **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. +- **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. -* **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. +- **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment ### Desafios -* **Escala computacional** +- **Escala computacional** - O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. + O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. -* **Muitas informações** +- **Muitas informações** - A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. + A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. -* **Em direção ao desconhecido** +- **Em direção ao desconhecido** - Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? + Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' @@ -67,7 +72,8 @@ alt = 'role of numpy' title = 'O papel do NumPy na criação da primeira imagem de um Buraco Negro' {{< /figure >}} -Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. +Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. +O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. {{< figure >}} src = '/images/content_images/cs/ehtim_numpy.png' @@ -75,7 +81,13 @@ alt = 'ehtim dependency map highlighting numpy' title = 'Diagrama de dependência de software do pacote ehtim evidenciando o NumPy' {{< /figure >}} -Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. +[ehtim]: https://github.com/achael/eht-imaging + +Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na _pipeline_ de processamento de dados para criar a imagem do buraco negro. +Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ ## Resumo @@ -86,12 +98,3 @@ src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Funcionalidades-chave do NumPy utilizadas' {{< /figure >}} - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole - -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment - -[ehtim]: https://github.com/achael/eht-imaging - -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ From 86be7dd0512711d8897e9bda7ac7a33d0ba86289 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:52 +0200 Subject: [PATCH 145/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 165 +++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 content/es/case-studies/cricket-analytics.md diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..347251d55a --- /dev/null +++ b/content/es/case-studies/cricket-analytics.md @@ -0,0 +1,165 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, the biggest Cricket Festival in India' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" + +> }} +> You don't play for the crowd, you play for the country. +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is +played in just about every nook and cranny of India, rural or urban, popular +with the young and the old alike, connecting billions in India unlike any other sport. +Cricket enjoys lots of media attention. There is a significant amount of +[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and +fame at stake. Over the last several years, technology has literally been a game +changer. Audiences are spoilt for choice with streaming media, tournaments, +affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket +league, founded in 2008. It is one of the most attended cricketing events in +the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) +in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken +by a bowler, the matches won by a cricket team, the number of times a batsman +responds in a certain way to a kind of bowling attack, etc. The capability to +dig into cricketing numbers for both improving performance and studying +the business opportunities, overall market, and economics of cricket via powerful +analytics tools, powered by numerical computing software such as NumPy, is a big +deal. Cricket analytics provides interesting insights into the game and +predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and +statistics available, e.g., ESPN +cricinfo and +[cricsheet](https://cricsheet.org). These and several such cricket databases +have been used for cricket +analysis +using the latest machine learning and predictive modelling algorithms. +Media and entertainment platforms along with professional sports bodies +associated with the game use technology and analytics for determining key +metrics for improving match winning chances: + +- batting performance moving average, +- score forecasting, +- gaining insights into fitness and performance of a player against different opposition, +- player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Cricket Pitch, the focal point in the field' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +- Sports data analytics are used not only in cricket but many other + sports for + improving the overall team performance and maximizing winning chances. +- Real-time data analytics can help in gaining insights even during the game + for changing tactics by the team and by associated businesses for economic + benefits and growth. +- Besides historical analysis, predictive models are + harnessed to determine the possible match outcomes that require significant + number crunching and data science know-how, visualization tools and capability + to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'Cricket Pose Estimator' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +- **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much + larger scale. The number of matches played every season across various + formats has increased and so has the data, the algorithms, newer sports data + analysis technologies and simulation models. Cricket data analysis requires + field mapping, player tracking, ball tracking, player shot analysis, and + several other aspects involved in how the ball is delivered, its angle, spin, + velocity, and trajectory. All these factors together have increased the + complexity of data cleaning and preprocessing. + +- **Dynamic Modeling** + + In cricket, just like any other sport, + there can be a large number of variables related to tracking various numbers + of players on the field, their attributes, the ball, and several possibilities + of potential actions. The complexity of data analytics and modeling is + directly proportional to the kind of predictive questions that are put forth + during analysis and are highly dependent on data representation and the + model. Things get even more challenging in terms of computation, data + comparisons when dynamic cricket play predictions are sought such as what + would have happened if the batsman had hit the ball at a different angle or + velocity. + +- **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how + often does a batsman play a certain kind of shot if the ball delivery is of a + particular type", or "how does a bowler change his line and length if the + batsman responds to his delivery in a certain way". + This kind of predictive analytics query requires highly granular dataset + availability and the capability to synthesize data and create generative + models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies +[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) +and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, +besides using the latest machine learning and AI techniques. NumPy has been used +for various kinds of cricket related sporting analytics such as: + +- **Statistical Analysis:** NumPy's numerical capabilities help estimate the + statistical significance of observational data or match events in the context + of various player and game tactics, estimating the game outcome by comparison + with a generative or static model. + [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) + and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) + are used for tactical analysis. + +- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are +played, especially how strategic decision making happens, which until recently +was primarily done based on “gut feeling" or adherence to past traditions. NumPy +forms a solid foundation for a large set of Python packages which provide higher +level functions related to data analytics, machine learning, and AI algorithms. +These packages are widely deployed to gain real-time insights that help in +decision making for game-changing outcomes, both on field as well as to draw +inferences and drive business around the game of cricket. Finding out the +hidden parameters, patterns, and attributes that lead to the outcome of a +cricket match helps the stakeholders to take notice of game insights that are +otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagram showing benefits of using NumPy for cricket analytics' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 91d8dde798d7e042d0d7fa7d3260b818ea89a47c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:54 +0200 Subject: [PATCH 146/586] New translations cricket-analytics.md (Arabic) --- content/ar/case-studies/cricket-analytics.md | 165 +++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 content/ar/case-studies/cricket-analytics.md diff --git a/content/ar/case-studies/cricket-analytics.md b/content/ar/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..347251d55a --- /dev/null +++ b/content/ar/case-studies/cricket-analytics.md @@ -0,0 +1,165 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, the biggest Cricket Festival in India' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" + +> }} +> You don't play for the crowd, you play for the country. +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is +played in just about every nook and cranny of India, rural or urban, popular +with the young and the old alike, connecting billions in India unlike any other sport. +Cricket enjoys lots of media attention. There is a significant amount of +[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and +fame at stake. Over the last several years, technology has literally been a game +changer. Audiences are spoilt for choice with streaming media, tournaments, +affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket +league, founded in 2008. It is one of the most attended cricketing events in +the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) +in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken +by a bowler, the matches won by a cricket team, the number of times a batsman +responds in a certain way to a kind of bowling attack, etc. The capability to +dig into cricketing numbers for both improving performance and studying +the business opportunities, overall market, and economics of cricket via powerful +analytics tools, powered by numerical computing software such as NumPy, is a big +deal. Cricket analytics provides interesting insights into the game and +predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and +statistics available, e.g., ESPN +cricinfo and +[cricsheet](https://cricsheet.org). These and several such cricket databases +have been used for cricket +analysis +using the latest machine learning and predictive modelling algorithms. +Media and entertainment platforms along with professional sports bodies +associated with the game use technology and analytics for determining key +metrics for improving match winning chances: + +- batting performance moving average, +- score forecasting, +- gaining insights into fitness and performance of a player against different opposition, +- player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Cricket Pitch, the focal point in the field' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +- Sports data analytics are used not only in cricket but many other + sports for + improving the overall team performance and maximizing winning chances. +- Real-time data analytics can help in gaining insights even during the game + for changing tactics by the team and by associated businesses for economic + benefits and growth. +- Besides historical analysis, predictive models are + harnessed to determine the possible match outcomes that require significant + number crunching and data science know-how, visualization tools and capability + to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'Cricket Pose Estimator' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +- **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much + larger scale. The number of matches played every season across various + formats has increased and so has the data, the algorithms, newer sports data + analysis technologies and simulation models. Cricket data analysis requires + field mapping, player tracking, ball tracking, player shot analysis, and + several other aspects involved in how the ball is delivered, its angle, spin, + velocity, and trajectory. All these factors together have increased the + complexity of data cleaning and preprocessing. + +- **Dynamic Modeling** + + In cricket, just like any other sport, + there can be a large number of variables related to tracking various numbers + of players on the field, their attributes, the ball, and several possibilities + of potential actions. The complexity of data analytics and modeling is + directly proportional to the kind of predictive questions that are put forth + during analysis and are highly dependent on data representation and the + model. Things get even more challenging in terms of computation, data + comparisons when dynamic cricket play predictions are sought such as what + would have happened if the batsman had hit the ball at a different angle or + velocity. + +- **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how + often does a batsman play a certain kind of shot if the ball delivery is of a + particular type", or "how does a bowler change his line and length if the + batsman responds to his delivery in a certain way". + This kind of predictive analytics query requires highly granular dataset + availability and the capability to synthesize data and create generative + models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies +[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) +and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, +besides using the latest machine learning and AI techniques. NumPy has been used +for various kinds of cricket related sporting analytics such as: + +- **Statistical Analysis:** NumPy's numerical capabilities help estimate the + statistical significance of observational data or match events in the context + of various player and game tactics, estimating the game outcome by comparison + with a generative or static model. + [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) + and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) + are used for tactical analysis. + +- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are +played, especially how strategic decision making happens, which until recently +was primarily done based on “gut feeling" or adherence to past traditions. NumPy +forms a solid foundation for a large set of Python packages which provide higher +level functions related to data analytics, machine learning, and AI algorithms. +These packages are widely deployed to gain real-time insights that help in +decision making for game-changing outcomes, both on field as well as to draw +inferences and drive business around the game of cricket. Finding out the +hidden parameters, patterns, and attributes that lead to the outcome of a +cricket match helps the stakeholders to take notice of game insights that are +otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagram showing benefits of using NumPy for cricket analytics' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From d7420906b42f588134b7cf361945de80975eb10e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:55 +0200 Subject: [PATCH 147/586] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 112 ++++++++++++++----- 1 file changed, 83 insertions(+), 29 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index b76249853f..2bbc4690ee 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -12,26 +12,47 @@ attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} {{< blockquote - cite="https://www.scoopwhoop.com/sports/ms-dhoni/" - by="M S Dhoni、 *インディアンチームの元キャプテン、インターナショナル・クリケットプレイヤー、チェンナイ・スーパー・キングスのためにIPLでプレイ*" ->}} -観客のために競技をするのではなく、国のために競技するのです。 -{{< /blockquote >}} +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni、 _インディアンチームの元キャプテン、インターナショナル・クリケットプレイヤー、チェンナイ・スーパー・キングスのためにIPLでプレイ_" -## クリケットについて - -インド人はクリケットが大好きだと言っても過言ではないでしょう。 この競技は、他のスポーツと異なり、インドの農村部や都市部を問わず、あらゆる場所でプレイされており、若者から年配の方まで広く人気があり、インドでは何十億人もの人々を結びつける役割を担っています。 クリケットは多くのメディアの注目を集めています。 クリケットは多くのメディアの注目を集め、非常に[多額のお金](https://www.statista.com/topics/4543/indian-premier-league-ipl/)と名声がかかっています。 過去数年間、テクノロジーは文字通りクリケットの試合を変えてきました。 視聴者はストリーミングメディア、トーナメント、モバイルベースの手頃なアクセスによるライブクリケット視聴などを享受しています。 - -インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は[67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League)だと評価されています。 +> }} +> You don't play for the crowd, you play for the country. +> {{< /blockquote >}} -クリケットは数のゲームです。 バッツマンによってスコアされたランの数、ボウラーによって取られたウィケットの数、クリケットチームによって獲得した試合の数、バッツマンがボウリング攻撃に特定の方法で応答する回数。 クリケットの数字を掘り下げてパフォーマンスを向上させるとともに、NumPyなどの数値計算ソフトウェアを利用した強力な分析ツールを介して、クリケットのビジネスチャンス、市場全体、経済性を研究することは、大きな意味を持ちます。 クリケット分析は、試合に関する興味深い洞察と、ゲームの結果に関する予測AIを提供します。 - -現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : [ESPN cricinfo や](https://stats.espncricinfo.com/ci/engine/stats/index.html) [cricsheet](https://cricsheet.org). これらのクリケットデータベースは、最新の機械学習と予測モデリングアルゴリズムを使用して、 [クリケット 分析](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) に使用されています。 メディアやプロスポーツ団体のエンターテインメントプラットフォームは、技術や分析を利用し、試合勝率を向上させるために、下記のような要素が主要なメトリックだと考え始めています。 +## クリケットについて -* バッティング成績の移動平均 -* スコア予測 -* プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 -* チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 +It would be an understatement to state that Indians love cricket. The game is +played in just about every nook and cranny of India, rural or urban, popular +with the young and the old alike, connecting billions in India unlike any other sport. +Cricket enjoys lots of media attention. There is a significant amount of +[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and +fame at stake. Over the last several years, technology has literally been a game +changer. Audiences are spoilt for choice with streaming media, tournaments, +affordable access to mobile based live cricket watching, and more. + +IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は[67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League)だと評価されています。 + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken +by a bowler, the matches won by a cricket team, the number of times a batsman +responds in a certain way to a kind of bowling attack, etc. The capability to +dig into cricketing numbers for both improving performance and studying +the business opportunities, overall market, and economics of cricket via powerful +analytics tools, powered by numerical computing software such as NumPy, is a big +deal. Cricket analytics provides interesting insights into the game and +predictive intelligence regarding game outcomes. + +現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : [ESPN cricinfo や](https://stats.espncricinfo.com/ci/engine/stats/index.html) [cricsheet](https://cricsheet.org). These and several such cricket databases +have been used for cricket +analysis +using the latest machine learning and predictive modelling algorithms. +Media and entertainment platforms along with professional sports bodies +associated with the game use technology and analytics for determining key +metrics for improving match winning chances: + +- バッティング成績の移動平均 +- スコア予測 +- プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 +- チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 {{< figure >}} src = '/images/content_images/cs/cricket-pitch.png' @@ -44,9 +65,9 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' ### データ分析の主要な目標 -* スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 -* リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 -* 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 +- スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 +- リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 +- 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 {{< figure >}} src = '/images/content_images/cs/player-pose-estimator.png' @@ -58,29 +79,62 @@ attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analy ### 課題 -* **データのクリーニングと前処理** +- **データのクリーニングと前処理** - IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 + IPL has expanded cricket beyond the classic test match format to a much + larger scale. The number of matches played every season across various + formats has increased and so has the data, the algorithms, newer sports data + analysis technologies and simulation models. Cricket data analysis requires + field mapping, player tracking, ball tracking, player shot analysis, and + several other aspects involved in how the ball is delivered, its angle, spin, + velocity, and trajectory. All these factors together have increased the + complexity of data cleaning and preprocessing. -* **動的モデリング** +- **動的モデリング** - クリケットでは、他のスポーツと同様、フィールド上の選手の様々な数字を追跡するために、関連する変数の数が多くなってしまいがちです。 たとえば、ボールやその属性情報、およびいくつかの行動をとるアクションのいくつかの可能性などの変数です。 データ分析とモデリングの複雑さは、分析中に必要となる予測のための質問の種類に正比例しており、データ表現とモデルにも大きく依存しています。 バッツマンが異なる角度や速度でボールを打った場合に何が起こるのかのような、動的なクリケットのプレーの予測が必要な場合、計算量やデータ比較が更に困難になります。 + In cricket, just like any other sport, + there can be a large number of variables related to tracking various numbers + of players on the field, their attributes, the ball, and several possibilities + of potential actions. The complexity of data analytics and modeling is + directly proportional to the kind of predictive questions that are put forth + during analysis and are highly dependent on data representation and the + model. Things get even more challenging in terms of computation, data + comparisons when dynamic cricket play predictions are sought such as what + would have happened if the batsman had hit the ball at a different angle or + velocity. -* **予測分析の複雑さ** +- **予測分析の複雑さ** クリケットにおいて、意思決定の多くは「ボウラーがある特定のタイプの場合、打者はどのくらいの頻度で特定の種類のショットを打つのか」「バッツマンが特定の方法であるボウラーに反応した場合、ボウラーはどのようにラインと長さを変更するのか 」などの質問に基づいています。 この種の予測分析クエリでは、精度の良いデータセットが利用できることと、データを合成して高精度な生成モデルを作成できることが必要とされます。 + This kind of predictive analytics query requires highly granular dataset + availability and the capability to synthesize data and create generative + models that are highly accurate. ## クリケット解析におけるNumPyの役割 -スポーツ分析は現在、非常に盛んな分野です。 多くの研究者や企業は、最新の機械学習やAI技術以外にも、NumPyや、Scikit-learn, SciPy, Matplotlib, Jupyterなどの他のPyDataパッケージを[使っています](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)。 NumPyは以下のように、クリケット関連の様々なスポーツ分析に使用されています。 +Sports Analytics is a thriving field. スポーツ分析は現在、非常に盛んな分野です。 多くの研究者や企業は、最新の機械学習やAI技術以外にも、NumPyや、Scikit-learn, SciPy, Matplotlib, Jupyterなどの他のPyDataパッケージを[使っています](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)。 NumPyは以下のように、クリケット関連の様々なスポーツ分析に使用されています。 NumPy has been used +for various kinds of cricket related sporting analytics such as: -* **統計分析:** NumPyの数値計算機能は、様々なプレイヤーやゲーム戦術のコンテキストでの観測データで、試合中のイベントの統計的有意性を推定し、生成モデルや静的モデルと比較して試合結果を推定するのに役立ちます。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) と [ビッグデータアプローチ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)が戦術的分析に使用されています。 +- **統計分析:** NumPyの数値計算機能は、様々なプレイヤーやゲーム戦術のコンテキストでの観測データで、試合中のイベントの統計的有意性を推定し、生成モデルや静的モデルと比較して試合結果を推定するのに役立ちます。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) と [ビッグデータアプローチ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)が戦術的分析に使用されています。 + [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) + and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) + are used for tactical analysis. -* **データ可視化:** データのグラフ化・[可視化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) は、さまざまなデータセット間の関係について、有益な洞察を与えてくれます。 +- **データ可視化:** データのグラフ化・[可視化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) は、さまざまなデータセット間の関係について、有益な洞察を与えてくれます。 ## まとめ -スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。 +Sports Analytics is a game changer when it comes to how professional games are +played, especially how strategic decision making happens, which until recently +was primarily done based on “gut feeling" or adherence to past traditions. NumPy +forms a solid foundation for a large set of Python packages which provide higher +level functions related to data analytics, machine learning, and AI algorithms. +These packages are widely deployed to gain real-time insights that help in +decision making for game-changing outcomes, both on field as well as to draw +inferences and drive business around the game of cricket. Finding out the +hidden parameters, patterns, and attributes that lead to the outcome of a +cricket match helps the stakeholders to take notice of game insights that are +otherwise hidden in numbers and statistics. {{< figure >}} src = '/images/content_images/cs/numpy_ca_benefits.png' From 83e01829d24c5758deb8c67b7dbf0bfecfb81572 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:56 +0200 Subject: [PATCH 148/586] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 165 +++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 content/ko/case-studies/cricket-analytics.md diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..347251d55a --- /dev/null +++ b/content/ko/case-studies/cricket-analytics.md @@ -0,0 +1,165 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, the biggest Cricket Festival in India' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" + +> }} +> You don't play for the crowd, you play for the country. +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is +played in just about every nook and cranny of India, rural or urban, popular +with the young and the old alike, connecting billions in India unlike any other sport. +Cricket enjoys lots of media attention. There is a significant amount of +[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and +fame at stake. Over the last several years, technology has literally been a game +changer. Audiences are spoilt for choice with streaming media, tournaments, +affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket +league, founded in 2008. It is one of the most attended cricketing events in +the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) +in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken +by a bowler, the matches won by a cricket team, the number of times a batsman +responds in a certain way to a kind of bowling attack, etc. The capability to +dig into cricketing numbers for both improving performance and studying +the business opportunities, overall market, and economics of cricket via powerful +analytics tools, powered by numerical computing software such as NumPy, is a big +deal. Cricket analytics provides interesting insights into the game and +predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and +statistics available, e.g., ESPN +cricinfo and +[cricsheet](https://cricsheet.org). These and several such cricket databases +have been used for cricket +analysis +using the latest machine learning and predictive modelling algorithms. +Media and entertainment platforms along with professional sports bodies +associated with the game use technology and analytics for determining key +metrics for improving match winning chances: + +- batting performance moving average, +- score forecasting, +- gaining insights into fitness and performance of a player against different opposition, +- player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Cricket Pitch, the focal point in the field' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +- Sports data analytics are used not only in cricket but many other + sports for + improving the overall team performance and maximizing winning chances. +- Real-time data analytics can help in gaining insights even during the game + for changing tactics by the team and by associated businesses for economic + benefits and growth. +- Besides historical analysis, predictive models are + harnessed to determine the possible match outcomes that require significant + number crunching and data science know-how, visualization tools and capability + to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'Cricket Pose Estimator' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +- **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much + larger scale. The number of matches played every season across various + formats has increased and so has the data, the algorithms, newer sports data + analysis technologies and simulation models. Cricket data analysis requires + field mapping, player tracking, ball tracking, player shot analysis, and + several other aspects involved in how the ball is delivered, its angle, spin, + velocity, and trajectory. All these factors together have increased the + complexity of data cleaning and preprocessing. + +- **Dynamic Modeling** + + In cricket, just like any other sport, + there can be a large number of variables related to tracking various numbers + of players on the field, their attributes, the ball, and several possibilities + of potential actions. The complexity of data analytics and modeling is + directly proportional to the kind of predictive questions that are put forth + during analysis and are highly dependent on data representation and the + model. Things get even more challenging in terms of computation, data + comparisons when dynamic cricket play predictions are sought such as what + would have happened if the batsman had hit the ball at a different angle or + velocity. + +- **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how + often does a batsman play a certain kind of shot if the ball delivery is of a + particular type", or "how does a bowler change his line and length if the + batsman responds to his delivery in a certain way". + This kind of predictive analytics query requires highly granular dataset + availability and the capability to synthesize data and create generative + models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies +[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) +and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, +besides using the latest machine learning and AI techniques. NumPy has been used +for various kinds of cricket related sporting analytics such as: + +- **Statistical Analysis:** NumPy's numerical capabilities help estimate the + statistical significance of observational data or match events in the context + of various player and game tactics, estimating the game outcome by comparison + with a generative or static model. + [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) + and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) + are used for tactical analysis. + +- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are +played, especially how strategic decision making happens, which until recently +was primarily done based on “gut feeling" or adherence to past traditions. NumPy +forms a solid foundation for a large set of Python packages which provide higher +level functions related to data analytics, machine learning, and AI algorithms. +These packages are widely deployed to gain real-time insights that help in +decision making for game-changing outcomes, both on field as well as to draw +inferences and drive business around the game of cricket. Finding out the +hidden parameters, patterns, and attributes that lead to the outcome of a +cricket match helps the stakeholders to take notice of game insights that are +otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagram showing benefits of using NumPy for cricket analytics' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 4bee1632539f7cdd1b9ec8870ee793f3fd70a9e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:58 +0200 Subject: [PATCH 149/586] New translations cricket-analytics.md (Russian) --- content/ru/case-studies/cricket-analytics.md | 165 +++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 content/ru/case-studies/cricket-analytics.md diff --git a/content/ru/case-studies/cricket-analytics.md b/content/ru/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..347251d55a --- /dev/null +++ b/content/ru/case-studies/cricket-analytics.md @@ -0,0 +1,165 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, the biggest Cricket Festival in India' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" + +> }} +> You don't play for the crowd, you play for the country. +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is +played in just about every nook and cranny of India, rural or urban, popular +with the young and the old alike, connecting billions in India unlike any other sport. +Cricket enjoys lots of media attention. There is a significant amount of +[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and +fame at stake. Over the last several years, technology has literally been a game +changer. Audiences are spoilt for choice with streaming media, tournaments, +affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket +league, founded in 2008. It is one of the most attended cricketing events in +the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) +in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken +by a bowler, the matches won by a cricket team, the number of times a batsman +responds in a certain way to a kind of bowling attack, etc. The capability to +dig into cricketing numbers for both improving performance and studying +the business opportunities, overall market, and economics of cricket via powerful +analytics tools, powered by numerical computing software such as NumPy, is a big +deal. Cricket analytics provides interesting insights into the game and +predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and +statistics available, e.g., ESPN +cricinfo and +[cricsheet](https://cricsheet.org). These and several such cricket databases +have been used for cricket +analysis +using the latest machine learning and predictive modelling algorithms. +Media and entertainment platforms along with professional sports bodies +associated with the game use technology and analytics for determining key +metrics for improving match winning chances: + +- batting performance moving average, +- score forecasting, +- gaining insights into fitness and performance of a player against different opposition, +- player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Cricket Pitch, the focal point in the field' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +- Sports data analytics are used not only in cricket but many other + sports for + improving the overall team performance and maximizing winning chances. +- Real-time data analytics can help in gaining insights even during the game + for changing tactics by the team and by associated businesses for economic + benefits and growth. +- Besides historical analysis, predictive models are + harnessed to determine the possible match outcomes that require significant + number crunching and data science know-how, visualization tools and capability + to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'Cricket Pose Estimator' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +- **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much + larger scale. The number of matches played every season across various + formats has increased and so has the data, the algorithms, newer sports data + analysis technologies and simulation models. Cricket data analysis requires + field mapping, player tracking, ball tracking, player shot analysis, and + several other aspects involved in how the ball is delivered, its angle, spin, + velocity, and trajectory. All these factors together have increased the + complexity of data cleaning and preprocessing. + +- **Dynamic Modeling** + + In cricket, just like any other sport, + there can be a large number of variables related to tracking various numbers + of players on the field, their attributes, the ball, and several possibilities + of potential actions. The complexity of data analytics and modeling is + directly proportional to the kind of predictive questions that are put forth + during analysis and are highly dependent on data representation and the + model. Things get even more challenging in terms of computation, data + comparisons when dynamic cricket play predictions are sought such as what + would have happened if the batsman had hit the ball at a different angle or + velocity. + +- **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how + often does a batsman play a certain kind of shot if the ball delivery is of a + particular type", or "how does a bowler change his line and length if the + batsman responds to his delivery in a certain way". + This kind of predictive analytics query requires highly granular dataset + availability and the capability to synthesize data and create generative + models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies +[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) +and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, +besides using the latest machine learning and AI techniques. NumPy has been used +for various kinds of cricket related sporting analytics such as: + +- **Statistical Analysis:** NumPy's numerical capabilities help estimate the + statistical significance of observational data or match events in the context + of various player and game tactics, estimating the game outcome by comparison + with a generative or static model. + [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) + and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) + are used for tactical analysis. + +- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are +played, especially how strategic decision making happens, which until recently +was primarily done based on “gut feeling" or adherence to past traditions. NumPy +forms a solid foundation for a large set of Python packages which provide higher +level functions related to data analytics, machine learning, and AI algorithms. +These packages are widely deployed to gain real-time insights that help in +decision making for game-changing outcomes, both on field as well as to draw +inferences and drive business around the game of cricket. Finding out the +hidden parameters, patterns, and attributes that lead to the outcome of a +cricket match helps the stakeholders to take notice of game insights that are +otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagram showing benefits of using NumPy for cricket analytics' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 0f276a415c0271f15855341e7adf1aba460ec692 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:13:59 +0200 Subject: [PATCH 150/586] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 165 +++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 content/zh/case-studies/cricket-analytics.md diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..347251d55a --- /dev/null +++ b/content/zh/case-studies/cricket-analytics.md @@ -0,0 +1,165 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, the biggest Cricket Festival in India' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" + +> }} +> You don't play for the crowd, you play for the country. +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is +played in just about every nook and cranny of India, rural or urban, popular +with the young and the old alike, connecting billions in India unlike any other sport. +Cricket enjoys lots of media attention. There is a significant amount of +[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and +fame at stake. Over the last several years, technology has literally been a game +changer. Audiences are spoilt for choice with streaming media, tournaments, +affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket +league, founded in 2008. It is one of the most attended cricketing events in +the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) +in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken +by a bowler, the matches won by a cricket team, the number of times a batsman +responds in a certain way to a kind of bowling attack, etc. The capability to +dig into cricketing numbers for both improving performance and studying +the business opportunities, overall market, and economics of cricket via powerful +analytics tools, powered by numerical computing software such as NumPy, is a big +deal. Cricket analytics provides interesting insights into the game and +predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and +statistics available, e.g., ESPN +cricinfo and +[cricsheet](https://cricsheet.org). These and several such cricket databases +have been used for cricket +analysis +using the latest machine learning and predictive modelling algorithms. +Media and entertainment platforms along with professional sports bodies +associated with the game use technology and analytics for determining key +metrics for improving match winning chances: + +- batting performance moving average, +- score forecasting, +- gaining insights into fitness and performance of a player against different opposition, +- player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Cricket Pitch, the focal point in the field' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +- Sports data analytics are used not only in cricket but many other + sports for + improving the overall team performance and maximizing winning chances. +- Real-time data analytics can help in gaining insights even during the game + for changing tactics by the team and by associated businesses for economic + benefits and growth. +- Besides historical analysis, predictive models are + harnessed to determine the possible match outcomes that require significant + number crunching and data science know-how, visualization tools and capability + to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'Cricket Pose Estimator' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +- **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much + larger scale. The number of matches played every season across various + formats has increased and so has the data, the algorithms, newer sports data + analysis technologies and simulation models. Cricket data analysis requires + field mapping, player tracking, ball tracking, player shot analysis, and + several other aspects involved in how the ball is delivered, its angle, spin, + velocity, and trajectory. All these factors together have increased the + complexity of data cleaning and preprocessing. + +- **Dynamic Modeling** + + In cricket, just like any other sport, + there can be a large number of variables related to tracking various numbers + of players on the field, their attributes, the ball, and several possibilities + of potential actions. The complexity of data analytics and modeling is + directly proportional to the kind of predictive questions that are put forth + during analysis and are highly dependent on data representation and the + model. Things get even more challenging in terms of computation, data + comparisons when dynamic cricket play predictions are sought such as what + would have happened if the batsman had hit the ball at a different angle or + velocity. + +- **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how + often does a batsman play a certain kind of shot if the ball delivery is of a + particular type", or "how does a bowler change his line and length if the + batsman responds to his delivery in a certain way". + This kind of predictive analytics query requires highly granular dataset + availability and the capability to synthesize data and create generative + models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies +[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) +and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, +besides using the latest machine learning and AI techniques. NumPy has been used +for various kinds of cricket related sporting analytics such as: + +- **Statistical Analysis:** NumPy's numerical capabilities help estimate the + statistical significance of observational data or match events in the context + of various player and game tactics, estimating the game outcome by comparison + with a generative or static model. + [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) + and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) + are used for tactical analysis. + +- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are +played, especially how strategic decision making happens, which until recently +was primarily done based on “gut feeling" or adherence to past traditions. NumPy +forms a solid foundation for a large set of Python packages which provide higher +level functions related to data analytics, machine learning, and AI algorithms. +These packages are widely deployed to gain real-time insights that help in +decision making for game-changing outcomes, both on field as well as to draw +inferences and drive business around the game of cricket. Finding out the +hidden parameters, patterns, and attributes that lead to the outcome of a +cricket match helps the stakeholders to take notice of game insights that are +otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagram showing benefits of using NumPy for cricket analytics' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 6531ea5cad2c9f258f59a811e52d5f6931850bb3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:00 +0200 Subject: [PATCH 151/586] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 52 +++++++++++--------- 1 file changed, 29 insertions(+), 23 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 8d70c776a6..54e9bc1fcd 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -12,26 +12,29 @@ attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} {{< blockquote - cite="https://www.scoopwhoop.com/sports/ms-dhoni/" - by="M S Dhoni, *Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL*" ->}} -Você não joga para a torcida, joga para o país. -{{< /blockquote >}} +cite="https://www.scoopwhoop.com/sports/ms-dhoni/" +by="M S Dhoni, _Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL_" + +> }} +> Você não joga para a torcida, joga para o país. +> {{< /blockquote >}} ## Sobre Críquete -Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. +Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. +O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. -A Primeira Liga Indiana (*Indian Premier League* - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. +A Primeira Liga Indiana (_Indian Premier League_ - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. -perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. A capacidade de investigar estatísticas do críquete para melhorar a performance dos times e estudar oportunidades de negócios, o mercado em si, e a economia do críquete através de ferramentas de análise poderosas alimentadas por softwares de computação numérica como o NumPy é um grande negócio. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. +perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. -Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) e [cricsheet](https://cricsheet.org). Estes e muitos outros bancos de dados de críquete foram usados para [análise de críquete](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) usando os mais modernos algoritmos de aprendizagem de máquina e modelagem preditiva. Plataformas de mídia e entretenimento, juntamente com entidades de esporte profissionais associadas ao jogo usam tecnologia e análise para determinar métricas chave para melhorar as chances de vitória: +Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) e [cricsheet](https://cricsheet.org). Estes e muitos outros bancos de dados de críquete foram usados para [análise de críquete](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) usando os mais modernos algoritmos de aprendizagem de máquina e modelagem preditiva. +Plataformas de mídia e entretenimento, juntamente com entidades de esporte profissionais associadas ao jogo usam tecnologia e análise para determinar métricas chave para melhorar as chances de vitória: -* média móvel do desempenho em rebatidas, -* previsão de pontuação, -* ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, -* contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time +- média móvel do desempenho em rebatidas, +- previsão de pontuação, +- ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, +- contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time {{< figure >}} src = '/images/content_images/cs/cricket-pitch.png' @@ -44,9 +47,9 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' ### Objetivos Principais da Análise de Dados -* A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. -* A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. -* Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. +- A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. +- A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. +- Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. {{< figure >}} src = '/images/content_images/cs/player-pose-estimator.png' @@ -58,29 +61,32 @@ attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analy ### Desafios -* **Limpeza e pré-processamento de dados** +- **Limpeza e pré-processamento de dados** A IPL expandiu o formato de jogo clássico de cricket para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, as tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados. -* **Modelagem Dinâmica** +- **Modelagem Dinâmica** No críquete, como em qualquer outro esporte, pode haver um grande número de variáveis relacionadas ao rastreamento de vários jogadores no campo, seus atributos, a bola e várias possibilidades de ações em potencial. A complexidade da análise e modelagem de dados é diretamente proporcional ao tipo de questões preditivas que são consideradas durante a análise e são altamente dependentes da representação de dados e do modelo. As coisas são ainda mais desafiadoras em termos de computação e comparações de dados quando previsões dinâmicas de jogo de críquete são desejadas, como o que teria acontecido se o batsman tivesse atingido a bola com um ângulo ou velocidade diferentes. -* **Complexidade da análise preditiva** +- **Complexidade da análise preditiva** - Muito da tomada de decisões em críquete se baseia em questões como "com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo", ou "como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira". Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos. + Muito da tomada de decisões em críquete se baseia em questões como "com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo", ou "como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira". + Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos. ## O papel do NumPy na análise de críquete A análise de dados esportivos é um campo próspero. Muitos pesquisadores e empresas [usam NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) e outros pacotes PyData como Scikit-learn, SciPy, Matplotlib, e Jupyter, além de usar as últimas técnicas de aprendizagem de máquina e IA. O NumPy foi usado para vários tipos de análise esportiva relacionada a críquete, como: -* **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. +- **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. + [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em _big data_](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. -* **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. +- **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. ## Resumo -A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas. +A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. +Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas. {{< figure >}} src = '/images/content_images/cs/numpy_ca_benefits.png' From 45977292e99658dc37416bdebe83c7fa115af95a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:01 +0200 Subject: [PATCH 152/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 185 ++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 content/es/case-studies/deeplabcut-dnn.md diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..4ef842d19d --- /dev/null +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -0,0 +1,185 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Analyzing mice hand-movement using DeepLapCut' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Colored dots track the positions of a racehorse’s body part' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +- **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture + of animals in a diverse settings. This data can be used, for example, in + neuroscience studies to understand how the brain controls movement, or to + elucidate how animals socially interact. Researchers have observed a + [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) + with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second + (FPS). + +- **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form + of an easy to use tool that can be adopted by researchers easily. So they have + created a complete, easy-to-use Python toolbox with project management features + as well. These enable not only automation of pose-estimation but also + managing the project end-to-end by helping the DeepLabCut Toolkit user right + from the dataset collection stage to creating shareable and reusable analysis + pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Pose estimation steps with DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + +### The Challenges + +- **Speed** + + Fast processing of animal behavior videos in order to measure their behavior + and at the same time make scientific experiments more efficient, accurate. + Extracting detailed animal poses for laboratory experiments, without + markers, in dynamically changing backgrounds, can be challenging, both + technically as well as in terms of resource needs and training data required. + Coming up with a tool that is easy to use without the need for skills such + as computer vision expertise that enables scientists to do research in more + real-world contexts, is a non-trivial problem to solve. + +- **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple + limbs into individual animal behavior. Assembling keypoints and their + connections into individual animal movements and linking them across time + is a complex process that requires heavy-duty numerical analysis, especially + in case of multi-animal movement tracking in experiment videos. + +- **Data Processing** + + Last but not the least, array manipulation - processing large stacks of + arrays corresponding to various images, target tensors and keypoints is + fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Pose estimation variety and complexity' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at +high speed for behavioural analytics. Besides NumPy, DeepLabCut employs +various Python software that utilize NumPy at their core, such as +[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), +[matplotlib](https://matplotlib.org), +[Tensorpack](https://github.com/tensorpack/tensorpack), +[imgaug](https://github.com/aleju/imgaug), +[scikit-learn](https://scikit-learn.org/stable/), +[scikit-image](https://scikit-image.org) and +[Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image +processing, combinatorics requirements and need for fast computation in +DeepLabCut pose estimation algorithms: + +- Vectorization +- Masked Array Operations +- Linear Algebra +- Random Sampling +- Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered +by the toolkit. In particular, NumPy is used for sampling distinct frames for +human annotation labeling, and for writing, editing and processing annotation +data. Within TensorFlow the neural network is trained by DeepLabCut technology +over thousands of iterations to predict the ground truth annotations from +frames. For this purpose, target densities (scoremaps) are created to cast pose +estimation as a image-to-image translation problem. To make the neural networks +robust, data augmentation is employed, which requires the calculation of target +scoremaps subject to various geometric and image processing steps. To make +training fast, NumPy’s vectorization capabilities are leveraged. For inference, +the most likely predictions from target scoremaps need to extracted and one +needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCut Workflow' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern +ethology, neuroscience, medicine, and technology. +[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) +allows researchers to estimate the pose of the subject, efficiently enabling +them to quantify the behavior. With only a small set of training images, +the DeepLabCut Python toolbox allows training a neural network to within human +level labeling accuracy, thus expanding its application to not only behavior +analysis in the laboratory, but to potentially also in sports, gait analysis, +medicine and rehabilitation studies. Complex combinatorics, data processing +challenges faced by DeepLabCut algorithms are addressed through the use of +NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 59c1bf98e64ee83baacbf84e592fc53d37715601 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:03 +0200 Subject: [PATCH 153/586] New translations deeplabcut-dnn.md (Arabic) --- content/ar/case-studies/deeplabcut-dnn.md | 185 ++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 content/ar/case-studies/deeplabcut-dnn.md diff --git a/content/ar/case-studies/deeplabcut-dnn.md b/content/ar/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..4ef842d19d --- /dev/null +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -0,0 +1,185 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Analyzing mice hand-movement using DeepLapCut' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Colored dots track the positions of a racehorse’s body part' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +- **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture + of animals in a diverse settings. This data can be used, for example, in + neuroscience studies to understand how the brain controls movement, or to + elucidate how animals socially interact. Researchers have observed a + [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) + with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second + (FPS). + +- **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form + of an easy to use tool that can be adopted by researchers easily. So they have + created a complete, easy-to-use Python toolbox with project management features + as well. These enable not only automation of pose-estimation but also + managing the project end-to-end by helping the DeepLabCut Toolkit user right + from the dataset collection stage to creating shareable and reusable analysis + pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Pose estimation steps with DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + +### The Challenges + +- **Speed** + + Fast processing of animal behavior videos in order to measure their behavior + and at the same time make scientific experiments more efficient, accurate. + Extracting detailed animal poses for laboratory experiments, without + markers, in dynamically changing backgrounds, can be challenging, both + technically as well as in terms of resource needs and training data required. + Coming up with a tool that is easy to use without the need for skills such + as computer vision expertise that enables scientists to do research in more + real-world contexts, is a non-trivial problem to solve. + +- **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple + limbs into individual animal behavior. Assembling keypoints and their + connections into individual animal movements and linking them across time + is a complex process that requires heavy-duty numerical analysis, especially + in case of multi-animal movement tracking in experiment videos. + +- **Data Processing** + + Last but not the least, array manipulation - processing large stacks of + arrays corresponding to various images, target tensors and keypoints is + fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Pose estimation variety and complexity' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at +high speed for behavioural analytics. Besides NumPy, DeepLabCut employs +various Python software that utilize NumPy at their core, such as +[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), +[matplotlib](https://matplotlib.org), +[Tensorpack](https://github.com/tensorpack/tensorpack), +[imgaug](https://github.com/aleju/imgaug), +[scikit-learn](https://scikit-learn.org/stable/), +[scikit-image](https://scikit-image.org) and +[Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image +processing, combinatorics requirements and need for fast computation in +DeepLabCut pose estimation algorithms: + +- Vectorization +- Masked Array Operations +- Linear Algebra +- Random Sampling +- Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered +by the toolkit. In particular, NumPy is used for sampling distinct frames for +human annotation labeling, and for writing, editing and processing annotation +data. Within TensorFlow the neural network is trained by DeepLabCut technology +over thousands of iterations to predict the ground truth annotations from +frames. For this purpose, target densities (scoremaps) are created to cast pose +estimation as a image-to-image translation problem. To make the neural networks +robust, data augmentation is employed, which requires the calculation of target +scoremaps subject to various geometric and image processing steps. To make +training fast, NumPy’s vectorization capabilities are leveraged. For inference, +the most likely predictions from target scoremaps need to extracted and one +needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCut Workflow' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern +ethology, neuroscience, medicine, and technology. +[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) +allows researchers to estimate the pose of the subject, efficiently enabling +them to quantify the behavior. With only a small set of training images, +the DeepLabCut Python toolbox allows training a neural network to within human +level labeling accuracy, thus expanding its application to not only behavior +analysis in the laboratory, but to potentially also in sports, gait analysis, +medicine and rehabilitation studies. Complex combinatorics, data processing +challenges faced by DeepLabCut algorithms are addressed through the use of +NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 5671f9e1a93e992c321fcfe7e606cb5e2d78ca99 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:04 +0200 Subject: [PATCH 154/586] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 107 +++++++++++++++------- 1 file changed, 73 insertions(+), 34 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 006dacadbb..3f2fa6259e 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -12,17 +12,20 @@ attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} {{< blockquote - cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" - by="Alexander Mathis、 *准教授、École polytechnology fe’rale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" ->}} -オープンソースソフトウェアは生体臨床医学を加速させています。 DeepLabCut を使用すると、深層学習を使用して動物の行動を自動的にビデオ解析することができます。 -{{< /blockquote >}} +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis、 _准教授、École polytechnology fe’rale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Open Source Software is accelerating Biomedicine. }} +> オープンソースソフトウェアは生体臨床医学を加速させています。 DeepLabCut を使用すると、深層学習を使用して動物の行動を自動的にビデオ解析することができます。 +> {{< /blockquote >}} +> {{< /blockquote >}} ## DeepLabCut について -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut)は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut)は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. -神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 {{< figure >}} src = '/images/content_images/cs/race-horse.gif' @@ -31,23 +34,36 @@ alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} -DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 +DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から [チーター][cheetah-movement]のようなより珍しい動物まで、動物の種類を問わず利用できます。 It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. -DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から [チーター][cheetah-movement]のようなより珍しい動物まで、動物の種類を問わず利用できます。 +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 -DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術を使用しています。 これにより必要な学習データの量を大幅に削減し、学習の収束を加速させることができます。 必要に応じて、より高速な推論を提供するさまざまなネットワークアーキテクチャ(MobileNetV2など)を選択することができ、リアルタイムの実験データフィードバックと組み合わせることもできます。 DeepLabCutはもともと[DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれるパフォーマンスのよい人用のポーズ推定アーキテクチャの特徴検出器を使用しており、これが名前の由来になりました。 今ではこのパッケージは大幅に変更され、追加のアーキテクチャ・データの水増し・一通りのユーザー用フロントエンドを含んでいます。 さらに、 大規模な生物学的実験をサポートするため、DeepLabCutはオンライン学習の機能を提供しています。 これにより、動画の時間をこえて学習データを増やすことができ、エッジケースをカバーしたり、特定のコンテキスト内でポーズ推定アルゴリズムを堅牢にしたりできます。 +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. -最近、[DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 +最近、[DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### 主な目標と結果 -* **科学研究のための動物姿勢解析の自動化:** +- **科学研究のための動物姿勢解析の自動化:** - DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで [10倍のパフォーマンス向上](https://www.biorxiv.org/content/10.1101/457242v1) が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 + DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで [10倍のパフォーマンス向上](https://www.biorxiv.org/content/10.1101/457242v1) が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 This data can be used, for example, in + neuroscience studies to understand how the brain controls movement, or to + elucidate how animals socially interact. Researchers have observed a + [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) + with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second + (FPS). -* **姿勢推定のための使いやすいPythonツールキットの作成:** +- **姿勢推定のための使いやすいPythonツールキットの作成:** - DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 + DeepLabCut wanted to share their animal pose-estimation technology in the form + of an easy to use tool that can be adopted by researchers easily. So they have + created a complete, easy-to-use Python toolbox with project management features + as well. These enable not only automation of pose-estimation but also + managing the project end-to-end by helping the DeepLabCut Toolkit user right + from the dataset collection stage to creating shareable and reusable analysis + pipelines. この[ツールキット][DLCToolkit] はオープンソースとして利用できます。 @@ -67,19 +83,28 @@ attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' {{< /figure >}} +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + ### 課題 -* **速度** +- **速度** - 動物行動動画の高速な処理は、動物の行動を測定し、科学実験をより効率的で正確にするために重要です。 動的に変化する背景の中で、マーカーを使用せずに、実験室での実験のために動物の詳細な姿勢を抽出することは、技術的にも、必要なリソース的にも、必要なトレーニングデータの面でも、困難な場合があります。 科学者が、より現実的な状況で研究を行うために、コンピュータビジョンなどの専門知識のスキルを必要とせずに使うことができるツールを開発することは、解決すべき重要な問題です。 + 動物行動動画の高速な処理は、動物の行動を測定し、科学実験をより効率的で正確にするために重要です。 動的に変化する背景の中で、マーカーを使用せずに、実験室での実験のために動物の詳細な姿勢を抽出することは、技術的にも、必要なリソース的にも、必要なトレーニングデータの面でも、困難な場合があります。 科学者が、より現実的な状況で研究を行うために、コンピュータビジョンなどの専門知識のスキルを必要とせずに使うことができるツールを開発することは、解決すべき重要な問題です。 + Extracting detailed animal poses for laboratory experiments, without + markers, in dynamically changing backgrounds, can be challenging, both + technically as well as in terms of resource needs and training data required. + Coming up with a tool that is easy to use without the need for skills such + as computer vision expertise that enables scientists to do research in more + real-world contexts, is a non-trivial problem to solve. -* **組み合わせ問題** +- **組み合わせ問題** - 組合せ問題とは、複数の四肢の動きを個々の動物行動に統合することを指します。 キーポイントと、その個々の動物行動との関連性を組み合わせ、時間的に結びつけることは、複雑なプロセスであり、非常に膨大な数値解析が必要となります。 特に、実験映像の中で複数の動物の動きを追跡する場合は大変です。 + Combinatorics involves assembly and integration of movement of multiple + limbs into individual animal behavior. 組合せ問題とは、複数の四肢の動きを個々の動物行動に統合することを指します。 キーポイントと、その個々の動物行動との関連性を組み合わせ、時間的に結びつけることは、複雑なプロセスであり、非常に膨大な数値解析が必要となります。 特に、実験映像の中で複数の動物の動きを追跡する場合は大変です。 -* **データ処理** +- **データ処理** - 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 + 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 {{< figure >}} src = '/images/content_images/cs/pose-estimation.png' @@ -92,17 +117,27 @@ attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' ## 姿勢推定の課題に対応するためのNumPyの役割 -NumPy は DeepLabCutにおける、行動分析の高速化のための数値計算の核となっています。 NumPyだけでなく、DeepLabCutは様々なNumPyをベースとしているPythonライブラリを利用しています。 [SciPy](https://www.scipy.org)、[Pandas](https://pandas.pydata.org)、[matplotlib](https://matplotlib.org)、[Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug)、[scikit-learn](https://scikit-learn.org/stable/)、[scikit-image](https://scikit-image.org)、[Tensorflow](https://www.tensorflow.org)などです。 +DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術を使用しています。 これにより必要な学習データの量を大幅に削減し、学習の収束を加速させることができます。 必要に応じて、より高速な推論を提供するさまざまなネットワークアーキテクチャ(MobileNetV2など)を選択することができ、リアルタイムの実験データフィードバックと組み合わせることもできます。 DeepLabCutはもともと[DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれるパフォーマンスのよい人用のポーズ推定アーキテクチャの特徴検出器を使用しており、これが名前の由来になりました。 今ではこのパッケージは大幅に変更され、追加のアーキテクチャ・データの水増し・一通りのユーザー用フロントエンドを含んでいます。 さらに、 大規模な生物学的実験をサポートするため、DeepLabCutはオンライン学習の機能を提供しています。 これにより、動画の時間をこえて学習データを増やすことができ、エッジケースをカバーしたり、特定のコンテキスト内でポーズ推定アルゴリズムを堅牢にしたりできます。 NumPy は DeepLabCutにおける、行動分析の高速化のための数値計算の核となっています。 NumPyだけでなく、DeepLabCutは様々なNumPyをベースとしているPythonライブラリを利用しています。 [SciPy](https://www.scipy.org)、[Pandas](https://pandas.pydata.org)、[matplotlib](https://matplotlib.org)、[Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug)、[scikit-learn](https://scikit-learn.org/stable/)、[scikit-image](https://scikit-image.org)、[Tensorflow](https://www.tensorflow.org)などです。 以下に挙げるNumPyの特徴が、DeepLabCutの姿勢推定アルゴリズムでの画像処理・組み合わせ処理・高速計算において、重要な役割を果たしました。 -* ベクトル化 -* マスクされた配列操作 -* 線形代数 -* ランダムサンプリング -* 大きな配列の再構成 - -DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 +- ベクトル化 +- マスクされた配列操作 +- 線形代数 +- ランダムサンプリング +- 大きな配列の再構成 + +DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 In particular, NumPy is used for sampling distinct frames for +human annotation labeling, and for writing, editing and processing annotation +data. Within TensorFlow the neural network is trained by DeepLabCut technology +over thousands of iterations to predict the ground truth annotations from +frames. For this purpose, target densities (scoremaps) are created to cast pose +estimation as a image-to-image translation problem. To make the neural networks +robust, data augmentation is employed, which requires the calculation of target +scoremaps subject to various geometric and image processing steps. To make +training fast, NumPy’s vectorization capabilities are leveraged. For inference, +the most likely predictions from target scoremaps need to extracted and one +needs to efficiently “link predictions to assemble individual animals”. {{< figure >}} src = '/images/content_images/cs/deeplabcut-workflow.png' @@ -114,14 +149,18 @@ attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The- ## まとめ -行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 +Observing and efficiently describing behavior is a core tenant of modern +ethology, neuroscience, medicine, and technology. +行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 With only a small set of training images, +the DeepLabCut Python toolbox allows training a neural network to within human +level labeling accuracy, thus expanding its application to not only behavior +analysis in the laboratory, but to potentially also in sports, gait analysis, +medicine and rehabilitation studies. Complex combinatorics, data processing +challenges faced by DeepLabCut algorithms are addressed through the use of +NumPy's array manipulation capabilities. {{< figure >}} src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'NumPyの主要機能' {{< /figure >}} - -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 - -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 28f9b5df9c8db9abdb78a2dfba056e69ba39645f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:05 +0200 Subject: [PATCH 155/586] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 185 ++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 content/ko/case-studies/deeplabcut-dnn.md diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..4ef842d19d --- /dev/null +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -0,0 +1,185 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Analyzing mice hand-movement using DeepLapCut' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Colored dots track the positions of a racehorse’s body part' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +- **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture + of animals in a diverse settings. This data can be used, for example, in + neuroscience studies to understand how the brain controls movement, or to + elucidate how animals socially interact. Researchers have observed a + [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) + with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second + (FPS). + +- **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form + of an easy to use tool that can be adopted by researchers easily. So they have + created a complete, easy-to-use Python toolbox with project management features + as well. These enable not only automation of pose-estimation but also + managing the project end-to-end by helping the DeepLabCut Toolkit user right + from the dataset collection stage to creating shareable and reusable analysis + pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Pose estimation steps with DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + +### The Challenges + +- **Speed** + + Fast processing of animal behavior videos in order to measure their behavior + and at the same time make scientific experiments more efficient, accurate. + Extracting detailed animal poses for laboratory experiments, without + markers, in dynamically changing backgrounds, can be challenging, both + technically as well as in terms of resource needs and training data required. + Coming up with a tool that is easy to use without the need for skills such + as computer vision expertise that enables scientists to do research in more + real-world contexts, is a non-trivial problem to solve. + +- **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple + limbs into individual animal behavior. Assembling keypoints and their + connections into individual animal movements and linking them across time + is a complex process that requires heavy-duty numerical analysis, especially + in case of multi-animal movement tracking in experiment videos. + +- **Data Processing** + + Last but not the least, array manipulation - processing large stacks of + arrays corresponding to various images, target tensors and keypoints is + fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Pose estimation variety and complexity' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at +high speed for behavioural analytics. Besides NumPy, DeepLabCut employs +various Python software that utilize NumPy at their core, such as +[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), +[matplotlib](https://matplotlib.org), +[Tensorpack](https://github.com/tensorpack/tensorpack), +[imgaug](https://github.com/aleju/imgaug), +[scikit-learn](https://scikit-learn.org/stable/), +[scikit-image](https://scikit-image.org) and +[Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image +processing, combinatorics requirements and need for fast computation in +DeepLabCut pose estimation algorithms: + +- Vectorization +- Masked Array Operations +- Linear Algebra +- Random Sampling +- Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered +by the toolkit. In particular, NumPy is used for sampling distinct frames for +human annotation labeling, and for writing, editing and processing annotation +data. Within TensorFlow the neural network is trained by DeepLabCut technology +over thousands of iterations to predict the ground truth annotations from +frames. For this purpose, target densities (scoremaps) are created to cast pose +estimation as a image-to-image translation problem. To make the neural networks +robust, data augmentation is employed, which requires the calculation of target +scoremaps subject to various geometric and image processing steps. To make +training fast, NumPy’s vectorization capabilities are leveraged. For inference, +the most likely predictions from target scoremaps need to extracted and one +needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCut Workflow' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern +ethology, neuroscience, medicine, and technology. +[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) +allows researchers to estimate the pose of the subject, efficiently enabling +them to quantify the behavior. With only a small set of training images, +the DeepLabCut Python toolbox allows training a neural network to within human +level labeling accuracy, thus expanding its application to not only behavior +analysis in the laboratory, but to potentially also in sports, gait analysis, +medicine and rehabilitation studies. Complex combinatorics, data processing +challenges faced by DeepLabCut algorithms are addressed through the use of +NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From ee99dea247139e07d4c6497720aa040498be5e72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:07 +0200 Subject: [PATCH 156/586] New translations deeplabcut-dnn.md (Russian) --- content/ru/case-studies/deeplabcut-dnn.md | 185 ++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 content/ru/case-studies/deeplabcut-dnn.md diff --git a/content/ru/case-studies/deeplabcut-dnn.md b/content/ru/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..4ef842d19d --- /dev/null +++ b/content/ru/case-studies/deeplabcut-dnn.md @@ -0,0 +1,185 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Analyzing mice hand-movement using DeepLapCut' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Colored dots track the positions of a racehorse’s body part' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +- **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture + of animals in a diverse settings. This data can be used, for example, in + neuroscience studies to understand how the brain controls movement, or to + elucidate how animals socially interact. Researchers have observed a + [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) + with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second + (FPS). + +- **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form + of an easy to use tool that can be adopted by researchers easily. So they have + created a complete, easy-to-use Python toolbox with project management features + as well. These enable not only automation of pose-estimation but also + managing the project end-to-end by helping the DeepLabCut Toolkit user right + from the dataset collection stage to creating shareable and reusable analysis + pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Pose estimation steps with DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + +### The Challenges + +- **Speed** + + Fast processing of animal behavior videos in order to measure their behavior + and at the same time make scientific experiments more efficient, accurate. + Extracting detailed animal poses for laboratory experiments, without + markers, in dynamically changing backgrounds, can be challenging, both + technically as well as in terms of resource needs and training data required. + Coming up with a tool that is easy to use without the need for skills such + as computer vision expertise that enables scientists to do research in more + real-world contexts, is a non-trivial problem to solve. + +- **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple + limbs into individual animal behavior. Assembling keypoints and their + connections into individual animal movements and linking them across time + is a complex process that requires heavy-duty numerical analysis, especially + in case of multi-animal movement tracking in experiment videos. + +- **Data Processing** + + Last but not the least, array manipulation - processing large stacks of + arrays corresponding to various images, target tensors and keypoints is + fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Pose estimation variety and complexity' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at +high speed for behavioural analytics. Besides NumPy, DeepLabCut employs +various Python software that utilize NumPy at their core, such as +[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), +[matplotlib](https://matplotlib.org), +[Tensorpack](https://github.com/tensorpack/tensorpack), +[imgaug](https://github.com/aleju/imgaug), +[scikit-learn](https://scikit-learn.org/stable/), +[scikit-image](https://scikit-image.org) and +[Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image +processing, combinatorics requirements and need for fast computation in +DeepLabCut pose estimation algorithms: + +- Vectorization +- Masked Array Operations +- Linear Algebra +- Random Sampling +- Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered +by the toolkit. In particular, NumPy is used for sampling distinct frames for +human annotation labeling, and for writing, editing and processing annotation +data. Within TensorFlow the neural network is trained by DeepLabCut technology +over thousands of iterations to predict the ground truth annotations from +frames. For this purpose, target densities (scoremaps) are created to cast pose +estimation as a image-to-image translation problem. To make the neural networks +robust, data augmentation is employed, which requires the calculation of target +scoremaps subject to various geometric and image processing steps. To make +training fast, NumPy’s vectorization capabilities are leveraged. For inference, +the most likely predictions from target scoremaps need to extracted and one +needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCut Workflow' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern +ethology, neuroscience, medicine, and technology. +[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) +allows researchers to estimate the pose of the subject, efficiently enabling +them to quantify the behavior. With only a small set of training images, +the DeepLabCut Python toolbox allows training a neural network to within human +level labeling accuracy, thus expanding its application to not only behavior +analysis in the laboratory, but to potentially also in sports, gait analysis, +medicine and rehabilitation studies. Complex combinatorics, data processing +challenges faced by DeepLabCut algorithms are addressed through the use of +NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From a9f1403eff8c78019d3fbe77aa3a14c6b345ee8e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:08 +0200 Subject: [PATCH 157/586] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 185 ++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 content/zh/case-studies/deeplabcut-dnn.md diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..4ef842d19d --- /dev/null +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -0,0 +1,185 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Analyzing mice hand-movement using DeepLapCut' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Colored dots track the positions of a racehorse’s body part' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +- **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture + of animals in a diverse settings. This data can be used, for example, in + neuroscience studies to understand how the brain controls movement, or to + elucidate how animals socially interact. Researchers have observed a + [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) + with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second + (FPS). + +- **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form + of an easy to use tool that can be adopted by researchers easily. So they have + created a complete, easy-to-use Python toolbox with project management features + as well. These enable not only automation of pose-estimation but also + managing the project end-to-end by helping the DeepLabCut Toolkit user right + from the dataset collection stage to creating shareable and reusable analysis + pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Pose estimation steps with DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + +### The Challenges + +- **Speed** + + Fast processing of animal behavior videos in order to measure their behavior + and at the same time make scientific experiments more efficient, accurate. + Extracting detailed animal poses for laboratory experiments, without + markers, in dynamically changing backgrounds, can be challenging, both + technically as well as in terms of resource needs and training data required. + Coming up with a tool that is easy to use without the need for skills such + as computer vision expertise that enables scientists to do research in more + real-world contexts, is a non-trivial problem to solve. + +- **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple + limbs into individual animal behavior. Assembling keypoints and their + connections into individual animal movements and linking them across time + is a complex process that requires heavy-duty numerical analysis, especially + in case of multi-animal movement tracking in experiment videos. + +- **Data Processing** + + Last but not the least, array manipulation - processing large stacks of + arrays corresponding to various images, target tensors and keypoints is + fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Pose estimation variety and complexity' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at +high speed for behavioural analytics. Besides NumPy, DeepLabCut employs +various Python software that utilize NumPy at their core, such as +[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), +[matplotlib](https://matplotlib.org), +[Tensorpack](https://github.com/tensorpack/tensorpack), +[imgaug](https://github.com/aleju/imgaug), +[scikit-learn](https://scikit-learn.org/stable/), +[scikit-image](https://scikit-image.org) and +[Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image +processing, combinatorics requirements and need for fast computation in +DeepLabCut pose estimation algorithms: + +- Vectorization +- Masked Array Operations +- Linear Algebra +- Random Sampling +- Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered +by the toolkit. In particular, NumPy is used for sampling distinct frames for +human annotation labeling, and for writing, editing and processing annotation +data. Within TensorFlow the neural network is trained by DeepLabCut technology +over thousands of iterations to predict the ground truth annotations from +frames. For this purpose, target densities (scoremaps) are created to cast pose +estimation as a image-to-image translation problem. To make the neural networks +robust, data augmentation is employed, which requires the calculation of target +scoremaps subject to various geometric and image processing steps. To make +training fast, NumPy’s vectorization capabilities are leveraged. For inference, +the most likely predictions from target scoremaps need to extracted and one +needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCut Workflow' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern +ethology, neuroscience, medicine, and technology. +[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) +allows researchers to estimate the pose of the subject, efficiently enabling +them to quantify the behavior. With only a small set of training images, +the DeepLabCut Python toolbox allows training a neural network to within human +level labeling accuracy, thus expanding its application to not only behavior +analysis in the laboratory, but to potentially also in sports, gait analysis, +medicine and rehabilitation studies. Complex combinatorics, data processing +challenges faced by DeepLabCut algorithms are addressed through the use of +NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 925d36746031c11f53294a62fea91b8528cead05 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:10 +0200 Subject: [PATCH 158/586] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 56 ++++++++++++----------- 1 file changed, 30 insertions(+), 26 deletions(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 557b336ab8..a163a46fcb 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -12,11 +12,12 @@ attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} {{< blockquote - cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" - by="Alexander Mathis, *Professor Assistente, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" ->}} -Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning. -{{< /blockquote >}} +cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" +by="Alexander Mathis, _Professor Assistente, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" + +> }} +> Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning. +> {{< /blockquote >}} ## Sobre o DeepLabCut @@ -33,19 +34,21 @@ attribution = '(Fonte: Mackenzie Mathis)' O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. -A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. +A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (_frames_) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 -A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. +A DeepLabCut usa um princípio chamado [aprendizado por transferência (_transfer learning_)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pré-treinados para várias espécies e condições experimentais, desde a análise facial em primatas até à posição de cães. Isso pode ser executado na nuvem, por exemplo, sem qualquer rotulagem de novos dados ou treinamento em rede neural, e não é necessária nenhuma experiência em programação. ### Principais Objetivos e Resultados -* **Automação da análise de poses animais para estudos científicos:** +- **Automação da análise de poses animais para estudos científicos:** O objetivo principal da tecnologia DeepLabCut é medir e rastrear a postura dos animais em várias configurações. Esses dados podem ser usados, por exemplo, em estudos de neurociência para entender como o cérebro controla o movimento, ou para elucidar como os animais interagem socialmente. Pesquisadores observaram que [desempenho é 10 vezes melhor](https://www.biorxiv.org/content/10.1101/457242v1) com o DeepLabCut. Poses podem ser inferidas off-line em até 1200 quadros por segundo (FPS). -* **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** +- **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** DeepLabCut queria compartilhar sua tecnologia de estimativa de poses animal na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. @@ -67,19 +70,23 @@ attribution = '(Fonte: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' {{< /figure >}} +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut + ### Desafios -* **Velocidade** +- **Velocidade** - Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. + Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. + Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. + Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. -* **Combinatória** +- **Combinatória** - Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. + Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. -* **Processamento de dados** +- **Processamento de dados** - Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. + Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. {{< figure >}} src = '/images/content_images/cs/pose-estimation.png' @@ -96,13 +103,13 @@ NumPy supre a principal necessidade da tecnologia DeepLabCut de cálculos numér As seguintes características da NumPy desempenharam um papel fundamental para atender às necessidades de processamento de imagens, combinatória e cálculos rápidos nos algoritmos de estimação de pose na DeepLabCut: -* Vetorização -* Operações em arrays com máscaras -* Álgebra linear -* Amostragem aleatória -* Reordenamento de matrizes grandes +- Vetorização +- Operações em arrays com máscaras +- Álgebra linear +- Random Sampling +- Reordenamento de matrizes grandes -A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. +A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. Para este propósito, densidades de alvo (_scoremaps_) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. {{< figure >}} src = '/images/content_images/cs/deeplabcut-workflow.png' @@ -114,14 +121,11 @@ attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The- ## Resumo -Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. +Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. +[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. {{< figure >}} src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Recursos chave do NumPy utilizados' {{< /figure >}} - -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 - -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 7fedcdea5e582daee07630a0d7a4d5e796b00c80 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:11 +0200 Subject: [PATCH 159/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 164 +++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 content/es/case-studies/gw-discov.md diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md new file mode 100644 index 0000000000..4bdbc88177 --- /dev/null +++ b/content/es/case-studies/gw-discov.md @@ -0,0 +1,164 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Gravitational Waves' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _LIGO Scientific Collaboration_" >}} +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by +cataclysmic events in the universe such as collision and merging of two black +holes or coalescing binary stars or supernovae. Observing GW can not only help +in studying gravity but also in understanding some of the obscure phenomena in +the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) +was designed to open the field of gravitational-wave astrophysics through the +direct detection of gravitational waves predicted by Einstein’s General Theory +of Relativity. It comprises two widely separated interferometers within the +United States — one in Hanford, Washington and the other in Livingston, +Louisiana — operated in unison to detect gravitational waves. Each of them has +multi-kilometer-scale gravitational wave detectors that use laser +interferometry. The LIGO Scientific Collaboration (LSC), is a group of more +than 1000 scientists from universities around the United States and in 14 +other countries supported by more than 90 universities and research institutes; +approximately 250 students actively contributing to the collaboration. The new +LIGO discovery is the first observation of gravitational waves themselves, +made by measuring the tiny disturbances the waves make to space and time as +they pass through the earth. It has opened up new astrophysical frontiers +that explore the warped side of the universe—objects and phenomena that are +made from warped spacetime. + +### Key Objectives + +- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to + detect gravitational waves from some of the most violent and energetic + processes in the Universe, the data LIGO collects may have far-reaching + effects on many areas of physics including gravitation, relativity, + astrophysics, cosmology, particle physics, and nuclear physics. +- Crunch observed data via numerical relativity computations that involves + complex maths in order to discern signal from noise, filter out relevant + signal and statistically estimate significance of observed data +- Data visualization so that the binary / numerical results can be + comprehended. + +### The Challenges + +- **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect + and have tiny interaction with matter. Processing and analyzing all of + LIGO's data requires a vast computing infrastructure.After taking care of + noise, which is billions of times of the signal, there is still very + complex relativity equations and huge amounts of data which present a + computational challenge: + [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) + spread on 6 dedicated LIGO clusters + +- **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges + posed by data deluge and finding a needle in a haystack rise multi-fold. + LIGO generates terabytes of data every day! Making sense of this data + requires an enormous effort for each and every detection. For example, the + signals being collected by LIGO must be matched by supercomputers against + hundreds of thousands of templates of possible gravitational-wave signatures. + +- **Visualization** + + Once the obstacles related to understanding Einstein’s equations well + enough to solve them using supercomputers are taken care of, the next big + challenge was making data comprehensible to the human brain. Simulation + modeling as well as signal detection requires effective visualization + techniques. Visualization also plays a role in lending more credibility + to numerical relativity in the eyes of pure science aficionados, who did + not give enough importance to numerical relativity until imaging and + simulations made it easier to comprehend results for a larger audience. + Speed of complex computations and rendering, re-rendering images and + simulations using latest experimental inputs and insights can be a time + consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Estimated gravitational-wave strain amplitude from GW150914' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any +technique except brute force numerical relativity using supercomputers. +The amount of data LIGO collects is as incomprehensibly large as gravitational +wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by +the software used for various tasks performed during the GW detection project +at LIGO. NumPy helped in solving complex maths and data manipulation at high +speed. Here are some examples: + +- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch + detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) + (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +- Data retrieval: Deciding which data can be analyzed, figuring out whether it + contains a signal - needle in a haystack +- Statistical analysis: estimate the statistical significance of observational + data, estimating the signal parameters (e.g. masses of stars, spin velocity, + and distance) by comparison with a model. +- Visualization of data + - Time series + - Spectrograms +- Compute Correlations +- Key [Software](https://github.com/lscsoft) developed in GW data analysis + such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and + [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for + providing object based interfaces to utilities, tools, and methods for + studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena +while providing new insight into many of the most profound astrophysical +phenomena known. Number crunching and data visualization is a crucial step +that helps scientists gain insights into data gathered from the scientific +observations and understand the results. The computations are complex and +cannot be comprehended by humans unless it is visualized using computer +simulations that are fed with the real observed data and analysis. NumPy +along with other Python packages such as matplotlib, pandas, and scikit-learn +is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to +answer complex questions and discover new horizons in our understanding of the +universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 537ef824529c948e5226ff11221fcdf9c9a420e1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:13 +0200 Subject: [PATCH 160/586] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 164 +++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 content/ar/case-studies/gw-discov.md diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md new file mode 100644 index 0000000000..4bdbc88177 --- /dev/null +++ b/content/ar/case-studies/gw-discov.md @@ -0,0 +1,164 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Gravitational Waves' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _LIGO Scientific Collaboration_" >}} +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by +cataclysmic events in the universe such as collision and merging of two black +holes or coalescing binary stars or supernovae. Observing GW can not only help +in studying gravity but also in understanding some of the obscure phenomena in +the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) +was designed to open the field of gravitational-wave astrophysics through the +direct detection of gravitational waves predicted by Einstein’s General Theory +of Relativity. It comprises two widely separated interferometers within the +United States — one in Hanford, Washington and the other in Livingston, +Louisiana — operated in unison to detect gravitational waves. Each of them has +multi-kilometer-scale gravitational wave detectors that use laser +interferometry. The LIGO Scientific Collaboration (LSC), is a group of more +than 1000 scientists from universities around the United States and in 14 +other countries supported by more than 90 universities and research institutes; +approximately 250 students actively contributing to the collaboration. The new +LIGO discovery is the first observation of gravitational waves themselves, +made by measuring the tiny disturbances the waves make to space and time as +they pass through the earth. It has opened up new astrophysical frontiers +that explore the warped side of the universe—objects and phenomena that are +made from warped spacetime. + +### Key Objectives + +- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to + detect gravitational waves from some of the most violent and energetic + processes in the Universe, the data LIGO collects may have far-reaching + effects on many areas of physics including gravitation, relativity, + astrophysics, cosmology, particle physics, and nuclear physics. +- Crunch observed data via numerical relativity computations that involves + complex maths in order to discern signal from noise, filter out relevant + signal and statistically estimate significance of observed data +- Data visualization so that the binary / numerical results can be + comprehended. + +### The Challenges + +- **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect + and have tiny interaction with matter. Processing and analyzing all of + LIGO's data requires a vast computing infrastructure.After taking care of + noise, which is billions of times of the signal, there is still very + complex relativity equations and huge amounts of data which present a + computational challenge: + [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) + spread on 6 dedicated LIGO clusters + +- **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges + posed by data deluge and finding a needle in a haystack rise multi-fold. + LIGO generates terabytes of data every day! Making sense of this data + requires an enormous effort for each and every detection. For example, the + signals being collected by LIGO must be matched by supercomputers against + hundreds of thousands of templates of possible gravitational-wave signatures. + +- **Visualization** + + Once the obstacles related to understanding Einstein’s equations well + enough to solve them using supercomputers are taken care of, the next big + challenge was making data comprehensible to the human brain. Simulation + modeling as well as signal detection requires effective visualization + techniques. Visualization also plays a role in lending more credibility + to numerical relativity in the eyes of pure science aficionados, who did + not give enough importance to numerical relativity until imaging and + simulations made it easier to comprehend results for a larger audience. + Speed of complex computations and rendering, re-rendering images and + simulations using latest experimental inputs and insights can be a time + consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Estimated gravitational-wave strain amplitude from GW150914' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any +technique except brute force numerical relativity using supercomputers. +The amount of data LIGO collects is as incomprehensibly large as gravitational +wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by +the software used for various tasks performed during the GW detection project +at LIGO. NumPy helped in solving complex maths and data manipulation at high +speed. Here are some examples: + +- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch + detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) + (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +- Data retrieval: Deciding which data can be analyzed, figuring out whether it + contains a signal - needle in a haystack +- Statistical analysis: estimate the statistical significance of observational + data, estimating the signal parameters (e.g. masses of stars, spin velocity, + and distance) by comparison with a model. +- Visualization of data + - Time series + - Spectrograms +- Compute Correlations +- Key [Software](https://github.com/lscsoft) developed in GW data analysis + such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and + [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for + providing object based interfaces to utilities, tools, and methods for + studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena +while providing new insight into many of the most profound astrophysical +phenomena known. Number crunching and data visualization is a crucial step +that helps scientists gain insights into data gathered from the scientific +observations and understand the results. The computations are complex and +cannot be comprehended by humans unless it is visualized using computer +simulations that are fed with the real observed data and analysis. NumPy +along with other Python packages such as matplotlib, pandas, and scikit-learn +is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to +answer complex questions and discover new horizons in our understanding of the +universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 66e65e678c81ca661f9f169cac854087481a1ad5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:14 +0200 Subject: [PATCH 161/586] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 108 +++++++++++++++++++-------- 1 file changed, 78 insertions(+), 30 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index c5275fde58..7d7311c141 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -12,39 +12,75 @@ attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} {{< blockquote - cite="https://www.youtube.com/watch?v=BIvezCVcsYs" - by="David Shoemaker, *LIGOサイエンティフィック・コラボレーション*" >}} +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _LIGOサイエンティフィック・コラボレーション_" >}} 科学計算のためのPythonエコシステムはLIGOで行われている研究のための重要なインフラです。 {{< /blockquote >}} +{{< /blockquote >}} ## [重力波](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) と [LIGO](https://www.ligo.caltech.edu) について -重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 - -\[レーザー干渉計重力波天文台(LIGO)\](https://www. ligo. caltech. edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 - +重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 Observing GW can not only help +in studying gravity but also in understanding some of the obscure phenomena in +the distant universe and its impact. + +\[レーザー干渉計重力波天文台(LIGO)\](https://www. It comprises two widely separated interferometers within the +United States — one in Hanford, Washington and the other in Livingston, +Louisiana — operated in unison to detect gravitational waves. Each of them has +multi-kilometer-scale gravitational wave detectors that use laser +interferometry. The LIGO Scientific Collaboration (LSC), is a group of more +than 1000 scientists from universities around the United States and in 14 +other countries supported by more than 90 universities and research institutes; +approximately 250 students actively contributing to the collaboration. The new +LIGO discovery is the first observation of gravitational waves themselves, +made by measuring the tiny disturbances the waves make to space and time as +they pass through the earth. It has opened up new astrophysical frontiers +that explore the warped side of the universe—objects and phenomena that are +made from warped spacetime. ### 主な目的 -* LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 -* 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 -* バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 - - +- LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 +- Crunch observed data via numerical relativity computations that involves + complex maths in order to discern signal from noise, filter out relevant + signal and statistically estimate significance of observed data +- バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 ### 課題 -* **計算** - - 合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 - -* **データの氾濫** - - 観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているのです！ この大量のデータを解釈するには、各検出ごとに多大な労力が必要です。 例えば、LIGOによって収集される信号は、数十万個の重力波シグネチャのテンプレートで構成されており、スーパーコンピュータでしか解析できません。 - -* **可視化** - - アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 +- **計算** + + Gravitational Waves are hard to detect as they produce a very small effect + and have tiny interaction with matter. Processing and analyzing all of + LIGO's data requires a vast computing infrastructure.After taking care of + noise, which is billions of times of the signal, there is still very + complex relativity equations and huge amounts of data which present a + computational challenge: + [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) + spread on 6 dedicated LIGO clusters + +- **データの氾濫** + + As observational devices become more sensitive and reliable, the challenges + posed by data deluge and finding a needle in a haystack rise multi-fold. + LIGO generates terabytes of data every day! Making sense of this data + requires an enormous effort for each and every detection. For example, the + signals being collected by LIGO must be matched by supercomputers against + hundreds of thousands of templates of possible gravitational-wave signatures. + +- **可視化** + + Once the obstacles related to understanding Einstein’s equations well + enough to solve them using supercomputers are taken care of, the next big + challenge was making data comprehensible to the human brain. Simulation + modeling as well as signal detection requires effective visualization + techniques. Visualization also plays a role in lending more credibility + to numerical relativity in the eyes of pure science aficionados, who did + not give enough importance to numerical relativity until imaging and + simulations made it easier to comprehend results for a larger audience. + Speed of complex computations and rendering, re-rendering images and + simulations using latest experimental inputs and insights can be a time + consuming activity that challenges researchers in this domain. {{< figure >}} src = '/images/content_images/cs/gw_strain_amplitude.png' @@ -57,17 +93,19 @@ attributionlink = 'https://www.researchgate.net/publication/293886905_Observatio ## 重力波の検出におけるNumPyの役割 合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。 +合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 -Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。 +Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。 NumPy helped in solving complex maths and data manipulation at high +speed. Here are some examples: -* [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 -* データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 -* 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 -* データ可視化 +- [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 +- データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 +- 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 +- データ可視化 - 時系列データ - スペクトログラム -* 相関計算 -* 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 +- 相関計算 +- 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 {{< figure >}} src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' @@ -85,7 +123,17 @@ title = 'PyCBCのNumPy依存グラフ' ## まとめ -一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 +GW detection has enabled researchers to discover entirely unexpected phenomena +while providing new insight into many of the most profound astrophysical +phenomena known. Number crunching and data visualization is a crucial step +that helps scientists gain insights into data gathered from the scientific +observations and understand the results. The computations are complex and +cannot be comprehended by humans unless it is visualized using computer +simulations that are fed with the real observed data and analysis. NumPy +along with other Python packages such as matplotlib, pandas, and scikit-learn +is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to +answer complex questions and discover new horizons in our understanding of the +universe. {{< figure >}} src = '/images/content_images/cs/numpy_bh_benefits.png' From a1f3a59b6a60980ce7bd130f29171ddb7ee4768b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:16 +0200 Subject: [PATCH 162/586] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 164 +++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 content/ko/case-studies/gw-discov.md diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md new file mode 100644 index 0000000000..4bdbc88177 --- /dev/null +++ b/content/ko/case-studies/gw-discov.md @@ -0,0 +1,164 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Gravitational Waves' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _LIGO Scientific Collaboration_" >}} +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by +cataclysmic events in the universe such as collision and merging of two black +holes or coalescing binary stars or supernovae. Observing GW can not only help +in studying gravity but also in understanding some of the obscure phenomena in +the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) +was designed to open the field of gravitational-wave astrophysics through the +direct detection of gravitational waves predicted by Einstein’s General Theory +of Relativity. It comprises two widely separated interferometers within the +United States — one in Hanford, Washington and the other in Livingston, +Louisiana — operated in unison to detect gravitational waves. Each of them has +multi-kilometer-scale gravitational wave detectors that use laser +interferometry. The LIGO Scientific Collaboration (LSC), is a group of more +than 1000 scientists from universities around the United States and in 14 +other countries supported by more than 90 universities and research institutes; +approximately 250 students actively contributing to the collaboration. The new +LIGO discovery is the first observation of gravitational waves themselves, +made by measuring the tiny disturbances the waves make to space and time as +they pass through the earth. It has opened up new astrophysical frontiers +that explore the warped side of the universe—objects and phenomena that are +made from warped spacetime. + +### Key Objectives + +- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to + detect gravitational waves from some of the most violent and energetic + processes in the Universe, the data LIGO collects may have far-reaching + effects on many areas of physics including gravitation, relativity, + astrophysics, cosmology, particle physics, and nuclear physics. +- Crunch observed data via numerical relativity computations that involves + complex maths in order to discern signal from noise, filter out relevant + signal and statistically estimate significance of observed data +- Data visualization so that the binary / numerical results can be + comprehended. + +### The Challenges + +- **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect + and have tiny interaction with matter. Processing and analyzing all of + LIGO's data requires a vast computing infrastructure.After taking care of + noise, which is billions of times of the signal, there is still very + complex relativity equations and huge amounts of data which present a + computational challenge: + [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) + spread on 6 dedicated LIGO clusters + +- **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges + posed by data deluge and finding a needle in a haystack rise multi-fold. + LIGO generates terabytes of data every day! Making sense of this data + requires an enormous effort for each and every detection. For example, the + signals being collected by LIGO must be matched by supercomputers against + hundreds of thousands of templates of possible gravitational-wave signatures. + +- **Visualization** + + Once the obstacles related to understanding Einstein’s equations well + enough to solve them using supercomputers are taken care of, the next big + challenge was making data comprehensible to the human brain. Simulation + modeling as well as signal detection requires effective visualization + techniques. Visualization also plays a role in lending more credibility + to numerical relativity in the eyes of pure science aficionados, who did + not give enough importance to numerical relativity until imaging and + simulations made it easier to comprehend results for a larger audience. + Speed of complex computations and rendering, re-rendering images and + simulations using latest experimental inputs and insights can be a time + consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Estimated gravitational-wave strain amplitude from GW150914' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any +technique except brute force numerical relativity using supercomputers. +The amount of data LIGO collects is as incomprehensibly large as gravitational +wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by +the software used for various tasks performed during the GW detection project +at LIGO. NumPy helped in solving complex maths and data manipulation at high +speed. Here are some examples: + +- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch + detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) + (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +- Data retrieval: Deciding which data can be analyzed, figuring out whether it + contains a signal - needle in a haystack +- Statistical analysis: estimate the statistical significance of observational + data, estimating the signal parameters (e.g. masses of stars, spin velocity, + and distance) by comparison with a model. +- Visualization of data + - Time series + - Spectrograms +- Compute Correlations +- Key [Software](https://github.com/lscsoft) developed in GW data analysis + such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and + [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for + providing object based interfaces to utilities, tools, and methods for + studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena +while providing new insight into many of the most profound astrophysical +phenomena known. Number crunching and data visualization is a crucial step +that helps scientists gain insights into data gathered from the scientific +observations and understand the results. The computations are complex and +cannot be comprehended by humans unless it is visualized using computer +simulations that are fed with the real observed data and analysis. NumPy +along with other Python packages such as matplotlib, pandas, and scikit-learn +is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to +answer complex questions and discover new horizons in our understanding of the +universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From caec2c79e4a02150df37e56b4eac48ebbbdcab5d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:17 +0200 Subject: [PATCH 163/586] New translations gw-discov.md (Russian) --- content/ru/case-studies/gw-discov.md | 164 +++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 content/ru/case-studies/gw-discov.md diff --git a/content/ru/case-studies/gw-discov.md b/content/ru/case-studies/gw-discov.md new file mode 100644 index 0000000000..4bdbc88177 --- /dev/null +++ b/content/ru/case-studies/gw-discov.md @@ -0,0 +1,164 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Gravitational Waves' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _LIGO Scientific Collaboration_" >}} +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by +cataclysmic events in the universe such as collision and merging of two black +holes or coalescing binary stars or supernovae. Observing GW can not only help +in studying gravity but also in understanding some of the obscure phenomena in +the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) +was designed to open the field of gravitational-wave astrophysics through the +direct detection of gravitational waves predicted by Einstein’s General Theory +of Relativity. It comprises two widely separated interferometers within the +United States — one in Hanford, Washington and the other in Livingston, +Louisiana — operated in unison to detect gravitational waves. Each of them has +multi-kilometer-scale gravitational wave detectors that use laser +interferometry. The LIGO Scientific Collaboration (LSC), is a group of more +than 1000 scientists from universities around the United States and in 14 +other countries supported by more than 90 universities and research institutes; +approximately 250 students actively contributing to the collaboration. The new +LIGO discovery is the first observation of gravitational waves themselves, +made by measuring the tiny disturbances the waves make to space and time as +they pass through the earth. It has opened up new astrophysical frontiers +that explore the warped side of the universe—objects and phenomena that are +made from warped spacetime. + +### Key Objectives + +- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to + detect gravitational waves from some of the most violent and energetic + processes in the Universe, the data LIGO collects may have far-reaching + effects on many areas of physics including gravitation, relativity, + astrophysics, cosmology, particle physics, and nuclear physics. +- Crunch observed data via numerical relativity computations that involves + complex maths in order to discern signal from noise, filter out relevant + signal and statistically estimate significance of observed data +- Data visualization so that the binary / numerical results can be + comprehended. + +### The Challenges + +- **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect + and have tiny interaction with matter. Processing and analyzing all of + LIGO's data requires a vast computing infrastructure.After taking care of + noise, which is billions of times of the signal, there is still very + complex relativity equations and huge amounts of data which present a + computational challenge: + [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) + spread on 6 dedicated LIGO clusters + +- **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges + posed by data deluge and finding a needle in a haystack rise multi-fold. + LIGO generates terabytes of data every day! Making sense of this data + requires an enormous effort for each and every detection. For example, the + signals being collected by LIGO must be matched by supercomputers against + hundreds of thousands of templates of possible gravitational-wave signatures. + +- **Visualization** + + Once the obstacles related to understanding Einstein’s equations well + enough to solve them using supercomputers are taken care of, the next big + challenge was making data comprehensible to the human brain. Simulation + modeling as well as signal detection requires effective visualization + techniques. Visualization also plays a role in lending more credibility + to numerical relativity in the eyes of pure science aficionados, who did + not give enough importance to numerical relativity until imaging and + simulations made it easier to comprehend results for a larger audience. + Speed of complex computations and rendering, re-rendering images and + simulations using latest experimental inputs and insights can be a time + consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Estimated gravitational-wave strain amplitude from GW150914' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any +technique except brute force numerical relativity using supercomputers. +The amount of data LIGO collects is as incomprehensibly large as gravitational +wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by +the software used for various tasks performed during the GW detection project +at LIGO. NumPy helped in solving complex maths and data manipulation at high +speed. Here are some examples: + +- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch + detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) + (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +- Data retrieval: Deciding which data can be analyzed, figuring out whether it + contains a signal - needle in a haystack +- Statistical analysis: estimate the statistical significance of observational + data, estimating the signal parameters (e.g. masses of stars, spin velocity, + and distance) by comparison with a model. +- Visualization of data + - Time series + - Spectrograms +- Compute Correlations +- Key [Software](https://github.com/lscsoft) developed in GW data analysis + such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and + [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for + providing object based interfaces to utilities, tools, and methods for + studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena +while providing new insight into many of the most profound astrophysical +phenomena known. Number crunching and data visualization is a crucial step +that helps scientists gain insights into data gathered from the scientific +observations and understand the results. The computations are complex and +cannot be comprehended by humans unless it is visualized using computer +simulations that are fed with the real observed data and analysis. NumPy +along with other Python packages such as matplotlib, pandas, and scikit-learn +is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to +answer complex questions and discover new horizons in our understanding of the +universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From ef989489dabf19a41695cd81bdf4d7a1c9ac6aa5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:18 +0200 Subject: [PATCH 164/586] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 164 +++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 content/zh/case-studies/gw-discov.md diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md new file mode 100644 index 0000000000..4bdbc88177 --- /dev/null +++ b/content/zh/case-studies/gw-discov.md @@ -0,0 +1,164 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Gravitational Waves' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _LIGO Scientific Collaboration_" >}} +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by +cataclysmic events in the universe such as collision and merging of two black +holes or coalescing binary stars or supernovae. Observing GW can not only help +in studying gravity but also in understanding some of the obscure phenomena in +the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) +was designed to open the field of gravitational-wave astrophysics through the +direct detection of gravitational waves predicted by Einstein’s General Theory +of Relativity. It comprises two widely separated interferometers within the +United States — one in Hanford, Washington and the other in Livingston, +Louisiana — operated in unison to detect gravitational waves. Each of them has +multi-kilometer-scale gravitational wave detectors that use laser +interferometry. The LIGO Scientific Collaboration (LSC), is a group of more +than 1000 scientists from universities around the United States and in 14 +other countries supported by more than 90 universities and research institutes; +approximately 250 students actively contributing to the collaboration. The new +LIGO discovery is the first observation of gravitational waves themselves, +made by measuring the tiny disturbances the waves make to space and time as +they pass through the earth. It has opened up new astrophysical frontiers +that explore the warped side of the universe—objects and phenomena that are +made from warped spacetime. + +### Key Objectives + +- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to + detect gravitational waves from some of the most violent and energetic + processes in the Universe, the data LIGO collects may have far-reaching + effects on many areas of physics including gravitation, relativity, + astrophysics, cosmology, particle physics, and nuclear physics. +- Crunch observed data via numerical relativity computations that involves + complex maths in order to discern signal from noise, filter out relevant + signal and statistically estimate significance of observed data +- Data visualization so that the binary / numerical results can be + comprehended. + +### The Challenges + +- **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect + and have tiny interaction with matter. Processing and analyzing all of + LIGO's data requires a vast computing infrastructure.After taking care of + noise, which is billions of times of the signal, there is still very + complex relativity equations and huge amounts of data which present a + computational challenge: + [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) + spread on 6 dedicated LIGO clusters + +- **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges + posed by data deluge and finding a needle in a haystack rise multi-fold. + LIGO generates terabytes of data every day! Making sense of this data + requires an enormous effort for each and every detection. For example, the + signals being collected by LIGO must be matched by supercomputers against + hundreds of thousands of templates of possible gravitational-wave signatures. + +- **Visualization** + + Once the obstacles related to understanding Einstein’s equations well + enough to solve them using supercomputers are taken care of, the next big + challenge was making data comprehensible to the human brain. Simulation + modeling as well as signal detection requires effective visualization + techniques. Visualization also plays a role in lending more credibility + to numerical relativity in the eyes of pure science aficionados, who did + not give enough importance to numerical relativity until imaging and + simulations made it easier to comprehend results for a larger audience. + Speed of complex computations and rendering, re-rendering images and + simulations using latest experimental inputs and insights can be a time + consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Estimated gravitational-wave strain amplitude from GW150914' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any +technique except brute force numerical relativity using supercomputers. +The amount of data LIGO collects is as incomprehensibly large as gravitational +wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by +the software used for various tasks performed during the GW detection project +at LIGO. NumPy helped in solving complex maths and data manipulation at high +speed. Here are some examples: + +- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch + detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) + (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +- Data retrieval: Deciding which data can be analyzed, figuring out whether it + contains a signal - needle in a haystack +- Statistical analysis: estimate the statistical significance of observational + data, estimating the signal parameters (e.g. masses of stars, spin velocity, + and distance) by comparison with a model. +- Visualization of data + - Time series + - Spectrograms +- Compute Correlations +- Key [Software](https://github.com/lscsoft) developed in GW data analysis + such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and + [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for + providing object based interfaces to utilities, tools, and methods for + studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena +while providing new insight into many of the most profound astrophysical +phenomena known. Number crunching and data visualization is a crucial step +that helps scientists gain insights into data gathered from the scientific +observations and understand the results. The computations are complex and +cannot be comprehended by humans unless it is visualized using computer +simulations that are fed with the real observed data and analysis. NumPy +along with other Python packages such as matplotlib, pandas, and scikit-learn +is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to +answer complex questions and discover new horizons in our understanding of the +universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From c288e15381967617746cb265d496948671342c3e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 17:14:19 +0200 Subject: [PATCH 165/586] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 42 ++++++++++++++-------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index cb371914fc..db8fd8a87a 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -12,8 +12,8 @@ attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} {{< blockquote - cite="https://www.youtube.com/watch?v=BIvezCVcsYs" - by="David Shoemaker, *Colaborador Científico no LIGO*" >}} +cite="https://www.youtube.com/watch?v=BIvezCVcsYs" +by="David Shoemaker, _Colaborador Científico no LIGO_" >}} O ecossistema científico Python é uma infraestrutura crítica para a pesquisa feita no LIGO. {{< /blockquote >}} @@ -23,28 +23,27 @@ Ondas gravitacionais são ondulações no tecido espaço-tempo, gerado por event O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. - ### Objetivos -* Embora sua [missão](https://www.ligo.caltech.edu/page/what-is-ligo) seja detectar ondas gravitacionais de alguns dos processos mais violentos e enérgicos no Universo, os dados que o LIGO coleta podem ter efeitos de grande alcance em muitas áreas da física, incluindo gravitação, relatividade, astrofísica, cosmologia, física de partículas e física nuclear. -* Processar dados observados através de cálculos numéricos de relatividade que envolvem matemática complexa para identificar o sinal e o ruído, filtrar o sinal relevante e estimar estatisticamente o significado dos dados observados. -* Visualização de dados para que os resultados binários/numéricos possam ser compreendidos. - - +- Embora sua [missão](https://www.ligo.caltech.edu/page/what-is-ligo) seja detectar ondas gravitacionais de alguns dos processos mais violentos e enérgicos no Universo, os dados que o LIGO coleta podem ter efeitos de grande alcance em muitas áreas da física, incluindo gravitação, relatividade, astrofísica, cosmologia, física de partículas e física nuclear. +- Processar dados observados através de cálculos numéricos de relatividade que envolvem matemática complexa para identificar o sinal e o ruído, filtrar o sinal relevante e estimar estatisticamente o significado dos dados observados. +- Visualização de dados para que os resultados binários/numéricos possam ser compreendidos. ### Desafios -* **Computação** +- **Computação** - As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Processar e analisar todos os dados do LIGO requer uma vasta infraestrutura de computação. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. + As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. -* **Sobrecarga de dados** +- **Sobrecarga de dados** - À medida que os dispositivos observacionais se tornam mais sensíveis e confiáveis, os desafios criados pela sobrecarga de dados e a procura por uma agulha em um palheiro se tornam muito maiores. O LIGO gera terabytes de dados todos os dias! Entender esses dados requer um enorme esforço para cada detecção. Por exemplo, os sinais sendo coletados pelo LIGO devem ser combinados por supercomputadores e comparados a centenas de milhares de modelos de possíveis assinaturas de ondas gravitacionais. + À medida que os dispositivos observacionais se tornam mais sensíveis e confiáveis, os desafios criados pela sobrecarga de dados e a procura por uma agulha em um palheiro se tornam muito maiores. + O LIGO gera terabytes de dados todos os dias! Entender esses dados requer um enorme esforço para cada detecção. Por exemplo, os sinais sendo coletados pelo LIGO devem ser combinados por supercomputadores e comparados a centenas de milhares de modelos de possíveis assinaturas de ondas gravitacionais. -* **Visualização** +- **Visualização** - Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. + Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. + A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. {{< figure >}} src = '/images/content_images/cs/gw_strain_amplitude.png' @@ -56,18 +55,19 @@ attributionlink = 'https://www.researchgate.net/publication/293886905_Observatio ## O papel da NumPy na detecção de ondas gravitacionais -Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. +Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. +A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: -* [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) -* Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro -* Análise estatística: estimar o significado estatístico dos dados observados, estimando os parâmetros do sinal (por exemplo, massa de estrelas, velocidade de giro e distância) em comparação com um modelo. -* Visualização de dados +- [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) +- Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro +- Análise estatística: estimar o significado estatístico dos dados observados, estimando os parâmetros do sinal (por exemplo, massa de estrelas, velocidade de giro e distância) em comparação com um modelo. +- Visualização de dados - Séries temporais - Espectrogramas -* Cálculo de correlações -* [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. +- Cálculo de correlações +- [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. {{< figure >}} src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' From e17404712fc3c9fd1fd4332cd86550a953bd1d01 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 17 Jun 2024 18:06:17 +0200 Subject: [PATCH 166/586] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index e5903d0a68..a094e972a8 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -95,7 +95,7 @@ params: libraries: - title: Quantum Computing - alttext: A computer chip. + alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - @@ -143,10 +143,10 @@ params: label: python-control - url: https://hyperspy.org/ - label: HyperSpy + label: HiperSpy - - title: Image Processing - alttext: An photograph of the mountains. + title: Processamento de imagens + alttext: Uma fotografia das montanhas. img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -159,8 +159,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Graphs and Networks - alttext: A simple graph. + title: Grafos e Redes + alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg links: - @@ -176,8 +176,8 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomy - alttext: A telescope. + title: Astronomia + alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - @@ -190,16 +190,16 @@ params: url: https://spacepy.github.io/ label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + title: Psicologia Cognitiva + alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - - title: Bioinformatics - alttext: A strand of DNA. + title: Bioinformática + alttext: Uma fita de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -283,8 +283,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: Processamento Geográfico + alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg links: - @@ -297,7 +297,7 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering + title: Arquitetura e Engenharia alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg links: From 14c8208c748deab6ab1ed9559157e3e837f160d7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:17 +0200 Subject: [PATCH 167/586] New translations news.md (Spanish) --- content/es/news.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 76b4f46cc3..76402114b5 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,28 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be @@ -385,6 +403,7 @@ Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 47d82e82bcfc13c29781f86e7b2af4a550984376 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:19 +0200 Subject: [PATCH 168/586] New translations news.md (Arabic) --- content/ar/news.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 76b4f46cc3..76402114b5 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,28 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be @@ -385,6 +403,7 @@ Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 26b936ea328d0b0434ac3cfec68177ee3903aadd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:20 +0200 Subject: [PATCH 169/586] New translations news.md (Japanese) --- content/ja/news.md | 27 +++++++++++++++++++++++---- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 157333976a..8f8dfe9f3a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,12 +1,30 @@ --- title: ニュース sidebar: false -newsHeader: NumPy 1.26.0 がリリースされました。 -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- ### NumPy 1.19.2 リリース +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Numpy 1.25. +- 多くの新しい非推奨(Deprecation)の追加 + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + +### NumPy 1.26.0 がリリースされました。 + _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new @@ -16,8 +34,8 @@ end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Numpy 1.25. -- 多くの新しい非推奨(Deprecation)の追加 +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser @@ -314,6 +332,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. - _2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. From e0eefe5f3f8448c8d58205761c4cb500315a87ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:22 +0200 Subject: [PATCH 170/586] New translations news.md (Korean) --- content/ko/news.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 76b4f46cc3..76402114b5 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,28 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be @@ -385,6 +403,7 @@ Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From a5284bc03381350986bc66833fc53fc5e9cce6cb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:24 +0200 Subject: [PATCH 171/586] New translations news.md (Russian) --- content/ru/news.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 76b4f46cc3..76402114b5 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,28 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be @@ -385,6 +403,7 @@ Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 7f4cbe14c86dddbe00f63f905c51e3082ee96374 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:26 +0200 Subject: [PATCH 172/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 76b4f46cc3..76402114b5 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,28 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be @@ -385,6 +403,7 @@ Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 94c5b1580e96dd0f7fd1ee7a3a6117dde5ccf6ab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:09:28 +0200 Subject: [PATCH 173/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 62db8878f4..541fe29b81 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,28 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: NumPy 2.0 released! +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 2023-09-16 This release has been over a year in the making, and @@ -15,7 +33,7 @@ end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser @@ -261,6 +279,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 de novembro de 2023_. From e31e97618ae9db9d9500c2234acb81946c54c109 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:51 +0200 Subject: [PATCH 174/586] New translations news.md (Spanish) --- content/es/news.md | 363 +++++++++++++-------------------------------- 1 file changed, 105 insertions(+), 258 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 76402114b5..a6f3d0a88a 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,120 +1,86 @@ --- title: News sidebar: false -newsHeader: NumPy 2.0 released! +newsHeader: "NumPy 2.0 released!" date: 2024-06-17 --- ### NumPy 2.0.0 released -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be -released on June 16, 2024. This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) -is now available. The highlights of the release are: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: -- Python 3.12.0 support. -- Cython 3.0.0 compatibility. -- Use of the Meson build system -- Updated SIMD support -- f2py fixes, meson and bind(x) support -- Support for the updated Accelerate BLAS/LAPACK library +* Soporte de Python 3.12.0. +* Compatibilidad con Cython 3.0.0. +* Utilización del sistema de compilación Meson +* Actualización del soporte de SIMD +* Correcciones de f2py, meson y soporte de bind(x) +* Soporte para la librería actualizada Accelerate BLAS/LAPACK -The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the -transition to the Meson build system and provision of support for Cython 3.0.0. -A total of 20 people contributed to this release and 59 pull requests were -merged. +La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la transición al sistema de compilación Meson y que provee soporte para Cython 3.0.0. Un total de 20 personas contribuyeron a esta versión y 59 solicitudes de cambios fueron fusionadas. -The Python versions supported by this release are 3.9-3.12. +Las versiones de Python compatibles con esta versión son 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: -Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ - -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) _Japanese:_ - -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. -Looking ahead, we’d love to translate the website into more languages. -If you’d like to help, please connect with the NumPy Translations Team on Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(Look for the #translations channel.) We are also building a Translations Team who will be -working on localizing documentation and educational content across the Scientific Python -ecosystem. If this piqued your interest, join us on the Scientific Python -Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) -is now available. The highlights of the release are: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -- Support for MUSL, there are now MUSL wheels. -- Support for the Fujitsu C/C++ compiler. -- Object arrays are now supported in einsum. -- Support for the inplace matrix multiplication (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, and clarify the -documentation. There has also been preparatory work for the future NumPy 2.0.0, -resulting in a large number of new and expired deprecations. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -A total of 148 people contributed to this release and 530 pull requests were -merged. +A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. @@ -122,148 +88,77 @@ The Python versions supported by this release are 3.9-3.11. _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -How can we be better when it comes to diversity and inclusion? -Read the report and find out how to get involved -[here](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy -documentation team leads replacing Melissa Mendonça. We thank Melissa for all her -contributions to the NumPy official documentation and educational materials, -and Mukulika and Ross for stepping up. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) -is now available. The highlights of the release are: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -- New "dtype" and "casting" keywords for stacking functions. -- New F2PY features and fixes. -- Many new deprecations, check them out. -- Many expired deprecations, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase execution speed, and clarify the documentation. -There are a large number of new and expired deprecations due to changes in -dtype promotion and cleanups. It is the work of 177 contributors spread over -444 pull requests. The supported Python versions are 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) -is now available. The highlights of the release are: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -- Implementation of `loadtxt` in C, greatly improving its performance. -- Exposure of DLPack at the Python level for easy data exchange. -- Changes to the promotion and comparisons of structured dtypes. -- Improvements to f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, clarify the documentation, -and expire old deprecations. It is the work of 151 contributors spread over -494 pull requests. The Python versions supported by this release 3.8-3.10. -Python 3.11 will be supported when it reaches the rc stage. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a -[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) -funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to -understand the barriers to participation that contributors, particularly those -from historically underrepresented groups, face in the open-source software -community. The research team would like to talk to new contributors, project -developers and maintainers, and those who have contributed in the past about -their experiences joining and contributing to NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) -which contains additional information on the research goals, privacy, and -confidentiality considerations. Your participation will be valuable to the -growth and sustainability of diverse and inclusive open-source software -communities. Accepted participants will participate in a 30-minute interview -with a research team member. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) -is now available. The highlights of the release are: - -- Type annotations of the main namespace are essentially complete. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. This is probably the most user visible enhancement in this - release. -- A preliminary version of the proposed - [array API Standard](https://data-apis.org/array-api/latest/) is provided - (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). - This is a step in creating a standard collection of functions that can be - used across libraries such as CuPy and JAX. -- NumPy now has a DLPack backend. DLPack provides a common interchange format - for array (tensor) data. -- New methods for `quantile`, `percentile`, and related functions. The new - methods provide a complete set of the methods commonly found in the - literature. -- The universal functions have been refactored to implement most of - [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). - This also unlocks the ability to experiment with the future DType API. -- A new configurable memory allocator for use by downstream projects. - -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread -over 609 pull requests. The Python versions supported by this release are -3.8-3.10. +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has -[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) -to support the onboarding, inclusion, and retention of people from historically -marginalized groups on scientific Python projects, and to structurally improve -the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. - -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), -this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) -will support the creation of dedicated Contributor Experience Lead positions to -identify, document, and implement practices to foster inclusive open-source -communities. This project will be led by Melissa Mendonça (NumPy), with -additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), -Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and -Joris Van den Bossche (Pandas). - -This is an ambitious project aiming to discover and implement activities that -should structurally improve the community dynamics of our projects. By -establishing these new cross-project roles, we hope to introduce a new -collaboration model to the Scientific Python communities, allowing -community-building work within the ecosystem to be done more efficiently and -with greater outcomes. We also expect to develop a clearer picture of what -works and what doesn't in our projects to engage and retain new contributors, -especially from historically underrepresented groups. Finally, we plan on -producing detailed reports on the actions executed, explaining how they have -impacted our projects in terms of representation and interaction with our -communities. - -The two-year project is expected to start by November 2021, and we are excited -to see the results from this work! -[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 -NumPy users from 75 countries participated in our inaugural survey last year. -The survey findings gave us a very good understanding of what we should focus -on for the next 12 months. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -It’s time for another survey, and we are counting on you once again. It will -take about 15 minutes of your time. Besides English, the survey questionnaire -is available in 8 additional languages: Bangla, French, Hindi, Japanese, -Mandarin, Portuguese, Russian, and Spanish. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + ### Numpy 1.21.0 release -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) -is now available. The highlights of the release are: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, @@ -272,121 +167,74 @@ is now available. The highlights of the release are: - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. -This NumPy release is the result of 581 merged pull requests contributed by 175 -people. The Python versions supported for this release are 3.7-3.9, support -for Python 3.10 will be added after Python 3.10 is released. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + ### 2020 NumPy survey results -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students -and faculty from the University of Michigan and the University of Maryland -conducted the first official NumPy community survey. Find the survey results -here: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) -is now available. This is the largest NumPy release to date, thanks to 180+ -contributors. The two most exciting new features are: +### Numpy 1.20.0 release -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule - containing `ArrayLike` and `DtypeLike` aliases that users and downstream - libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, - AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant - performance improvements for many functions (examples: - [sin/cos](https://github.com/numpy/numpy/pull/17587), - [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + ### First official NumPy paper published in Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of -[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) -as a review article in Nature. This comes 14 years after the release of NumPy 1.0. -The paper covers applications and fundamental concepts of array programming, -the rich scientific Python ecosystem built on top of NumPy, and the recently added -array protocols to facilitate interoperability with external array and tensor -libraries like CuPy, Dask, and JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + ### Python 3.9 is coming, when will NumPy release binary wheels? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an -early adopter of Python versions, you may be dissapointed to find that NumPy -(and other binary packages like SciPy) will not have binary wheels ready on the -day of the release. It is a major effort to adapt the build infrastructure to a -new Python version and it typically takes a few weeks for the packages to appear -on PyPI and conda-forge. In preparation for this event, please make sure to +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -- update your `pip` to version 20.1 at least to support `manylinux2010` and - `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from - trying to build from source. ### Numpy 1.19.2 release -_Sep 10, 2020_ -- NumPy -1.19.2 is now available. -This latest release in the 1.19 series fixes several bugs, prepares for the -upcoming Cython 3.x -release and pins -setuptools to keep distutils working while upstream modifications are ongoing. -The aarch64 wheels are built with the latest manylinux2014 release that fixes -the problem of differing page sizes used by different linux distros. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for -decision-making about the development of NumPy as software and as a community. -The survey is available in 8 additional languages besides English: -Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -Please help us make NumPy better and take the survey -[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: - +NumPy logo + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -The logo is a modern take on the old one, with a cleaner design. Thanks to -Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught -for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release -without Python 2 support, hence it was a "clean-up release". The minimum -supported Python version is now Python 3.6. An important new feature is that -the random number generation infrastructure that was introduced in NumPy 1.17.0 -is now accessible from Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + ### Season of Docs acceptance -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for -the Google Season of Docs program. We are excited about the opportunity to -work with a technical writer to improve NumPy's documentation once again! For more -details, please see -[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our -[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + ### NumPy 1.18.0 release -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in -1.17.0, this is a consolidation release. It is the last minor release that will -support Python 3.5. Highlights of the release includes the addition of basic -infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. @@ -395,20 +243,19 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + ## Releases -Here is a list of NumPy releases, with links to release notes. Bugfix -releases (only the `z` changes in the `x.y.z` version number) have no new -features; minor releases (the `y` increases) do. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. -- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. -- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. From 68ad8afc4bbd626b979c112d5d7dffb54c3700ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:52 +0200 Subject: [PATCH 175/586] New translations news.md (Arabic) --- content/ar/news.md | 357 +++++++++++++-------------------------------- 1 file changed, 102 insertions(+), 255 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 76402114b5..706b609976 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,120 +1,86 @@ --- title: News sidebar: false -newsHeader: NumPy 2.0 released! +newsHeader: "NumPy 2.0 released!" date: 2024-06-17 --- ### NumPy 2.0.0 released -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be -released on June 16, 2024. This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) -is now available. The highlights of the release are: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: -- Python 3.12.0 support. -- Cython 3.0.0 compatibility. -- Use of the Meson build system -- Updated SIMD support -- f2py fixes, meson and bind(x) support -- Support for the updated Accelerate BLAS/LAPACK library +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library -The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the -transition to the Meson build system and provision of support for Cython 3.0.0. -A total of 20 people contributed to this release and 59 pull requests were -merged. +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: -Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ - -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) _Japanese:_ - -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. -Looking ahead, we’d love to translate the website into more languages. -If you’d like to help, please connect with the NumPy Translations Team on Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(Look for the #translations channel.) We are also building a Translations Team who will be -working on localizing documentation and educational content across the Scientific Python -ecosystem. If this piqued your interest, join us on the Scientific Python -Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) -is now available. The highlights of the release are: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -- Support for MUSL, there are now MUSL wheels. -- Support for the Fujitsu C/C++ compiler. -- Object arrays are now supported in einsum. -- Support for the inplace matrix multiplication (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, and clarify the -documentation. There has also been preparatory work for the future NumPy 2.0.0, -resulting in a large number of new and expired deprecations. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -A total of 148 people contributed to this release and 530 pull requests were -merged. +A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. @@ -122,148 +88,77 @@ The Python versions supported by this release are 3.9-3.11. _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -How can we be better when it comes to diversity and inclusion? -Read the report and find out how to get involved -[here](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy -documentation team leads replacing Melissa Mendonça. We thank Melissa for all her -contributions to the NumPy official documentation and educational materials, -and Mukulika and Ross for stepping up. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) -is now available. The highlights of the release are: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -- New "dtype" and "casting" keywords for stacking functions. -- New F2PY features and fixes. -- Many new deprecations, check them out. -- Many expired deprecations, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase execution speed, and clarify the documentation. -There are a large number of new and expired deprecations due to changes in -dtype promotion and cleanups. It is the work of 177 contributors spread over -444 pull requests. The supported Python versions are 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) -is now available. The highlights of the release are: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -- Implementation of `loadtxt` in C, greatly improving its performance. -- Exposure of DLPack at the Python level for easy data exchange. -- Changes to the promotion and comparisons of structured dtypes. -- Improvements to f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, clarify the documentation, -and expire old deprecations. It is the work of 151 contributors spread over -494 pull requests. The Python versions supported by this release 3.8-3.10. -Python 3.11 will be supported when it reaches the rc stage. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a -[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) -funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to -understand the barriers to participation that contributors, particularly those -from historically underrepresented groups, face in the open-source software -community. The research team would like to talk to new contributors, project -developers and maintainers, and those who have contributed in the past about -their experiences joining and contributing to NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) -which contains additional information on the research goals, privacy, and -confidentiality considerations. Your participation will be valuable to the -growth and sustainability of diverse and inclusive open-source software -communities. Accepted participants will participate in a 30-minute interview -with a research team member. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) -is now available. The highlights of the release are: - -- Type annotations of the main namespace are essentially complete. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. This is probably the most user visible enhancement in this - release. -- A preliminary version of the proposed - [array API Standard](https://data-apis.org/array-api/latest/) is provided - (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). - This is a step in creating a standard collection of functions that can be - used across libraries such as CuPy and JAX. -- NumPy now has a DLPack backend. DLPack provides a common interchange format - for array (tensor) data. -- New methods for `quantile`, `percentile`, and related functions. The new - methods provide a complete set of the methods commonly found in the - literature. -- The universal functions have been refactored to implement most of - [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). - This also unlocks the ability to experiment with the future DType API. -- A new configurable memory allocator for use by downstream projects. - -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread -over 609 pull requests. The Python versions supported by this release are -3.8-3.10. +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has -[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) -to support the onboarding, inclusion, and retention of people from historically -marginalized groups on scientific Python projects, and to structurally improve -the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. - -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), -this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) -will support the creation of dedicated Contributor Experience Lead positions to -identify, document, and implement practices to foster inclusive open-source -communities. This project will be led by Melissa Mendonça (NumPy), with -additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), -Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and -Joris Van den Bossche (Pandas). - -This is an ambitious project aiming to discover and implement activities that -should structurally improve the community dynamics of our projects. By -establishing these new cross-project roles, we hope to introduce a new -collaboration model to the Scientific Python communities, allowing -community-building work within the ecosystem to be done more efficiently and -with greater outcomes. We also expect to develop a clearer picture of what -works and what doesn't in our projects to engage and retain new contributors, -especially from historically underrepresented groups. Finally, we plan on -producing detailed reports on the actions executed, explaining how they have -impacted our projects in terms of representation and interaction with our -communities. - -The two-year project is expected to start by November 2021, and we are excited -to see the results from this work! -[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 -NumPy users from 75 countries participated in our inaugural survey last year. -The survey findings gave us a very good understanding of what we should focus -on for the next 12 months. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -It’s time for another survey, and we are counting on you once again. It will -take about 15 minutes of your time. Besides English, the survey questionnaire -is available in 8 additional languages: Bangla, French, Hindi, Japanese, -Mandarin, Portuguese, Russian, and Spanish. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + ### Numpy 1.21.0 release -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) -is now available. The highlights of the release are: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, @@ -272,121 +167,74 @@ is now available. The highlights of the release are: - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. -This NumPy release is the result of 581 merged pull requests contributed by 175 -people. The Python versions supported for this release are 3.7-3.9, support -for Python 3.10 will be added after Python 3.10 is released. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + ### 2020 NumPy survey results -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students -and faculty from the University of Michigan and the University of Maryland -conducted the first official NumPy community survey. Find the survey results -here: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) -is now available. This is the largest NumPy release to date, thanks to 180+ -contributors. The two most exciting new features are: +### Numpy 1.20.0 release -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule - containing `ArrayLike` and `DtypeLike` aliases that users and downstream - libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, - AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant - performance improvements for many functions (examples: - [sin/cos](https://github.com/numpy/numpy/pull/17587), - [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + ### First official NumPy paper published in Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of -[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) -as a review article in Nature. This comes 14 years after the release of NumPy 1.0. -The paper covers applications and fundamental concepts of array programming, -the rich scientific Python ecosystem built on top of NumPy, and the recently added -array protocols to facilitate interoperability with external array and tensor -libraries like CuPy, Dask, and JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + ### Python 3.9 is coming, when will NumPy release binary wheels? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an -early adopter of Python versions, you may be dissapointed to find that NumPy -(and other binary packages like SciPy) will not have binary wheels ready on the -day of the release. It is a major effort to adapt the build infrastructure to a -new Python version and it typically takes a few weeks for the packages to appear -on PyPI and conda-forge. In preparation for this event, please make sure to +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -- update your `pip` to version 20.1 at least to support `manylinux2010` and - `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from - trying to build from source. ### Numpy 1.19.2 release -_Sep 10, 2020_ -- NumPy -1.19.2 is now available. -This latest release in the 1.19 series fixes several bugs, prepares for the -upcoming Cython 3.x -release and pins -setuptools to keep distutils working while upstream modifications are ongoing. -The aarch64 wheels are built with the latest manylinux2014 release that fixes -the problem of differing page sizes used by different linux distros. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for -decision-making about the development of NumPy as software and as a community. -The survey is available in 8 additional languages besides English: -Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -Please help us make NumPy better and take the survey -[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: - +NumPy logo + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -The logo is a modern take on the old one, with a cleaner design. Thanks to -Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught -for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release -without Python 2 support, hence it was a "clean-up release". The minimum -supported Python version is now Python 3.6. An important new feature is that -the random number generation infrastructure that was introduced in NumPy 1.17.0 -is now accessible from Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + ### Season of Docs acceptance -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for -the Google Season of Docs program. We are excited about the opportunity to -work with a technical writer to improve NumPy's documentation once again! For more -details, please see -[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our -[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + ### NumPy 1.18.0 release -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in -1.17.0, this is a consolidation release. It is the last minor release that will -support Python 3.5. Highlights of the release includes the addition of basic -infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. @@ -395,13 +243,12 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + ## Releases -Here is a list of NumPy releases, with links to release notes. Bugfix -releases (only the `z` changes in the `x.y.z` version number) have no new -features; minor releases (the `y` increases) do. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From 823c4edc110d2b9c68d7bd8aeac8f64a506a3960 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:53 +0200 Subject: [PATCH 176/586] New translations news.md (Japanese) --- content/ja/news.md | 312 +++++++++++++++++---------------------------- 1 file changed, 115 insertions(+), 197 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 8f8dfe9f3a..3cff82620f 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,234 +1,164 @@ --- title: ニュース sidebar: false -newsHeader: NumPy 2.0 released! -date: 2024-06-17 +newsHeader: "NumPy 2.0 released!" +date: 2023-09-16 --- -### NumPy 1.19.2 リリース +### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Numpy 1.25. -- 多くの新しい非推奨(Deprecation)の追加 +- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -### NumPy 1.26.0 がリリースされました。 -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be -released on June 16, 2024. This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +### NumPy 2.0 リリース日: 6月16日 + +_ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### NumFOCUSの年末の資金調達 +_2023年12月19日_ -- NumFOCUSは、年末キャンペーンでPyCharmチームと協力し、PyCharmライセンスの初回購入に30%の割引を提供しています。 2023年12月23日までのPyCharm購入による1年目の収益は全てNumFOCUSのプログラムに直接寄付されます。 -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +購入される方はこちらのURLか: https://lp.jetbrains.com/support-data-science/ こちらのクーポンコードを利用してください: ISUPPORTDATASCIENCE  ### NumPy 1.26.0 がリリースされました。 -_2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 The highlights of the release are: +_2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 -- Python 3.12.0 のサポート -- Cython 3.0.0 との互換性 -- Mesonビルドシステムの利用 -- SIMD サポートの改善 -- f2py のバグ修正, meson と bind(x) のサポート -- 更新された BLAS/LAPACK の高速化ライブラリのサポート +* Python 3.12.0 のサポート +* Cython 3.0.0 への互換性 +* Mesonビルドシステムの利用 +* SIMD サポートの改善 +* f2py のバグ修正, meson と bind(x) のサポート +* 更新された BLAS/LAPACK の高速化ライブラリのサポート -Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0のサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。 -A total of 20 people contributed to this release and 59 pull requests were -merged. +Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0へのサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。 このリリースでサポートされている Python のバージョンは3.9から 3.12 です。 ### numpy.orgが日本語とポルトガル語で利用可能になりました -_2023年4月2日_ -- numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： This wouldn’t be possible without our dedicated volunteers: +_2023年4月2日_ -- numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： _ポルトガル語_ - -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) _日本語：_ - -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) 翻訳インフラストラクチャに関するプロジェクトは、CZIからの資金援助でサポートされています。 -Looking ahead, we’d love to translate the website into more languages. -今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(#translation チャンネルを探してください) (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な[参加者の興味](https://numfocus.typeform.com/to/WBWVJSqe)フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。 umd. +今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. (#translation チャンネルを探してください) ### NumPy 1.25.0 リリース -_2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 The highlights of the release are: +_2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 -- MUSLのサポート。MUSLのWheelが準備されました。 -- 富士通のC/C++コンパイラサポート -- einsum でオブジェクト配列がサポートされるようになりました. -- 行列の置き換え(inplace)掛け算のサポート (`@=`). +* MUSLのサポート。MUSLのWheelが準備されました。 +* 富士通のC/C++コンパイラサポート +* einsum でオブジェクト配列がサポートされるようになりました. +* 行列の置き換え(inplace)掛け算のサポート (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, and clarify the -documentation. There has also been preparatory work for the future NumPy 2.0.0, -resulting in a large number of new and expired deprecations. +Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。 合計148人がこのリリースに貢献し、530個のプルリクエストが マージされました。 -この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。 +このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。 ### インクルーシブな文化の育成: 参加の募集 _2023年5月10日_ -- インクルーシブ・カルチャーの育成: 参加募集 -How can we be better when it comes to diversity and inclusion? -Read the report and find out how to get involved -[here](https://contributor-experience.org/docs/posts/dei-report/). +NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの [レポート](https://contributor-experience.org/docs/posts/dei-report/) を読んで参加する方法を確認してください。 ### NumPy ドキュメンテーションチームのリーダーの変更 -_2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。 We thank Melissa for all her -contributions to the NumPy official documentation and educational materials, -and Mukulika and Ross for stepping up. +_2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。 ### NumPy 1.24.0 リリース -_2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 The highlights of the release are: +_2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 -- スタッキング関数のための新しい"dtype"と"casting"キーワードの追加 -- F2PYの新機能追加とバグ修正 -- Many new deprecations, check them out. -- Many expired deprecations, +* スタッキング関数のための新しい"dtype"と"casting"キーワードの追加 +* F2PYの新機能追加とバグ修正 +* 多くの新しい非推奨(Deprecation)の追加 +* 多くの期限切れの非推奨(Deprecation)の削除 -The NumPy 1.24.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase execution speed, and clarify the documentation. -There are a large number of new and expired deprecations due to changes in -dtype promotion and cleanups. It is the work of 177 contributors spread over -444 pull requests. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。 +Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。 ### Numpy 1.23.0 リリース -_2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 The highlights of the release are: +_2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 -- `loadtxt` がCで実装されたことによる、大幅なパフォーマンス向上 -- より簡単なデータ交換のためのPythonレベルでのDLPackの公開 -- 構造化されたdtypesのプロモーションと比較方法の変更 -- f2pyの改善 +* `loadtxt` がCで実装されたことによる、大幅なパフォーマンス向上 +* より簡単なデータ交換のためのPythonレベルでのDLPackの公開 +* 構造化されたdtypesのプロモーションと比較方法の変更 +* f2pyの改善 -リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。 It is the work of 151 contributors spread over -494 pull requests. このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。 -edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。 +Numpy 1.23. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。 ### NumFOCUS DEI研究への参加募集 -_2022年4月13日_ -- NumPyは、[NumFOCUS](http://numfocus.org/)と協力して、[ある研究プロジェクト](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)を進めており、これは[Gordon & Betty Moore Foundation](https://www.moore.org/)によって資金提供されています。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。 The research team would like to talk to new contributors, project -developers and maintainers, and those who have contributed in the past about -their experiences joining and contributing to NumPy. +_2022年4月13日_ -- NumPyは、[NumFOCUS](http://numfocus.org/)と協力して、[ある研究プロジェクト](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)を進めており、これは[Gordon & Betty Moore Foundation](https://www.moore.org/)によって資金提供されています。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。 **あなたの経験を共有することに興味がありますか?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) -which contains additional information on the research goals, privacy, and -confidentiality considerations. Your participation will be valuable to the -growth and sustainability of diverse and inclusive open-source software -communities. Accepted participants will participate in a 30-minute interview -with a research team member. - -### Numpy 1.25. - -_2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 The highlights of the release are: - -- Type annotations of the main namespace are essentially complete. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. This is probably the most user visible enhancement in this - release. -- 以前から提案されていた [array API 標準](https://data-apis.org/array-api/latest/) のベータ版が提供されています ( [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。 - This is a step in creating a standard collection of functions that can be - used across libraries such as CuPy and JAX. -- NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。 DLPack provides a common interchange format - for array (tensor) data. -- `quantile`, `percentile`, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。 The new - methods provide a complete set of the methods commonly found in the - literature. -- ユニバーサル関数は、[NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。 - This also unlocks the ability to experiment with the future DType API. -- ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。 - -NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。 +もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な[参加者の興味](https://numfocus.typeform.com/to/WBWVJSqe)フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。 + +### NumPy 1.19.2 リリース + +_2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 + +* メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。 +* 以前から提案されていた [array API 標準](https://data-apis.org/array-api/latest/) のベータ版が提供されています ( [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。 +* NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。 +* `quantile`, `percentile`, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。 +* ユニバーサル関数は、[NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。 +* ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。 + +NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 ### 科学的なPythonエコシステムにおける包括的な文化の前進 _ 2021年8月31日_ -- この度、Chan Zuckerberg Initiativeより、科学的なPythonプロジェクトにおいて、歴史的に疎外されてきたグループの人々のオンボーディング、インクルージョン、リテンションを支援し、NumPy、SciPy、Matplotlib、Pandasのコミュニティダイナミクスを構造的に改善するための [ 助成金を授与されました ](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) ことをお知らせします。 -[ CZIのEssential Open Source Software for Scienceプログラム ](https://chanzuckerberg.com/eoss/)の一環として、この[ Diversity & Inclusion補助金 ](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。 This project will be led by Melissa Mendonça (NumPy), with -additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), -Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and -Joris Van den Bossche (Pandas). - -This is an ambitious project aiming to discover and implement activities that -should structurally improve the community dynamics of our projects. By -establishing these new cross-project roles, we hope to introduce a new -collaboration model to the Scientific Python communities, allowing -community-building work within the ecosystem to be done more efficiently and -with greater outcomes. We also expect to develop a clearer picture of what -works and what doesn't in our projects to engage and retain new contributors, -especially from historically underrepresented groups. Finally, we plan on -producing detailed reports on the actions executed, explaining how they have -impacted our projects in terms of representation and interaction with our -communities. - -2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! -このプロジェクトの提案書に関しては、[こちら](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) から全文を読むことができます. +[ CZIのEssential Open Source Software for Scienceプログラム ](https://chanzuckerberg.com/eoss/)の一環として、この[ Diversity & Inclusion補助金 ](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。 + +このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。 + +2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! このプロジェクトの提案書に関しては、[こちら](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) から全文を読むことができます. ### 2021年度NumPyアンケート -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 -NumPy users from 75 countries participated in our inaugural survey last year. -The survey findings gave us a very good understanding of what we should focus -on for the next 12 months. +_2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 -It’s time for another survey, and we are counting on you once again. It will -take about 15 minutes of your time. Besides English, the survey questionnaire -is available in 8 additional languages: Bangla, French, Hindi, Japanese, -Mandarin, Portuguese, Russian, and Spanish. +今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。 こちらのリンク先から、アンケートを始めることができます: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSL4q. -### Numpy 1.18.0 リリース -_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 The highlights of the release are: +### NumPy 1.19.0 リリース + +_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 - より多くの機能やプラットフォームをカバーするためのSIMD関連の改善が実施されました。 - dtypeのための新しいインフラとキャストの準備 @@ -237,104 +167,92 @@ _2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0 - アノテーションの改善 - 乱数生成用の新しい `PCG64DXSM` ビット生成機 -This NumPy release is the result of 581 merged pull requests contributed by 175 -people. 今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。 +今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。 + ### 2020年度 NumPy アンケート結果 -_2021年6月22日_ -- NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/ 今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。 +_2021年6月22日_ -- NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/ -### NumPy 1.20.0 リリース -_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。 This is the largest NumPy release to date, thanks to 180+ -contributors. The two most exciting new features are: +### NumPy 1.18.0 リリース +_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。 - NumPyの大部分のコードに型注釈が追加されました。 そして新しいサブモジュールである`numpy.typing`が追加されました。 このサブモジュールは`ArrayLike` や`DtypeLike`という型注釈のエイリアスが定義されており、これによりユーザーやダウンストリームのライブラリはこの型注釈を使うことができます。 -- X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). This yielded significant - performance improvements for many functions (examples: - [sin/cos](https://github.com/numpy/numpy/pull/17587), - [einsum](https://github.com/numpy/numpy/pull/18194)). +- X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### NumPyプロジェクトの多様性 _2020年9月20日に_ 、私たちは[ NumPyプロジェクトにおけるダイバーシティやインクルージョンの状況や、ソーシャルメディア上での議論についての宣言 ](/diversity_sep2020)について書きました。 + ### Natureに初の公式NumPy論文が掲載されました! -提案されたイニシアチブとその成果の詳細については、 [フルグラントプロポーザル](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。 This comes 14 years after the release of NumPy 1.0. -The paper covers applications and fundamental concepts of array programming, -the rich scientific Python ecosystem built on top of NumPy, and the recently added -array protocols to facilitate interoperability with external array and tensor -libraries like CuPy, Dask, and JAX. +_2020年9月16日_ -- NumPyに関する [ 最初の公式の論文 ](https://www.nature.com/articles/s41586-020-2649-2)がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。 -### Python 3.9のリリースに伴い、いつNumPyのバイナリwheelがリリースされるのですか？ -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) がリリースされました。 この 1.19 シリーズの最新リリースでは、いくつかのバグが修正され、[ 来るべき Cython 3.xリリース ](http:/docs.cython.orgenlatestsrcchanges.html)への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。 _2020年7月2日_ -- このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語. _2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。 In preparation for this event, please make sure to +### Python 3.9のリリースに伴い、いつNumPyのバイナリwheelがリリースされるのですか？ +_2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。 - `pip` が`manylinux2010` と `manylinux2014` をサポートするためにpipを少なくともバージョン 20.1 に更新する。 - [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) または `--only-binary=:all:` を`pip`がソースからビルドしようとするのを防ぐために使用します。 + ### NumPy 1.19.2 リリース -_2020年9月16日_ -- NumPyに関する [ 最初の公式の論文 ](https://www.nature.com/articles/s41586-020-2649-2)がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。 -メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。 -The aarch64 wheels are built with the latest manylinux2014 release that fixes -the problem of differing page sizes used by different linux distros. +_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) がリリースされました。 この 1.19 シリーズの最新リリースでは、いくつかのバグが修正され、[ 来るべき Cython 3.xリリース ](http:/docs.cython.orgenlatestsrcchanges.html)への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。 ### 初めてのNumPyの調査が公開されました!! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for -decision-making about the development of NumPy as software and as a community. -The survey is available in 8 additional languages besides English: -Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_2020年7月2日_ -- このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語. + +NumPy をより良くするために、こちらの [アンケート](https://umdsurvey. umd. edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。 -NumPy をより良くするために、こちらの [アンケート](https://umdsurvey. ### NumPy に新しいロゴができました! _2020年6月24日_ -- NumPyのロゴが新しくなりました: - +NumPyのロゴ -The logo is a modern take on the old one, with a cleaner design. 新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。 +新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。 -### NumPy 1.19.0 リリース -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. 多くの期限切れの非推奨(Deprecation)の削除 _2019年12月22日_ -- NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、`numpy.random`のための新しいC-APIの追加などが行われました。 _2020年6月20日_ -- NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、"クリーンアップ用のリリース" です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。 +### NumPy 1.20.0 リリース + +_2020年6月20日_ -- NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、"クリーンアップ用のリリース" です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。 + ### ドキュメント受諾期間 -_2020年5月11日_ -- NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの [レポート](https://contributor-experience.org/docs/posts/dei-report/) を読んで参加する方法を確認してください。 詳細については、 [シーズンオブドキュメント公式サイト](https://developers.google.com/season-of-docs/) と [アイデアページ](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) をご覧ください。 +_2020年5月11日_ -- NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! 詳細については、 [シーズンオブドキュメント公式サイト](https://developers.google.com/season-of-docs/) と [アイデアページ](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) をご覧ください。 -### NumPy 1.18.0 リリース -2023-09-16 After the major changes in -1.17.0, this is a consolidation release. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。 Highlights of the release includes the addition of basic -infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +### Numpy 1.18.0 リリース + +_2019年12月22日_ -- NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、`numpy.random`のための新しいC-APIの追加などが行われました。 詳細については、 [リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.0) を参照してください。 + ### NumPyはChan Zuckerberg財団から助成金を受けました。 _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つであるOpenBLASが、Chan Zuckerberg財団の[Essential Open Source Software for Scienceプログラム](https:/chanzuckerberg.comeoss)を通じて、科学に不可欠なオープンソースツールのソフトウェアのメンテナンス、成長、開発、コミュニティへの参加などを支援する195,000ドルの共同助成金を獲得したことを発表しました。 -This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。 + +提案されたイニシアチブとその成果の詳細については、 [フルグラントプロポーザル](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。 -More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. ## 過去のリリース -Here is a list of NumPy releases, with links to release notes. こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. -- NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. -- _2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 +- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. @@ -353,7 +271,7 @@ Here is a list of NumPy releases, with links to release notes. こちらは、 - NumPy 1.23.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022年7月8日_. - NumPy 1.23.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022年6月22日_. - NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022年5月20日_. -- Numpy 1.23. +- NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. - NumPy 1.22.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2022年3月7日_. - NumPy 1.22.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022年2月3日_. - NumPy 1.22.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022年1月14日_. From fc65e095a177a24da2b4cac9ca9860349f63057f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:54 +0200 Subject: [PATCH 177/586] New translations news.md (Korean) --- content/ko/news.md | 605 +++++++++++++++++---------------------------- 1 file changed, 226 insertions(+), 379 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 76402114b5..447e02eaf7 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,444 +1,291 @@ --- -title: News +title: 소식 sidebar: false -newsHeader: NumPy 2.0 released! -date: 2024-06-17 +newsHeader: "NumPy 2.0 released!" +date: 2023-09-16 --- -### NumPy 2.0.0 released +### NumPy 2.0 출시일: 6월 16일 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) +- 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. -### NumPy 2.0 release date: June 16 +### NumPy 2.0 출시일: 6월 16일 -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be -released on June 16, 2024. This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### NumPy 1.26.0 출시 +_2023년 12월 19_ -- NumFOCUS에서 연말 캠페인 기간 동안 PyCharm과 협력해 최초 PyCharm 이용자의 라이선스를 30% 할인된 가격에 제공했습니다. 지금부터 2023년 12월 23일까지 PyCharm 구매로 발생한 모든 수익은 NumFOCUS 프로그램으로 직접 전달됩니다. + +구매를 추적할 수 있는 고유 URL을 이용하거나: https://lp.jetbrains.com/support-data-science/ 쿠폰 코드를 사용하세요: ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 출시 + +_2023년 12월 16일_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* 파이썬 3.12.0 지원 +* Cython 3.0.0 호환 +* Meson 빌드 시스템 사용 +* 업데이트된 SIMD 지원 +* f2py 수정, meson 및 bind(x) 지원 +* 업데이트된 Accelerate BLAS/LAPACK 라이브러리 지원 + +NumPy 1.26.0 릴리스는 Meson 빌드 시스템으로의 전환과 Cython 3.0.0 지원을 표시하는 1.25.x 시리즈의 연장입니다. 총 20명의 사람들이 이 릴리스에 기여하였으며 59개의 풀 리퀘스트가 병합되었습니다. + +본 릴리즈에서 지원하는 Python 버전은 3.3.9-3.12입니다. + +### numpy.org은 이제 일본어와 포르투갈어로도 이용 가능합니다. + +_2023년 8월 2일_ - numpy.org은 이제 추가로 일본어와 포르투갈어로 이용 가능합니다. 이는 다음의 헌신적인 자원봉사자들의 노력 없이는 가능하지 않았을 것입니다: + +_포르투갈어_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_일본어_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +번역 인프라에 대한 작업은 CZI로부터의 자금 지원을 받아 진행되었습니다. + +나아가서 NumPy 웹사이트가 더 많은 언어로 번역되기를 바랍니다. 도움을 주시려면 다음 Slack 링크를 통해 NumPy Translations Team 에 연락을 주십시오: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translations 채널을 add 해주세요) 또한 과학적 파이썬 생태계 전반에서 문서 및 교육 콘텐츠를 지역화하는데 참여할 Translations Team을 구축하고 있습니다. 이에 흥미를 느낀다면 Scientific Python Discord에서 함께해 주세요: https://discord.gg/khWtqY6RKr. (#translation 채널을 찾아보세요) + +### NumPy 1.25.0 출시 + +_2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html)을 이용할 수 있습니다. 주요 기능들은 다음과 같습니다: + +* MUSL 지원, 이제 MUSL Wheel도 배포됩니다. +* Fujitsu C/C++ 컴파일러 지원 +* Einsum에서 객체 배열 지원 +* Inplace 행렬 곱셈 (`@=`) 지원 + +NumPy 1.25.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. 미래의 NumPy 2.0.0을 위한 준비 작업도 있었는데, 이로 인해 수많은 기능들이 지원 종료 예정에 새로 포함되거나 완전히 만료되었습니다. + +총 148명의 사람들이 이 릴리스에 기여하였으며 530개의 풀 리퀘스트가 병합되었습니다. + +본 릴리즈에서 지원하는 Python 버전은 3.9-3.11입니다. + +### 포용적인 문화 조성: 참여 요청 + +_2023년 5월 10일_ -- 포용적인 문화 조성: 참여 요청 + +다양성과 포용성의 측면에서 우리는 어떻게 더 나아질 수 있을까요? [여기](https://contributor-experience.org/docs/posts/dei-report/)에서 보고서를 읽고 함께 참여하는 방법을 알아보세요. + +### NumPy 문서 팀 리더 변경 + +_2023년 1월 6일_ –- Mukulika Pahari, Ross Barnowski가 Melissa Mendonça를 대신해 새 NumPy 문서 팀 리더로 임명되었습니다. NumPy 공식 문서와 교육 자료에 기여한 Melissa와 한 걸음 더 나아간 Mukulika, Ross에게 감사를 표합니다. + +### NumPy 1.24.0 출시 + +_2022년 12월 18일_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* 스태킹 함수를 위한 새 "dtype" 및 "casting" 키워드. +* 새 F2PY 기능 및 수정. +* 수많은 지원 종료 예정 기능, 확인하세요. +* 수많은 만료된 기능, + +NumPy 1.24.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. dtype의 형변환 및 정리를 변경하는 과정에서 수많은 기능들이 지원 종료 예정에 새로 포함되거나 완전히 만료되었습니다. 177명의 기여자가 생성한 444개의 풀 요청을 바탕으로 한 성과입니다. 지원하는 Python 버전은 3.8-3.11입니다. + +### NumPy 1.23.0 출시 + +_2022년 6월 22일_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* `loadtxt`를 C로 구현하여 성능이 크게 향상되었습니다. +* 데이터 교환을 쉽게 하기 위해 Python 수준에서 DLPack을 개방합니다. +* 구조화된 dtype의 형변환 및 비교 방법을 변경했습니다. +* f2py를 개선했습니다. + +NumPy 1.23.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업, 오래된 지원 종료 예정 기능을 완전히 만료시키는 작업을 계속하고 있습니다. 151명의 기여자가 생성한 494개의 풀 요청을 바탕으로 한 성과입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. Python 3.11은 rc 단계에 다다르면 지원할 예정입니다. + +### NumFOCUS DEI 연구: 참여 요청 + +_2022년 4월 13일_ -- NumPy는 [NumFOCUS](http://numfocus.org/)와 협력하여 [Gordon & Betty Moore 재단](https://www.moore.org/)에서 기금을 제공하는 [연구 프로젝트](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)를 진행합니다. 본 연구는 오픈 소스 소프트웨어 커뮤니티에 기여자, 특히 역사적으로 과소평가된 집단의 기여자가 참여할 때 직면하는 장벽을 이해하는 것을 목표로 합니다. 연구팀은 새 기여자, 프로젝트 개발자 및 유지관리자, 과거에 기여한 사람들과 NumPy에 참여하고 기여한 경험에 대해 이야기하고자 합니다. + +**경험을 공유하고 싶으신가요?** + +간단한 ["참여 희망" 양식](https://numfocus.typeform.com/to/WBWVJSqe)을 작성해주세요. 양식에서 연구 목표, 개인정보 보호, 기밀 유지 사항에 대한 추가 정보를 확인할 수 있습니다. 당신의 참여가 다양성과 포용성을 갖춘 오픈 소스 소프트웨어 커뮤니티의 성장과 지속 가능성에 도움이 될 것입니다. 승인된 참가자는 연구팀과 30분 면담을 진행하게 됩니다. + +### Numpy 1.22.0 출시 + +_2021년 12월 31일_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* 기본 네임스페이스에 대해 유형 주석의 지원을 거의 완료했습니다. 업스트림 코드는 항상 변하므로 추가 개선이 있을 수 있지만 주요 작업은 완료되었습니다. 아마도 이 릴리스에서 가장 체감되는 개선 사항일 것입니다. +* 제안된 [배열 API 표준의 예비 버전](https://data-apis.org/array-api/latest/)이 제공됩니다([NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) 참조). 이는 CuPy 및 JAX와 같은 라이브러리에서 사용할 수 있는 표준 함수 모음을 만드는 단계입니다. +* NumPy가 DLPack 백엔드로 구동됩니다. DLPack은 배열(텐서) 데이터에 대한 공통 교환 형식을 제공합니다. +* `quantile`, `percentile` 관련 함수를 위한 새 메서드를 추가했습니다. 새 메서드를 이용해 문헌에서 일반적으로 쓰이는 처리를 진행할 수 있습니다. +* 범용 함수가 대부분의 [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html)을 구현하도록 리팩터링되었습니다. 이를 통해 미래의 DType API를 실험할 수 있는 능력도 갖췄습니다. +* 새 구성 가능한 메모리 할당자를 다운스트림 프로젝트에서 사용할 수 있습니다. + +NumPy 1.22.0은 153명의 기여자가 생성한 609개의 풀 요청을 바탕으로 만들어진 대형 릴리즈입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. + +### 과학 Python 생태계에서 포용적 문화 발전 + +_2021년 8월 31일_ -- Chan Zuckerberg Initiative가 과학적 Python 프로젝트에서 역사적으로 소외된 그룹의 사람들을 온보딩, 포함 및 유지하고 NumPy, SciPy, Matplotlib 그리고 Pandas 의 커뮤니티 역학을 구조적으로 개선하기 위한 [보조금을 수여](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/)했음을 발표하게 되어 기쁩니다. + +[CZI의 Essential Open Source Software for Science 프로그램](https://chanzuckerberg.com/eoss/)의 일환으로 이 [Diversity & 포함 추가 보조금](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)은 포괄적인 오픈 소스 커뮤니티를 육성하기 위한 관행을 식별, 문서화 및 구현하기 위한 전담 기여자 경험 리드 직책 생성을 지원합니다. 이 프로젝트는 Melissa Mendonça(NumPy) 님이 이끌고 Ralf Gommers(NumPy, SciPy), Hannah Aizenman, Thomas Caswell(Matplotlib), Matt Haberland(SciPy), Joris Van den Bossche(Pandas) 님이 추가 멘토링 및 지침을 제공합니다. + +이것은 프로젝트의 커뮤니티 역학을 구조적으로 개선해야 하는 활동을 발견하고 구현하는 것을 목표로 하는 야심 찬 프로젝트입니다. 새로운 교차 프로젝트 역할을 설정함으로써 과학적 Python 커뮤니티에 새로운 협업 모델을 도입하여 생태계 내에서 커뮤니티 구축 작업을 보다 효율적으로 수행하고 더 큰 결과를 얻을 수 있을 것으로 기대됩니다. 또한 특히 역사적으로 과소대표된 집단의 새로운 기여자를 참여시키고 유지하기 위해, 프로젝트에서 효과적인 것과 그렇지 않은 것에 대한 명확한 그림을 구축할 것으로 기대합니다. 마지막으로, 시행된 조치에 대해 자세한 보고서를 작성하여 커뮤니티와의 대표 및 상호 작용 측면에서 프로젝트에 어떤 영향을 미쳤는지 설명할 계획입니다. + +2개년 프로젝트가 2021년 11월부터 시작될 예정입니다. 프로젝트의 결과를 볼 날이 기대되네요! [여기에서 전체 제안서를 열람할 수 있습니다](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021년도 NumPy 설문조사 + +_2021년 7월 12일_ -- NumPy에서, 우리는 커뮤니티의 힘을 믿습니다. 작년에 75개국에서 1,236명의 NumPy 사용자가 첫 번째 설문조사에 참여했습니다. 설문 조사 결과를 통해 다음 12개월 동안 우리가 어떤 것에 집중해야 할지 아주 잘 이해할 수 있었습니다. + +이제 또다른 설문 조사를 진행할 시간이고, 여러분의 도움이 다시 한 번 필요합니다. 완료하는 데 약 15분 정도 소요될 겁니다. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. + +시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 출시 -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +_2021년 9월 23일_ -- [NumPy 1.1.21](https://numpy.org/doc/stable/release/1.21.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: -### NumPy 1.26.0 released +- 더 많은 기능과 플랫폼을 다루는 지속적인 SIMD 작업, +- 새로운 dtype 인프라 및 캐스팅에 대한 초기 작업, +- Mac의 Python 3.8 및 Python 3.9용 universal2 휠, +- 문서화 향상, +- 주석 향상, +- 난수 생성에 이용되는 새 `PCG64DXSM` 비트 생성기. -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) -is now available. The highlights of the release are: +이번 NumPy 릴리즈는 175명이 기여해주신 581개의 풀 리퀘스트가 합쳐진 결과입니다. 본 릴리즈에서 지원하는 Python 버전은 3.7-3.9입니다. Python 3.10은 Python 3.10 릴리즈 이후 지원할 예정입니다. -- Python 3.12.0 support. -- Cython 3.0.0 compatibility. -- Use of the Meson build system -- Updated SIMD support -- f2py fixes, meson and bind(x) support -- Support for the updated Accelerate BLAS/LAPACK library -The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the -transition to the Meson build system and provision of support for Cython 3.0.0. -A total of 20 people contributed to this release and 59 pull requests were -merged. +### 2020년도 NumPy 설문조사 결과 -The Python versions supported by this release are 3.9-3.12. +_2021년 6월 22일_ -- 2020년에, NumPy 조사 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 여기서 조사 결과를 확인하세요: https://numpy.org/user-survey-2020/. -### numpy.org is now available in Japanese and Portuguese -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: -Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +### Numpy 1.20.0 출시 -_Portuguese:_ +_2021년 9월 30일_ -- [NumPy 1.1.20](https://numpy.org/doc/stable/release/1.20.0-notes.html)이 출시되었습니다. 역대 최대의 NumPy 릴리즈입니다. 180명이 넘는 기여자분들께 감사드립니다. 다음은 이번 출시에서 가장 흥미로운 두가지 기능들 입니다. +- NumPy의 많은 부분에 대한 유형 주석 및 사용자와 다운스트림 라이브러리가 추가할 때 사용할 수 있는 `ArrayLike` 및 `DtypeLike` 별칭을 포함하는 새로운 `numpy.typing` 하위 모듈 자체 코드에 주석을 입력합니다. +- x86(SSE, AVX), ARM64(Neon) 및 PowerPC(VSX) 명령을 지원하는 다중 플랫폼 SIMD 컴파일러 최적화 입니다. 이는 많은 함수들의 상당한 성능향상을 가져왔습니다 (예: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) +### NumPy 프로젝트 내 다양성 -_Japanese:_ +_2020년 9월 20일_ -- 우리는 [NumPy 프로젝트 안에서의 다양성과 포용성에 관한 소셜 미디어의 상태 및 토론에 대한 성명서를 작성했습니다](/diversity_sep2020). -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) -The work on the translation infrastructure is supported with funding from CZI. +### Nature에 첫 공식 NumPy 논문 발표! -Looking ahead, we’d love to translate the website into more languages. -If you’d like to help, please connect with the NumPy Translations Team on Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(Look for the #translations channel.) We are also building a Translations Team who will be -working on localizing documentation and educational content across the Scientific Python -ecosystem. If this piqued your interest, join us on the Scientific Python -Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +_2020년 9월 16일_ -- [NumPy에 대한 첫 번째 공식 논문](https://www.nature.com/articles/s41586-020-2649-2)이 Nature에 리뷰 기사로 게재되었음을 발표하게 되어 기쁩니다. NumPy 1.0이 나온 지 14년 만입니다. 이 백서에서는 배열 프로그래밍의 응용 프로그램 및 기본 개념, NumPy 위에 구축된 풍부한 과학적 Python 생태계, CuPy, Dask 및 JAX와 같은 외부 배열 및 텐서 라이브러리와의 상호 운용성을 촉진하기 위해 최근에 추가된 배열 프로토콜을 다룹니다. -### NumPy 1.25.0 released -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) -is now available. The highlights of the release are: +### Python 3.9가 곧 출시하는데, NumPy는 바이너리 Wheel을 언제 출시합니까? -- Support for MUSL, there are now MUSL wheels. -- Support for the Fujitsu C/C++ compiler. -- Object arrays are now supported in einsum. -- Support for the inplace matrix multiplication (`@=`). - -The NumPy 1.25.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, and clarify the -documentation. There has also been preparatory work for the future NumPy 2.0.0, -resulting in a large number of new and expired deprecations. +_2020년 9월 14일_ -- Python 3.9가 몇 주 내로 출시될 것입니다. 만약 Python 얼리어답터라면, NumPy (그리고 SciPy 등 다른 바이너리 패키지) 가 릴리즈 시일에 바이너리 Wheel을 준비하지 못한다는 것을 알고 실망했을 수 있습니다. 새로운 Python 버전에 빌드 환경을 맞추는 것은 많은 노력을 요하고, 패키지가 PyPI 및 conda-forge에 배포되는 데에는 일반적으로 몇 주가 걸립니다. 출시를 대비하려면 아래 요건을 충족하도록 하십시오. +- `pip` 버전을 최소 20.1로 업데이트하여 `manylinux2010` 및 `manylinux2014`를 지원하도록 합니다 +- [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary)를 사용하거나 또는 `--only-binary=:all:`을 사용하여 `pip`가 소스에서 빌드하는 것을 막아주세요. -A total of 148 people contributed to this release and 530 pull requests were -merged. -The Python versions supported by this release are 3.9-3.11. +### NumPy 1.19.2 출시 -### Fostering an Inclusive Culture: Call for Participation - -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html)이 출시되었습니다. 1.19 시리즈의 이 최신 릴리스는 몇 가지 버그를 수정하고 [다가오는 Cython 3.x 릴리스](http://docs.cython.org/en/latest/src/changes.html)를 준비하며 setuptools를 고정하여 업스트림 수정이 진행되는 동안 distutils가 계속 작동하도록 합니다. aarch64 휠은 다양한 Linux 배포판에서 사용되는 다양한 페이지 크기 문제를 해결하는 최신 manylinux2014 릴리스로 제작되었습니다. -How can we be better when it comes to diversity and inclusion? -Read the report and find out how to get involved -[here](https://contributor-experience.org/docs/posts/dei-report/). - -### NumPy documentation team leadership transition - -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy -documentation team leads replacing Melissa Mendonça. We thank Melissa for all her -contributions to the NumPy official documentation and educational materials, -and Mukulika and Ross for stepping up. - -### NumPy 1.24.0 released - -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) -is now available. The highlights of the release are: - -- New "dtype" and "casting" keywords for stacking functions. -- New F2PY features and fixes. -- Many new deprecations, check them out. -- Many expired deprecations, - -The NumPy 1.24.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase execution speed, and clarify the documentation. -There are a large number of new and expired deprecations due to changes in -dtype promotion and cleanups. It is the work of 177 contributors spread over -444 pull requests. The supported Python versions are 3.8-3.11. - -### Numpy 1.23.0 released - -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) -is now available. The highlights of the release are: - -- Implementation of `loadtxt` in C, greatly improving its performance. -- Exposure of DLPack at the Python level for easy data exchange. -- Changes to the promotion and comparisons of structured dtypes. -- Improvements to f2py. - -The NumPy 1.23.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, clarify the documentation, -and expire old deprecations. It is the work of 151 contributors spread over -494 pull requests. The Python versions supported by this release 3.8-3.10. -Python 3.11 will be supported when it reaches the rc stage. - -### NumFOCUS DEI research study: call for participation - -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a -[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) -funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to -understand the barriers to participation that contributors, particularly those -from historically underrepresented groups, face in the open-source software -community. The research team would like to talk to new contributors, project -developers and maintainers, and those who have contributed in the past about -their experiences joining and contributing to NumPy. - -**Interested in sharing your experiences?** - -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) -which contains additional information on the research goals, privacy, and -confidentiality considerations. Your participation will be valuable to the -growth and sustainability of diverse and inclusive open-source software -communities. Accepted participants will participate in a 30-minute interview -with a research team member. - -### Numpy 1.22.0 release - -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) -is now available. The highlights of the release are: - -- Type annotations of the main namespace are essentially complete. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. This is probably the most user visible enhancement in this - release. -- A preliminary version of the proposed - [array API Standard](https://data-apis.org/array-api/latest/) is provided - (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). - This is a step in creating a standard collection of functions that can be - used across libraries such as CuPy and JAX. -- NumPy now has a DLPack backend. DLPack provides a common interchange format - for array (tensor) data. -- New methods for `quantile`, `percentile`, and related functions. The new - methods provide a complete set of the methods commonly found in the - literature. -- The universal functions have been refactored to implement most of - [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). - This also unlocks the ability to experiment with the future DType API. -- A new configurable memory allocator for use by downstream projects. - -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread -over 609 pull requests. The Python versions supported by this release are -3.8-3.10. - -### Advancing an inclusive culture in the scientific Python ecosystem - -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has -[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) -to support the onboarding, inclusion, and retention of people from historically -marginalized groups on scientific Python projects, and to structurally improve -the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. - -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), -this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) -will support the creation of dedicated Contributor Experience Lead positions to -identify, document, and implement practices to foster inclusive open-source -communities. This project will be led by Melissa Mendonça (NumPy), with -additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), -Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and -Joris Van den Bossche (Pandas). - -This is an ambitious project aiming to discover and implement activities that -should structurally improve the community dynamics of our projects. By -establishing these new cross-project roles, we hope to introduce a new -collaboration model to the Scientific Python communities, allowing -community-building work within the ecosystem to be done more efficiently and -with greater outcomes. We also expect to develop a clearer picture of what -works and what doesn't in our projects to engage and retain new contributors, -especially from historically underrepresented groups. Finally, we plan on -producing detailed reports on the actions executed, explaining how they have -impacted our projects in terms of representation and interaction with our -communities. - -The two-year project is expected to start by November 2021, and we are excited -to see the results from this work! -[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). - -### 2021 NumPy survey - -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 -NumPy users from 75 countries participated in our inaugural survey last year. -The survey findings gave us a very good understanding of what we should focus -on for the next 12 months. - -It’s time for another survey, and we are counting on you once again. It will -take about 15 minutes of your time. Besides English, the survey questionnaire -is available in 8 additional languages: Bangla, French, Hindi, Japanese, -Mandarin, Portuguese, Russian, and Spanish. - -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. - -### Numpy 1.21.0 release - -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) -is now available. The highlights of the release are: - -- continued SIMD work covering more functions and platforms, -- initial work on the new dtype infrastructure and casting, -- universal2 wheels for Python 3.8 and Python 3.9 on Mac, -- improved documentation, -- improved annotations, -- new `PCG64DXSM` bitgenerator for random numbers. - -This NumPy release is the result of 581 merged pull requests contributed by 175 -people. The Python versions supported for this release are 3.7-3.9, support -for Python 3.10 will be added after Python 3.10 is released. - -### 2020 NumPy survey results - -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students -and faculty from the University of Michigan and the University of Maryland -conducted the first official NumPy community survey. Find the survey results -here: https://numpy.org/user-survey-2020/. - -### Numpy 1.20.0 release - -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) -is now available. This is the largest NumPy release to date, thanks to 180+ -contributors. The two most exciting new features are: - -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule - containing `ArrayLike` and `DtypeLike` aliases that users and downstream - libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, - AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant - performance improvements for many functions (examples: - [sin/cos](https://github.com/numpy/numpy/pull/17587), - [einsum](https://github.com/numpy/numpy/pull/18194)). - -### Diversity in the NumPy project - -_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). - -### First official NumPy paper published in Nature! - -_Sep 16, 2020_ -- We are pleased to announce the publication of -[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) -as a review article in Nature. This comes 14 years after the release of NumPy 1.0. -The paper covers applications and fundamental concepts of array programming, -the rich scientific Python ecosystem built on top of NumPy, and the recently added -array protocols to facilitate interoperability with external array and tensor -libraries like CuPy, Dask, and JAX. +### 최초의 NumPy 설문조사가 진행 중입니다! -### Python 3.9 is coming, when will NumPy release binary wheels? +_2020년 7월 2일_ -- 본 설문조사는 소프트웨어 및 커뮤니티로서의 NumPy 개발에 대하여, 의사결정의 우선 순위를 안내하고 설정하기 위해 실시됩니다. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an -early adopter of Python versions, you may be dissapointed to find that NumPy -(and other binary packages like SciPy) will not have binary wheels ready on the -day of the release. It is a major effort to adapt the build infrastructure to a -new Python version and it typically takes a few weeks for the packages to appear -on PyPI and conda-forge. In preparation for this event, please make sure to +NumPy를 개선하게 도와주시고 이를위해 설문조사에 참여해 주세요. [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -- update your `pip` to version 20.1 at least to support `manylinux2010` and - `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from - trying to build from source. -### Numpy 1.19.2 release +### NumPy에 새로운 로고가 생겼습니다! -_Sep 10, 2020_ -- NumPy -1.19.2 is now available. -This latest release in the 1.19 series fixes several bugs, prepares for the -upcoming Cython 3.x -release and pins -setuptools to keep distutils working while upstream modifications are ongoing. -The aarch64 wheels are built with the latest manylinux2014 release that fixes -the problem of differing page sizes used by different linux distros. +_2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. -### The inaugural NumPy survey is live! +NumPy 로고 -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for -decision-making about the development of NumPy as software and as a community. -The survey is available in 8 additional languages besides English: -Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +이전 로고를 깔끔하고 현대적으로 다시 디자인했습니다. 새 로고를 만들어 주신 Isabela Presedo-Floyd님께 감사드립니다. 또 15년이 넘는 기간 동안 저희가 사용했던 로고를 만들어 주신 Travis Vaught님께도 감사의 말씀을 드립니다. -Please help us make NumPy better and take the survey -[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy has a new logo! +### NumPy 1.19.0 출시 -_Jun 24, 2020_ -- NumPy now has a new logo: +_2020년 6월 20일_ -- NumPy 1.19.0이 출시되었습니다. Python 2의 지원을 중단한 첫 릴리즈라서 "정리 릴리즈"라고도 불립니다. 이제 지원하는 Python 최소 버전은 3.6입니다. 중요한 새 기능을 꼽자면, NumPy 1.17.0에 도입된 난수 생성 인프라를 Cython에서 접근할 수 있게 되었다는 것입니다. - -The logo is a modern take on the old one, with a cleaner design. Thanks to -Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught -for the old logo that served us well for 15+ years. +### Season of Docs 승인 -### NumPy 1.19.0 release +_2020년 5월 11일_ -- NumPy가 Google Season of Docs 프로그램의 선도 조직으로 승인되었습니다. 테크니컬 라이터와 협력해서 NumPy 문서를 다시 한 번 개선할 수 있는 기회를 갖게 되어 좋습니다! 이상 자세한 내용은 [공식 문서 시즌 사이트](https://developers.google.com/season-of-docs/) 및 [아이디어 페이지](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) 를 참조하세요. -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release -without Python 2 support, hence it was a "clean-up release". The minimum -supported Python version is now Python 3.6. An important new feature is that -the random number generation infrastructure that was introduced in NumPy 1.17.0 -is now accessible from Cython. -### Season of Docs acceptance +### NumPy 1.18.0 출시 -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for -the Google Season of Docs program. We are excited about the opportunity to -work with a technical writer to improve NumPy's documentation once again! For more -details, please see -[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our -[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_2019년 12월 22일_ -- NumPy 1.18.0이 출시되었습니다. 1.17.0에서의 주요 변경점을 통합하는 릴리즈입니다. 본 릴리즈는 Python 3.5를 지원하는 마지막 마이너 릴리즈입니다. 릴리즈의 주요 내용으로는, 64비트 BLAS 및 LAPACK 라이브러리와 연결하기 위한 환경 조성, `numpy.random`을 위한 새로운 C-API 등이 있습니다. -### NumPy 1.18.0 release +자세한 내용은 [출시 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)를 참조하세요. -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in -1.17.0, this is a consolidation release. It is the last minor release that will -support Python 3.5. Highlights of the release includes the addition of basic -infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받았습니다 -### NumPy receives a grant from the Chan Zuckerberg Initiative +_2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 OpenBLAS가 챈 저커버그 이니셔티브의 [과학 프로그램용 중요 오픈소스 소프트웨어](https://chanzuckerberg.com/eoss/) 지원을 통해 19만 5천 달러에 달하는 공동 보조금을 받았다는 소식을 전할 수 있어 기쁩니다. 이곳에서는 과학에 중요한 오픈소스 도구에 대해 유지 관리, 성장, 개발 및 커뮤니티 참여를 지원합니다. -_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. +이 보조금은 NumPy 문서, 웹사이트 재설계 및 커뮤니티 개발을 개선하여 빠르게 성장하는 대규모 사용자 기반에 더 나은 서비스를 제공하고 프로젝트의 장기적인 지속 가능성을 보장하는 데 사용될 것입니다. OpenBLAS 팀은 OpenBLAS가 의존하는 ReLAPACK(Recursive LAPACK) 의 알고리즘 개선뿐만 아니라 특히 스레드 안전성, AVX-512 및 스레드 로컬 스토리지(TLS) 문제와 같은 일련의 핵심 기술 문제를 해결하는 데 집중할 것입니다. -This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +제안된 계획 및 결과물에 대한 자세한 내용은 [전체 보조금 제안](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167)에서 확인할 수 있습니다. 2019년 12월 1일부터 작업을 시작하여 다음 12개월 동안 진행할 예정입니다. -More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. -## Releases +## 릴리즈 -Here is a list of NumPy releases, with links to release notes. Bugfix -releases (only the `z` changes in the `x.y.z` version number) have no new -features; minor releases (the `y` increases) do. +NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. -- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. -- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. -- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. -- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. -- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. -- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. -- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. -- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. -- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. -- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. -- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. -- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. -- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. -- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. -- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. -- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. -- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. -- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. -- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. -- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. -- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. -- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. -- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. -- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. -- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. -- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. -- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. -- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. -- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. -- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. -- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. -- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. -- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. +- NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. +- NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2024년 1월 2일_. +- NumPy 1.26.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023년 1월 2일_. +- NumPy 1.26.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023년 10월 14일_. +- NumPy 1.26.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023년 16월 9일_. +- NumPy 1.25.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. +- NumPy 1.25.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _2023년 7월 8일_. +- NumPy 1.24.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _2023년 6월 26일_. +- NumPy 1.25.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _2023년 6월 17일_. +- NumPy 1.24.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _2023년 4월 22일_. +- NumPy 1.24.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _2023년 2월 5일_. +- NumPy 1.24.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _2022년 12월 26일_. +- NumPy 1.24.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _2022년 12월 18일_. +- NumPy 1.23.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _2022년 11월 19일_. +- NumPy 1.23.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _2022년 10월 12일_. +- NumPy 1.23.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _2022년 9월 9일_. +- NumPy 1.23.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _2022년 8월 14일_. +- NumPy 1.23.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022년 7월 8일_. +- NumPy 1.23.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022년 6월 22일_. +- NumPy 1.22.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022년 5월 20일_. +- NumPy 1.21.6 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022년 4월 12일_. +- NumPy 1.22.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _2022년 3월 7일_. +- NumPy 1.22.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022년 2월 3일_. +- NumPy 1.22.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022년 1월 14일_. +- NumPy 1.22.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _2021년 12월 31일_. +- NumPy 1.21.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _2021년 12월 19일_. +- NumPy 1.21.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021년 6월 22일_. +- NumPy 1.20.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021년 5월 10일_. +- NumPy 1.20.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021년 1월 30일_. +- NumPy 1.19.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021년 1월 5일_. +- NumPy 1.19.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020년 6월 20일_. +- NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. +- NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. +- NumPy 1.18.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019년 12월 22일_. +- NumPy 1.17.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019년 7월 26일_. +- NumPy 1.16.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019년 1월 14일_. +- NumPy 1.15.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018년 7월 23일_. +- NumPy 1.14.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018년 1월 7일_. From e9b6fce5d6a8b8c0be0a285c54b2d4203660bb67 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:55 +0200 Subject: [PATCH 178/586] New translations news.md (Russian) --- content/ru/news.md | 357 +++++++++++++-------------------------------- 1 file changed, 102 insertions(+), 255 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 76402114b5..706b609976 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,120 +1,86 @@ --- title: News sidebar: false -newsHeader: NumPy 2.0 released! +newsHeader: "NumPy 2.0 released!" date: 2024-06-17 --- ### NumPy 2.0.0 released -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be -released on June 16, 2024. This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) -is now available. The highlights of the release are: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: -- Python 3.12.0 support. -- Cython 3.0.0 compatibility. -- Use of the Meson build system -- Updated SIMD support -- f2py fixes, meson and bind(x) support -- Support for the updated Accelerate BLAS/LAPACK library +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library -The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the -transition to the Meson build system and provision of support for Cython 3.0.0. -A total of 20 people contributed to this release and 59 pull requests were -merged. +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: -Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ - -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) _Japanese:_ - -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. -Looking ahead, we’d love to translate the website into more languages. -If you’d like to help, please connect with the NumPy Translations Team on Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(Look for the #translations channel.) We are also building a Translations Team who will be -working on localizing documentation and educational content across the Scientific Python -ecosystem. If this piqued your interest, join us on the Scientific Python -Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) -is now available. The highlights of the release are: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -- Support for MUSL, there are now MUSL wheels. -- Support for the Fujitsu C/C++ compiler. -- Object arrays are now supported in einsum. -- Support for the inplace matrix multiplication (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, and clarify the -documentation. There has also been preparatory work for the future NumPy 2.0.0, -resulting in a large number of new and expired deprecations. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -A total of 148 people contributed to this release and 530 pull requests were -merged. +A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. @@ -122,148 +88,77 @@ The Python versions supported by this release are 3.9-3.11. _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -How can we be better when it comes to diversity and inclusion? -Read the report and find out how to get involved -[here](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy -documentation team leads replacing Melissa Mendonça. We thank Melissa for all her -contributions to the NumPy official documentation and educational materials, -and Mukulika and Ross for stepping up. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) -is now available. The highlights of the release are: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -- New "dtype" and "casting" keywords for stacking functions. -- New F2PY features and fixes. -- Many new deprecations, check them out. -- Many expired deprecations, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase execution speed, and clarify the documentation. -There are a large number of new and expired deprecations due to changes in -dtype promotion and cleanups. It is the work of 177 contributors spread over -444 pull requests. The supported Python versions are 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) -is now available. The highlights of the release are: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -- Implementation of `loadtxt` in C, greatly improving its performance. -- Exposure of DLPack at the Python level for easy data exchange. -- Changes to the promotion and comparisons of structured dtypes. -- Improvements to f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, clarify the documentation, -and expire old deprecations. It is the work of 151 contributors spread over -494 pull requests. The Python versions supported by this release 3.8-3.10. -Python 3.11 will be supported when it reaches the rc stage. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a -[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) -funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to -understand the barriers to participation that contributors, particularly those -from historically underrepresented groups, face in the open-source software -community. The research team would like to talk to new contributors, project -developers and maintainers, and those who have contributed in the past about -their experiences joining and contributing to NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) -which contains additional information on the research goals, privacy, and -confidentiality considerations. Your participation will be valuable to the -growth and sustainability of diverse and inclusive open-source software -communities. Accepted participants will participate in a 30-minute interview -with a research team member. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) -is now available. The highlights of the release are: - -- Type annotations of the main namespace are essentially complete. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. This is probably the most user visible enhancement in this - release. -- A preliminary version of the proposed - [array API Standard](https://data-apis.org/array-api/latest/) is provided - (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). - This is a step in creating a standard collection of functions that can be - used across libraries such as CuPy and JAX. -- NumPy now has a DLPack backend. DLPack provides a common interchange format - for array (tensor) data. -- New methods for `quantile`, `percentile`, and related functions. The new - methods provide a complete set of the methods commonly found in the - literature. -- The universal functions have been refactored to implement most of - [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). - This also unlocks the ability to experiment with the future DType API. -- A new configurable memory allocator for use by downstream projects. - -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread -over 609 pull requests. The Python versions supported by this release are -3.8-3.10. +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has -[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) -to support the onboarding, inclusion, and retention of people from historically -marginalized groups on scientific Python projects, and to structurally improve -the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. - -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), -this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) -will support the creation of dedicated Contributor Experience Lead positions to -identify, document, and implement practices to foster inclusive open-source -communities. This project will be led by Melissa Mendonça (NumPy), with -additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), -Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and -Joris Van den Bossche (Pandas). - -This is an ambitious project aiming to discover and implement activities that -should structurally improve the community dynamics of our projects. By -establishing these new cross-project roles, we hope to introduce a new -collaboration model to the Scientific Python communities, allowing -community-building work within the ecosystem to be done more efficiently and -with greater outcomes. We also expect to develop a clearer picture of what -works and what doesn't in our projects to engage and retain new contributors, -especially from historically underrepresented groups. Finally, we plan on -producing detailed reports on the actions executed, explaining how they have -impacted our projects in terms of representation and interaction with our -communities. - -The two-year project is expected to start by November 2021, and we are excited -to see the results from this work! -[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 -NumPy users from 75 countries participated in our inaugural survey last year. -The survey findings gave us a very good understanding of what we should focus -on for the next 12 months. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -It’s time for another survey, and we are counting on you once again. It will -take about 15 minutes of your time. Besides English, the survey questionnaire -is available in 8 additional languages: Bangla, French, Hindi, Japanese, -Mandarin, Portuguese, Russian, and Spanish. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + ### Numpy 1.21.0 release -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) -is now available. The highlights of the release are: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, @@ -272,121 +167,74 @@ is now available. The highlights of the release are: - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. -This NumPy release is the result of 581 merged pull requests contributed by 175 -people. The Python versions supported for this release are 3.7-3.9, support -for Python 3.10 will be added after Python 3.10 is released. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + ### 2020 NumPy survey results -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students -and faculty from the University of Michigan and the University of Maryland -conducted the first official NumPy community survey. Find the survey results -here: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) -is now available. This is the largest NumPy release to date, thanks to 180+ -contributors. The two most exciting new features are: +### Numpy 1.20.0 release -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule - containing `ArrayLike` and `DtypeLike` aliases that users and downstream - libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, - AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant - performance improvements for many functions (examples: - [sin/cos](https://github.com/numpy/numpy/pull/17587), - [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + ### First official NumPy paper published in Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of -[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) -as a review article in Nature. This comes 14 years after the release of NumPy 1.0. -The paper covers applications and fundamental concepts of array programming, -the rich scientific Python ecosystem built on top of NumPy, and the recently added -array protocols to facilitate interoperability with external array and tensor -libraries like CuPy, Dask, and JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + ### Python 3.9 is coming, when will NumPy release binary wheels? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an -early adopter of Python versions, you may be dissapointed to find that NumPy -(and other binary packages like SciPy) will not have binary wheels ready on the -day of the release. It is a major effort to adapt the build infrastructure to a -new Python version and it typically takes a few weeks for the packages to appear -on PyPI and conda-forge. In preparation for this event, please make sure to +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -- update your `pip` to version 20.1 at least to support `manylinux2010` and - `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from - trying to build from source. ### Numpy 1.19.2 release -_Sep 10, 2020_ -- NumPy -1.19.2 is now available. -This latest release in the 1.19 series fixes several bugs, prepares for the -upcoming Cython 3.x -release and pins -setuptools to keep distutils working while upstream modifications are ongoing. -The aarch64 wheels are built with the latest manylinux2014 release that fixes -the problem of differing page sizes used by different linux distros. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for -decision-making about the development of NumPy as software and as a community. -The survey is available in 8 additional languages besides English: -Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -Please help us make NumPy better and take the survey -[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: - +NumPy logo + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -The logo is a modern take on the old one, with a cleaner design. Thanks to -Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught -for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release -without Python 2 support, hence it was a "clean-up release". The minimum -supported Python version is now Python 3.6. An important new feature is that -the random number generation infrastructure that was introduced in NumPy 1.17.0 -is now accessible from Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + ### Season of Docs acceptance -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for -the Google Season of Docs program. We are excited about the opportunity to -work with a technical writer to improve NumPy's documentation once again! For more -details, please see -[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our -[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + ### NumPy 1.18.0 release -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in -1.17.0, this is a consolidation release. It is the last minor release that will -support Python 3.5. Highlights of the release includes the addition of basic -infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. @@ -395,13 +243,12 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + ## Releases -Here is a list of NumPy releases, with links to release notes. Bugfix -releases (only the `z` changes in the `x.y.z` version number) have no new -features; minor releases (the `y` increases) do. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From aeab212aaf5ecb2ef4516d0103ac10248e8567bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:57 +0200 Subject: [PATCH 179/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 357 +++++++++++++-------------------------------- 1 file changed, 102 insertions(+), 255 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 76402114b5..706b609976 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,120 +1,86 @@ --- title: News sidebar: false -newsHeader: NumPy 2.0 released! +newsHeader: "NumPy 2.0 released!" date: 2024-06-17 --- ### NumPy 2.0.0 released -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be -released on June 16, 2024. This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) -is now available. The highlights of the release are: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: -- Python 3.12.0 support. -- Cython 3.0.0 compatibility. -- Use of the Meson build system -- Updated SIMD support -- f2py fixes, meson and bind(x) support -- Support for the updated Accelerate BLAS/LAPACK library +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library -The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the -transition to the Meson build system and provision of support for Cython 3.0.0. -A total of 20 people contributed to this release and 59 pull requests were -merged. +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: -Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ - -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) _Japanese:_ - -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. -Looking ahead, we’d love to translate the website into more languages. -If you’d like to help, please connect with the NumPy Translations Team on Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(Look for the #translations channel.) We are also building a Translations Team who will be -working on localizing documentation and educational content across the Scientific Python -ecosystem. If this piqued your interest, join us on the Scientific Python -Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) -is now available. The highlights of the release are: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -- Support for MUSL, there are now MUSL wheels. -- Support for the Fujitsu C/C++ compiler. -- Object arrays are now supported in einsum. -- Support for the inplace matrix multiplication (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, and clarify the -documentation. There has also been preparatory work for the future NumPy 2.0.0, -resulting in a large number of new and expired deprecations. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -A total of 148 people contributed to this release and 530 pull requests were -merged. +A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. @@ -122,148 +88,77 @@ The Python versions supported by this release are 3.9-3.11. _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -How can we be better when it comes to diversity and inclusion? -Read the report and find out how to get involved -[here](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy -documentation team leads replacing Melissa Mendonça. We thank Melissa for all her -contributions to the NumPy official documentation and educational materials, -and Mukulika and Ross for stepping up. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) -is now available. The highlights of the release are: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -- New "dtype" and "casting" keywords for stacking functions. -- New F2PY features and fixes. -- Many new deprecations, check them out. -- Many expired deprecations, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase execution speed, and clarify the documentation. -There are a large number of new and expired deprecations due to changes in -dtype promotion and cleanups. It is the work of 177 contributors spread over -444 pull requests. The supported Python versions are 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) -is now available. The highlights of the release are: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -- Implementation of `loadtxt` in C, greatly improving its performance. -- Exposure of DLPack at the Python level for easy data exchange. -- Changes to the promotion and comparisons of structured dtypes. -- Improvements to f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and -promotion of dtypes, increase the execution speed, clarify the documentation, -and expire old deprecations. It is the work of 151 contributors spread over -494 pull requests. The Python versions supported by this release 3.8-3.10. -Python 3.11 will be supported when it reaches the rc stage. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a -[research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) -funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to -understand the barriers to participation that contributors, particularly those -from historically underrepresented groups, face in the open-source software -community. The research team would like to talk to new contributors, project -developers and maintainers, and those who have contributed in the past about -their experiences joining and contributing to NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) -which contains additional information on the research goals, privacy, and -confidentiality considerations. Your participation will be valuable to the -growth and sustainability of diverse and inclusive open-source software -communities. Accepted participants will participate in a 30-minute interview -with a research team member. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) -is now available. The highlights of the release are: - -- Type annotations of the main namespace are essentially complete. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. This is probably the most user visible enhancement in this - release. -- A preliminary version of the proposed - [array API Standard](https://data-apis.org/array-api/latest/) is provided - (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). - This is a step in creating a standard collection of functions that can be - used across libraries such as CuPy and JAX. -- NumPy now has a DLPack backend. DLPack provides a common interchange format - for array (tensor) data. -- New methods for `quantile`, `percentile`, and related functions. The new - methods provide a complete set of the methods commonly found in the - literature. -- The universal functions have been refactored to implement most of - [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). - This also unlocks the ability to experiment with the future DType API. -- A new configurable memory allocator for use by downstream projects. - -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread -over 609 pull requests. The Python versions supported by this release are -3.8-3.10. +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has -[awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) -to support the onboarding, inclusion, and retention of people from historically -marginalized groups on scientific Python projects, and to structurally improve -the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. - -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), -this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) -will support the creation of dedicated Contributor Experience Lead positions to -identify, document, and implement practices to foster inclusive open-source -communities. This project will be led by Melissa Mendonça (NumPy), with -additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), -Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and -Joris Van den Bossche (Pandas). - -This is an ambitious project aiming to discover and implement activities that -should structurally improve the community dynamics of our projects. By -establishing these new cross-project roles, we hope to introduce a new -collaboration model to the Scientific Python communities, allowing -community-building work within the ecosystem to be done more efficiently and -with greater outcomes. We also expect to develop a clearer picture of what -works and what doesn't in our projects to engage and retain new contributors, -especially from historically underrepresented groups. Finally, we plan on -producing detailed reports on the actions executed, explaining how they have -impacted our projects in terms of representation and interaction with our -communities. - -The two-year project is expected to start by November 2021, and we are excited -to see the results from this work! -[You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 -NumPy users from 75 countries participated in our inaugural survey last year. -The survey findings gave us a very good understanding of what we should focus -on for the next 12 months. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -It’s time for another survey, and we are counting on you once again. It will -take about 15 minutes of your time. Besides English, the survey questionnaire -is available in 8 additional languages: Bangla, French, Hindi, Japanese, -Mandarin, Portuguese, Russian, and Spanish. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + ### Numpy 1.21.0 release -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) -is now available. The highlights of the release are: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, @@ -272,121 +167,74 @@ is now available. The highlights of the release are: - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. -This NumPy release is the result of 581 merged pull requests contributed by 175 -people. The Python versions supported for this release are 3.7-3.9, support -for Python 3.10 will be added after Python 3.10 is released. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + ### 2020 NumPy survey results -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students -and faculty from the University of Michigan and the University of Maryland -conducted the first official NumPy community survey. Find the survey results -here: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) -is now available. This is the largest NumPy release to date, thanks to 180+ -contributors. The two most exciting new features are: +### Numpy 1.20.0 release -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule - containing `ArrayLike` and `DtypeLike` aliases that users and downstream - libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, - AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant - performance improvements for many functions (examples: - [sin/cos](https://github.com/numpy/numpy/pull/17587), - [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + ### First official NumPy paper published in Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of -[the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) -as a review article in Nature. This comes 14 years after the release of NumPy 1.0. -The paper covers applications and fundamental concepts of array programming, -the rich scientific Python ecosystem built on top of NumPy, and the recently added -array protocols to facilitate interoperability with external array and tensor -libraries like CuPy, Dask, and JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + ### Python 3.9 is coming, when will NumPy release binary wheels? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an -early adopter of Python versions, you may be dissapointed to find that NumPy -(and other binary packages like SciPy) will not have binary wheels ready on the -day of the release. It is a major effort to adapt the build infrastructure to a -new Python version and it typically takes a few weeks for the packages to appear -on PyPI and conda-forge. In preparation for this event, please make sure to +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -- update your `pip` to version 20.1 at least to support `manylinux2010` and - `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from - trying to build from source. ### Numpy 1.19.2 release -_Sep 10, 2020_ -- NumPy -1.19.2 is now available. -This latest release in the 1.19 series fixes several bugs, prepares for the -upcoming Cython 3.x -release and pins -setuptools to keep distutils working while upstream modifications are ongoing. -The aarch64 wheels are built with the latest manylinux2014 release that fixes -the problem of differing page sizes used by different linux distros. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for -decision-making about the development of NumPy as software and as a community. -The survey is available in 8 additional languages besides English: -Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -Please help us make NumPy better and take the survey -[here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: - +NumPy logo + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -The logo is a modern take on the old one, with a cleaner design. Thanks to -Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught -for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release -without Python 2 support, hence it was a "clean-up release". The minimum -supported Python version is now Python 3.6. An important new feature is that -the random number generation infrastructure that was introduced in NumPy 1.17.0 -is now accessible from Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + ### Season of Docs acceptance -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for -the Google Season of Docs program. We are excited about the opportunity to -work with a technical writer to improve NumPy's documentation once again! For more -details, please see -[the official Season of Docs site](https://developers.google.com/season-of-docs/) and our -[ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + ### NumPy 1.18.0 release -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in -1.17.0, this is a consolidation release. It is the last minor release that will -support Python 3.5. Highlights of the release includes the addition of basic -infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. @@ -395,13 +243,12 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + ## Releases -Here is a list of NumPy releases, with links to release notes. Bugfix -releases (only the `z` changes in the `x.y.z` version number) have no new -features; minor releases (the `y` increases) do. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From 67a49e62d50c098fdf758e7eac2aaaa9a475c6ff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 09:28:58 +0200 Subject: [PATCH 180/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 181 +++++++++++++++++++-------------------------- 1 file changed, 76 insertions(+), 105 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 541fe29b81..d3513d18a6 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,63 +1,47 @@ --- title: Notícias sidebar: false -newsHeader: NumPy 2.0 released! -date: 2024-06-17 +newsHeader: "NumPy 2.0 released!" +date: 2023-09-16 --- ### NumPy 2.0.0 released -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the -result of 11 months of development since the last feature release and is the -work of 212 contributors spread over 1078 pull requests. It contains a large -number of exciting new features as well as changes to both the Python and C -APIs. It includes breaking changes that could not happen in a regular minor -release - including an ABI break, changes to type promotion rules, and API -changes which may not have been emitting deprecation warnings in 1.26.x. Key -documents related to how to adapt to changes in NumPy 2.0 include: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) -tells a bit of the story about how this release came together. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + ### NumPy 2.0 release date: June 16 -2023-09-16 This release has been over a year in the making, and -is the first major release since 2006. Importantly, in addition to many new -features and performance improvement, it contains **breaking changes** to the -ABI as well as the Python and C APIs. It is likely that downstream packages and -end user code needs to be adapted - if you can, please verify whether your code -works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount -on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now -until December 23rd, 2023 will go directly to the NumFOCUS programs. +### Lançado o NumPy versão 1.26.0 +_19 de dez, 2023_ -- O NumFOCUS se juntou ao PyCharm durante sua campanha de final de ano para oferecer 30% de desconto em licenças de PyCharm para novos usuários. Todas as receitas do primeiro ano das compras do PyCharm a partir de agora até 23 de dezembro, 2023 irão diretamente para os programas NumFOCUS. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ -or a coupon code ISUPPORTDATASCIENCE  +Use a URL única que permitirá rastrear as compras https://lp.jetbrains.com/support-data-science/ ou um código de cupom ISUPPORTDATASCIENCE  -### NumPy versão 1.26.0 +### Lançado o NumPy versão 1.26.0 _16 de setembro de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) está disponível. Os destaques desta versão são: -- Suporte ao Python 3.12.0. -- Compatibilidade com Cython 3.0.0. -- Utilização do sistema Meson para compilação -- Suport a SIMD atualizado -- Melhorias para f2py, suporte a meson e bind(x) -- Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK +* Suport ao Python 3.12.0. +* Compatibilidade com Cython 3.0.0. +* Utilização do sistema Meson para compilação +* Suport a SIMD atualizado +* Melhorias para f2py, suporte a meson e bind(x) +* Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK -A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.0.0. -Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas. +A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.0.0. Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas. As versões do Python suportadas por esta versão são 3.9-3.12. @@ -66,39 +50,33 @@ As versões do Python suportadas por esta versão são 3.9-3.12. _2 de agosto de 2023_ -- numpy.org agora está disponível em 2 idiomas adicionais: japonês e português. Isto não seria possível sem nossos voluntários dedicados: _Português:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) -- Melissa Weber Mendonça (melissawm) -- Ricardo Prins (ricardoprins) -- Getúlio Silva (getuliosilva) -- Julio Batista Silva (jbsilva) -- Alexandre de Siqueira (alexdesiqueira) -- Alexandre B A Villares (villares) -- Vini Salazar (vinisalazar) - -Japonês: +_Japonês:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) -- Atsushi Sakai (AtsushiSakai) -- KKunai -- Tom Kelly (TomKellyGenetics) -- Yuji Kanagawa (kngwyu) -- Tetsuo Koyama (tkoyama010) +O trabalho na infraestrutura de tradução é apoiado com financiamento da CZI. -O trabalho na infraestrutura de traduções é financiado pela CZI. +Futuramente, nós adoraríamos traduzir o site para mais idiomas. Se você quiser ajudar, conecte-se com a Equipe de Traduções NumPy no Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Procure pelo canal de #translations.) Também estamos construindo uma Equipe de Traduções que vai trabalhar na localização da documentação e conteúdo educacional por todo o ecossistema de Python científico. Se isto despertou o seu interesse, junte-se a nós no servidor Discord Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation.) -No futuro, adoraríamos traduzir o site para mais línguas. -Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. -(Look for the #translations channel.) (Procure pelo canal #translations) -Também estamos organizando um time de tradutores que serão responsáveis por trabalhar na localização da documentação e conteúdo educacional para o ecossistema Scientific Python. Se esse trabalho te interessa, junte-se a nós no Discord do projeto Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation) - -### Lançado o NumPy versão 1.26.0 +### Lançado o NumPy 1.25.0 _17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Os destaques desta versão são: -- Suporte para MUSL, agora existem rodas MUSL. -- Suporte para o compilador Fujitsu C/C++. -- Arrays de objetos agora são suportados em einsum. -- Suporte para a multiplicação da matriz inplace (`@=`). +* Suporte para MUSL, agora existem rodas MUSL. +* Suporte para o compilador Fujitsu C/C++. +* Arrays de objetos agora são suportados em einsum. +* Suporte para a multiplicação da matriz inplace (`@=`). A versão 1.25.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Também tem havido trabalho preparatório para a futura versão 2.0.0, resultando em um grande número de depreciações novas e expiradas. @@ -110,8 +88,7 @@ As versões do Python suportadas por esta versão são 3.9-3.11. _10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação -Como podemos ser melhores quando se trata de diversidade e de inclusão? -Leia o relatório e descubra como colaborar [aqui](https://contributor-experience.org/docs/posts/dei-report/). +Como podemos ser melhores quando se trata de diversidade e de inclusão? Leia o relatório e descubra como colaborar [aqui](https://contributor-experience.org/docs/posts/dei-report/). ### Transição de liderança do time de documentação do NumPy @@ -121,29 +98,27 @@ _6 de janeiro de 2023_ –- Mukulika Pahari e Ross Barnowski são nomeados como _18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. Os destaques desta versão são: -- Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. -- Novas funcionalidades e correções do F2PY. -- Muitas depreciações novas, confira. -- Muitas depreciações expiradas. +* Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. +* Novas funcionalidades e correções do F2PY. +* Muitas depreciações novas, confira. +* Muitas depreciações expiradas. -A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. -Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11. +A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11. ### NumPy versão 1.23.0 _22 de junho de 2022_ -- O [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) está disponível. Os destaques desta versão são: -- Implementação de `loadtxt` em C, melhorando muito seu desempenho. -- Exposição do DLPack ao nível de Python para facilitar a troca de dados. -- Mudanças na promoção e comparações de dtypes estruturados. -- Melhorias no f2py. +* Implementação de `loadtxt` em C, melhorando muito seu desempenho. +* Exposição do DLPack ao nível de Python para facilitar a troca de dados. +* Mudanças na promoção e comparações de dtypes estruturados. +* Melhorias no f2py. -A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão 3.8-3.10. -Python 3.11 será suportado quando chegar na etapa rc. +A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão 3.8-3.10. Python 3.11 será suportado quando chegar na etapa rc. ### Pesquisa NumFOCUS DEI: chamada para participação -_13 de abril de 2022_ -- O NumPy está trabalhando com a [NumFOCUS](http://numfocus.org/) em um [projeto de pesquisa](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent\&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado pela [Gordon & Betty Moore Foundation](https://www.moore.org/) para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy. +_13 de abril de 2022_ -- O NumPy está trabalhando com a [NumFOCUS](http://numfocus.org/) em um [projeto de pesquisa](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado pela [Gordon & Betty Moore Foundation](https://www.moore.org/) para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy. **Quer compartilhar suas experiências?** @@ -153,16 +128,12 @@ Por favor, preencha este breve formulário: ["Participant Interest form"](https: _31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) está agora disponível. Os destaques desta versão são: -- Anotações de tipo do namespace principal estão praticamente completas. Upstream is - a moving target, so there will likely be further improvements, but the major - work is done. Esta é provavelmente a melhoria mais visível para os usuários nesta versão. -- Uma versão preliminar da proposta do [array API Standard](https://data-apis.org/array-api/latest/) está disponível (veja [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). - Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX. -- NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores). -- Novos métodos para `quantile`, `percentile`, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura. -- As funções universais foram refatoradas para implementar a maior parte da [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). - Isso também desbloqueia a capacidade de experimentar a futura API DType. -- Um novo alocador de memória configurável para uso pelos projetos downstream. +* Anotações de tipo do namespace principal estão praticamente completas. Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. Esta é provavelmente a melhoria mais visível para os usuários nesta versão. +* Uma versão preliminar da proposta do [array API Standard](https://data-apis.org/array-api/latest/) está disponível (veja [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX. +* NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores). +* Novos métodos para `quantile`, `percentile`, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura. +* As funções universais foram refatoradas para implementar a maior parte da [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Isso também desbloqueia a capacidade de experimentar a futura API DType. +* Um novo alocador de memória configurável para uso pelos projetos downstream. NumPy 1.22.0 é uma versão importante com o trabalho de 153 contribuidores espalhados por mais de 609 pull requests. As versões do Python suportadas por esta versão são 3.8-3.10. @@ -174,18 +145,17 @@ Como parte do programa [CZI's Essential Open Source Software for Science](https: Esse é um projeto ambicioso que visa descobrir e implementar atividades que devem estruturalmente melhorar a dinâmica da comunidade de nossos projetos. Ao criar essas novas funções entre projetos, esperamos introduzir um novo modelo de colaboração às comunidades de Python científico, permitir que o trabalho de construção da comunidade no ecossistema seja feito de forma mais eficiente e com maiores resultados. Também esperamos desenvolver uma imagem mais clara do que funciona e o que não funciona em nossos projetos para engajar e reter novos colaboradores, especialmente de grupos historicamente sub-representados. Finalmente, planejamos produzir relatórios detalhados sobre as ações executadas, explicando como eles afetaram nossos projetos em termos de representação e interação com nossas comunidades. -O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! -[Você pode ler a proposta completa aqui](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! [Você pode ler a proposta completa aqui](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### Pesquisa NumPy 2021 -_12 de julho de 2021_ -- Nós do NumPy acreditamos no poder da nossa comunidade. 1,236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. -Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes. +_12 de julho de 2021_ -- Nós do NumPy acreditamos no poder da nossa comunidade. 1,236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes. Chegou a hora de fazer outra pesquisa e estamos contando com você novamente. Vai levar cerca de 15 minutos do seu tempo. Além de Inglês, o questionário de pesquisa está disponível em 8 idiomas adicionais: Bangla, Francês, Hindi, Japonês, Mandarim, Português, Russo e Espanhol. Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + ### NumPy versão 1.19.0 _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) está disponível. Os destaques desta versão são: @@ -199,14 +169,15 @@ _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1 Esta versão do NumPy é o resultado de 581 pull requests aceitos, a partir das contribuições de 175 pessoas. As versões do Python suportadas por esta versão são 3.7-3.9; o suporte para o Python 3.10 será adicionado após o lançamento do Python 3.10. + ### Resultados da pesquisa NumPy 2020 _22 de junho de 2021_ -- Em 2020, o time de pesquisas NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, realizou a primeira pesquisa oficial sobre a comunidade NumPy. Encontre os resultados da pesquisa aqui: https://numpy.org/user-survey-2020/. + ### NumPy versão 1.20.0 _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) está disponível. Este é o maior lançamento do NumPy até hoje, graças a mais de 180 colaboradores. As duas novidades mais emocionantes são: - - Anotações de tipos para grandes partes do NumPy, e um novo submódulo `numpy.typing` contendo aliases `ArrayLike` e `DtypeLike` que usuários e bibliotecas downstream podem usar quando quiserem adicionar anotações de tipos em seu próprio código. - Otimizações de compilação SIMD multi-plataforma, com suporte para instruções x86 (SSE, AVX), ARM64 (Neon) e PowerPC (VSX). Isso rendeu melhorias significativas de desempenho para muitas funções (exemplos: [sen/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). @@ -214,50 +185,48 @@ _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release _20 de setembro de 2020_ -- Escrevemos uma [declaração sobre o estado da diversidade e inclusão no projeto NumPy e discussões em redes sociais sobre isso.](/diversity_sep2020). + ### Primeiro artigo oficial do NumPy publicado na Nature! -_16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. -O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. +_16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. + ### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se você for quiser usar imediatamente a nova versão do Python, você pode ficar desapontado ao descobrir que o NumPy (e outros pacotes binários como SciPy) não terão wheels no dia do lançamento. É um grande esforço adaptar a infraestrutura de compilação a uma nova versão de Python e normalmente leva algumas semanas para que os pacotes apareçam no PyPI e no conda-forge. Em preparação para este evento, por favor, certifique-se de - - atualizar seu `pip` para a versão 20.1 pelo menos para suportar `manylinux2010` e `manylinux2014` - usar [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) ou `--only-binary=:all:` para impedir `pip` de tentar compilar a partir do código fonte. + ### NumPy versão 1.19.2 -_10 de setembro de 2020_ -- O [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) está disponível. -Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento [do Cython 3](http://docs.cython.org/en/latest/src/changes.html) e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. -As wheels para aarch64 são compiladas com manylinux2014 mais recente que conserta um problema com distribuições linux diferentes. +_10 de setembro de 2020_ -- O [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) está disponível. Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento [do Cython 3](http://docs.cython.org/en/latest/src/changes.html) e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. As wheels para aarch64 são compiladas com manylinux2014 mais recente que conserta um problema com distribuições linux diferentes. ### A primeira pesquisa NumPy está aqui! -_2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. -A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. +_2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + ### O NumPy tem um novo logo! _24 de junho de 2020_ -- NumPy agora tem um novo logo: - +NumPy logo O logotipo é uma versão moderna do antigo, com um design mais limpo. Obrigado à Isabela Presedo-Floyd por projetar o novo logotipo, bem como ao Travis Vaught pelo o logotipo antigo que nos serviu bem durante mais de 15 anos. + ### NumPy versão 1.19.0 _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira versão sem suporte ao Python 2, portanto foi uma "versão de limpeza". A versão mínima de Python suportada agora é Python 3.6. Uma característica nova importante é que a infraestrutura de geração de números aleatórios que foi introduzida na NumPy 1.17.0 agora está acessível a partir do Cython. + ### Aceitação no programa Season of Docs -_11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um _technical writer_ para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um *technical writer* para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + ### NumPy versão 1.18.0 @@ -265,14 +234,16 @@ _22 de dezembro de 2019_ -- O NumPy 1.18.0 está disponível. Após as principai Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. + ### O NumPy recebe financiamento da Chan Zuckerberg Initiative _15 de novembro de 2019_ -- Estamos felizes em anunciar que o NumPy e a OpenBLAS, uma das dependências-chave do NumPy, receberam um auxílio conjunto de $195,000 da Chan Zuckerberg Initiative através do seu programa [Essential Open Source Software for Science](https://chanzuckerberg.com/eoss/) que apoia a manutenção, crescimento, desenvolvimento e envolvimento da comunidade em ferramentas de código aberto fundamentais para a ciência. -Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a _thread-safety_, AVX-512, e _thread-local storage_ (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende. +Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a *thread-safety*, AVX-512, e *thread-local storage* (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende. Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrados na [proposta completa de concessão de auxílio](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). O trabalho está agendado para começar no dia 1 de dezembro de 2019 e continuar pelos próximos 12 meses. + ## Lançamentos From d7f01e74698c24895fc9e239d15974203650ae3c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:48:53 +0200 Subject: [PATCH 181/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 45a10aec3faab837b74a3f6ca29e5c997dc88477 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:48:54 +0200 Subject: [PATCH 182/586] New translations config.yaml (Arabic) --- content/ar/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From dd4be8da69786834c5de0228e14d10cb8f531c4b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:48:56 +0200 Subject: [PATCH 183/586] New translations config.yaml (Korean) --- content/ko/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From a47867e191b1c4f4a5d0a8ca147c90597009c20e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:48:57 +0200 Subject: [PATCH 184/586] New translations config.yaml (Russian) --- content/ru/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From ddb43660f7d1e6a3ba02721091a4f7cc18578208 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:48:58 +0200 Subject: [PATCH 185/586] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From b175ba566b1ccef975ddfe823deddc9ed7cff854 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:51:13 +0200 Subject: [PATCH 186/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 86 +++++++++++++++++++++--------------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 0ee45e8b33..1205ea6f24 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -1,6 +1,6 @@ -languageName: English +languageName: Inglés params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + description: '¿Por qué NumPy? Potentes matrices n-dimensionales. Herramientas de cálculo numérico. Interoperable. Rendimiento. Código abierto.' navbarlogo: image: logo.svg text: NumPy @@ -9,71 +9,71 @@ params: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: El paquete fundamental para la computación científica con Python #Button text - buttontext: "Latest release: NumPy 2.0. View all releases" + buttontext: "Última versión: NumPy 1.25. Ver todas las versiones" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: - title: placeholder + title: marcador intro: - - title: Try NumPy - text: Use the interactive shell to try NumPy in the browser - docslink: Don't forget to check out the docs. + title: Prueba NumPy + text: Utilice el terminal interactivo para probar NumPy en el navegador + docslink: No olvides echarle un ojo a la documentación. casestudies: - title: CASE STUDIES + title: CASOS DE ESTUDIO features: - - title: First Image of a Black Hole - text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + title: Primera imagen de un Agujero Negro + text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Event Horizon Telescope producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png - alttext: First image of a black hole. It is an orange circle in a black background. + alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. url: /case-studies/blackhole-image - - title: Detection of Gravitational Waves - text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + title: Detección de Ondas Gravitacionales + text: En 1916 Albert Einstein predijo las ondas gravitacionales; 100 años después se confirmó su existencia por científicos del LIGO, utilizando NumPy. img: /images/content_images/case_studies/gravitional.png - alttext: Two orbs orbiting each other. They are displacing gravity around them. + alttext: Dos cuerpos orbitándose mutuamente. Estos desplazan la gravedad a su alrededor. url: /case-studies/gw-discov - - title: Sports Analytics - text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + title: Analíticas Deportivas + text: El uso de Analíticas está cambiando al Cricket, al mejorar el rendimiento de los jugadores y equipos, mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. img: /images/content_images/case_studies/sports.jpg - alttext: Cricket ball on green field. + alttext: Bola de Cricket sobre un campo verde. url: /case-studies/cricket-analytics - - title: Pose Estimation using deep learning - text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + title: Estimación de la pose mediante aprendizaje profundo + text: DeepLabCut utiliza NumPy para acelerar estudios científicos que implican la observación del comportamiento animal para una mejor comprensión del control motriz, a través de especies y escalas de tiempo. img: /images/content_images/case_studies/deeplabcut.png - alttext: Cheetah pose analysis + alttext: Análisis de la pose de un Guepardo url: /case-studies/deeplabcut-dnn tabs: - title: ECOSYSTEM + title: ECOSISTEMA section5: false navbar: - - title: Install + title: Instalar url: /install - - title: Documentation + title: Documentación url: https://numpy.org/doc/stable - - title: Learn + title: Aprende url: /learn - - title: Community + title: Comunidad url: /community - - title: About Us + title: Acerca de nosotros url: /about - - title: News + title: Noticias url: /news - - title: Contribute + title: Contribuye url: /contribute footer: logo: logo.svg @@ -93,48 +93,48 @@ params: title: "" links: - - text: Install + text: Instalar link: /install - - text: Documentation + text: Documentación link: https://numpy.org/doc/stable - - text: Learn + text: Aprende link: /learn - - text: Citing Numpy + text: Citando a NumPy link: /citing-numpy - - text: Roadmap + text: Mapa de ruta link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us + text: Acerca de nosotros link: /about - - text: Community + text: Comunidad link: /community - - text: User surveys + text: Encuestas a usuarios link: /user-surveys - - text: Contribute + text: Contribuye link: /contribute - - text: Code of conduct + text: Código de Conducta link: /code-of-conduct column3: links: - - text: Get help + text: Buscar ayuda link: /gethelp - - text: Terms of use + text: Términos de uso link: /terms - - text: Privacy + text: Confidencialidad link: /privacy - - text: Press kit + text: Kit de prensa link: /press-kit From b5da6d4e67a104dd539bf45874af945c23eb018b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:51:15 +0200 Subject: [PATCH 187/586] New translations config.yaml (Japanese) --- content/ja/config.yaml | 52 +++++++++++++++++++++--------------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 6b2d8b59a3..ec90ad7023 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: Pythonによる科学技術計算の基礎パッケージ #Button text - buttontext: "最新リリース: Numpy 1.26. すべてのリリースを表示する" + buttontext: "最新リリース: Numpy 1.25. すべてのリリースを表示する" #Where the main hero button links to buttonlink: "/ja/news/#releases" #Hero image (from static/images/___) @@ -56,25 +56,25 @@ params: navbar: - title: インストール - url: /ja/install + url: /install - title: ドキュメント url: https://numpy.org/doc/stable - - title: 学び方 - url: /ja/learn + title: 学習方法 + url: /learn - title: コミュニティ - url: /ja/community + url: /community - - title: 私達について - url: /ja/about + title: 私たちについて + url: /about - title: ニュース - url: /ja/news + url: /news - - title: NumPyに貢献する - url: /ja/contribute + title: 貢献 + url: /contribute footer: logo: logo.svg socialmediatitle: "" @@ -94,16 +94,16 @@ params: links: - text: インストール - link: /ja/install + link: /install - text: ドキュメント link: https://numpy.org/doc/stable - - text: 学び方 - link: /ja/learn + text: 学習方法 + link: /learn - - text: 引用する - link: /ja/citing-numpy + text: NumPy を引用する + link: /citing-numpy - text: ロードマップ link: https://numpy.org/neps/roadmap.html @@ -111,30 +111,30 @@ params: links: - text: 私達について - link: /ja/about + link: /about - text: コミュニティ - link: /ja/community + link: /community - - text: ユーザーの調査 - link: /ja/user-surveys + text: ユーザ調査 + link: /user-surveys - - text: NumPyに貢献する - link: /ja/contribute + text: 貢献 + link: /contribute - text: 行動規範 - link: /ja/code-of-conduct + link: /code-of-conduct column3: links: - text: サポートを得る方法 - link: /ja/gethelp + link: /gethelp - text: 利用規約 - link: /ja/terms + link: /terms - text: プライバシーポリシー - link: /ja/privacy + link: /privacy - text: プレス用資料 - link: /ja/press-kit + link: /press-kit From c2bea731cdf20213c290f891d8efa9effbc56e35 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jun 2024 12:51:16 +0200 Subject: [PATCH 188/586] New translations config.yaml (Korean) --- content/ko/config.yaml | 130 ++++++++++++++++++++--------------------- 1 file changed, 65 insertions(+), 65 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 0ee45e8b33..9f0b9cfdc3 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -1,80 +1,80 @@ -languageName: English +languageName: 한국어 params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + description: 왜 NumPy인가? 강력한 n차원 배열. 수치 컴퓨팅 도구. 상호운용성. 고성능. 오픈소스. navbarlogo: image: logo.svg text: NumPy - link: / + link: /ko/ hero: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: Python으로 과학적 컴퓨팅을 하기 위한 기초 패키지 #Button text - buttontext: "Latest release: NumPy 2.0. View all releases" + buttontext: "최신 릴리: NumPy 1.26. 모든 릴리즈 보기" #Where the main hero button links to - buttonlink: "/news/#releases" + buttonlink: "/ko/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: - title: placeholder + title: 자리 표시자 intro: - - title: Try NumPy - text: Use the interactive shell to try NumPy in the browser - docslink: Don't forget to check out the docs. + title: NumPy 써 보기 + text: 대화형 셸을 이용해 브라우저에서 NumPy를 사용해보세요 + docslink: 문서도 한 번 열람해보세요. casestudies: - title: CASE STUDIES + title: 사례 연구 features: - - title: First Image of a Black Hole - text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + title: 최초의 블랙홀 사진 + text: NumPy 및 NumPy에 의존하는 SciPy, Matplotlib와 같은 라이브러리가 사건의 지평선 망원경으로 최초의 블랙홀 사진을 생성할 수 있었던 방법 img: /images/content_images/case_studies/blackhole.png - alttext: First image of a black hole. It is an orange circle in a black background. + alttext: 최초의 블랙홀 사진. 검은 배경의 주황색 원입니다. url: /case-studies/blackhole-image - - title: Detection of Gravitational Waves - text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + title: 중력파 검출 + text: 1916년, 알베르트 아인슈타인이 중력파를 예측했습니다. LIGO 과학자들이 NumPy를 이용하여 이것이 존재함을 증명하기 100년 전이었습니다. img: /images/content_images/case_studies/gravitional.png - alttext: Two orbs orbiting each other. They are displacing gravity around them. + alttext: 서로의 궤도를 도는 두 구체. 주위의 중력을 변화시키고 있습니다. url: /case-studies/gw-discov - - title: Sports Analytics - text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + title: 스포츠 분석 + text: 크리켓 분석은 통계적 모델링과 예측 분석을 통해 선수와 팀의 성과를 개선하여 게임을 바꾸고 있습니다. NumPy는 이런 많은 분석을 가능하게 합니다. img: /images/content_images/case_studies/sports.jpg - alttext: Cricket ball on green field. - url: /case-studies/cricket-analytics + alttext: 크리켓 공이 녹지 위에 있습니다. + url: /ko/case-studies/cricket-analytics - - title: Pose Estimation using deep learning - text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + title: 딥러닝을 통한 자세 추정 + text: DeepLabCut은 동물의 행동을 관찰하는 과학 연구의 속도를 개선하기 위해, NumPy를 사용하여 종이나 시간에 따른 운동 제어 방식을 잘 이해할 수 있도록 하였습니다. img: /images/content_images/case_studies/deeplabcut.png - alttext: Cheetah pose analysis - url: /case-studies/deeplabcut-dnn + alttext: 치타 자세 분석 + url: /ko/case-studies/deeplabcut-dnn tabs: - title: ECOSYSTEM + title: 생태계 section5: false navbar: - - title: Install - url: /install + title: 설치 + url: /ko/install - - title: Documentation + title: 문서 url: https://numpy.org/doc/stable - - title: Learn - url: /learn + title: 배움 + url: /ko/learn - - title: Community - url: /community + title: 커뮤니티 + url: /ko/community - - title: About Us - url: /about + title: NumPy 정보 + url: /ko/about - - title: News - url: /news + title: 소식 + url: /ko/news - - title: Contribute - url: /contribute + title: 기여 + url: /ko/contribute footer: logo: logo.svg socialmediatitle: "" @@ -93,48 +93,48 @@ params: title: "" links: - - text: Install - link: /install + text: 설치 + link: /ko/install - - text: Documentation + text: 문서 link: https://numpy.org/doc/stable - - text: Learn - link: /learn + text: 배움 + link: /ko/learn - - text: Citing Numpy - link: /citing-numpy + text: Numpy 인용 + link: /ko/citing-numpy - - text: Roadmap + text: 로드맵 link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us - link: /about + text: 정보 + link: /ko/about - - text: Community - link: /community + text: 커뮤니티 + link: /ko/community - - text: User surveys - link: /user-surveys + text: 사용자 설문조사 + link: /ko/user-surveys - - text: Contribute - link: /contribute + text: 기여 + link: /ko/contribute - - text: Code of conduct - link: /code-of-conduct + text: 이용약관 + link: /ko/code-of-conduct column3: links: - - text: Get help - link: /gethelp + text: 도움받기 + link: /ko/gethelp - - text: Terms of use - link: /terms + text: 이용약관 + link: /ko/terms - - text: Privacy - link: /privacy + text: 개인정보처리방침 + link: /ko/privacy - - text: Press kit - link: /press-kit + text: 홍보 자료 + link: /ko/press-kit From 1f8f184ab55785135ca9be33ce7346362ec4ec60 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:24 +0200 Subject: [PATCH 189/586] New translations _index.md (Japanese) --- content/ja/_index.md | 34 +++++++++++++--------------------- 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/content/ja/_index.md b/content/ja/_index.md index 29646d6db4..be88f9e642 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -6,52 +6,44 @@ title: null [[item]] type = 'card' -title = '強力な多次元配列' +title = 'Powerful N-dimensional arrays' body = ''' -NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ>ンダードです。 -''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. ''' [[item]] type = 'card' -title = '数値計算ツール群' +title = 'Numerical computing tools' body = ''' -NumPyは、様々な数学関数、乱数生成器、線形代数ルーチン、フーリエ変換などを提供しています。 -''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' [[item]] type = 'card' -title = 'オープンソース' +title = 'Open source' body = ''' -NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt)で公開されています。NumPyは活発で、互>いを尊重し、多様性を認め合う[コミュニティ](/ja/community)によって、 [GitHub](https://github.com/numpy/numpy)上でオープンに開発されていま -す. +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). ''' [[item]] type = 'card' -title = '相互運用性' +title = 'Interoperable' body = ''' -NumPyは、幅広いハードウェアとコンピューティング・プラットフォームをサポートしており、分散処理、GPU、疎行列ライブラリにも対 -応しています。 -''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. ''' [[item]] type = 'card' -title = '高パフォーマンス' +title = 'Performant' body = ''' -NumPyの大部分は最適化されたC言語のコードで構成されています。これによりPythonの柔軟性とコンパイルされたコードの高速性の両方 -を享受できます。 -''' Enjoy the flexibility of Python with the speed of compiled code. +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. ''' [[item]] type = 'card' -title = '使いやすさ' +title = 'Easy to use' body = ''' -NumPyの高水準なシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高め>ることができます。 -''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid >}} +{{< /grid>}} From 27b276aff8d2781e6cdec69b5eed880d99069292 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:25 +0200 Subject: [PATCH 190/586] New translations _index.md (Korean) --- content/ko/_index.md | 50 ++++++++++++++++++++++---------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ko/_index.md b/content/ko/_index.md index be88f9e642..0f06c2c787 100644 --- a/content/ko/_index.md +++ b/content/ko/_index.md @@ -4,46 +4,46 @@ title: null {{< grid columns="1 2 2 3" >}} -[[item]] -type = 'card' -title = 'Powerful N-dimensional arrays' +{{< card >}} +title = '강력한 N차원 배열' body = ''' -Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +빠르고 다재다능한 NumPy의 벡터화, 인덱싱, 전송 구성은 오늘날 배열 컴퓨팅의 사실상 표준입니다. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Numerical computing tools' +{{< card >}} +title = '수치적 컴퓨팅 도구' body = ''' -NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +NumPy는 포괄적인 수학 함수, 난수 생성기, 선형 대수 루틴, 푸리에 변환 등을 제공합니다. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Open source' +{{< card >}} +title = '오픈 소스' body = ''' -Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +자유 [BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt)에 따라, NumPy는 흥미에 찼으며, 반응이 빠르고, 다양성이 넘치는 [커뮤니티](/community)에 의하여 [GitHub](https://github.com/numpy/numpy)에서 공개적으로 개발되고 유지됩니다. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Interoperable' +{{< card >}} +title = '상호운용성' body = ''' -NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +NumPy는 광범위한 하드웨어 및 컴퓨팅 플랫폼을 지원합니다. 또 분산형, GPU, 희소 배열 라이브러리와도 잘 작동합니다. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Performant' +{{< card >}} +title = '효율성' body = ''' -The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +NumPy의 핵심은 최적화된 C 코드로 구성되어 있습니다. 컴파일된 코드의 속도와 함께 Python의 유연함을 즐기세요. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Easy to use' +{{< card >}} +title = '쉬운 사용법' body = ''' -NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +NumPy의 고수준 문법은 어떤 배경이나 수준을 가지고 있는 프로그래머든 쉽게 접근하여 생산적인 일을 할 수 있도록 만들어줍니다. ''' +{{< /card >}} -{{< /grid>}} +{{< /grid >}} From baa80b6e57097784ce2413a9b06b52a4ce8f4612 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:28 +0200 Subject: [PATCH 191/586] New translations _index.md (Portuguese, Brazilian) --- content/pt/_index.md | 45 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 23 deletions(-) diff --git a/content/pt/_index.md b/content/pt/_index.md index deb9baacd4..96a58860fc 100644 --- a/content/pt/_index.md +++ b/content/pt/_index.md @@ -4,47 +4,46 @@ title: null {{< grid columns="1 2 2 3" >}} -[[item]] -type = 'card' -title = 'Arrays n-dimensionais poderosas' +{{< card >}} +title = 'Potentes arrays N-dimensionais.' body = ''' -Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays. +Rápido e versátil, os conceitos de vetorização, indexação e broadcasting do NumPy são os padrões de fato da computação de arrays hoje em dia. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Ferramentas de computação numérica' +{{< card >}} +title = 'Ferramentas numéricas de computação' body = ''' -O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. +O NumPy oferece funções matemáticas abrangentes, geradores de números aleatórios, rotinas de álgebra linear, transformações de Fourier e muito mais. ''' +{{< /card >}} -[[item]] -type = 'card' +{{< card >}} title = 'Código aberto' body = ''' -Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt) liberal, o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/pt/community) vibrante, responsiva, e diversa. +Distribuído sob uma licença liberal [BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt), o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/community) vibrante, responsiva e diversa. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Interoperabilidade' +{{< card >}} +title = 'Interoperável' body = ''' -O NumPy suporta um grande número de plataformas de hardware e computação, e pode ser combinado com bibliotecas de computação com arrays esparsas, distribuidas ou em GPUs. +O NumPy suporta uma ampla gama de plataformas de hardware e computação, e se integra bem com bibliotecas distribuídas, de GPU e de arrays esparsos. ''' +{{< /card >}} -[[item]] -type = 'card' -title = 'Alto desempenho' +{{< card >}} +title = 'Eficiente' body = ''' -O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. Enjoy the flexibility of Python with the speed of compiled code. +O núcleo do NumPy é composto por código C bem otimizado. Aproveite a flexibilidade do Python com a velocidade de código compilado. ''' +{{< /card >}} -[[item]] -type = 'card' +{{< card >}} title = 'Fácil de usar' body = ''' -A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. -''' +A sintaxe de alto nível do NumPy o torna acessível e produtivo para programadores de qualquer formação ou nível de experiência. ''' +{{< /card >}} {{< /grid >}} From ab305562b53f148ba291a6e771d9f805bb9d265b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:29 +0200 Subject: [PATCH 192/586] New translations user-surveys.md (Spanish) --- content/es/user-surveys.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/content/es/user-surveys.md b/content/es/user-surveys.md index 529a6c1ea7..ef8467358d 100644 --- a/content/es/user-surveys.md +++ b/content/es/user-surveys.md @@ -1,11 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: ENCUESTAS DE USUARIOS DE NUMPY sidebar: false --- -**2020** -The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). +**2020** El equipo de encuestas de NumPy, en asociación con estudiantes y profesores de la Universidad de Michigan y de la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad de NumPy. Encuentra los resultados de la encuesta [aquí](https://numpy.org/user-survey-2020/). -**2021** The collected data is currently being analyzed. +**2021** Los datos recolectados están siendo analizados actualmente. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +Si tienes alguna pregunta o sugerencia sobre las encuestas pasadas o futuras, por favor abre una propuesta [aquí](https://github.com/numpy/numpy-surveys/issues). From 553f9c01e8d642978701d05fc77cfc9f73870f8c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:30 +0200 Subject: [PATCH 193/586] New translations user-surveys.md (Arabic) --- content/ar/user-surveys.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/content/ar/user-surveys.md b/content/ar/user-surveys.md index 529a6c1ea7..89a2aa0460 100644 --- a/content/ar/user-surveys.md +++ b/content/ar/user-surveys.md @@ -3,9 +3,8 @@ title: NUMPY USER SURVEYS sidebar: false --- -**2020** -The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). +**2020** The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). **2021** The collected data is currently being analyzed. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 6d2558607ed1948ed83ef062daaff20cbeacc364 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:31 +0200 Subject: [PATCH 194/586] New translations user-surveys.md (Japanese) --- content/ja/user-surveys.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/user-surveys.md b/content/ja/user-surveys.md index bc952ab2a9..7be9979c3a 100644 --- a/content/ja/user-surveys.md +++ b/content/ja/user-surveys.md @@ -1,10 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: NumPyユーザアンケート sidebar: false --- -**2020** NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果は[こちら](https://numpy.org/user-survey-2020/)をご覧ください。 Find the survey results [here](https://numpy.org/user-survey-2020/). +**2020** NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果は[こちら](https://numpy.org/user-survey-2020/)をご覧ください。 **2021** 収集された調査データは現在解析中です。 -過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、[こちら](https://github.com/numpy/numpy-surveys/issues)にイシューを作成してください。 +過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、[こちら](https://github.com/numpy/numpy-surveys/issues)にイシューを作成してください。 From 6b3286630714328d5a686c84c1fd591bf70f29ae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:32 +0200 Subject: [PATCH 195/586] New translations user-surveys.md (Korean) --- content/ko/user-surveys.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/content/ko/user-surveys.md b/content/ko/user-surveys.md index 529a6c1ea7..9fec78190d 100644 --- a/content/ko/user-surveys.md +++ b/content/ko/user-surveys.md @@ -1,11 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: NUMPY 사용자 설문조사 sidebar: false --- -**2020** -The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). +**2020년** NumPy 조사 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. [여기](https://numpy.org/user-survey-2020/)서 조사 결과를 확인하세요. -**2021** The collected data is currently being analyzed. +**2021년** 수집한 데이터가 현재 분석 중입니다. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +과거나 미래 설문조사에 대해 질문이나 제안 사항이 있으시면, [여기](https://github.com/numpy/numpy-surveys/issues)서 이슈를 생성하세요. From cb562a318ea2a8043995d7bd16cdb8a46d3d18b3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:33 +0200 Subject: [PATCH 196/586] New translations user-surveys.md (Russian) --- content/ru/user-surveys.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/content/ru/user-surveys.md b/content/ru/user-surveys.md index 529a6c1ea7..89a2aa0460 100644 --- a/content/ru/user-surveys.md +++ b/content/ru/user-surveys.md @@ -3,9 +3,8 @@ title: NUMPY USER SURVEYS sidebar: false --- -**2020** -The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). +**2020** The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). **2021** The collected data is currently being analyzed. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 8f9967669f6c80dbb0386470d979b8cb9b668705 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:34 +0200 Subject: [PATCH 197/586] New translations user-surveys.md (Chinese Simplified) --- content/zh/user-surveys.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/content/zh/user-surveys.md b/content/zh/user-surveys.md index 529a6c1ea7..89a2aa0460 100644 --- a/content/zh/user-surveys.md +++ b/content/zh/user-surveys.md @@ -3,9 +3,8 @@ title: NUMPY USER SURVEYS sidebar: false --- -**2020** -The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). +**2020** The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). **2021** The collected data is currently being analyzed. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From b634b4ad12e61ee8d36c8f00a9da8f0f80c504df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:35 +0200 Subject: [PATCH 198/586] New translations user-surveys.md (Portuguese, Brazilian) --- content/pt/user-surveys.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/user-surveys.md b/content/pt/user-surveys.md index 1c0e4f62af..4f60686926 100644 --- a/content/pt/user-surveys.md +++ b/content/pt/user-surveys.md @@ -7,4 +7,4 @@ sidebar: false **2021** Os dados coletados estão em análise. -Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue [aqui](https://github.com/numpy/numpy-surveys/issues). +Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue [aqui](https://github.com/numpy/numpy-surveys/issues). From 6f91740db271b16d7f417e61caf2a1243b4cd7b7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:36 +0200 Subject: [PATCH 199/586] New translations 404.md (Spanish) --- content/es/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/404.md b/content/es/404.md index 7fe5d79a2c..b38d73b758 100644 --- a/content/es/404.md +++ b/content/es/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +¡Oh, oh! Has llegado a un callejón sin salida. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +Si crees que algo debería estar aquí, puedes [reportar este problema](https://github.com/numpy/numpy.org/issues) en GitHub. From ef1bfe62d2b0c11c82377db1cdaf17e5e12ceab2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:37 +0200 Subject: [PATCH 200/586] New translations 404.md (Arabic) --- content/ar/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/404.md b/content/ar/404.md index 7fe5d79a2c..da192c53c0 100644 --- a/content/ar/404.md +++ b/content/ar/404.md @@ -5,4 +5,4 @@ sidebar: false Oops! You've reached a dead end. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 9f5568bacecaccc6279242d48aa485a8c2ed522c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:38 +0200 Subject: [PATCH 201/586] New translations 404.md (Japanese) --- content/ja/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/404.md b/content/ja/404.md index 8af76ac163..8e4db85255 100644 --- a/content/ja/404.md +++ b/content/ja/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +おっとっと！ 間違った所にアクセスしているようです。 -何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 +何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 From 4ba6ca31d018910b5e7511a3707ee3bd31e68a9e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:39 +0200 Subject: [PATCH 202/586] New translations 404.md (Korean) --- content/ko/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/404.md b/content/ko/404.md index 7fe5d79a2c..41504d0c8a 100644 --- a/content/ko/404.md +++ b/content/ko/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +앗! 잘못된 접근입니다. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +만약 이곳에 어떤 페이지가 있어야 한다면 [Issue 열기](https://github.com/numpy/numpy.org/issues)에서 문제를 제기할 수 있습니다. From c85b1aae9fbf65b835015fb786e35f9b0b3b7b08 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:40 +0200 Subject: [PATCH 203/586] New translations 404.md (Russian) --- content/ru/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/404.md b/content/ru/404.md index 7fe5d79a2c..da192c53c0 100644 --- a/content/ru/404.md +++ b/content/ru/404.md @@ -5,4 +5,4 @@ sidebar: false Oops! You've reached a dead end. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From b0a17b5211623b533e8d128d65753064ece409b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:41 +0200 Subject: [PATCH 204/586] New translations 404.md (Chinese Simplified) --- content/zh/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/404.md b/content/zh/404.md index 7fe5d79a2c..0f27a36ee9 100644 --- a/content/zh/404.md +++ b/content/zh/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +抱歉······ 目标网页并不存在。 -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +如果您认为这个页面应该展示些什么东西，请在 GitHub 上面 [发起一个 issue](https://github.com/numpy/numpy.org/issues). From 2f5c96a6c32973debae1eb33ea2917a0ef0b8085 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:42 +0200 Subject: [PATCH 205/586] New translations 404.md (Portuguese, Brazilian) --- content/pt/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/404.md b/content/pt/404.md index ac3a516791..627cde96d0 100644 --- a/content/pt/404.md +++ b/content/pt/404.md @@ -5,4 +5,4 @@ sidebar: false Oops! Você atingiu um beco sem saída. -Se você acha que algo deveria estar aqui, você pode [abrir uma issue](https://github.com/numpy/numpy.org/issues) no GitHub. +Se você acha que algo deveria estar aqui, você pode [abrir uma issue](https://github.com/numpy/numpy.org/issues) no GitHub. From 9636bb42b6b6208cd75c8f63c6ca7bb59a56ef0e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:43 +0200 Subject: [PATCH 206/586] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 38 ++++++++++-------------------------- 1 file changed, 10 insertions(+), 28 deletions(-) diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md index 0771101eff..b473f9330f 100644 --- a/content/es/arraycomputing.md +++ b/content/es/arraycomputing.md @@ -1,39 +1,21 @@ --- -title: Array Computing +title: Cómputo Matricial sidebar: false --- -_Array computing is the foundation of statistical, mathematical, scientific computing -in various contemporary data science and analytics applications such as data -visualization, digital signal processing, image processing, bioinformatics, -machine learning, AI, and several others._ +*El cómputo matricial es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y aplicaciones de analíticas, como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática, el aprendizaje automático, la IA y entre otras.* -Large scale data manipulation and transformation depends on efficient, -high-performance array computing. The language of choice for data analytics, -machine learning, and productive numerical computing is **Python.** +La manipulación y transformación de datos a gran escala depende de una computación matricial eficiente y de alto rendimiento. El lenguaje de elección para la analítica de datos, el aprendizaje automático y el cómputo numérico productivo es **Python.** -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming -language library that supports large, multi-dimensional arrays and matrices, -and comes with a vast collection of high-level mathematical functions to -operate on these arrays. +**Num**erical **Py**thon o NumPy es la biblioteca estándar de-facto del lenguaje de programación Python que soporta conjuntos y matrices multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos conjuntos. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, -and it was not until a couple of years ago that several array computing -libraries showed up in succession, crowding the array computing landscape. -Many of these newer libraries mimic NumPy-like features and capabilities, and -pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +Tras el lanzamiento de NumPy en 2006, Pandas apareció en el panorama en 2008, y no fue hasta hace un par de años que aparecieron sucesivamente varias bibliotecas de cómputo matricial, poblando este escenario. Muchas de estas nuevas bibliotecas imitan las características y capacidades de NumPy, y contienen nuevos algoritmos y características orientadas a las aplicaciones de aprendizaje automático e inteligencia artificial. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="Panorama del Cómputo Matricial" /> -**Array computing** is based on **arrays** data structures. _Arrays_ are used -to organize vast amounts of data such that a related set of values can be easily -sorted, searched, mathematically manipulated, and transformed easily and quickly. +El **cómputo matricial** está basado en los **conjuntos** como estructura de datos. *Los conjuntos* se utilizan para organizar grandes cantidades de datos de manera que un conjunto de valores relacionados pueda ordenarse, buscarse, manipularse matemáticamente y transformarse con facilidad y rapidez. -Array computing is _unique_ as it involves operating on the data array _at -once_. What this means is that any array operation applies to an entire set of -values in one shot. This vectorized approach provides speed and simplicity by -enabling programmers to code and operate on aggregates of data, without having -to use loops of individual scalar operations. +La computación matricial es *única* ya que implica operar sobre todo el conjunto de datos *al mismo tiempo*. Esto significa que cualquier operación de conjuntos se aplica a un conjunto completo de valores de una sola vez. Este enfoque vectorial proporciona velocidad y simplicidad, al permitir a los programadores codificar y trabajar sobre los datos agregados, sin tener que utilizar bucles de instrucciones escalares individuales. From 2fa002691c5b7440e7e73a500d81f75a53ca068f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:44 +0200 Subject: [PATCH 207/586] New translations arraycomputing.md (Arabic) --- content/ar/arraycomputing.md | 36 +++++++++--------------------------- 1 file changed, 9 insertions(+), 27 deletions(-) diff --git a/content/ar/arraycomputing.md b/content/ar/arraycomputing.md index 0771101eff..abd29d11c1 100644 --- a/content/ar/arraycomputing.md +++ b/content/ar/arraycomputing.md @@ -3,37 +3,19 @@ title: Array Computing sidebar: false --- -_Array computing is the foundation of statistical, mathematical, scientific computing -in various contemporary data science and analytics applications such as data -visualization, digital signal processing, image processing, bioinformatics, -machine learning, AI, and several others._ +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* -Large scale data manipulation and transformation depends on efficient, -high-performance array computing. The language of choice for data analytics, -machine learning, and productive numerical computing is **Python.** +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming -language library that supports large, multi-dimensional arrays and matrices, -and comes with a vast collection of high-level mathematical functions to -operate on these arrays. +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, -and it was not until a couple of years ago that several array computing -libraries showed up in succession, crowding the array computing landscape. -Many of these newer libraries mimic NumPy-like features and capabilities, and -pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="Array Computing Landscape" /> -**Array computing** is based on **arrays** data structures. _Arrays_ are used -to organize vast amounts of data such that a related set of values can be easily -sorted, searched, mathematically manipulated, and transformed easily and quickly. +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. -Array computing is _unique_ as it involves operating on the data array _at -once_. What this means is that any array operation applies to an entire set of -values in one shot. This vectorized approach provides speed and simplicity by -enabling programmers to code and operate on aggregates of data, without having -to use loops of individual scalar operations. +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From ec20e7817f4245e21da645d802692c90c5d8483b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:44 +0200 Subject: [PATCH 208/586] New translations arraycomputing.md (Japanese) --- content/ja/arraycomputing.md | 22 +++++++--------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/content/ja/arraycomputing.md b/content/ja/arraycomputing.md index 7b60583a4b..7713e7e0f2 100644 --- a/content/ja/arraycomputing.md +++ b/content/ja/arraycomputing.md @@ -3,27 +3,19 @@ title: 配列演算 sidebar: false --- -_Array computing is the foundation of statistical, mathematical, scientific computing -in various contemporary data science and analytics applications such as data -visualization, digital signal processing, image processing, bioinformatics, -machine learning, AI, and several others._ +*配列演算は統計、数学、科学計算の基礎です。可視化、信号処理、画像処理、生命情報学、機械学習、人工知能など、現代のデータサイエンスやデータ分析の様々な分野で配列演算は中核を担っています。* -Large scale data manipulation and transformation depends on efficient, -high-performance array computing. The language of choice for data analytics, -machine learning, and productive numerical computing is **Python.** +大規模なデータ処理やデータ変換には、効率的な配列演算が重要です。 データ分析や、機械学習、効率的な数値計算に最適な言語のひとつは **Python** です。 **Num**erical **Py**thon: NumPyは、Pythonにおけるデファクトスタンダードなライブラリであり、大規模な多次元配列や行列、そして、それらの配列を処理する様々な分野の数学ルーチンをサポートしています。 2006年にNumPyが発表されてから、2008年にPandasが登場し、その後、数年間にいくつかの配列演算関連のライブラリが次々と現れるようになりました。 これらの新しい配列演算ライブラリの多くは、NumPyの機能や能力を模倣しており、機械学習や人工知能向けの新しいアルゴリズムや機能を持っています。 -Many of these newer libraries mimic NumPy-like features and capabilities, and -pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="配列演算の概略" /> -**配列演算** は **配列** のデータ構造に基づいています。 _配列_ は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 大規模なデータ処理やデータ変換には、効率的な配列演算が重要です。 データ分析や、機械学習、効率的な数値計算に最適な言語のひとつは **Python** です。 +**配列演算** は **配列** のデータ構造に基づいています。 *配列* は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 -_配列演算は統計、数学、科学計算の基礎です。可視化、信号処理、画像処理、生命情報学、機械学習、人工知能など、現代のデータサイエンスやデータ分析の様々な分野で配列演算は中核を担っています。_ What this means is that any array operation applies to an entire set of -values in one shot. 配列演算は _一度に_ 配列のデータの複数の要素を操作するため、 \* ユニーク\* な処理と言えます。 これは、配列操作が一回の処理で、配列内の 全ての値に適用されることを意味しています。 このベクトル化手法は、速さと単純さという恩恵をもたらします。 プログラマーはループを回して個々の要素のスカラー演算を行うことなく、データの集合を操作しコーディングすることができるのです。 +配列演算は *一度に* 配列のデータの複数の要素を操作するため、 * ユニーク* な処理と言えます。 これは、配列操作が一回の処理で、配列内の 全ての値に適用されることを意味しています。 このベクトル化手法は、速さと単純さという恩恵をもたらします。 プログラマーはループを回して個々の要素のスカラー演算を行うことなく、データの集合を操作しコーディングすることができるのです。 From 46fde9cc3443a2778f081e6e3399e288d26bfe6a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:46 +0200 Subject: [PATCH 209/586] New translations arraycomputing.md (Korean) --- content/ko/arraycomputing.md | 38 ++++++++++-------------------------- 1 file changed, 10 insertions(+), 28 deletions(-) diff --git a/content/ko/arraycomputing.md b/content/ko/arraycomputing.md index 0771101eff..9c661978d2 100644 --- a/content/ko/arraycomputing.md +++ b/content/ko/arraycomputing.md @@ -1,39 +1,21 @@ --- -title: Array Computing +title: 배열 연산 sidebar: false --- -_Array computing is the foundation of statistical, mathematical, scientific computing -in various contemporary data science and analytics applications such as data -visualization, digital signal processing, image processing, bioinformatics, -machine learning, AI, and several others._ +*배열 연산은 통계와 수학 뿐만 아니라 현대의 다양한 분야에 적용되는 데이터 사이언스, 데이터 시각화나 디지털 신호 처리, 영상 처리, 의생명 정보 공학, 기계학습, AI 등 다양한 분야에서 적용되는 데이터 분석 어플리케이션의 기반입니다.* -Large scale data manipulation and transformation depends on efficient, -high-performance array computing. The language of choice for data analytics, -machine learning, and productive numerical computing is **Python.** +대규모 데이터의 조작과 연산은 고효율, 고성능의 배열 연산에 달려있습니다. **Python**은 데이터 과학자, 머신 러닝 개발자, 그리고 효율적인 수치 계산을 필요로 하는 분야에서 선택되는 프로그래밍 언어입니다. -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming -language library that supports large, multi-dimensional arrays and matrices, -and comes with a vast collection of high-level mathematical functions to -operate on these arrays. +**Num**erical **Py**thon 또는 NumPy 는 파이썬의 표준라이브러리에는 포함되지 않지만, 대규모, 다차원 행렬을 표현할 수 있고, 배열 연산을 위한 고수준의 수학 함수들을 포함한 라이브러리입니다. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, -and it was not until a couple of years ago that several array computing -libraries showed up in succession, crowding the array computing landscape. -Many of these newer libraries mimic NumPy-like features and capabilities, and -pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +2006년에 NumPy가 출시된 이후로, 2008년에 이를 기반으로 Pandas가 나타났습니다. 그리고 몇년전까지도, 다양한 배열 연산 라이브러리가 잇따라 나오며 배열 연산 분야가 더욱 활발해 졌습니다. 최신의 라이브러리들중 대부분은 NumPy 같은 특징과 성능을 모방하고, 새로운 알고리즘이나 머신러닝이나 인공지능 어플리케이션을 위한 특화된 기능을 포함하고 있습니다. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="Array Computing Landscape" /> -**Array computing** is based on **arrays** data structures. _Arrays_ are used -to organize vast amounts of data such that a related set of values can be easily -sorted, searched, mathematically manipulated, and transformed easily and quickly. +**배열 연산**의 기반은 **array ** 자료구조 입니다. *배열*은 대규모의 데이터를 정렬, 검색, 수학 계산, 그리고 변형을 쉽고 빠르게 처리하는데 사용됩니다. -Array computing is _unique_ as it involves operating on the data array _at -once_. What this means is that any array operation applies to an entire set of -values in one shot. This vectorized approach provides speed and simplicity by -enabling programmers to code and operate on aggregates of data, without having -to use loops of individual scalar operations. +배열 연산은 *한번에 * 데이터 배열에 *모든 연산이* 계산 됩니다. 다시 말해서, 모든 배열 연산은 전체 데이터에 한번에 적용됩니다. 이 벡터화 접근법은 배열 연산을 위해 루프를 활용하여 개별적인 데이터에 접근하여 연산하는 코드를 작성하지 않고, 배열에 바로 연산하는 코드를 작성하여, 개발자가 보다 개발 빠르고 간단하게 할수 있게 해줍니다. From 236e79e9a42aa1e769548a627343687b5e4ef392 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:47 +0200 Subject: [PATCH 210/586] New translations arraycomputing.md (Russian) --- content/ru/arraycomputing.md | 36 +++++++++--------------------------- 1 file changed, 9 insertions(+), 27 deletions(-) diff --git a/content/ru/arraycomputing.md b/content/ru/arraycomputing.md index 0771101eff..abd29d11c1 100644 --- a/content/ru/arraycomputing.md +++ b/content/ru/arraycomputing.md @@ -3,37 +3,19 @@ title: Array Computing sidebar: false --- -_Array computing is the foundation of statistical, mathematical, scientific computing -in various contemporary data science and analytics applications such as data -visualization, digital signal processing, image processing, bioinformatics, -machine learning, AI, and several others._ +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* -Large scale data manipulation and transformation depends on efficient, -high-performance array computing. The language of choice for data analytics, -machine learning, and productive numerical computing is **Python.** +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming -language library that supports large, multi-dimensional arrays and matrices, -and comes with a vast collection of high-level mathematical functions to -operate on these arrays. +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, -and it was not until a couple of years ago that several array computing -libraries showed up in succession, crowding the array computing landscape. -Many of these newer libraries mimic NumPy-like features and capabilities, and -pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="Array Computing Landscape" /> -**Array computing** is based on **arrays** data structures. _Arrays_ are used -to organize vast amounts of data such that a related set of values can be easily -sorted, searched, mathematically manipulated, and transformed easily and quickly. +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. -Array computing is _unique_ as it involves operating on the data array _at -once_. What this means is that any array operation applies to an entire set of -values in one shot. This vectorized approach provides speed and simplicity by -enabling programmers to code and operate on aggregates of data, without having -to use loops of individual scalar operations. +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 523f00ba04cb8dfca57a7a996e03e58cb59a9ee4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:48 +0200 Subject: [PATCH 211/586] New translations arraycomputing.md (Chinese Simplified) --- content/zh/arraycomputing.md | 36 +++++++++--------------------------- 1 file changed, 9 insertions(+), 27 deletions(-) diff --git a/content/zh/arraycomputing.md b/content/zh/arraycomputing.md index 0771101eff..abd29d11c1 100644 --- a/content/zh/arraycomputing.md +++ b/content/zh/arraycomputing.md @@ -3,37 +3,19 @@ title: Array Computing sidebar: false --- -_Array computing is the foundation of statistical, mathematical, scientific computing -in various contemporary data science and analytics applications such as data -visualization, digital signal processing, image processing, bioinformatics, -machine learning, AI, and several others._ +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* -Large scale data manipulation and transformation depends on efficient, -high-performance array computing. The language of choice for data analytics, -machine learning, and productive numerical computing is **Python.** +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming -language library that supports large, multi-dimensional arrays and matrices, -and comes with a vast collection of high-level mathematical functions to -operate on these arrays. +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, -and it was not until a couple of years ago that several array computing -libraries showed up in succession, crowding the array computing landscape. -Many of these newer libraries mimic NumPy-like features and capabilities, and -pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="Array Computing Landscape" /> -**Array computing** is based on **arrays** data structures. _Arrays_ are used -to organize vast amounts of data such that a related set of values can be easily -sorted, searched, mathematically manipulated, and transformed easily and quickly. +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. -Array computing is _unique_ as it involves operating on the data array _at -once_. What this means is that any array operation applies to an entire set of -values in one shot. This vectorized approach provides speed and simplicity by -enabling programmers to code and operate on aggregates of data, without having -to use loops of individual scalar operations. +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From cdcc493900774b7a2df3def164db5ccb8e27fbb2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:49 +0200 Subject: [PATCH 212/586] New translations arraycomputing.md (Portuguese, Brazilian) --- content/pt/arraycomputing.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/pt/arraycomputing.md b/content/pt/arraycomputing.md index b43b5d58b4..941f69fe42 100644 --- a/content/pt/arraycomputing.md +++ b/content/pt/arraycomputing.md @@ -3,20 +3,19 @@ title: Computação com Arrays sidebar: false --- -_A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras._ +*A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras.* A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alta performance com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é **Python.** **Num**erical **Py**thon (Python Numérico) ou NumPy é a biblioteca em Python padrão para o suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays. -Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. -Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. +Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. + src="/images/content_images/array_c_landscape.png" + alt="arraycl" + title="Panorama de Computação com Arrays" /> -A **computação com arrays** é baseada em estruturas de dados chamadas **arrays**. _Arrays_ são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente. +A **computação com arrays** é baseada em estruturas de dados chamadas **arrays**. *Arrays* são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente. -A computação com arrays é _única_ pois envolve operar nos valores de um array de dados _de uma vez_. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais. +A computação com arrays é *única* pois envolve operar nos valores de um array de dados *de uma vez*. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais. From 415cdb3cfe9e7854a0d44989219ad5a0ca078ab9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:50 +0200 Subject: [PATCH 213/586] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 50 +++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 93a1708556..50b9bb2b78 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -1,35 +1,35 @@ --- -title: Citing NumPy +title: Citando a NumPy sidebar: false --- -If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: +Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento: -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). -_In BibTeX format:_ +_En formato BibTeX:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del - R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From 70e1302d11596b46cfac417255334e5d7b2f4ab5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:51 +0200 Subject: [PATCH 214/586] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index 93a1708556..5bb5d791b4 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -5,31 +5,31 @@ sidebar: false If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). _In BibTeX format:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del - R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From dfa2947326306eb67c12e5ffde30d19bce185890 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:52 +0200 Subject: [PATCH 215/586] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 42 +++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index fddd4696dc..397ca192ab 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -5,30 +5,30 @@ sidebar: false もしあなたの研究においてNumPyが重要な役割を果たし、論文でこのプロジェクトについて言及したい場合は、こちらの論文を引用して下さい。 -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). _BibTeX形式:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{'{a}}ndez del - R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{'{a}}ndez del + R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From b381b45fd8981bb00f12aad69ed8e92bb8b77217 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:53 +0200 Subject: [PATCH 216/586] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 50 +++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md index 93a1708556..cf1458e657 100644 --- a/content/ko/citing-numpy.md +++ b/content/ko/citing-numpy.md @@ -1,35 +1,35 @@ --- -title: Citing NumPy +title: NumPy 인용하기 sidebar: false --- -If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: +진행한 연구에서 NumPy가 중요한 부분을 차지하고 있고 학술지에 출판한다면, 아래의 논문을 참조문헌에 써주시길 바랍니다. -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([링크](https://www.nature.com/articles/s41586-020-2649-2)). -_In BibTeX format:_ +_BibTeX 형식:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del - R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From d2b937052217b062df0b4b2f7f86e24cb7f841ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:54 +0200 Subject: [PATCH 217/586] New translations citing-numpy.md (Russian) --- content/ru/citing-numpy.md | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/ru/citing-numpy.md b/content/ru/citing-numpy.md index 93a1708556..5bb5d791b4 100644 --- a/content/ru/citing-numpy.md +++ b/content/ru/citing-numpy.md @@ -5,31 +5,31 @@ sidebar: false If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). _In BibTeX format:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del - R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From 02b872f905483bcfc4a3a87b932055095dd8c678 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:55 +0200 Subject: [PATCH 218/586] New translations citing-numpy.md (Chinese Simplified) --- content/zh/citing-numpy.md | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/zh/citing-numpy.md b/content/zh/citing-numpy.md index 93a1708556..5bb5d791b4 100644 --- a/content/zh/citing-numpy.md +++ b/content/zh/citing-numpy.md @@ -5,31 +5,31 @@ sidebar: false If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). _In BibTeX format:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del - R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From 68a328151c737697a7e3103be4bdb739921cc1ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:56 +0200 Subject: [PATCH 219/586] New translations citing-numpy.md (Portuguese, Brazilian) --- content/pt/citing-numpy.md | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/pt/citing-numpy.md b/content/pt/citing-numpy.md index f73f541896..f947689548 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -5,31 +5,31 @@ sidebar: false Se a NumPy é importante na sua pesquisa, e você gostaria de dar reconhecimento ao projeto na sua publicação acadêmica, sugerimos citar os seguintes documentos: -- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Link da editora](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Link da editora](https://www.nature.com/articles/s41586-020-2649-2)). _Em formato BibTeX:_ -``` + ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David - Cournapeau and Eric Wieser and Julian Taylor and Sebastian - Berg and Nathaniel J. Smith and Robert Kern and Matti Picus - and Stephan Hoyer and Marten H. van Kerkwijk and Matthew - Brett and Allan Haldane and Jaime Fern{'{a}}ndez del - R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre - G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and - Warren Weckesser and Hameer Abbasi and Christoph Gohlke and - Travis E. Oliphant}, - year = {2020}, - month = sep, - journal = {Nature}, - volume = {585}, - number = {7825}, - pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, - publisher = {Springer Science and Business Media {LLC}}, - url = {https://doi.org/10.1038/s41586-020-2649-2} + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{'{a}}ndez del + R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} } ``` From c82f3b06e8ac2bbfa76198e5ba4c02bf85c7356a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:57 +0200 Subject: [PATCH 220/586] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index 99182de7a5..d6d502b29a 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -1,25 +1,18 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: ENCUESTA DE LA COMUNIDAD NUMPY 2020 sidebar: false --- -In 2020, the NumPy survey team in partnership with students and faculty from a -Master’s course in Survey Methodology jointly hosted by the University of -Michigan and the University of Maryland conducted the first official NumPy -community survey. Over 1,200 users from 75 countries participated to help us -map out a landscape of the NumPy community and voiced their thoughts about the -future of the project. +En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' -width = '250' +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250">}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' {{< /figure >}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** -to take a closer look at the survey findings. +**[Descarga el informe](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver a detalle los resultados de la encuesta. -For the highlights, check out -**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. -Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. +Para los puntos destacados, echa un vistazo a **[esta infografía](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +¿Listo para una inmersión profunda? Visita **https://numpy.org/user-survey-2020-details/**. + From b3ff53683e502ff84a7d4a31f41e5d61811cb893 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:58 +0200 Subject: [PATCH 221/586] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md index 99182de7a5..b4349bcb7d 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -3,23 +3,16 @@ title: 2020 NUMPY COMMUNITY SURVEY sidebar: false --- -In 2020, the NumPy survey team in partnership with students and faculty from a -Master’s course in Survey Methodology jointly hosted by the University of -Michigan and the University of Maryland conducted the first official NumPy -community survey. Over 1,200 users from 75 countries participated to help us -map out a landscape of the NumPy community and voiced their thoughts about the -future of the project. +In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. {{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' -width = '250' +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' {{< /figure >}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** -to take a closer look at the survey findings. +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. -For the highlights, check out -**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. + From 1ffd46bdb93226cc9678f41926d8f1d45b811d58 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:24:59 +0200 Subject: [PATCH 222/586] New translations user-survey-2020.md (Japanese) --- content/ja/user-survey-2020.md | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index 46e47e5073..a6533d3cb8 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -3,21 +3,16 @@ title: 2020年 NumPyコミュニティ調査 sidebar: false --- -In 2020, the NumPy survey team in partnership with students and faculty from a -Master’s course in Survey Methodology jointly hosted by the University of -Michigan and the University of Maryland conducted the first official NumPy -community survey. Over 1,200 users from 75 countries participated to help us -map out a landscape of the NumPy community and voiced their thoughts about the -future of the project. +2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。 -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 Numpy User survey report, titled "Numpyコミュニティ調査2020 - 結果"' -width = '250' +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 Numpy User survey report, titled 'Numpyコミュニティ調査2020 - 結果'" width="250">}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' {{< /figure >}} 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。 + 結果の概要については、 **[こちらの図](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)** をチェックしてください。 -Ready for a deep dive? より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 +より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 + From ae70ccf10f3e0ebbf08fa5d38063a6af212dcb88 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:00 +0200 Subject: [PATCH 223/586] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md index 99182de7a5..68505b30f9 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -1,25 +1,18 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: 2020 NUMPY 커뮤니티 설문조사 sidebar: false --- -In 2020, the NumPy survey team in partnership with students and faculty from a -Master’s course in Survey Methodology jointly hosted by the University of -Michigan and the University of Maryland conducted the first official NumPy -community survey. Over 1,200 users from 75 countries participated to help us -map out a landscape of the NumPy community and voiced their thoughts about the -future of the project. +2020년, NumPy 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 75개국 내 1200명 이상의 사용자 여러분들께서 저희가 NumPy 커뮤니티의 가닥을 잡을 수 있도록 도와주기 위해 참여해주셨으며 프로젝트의 미래에 대한 생각을 표현해주셨습니다. -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' -width = '250' +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="'NumPy Community Survey 2020 - results'라는 제목이 붙은 2020년 NumPy 사용자 설문조사 보고서 표지" width="250">}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = '"2020 NumPy 커뮤니티 설문조사 - 결과"라는 제목이 붙은 2020 NumPy 사용자 설문조사 보고서의 표지' width = '250' {{< /figure >}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** -to take a closer look at the survey findings. +**[보고서를 내려받아서](/surveys/NumPy_usersurvey_2020_report.pdf)** 설문조사 결과를 자세히 들여다 보세요. -For the highlights, check out -**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. -Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. +요점만 보시려면, **[이 인포그래픽](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**을 참고하시기 바랍니다. + +더욱 자세한 정보가 궁금하신가요? **https://numpy.org/user-survey-2020-details/** 페이지를 방문하세요. + From 1f222c2b8b5e6e01306399be81d29a7eaa51fa49 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:01 +0200 Subject: [PATCH 224/586] New translations user-survey-2020.md (Russian) --- content/ru/user-survey-2020.md | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/content/ru/user-survey-2020.md b/content/ru/user-survey-2020.md index 99182de7a5..b4349bcb7d 100644 --- a/content/ru/user-survey-2020.md +++ b/content/ru/user-survey-2020.md @@ -3,23 +3,16 @@ title: 2020 NUMPY COMMUNITY SURVEY sidebar: false --- -In 2020, the NumPy survey team in partnership with students and faculty from a -Master’s course in Survey Methodology jointly hosted by the University of -Michigan and the University of Maryland conducted the first official NumPy -community survey. Over 1,200 users from 75 countries participated to help us -map out a landscape of the NumPy community and voiced their thoughts about the -future of the project. +In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. {{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' -width = '250' +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' {{< /figure >}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** -to take a closer look at the survey findings. +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. -For the highlights, check out -**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. + From 94eee8a38e729397652a1cea9fbcf3ce750ced3e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:02 +0200 Subject: [PATCH 225/586] New translations user-survey-2020.md (Chinese Simplified) --- content/zh/user-survey-2020.md | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/content/zh/user-survey-2020.md b/content/zh/user-survey-2020.md index 99182de7a5..b4349bcb7d 100644 --- a/content/zh/user-survey-2020.md +++ b/content/zh/user-survey-2020.md @@ -3,23 +3,16 @@ title: 2020 NUMPY COMMUNITY SURVEY sidebar: false --- -In 2020, the NumPy survey team in partnership with students and faculty from a -Master’s course in Survey Methodology jointly hosted by the University of -Michigan and the University of Maryland conducted the first official NumPy -community survey. Over 1,200 users from 75 countries participated to help us -map out a landscape of the NumPy community and voiced their thoughts about the -future of the project. +In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. {{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' -width = '250' +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' {{< /figure >}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** -to take a closer look at the survey findings. +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. -For the highlights, check out -**[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. + From 8da9398c16151c05b467bef953424b3acfc4f608 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:03 +0200 Subject: [PATCH 226/586] New translations user-survey-2020.md (Portuguese, Brazilian) --- content/pt/user-survey-2020.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index 5f2a397baf..bbf3cb6a17 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -5,14 +5,14 @@ sidebar: false Em 2020, o time de pesquisas do NumPy realizou a primeira pesquisa oficial sobre a comunidade NumPy, em parceria com alunos e docentes de um Mestrado em metodologia de pesquisa realizado conjuntamente pela Universidade de Michigan e pela Universidade da Maryland. Mais de 1200 usuários de 75 países participaram para nos ajudar a mapear uma paisagem da comunidade NumPy e expressaram seus pensamentos sobre o futuro do projeto. -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado "NumPy Community Survey 2020 - results"' -width = '250' +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado 'NumPy Community Survey 2020 - results'" width="250">}} +src = '/surveys/NumPy_usersurvey_2020_report_cover. ng' alt = 'Página de rosto do relatório da pesquisa de usuário NumPy de 2020, intitulado "NumPy Community Survey 2020 - resultados"' width = '250' {{< /figure >}} **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. + Para os destaques, confira **[este infográfico](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. -Ready for a deep dive? Visite **https://numpy.org/user-survey-2020-details/**. +Quer saber mais? Visite **https://numpy.org/user-survey-2020-details/**. + From b9a2fea859bea4289540b8b6e596049e7938c6e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:04 +0200 Subject: [PATCH 227/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 66 +++++++++++++++------------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 161757fe41..5586668cba 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -7,40 +7,44 @@ This is the manual followed by NumPy’s Code of Conduct Committee. It’s used Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: -- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + ## Mediation Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: -- Find a candidate who can serve as a mediator. -- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -- Obtain the agreement of the reported person(s). -- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -- Establish a timeline for mediation to complete, ideally within two weeks. +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + ## How the Committee will respond to reports When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + ## Clear and severe breach actions We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: -- Immediately disconnect the originator from all NumPy communication channels. -- Reply to the reporter that their report has been received and that the originator has been disconnected. -- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + ## Report handling @@ -50,10 +54,10 @@ If a report doesn’t contain enough information, the Committee will obtain all The Committee will then review the incident and determine, to the best of their ability: -- What happened. -- Whether this event constitutes a Code of Conduct violation. -- Who are the responsible party(ies). -- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). @@ -61,22 +65,23 @@ It is important to retain an archive of all activities of this Committee to ensu The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + ## Resolutions The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. Possible responses may include: -- Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. @@ -84,6 +89,7 @@ Finally, the Committee will make a report to the NumPy Steering Council (as well The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + ## Conflicts of Interest In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 89c05219a228910f96a08d334c6f7fee2d0ac3cf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:05 +0200 Subject: [PATCH 228/586] New translations report-handling-manual.md (Arabic) --- content/ar/report-handling-manual.md | 66 +++++++++++++++------------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/content/ar/report-handling-manual.md b/content/ar/report-handling-manual.md index 161757fe41..5586668cba 100644 --- a/content/ar/report-handling-manual.md +++ b/content/ar/report-handling-manual.md @@ -7,40 +7,44 @@ This is the manual followed by NumPy’s Code of Conduct Committee. It’s used Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: -- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + ## Mediation Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: -- Find a candidate who can serve as a mediator. -- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -- Obtain the agreement of the reported person(s). -- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -- Establish a timeline for mediation to complete, ideally within two weeks. +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + ## How the Committee will respond to reports When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + ## Clear and severe breach actions We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: -- Immediately disconnect the originator from all NumPy communication channels. -- Reply to the reporter that their report has been received and that the originator has been disconnected. -- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + ## Report handling @@ -50,10 +54,10 @@ If a report doesn’t contain enough information, the Committee will obtain all The Committee will then review the incident and determine, to the best of their ability: -- What happened. -- Whether this event constitutes a Code of Conduct violation. -- Who are the responsible party(ies). -- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). @@ -61,22 +65,23 @@ It is important to retain an archive of all activities of this Committee to ensu The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + ## Resolutions The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. Possible responses may include: -- Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. @@ -84,6 +89,7 @@ Finally, the Committee will make a report to the NumPy Steering Council (as well The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + ## Conflicts of Interest In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From e26d72e5f75b1d199b26502c88ee41121253e1da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:07 +0200 Subject: [PATCH 229/586] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 96 +++++++++++++++------------- 1 file changed, 51 insertions(+), 45 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 62363d58cb..b200124145 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -3,87 +3,93 @@ title: NumPy行動規範 - 報告書のフォローアップ方法 sidebar: false --- -NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 It’s used when we respond to an issue to make sure we’re consistent and fair. +NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 -Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: +[行動規範](/ja/code-of-conduct) を施行することは、私たちのコミュニティの現在のため、未来のために重要です。 この施行は、軽いものではありません。 施行の基準を見直す際、行動規範委員会は以下の考え方とガイドラインに留意するようにします。 -- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。 -- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -- 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。 -- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* 機械的ではなく、人間的に行動します。 委員会は、当事者のプライバシーと報告者の必要なだけの機密性を尊重しながら、状況を理解するように働きかけることができます. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。 +* 行動を判断するのではなく、個人への共感を強調し、「良い」と「悪い」の二値評価を避けます。 明確な攻撃性とハラスメントが存在した場合、私たちはそれらに対処します。 しかし、解決が困難なシナリオの多くは、通常の意見の相違が、複数の当事者による無益または有害な行動に発展した場合です。 完全に文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、コミュニティにとって最終的に最も有益な方法です。 +* 私たちは、電子メールが判断に困難な媒体であり、独立して利用できることを理解しています。 個人の情報なしに電子メール上で批判を受けることは、特に苦痛である場合もあります。 そこで、他者の見解に対して、開放的で、敬意を持った雰囲気を保つことが重要になります。 それはまた、私たちの行動が透明でなければならないことを意味します。 全てのメンバーが公平かつ同情をもって扱われるようにするため、私たちは全力を尽くします。 +* 差別の境界は時に曖昧で、また無意識に行われている場合もあります。 これにより、普通の人との関わりの中で、不公平感や敵意として現れてくるのです。 私達は、このようなことが起こることはわかっているので、気をつけて見ていきたいと思います。 不当な扱いを受けたと思われる方は、ぜひご連絡ください。 +* 良い議論を実践することで、エンゲージメントの向上に取り組みます。例えば議論がどこで止まっているのかを特定したり、 実践的な情報、指針、資源を提供することで、これらの問題を前向きな方向に変えていきます。 +* 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。 +* 一人一人の文化的背景や母国語は異なります。 ネイティブでない人が起こした悪気のない誤解を確認し、問題を理解してもらい、不快感を与えないために何を変えればよいかを教えてあげてください。 外国語での複雑な議論はとても難しいものであり、国籍や文化を超えて多様性を育てていきたいと考えています。 -## Mediation -Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: +## 仲介 -- Find a candidate who can serve as a mediator. -- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -- Obtain the agreement of the reported person(s). -- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -- Establish a timeline for mediation to complete, ideally within two weeks. +自主的な非公式の調停は、私たちの重要な役割です。 2つのグループ以上の当事者が不適切な行動をエスカレートした場合(人類の紛争では悲しいことに一般的なものですが)、調停プロセスを促進するは非常に重要です。 ちなみに、これは一例に過ぎません。委員会は、どのようなケースでも調停を検討することができますが、このプロセスはあくまでも自発的なものであり、当事者に参加を迫ることはできないことを念頭に置いて下さい。 委員会が調停を提案する場合は、次のようにすべきです。 + +* 調停者として役立つ候補者を見つけます。 +* 報告者の合意を取得します。 報告者は、調停のアイデアを拒否したり、代替の調停者を提案する権利を持ちます。 +* 報告者の同意を取得します。 +* 調停人を決定します。当事者は、提案された候補者とは別の調停人を提案することができます。すべての条件で共通の合意に達した場合のみ、プロセスを進めることができます。 +* 調停が完了するまでのタイムラインを設定し、理想的には2週間以内に完了させます。 + +調停者は、すべての当事者と関わり、すべての人に満足のいく決議を求めていきます。 終了後、調停人は（プロセスの全当事者によって吟味された）報告書を委員会に提出し、今後のステップに関する推奨事項を提示します。 委員会は、これらの結果(満足のいく決議が達成されたか否か) を評価し、必要と判断される追加的な措置を決定します。 -The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. ## 報告に対する委員会の対応 -When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. +委員会(または委員) が行動規範違反報告を受けた時、その報告が明確で深刻な違反であるかどうかは判断されます(以下に違反項目を定義します)。 違反判定された場合は、通常のレポート処理プロセスに加えて、即時の対応が必要になります。 + ## 明確かつ深刻な違反行為の解決 -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. +私たちは、インターネットでの会話が簡単にひどい誹謗中傷になってしまうことを、痛いほど知っています。 個人的な脅迫、暴力的、性差別的、人種差別的な言葉など、明らかで深刻な違反に対しては、迅速に対処します。 行動規範委員会のメンバーは、明確かつ深刻な違反に気づいた場合、以下のように行動します。 -- Immediately disconnect the originator from all NumPy communication channels. -- Reply to the reporter that their report has been received and that the originator has been disconnected. -- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* 直ちにすべてのNumPyのオンラインコミュニティから違反者を排除します。 +* 報告が受信され、違反者が排除されたことを報告者に連絡します。 +* どのような場合でも、モデレーターは違反者に連絡するための合理的な努力を行い、違反者の言葉や行動がどのように「明確かつ重大な違反」に該当するのかを具体的に伝えるべきです。 モデレーターは、違反者がこれは不当だと思う場合、あるいはNumPyチャンネルとの再接続を望む場合には、行動規範委員会による以下のような審査を求める権利があることも述べるべきです。 モデレータは、この説明を行動規範委員会に転送する必要があります。 +* 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし署名することで、よくある盛り上がりすぎた論争を諫めるためこのプロセスが使用されたのでないことを確認します。 + ## 報告の処理 -When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. +報告が委員会に送られると、直ちに報告者に返信して報告を受領したことを確認します。 この返信は72時間以内に送信される必要があり、委員会はそれよりもはるかに迅速に対応するよう努める必要があります。 -レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。 The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. +レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。 -The Committee will then review the incident and determine, to the best of their ability: +その後、委員会は今回の問題を見直し、効果を最大限に発揮する対策を決定します。 -- What happened. -- 今回の事情が行動規範違反であるかどうか。 -- 責任者が誰であるか -- これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。 +* 問題の種類 +* 今回の事情が行動規範違反であるかどうか。 +* 責任者が誰であるか +* これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。 これらの情報は書面で収集され、可能な限りグループの審議が記録され、保持されます (例えば、チャットの記録、Eメールのディスカッション、会議通話の記録、音声会話の概要など)。 -It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. +行動の一貫性を確保し、プロジェクトのために記録を残すために、委員会のすべての活動のアーカイブを保持することが重要です。 この活動支援するために、委員会のデフォルトの議論チャネルは、正当化された要求に応じて、委員会の現在および将来のメンバー、および運営委員会のメンバーがアクセスできるプライベートメーリングリストにします。 委員会がリストにはない連絡方法を使用する必要がある場合(例: 早期/迅速な対応を求める電話など)、そのプロセスの良い記録となるように、これらをリストにまとめて戻すべきです。 + +行動規範委員会は、2週間以内に決議の合意を目指すべきです。 その期間内に決議が確定できない場合。 委員会は、レポーターに対して現状の更新と今後のタイムラインを連絡します。 -The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. ## 解決方法 -The Committee must agree on a resolution by consensus. 委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 +委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 ありうる返答は次のとおりです: -- これ以上アクションを取らない。 - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -- 公の場における説明。 どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。 -- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. 「相互に合意した休止」の要求。 これは、委員会から個人への、コミュニティへの参加を一時的に控えるような要請です。 対象者が自発的に一時的な休みを取らないことを選択した場合、委員会は「冷却期限」を準備することがあります。 -- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* これ以上アクションを取らない。 + - 違反が起きていないと判断された + - 検討中に問題が明らかに解決された +* 調停の調整。すべての関係者が合意した場合、委員会は上記のように調停プロセスを促進することができます。 +* 公の場における説明。 どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。 +* 委員会から関係者(複数可) への非公開処分の実施。 この場合、委員会は、電子メールを介して、グループにccを入れながら、対象者に問題の指摘を連絡します。 +* 公の場での指摘。 この場合、委員会の議長は、違反が発生したのと同じ場所で、実用性の範囲内で叱責を行います。 例えば、メールルールの違反の元のメーリングリストなどです。しかし、人や状況がかわるかもしれないチャットルームなどの場合、他の手段を利用する可能性もあります。 文書化のため、この問題のメッセージを他の場所で公開することを対策グループが選択する場合もあります。 +* 報告者がこの考えに同意することを前提とした、公的または私的な謝罪の要求。 報告者は自分の裁量で、違反者とのさらなる接触を拒否することもできます。 委員会がこの要求をお届けします。 委員会は、必要に応じてこの要求に「条件」を付けることができます。例えば、メーリングリストの会員資格を維持するために、違反者に謝罪を求めることができます。 +* 「相互に合意した休止」の要求。 これは、委員会から個人への、コミュニティへの参加を一時的に控えるような要請です。 対象者が自発的に一時的な休みを取らないことを選択した場合、委員会は「冷却期限」を準備することがあります。 +* これは、一部またはすべてのNumPyオンラインコミュニティ (メーリングリスト、gitter.im など) からの永続的または一時的な出入り禁止。 将来的に禁止が見直されるのか、維持されるか決定できるよう、対策グループは出入り禁止の記録を全て保持します。 -Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. +決議が合意されると制定される前に、委員会は、元の報告者およびその他の影響を受けた当事者に連絡し、提案された決議を説明します。 委員会は、この決議が受け入れられるかどうかを尋ねます。 そして、記録のためのフィードバックに注意を払います。 最後に 委員会は、NumPy Steering Councilに報告を行います(NumPy Coreチームにも、出入り禁止など進行中の出来事については報告します)。 委員会はこの問題について公に議論することはありません。 すべての公開声明は、行動規範委員会またはNumPy Steering Councilの議長によって行われます。 + ## 利益相反 -In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. +利益相反が発生した場合、委員会メンバーは直ちに他のメンバーに通知し、必要に応じて対応を辞退しなければなりません。 From 5e5a9ab6bd7a01f1ad958545def6f2eae83fc939 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:08 +0200 Subject: [PATCH 230/586] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 118 ++++++++++++++------------- 1 file changed, 62 insertions(+), 56 deletions(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 161757fe41..cdd686956e 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -1,89 +1,95 @@ --- -title: NumPy Code of Conduct - How to follow up on a report +title: NumPy 이용 약관 - 보고서의 후속 조치 방법 sidebar: false --- -This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. +NumPy 행동 강령 위원회는 본 설명을 따릅니다. 문제를 해결할 때 일관성과 공정성을 확보하기 위한 지침입니다. -Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: +[행동 강령](/code-of-conduct)을 시행하면 현재와 미래의 커뮤니티에 영향을 미칩니다. 우리가 가볍게 받아들이지 않는 행동입니다. 집행 조치를 검토할 때 행동 강령 위원회는 다음 가치와 지침을 염두에 둘 것입니다. -- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* 비인간적이기보다는 개인적인 방식으로 행동하십시오. 위원회는 신고자의 사생활과 필요한 기밀을 존중하면서 상황을 이해하도록 당사자들을 참여시킬 수 있습니다. 그러나 때때로 한 명 이상의 개인과 직접 소통해야 할 필요가 있습니다. 위원회의 목표는 단지 공식적인 결정을 내리는 것이 아니라 우리 지역 사회의 건강을 개선하는 것입니다. +* 행동을 판단하기보다는 개인에 대한 공감을 강조하고 "좋음"과 "나쁨/악"이라는 이분법적인 레이블을 피하십시오. 노골적이고 분명한 공격성과 괴롭힘이 존재하며 우리는 단호하게 대처할 것입니다. 그러나 해결하기 어려운 것으로 입증될 수 있는 많은 시나리오는 정상적인 불일치가 여러 당사자의 도움이 되지 않거나 해로운 행동으로 귀결되는 시나리오입니다. 전체 맥락을 이해하고 모두를 다시 참여시키는 경로를 찾는 것은 어렵지만 궁극적으로 우리 커뮤니티에 가장 생산적입니다. +* 우리는 이메일이 어려운 매체이며 고립될 수 있음을 이해합니다. 개인적인 연락 없이 이메일을 통해 비판을 받는 것은 특히 고통스러울 수 있습니다. 따라서 다른 사람의 견해를 열린 마음으로 존중하는 분위기를 유지하는 것이 특히 중요합니다. 또한 투명하게 행동해야 하며 모든 구성원이 공정하고 공감하는 대우를 받을 수 있도록 최선을 다하겠다는 의미이기도 합니다. +* 차별은 미묘할 수도 있고 무의식적일 수도 있습니다. 일상적인 상호 작용에서 불공평과 적대감으로 나타날 수 있습니다. 저희는 이런 차별이 발생한다는 점을 인지하고 있으며 주의를 기울이고 조심할 것입니다. 저희는 귀하가 부당한 대우를 받았다고 느끼시는 경우 귀하의 의견을 듣고 싶습니다. 귀하의 불만 사항을 듣고 해결하기 위한 절차를 밟을 것입니다. +* 좋은 토론 관행에 참여를 늘리도록 도와주세요: 토론이 중단되었을 수 있는 부분을 파악하고 이러한 점에서 긍정적인 변화를 가져올 수 있는 실행 가능한 정보, 포인터 및 리소스를 제공하세요. +* 신입 회원의 필요를 염두에 두십시오. 특히 소외된 그룹의 참여를 늘리는 것을 목표로 명시적인 지원과 배려를 제공하십시오. +* 개인은 서로 다른 문화적 배경과 모국어를 가지고 있습니다. 원어민이 아닌 사람으로 인한 정직한 오해를 식별하고 문제를 이해하고 불쾌감을 주지 않도록 변경할 수 있는 사항을 이해하도록 돕습니다. 외국어로 복잡한 토론을 하는 것은 매우 위협적일 수 있으며 국적과 문화를 넘어 다양성을 키우고자 합니다. -## Mediation -Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: +## 중재 -- Find a candidate who can serve as a mediator. -- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -- Obtain the agreement of the reported person(s). -- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -- Establish a timeline for mediation to complete, ideally within two weeks. +자발적인 비공식 중재는 우리가 사용할 수 있는 도구입니다. 두 명 이상의 당사자가 모두 부적절한 행동 (인간 갈등에서 슬프게도 흔한 일) 의 지점까지 확대된 경우와 같은 맥락에서 중재 프로세스를 촉진하는 것이 유용할 수 있습니다. 이것은 단지 예일 뿐입니다. 위원회는 어떤 경우에도 중재를 고려할 수 있으며, 그 과정은 엄격하게 자발적으로 이루어지며 어느 당사자도 참여하도록 압력을 받을 수 없다는 점을 염두에 두어야 합니다. 위원회가 중재를 제안하는 경우 위원회는 다음을 수행해야 합니다. -The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. +* 중재자 역할을 할 수 있는 후보를 찾으십시오. +* 신고자(들) 의 동의를 얻습니다. 신고자는 중재 아이디어를 거부하거나 대체 중재자를 제안할 수 있는 완전한 자유가 있습니다. +* 피신고자의 동의를 얻습니다. +* 중재자 결정: 당사자는 제안된 후보와 다른 중재자를 제안할 수 있지만 모든 조건에 대해 공통된 합의에 도달한 경우에만 프로세스를 진행할 수 있습니다. +* 중재가 완료되기 위한 일정을 설정합니다 (이상적으로는 2주 이내). -## How the Committee will respond to reports +중재자는 모든 당사자와 관계를 맺고 모두가 만족할 만한 해결책을 모색합니다. 완료되면 조정자는 추가 단계에 대한 권장 사항과 함께 보고서(프로세스의 모든 당사자가 심사한) 를 위원회에 제공합니다. 그런 다음 위원회는 이러한 결과를 평가하고(만족스러운 해결 여부에 관계없이) 필요하다고 판단되는 추가 조치를 결정합니다. -When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. -## Clear and severe breach actions +## 위원회가 신고에 응답하는 방법 -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. +위원회(또는 위원회 위원) 는 보고서를 받으면 먼저 보고서가 명확하고 심각한 위반에 관한 것인지 여부를 결정합니다(아래에 정의됨). 만약 그렇다면, 정기적인 신고 처리 프로세스와 더불어 즉각적인 조치가 취해져야 합니다. -When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: -- Immediately disconnect the originator from all NumPy communication channels. -- Reply to the reporter that their report has been received and that the originator has been disconnected. -- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +## 명확하고 심각한 권리침해 행위 -## Report handling +우리는 인터넷 통신이 명백하고 노골적인 남용에서 시작되거나 악화되는 것이 매우 흔한 일이라는 것을 알고 있습니다. 우리는 개인적인 위협, 폭력적, 성차별적 또는 인종차별적 언어와 같은 명확하고 심각한 위반을 신속하게 처리할 것입니다. -When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. +행동 강령 위원회 위원이 명백하고 심각한 위반 사실을 알게 되면 다음과 같은 조치를 취합니다. -If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. +* 모든 NumPy 통신 채널에서 발신자를 즉시 연결 해제합니다. +* 보고자에게 보고가 접수되었으며 발신자의 연결이 끊어졌다고 회신합니다. +* 모든 경우에 중재자는 작성자에게 연락하기 위해 합당한 노력을 기울여야 하며, 그들의 언어나 행동이 어떻게 "명백하고 심각한 위반"에 해당하는지 구체적으로 알려야 합니다. 중재자는 또한 작성자가 이것이 불공평하다고 생각하거나 NumPy에 다시 연결되기를 원하는 경우 아래와 같이 행동 강령 위원회에 검토를 요청할 권리가 있다고 말해야 합니다. 중재자는 이 설명을 행동 강령 위원회에 발송해야 합니다. +* 행동 강령 위원회는 이 메커니즘이 일반적인 열띤 의견 불일치를 통제하는 데 사용되지 않도록 하기 위해 이 메커니즘이 적용된 모든 사례를 공식적으로 검토하고 승인할 것입니다. -The Committee will then review the incident and determine, to the best of their ability: -- What happened. -- Whether this event constitutes a Code of Conduct violation. -- Who are the responsible party(ies). -- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +## 신고 대응 -This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). +보고서가 위원회에 보내지면 위원회는 접수 확인을 위해 즉시 보고자에게 회신할 것입니다. 이 회신은 72시간 이내에 보내야 하며 그룹은 그보다 훨씬 빨리 회신하기 위해 노력해야 합니다. -It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. +보고서에 충분한 정보가 포함되어 있지 않은 경우 위원회는 조치를 취하기 전에 모든 관련 데이터를 수집합니다. 위원회는 사건에 대한 보다 완전한 설명을 얻기 위해 관련된 모든 개인에게 연락할 때 운영 위원회를 대신하여 행동할 권한이 있습니다. -The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. +그런 다음 위원회는 사건을 검토하고 최선을 다해 다음을 결정할 것입니다. -## Resolutions +* 무슨 일이 일어났는지 +* 이 이벤트가 행동 강령 위반에 해당하는지 여부 +* 책임자는 누구인지 +* 이것이 현재 진행 중인 상황인지, 누군가의 신체적 안전에 대한 위협이 있는지 여부 -The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. +이 정보는 서면으로 수집되며 가능할 때마다 그룹의 심의가 녹음 및 보관됩니다(예: 채팅 내용, 이메일 토론, 녹음된 회의 통화, 음성 대화 요약 등). -Possible responses may include: +행동의 일관성을 보장하고 프로젝트에 대한 제도적 기억을 제공하기 위해 이 위원회의 모든 활동 기록을 보관하는 것이 중요합니다. 이를 지원하기 위해 이 위원회의 기본 토론 채널은 정당한 요청이 있는 경우 현재 및 미래의 위원회 구성원과 운영 위원회 구성원이 액세스할 수 있는 개인 메일링 리스트가 될 것입니다. 위원회가 목록 외 커뮤니케이션(예: 조기/신속한 응답을 위한 전화 통화)을 사용해야 할 필요성을 발견하는 경우 모든 경우에 이를 목록에 다시 요약하여 프로세스에 대한 좋은 기록이 남도록 해야 합니다. -- Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +행동 강령 위원회는 2주 이내에 결의안에 동의하는 것을 목표로 해야 합니다. 그 시간 내에 해결 방법을 결정할 수 없는 경우 위원회는 보고자에게 해결을 위한 업데이트 및 예상 일정에 대해 응답합니다. -Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. -Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). +## 해결 -The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. +위원회는 반드시 합의를 바탕으로 결의를 내야 합니다. 그룹에서 합의가 이루어지지 못하고 1주 넘게 교착 상태에 빠진 경우, 결의를 내기 위해 해당 의제는 조정위원회로 이양됩니다. -## Conflicts of Interest +가능한 응답은 다음과 같습니다. -In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. +* 추가 조치 없음: + - 위반이 발생하지 않았다고 판단되는 경우 + - 위원회가 응답을 고려하는 동안 문제가 공개적으로 해결된 경우. +* 자발적 중재 조정: 모든 관련 당사자가 동의하는 경우 위원회는 위에서 설명한 대로 중재 절차를 촉진할 수 있습니다. +* 공개적으로 상기시키고 일부 행동/행동/언어가 부적절하다고 판단되었으며 그 이유를 현재 상황에서 지적하거나 일부 사람들에게 상처를 줄 수 있으며 커뮤니티가 자체 조정하도록 요청합니다. +* 관련된 개인에 대한 위원회의 사적인 견책. 이 경우 그룹 의장은 그룹을 참조하여 이메일을 통해 개인(들) 에게 질책을 전달합니다. +* 공개 질책. 이 경우 위원회 위원장은 위반이 발생한 동일한 장소에서 실행 가능한 범위 내에서 질책합니다. 예를 들어 이메일 위반에 대한 원래 메일링 리스트이지만 사람/컨텍스트가 없을 수 있는 채팅방 토론의 경우 다른 방법으로 도달할 수 있습니다. 그룹은 문서화 목적으로 이 메시지를 다른 곳에 게시하도록 선택할 수 있습니다. +* 신고자가 이 생각에 동의한다고 가정하고 공개 또는 비공개 사과 요청: 신고자는 재량에 따라 위반자와의 추가 연락을 거부할 수 있습니다. 의장이 이 요청을 전달할 것입니다. 위원회는 선택하는 경우 이 요청에 "문자열"을 첨부할 수 있습니다. 예를 들어 그룹은 위반자에게 메일링 리스트의 회원 자격을 유지하기 위해 사과를 요청할 수 있습니다. +* 위원회가 개인에게 일시적으로 커뮤니티 참여를 자제하도록 요청하는 "상호 합의 중단". 개인이 자발적으로 일시적인 휴식을 취하지 않기로 선택한 경우 위원회는 "필수 냉각 기간"을 발행할 수 있습니다. +* 일부 또는 모든 NumPy 공간(메일링 리스트, gitter.im 등)에서 영구적 또는 일시적 금지. 그룹은 이러한 모든 금지에 대한 기록을 유지하여 나중에 검토하거나 다른 방법으로 유지할 수 있습니다. + +일단 결의안이 합의되면, 그러나 그것이 시행되기 전에 위원회는 원래 신고자와 영향을 받는 다른 당사자들에게 연락하여 제안된 결의안을 설명합니다. 위원회는 이 결의안이 수용 가능한지 묻고 기록을 위해 피드백을 기록해야 합니다. + +최종적으로 위원회는 NumPy 조정위원회에 보고서를 만들어 제출하게 됩니다 (추방 등 효력이 지속되는 결의가 발생하는 경우 NumPy 핵심 팀에게도 보고합니다). + +위원회는 문제를 반드시 비공개 상태로 다룰 것입니다. 모든 공개 성명은 행동강령 위원회 혹은 NumPy 조정위원회에서 담당합니다. + + +## 이해관계 충돌 + +이해관계 충돌이 일어난 경우, 위원회 회원은 즉시 이 사실을 다른 회원에게 고지하고 필요한 경우 자진 사퇴해야 합니다. From e9e2553a233103813ddbdaab73fbb21811d39a44 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:09 +0200 Subject: [PATCH 231/586] New translations report-handling-manual.md (Russian) --- content/ru/report-handling-manual.md | 66 +++++++++++++++------------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/content/ru/report-handling-manual.md b/content/ru/report-handling-manual.md index 161757fe41..5586668cba 100644 --- a/content/ru/report-handling-manual.md +++ b/content/ru/report-handling-manual.md @@ -7,40 +7,44 @@ This is the manual followed by NumPy’s Code of Conduct Committee. It’s used Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: -- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + ## Mediation Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: -- Find a candidate who can serve as a mediator. -- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -- Obtain the agreement of the reported person(s). -- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -- Establish a timeline for mediation to complete, ideally within two weeks. +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + ## How the Committee will respond to reports When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + ## Clear and severe breach actions We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: -- Immediately disconnect the originator from all NumPy communication channels. -- Reply to the reporter that their report has been received and that the originator has been disconnected. -- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + ## Report handling @@ -50,10 +54,10 @@ If a report doesn’t contain enough information, the Committee will obtain all The Committee will then review the incident and determine, to the best of their ability: -- What happened. -- Whether this event constitutes a Code of Conduct violation. -- Who are the responsible party(ies). -- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). @@ -61,22 +65,23 @@ It is important to retain an archive of all activities of this Committee to ensu The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + ## Resolutions The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. Possible responses may include: -- Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. @@ -84,6 +89,7 @@ Finally, the Committee will make a report to the NumPy Steering Council (as well The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + ## Conflicts of Interest In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 9f4909fa9f1a6857a8a66f5af4781c4e87b27de6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:10 +0200 Subject: [PATCH 232/586] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 66 +++++++++++++++------------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 161757fe41..5586668cba 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -7,40 +7,44 @@ This is the manual followed by NumPy’s Code of Conduct Committee. It’s used Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: -- Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -- Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -- We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -- Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -- Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -- Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -- Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + ## Mediation Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: -- Find a candidate who can serve as a mediator. -- Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -- Obtain the agreement of the reported person(s). -- Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -- Establish a timeline for mediation to complete, ideally within two weeks. +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + ## How the Committee will respond to reports When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + ## Clear and severe breach actions We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: -- Immediately disconnect the originator from all NumPy communication channels. -- Reply to the reporter that their report has been received and that the originator has been disconnected. -- In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -- The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + ## Report handling @@ -50,10 +54,10 @@ If a report doesn’t contain enough information, the Committee will obtain all The Committee will then review the incident and determine, to the best of their ability: -- What happened. -- Whether this event constitutes a Code of Conduct violation. -- Who are the responsible party(ies). -- Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). @@ -61,22 +65,23 @@ It is important to retain an archive of all activities of this Committee to ensu The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + ## Resolutions The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. Possible responses may include: -- Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -- Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -- Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -- A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -- A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -- A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -- A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -- A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. @@ -84,6 +89,7 @@ Finally, the Committee will make a report to the NumPy Steering Council (as well The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + ## Conflicts of Interest In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From cba307546f178f030dbc43f335c3deedc432ec43 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:11 +0200 Subject: [PATCH 233/586] New translations report-handling-manual.md (Portuguese, Brazilian) --- content/pt/report-handling-manual.md | 70 +++++++++++++++------------- 1 file changed, 38 insertions(+), 32 deletions(-) diff --git a/content/pt/report-handling-manual.md b/content/pt/report-handling-manual.md index cedb1d4c5a..14418d0e11 100644 --- a/content/pt/report-handling-manual.md +++ b/content/pt/report-handling-manual.md @@ -7,40 +7,44 @@ Este é o manual seguido pelo Comitê do Código de Conduta do NumPy. É usado q Garantir que o [Código de Conduta](/code-of-conduct) seja respeitado afeta nossa comunidade hoje e no futuro. É uma ação que levamos muito a sério. Ao analisar medidas de aplicação do Código de Conduta, o Comitê terá em mente os seguintes valores e orientações: -- Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. -- Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. -- Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. -- A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. -- Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. -- Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. -- As pessoas vêm de meios culturais e linguísticos diferentes. Tentar identificar quaisquer mal-entendidos honestos causados por falantes não-nativos e ajudá-los a entender a questão e o que pode ser modificado para evitar causar ofensa. Uma discussão complexa numa língua estrangeira pode ser muito intimidante, e queremos aumentar a nossa diversidade também entre nacionalidades e culturas. +* Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. +* Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. +* Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. +* A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. +* Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. +* Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. +* As pessoas vêm de meios culturais e linguísticos diferentes. Tentar identificar quaisquer mal-entendidos honestos causados por falantes não-nativos e ajudá-los a entender a questão e o que pode ser modificado para evitar causar ofensa. Uma discussão complexa numa língua estrangeira pode ser muito intimidante, e queremos aumentar a nossa diversidade também entre nacionalidades e culturas. + ## Mediação -A mediação informal voluntária é um instrumento à nossa disposição. Em contextos em que duas ou mais partes escalaram ao ponto de demonstrarem comportamento inapropriado (algo tristemente comum no conflito humano), poderá ser útil facilitar um processo de mediação. Isto é apenas um exemplo: em todo caso, o Comitê pode considerar a mediação, tendo em conta que o processo se destina a ser estritamente voluntário e que nenhuma das partes pode ser pressionada a participar. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. Se o Comitê sugerir mediação, deve: +A mediação informal voluntária é um instrumento à nossa disposição. Em contextos em que duas ou mais partes escalaram ao ponto de demonstrarem comportamento inapropriado (algo tristemente comum no conflito humano), poderá ser útil facilitar um processo de mediação. Isto é apenas um exemplo: em todo caso, o Comitê pode considerar a mediação, tendo em conta que o processo se destina a ser estritamente voluntário e que nenhuma das partes pode ser pressionada a participar. Se o Comitê sugerir mediação, deve: -- Encontrar uma pessoa candidata que possa servir de mediadora. -- Obter o acordo da(s) pessoa(s) relatada(s). A(s) pessoa(s) relatante(s) têm total liberdade para recusar a ideia de mediação ou propor um mediador alternativo. -- Obter o acordo da(s) pessoa(s) relatante(s). -- Estabelecer uma pessoa mediadora: enquanto as partes podem propor um mediador diferente da pessoa sugerida, o processo só poderá avançar se for alcançado um acordo comum em todos os termos. -- Estabelecer um cronograma para a mediação ser concluida, idealmente dentro de duas semanas. +* Encontrar uma pessoa candidata que possa servir de mediadora. +* Obter o acordo da(s) pessoa(s) relatante(s). A(s) pessoa(s) relatante(s) têm total liberdade para recusar a ideia de mediação ou propor um mediador alternativo. +* Obter o acordo da(s) pessoa(s) relatada(s). +* Estabelecer uma pessoa mediadora: enquanto as partes podem propor um mediador diferente da pessoa sugerida, o processo só poderá avançar se for alcançado um acordo comum em todos os termos. +* Estabelecer um cronograma para a mediação ser concluida, idealmente dentro de duas semanas. A pessoa mediadora entrará em contato com todas as partes e procurará uma resolução satisfatória para todos. Após a sua conclusão, a pessoa mediadora apresentará ao Comitê um relatório (examinado por todas as partes envolvidas no processo) com recomendações sobre outras medidas. O Comitê avaliará então esses resultados (em caso de resolução satisfatória ou não) e decidirá sobre quaisquer medidas adicionais consideradas necessárias. + ## Como o Comitê responderá aos relatórios Quando o Comitê (ou um membro do Comitê) recebe um relatório, será inicialmente determinado se o relatório é sobre uma violação clara e severa (como definido abaixo). Em caso afirmativo, medidas imediatas serão tomadas para além do processo regular de tratamento dos relatórios. + ## Ações claras e severas de violação Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Trataremos rapidamente de violações claras e severas como ameaças pessoais, linguagem violenta, sexista ou racista. Quando um membro do Comitê do Código de Conduta tomar conhecimento de uma violação clara e grave, fará o seguinte: -- Desligará imediatamente a pessoa originadora de todos os canais de comunicação do NumPy. -- Responderá à pessoa relatante para informá-la que seu relatório foi recebido e que a pessoa originadora foi desligada. -- Em todos os casos, a pessoa moderadora deve fazer um esforço razoável para entrar em contato com a pessoa originadora, e dizer-lhes especificamente como sua linguagem ou ações se qualificam como uma "violação clara e severa". A pessoa moderadora deve também dizer que, se a pessoa originadora considerar que isso é injusto ou quiser ser reconectada ao NumPy, tem o direito de solicitar uma revisão, de acordo com as disposições do Comitê do Código de Conduta. A pessoa moderadora deve copiar esta explicação para o Comitê do Código de Conduta. -- O Comitê do Código de Conduta procederá formalmente à análise e decisão em todos os casos em que este mecanismo tenha sido aplicado para garantir que não seja utilizado para controlar desentendimentos acalorados comuns. +* Desligará imediatamente a pessoa originadora de todos os canais de comunicação do NumPy. +* Responderá à pessoa relatante para informá-la que seu relatório foi recebido e que a pessoa originadora foi desligada. +* Em todos os casos, a pessoa moderadora deve fazer um esforço razoável para entrar em contato com a pessoa originadora, e dizer-lhes especificamente como sua linguagem ou ações se qualificam como uma "violação clara e severa". A pessoa moderadora deve também dizer que, se a pessoa originadora considerar que isso é injusto ou quiser ser reconectada ao NumPy, tem o direito de solicitar uma revisão, de acordo com as disposições do Comitê do Código de Conduta. A pessoa moderadora deve copiar esta explicação para o Comitê do Código de Conduta. +* O Comitê do Código de Conduta procederá formalmente à análise e decisão em todos os casos em que este mecanismo tenha sido aplicado para garantir que não seja utilizado para controlar desentendimentos acalorados comuns. + ## Tratamento de relatórios @@ -50,10 +54,10 @@ Se um relatório não contiver informações suficientes, o Comitê obterá todo O Comitê analisará então o incidente e determinará, do melhor jeito possível: -- O que aconteceu. -- Se este evento constitui ou não uma violação do Código de Conduta. -- Quem são as pessoas responsáveis. -- Se se trata de uma situação contínua, e existe uma ameaça para a segurança física de alguém. +* O que aconteceu. +* Se este evento constitui ou não uma violação do Código de Conduta. +* Quem são as pessoas responsáveis. +* Se se trata de uma situação contínua, e existe uma ameaça para a segurança física de alguém. Estas informações serão recolhidas por escrito e, sempre que possível, as deliberações do grupo serão gravadas e armazenadas (por exemplo, transcrições de conversas, discussões por e-mail, chamadas gravadas de videoconferência, resumos de conversas por voz, etc). @@ -61,29 +65,31 @@ Estas informações serão recolhidas por escrito e, sempre que possível, as de O Comitê do Código de Conduta deve ter por objetivo chegar a um acordo sobre uma resolução no prazo de duas semanas. Caso uma resolução não possa ser determinada nesse período, o Comitê responderá à(s) pessoa(s) relatante(s) com uma atualização e cronograma previsto para a resolução. + ## Resoluções O Comitê tem de chegar a um acordo sobre uma resolução por consenso. Se o grupo não conseguir chegar a um consenso e permanece bloqueado durante mais de uma semana, o grupo encaminhará o assunto para o Conselho Diretor para resolução. Possíveis respostas podem incluir: -- Não tomar nenhuma outra ação: - - se determinarmos que não ocorreram violações; - - se a questão tiver sido resolvida publicamente enquanto o Comitê estava considerando uma resposta. -- Coordenação de mediação voluntária: se todas as partes envolvidas concordarem, o Comitê poderá facilitar um processo de mediação, conforme detalhado acima. -- Salientar publicamente que alguns comportamentos, ações ou linguagem foram julgados inapropriados ou podem ser considerados danosos para algumas pessoas, explicando por que no contexto atual e solicitando que a comunidade se auto-ajuste. -- Uma advertência privada do Comitê para a(s) pessoa(s) envolvida(s). Neste caso, a pessoa presidente do Comitê irá entregar essa advertência à(s) pessoa(s) por e-mail, em cópia (CC) ao grupo. -- Uma advertência pública. Neste caso, a pessoa presidente do Comitê vai apresentar essa advertência no mesmo fórum em que ocorreu a violação, dentro dos limites da viabilidade. Exemplo: a lista original para uma violação de e-mail, mas para uma discussão em sala de bate-papo onde a pessoa/contexto pode sumir, isto pode ser feito por outros meios. O grupo pode optar por publicar esta mensagem em outro local para fins de documentação. -- Um pedido de desculpas públicas ou privadas, supondo que a(s) pessoa(s) relatante(s) concorde(m) com esta ideia: a(s) pessoa(s) pode(m), a seu critério, recusar contatos adicionais com a pessoa relatada. A Presidência dará seguimento a este pedido. O Comitê, se escolher, pode anexar condições adicionais a este pedido inicial: por exemplo, o grupo pode pedir à pessoa relatada que se desculpe para que tenha o direito de manter a sua adesão a uma lista de e-mails. -- Um “acordo mútuo de trégua” onde o Comitê solicita à pessoa que se abstenha temporariamente da participação na comunidade. Se a pessoa optar por não fazer uma pausa temporária voluntariamente, o Comitê pode aplicar um “período de afastamento obrigatório”. -- Um banimento permanente ou temporário de alguns ou todos os espaços do NumPy (listas de e-mails, gitter.im, etc.). O grupo manterá registro de todas essas proibições, para que elas possam ser revistas no futuro ou mantidas. +* Não tomar nenhuma outra ação: + - se determinarmos que não ocorreram violações; + - se a questão tiver sido resolvida publicamente enquanto o Comitê estava considerando uma resposta. +* Coordenação de mediação voluntária: se todas as partes envolvidas concordarem, o Comitê poderá facilitar um processo de mediação, conforme detalhado acima. +* Salientar publicamente que alguns comportamentos, ações ou linguagem foram julgados inapropriados ou podem ser considerados danosos para algumas pessoas, explicando por que no contexto atual e solicitando que a comunidade se auto-ajuste. +* Uma advertência privada do Comitê para a(s) pessoa(s) envolvida(s). Neste caso, a pessoa presidente do Comitê irá entregar essa advertência à(s) pessoa(s) por e-mail, em cópia (CC) ao grupo. +* Uma advertência pública. Neste caso, a pessoa presidente do Comitê vai apresentar essa advertência no mesmo fórum em que ocorreu a violação, dentro dos limites da viabilidade. Exemplo: a lista original para uma violação de e-mail, mas para uma discussão em sala de bate-papo onde a pessoa/contexto pode sumir, isto pode ser feito por outros meios. O grupo pode optar por publicar esta mensagem em outro local para fins de documentação. +* Um pedido de desculpas públicas ou privadas, supondo que a(s) pessoa(s) relatante(s) concorde(m) com esta ideia: a(s) pessoa(s) pode(m), a seu critério, recusar contatos adicionais com a pessoa relatada. A Presidência dará seguimento a este pedido. O Comitê, se escolher, pode anexar condições adicionais a este pedido inicial: por exemplo, o grupo pode pedir à pessoa relatada que se desculpe para que tenha o direito de manter a sua adesão a uma lista de e-mails. +* Um “acordo mútuo de trégua” onde o Comitê solicita à pessoa que se abstenha temporariamente da participação na comunidade. Se a pessoa optar por não fazer uma pausa temporária voluntariamente, o Comitê pode aplicar um “período de afastamento obrigatório”. +* Um banimento permanente ou temporário de alguns ou todos os espaços do NumPy (listas de e-mails, gitter.im, etc.). O grupo manterá registro de todas essas proibições, para que elas possam ser revistas no futuro ou mantidas. Uma vez aprovada uma resolução, mas antes de ser efetivamente aplicada, o Comitê entrará em contato com a pessoa relatante original e quaisquer outras partes afetadas e explicará a resolução proposta. O Comitê perguntará se esta resolução é aceitável e terá de tomar nota da sua resposta para registro futuro. -Finalmente, o Comitê apresentará um relatório ao Conselho Diretor do NumPy (bem como ao time _core_ do NumPy no caso de uma resolução em curso, como um banimento). +Finalmente, o Comitê apresentará um relatório ao Conselho Diretor do NumPy (bem como ao time *core* do NumPy no caso de uma resolução em curso, como um banimento). O Comitê nunca discutirá publicamente a questão; todas as declarações públicas serão feitas pela pessoa presidente do Comitê do Código de Conduta ou pelo Conselho Diretor do NumPy. + ## Conflitos de Interesse Em caso de conflito de interesses, um membro do Comitê deve notificar imediatamente os outros membros e abdicar de sua participação no processo caso seja necessário. From 5cb2fb545085a7a6f908a2a32fa37e064c021d1a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:12 +0200 Subject: [PATCH 234/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 116 +++++++++++++-------------------------- 1 file changed, 37 insertions(+), 79 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 3e9565acf4..75e24296f6 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -1,108 +1,66 @@ --- -title: Contribute to NumPy +title: Contribuye a NumPy sidebar: false --- -The NumPy project welcomes your expertise and enthusiasm! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. +¡El proyecto NumPy agradece tu experiencia y entusiasmo! Tus opciones no se limitan a la programación. Como puedes ver más abajo, existen muchas áreas en las que necesitamos **tu** ayuda. -If you're unsure where to start or how your skills fit in, _reach out!_ You -can ask on the mailing -list or -[GitHub](http://github.com/numpy/numpy) (open an -[issue](https://github.com/numpy/numpy/issues) or comment on a relevant -issue). +Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [propuesta](https://github.com/numpy/numpy/issues) o comenta en una relevante). -Those are our preferred channels (open source is open by nature), but -if you prefer to talk privately, contact our community coordinators at -numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) -(write numpy-team@googlegroups.com for an invite). +Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la communidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). -We also have a biweekly _community call_, details of which are announced on -the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). -You are very welcome to join. -If you are new to contributing to open source, we also highly recommend reading -[this guide](https://opensource.guide/how-to-contribute/). +También hacemos _llamadas a la comunidad_ de manera quincenal, cuyos detalles se anuncian en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion). Te invitamos a unirte. Si es la primera vez que contribuyes al código abierto, también te recomendamos encarecidamente que leas [esta guía](https://opensource.guide/how-to-contribute/). -Our community aspires to treat everyone equally and to value all -contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open -and welcoming environment. +Nuestra comunidad aspira a tratar a todos por igual y a valorar todas las contribuciones. Tenemos un [Código de Conducta](/code-of-conduct) para fomentar un entorno abierto y acogedor. -### Writing code +### Escribiendo código -Programmers, this -[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) -explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica cómo contribuir al código base.
También revisa nuestro [canal de YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) por consejos adicionales. -### Reviewing pull requests -The project has more than 250 open pull requests -- meaning many potential -improvements and many open-source contributors waiting for feedback. If you're -a developer who knows NumPy, you can help even if you're not familiar with the -codebase. You can: +### Revisando solicitudes de cambios +El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: +* resumir un debate extenso +* categorizar PRs de documentación +* probar los cambios propuestos -- summarize a long-running discussion -- triage documentation PRs -- test proposed changes -### Developing educational materials +### Creando material educativo -NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. -We're in need of new tutorials, how-to's, and deep-dive explanations, and the -site needs restructuring. Opportunities aren't limited to writers. We'd also -welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the -NumPyDocumentation -lays out our ideas -- and you may have others. +La [Guía de usuario](https://numpy.org/devdocs) de NumPy está en proceso de rehabilitación. Necesitamos nuevos tutoriales, instrucciones y explicaciones detalladas, y la página necesita una reestructuración. Las oportunidades no se limitan a escritores. También ejemplos prácticos, notebooks y vídeos. La propuesta [NEP 44 - Reestructuración de la Documentación NumPy](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) expone nuestras ideas -- y tal vez tú puedas tener otras. -### Issue triaging -The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ -of open issues. Some are no longer valid, some should be prioritized, and some -would make good issues for new contributors. You can: +### Clasificación de propuestas -- check if older bugs are still present -- find duplicate issues and link related ones -- add good self-contained reproducers to issues -- label issues correctly (this requires triage rights -- just ask) +El [rastreador de propuestas de NumPy](https://github.com/numpy/numpy/issues) tiene _muchos_ temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes: -Please just dive in. +* revisar si errores antiguos siguen presentes +* encontrar propuestas duplicadas, y enlazar las relacionadas +* añadir buenos reproductores autónomos de las incidencias +* etiquetar correctamente las propuestas (para ello es necesario tener derechos de categorización, solo necesitas preguntar) -### Website development +Por favor, solo sumérgete. -We've just revamped our website, but we're far from done. If you love web -development, these -[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) -list some of our unmet needs -- and feel free to share your own ideas. -### Graphic design +### Desarrollo de la Página Web -We can barely begin to list the contributions a graphic designer can make here. -Our docs are parched for illustration; our growing website craves images -- -opportunities abound. +Acabamos de renovar nuestro sitio web, pero aún no hemos terminado. Si te gusta el desarrollo web, estas [incidencias](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) enumeran algunas de nuestras necesidades insatisfechas -- y no dudes en compartir tus propias ideas. -### Translating website content -We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) -for background; comment on this GitHub -issue to sign up. +### Diseño gráfico -### Community coordination and outreach +Apenas podemos empezar a enumerar las aportaciones que puede hacer un diseñador gráfico. Nuestra documentación está sedienta de ilustraciones; nuestro sitio web, en pleno crecimiento, ansía imágenes... las oportunidades abundan. -Through community contact we share our work more widely and learn where we're -falling short. We're eager to get more people involved in efforts like our -[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code -sprints, a newsletter, and perhaps a blog. -### Fundraising +### Traduciendo el contenido de la página web -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. This -SciPy'19 talk explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +Planeamos múltiples traducciones de [numpy.org](https://numpy.org) para hacer a NumPy accesible a los usuarios en su lengua materna. Los traductores voluntarios son el núcleo de este esfuerzo. Consulta [aquí](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para más información; comenta en [este tema de GitHub](https://github.com/numpy/numpy.org/issues/55) para inscribirte. + + +### Coordinación y divulgación de la comunidad + +A través del contacto con la comunidad compartimos nuestro trabajo más ampliamente, y aprendemos en dónde nos estamos quedando cortos. Estamos ansiosos por conseguir más gente involucrada en esfuerzos como nuestra cuenta de [Twitter](https://twitter.com/numpy_team), organizando [code sprints](https://scisprints.github.io/) de NumPy, un boletín y quizás un blog. + +### Recaudación de fondos + +NumPy fue durante muchos años un proyecto voluntario, pero a medida que crecía su importancia se hizo evidente que necesitaríamos apoyo financiero para garantizar su estabilidad y crecimiento. [Esta charla en SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica cuánta diferencia ha supuesto este apoyo. Como todo en el mundo sin ánimo de lucro, estamos constantemente en busca de subvenciones, patrocinios y otros tipos de ayuda. Tenemos varias ideas y, por supuesto, aceptamos más. La recaudación de fondos es una habilidad escasa aquí -- apreciaríamos tu ayuda. From 03df283090f9bc99fcdc58a674f988a1e9c975b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:14 +0200 Subject: [PATCH 235/586] New translations contribute.md (Arabic) --- content/ar/contribute.md | 104 ++++++++++++--------------------------- 1 file changed, 31 insertions(+), 73 deletions(-) diff --git a/content/ar/contribute.md b/content/ar/contribute.md index 3e9565acf4..6efff53624 100644 --- a/content/ar/contribute.md +++ b/content/ar/contribute.md @@ -3,106 +3,64 @@ title: Contribute to NumPy sidebar: false --- -The NumPy project welcomes your expertise and enthusiasm! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. - -If you're unsure where to start or how your skills fit in, _reach out!_ You -can ask on the mailing -list or -[GitHub](http://github.com/numpy/numpy) (open an -[issue](https://github.com/numpy/numpy/issues) or comment on a relevant -issue). - -Those are our preferred channels (open source is open by nature), but -if you prefer to talk privately, contact our community coordinators at -numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) -(write numpy-team@googlegroups.com for an invite). - -We also have a biweekly _community call_, details of which are announced on -the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). -You are very welcome to join. -If you are new to contributing to open source, we also highly recommend reading -[this guide](https://opensource.guide/how-to-contribute/). - -Our community aspires to treat everyone equally and to value all -contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open -and welcoming environment. +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). + +Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). + +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. ### Writing code -Programmers, this -[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) -explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. -### Reviewing pull requests -The project has more than 250 open pull requests -- meaning many potential -improvements and many open-source contributors waiting for feedback. If you're -a developer who knows NumPy, you can help even if you're not familiar with the -codebase. You can: +### Reviewing pull requests +The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: +* summarize a long-running discussion +* triage documentation PRs +* test proposed changes -- summarize a long-running discussion -- triage documentation PRs -- test proposed changes ### Developing educational materials -NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. -We're in need of new tutorials, how-to's, and deep-dive explanations, and the -site needs restructuring. Opportunities aren't limited to writers. We'd also -welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the -NumPyDocumentation -lays out our ideas -- and you may have others. +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. + ### Issue triaging -The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ -of open issues. Some are no longer valid, some should be prioritized, and some -would make good issues for new contributors. You can: +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: -- check if older bugs are still present -- find duplicate issues and link related ones -- add good self-contained reproducers to issues -- label issues correctly (this requires triage rights -- just ask) +* check if older bugs are still present +* find duplicate issues and link related ones +* add good self-contained reproducers to issues +* label issues correctly (this requires triage rights -- just ask) Please just dive in. + ### Website development -We've just revamped our website, but we're far from done. If you love web -development, these -[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) -list some of our unmet needs -- and feel free to share your own ideas. +We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. + ### Graphic design -We can barely begin to list the contributions a graphic designer can make here. -Our docs are parched for illustration; our growing website craves images -- -opportunities abound. +We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. + ### Translating website content -We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) -for background; comment on this GitHub -issue to sign up. +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. + ### Community coordination and outreach -Through community contact we share our work more widely and learn where we're -falling short. We're eager to get more people involved in efforts like our -[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code -sprints, a newsletter, and perhaps a blog. +Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. This -SciPy'19 talk explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. From 4d883a0e7fcd8bd6edb8caa4ada1845965ca9502 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:15 +0200 Subject: [PATCH 236/586] New translations contribute.md (Japanese) --- content/ja/contribute.md | 70 +++++++++++++++------------------------- 1 file changed, 26 insertions(+), 44 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 644e7eaa61..90db608852 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -3,82 +3,64 @@ title: NumPy に貢献する sidebar: false --- -The NumPy project welcomes your expertise and enthusiasm! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. +NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページには**あなたができる** 様々な種類の貢献方法が示されています。 もしどこから始めればいいか、あなたのスキルをどう生かせばいいかがわからない場合は、 _是非ご連絡下さい。 _ 連絡の方法としては、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) 、 [GitHub](http://github.com/numpy/numpy)、 [イシューの作成](https://github.com/numpy/numpy/issues) 、関連するイシューへのコメントがあります。 -連絡先としては、 numpy-team@googlegroups.com または、[Slack](https://numpy-team.slack.com) (グループに招待するためにこちらに連絡お願いします: numpy-team@googlegroups.com)があります。 +連絡先としては、 または、[Slack](https://numpy-team.slack.com) (グループに招待するためにこちらに連絡お願いします: )があります。 また、隔週の _コミュニティミーティング_もあり、詳細は [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) で発表されています。 あなたの参加を大いに歓迎します。 オープンソースプロジェクトに貢献するのが初めての方は、是非、 [このガイド](https://opensource.guide/how-to-contribute/) を読んでみて下さい。 -You are very welcome to join. -If you are new to contributing to open source, we also highly recommend reading -[this guide](https://opensource.guide/how-to-contribute/). -私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために [行動基準](/ja/code-of-conduct) を制定しています。 We have a [Code of Conduct](/code-of-conduct) to foster an open -and welcoming environment. +私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために [行動基準](/ja/code-of-conduct) を制定しています。 ### コードを書く -プログラマーの方には、こちらの [ガイド](https://numpy.org/devdocs/dev/index.html#development-process-summary)でNumPyのコードに貢献する方法を説明しています。
追加情報に関しては、 こちらの[YouTube チャンネル](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) もご覧ください。
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +プログラマーの方には、こちらの [ガイド](https://numpy.org/devdocs/dev/index.html#development-process-summary)でNumPyのコードに貢献する方法を説明しています。
追加情報に関しては、 こちらの[YouTube チャンネル](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) もご覧ください。 -### プルリクエストのレビュー -The project has more than 250 open pull requests -- meaning many potential -improvements and many open-source contributors waiting for feedback. If you're -a developer who knows NumPy, you can help even if you're not familiar with the -codebase. You can: +### プルリクエストのレビュー +NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 +* 長期にわたる議論をまとめる +* ドキュメントのPRをトリアージする +* 提案された変更をテストする -- 長期にわたる議論をまとめる -- ドキュメントのPRをトリアージする -- 提案された変更をテストする ### 教育用の資料を作成する NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模な再設計中です。 新しいNumPyのWebページは、新しいチュートリアルや、NumPyの使い方、NumPy内部の深い説明など必要としており、サイト全体にも再設計と再構築が必要です。 このウェブサイトの再構築の作業は、ドキュメントを書くだけではありません。 コード例や、ノートブック、ビデオなどの作成も歓迎しています。 [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)に、ウェブサイトの再構築についての詳細が説明されています。 -We're in need of new tutorials, how-to's, and deep-dive explanations, and the -site needs restructuring. Opportunities aren't limited to writers. We'd also -welcome worked examples, notebooks, and videos. NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページには**あなたができる** 様々な種類の貢献方法が示されています。 -### Issue triaging -[NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: Some are no longer valid, some should be prioritized, and some -would make good issues for new contributors. You can: +### イシューのトリアージ + +[NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: + +* 古いバグがまだ残っているか確認する +* 重複したイシューを見つけ、お互いに関連づける +* 問題を再現するコードを作成する +* イシューに正しいラベル付けをする (トリアージ権が必要なので、連絡下さい) -- 古いバグがまだ残っているか確認する -- find duplicate issues and link related ones -- add good self-contained reproducers to issues -- 問題を再現するコードを作成する イシューに正しいラベル付けをする (トリアージ権が必要なので、連絡下さい) +ぜひ、やってみて下さい。 -Please just dive in. ### ウェブサイトの開発 -We've just revamped our website, but we're far from done. 私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、この[イシュー](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。 +私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、この[イシュー](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。 + ### グラフィックデザイン -We can barely begin to list the contributions a graphic designer can make here. -Our docs are parched for illustration; our growing website craves images -- -opportunities abound. +グラフィックデザイナーの方が可能な貢献は、枚挙にいとまがありません。 しかし、私たちのドキュメントは説明のために可視化が重要であり、私たちの拡大しているウェブサイトは良い画像を求めていることから、 貢献する機会が沢山あると言えます。 + ### ウェブサイトの翻訳 -NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 Volunteer translators are at the heart -of this effort. 私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 +私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 + ### コミュニティとの連携とアウトリーチ -Through community contact we share our work more widely and learn where we're -falling short. コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 +コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 ### 資金調達 -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. This -SciPy'19 talk explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +NumPyは何年にも渡ってボランティアだけ活動していましたが、その重要性が高まるにつれ、安定性と成長のためには資金面での支援が必要であることがわかってきました。 こちらの[SciPy'19のプレゼン](https://www.youtube.com/watch?v=dBTJD_FDVjU) では、資金的なサポートを受けたことで、どれだけ違いが出たかを説明しています。 他の非営利団体のように、私たちは助成金や、スポンサーシップ、その他の資金支援を常に探しています。 私たちはすでにいくつかの資金調達のアイデアを持っていますが、他にもより多くを資金調達を受けたいと思っています。 資金調達に関する知識は、我々には不足しているスキルです。 是非、あなたのサポートをお待ちしています。 From 55b873c8eac3d0c94be0b43e4720c28109f4d0fe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:16 +0200 Subject: [PATCH 237/586] New translations contribute.md (Korean) --- content/ko/contribute.md | 116 +++++++++++++-------------------------- 1 file changed, 37 insertions(+), 79 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 3e9565acf4..51ce2bd4aa 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -1,108 +1,66 @@ --- -title: Contribute to NumPy +title: NumPy 프로젝트에 기여하기 sidebar: false --- -The NumPy project welcomes your expertise and enthusiasm! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. +NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! 당신의 선택지는 프로그래밍에만 국한되어 있지않습니다. 아래의 참여방법들을 확인하면 많은 부분에서 **당신의 도움**이 필요한 것을 알수있습니다. -If you're unsure where to start or how your skills fit in, _reach out!_ You -can ask on the mailing -list or -[GitHub](http://github.com/numpy/numpy) (open an -[issue](https://github.com/numpy/numpy/issues) or comment on a relevant -issue). +시작점을 찾기 힘들거나 재능을 어떻게 활용해야 할지 잘 모르겠다면, _물어보세요!_ [메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion)나 [GitHub](http://github.com/numpy/numpy) ([이슈](https://github.com/numpy/numpy/issues)를 생성하거나 관련 이슈에 답글을 다세요)에서 질문하시면 됩니다. -Those are our preferred channels (open source is open by nature), but -if you prefer to talk privately, contact our community coordinators at -numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) -(write numpy-team@googlegroups.com for an invite). +앞서 소개드린 것들이 저희가 선호하는 연락 채널입니다. (오픈 소스는 원래 개방되어 있으니까요) 하지만 비공개적으로 대화를 나누고 싶으시다면, 을 통해 커뮤니티 코디네이터로 연락하시거나 [Slack](https://numpy-team.slack.com)을 이용하시면 됩니다. (초대를 받으시려면 을 쓰시면 됩니다). -We also have a biweekly _community call_, details of which are announced on -the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). -You are very welcome to join. -If you are new to contributing to open source, we also highly recommend reading -[this guide](https://opensource.guide/how-to-contribute/). +또한 저희는 격주마다 _커뮤니티 연락_을 합니다. 자세한 정보는 [메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion)로 알립니다. 당신의 참여를 매우 환영합니다. 오픈소스에 기여하는 게 처음이시라면 [이 도움말](https://opensource.guide/how-to-contribute/)을 읽어 보시는 것을 적극 권장합니다. -Our community aspires to treat everyone equally and to value all -contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open -and welcoming environment. +저희 커뮤니티는 모두를 평등하게 대하고 모든 기여의 가치를 인정하려는 뜻을 품고 있습니다. 개방적이고 참여를 환영하는 분위기를 조성하기 위해 [이용약관](/code-of-conduct)을 만들었습니다. -### Writing code +### 코드 작성 -Programmers, this -[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) -explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +프로그래머 여러분, 이 [도움말](https://numpy.org/devdocs/dev/index.html#development-process-summary)에 어떻게 코드베이스에 기여하는지 설명되어 있습니다.
저희의 [유투브 채널](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS)을 통해서 추가적인 정보를 확인 해주세요. -### Reviewing pull requests -The project has more than 250 open pull requests -- meaning many potential -improvements and many open-source contributors waiting for feedback. If you're -a developer who knows NumPy, you can help even if you're not familiar with the -codebase. You can: +### Pull Request 리뷰 +프로젝트의 열린 풀 요청만 250개가 넘습니다. 즉 많은 잠재적 개선점과 오픈소스 기여자들이 피드백을 기다리고 있다는 것입니다. NumPy를 알고 있는 개발자라면, 코드베이스에 대해 잘 알지 못해도 기여할 수 있습니다. 아래와 같은 기여를 해 보십시오. +* 늘어지는 토론 요약 +* 문서의 풀 요청 심사 +* 제안된 변경 사항 테스트 -- summarize a long-running discussion -- triage documentation PRs -- test proposed changes -### Developing educational materials +### 교육 자료 개발 -NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. -We're in need of new tutorials, how-to's, and deep-dive explanations, and the -site needs restructuring. Opportunities aren't limited to writers. We'd also -welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the -NumPyDocumentation -lays out our ideas -- and you may have others. +NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 재구성되고 있습니다. 현재 새로운 튜토리얼, 방법, 심층적 설명이 필요하고, 사이트의 구조를 다시 짜야 합니다. 글을 쓰는 사람에게만 기회가 주어지는 것은 아닙니다. 코드 예제와 노트북, 동영상 등을 통한 기여도 환영합니다. [NEP 44 — NumPy 문서의 재구성](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)에 사이트 재구성에 대하여 자세한 내용이 설명되어 있습니다. -### Issue triaging -The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ -of open issues. Some are no longer valid, some should be prioritized, and some -would make good issues for new contributors. You can: +### 이슈 확인 -- check if older bugs are still present -- find duplicate issues and link related ones -- add good self-contained reproducers to issues -- label issues correctly (this requires triage rights -- just ask) +[NumPy 이슈 트래커](https://github.com/numpy/numpy/issues)에는 _정말 많은_ 이슈들이 현재 열린 상태로 있습니다. 일부는 더 이상 유효하지 않은 이슈고, 일부는 우선 순위를 지정해야 하며, 일부는 새로운 기여자들이 볼 만한 좋은 이슈가 될 수 있을 것입니다. 아래와 같은 기여를 해 보십시오. -Please just dive in. +* 오래된 버그가 현재도 남아 있는지 확인 +* 중복된 이슈를 찾아 하나로 묶기 +* 이슈를 재현하는 코드를 추가 +* 이슈를 올바르게 라벨링 (이 작업에는 심사 권한이 필요합니다. 필요한 경우 요청하십시오) -### Website development +한 번 참여해 보시길 바랍니다. -We've just revamped our website, but we're far from done. If you love web -development, these -[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) -list some of our unmet needs -- and feel free to share your own ideas. -### Graphic design +### 웹사이트 개발 -We can barely begin to list the contributions a graphic designer can make here. -Our docs are parched for illustration; our growing website craves images -- -opportunities abound. +사이트를 막 뜯어 고친 상태이지만, 아직 끝이라기엔 멀었습니다. 웹 개발을 좋아하신다면, [여기](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign)에서 저희가 이루지 못했던 사항의 목록을 볼 수 있습니다. 자신만의 아이디어를 마음껏 공유해 주십시오. -### Translating website content -We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) -for background; comment on this GitHub -issue to sign up. +### 그래픽 디자인 -### Community coordination and outreach +그래픽 디자이너분들이 할 수 있는 기여의 목록을 여기에 열거하는 건 어렵습니다. 저희 문서에는 일러스트가 많이 부족합니다. 성장하는 사이트에는 이미지가 필요하기 때문에, 기여할 수 있는 기회가 많을 것입니다. -Through community contact we share our work more widely and learn where we're -falling short. We're eager to get more people involved in efforts like our -[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code -sprints, a newsletter, and perhaps a blog. -### Fundraising +### 웹사이트 번역 -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. This -SciPy'19 talk explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +사용자가 모국어로 NumPy를 이용할 수 있도록 [numpy.org](https://numpy.org)의 여러 번역을 계획하고 있습니다. 이를 위해서는 자원봉사자분들의 통역이 필요합니다. 자세한 내용은 [여기](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)를 참고하십시오. [이 GitHub 이슈](https://github.com/numpy/numpy.org/issues/55)에 댓글을 달아 번역에 참여하십시오. + + +### 커뮤니티 조직 및 확산 + +우리는 커뮤니티 연락처를 통해 작업물을 더 널리 공유하고 미흡한 부분을 배워 나갑니다. 우리는 [Twitter](https://twitter.com/numpy_team) 계정, NumPy [코드 스프린트](https://scisprints.github.io/) 개최, 뉴스레터 발행, 그리고 아마 블로그 등을 통해서 더 많은 사람들이 커뮤니티에 참여하기를 간절히 바라고 있습니다. + +### 모금 + +NumPy는 오랜 기간 동안 자원봉사의 형태로 유지되었으나, 그 중요성이 커짐에 따라 안정성 및 성장을 보장하려면 경제적 지원이 필요함이 분명해졌습니다. 이런 지원이 얼마나 큰 차이를 만들어 냈는지 [SciPy'19 강연](https://www.youtube.com/watch?v=dBTJD_FDVjU)에서 확인하실 수 있습니다. 모든 비영리 조직과 마찬가지로 저희는 지속적으로 보조금, 후원 및 기타 종류의 지원을 끊임없이 찾고 있습니다. 모금을 받을 아이디어가 몇 개 있지만 당연히 더 많은 자금을 받게 된다면 좋을 것입니다. 모금도 정말 희귀한 능력 중 하나입니다 - 도움을 주신다면 감사드리겠습니다. From 3ed1688c7b40974a54c01a8260a3cd276ed44120 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:17 +0200 Subject: [PATCH 238/586] New translations contribute.md (Russian) --- content/ru/contribute.md | 104 ++++++++++++--------------------------- 1 file changed, 31 insertions(+), 73 deletions(-) diff --git a/content/ru/contribute.md b/content/ru/contribute.md index 3e9565acf4..6efff53624 100644 --- a/content/ru/contribute.md +++ b/content/ru/contribute.md @@ -3,106 +3,64 @@ title: Contribute to NumPy sidebar: false --- -The NumPy project welcomes your expertise and enthusiasm! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. - -If you're unsure where to start or how your skills fit in, _reach out!_ You -can ask on the mailing -list or -[GitHub](http://github.com/numpy/numpy) (open an -[issue](https://github.com/numpy/numpy/issues) or comment on a relevant -issue). - -Those are our preferred channels (open source is open by nature), but -if you prefer to talk privately, contact our community coordinators at -numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) -(write numpy-team@googlegroups.com for an invite). - -We also have a biweekly _community call_, details of which are announced on -the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). -You are very welcome to join. -If you are new to contributing to open source, we also highly recommend reading -[this guide](https://opensource.guide/how-to-contribute/). - -Our community aspires to treat everyone equally and to value all -contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open -and welcoming environment. +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). + +Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). + +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. ### Writing code -Programmers, this -[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) -explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. -### Reviewing pull requests -The project has more than 250 open pull requests -- meaning many potential -improvements and many open-source contributors waiting for feedback. If you're -a developer who knows NumPy, you can help even if you're not familiar with the -codebase. You can: +### Reviewing pull requests +The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: +* summarize a long-running discussion +* triage documentation PRs +* test proposed changes -- summarize a long-running discussion -- triage documentation PRs -- test proposed changes ### Developing educational materials -NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. -We're in need of new tutorials, how-to's, and deep-dive explanations, and the -site needs restructuring. Opportunities aren't limited to writers. We'd also -welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the -NumPyDocumentation -lays out our ideas -- and you may have others. +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. + ### Issue triaging -The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ -of open issues. Some are no longer valid, some should be prioritized, and some -would make good issues for new contributors. You can: +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: -- check if older bugs are still present -- find duplicate issues and link related ones -- add good self-contained reproducers to issues -- label issues correctly (this requires triage rights -- just ask) +* check if older bugs are still present +* find duplicate issues and link related ones +* add good self-contained reproducers to issues +* label issues correctly (this requires triage rights -- just ask) Please just dive in. + ### Website development -We've just revamped our website, but we're far from done. If you love web -development, these -[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) -list some of our unmet needs -- and feel free to share your own ideas. +We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. + ### Graphic design -We can barely begin to list the contributions a graphic designer can make here. -Our docs are parched for illustration; our growing website craves images -- -opportunities abound. +We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. + ### Translating website content -We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) -for background; comment on this GitHub -issue to sign up. +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. + ### Community coordination and outreach -Through community contact we share our work more widely and learn where we're -falling short. We're eager to get more people involved in efforts like our -[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code -sprints, a newsletter, and perhaps a blog. +Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. This -SciPy'19 talk explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. From 0daf99cb2ce66ca45a14f5115d39ee14f077e13d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:18 +0200 Subject: [PATCH 239/586] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 104 ++++++++++++--------------------------- 1 file changed, 31 insertions(+), 73 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 3e9565acf4..6efff53624 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -3,106 +3,64 @@ title: Contribute to NumPy sidebar: false --- -The NumPy project welcomes your expertise and enthusiasm! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. - -If you're unsure where to start or how your skills fit in, _reach out!_ You -can ask on the mailing -list or -[GitHub](http://github.com/numpy/numpy) (open an -[issue](https://github.com/numpy/numpy/issues) or comment on a relevant -issue). - -Those are our preferred channels (open source is open by nature), but -if you prefer to talk privately, contact our community coordinators at -numpy-team@googlegroups.com or on [Slack](https://numpy-team.slack.com) -(write numpy-team@googlegroups.com for an invite). - -We also have a biweekly _community call_, details of which are announced on -the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). -You are very welcome to join. -If you are new to contributing to open source, we also highly recommend reading -[this guide](https://opensource.guide/how-to-contribute/). - -Our community aspires to treat everyone equally and to value all -contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open -and welcoming environment. +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). + +Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). + +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. ### Writing code -Programmers, this -[guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) -explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. -### Reviewing pull requests -The project has more than 250 open pull requests -- meaning many potential -improvements and many open-source contributors waiting for feedback. If you're -a developer who knows NumPy, you can help even if you're not familiar with the -codebase. You can: +### Reviewing pull requests +The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: +* summarize a long-running discussion +* triage documentation PRs +* test proposed changes -- summarize a long-running discussion -- triage documentation PRs -- test proposed changes ### Developing educational materials -NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. -We're in need of new tutorials, how-to's, and deep-dive explanations, and the -site needs restructuring. Opportunities aren't limited to writers. We'd also -welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the -NumPyDocumentation -lays out our ideas -- and you may have others. +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. + ### Issue triaging -The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ -of open issues. Some are no longer valid, some should be prioritized, and some -would make good issues for new contributors. You can: +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: -- check if older bugs are still present -- find duplicate issues and link related ones -- add good self-contained reproducers to issues -- label issues correctly (this requires triage rights -- just ask) +* check if older bugs are still present +* find duplicate issues and link related ones +* add good self-contained reproducers to issues +* label issues correctly (this requires triage rights -- just ask) Please just dive in. + ### Website development -We've just revamped our website, but we're far from done. If you love web -development, these -[issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) -list some of our unmet needs -- and feel free to share your own ideas. +We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. + ### Graphic design -We can barely begin to list the contributions a graphic designer can make here. -Our docs are parched for illustration; our growing website craves images -- -opportunities abound. +We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. + ### Translating website content -We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) -for background; comment on this GitHub -issue to sign up. +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. + ### Community coordination and outreach -Through community contact we share our work more widely and learn where we're -falling short. We're eager to get more people involved in efforts like our -[Twitter](https://twitter.com/numpy_team) account, organizing NumPy code -sprints, a newsletter, and perhaps a blog. +Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. This -SciPy'19 talk explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. From f525a48c55a1d360a7f051b461660ea693a8fcdb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:19 +0200 Subject: [PATCH 240/586] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 44 +++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 23 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index fe039ce94c..65b82636b8 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -3,17 +3,13 @@ title: Contribua com o NumPy sidebar: false --- -O projeto NumPy precisa de sua experiência e entusiasmo! -Your choices aren't limited to programming, as you can -see below there are many areas where we need **your** help. +O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação -- além de Se você não sabe por onde começar ou como suas habilidades podem ajudar, _fale conosco!_ Você pode perguntar na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion) ou [GitHub](http://github.com/numpy/numpy) (abrindo uma [issue](https://github.com/numpy/numpy/issues) ou comentando em uma issue relevante). -No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em numpy-team@googlegroups.com ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para numpy-team@googlegroups.com para obter um convite antes de entrar). +Estes são os nossos canais de comunicação preferidos (projetos de código aberto são abertos por natureza!). No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para para obter um convite antes de entrar). -Os detalhes são anunciados na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion). -You are very welcome to join. -Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). +Nós também temos uma _reunião aberta da comunidade_ a cada duas semanas. Os detalhes são anunciados na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion). Convidamos você a participar. Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contribuições. Temos um [Código de Conduta](/pt/code-of-conduct) para promover um ambiente aberto e acolhedor. @@ -21,43 +17,45 @@ Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contrib Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código.
Confira também nosso [canal do YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) para obter informações adicionais. -### Revisar pull requests +### Revisar pull requests O projeto tem mais de 250 pull requests abertos -- o que significa que muitas potenciais melhorias e muitos contribuidores de código aberto estão aguardando feedback. Se você é uma pessoa programadora que conhece o NumPy, você pode ajudar, mesmo que não tenha familiaridade com o código. Você pode: +* resumir uma discussão longa +* fazer triagem de PRs de documentação +* testar alterações propostas -- resumir uma discussão longa -- fazer triagem de PRs de documentação -- testar alterações propostas ### Desenvolvimento de materiais educacionais -O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. -Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. +O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. + ### Triagem de Issues -O [_issue tracker_ do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: Sinta-se à vontade! +O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: + +* verificar se erros mais antigos ainda estão presentes +* encontrar issues duplicadas e criar links entre issues relacionadas +* adicionar bons exemplos autocontidos que reproduzam issues +* rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) -- verificar se erros mais antigos ainda estão presentes -- encontrar issues duplicadas e criar links entre issues relacionadas -- adicionar bons exemplos autocontidos que reproduzam issues -- rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) +Sinta-se à vontade! -Please just dive in. ### Desenvolvimento do site Acabamos de renovar o nosso site, mas estamos longe de terminar. Se você adora o desenvolvimento web, estas [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) listam algumas de nossas necessidades não atendidas -- e sinta-se livre para compartilhar suas próprias ideias. + ### Design gráfico -Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. -Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. +Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. + ### Traduzir conteúdo do site -Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Volunteer translators are at the heart -of this effort. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. +Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. + ### Coordenação e promoção na comunidade From c4c105154456bdf42edbb507c44bda6147c0de30 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:20 +0200 Subject: [PATCH 241/586] New translations learn.md (Spanish) --- content/es/learn.md | 84 ++++++++++++++++++++++----------------------- 1 file changed, 42 insertions(+), 42 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index 0da095930c..5140f5c664 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -1,76 +1,76 @@ --- -title: Learn +title: Aprende sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +Para la **documentación oficial de NumPy** visita [numpy.org/doc/stable](https://numpy.org/doc/stable). *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +A continuación se muestra una colección de recursos educativos, tanto para el autoaprendizaje como para enseñar a otros, desarrollados por colaboradores de NumPy y aprobados por la comunidad. -## Beginners +## Principiantes -There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: +Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recomendamos encarecidamente estos: - **Tutorials** + **Tutoriales** -- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). -- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. -- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPy User Guide](https://numpy.org/devdocs) +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [Lecturas de SciPy](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) - **Books** + **Libros** -- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). -- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ +* [Guide to NumPy *by Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Esta es la primera versión gratuita de 2006. Para conseguir la última versión (2015) mira [aquí](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* -You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. +También puedes echar un vistazo a esta [lista de Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre el tema "Python+SciPy". La mayoría de esos libros son sobre el "ecosistema SciPy", que tiene NumPy en su núcleo. - **Videos** + **Vídeos** -- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** -## Advanced +## Avanzado -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +Pruebe estos recursos avanzados para comprender mejor los conceptos de NumPy como indexación avanzada, división, apilamiento, álgebra lineal y mucho más. - **Tutorials** + **Tutoriales** -- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ -- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ -- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). - **Books** + **Libros** -- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ -- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ -- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* - **Videos** + **Vídeos** -- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** -## NumPy Talks +## Charlas de NumPy -- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) -- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) -- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) -- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) -- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** -## Citing NumPy +## Citando a NumPy -If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). +Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, consulta esta [información de citado](/citing-numpy). From 321a5676886135f75dc155c6d678b1e04c0179f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:21 +0200 Subject: [PATCH 242/586] New translations learn.md (Arabic) --- content/ar/learn.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 0da095930c..4f9fa53ae3 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -15,26 +15,26 @@ There's a ton of information about NumPy out there. If you are just starting, we **Tutorials** -- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). -- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. -- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPy User Guide](https://numpy.org/devdocs) +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) **Books** -- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). -- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. **Videos** -- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** @@ -44,30 +44,30 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Tutorials** -- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ -- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ -- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). **Books** -- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ -- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ -- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* **Videos** -- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks -- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) -- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) -- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) -- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) -- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** From 070c466be44dbeb378242cf1bf64bd3dbf6287d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:22 +0200 Subject: [PATCH 243/586] New translations learn.md (Japanese) --- content/ja/learn.md | 64 ++++++++++++++++++++++----------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 8b95019964..7ab2dcde53 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -1,5 +1,5 @@ --- -title: Learn +title: NumPyの学び方 sidebar: false --- @@ -11,30 +11,30 @@ sidebar: false ## 初心者向け -NumPyについての資料は多数存在しています。 初心者の方にはこちらの資料を強くお勧めします： If you are just starting, we'd strongly recommend the following: +NumPyについての資料は多数存在しています。 初心者の方にはこちらの資料を強くお勧めします： - **書籍** + **動画** -- [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) -- [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm -- [イラストで学ぶNumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Pythonレクチャー](https://lectures.scientific-python.org/) NumPyだけでなく、科学的なPythonソフトウェアエコシステムを広く紹介しています。 -- [NumPy: 初心者のための基本](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy チュートリアル _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [スタンフォード大学 CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPyユーザーガイド](https://numpy.org/devdocs) +* [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) +* [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 +* [イラストで学ぶNumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [SciPyレクチャー](https://scipy-lectures.org/) NumPyだけでなく、科学的なPythonソフトウェアエコシステムを広く紹介しています。 +* [NumPy: 初心者のための基本](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy チュートリアル *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [スタンフォード大学 CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPyユーザーガイド](https://numpy.org/devdocs) **チュートリアル** -- [NumPガイド _Travelis E. Oliphant著_](http://web.mit.edu/dvp/Public/numpybook.pdf) これは2006年の無料版の初版です 最新版(2015年)については、こちら [を参照ください](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). -- [PythonにおけるNumPy (発展編)](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [エレガントなSciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著_ +* [NumPガイド *Travelis E. Oliphant著*](http://web.mit.edu/dvp/Public/numpybook.pdf) これは2006年の無料版の初版です 最新版(2015年)については、こちら [を参照ください](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [PythonにおけるNumPy (発展編)](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [エレガントなSciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著* -また、「Python+SciPy」を題材にした[推薦本リスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの本にはNumPyを核とした「SciPyエコシステム」が説明されています。 Most books there are about the "SciPy ecosystem," which has NumPy at its core. +また、「Python+SciPy」を題材にした[推薦本リスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの本にはNumPyを核とした「SciPyエコシステム」が説明されています。 - **書籍** + **動画** -- [NumPy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ +* [NumPy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** @@ -42,32 +42,32 @@ NumPyについての資料は多数存在しています。 初心者の方に 高度なインデックス指定、分割、スタッキング、線形代数など、NumPyの概念をより深く理解するためには、これらの上級者向け資料を試してみてください。 - **動画** + **書籍** -- NumPyの学び方 -- [NumPyとSciPyへのイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _M. Scott Shell著_ -- [NumPy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) _Stéfan van der Walt著_ -- [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm +* [NumPyとSciPyへのイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *M. Scott Shell著* +* [NumPy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) *Stéfan van der Walt著* +* [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 **チュートリアル** -- [Pythonデータサイエンスハンドブック](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) _Jake Vanderplas著_ -- [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) _Wes McKinney著_ -- [数値解析Python: NumPy, SciPy, Matplotlibによる数値計算とデータサイエンスアプリケーション](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _Robert Johansson著_ +* [Pythonデータサイエンスハンドブック](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *Jake Vanderplas著* +* [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *Wes McKinney著* +* [数値解析Python: NumPy, SciPy, Matplotlibによる数値計算とデータサイエンスアプリケーション](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *Robert Johansson著* - **動画** + **書籍** -- [アドバンスドNumPy - ブロードキャストルール・ストライド・高度なインデックス指定](https://www.youtube.com/watch?v=cYugp9IN1-Q) _Fan Nunuz-Iglesias著_ +* [アドバンスドNumPy - ブロードキャストルール・ストライド・高度なインデックス指定](https://www.youtube.com/watch?v=cYugp9IN1-Q) *Fan Nunuz-Iglesias著* *** ## NumPyに関する講演 -- [NumPyにおけるインデックス指定の未来](https://www.youtube.com/watch?v=o0EacbIbf58) _Jaime Fernadezによる_ (2016) -- [Pythonにおける配列計算の進化](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _Ralf Gommersによる_ (2019) -- [NumPy: 今までどう変わってきて、今後どう変わっていくのか？ ](https://www.youtube.com/watch?v=YFLVQFjRmPY) _Matti Picusによる_ (2019) -- [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) _Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harrisによる_ (2019) -- [Pythonにおける配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) _Travis Oliphantによる_ (2019) +* [NumPyにおけるインデックス指定の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *Jaime Fernadezによる* (2016) +* [Pythonにおける配列計算の進化](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *Ralf Gommersによる* (2019) +* [NumPy: 今までどう変わってきて、今後どう変わっていくのか？ ](https://www.youtube.com/watch?v=YFLVQFjRmPY) *Matti Picusによる* (2019) +* [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) *Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harrisによる* (2019) +* [Pythonにおける配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) *Travis Oliphantによる* (2019) *** From d5a411964973fac5d9e6ada7eefce151458de8b7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:23 +0200 Subject: [PATCH 244/586] New translations learn.md (Korean) --- content/ko/learn.md | 84 ++++++++++++++++++++++----------------------- 1 file changed, 42 insertions(+), 42 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 0da095930c..3f32c000f3 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -1,76 +1,76 @@ --- -title: Learn +title: 배우기 sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +**공식 NumPy 문서**는 [numpy.org/doc/stable](https://numpy.org/doc/stable)에 있습니다. *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +아래는 NumPy 기여자들이 개발하고 커뮤니티에서 승인한 자기 학습 및 교육 자원들을 선별한 컬렉션입니다. -## Beginners +## 초심자 -There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: +내외적으로 NumPy에 대한 정보가 많이 있습니다. 처음 시작하는 경우 다음 자료들을 강력히 추천합니다: - **Tutorials** + **튜토리얼** -- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). -- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. -- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPy User Guide](https://numpy.org/devdocs) +* [NumPy 빠른 시작 튜토리얼](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy 튜토리얼](https://numpy.org/numpy-tutorials) - NumPy 문서 팀에서 개발 및 유지보수하는 Jupyter 노트북 형식의 튜토리얼 및 교육 자료 모음입니다. 만약 추가하고 싶은 내용이 생기는 경우 [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials)를 확인해 주십시오. +* [NumPy Illustrated: The Visual Guide to NumPy - *Lev Maximov 저*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [SciPy Lectures](https://scipy-lectures.org/) - 여기서는 NumPy를 다루는 것 외에도 Python 생태계에 대하여 광범위한 소개를 볼 수 있습니다. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [From Python to NumPy - *Nicolas P. Rougier 저*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 - *Justin Johnson 저*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) - **Books** + **도서** -- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). -- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ +* [Guide to NumPy - *Travis E. Oliphant 저*](http://web.mit.edu/dvp/Public/numpybook.pdf) 이건 2006년의 무료 버전 초판입니다. 최근 판(2015)은 [여기에서](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007) 볼 수 있습니다. +* [From Python to NumPy - *Nicolas P. Rougier 저*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy - ](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *Juan Nunez-Iglesias, Stefan van der Walt, Harriet Dashnow 저* -You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. +"Python+SciPy" 주제에 관한 [Goodreads 목록](https://www.goodreads.com/shelf/show/python-scipy)도 확인해보시기를 권장합니다. 거기서 대부분의 책은 "SciPy 생태계"에 관한 것이며, 이 생태계의 핵심에는 NumPy가 포함되어 있습니다. - **Videos** + **영상** -- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ +* [Introduction to Numerical Computing with NumPy - ](http://youtu.be/ZB7BZMhfPgk) *Alex Chabot-Leclerc 저* *** -## Advanced +## 숙련자 -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +Indexing, Splitting, Stacking, 선형대수 등과 같은 NumPy의 개념을 더 잘 이해하러면 이 고급 자료들을 참조 해보세요. - **Tutorials** + **튜토리얼** -- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ -- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ -- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPy 튜토리얼](https://numpy.org/numpy-tutorials) - NumPy 문서 팀에서 개발 및 유지보수하는 Jupyter 노트북 형식의 튜토리얼 및 교육 자료 모음입니다. 만약 추가하고 싶은 내용이 생기는 경우 [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials)를 확인해 주십시오. - **Books** + **도서** -- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ -- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ -- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* - **Videos** + **영상** -- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** -## NumPy Talks +## NumPy 이야기 -- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) -- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) -- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) -- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) -- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** -## Citing NumPy +## NumPy 인용하기 -If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). +만약 당신의 연구에서 NumPy가 중요한 역할을 수행하였고 학술 간행물에서 출판하기 위해서는 [이 인용 정보](/citing-numpy)를 참조하세요. From dc19668dbdeac5b5f2079c3fcc7d663477a133c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:24 +0200 Subject: [PATCH 245/586] New translations learn.md (Russian) --- content/ru/learn.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ru/learn.md b/content/ru/learn.md index 0da095930c..4f9fa53ae3 100644 --- a/content/ru/learn.md +++ b/content/ru/learn.md @@ -15,26 +15,26 @@ There's a ton of information about NumPy out there. If you are just starting, we **Tutorials** -- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). -- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. -- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPy User Guide](https://numpy.org/devdocs) +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) **Books** -- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). -- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. **Videos** -- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** @@ -44,30 +44,30 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Tutorials** -- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ -- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ -- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). **Books** -- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ -- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ -- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* **Videos** -- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks -- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) -- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) -- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) -- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) -- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** From f3a1b12b09a863d25279c244534e967a80288e2e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:25 +0200 Subject: [PATCH 246/586] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index 0da095930c..4f9fa53ae3 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -15,26 +15,26 @@ There's a ton of information about NumPy out there. If you are just starting, we **Tutorials** -- [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). -- [NumPy Illustrated: The Visual Guide to NumPy _by Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. -- [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy tutorial _by Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [Stanford CS231 _by Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPy User Guide](https://numpy.org/devdocs) +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) **Books** -- [Guide to NumPy _by Travis E. Oliphant_](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). -- [From Python to NumPy _by Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow_ +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. **Videos** -- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _by Alex Chabot-Leclerc_ +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** @@ -44,30 +44,30 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Tutorials** -- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _by Nicolas P. Rougier_ -- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _by M. Scott Shell_ -- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _by Stéfan van der Walt_ -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). **Books** -- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) _by Jake Vanderplas_ -- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) _by Wes McKinney_ -- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _by Robert Johansson_ +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* **Videos** -- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _by Juan Nunez-Iglesias_ +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks -- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _by Jaime Fernández_ (2016) -- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _by Ralf Gommers_ (2019) -- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _by Matti Picus_ (2019) -- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) -- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _by Travis Oliphant_ (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** From 66bc956a0334553c79702c274a8fcbd810171ba4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:26 +0200 Subject: [PATCH 247/586] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index c5290c33d6..e20462b9c1 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -15,26 +15,26 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa **Tutoriais** -- [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). -- [NumPy Illustrated: The Visual Guide to NumPy _por Lev Maximov_](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -- [Scientific Python Lectures](https://lectures.scientific-python.org/) Além de incluir conteúdo sobre a NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python. -- [NumPy: the absolute basics for beginners ("o básico absoluto para inciantes")](https://numpy.org/devdocs/user/absolute_beginners.html) -- [NumPy tutorial _por Nicolas Rougier_](https://github.com/rougier/numpy-tutorial) -- [Stanford CS231 _por Justin Johnson_](http://cs231n.github.io/python-numpy-tutorial/) -- [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) +* [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *por Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [SciPy Lectures](https://scipy-lectures.org/) Além de incluir conteúdo sobre a NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python. +* [NumPy: the absolute basics for beginners ("o básico absoluto para inciantes")](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *por Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) **Livros** -- [Guide to NumPy _de Travis E. Oliphant_](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -- [From Python to NumPy _por Nicolas P. Rougier_](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -- [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) _por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow_ +* [Guide to NumPy *de Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [From Python to NumPy *por Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow* Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. **Vídeos** -- [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) _por Alex Chabot-Leclerc_ +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** @@ -44,30 +44,30 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos **Tutoriais** -- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) _por Nicolas P. Rougier_ -- [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) _por M. Scott Shell_ -- [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) _por Stéfan van der Walt_ -- [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *por Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). **Livros** -- [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) _por Jake Vanderplas_ -- [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) _por Wes McKinney_ -- [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) _por Robert Johansson_ +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *por Robert Johansson* **Vídeos** -- [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) _por Juan Nunuz-Iglesias_ +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunuz-Iglesias* *** ## Palestras sobre NumPy -- [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) _por Jaime Fernández_ (2016) -- [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM\&t=10s) _por Ralf Gommers_ (2019) -- [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) _por Matti Picus_ (2019) -- [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) _por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris_ (2019) -- [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) _por Travis Oliphant_ (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *por Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *por Travis Oliphant* (2019) *** From 01585199429a0a1744cb017e3b26438349aab3b9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:27 +0200 Subject: [PATCH 248/586] New translations community.md (Spanish) --- content/es/community.md | 68 +++++++++++++++++++---------------------- 1 file changed, 31 insertions(+), 37 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index d4f188d6ce..0e1e53211b 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -1,72 +1,66 @@ --- -title: Community +title: Comunidad sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. +NumPy es un proyecto de código abierto impulsado por la comunidad y desarrollado por un grupo diverso de [colaboradores](/teams/). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta de NumPy](/code-of-conduct) para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere. -We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. +Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad de NumPy. -## Participate online -The following are ways to engage directly with the NumPy project and community. -_Please note that we encourage users and community members to support each other -for usage questions - see [Get Help](/gethelp)._ +## Participa en línea -### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) +Las siguientes son formas de relacionarse directamente con el proyecto y la comunidad de NumPy. _Ten en cuenta que animamos a los usuarios y a los miembros de la comunidad a apoyarse mutuamente por preguntas de uso - ver [Obtener ayuda](/gethelp)._ -This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. -Announcements about NumPy, such as for releases, developer meetings, sprints or -conference talks are also made on this list. -On this list please use bottom posting, reply to the list (rather than to -another sender), and don't reply to digests. A searchable archive of this list -is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). +### [Lista de correo de NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) + +Este es el foro principal para discusiones más extensas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones sobre el proyecto. Aquí también se hacen los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o conferencias. + +En esta lista, por favor, utiliza el botón de envío inferior, responde a la lista (en lugar de a otro remitente) y no respondas a los resúmenes. El archivo de consulta de esta lista está disponible [aquí](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** -### [GitHub issue tracker](https://github.com/numpy/numpy/issues) +### [Seguimiento de incidencias en GitHub](https://github.com/numpy/numpy/issues) -- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); -- documentation issues (e.g. "I found this section unclear"); -- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). +- Para informes de error (por ejemplo, "`np.arange(3).shape` devuelve `(5,)`, cuando debería devolver `(3,)`"); +- problemas en la documentación (por ejemplo, "Esta sección me pareció poco clara"); +- y solicitudes de funcionalidades (por ejemplo, "Me gustaría tener un nuevo método de interpolación en `np.percentile`"). -_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ +_Ten en cuenta que GitHub no es el lugar adecuado para reportar una vulnerabilidad de seguridad. Si crees que has encontrado una vulnerabilidad de seguridad en NumPy, por favor repórtalo [aquí](https://tidelift.com/docs/security)._ *** ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. -This is a private space, specifically meant for people who are hesitant to -bring up their questions or ideas on a large public mailing list or GitHub. -Please see -[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more -details and how to get an invite. +Una sala de chat en tiempo real para hacer preguntas sobre las _contribuciones_ a NumPy. Este es un espacio privado, destinado específicamente a las personas que no se atreven a plantear sus preguntas o ideas en la lista de correo pública o en GitHub. Por favor, visita [aquí](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para más detalles, y sobre cómo obtener una invitación. + -## Study Groups and Meetups +## Grupos de Estudio y Reuniones -If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). +Si desea encontrar un grupo de estudio o reunión local para aprender más sobre NumPy y el ecosistema más amplio de paquetes de Python para ciencia de datos y computación científica, te recomendamos que explores los [PyData meetups](https://www.meetup.com/pro/pydata/) (más de 150 reuniones, más de 100,000 miembros). -NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). +NumPy también organiza ocasionalmente sprints presenciales para su equipo y colaboradores interesados. Estos normalmente se planifican con varios meses de anticipación y se anunciarán en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion) y en [Twitter](https://twitter.com/numpy_team). -## Conferences -The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: +## Conferencias + +El proyecto NumPy no organiza sus propias conferencias. Las conferencias que tradicionalmente han sido más populares entre los responsables, colaboradores y usuarios de NumPy son la serie de conferencias de SciPy y PyData: - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) -- [SciPy Latin America](https://www.scipyla.org) +- [SciPy Latinoamérica](https://www.scipyla.org) - [SciPy India](https://scipy.in) - [SciPy Japan](https://conference.scipy.org) -- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) +- [Conferencias PyData](https://pydata.org/event-schedule/) (de 15 a 20 eventos al año, repartidos entre muchos países) + +Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy, en donde puedes aprender cómo contribuir a Numpy o proyectos de código abierto relacionados. -Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. -## Join the NumPy community +## Únete a la comunidad Numpy -To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. +Para prosperar, el proyecto NumPy necesita tu experiencia y entusiasmo. ¿No sabes programar? ¡No es un problema! Hay muchas maneras de contribuir a NumPy. -If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. +Si te interesa colaborar en NumPy (¡yupi!) te recomendamos que visites nuestra página [Contribuir](/contribute). -Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). +No dudes en pasar a saludarnos en uno de nuestros encuentros de la comunidad. Para enterarte del próximo, consulta nuestro calendario de eventos [aquí](https://scientific-python.org/calendars/). From 08f7517ded3edb22463ba5481cb5da3903108d2e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:28 +0200 Subject: [PATCH 249/586] New translations community.md (Arabic) --- content/ar/community.md | 24 +++++++++--------------- 1 file changed, 9 insertions(+), 15 deletions(-) diff --git a/content/ar/community.md b/content/ar/community.md index d4f188d6ce..5034fba239 100644 --- a/content/ar/community.md +++ b/content/ar/community.md @@ -7,21 +7,17 @@ NumPy is a community-driven open source project developed by a diverse group of We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + ## Participate online -The following are ways to engage directly with the NumPy project and community. -_Please note that we encourage users and community members to support each other -for usage questions - see [Get Help](/gethelp)._ +The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ + ### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) -This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. -Announcements about NumPy, such as for releases, developer meetings, sprints or -conference talks are also made on this list. +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. -On this list please use bottom posting, reply to the list (rather than to -another sender), and don't reply to digests. A searchable archive of this list -is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). +On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** @@ -37,12 +33,8 @@ _Please note that GitHub is not the right place to report a security vulnerabili ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. -This is a private space, specifically meant for people who are hesitant to -bring up their questions or ideas on a large public mailing list or GitHub. -Please see -[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more -details and how to get an invite. +A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. + ## Study Groups and Meetups @@ -50,6 +42,7 @@ If you would like to find a local meetup or study group to learn more about NumP NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + ## Conferences The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: @@ -63,6 +56,7 @@ The NumPy project doesn't organize its own conferences. The conferences that hav Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + ## Join the NumPy community To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. From 31f736a842650a36dcf372e1aa03464a00f97b97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:29 +0200 Subject: [PATCH 250/586] New translations community.md (Japanese) --- content/ja/community.md | 35 +++++++++++++++-------------------- 1 file changed, 15 insertions(+), 20 deletions(-) diff --git a/content/ja/community.md b/content/ja/community.md index 5178b60aa3..2629f72358 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,24 +3,21 @@ title: コミュニティ sidebar: false --- -NumPy は 常に多様な[コントリビュータ](/ja/teams/) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 NumPy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/ja/code-of-conduct) をご覧ください。 The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. +NumPy は 常に多様な[コントリビュータ](/ja/teams/) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 NumPy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/ja/code-of-conduct) をご覧ください。 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 + ## オンラインで参加する方法 -The following are ways to engage directly with the NumPy project and community. -_Please note that we encourage users and community members to support each other -for usage questions - see [Get Help](/gethelp)._ +NumPy プロジェクトやコミュニティと直接交流する方法は次の通りです。 _重要: 私たちはユーザとコミュニティメンバーに互いにNumPyの使い方の質問に関して助言し合って欲しいと思っています。 - 参照[サポート](/gethelp)._ + ### [NumPyメーリングリスト：](https://mail.python.org/mailman/listinfo/numpy-discussion) このメーリングリストは、NumPy に新しい機能を追加するなど、より長い期間の議論のための主なコミュニケーションの場です。 NumPyのRoadmapに変更を加えたり、プロジェクト全体での意思決定を行います。 このメーリングリストでは、リリース、開発者会議、スプリント、カンファレンストークなど、NumPy についてのアナウンスなどにも利用されます。 -Announcements about NumPy, such as for releases, developer meetings, sprints or -conference talks are also made on this list. -On this list please use bottom posting, reply to the list (rather than to -another sender), and don't reply to digests. このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 このメーリングリストの検索可能なアーカイブは [こちら](https://mail.python.org/archives/list/numpy-discussion@python.org/) にあります。 +このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 このメーリングリストの検索可能なアーカイブは [こちら](https://mail.python.org/archives/list/numpy-discussion@python.org/) にあります。 *** @@ -30,28 +27,25 @@ another sender), and don't reply to digests. このメーリングリストで - ドキュメントの問題 (例: "I find this section unclear"); - 機能追加リクエスト (例: "I would like to have a new interpolation method in `np.percentile`"). -_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ +_ちなみに、セキュリティの脆弱性を報告するには、GitHubのイシュートラッカーは適切な場所ではないことに注意してください。 NumPy でセキュリティ上の脆弱性を発見したと思われる場合は、 [こちら](https://tidelift.com/docs/security) から報告してください。_ *** ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. -This is a private space, specifically meant for people who are hesitant to -bring up their questions or ideas on a large public mailing list or GitHub. -Please see -[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more -details and how to get an invite. +SlackはNumpyに_ 貢献するための質問をするための_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 + ## 勉強会とミートアップ NumPyや、データサイエンス、科学技術計算などのより広いエコシステムのためのPythonパッケージついて、もっと学ぶためのローカルミートアップや勉強会を見つけたい場合、 [PyData ミートアップ](https://www.meetup.com/pro/pydata/) (150人以上のミートアップ、10万人以上のメンバーをまとめたもの) を調べてみることをお勧めします。 -NumPy also organizes in-person sprints for its team and interested contributors occasionally. 加えて、NumPy では開発チームと参加に興味があるコントリビュータのために、対面でのスプリントを時折開催しています。 この開発スプリントは通常数ヶ月に一度に開催されており、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) と [Twitter](https://twitter.com/numpy_team) で開催連絡されます。 +加えて、NumPy では開発チームと参加に興味があるコントリビュータのために、対面でのスプリントを時折開催しています。 この開発スプリントは通常数ヶ月に一度に開催されており、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) と [Twitter](https://twitter.com/numpy_team) で開催連絡されます。 + ## カンファレンス -The NumPy project doesn't organize its own conferences. NumPy プロジェクトは独自のカンファレンスは開催していません。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 +NumPy プロジェクトは独自のカンファレンスは開催していません。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) @@ -62,10 +56,11 @@ The NumPy project doesn't organize its own conferences. NumPy プロジェクト これらのカンファレンスの多くは、NumPyの使い方や関連するオープンソースプロジェクトに貢献する方法を学ぶことができるチュートリアルを開催しています。 + ## NumPy コミュニティに参加する -To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. +NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 プログラマーじゃないから参加できない？ そんなことはありません！ NumPy に貢献する様々な方法があります。 -If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. +もし、NumPyに貢献したい場合は、 [コントリビュート](/ja/contribute) ページをご覧いただくことをお勧めします。 -Also, feel free to stop by and say hi at one of our community meetings. また、私たちのコミュニティミーティングにもぜひ参加してみてください。 コミュニティミーティングの活動を確認するには、[こちら](https://scientific-python.org/calendars/)のイベントカレンダーを確認ください。 +また、私たちのコミュニティミーティングにもぜひ参加してみてください。 コミュニティミーティングの活動を確認するには、[こちら](https://scientific-python.org/calendars/)のイベントカレンダーを確認ください。 From 342459fb92eb3cc3a76c6148bab5425d0281e3b8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:30 +0200 Subject: [PATCH 251/586] New translations community.md (Korean) --- content/ko/community.md | 64 +++++++++++++++++++---------------------- 1 file changed, 29 insertions(+), 35 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index d4f188d6ce..7a12d27e8d 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -1,72 +1,66 @@ --- -title: Community +title: 커뮤니티 sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. +NumPy는 다양한 [기여자](/teams/) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 운영진들은 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울여오고 있습니다. 커뮤니티를 발전시키기 위한 다른 이용자들과의 상호작용에 대한 가이드라인은 [NumPy 이용약관](/code-of-conduct)을 통해 확인하실 수 있습니다. -We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. +NumPy 커뮤니티에서는 배우고, 지식을 공유하고, 다른 사람들과 협력할 수 있는 여러 커뮤니케이션 채널을 제공합니다. -## Participate online -The following are ways to engage directly with the NumPy project and community. -_Please note that we encourage users and community members to support each other -for usage questions - see [Get Help](/gethelp)._ +## 온라인으로 참여하기 -### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) +NumPy 프로젝트 및 커뮤니티에 곧장 참여할 수 있는 방법들입니다. _사용자와 커뮤니티 회원이 사용 중 질문에 대하여 서로 도움을 주고받기를 권장한다는 것을 명심하십시오. [도움말](/gethelp)을 참고하세요._ -This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. -Announcements about NumPy, such as for releases, developer meetings, sprints or -conference talks are also made on this list. -On this list please use bottom posting, reply to the list (rather than to -another sender), and don't reply to digests. A searchable archive of this list -is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). +### [NumPy 메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion) + +이 리스트는 NumPy 신기능 추가, NumPy 로드맵 변경 등 모든 종류의 프로젝트 전체 의사 결정과 같은 장기적인 토론을 이끄는 주요 포럼이라 할 수 있습니다. 출시, 개발자 모임, 일반 모임, 컨퍼런스 강연과 같은 NumPy에 대한 공지도 이 리스트를 통해 받아볼 수 있습니다. + +리스트에 회신하려면 (다른 발신자에게 회신하기보다는) 하단의 게시물을 이용하십시오. 또, 자동 발신 메일에 회신하지 마십시오. 검색 가능한 아카이브는 [여기](https://mail.python.org/archives/list/numpy-discussion@python.org/)에서 이용할 수 있습니다. *** ### [GitHub issue tracker](https://github.com/numpy/numpy/issues) -- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); -- documentation issues (e.g. "I found this section unclear"); -- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). +- 버그 제보 (예: "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- 문서 관련 문제점 (예: "I found this section unclear"); +- 기능 요청 (예: "I would like to have a new interpolation method in `np.percentile`"). -_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ +_GitHub은 보안 취약점을 제보하는 곳이 아님을 명심하십시오. NumPy의 보안 취약점을 발견한 것 같으시다면, [여기](https://tidelift.com/docs/security)에서 제보하십시오._ *** ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. -This is a private space, specifically meant for people who are hesitant to -bring up their questions or ideas on a large public mailing list or GitHub. -Please see -[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more -details and how to get an invite. +NumPy에 _기여하는_ 방법에 대하여 질문하는 실시간 채팅방입니다. 여기는 비공개 공간으로, 공용 메일링 리스트나 GitHub에 질문 또는 아이디어를 올리는 것을 주저하는 사람들을 위한 곳입니다. [여기](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)에서 자세한 내용과 초대를 받는 방법을 알아보세요. + -## Study Groups and Meetups +## 학술 그룹 및 모임 -If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). +NumPy와 데이터 과학 및 과학적 컴퓨팅을 위한 Python 패키지의 생태계에 대해 자세히 알아보기 위하여, 지역 모임이나 학술 그룹을 찾고 싶다면 [PyData 모임](https://www.meetup.com/pro/pydata/) (150개 이상의 모임, 10만 명 이상의 회원) 사이트를 돌아보시는 것을 추천해 드립니다. -NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). +NumPy에서도 가끔 자체 팀이나 관심 있는 기여자들을 위하여 직접 모임을 조직하기도 합니다. 보통 몇 달 전부터 미리 계획되며 [메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion) 및 [트위터](https://twitter.com/numpy_team)로 해당 사실을 알립니다. -## Conferences -The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: +## 컨퍼런스 + +NumPy 프로젝트에서는 자체 컨퍼런스를 추진하지 않습니다. 보통 NumPy 관리자나 기여자, 사용자들에게 가장 인기 있는 컨퍼런스는 SciPy나 PyData 쪽 컨퍼런스입니다. - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) - [SciPy Latin America](https://www.scipyla.org) - [SciPy India](https://scipy.in) - [SciPy Japan](https://conference.scipy.org) -- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) +- [PyData 컨퍼런스](https://pydata.org/event-schedule/) (세계 곳곳의 여러 나라에서 1년에 15~20개의 이벤트를 개최합니다) + +이런 컨퍼런스 대부분에는 NumPy를 배우는 튜토리얼의 날이나 NumPy 혹은 관련 오픈소스 프로젝트에 기여하는 방법을 배울 수 있는 장이 마련되어 있습니다. -Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. -## Join the NumPy community +## NumPy 커뮤니티에 가입하기 -To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. +더욱 성장하기 위해, NumPy 프로젝트에서는 당신의 경험과 의욕을 필요로 합니다. 프로그래머가 아니라고요? 걱정하지 마세요! NumPy에 기여하는 방법에는 여러 가지가 있습니다. -If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. +NumPy 기여자가 되는 데 관심이 있으시다면 [기여](/contribute) 페이지를 방문하시는 것을 추천해 드립니다. -Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). +또한, 부담없이 커뮤니티 미팅에 참석 해주시길 바랍니다. 정확한 날짜들은 [행사달력](https://scientific-python.org/calendars/)을 확인해주세요. From 923121b553248dd7f742d53645478e2ef54e1c2c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:31 +0200 Subject: [PATCH 252/586] New translations community.md (Russian) --- content/ru/community.md | 24 +++++++++--------------- 1 file changed, 9 insertions(+), 15 deletions(-) diff --git a/content/ru/community.md b/content/ru/community.md index d4f188d6ce..5034fba239 100644 --- a/content/ru/community.md +++ b/content/ru/community.md @@ -7,21 +7,17 @@ NumPy is a community-driven open source project developed by a diverse group of We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + ## Participate online -The following are ways to engage directly with the NumPy project and community. -_Please note that we encourage users and community members to support each other -for usage questions - see [Get Help](/gethelp)._ +The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ + ### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) -This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. -Announcements about NumPy, such as for releases, developer meetings, sprints or -conference talks are also made on this list. +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. -On this list please use bottom posting, reply to the list (rather than to -another sender), and don't reply to digests. A searchable archive of this list -is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). +On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** @@ -37,12 +33,8 @@ _Please note that GitHub is not the right place to report a security vulnerabili ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. -This is a private space, specifically meant for people who are hesitant to -bring up their questions or ideas on a large public mailing list or GitHub. -Please see -[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more -details and how to get an invite. +A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. + ## Study Groups and Meetups @@ -50,6 +42,7 @@ If you would like to find a local meetup or study group to learn more about NumP NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + ## Conferences The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: @@ -63,6 +56,7 @@ The NumPy project doesn't organize its own conferences. The conferences that hav Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + ## Join the NumPy community To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. From 23f06a9f123d9c8555c892e8b74ddc8a95031a60 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:32 +0200 Subject: [PATCH 253/586] New translations community.md (Chinese Simplified) --- content/zh/community.md | 24 +++++++++--------------- 1 file changed, 9 insertions(+), 15 deletions(-) diff --git a/content/zh/community.md b/content/zh/community.md index d4f188d6ce..5034fba239 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -7,21 +7,17 @@ NumPy is a community-driven open source project developed by a diverse group of We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + ## Participate online -The following are ways to engage directly with the NumPy project and community. -_Please note that we encourage users and community members to support each other -for usage questions - see [Get Help](/gethelp)._ +The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ + ### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) -This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. -Announcements about NumPy, such as for releases, developer meetings, sprints or -conference talks are also made on this list. +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. -On this list please use bottom posting, reply to the list (rather than to -another sender), and don't reply to digests. A searchable archive of this list -is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). +On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** @@ -37,12 +33,8 @@ _Please note that GitHub is not the right place to report a security vulnerabili ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. -This is a private space, specifically meant for people who are hesitant to -bring up their questions or ideas on a large public mailing list or GitHub. -Please see -[here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more -details and how to get an invite. +A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. + ## Study Groups and Meetups @@ -50,6 +42,7 @@ If you would like to find a local meetup or study group to learn more about NumP NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + ## Conferences The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: @@ -63,6 +56,7 @@ The NumPy project doesn't organize its own conferences. The conferences that hav Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + ## Join the NumPy community To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. From 6a17d0b9090556dfd7d3256446e00e4bf42b1f87 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:33 +0200 Subject: [PATCH 254/586] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/content/pt/community.md b/content/pt/community.md index 23e7fe3aef..7992ff2fd6 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -7,17 +7,17 @@ NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros dentro da comunidade NumPy. + ## Participar online -Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade do NumPy. -_Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja [Obter Ajuda](/gethelp)._ +Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade do NumPy. _Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja [Obter Ajuda](/gethelp)._ + ### [Lista de discussões NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) -Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. -Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. +Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. -Nesta lista, por favor, use _bottom posting_, responda à lista (em vez de a outro remetente), e não responda aos _digests_. Um arquivo pesquisável desta lista está disponível [aqui](https://mail.python.org/archives/list/numpy-discussion@python.org/). +Nesta lista, por favor, use *bottom posting*, responda à lista (em vez de a outro remetente), e não responda aos *digests*. Um arquivo pesquisável desta lista está disponível [aqui](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** @@ -33,9 +33,8 @@ _Por favor, note que o GitHub não é o lugar certo para relatar uma vulnerabili ### [Slack](https://numpy-team.slack.com) -Uma sala de bate-papo em tempo real para fazer perguntas sobre _contribuir_ para o NumPy. -Este é um fórum privado, especificamente para pessoas hesitantes em levantar suas perguntas ou idéias em uma grande lista de e-mails públicos ou no GitHub. -Por favor, clique [aqui](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para mais detalhes e como obter um convite. +Uma sala de bate-papo em tempo real para fazer perguntas sobre _contribuir_ para o NumPy. Este é um fórum privado, especificamente para pessoas hesitantes em levantar suas perguntas ou idéias em uma grande lista de e-mails públicos ou no GitHub. Por favor, clique [aqui](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para mais detalhes e como obter um convite. + ## Grupos de Estudo e Meetups @@ -43,6 +42,7 @@ Se você gostaria de encontrar um encontro ou grupo de estudo local para aprende O NumPy também organiza sprints presenciais para sua equipe e colaboradores interessados ocasionalmente. Estes eventos são normalmente planejados com vários meses de antecedência e serão anunciados na [lista de discussão](https://mail.python.org/mailman/listinfo/numpy-discussion) e no [Twitter](https://twitter.com/numpy_team). + ## Conferências O projeto NumPy não organiza suas próprias conferências. As conferências que tradicionalmente têm sido mais populares com mantenedores, colaboradores e usuários são as conferências SciPy e PyData: @@ -56,6 +56,7 @@ O projeto NumPy não organiza suas próprias conferências. As conferências que Muitas dessas conferências incluem dias de tutorial sobre o NumPy e/ou sprints onde você pode aprender como contribuir com o NumPy ou projetos de código aberto relacionados. + ## Junte-se à comunidade NumPy Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não é uma pessoa programadora? Sem problemas! Existem muitas maneiras de contribuir com o NumPy. From 45d64043240f8300c75692aa6bee7dc833114497 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:34 +0200 Subject: [PATCH 255/586] New translations press-kit.md (Spanish) --- content/es/press-kit.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/press-kit.md b/content/es/press-kit.md index 2c8970bb29..9b66e3e683 100644 --- a/content/es/press-kit.md +++ b/content/es/press-kit.md @@ -1,8 +1,8 @@ --- -title: Press kit +title: Kit de prensa sidebar: false --- -We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. +Nos gustaría facilitarte el trabajo para incluir la identidad del proyecto NumPy en tu próximo documento académico, material de curso o presentación. -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +[Aquí](https://github.com/numpy/numpy/tree/main/branding/logo) encontrarás varias versiones en alta resolución del logo de NumPy. Ten en cuenta que al utilizar los recursos de numpy.org, aceptas el [Código de Conducta de NumPy](/code-of-conduct). From 195796c2c3305e0932766649c897ac9a301575a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:36 +0200 Subject: [PATCH 256/586] New translations press-kit.md (Japanese) --- content/ja/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/press-kit.md b/content/ja/press-kit.md index 39f88f2388..6d28214989 100644 --- a/content/ja/press-kit.md +++ b/content/ja/press-kit.md @@ -5,4 +5,4 @@ sidebar: false 私たちはユーザーの皆さんが次に書く学術論文や、コース教材、プレゼンテーションなどに、NumPyプロジェクトのロゴを簡単に盛り込めるようにしたいと考えています。 -こちらから、様々な解像度のNumPyロゴのファイルをダウンロードできます: [ロゴリンク](https://github.com/numpy/numpy/tree/main/branding/logo)。 numpy.orgのリソースを使用することで、[NumPy行動規範](/code-of-conduct) を受け入れたことになることに注意してください。 Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +こちらから、様々な解像度のNumPyロゴのファイルをダウンロードできます: [ロゴリンク](https://github.com/numpy/numpy/tree/main/branding/logo)。 numpy.orgのリソースを使用することで、[NumPy行動規範](/code-of-conduct) を受け入れたことになることに注意してください。 From 913f74f8533fac6a63ebedcf8258882b23a7a6fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:37 +0200 Subject: [PATCH 257/586] New translations press-kit.md (Korean) --- content/ko/press-kit.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/press-kit.md b/content/ko/press-kit.md index 2c8970bb29..ddce954013 100644 --- a/content/ko/press-kit.md +++ b/content/ko/press-kit.md @@ -1,8 +1,8 @@ --- -title: Press kit +title: 홍보 자료 sidebar: false --- -We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. +저희는 당신이 NumPy 프로젝트의 상징을 논문, 코스 자료, 발표 자료 등에 삽입하기 쉽도록 하고자 합니다. -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +[여기에서](https://github.com/numpy/numpy/tree/main/branding/logo) 여러 버전의 고화질 NumPy 로고를 찾을 수 있습니다. numpy.org 자료를 이용하는 경우, [NumPy 이용약관](/code-of-conduct)에 동의하게 됨을 명심하십시오. From 4b964662d63bc52fb6600f2df652a3c795fcd57b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:40 +0200 Subject: [PATCH 258/586] New translations news.md (Spanish) --- content/es/news.md | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index a6f3d0a88a..7fc9ef2d82 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,21 +1,10 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 --- -### NumPy 2.0.0 released - -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -250,7 +239,6 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 8539b5e50aa0dcfca493abfa447a2c69bee61842 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:41 +0200 Subject: [PATCH 259/586] New translations news.md (Arabic) --- content/ar/news.md | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 706b609976..793619c0d1 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,21 +1,10 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 --- -### NumPy 2.0.0 released - -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -250,7 +239,6 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 2485abe60facbe759fc997b748d420c6e8369c1e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:43 +0200 Subject: [PATCH 260/586] New translations news.md (Japanese) --- content/ja/news.md | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 3cff82620f..8d0cfb5b39 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,29 +1,18 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.0 リリース日: 6月16日" date: 2023-09-16 --- ### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - -### NumPy 2.0 リリース日: 6月16日 - -_ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - ### NumFOCUSの年末の資金調達 _2023年12月19日_ -- NumFOCUSは、年末キャンペーンでPyCharmチームと協力し、PyCharmライセンスの初回購入に30%の割引を提供しています。 2023年12月23日までのPyCharm購入による1年目の収益は全てNumFOCUSのプログラムに直接寄付されます。 @@ -250,7 +239,6 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. From c159f6103e58f8fc4601195485d6080e7ea14cef Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:44 +0200 Subject: [PATCH 261/586] New translations news.md (Korean) --- content/ko/news.md | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 447e02eaf7..6f52f07394 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,29 +1,18 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.0 출시일: 6월 16일" date: 2023-09-16 --- ### NumPy 2.0 출시일: 6월 16일 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** - [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) - 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - -### NumPy 2.0 출시일: 6월 16일 - -_2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - ### NumPy 1.26.0 출시 _2023년 12월 19_ -- NumFOCUS에서 연말 캠페인 기간 동안 PyCharm과 협력해 최초 PyCharm 이용자의 라이선스를 30% 할인된 가격에 제공했습니다. 지금부터 2023년 12월 23일까지 PyCharm 구매로 발생한 모든 수익은 NumFOCUS 프로그램으로 직접 전달됩니다. @@ -250,7 +239,6 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. - NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2024년 1월 2일_. - NumPy 1.26.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023년 1월 2일_. From 35b7d07ce0411a8a34b8e05c54ef97e9203b4240 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:45 +0200 Subject: [PATCH 262/586] New translations news.md (Russian) --- content/ru/news.md | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 706b609976..793619c0d1 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,21 +1,10 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 --- -### NumPy 2.0.0 released - -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -250,7 +239,6 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 214fbfe6fe96cb2803ec163db09c82138880e8d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:46 +0200 Subject: [PATCH 263/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 706b609976..793619c0d1 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,21 +1,10 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 --- -### NumPy 2.0.0 released - -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -250,7 +239,6 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 329ba1c193873945b39c0e3dd75b895965839fa5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:47 +0200 Subject: [PATCH 264/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index d3513d18a6..085a49d625 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,21 +1,10 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.0 release date: June 16" date: 2023-09-16 --- -### NumPy 2.0.0 released - -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) - -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. - - ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -250,7 +239,6 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 de novembro de 2023_. From 152864faae0d3c0f7ea7f744c934d0163a5ae461 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:48 +0200 Subject: [PATCH 265/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 92 ++++++++++++++++++------------------- 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index d74cba9bce..7eabca58e1 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -2,96 +2,96 @@ params: machinelearning: paras: - - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. - para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + para1: NumPy constituye la base de potentes bibliotecas de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series temporales y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión por ordenador y procesamiento del lenguaje natural. [MXNet](https://github.com/apache/incubator-mxnet) es otro paquete de IA que proporciona modelos y plantillas para el aprendizaje profundo. + para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. arraylibraries: intro: - - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + text: La API de NumPy es el punto de partida cuando se escriben bibliotecas para explotar hardware innovador, crear tipos de matrices especializadas o añadir capacidades más allá de lo que NumPy proporciona. headers: - - text: Array Library + text: Biblioteca de matrices - - text: Capabilities & Application areas + text: Capacidades y áreas de aplicación libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + text: Matrices distribuidas y paralelismo avanzado para análisis, que permiten un rendimiento a escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy - text: NumPy-compatible array library for GPU-accelerated computing with Python. + text: Biblioteca de matrices compatible con NumPy para cálculo acelerado en la GPU con Python. img: /images/content_images/arlib/cupy.png alttext: CuPy - url: https://cupy.dev + url: https://cupy.chainer.org - title: JAX - text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + text: "Transformaciones componibles de programas NumPy: diferenciar, vectorizar, compilación justo-a-tiempo a GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://jax.readthedocs.io/ + url: https://github.com/google/jax - title: Xarray - text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + text: Matrices multidimensionales indexadas y etiquetadas para análisis y visualización avanzados img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse - text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + text: Biblioteca de matrices dispersas compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch - text: Deep learning framework that accelerates the path from research prototyping to production deployment. + text: Marco de aprendizaje profundo que acelera el camino desde la creación de prototipos de investigación hasta la implantación en producción. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow - text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + text: Una plataforma integral de aprendizaje automático para crear y desplegar fácilmente aplicaciones basadas en ML. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org + - + title: MXNet + text: Marco de aprendizaje profundo adecuado para la creación flexible de prototipos de investigación y la producción. + img: /images/content_images/arlib/mxnet_logo.png + alttext: MXNet + url: https://mxnet.apache.org/ - title: Arrow - text: A cross-language development platform for columnar in-memory data and analytics. + text: Plataforma de desarrollo multilenguaje para datos y análisis columnares en memoria. img: /images/content_images/arlib/arrow.png alttext: arrow - url: https://arrow.apache.org/ + url: https://github.com/apache/arrow - title: xtensor - text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + text: Matrices multidimensionales con emisión y computación de llamada-bajo-demanda para el análisis numérico. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array - text: Manipulate JSON-like data with NumPy-like idioms. + text: Manipula datos tipo JSON con modismos similares a los de NumPy. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray - text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + text: Sistema de backend de Python que desvincula la API de la implementación; unumpy proporciona una API de NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - - text: Nearly every scientist working in Python draws on the power of NumPy. + text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. - - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + text: "NumPy lleva la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." libraries: - title: Quantum Computing @@ -311,63 +311,63 @@ params: url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: - intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + intro: "NumPy es el núcleo de un rico ecosistema de librerías de ciencia de datos. Un flujo de trabajo exploratorio típico de ciencia de datos podría verse así:" image1: - img: /images/content_images/ds-landscape.png - alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + alttext: Diagrama de las librerías de Python. Las cinco categorías son "Extraer, Transformar, Cargar", "Exploración de Datos", "Modelado de Datos", "Evaluación de Datos" y "Presentación de Datos". image2: - img: /images/content_images/data-science.png - alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Informática" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + text: "Extraer, Transformar, Cargar: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "Análisis Exploratorio: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "Modelado y evaluación: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + text: "Informes en paneles: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png - alttext: A streamplot made in matplotlib + alttext: Un diagrama de flujo hecho en matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png - alttext: A scatter-plot graph made in ggpy + alttext: Un diagrama de dispersión hecho en ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png - alttext: A box-plot made in plotly + alttext: Un diagrama de caja hecho en plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + alttext: Un diagrama de flujo hecho en altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png - alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + alttext: Un gráfico de pares de dos tipos de gráficos, un gráfico de trazado y un gráfico de frecuencias hecho en seaborn - - url: https://docs.pyvista.org/ + url: https://docs.pyvista.org/examples/index.html img: /images/content_images/v_pyvista.png - alttext: A 3D volume rendering made in PyVista. + alttext: Un renderizado de volumen 3D realizado en PyVista. - url: https://napari.org img: /images/content_images/v_napari.png - alttext: A multi-dimensionan image made in napari. + alttext: Una imagen multidimensional hecha en napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png - alttext: A Voronoi diagram made in vispy. + alttext: Un diagrama de Voronoi hecho en vispy. content: - - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + text: NumPy es un componente esencial en el floreciente [panorama de visualización de Python](https://pyviz.org/overviews/index.html), que incluye [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), y [PyVista](https://github.com/pyvista/pyvista), por nombrar algunos. - - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + text: El procesamiento acelerado de matrices de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. From 47e077facad49c7d9e43d6ce9aa7a9f4545ff2e1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:50 +0200 Subject: [PATCH 266/586] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 78 ++++++++++++++++++------------------- 1 file changed, 39 insertions(+), 39 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index de3a75a312..5a208d9f6b 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -75,7 +75,7 @@ params: alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - - title: Awkward + title: XND text: Numpy のような イディオムを使って JSON のようなデータを操作するライブラリ img: /images/content_images/arlib/xnd.png alttext: awkward @@ -94,8 +94,8 @@ params: text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" libraries: - - title: Quantum Computing - alttext: A computer chip. + title: 量子コンピューティング + alttext: コンピューターチップ img: /images/content_images/sc_dom_img/quantum_computing.svg links: - @@ -111,25 +111,25 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: 統計コンピューティング + alttext: 線グラフが上に移動します。 img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - - url: https://www.statsmodels.org/ + url: https://github.com/statsmodels/statsmodels label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - - url: https://seaborn.pydata.org/ + url: https://github.com/mwaskom/seaborn label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + title: 信号処理 + alttext: 正と負の値を持つ棒グラフ。 img: /images/content_images/sc_dom_img/signal_processing.svg links: - @@ -145,8 +145,8 @@ params: url: https://hyperspy.org/ label: HyperSpy - - title: Image Processing - alttext: An photograph of the mountains. + title: 画像処理 + alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -159,8 +159,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Graphs and Networks - alttext: A simple graph. + title: グラフとネットワーク + alttext: シンプルなグラフ img: /images/content_images/sc_dom_img/sd6.svg links: - @@ -176,30 +176,30 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomy - alttext: A telescope. + title: 天文学 + alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - - url: https://sunpy.org/ + url: https://github.com/sunpy/sunpy label: SunPy - - url: https://spacepy.github.io/ + url: https://github.com/spacepy/spacepy label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + title: 認知心理学 + alttext: ギアをつけた人間の頭部 img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - - title: Bioinformatics - alttext: A strand of DNA. + title: 生命情報科学 + alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -215,8 +215,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: ベイズ推論 + alttext: 鐘形の曲線のグラフ img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - @@ -232,8 +232,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: 数学的分析 + alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - @@ -243,14 +243,14 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://www.cvxpy.org/ + url: https://github.com/cvxgrp/cvxpy label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - - title: Chemistry - alttext: A test tube. + title: 化学 + alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg links: - @@ -266,8 +266,8 @@ params: url: https://www.pybamm.org/ label: PyBaMM - - title: Geoscience - alttext: The Earth. + title: 地球科学 + alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -283,8 +283,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: 地理情報処理 + alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg links: - @@ -297,8 +297,8 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: アーキテクチャとエンジニアリング + alttext: マイクロプロセッサ開発ボード img: /images/content_images/sc_dom_img/robotics.svg links: - @@ -306,7 +306,7 @@ params: label: COMPAS - url: https://cityenergyanalyst.com/ - label: City Energy Analyst + label: 都市エネルギー分析 - url: https://nortikin.github.io/sverchok/ label: Sverchok @@ -322,13 +322,13 @@ params: alttext: 三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。 examples: - - text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "探索的解析: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - text: "モデリングと評価: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "ダッシュボードでのレポート: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "ダッシュボードでのレポート: [Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" content: - text: 大規模データに対して、[Dask](https://dask.org)と[Ray](https://ray.io/)はスケールすることを目指して設計されています。安定したデプロイメントに関しては、データのバージョニング（[DVC](https://dvc.org)）、実験の追跡（[MLFlow](https://mlflow.org)）、ワークフローの自動化（[Airflow](https://airflow.apache.org)および[Prefect](https://www.prefect.io)が重要ですが様々なNumPyベースのツールが提供されています。 @@ -355,7 +355,7 @@ params: img: /images/content_images/v_seaborn.png alttext: 2種類のグラフによるペアプロット。seabornで作られたプロットと周波数グラフ" - - url: https://docs.pyvista.org/ + url: https://docs.pyvista.org/examples/index.html img: /images/content_images/v_pyvista.png alttext: PyVista製の3Dボリュームレンダリング - From 1c6a806e4cd4885c8d898d0fc22a4ebfd3ff3211 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:51 +0200 Subject: [PATCH 267/586] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 158 ++++++++++++++++++------------------ 1 file changed, 79 insertions(+), 79 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index d74cba9bce..4fa0164021 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -2,100 +2,100 @@ params: machinelearning: paras: - - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. - para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + para1: NumPy는 [scikit-learn](https://scikit-learn.org) 및 [SciPy](https://www.scipy.org)와 같은 강력한 기계 학습 라이브러리의 기반을 형성합니다. 기계 학습이 성장함에 따라 NumPy에 구축된 라이브러리 목록도 늘어납니다. [TensorFlow의](https://www.tensorflow.org) 딥 러닝 기능은 폭넓게 응용할 수 있습니다. — 그 중에는 음성 및 이미지 인식, 텍스트 기반 애플리케이션, 시계열 분석 및 비디오 감지가 있습니다. 또 다른 딥 러닝 라이브러리인 [PyTorch](https://pytorch.org)는 컴퓨터 비전 및 자연어 처리 연구자들 사이에서 인기가 있습니다. [MXNet](https://github.com/apache/incubator-mxnet)은 딥 러닝을 위한 청사진과 템플릿을 제공하는 또 다른 AI 패키지입니다. + para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) 메서드라고 하는 비닝(binning), 배깅(bagging), 스태킹(stacking), 부스팅(boosting)과 같은 통계 기법은 다음과 같은 도구로 구현되는 ML 알고리즘에 속합니다. [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) 및 [CatBoost](https://catboost.ai) — – 가장 빠른 추론 엔진 중 하나입니다. [Yellowbrick](https://www.scikit-yb.org/en/latest/) 및 [Eli5](https://eli5.readthedocs.io/en/latest/)는 기계 학습 시각화를 제공합니다.' arraylibraries: intro: - - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + text: NumPy의 API는 라이브러리가 혁신적인 하드웨어를 활용하거나, 특수 배열 유형을 생성하거나, NumPy가 제공하는 것 이상의 기능을 추가하도록 작성되는 출발점입니다. headers: - - text: Array Library + text: 배열 라이브러리 - - text: Capabilities & Application areas + text: 기능 및 응용 분야 libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + text: 분석을 위한 분산 배열 및 고급 병렬 처리를 통해 규모에 맞는 성능을 구현합니다. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy - text: NumPy-compatible array library for GPU-accelerated computing with Python. + text: Python에서 GPU 가속 컴퓨팅을 구현해주며 NumPy와 호환되는 배열 라이브러리. img: /images/content_images/arlib/cupy.png alttext: CuPy - url: https://cupy.dev + url: https://cupy.chainer.org - title: JAX - text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + text: "NumPy 프로그램을 부분적으로 변환하여 벡터화, GPU/TPU의 적시 컴파일을 제공하는 라이브러리." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://jax.readthedocs.io/ + url: https://github.com/google/jax - title: Xarray - text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + text: 고급 통계 및 시각화를 구동하기 위하여 라벨링 및 인덱싱이 이뤄진 다차원 배열을 제공 img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse - text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + text: Dask 및 SciPy의 희소 선형 대수와 통합되는 NumPy 호환 희소 배열 라이브러리입니다. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch - text: Deep learning framework that accelerates the path from research prototyping to production deployment. + text: 연구 프로토타이핑에서 프로덕션 배포로의 경로를 가속화하는 딥 러닝 프레임워크입니다. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow - text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + text: 기계 학습을 위한 엔드 투 엔드 플랫폼으로 ML 기반 애플리케이션을 쉽게 구축하고 배포할 수 있습니다. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org + - + title: MXNet + text: 유연한 연구 프로토타이핑 및 생산에 적합한 딥 러닝 프레임워크입니다. + img: /images/content_images/arlib/mxnet_logo.png + alttext: MXNet + url: https://mxnet.apache.org/ - title: Arrow - text: A cross-language development platform for columnar in-memory data and analytics. + text: 열 기반 메모리 내 데이터 및 분석을 위한 교차 언어 개발 플랫폼입니다. img: /images/content_images/arlib/arrow.png alttext: arrow - url: https://arrow.apache.org/ + url: https://github.com/apache/arrow - title: xtensor - text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + text: 수치 분석을 위한 브로드캐스팅 및 지연 컴퓨팅이 포함된 다차원 배열. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array - text: Manipulate JSON-like data with NumPy-like idioms. + text: NumPy와 유사한 관용어로 JSON과 유사한 데이터를 조작합니다. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray - text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + text: 구현에서 API를 분리하는 Python 백엔드 시스템; unumpy는 NumPy API를 제공합니다. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - - text: Nearly every scientist working in Python draws on the power of NumPy. + text: Python으로 작업하는 거의 모든 과학자는 NumPy의 힘을 이용합니다. - - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + text: "NumPy는 C 및 Fortran과 같은 언어의 계산 능력을 배우고 사용하기 훨씬 쉬운 언어인 Python으로 가져옵니다. 이 힘에는 단순함이 있습니다. NumPy의 솔루션은 종종 명확하고 우아합니다." libraries: - - title: Quantum Computing - alttext: A computer chip. + title: 양자 컴퓨팅 + alttext: 컴퓨터 칩 img: /images/content_images/sc_dom_img/quantum_computing.svg links: - @@ -111,25 +111,25 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: 통계적 컴퓨팅 + alttext: 선이 위로 이동하는 선그래프 img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - - url: https://www.statsmodels.org/ + url: https://github.com/statsmodels/statsmodels label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - - url: https://seaborn.pydata.org/ + url: https://github.com/mwaskom/seaborn label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + title: 신호 처리 + alttext: 양의 값과 음의 값을 가지는 막대 차트 img: /images/content_images/sc_dom_img/signal_processing.svg links: - @@ -145,8 +145,8 @@ params: url: https://hyperspy.org/ label: HyperSpy - - title: Image Processing - alttext: An photograph of the mountains. + title: 이미지 처리 + alttext: 산이 찍힌 사진 img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -159,8 +159,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Graphs and Networks - alttext: A simple graph. + title: 그래프 및 네트워크 + alttext: 간단한 그래프. img: /images/content_images/sc_dom_img/sd6.svg links: - @@ -176,30 +176,30 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomy - alttext: A telescope. + title: 천문학 + alttext: 망원경 img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - - url: https://sunpy.org/ + url: https://github.com/sunpy/sunpy label: SunPy - - url: https://spacepy.github.io/ + url: https://github.com/spacepy/spacepy label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + title: 인지심리학 + alttext: 톱니바퀴가 안에서 돌아가는 사람의 머리 img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - - title: Bioinformatics - alttext: A strand of DNA. + title: 생물정보학 + alttext: DNA 가닥 img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -215,8 +215,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: 베이지안 추론 + alttext: 종 모양 곡선이 그려진 그래프 img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - @@ -232,8 +232,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: 수학적 분석 + alttext: 수학 기호 4개 img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - @@ -243,14 +243,14 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://www.cvxpy.org/ + url: https://github.com/cvxgrp/cvxpy label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - - title: Chemistry - alttext: A test tube. + title: 화학 + alttext: 시험관 img: /images/content_images/sc_dom_img/chemistry.svg links: - @@ -266,8 +266,8 @@ params: url: https://www.pybamm.org/ label: PyBaMM - - title: Geoscience - alttext: The Earth. + title: 지구과학 + alttext: 지구 img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -283,8 +283,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: 지리학적 처리 + alttext: 지도 img: /images/content_images/sc_dom_img/GIS.svg links: - @@ -297,8 +297,8 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: 아키텍처 및 엔지니어링 + alttext: 마이크로프로세서 개발 보드 img: /images/content_images/sc_dom_img/robotics.svg links: - @@ -306,68 +306,68 @@ params: label: COMPAS - url: https://cityenergyanalyst.com/ - label: City Energy Analyst + label: 도시 에너지 분석가 - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: - intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + intro: "NumPy는 풍부한 데이터 과학 라이브러리 생태계의 핵심에 있습니다. 일반적인 탐색적 데이터 과학 워크플로는 다음과 같습니다." image1: - img: /images/content_images/ds-landscape.png - alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + alttext: Python 라이브러리 다이어그램. 다섯 가지 범주는 '추출, 변환, 로드', '데이터 탐색', '데이터 모델링', '데이터 평가' 및 '데이터 프레젠테이션'입니다. image2: - img: /images/content_images/data-science.png - alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + alttext: 세 개의 겹치는 원의 다이어그램입니다. 원에는 '수학', '컴퓨터 과학' 및 '영역 전문성'이라는 레이블이 지정되어 있습니다. 세 개의 원이 겹치는 다이어그램의 중간에는 'Data Science'라는 레이블이 붙은 영역이 있습니다. examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + text: "추출, 변환, 로드: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https:/ /pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "탐색적 분석: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "모델링 및 평가: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + text: "대시보드에서 보고: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + text: 대용량 데이터의 경우 [Dask](https://dask.org) 및 [Ray](https://ray.io/)가 확장되도록 설계되었습니다. 안정적인 배포는 데이터 버전 관리([DVC](https://dvc.org)), 실험 추적([MLFlow](https://mlflow.org)) 및 워크플로 자동화([Airflow](https:// airflow.apache.org), Dagster](https://dagster.io) 와 [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png - alttext: A streamplot made in matplotlib + alttext: Matplotlib으로 만든 streamplot - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png - alttext: A scatter-plot graph made in ggpy + alttext: ggpy로 만든 산점도 - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png - alttext: A box-plot made in plotly + alttext: plotly로 만든 상자 그림 - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + alttext: altair로 만든 스트림 그래프 - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png - alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + alttext: Seaborn에서 만든 두 가지 유형의 그래프, 플롯 그래프와 빈도 그래프의 pairplot - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png - alttext: A 3D volume rendering made in PyVista. + alttext: PyVista로 만든 3D 볼륨 렌더링. - url: https://napari.org img: /images/content_images/v_napari.png - alttext: A multi-dimensionan image made in napari. + alttext: napari로 만든 다차원 이미지. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png - alttext: A Voronoi diagram made in vispy. + alttext: vispy로 만든 보로노이 다이어그램. content: - - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + text: 몇 가지만 예를 들자면 NumPy는 [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), [PyVista](https://github.com/pyvista/pyvista) 등이 포함되어 있으며 급격히 성장해나가고 있는 [Python visualization landscape](https://pyviz.org/overviews/index.html)의 핵심 구성 요소 중 하나입니다. - - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + text: NumPy는 큰 배열을 고속으로 처리할 수 있어 연구자가 기존 Python이 처리할 수 있는 데이터셋보다 훨씬 큰 것도 시각화할 수 있도록 합니다. From 35d823a166efe254e28aab74b666895e235c31d6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:53 +0200 Subject: [PATCH 268/586] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 50 ++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index a094e972a8..b84da40386 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -28,7 +28,7 @@ params: url: https://cupy.chainer.org - title: JAX - text: "Transformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU." + text: "Composable transformations of NumPy programs differentiate: vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax @@ -76,7 +76,7 @@ params: url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array - text: Manipulação de dados JSON-like com sintaxe NumPy-like. + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ @@ -94,7 +94,7 @@ params: text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." libraries: - - title: Quantum Computing + title: Computação quântica alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: @@ -111,25 +111,25 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: Computação estatística + alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - - url: https://www.statsmodels.org/ + url: https://github.com/statsmodels/statsmodels label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - - url: https://seaborn.pydata.org/ + url: https://github.com/mwaskom/seaborn label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + title: Processamento de sinais + alttext: Um gráfico de barras com valores positivos e negativos. img: /images/content_images/sc_dom_img/signal_processing.svg links: - @@ -143,7 +143,7 @@ params: label: python-control - url: https://hyperspy.org/ - label: HiperSpy + label: HyperSpy - title: Processamento de imagens alttext: Uma fotografia das montanhas. @@ -159,8 +159,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Grafos e Redes - alttext: Um grafo simples. + title: Graphs and Networks + alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg links: - @@ -176,18 +176,18 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomia - alttext: Um telescópio. + title: Astronomy + alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - - url: https://sunpy.org/ + url: https://github.com/sunpy/sunpy label: SunPy - - url: https://spacepy.github.io/ + url: https://github.com/spacepy/spacepy label: SpacePy - title: Psicologia Cognitiva @@ -215,8 +215,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: Inferência Bayesiana + alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - @@ -232,8 +232,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: Análise Matemática + alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - @@ -243,14 +243,14 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://www.cvxpy.org/ + url: https://github.com/cvxgrp/cvxpy label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Chemistry - alttext: A test tube. + alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg links: - @@ -267,7 +267,7 @@ params: label: PyBaMM - title: Geoscience - alttext: The Earth. + alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -298,7 +298,7 @@ params: label: Folium - title: Arquitetura e Engenharia - alttext: A microprocessor development board. + alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg links: - @@ -322,7 +322,7 @@ params: alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - From 680469faf40f1bdb5f2a2a79d2e13a5020229f38 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:54 +0200 Subject: [PATCH 269/586] New translations history.md (Spanish) --- content/es/history.md | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/content/es/history.md b/content/es/history.md index eafe550ab0..e4e988a3df 100644 --- a/content/es/history.md +++ b/content/es/history.md @@ -1,20 +1,21 @@ --- -title: History of NumPy +title: Historia de NumPy sidebar: false --- -NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de matrices y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la biblioteca contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en informática y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amistosa con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados en el análisis de ondas gravitacionales utilizan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. -For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). +Para conocer en profundidad los hitos en el desarrollo de NumPy y las bibliotecas relacionadas, consulta [arxiv.org](arxiv.org/abs/1907.10121). -If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: +Si deseas obtener una copia de las bibliotecas originales Numeric y Numarray, sigue los siguientes enlaces: -[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* +[Página de Descarga de *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* +[Página de Descarga de *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con matrices o que refactoricen cualquier código preexistente para utilizar la biblioteca NumPy. -### Historic Documentation +### Documentación Histórica + +[Descarga el Manual de *`Numeric'*](static/numeric-manual.pdf) -[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From 5154fdac5f4f45cd20407c0de9de5b532c63602b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:55 +0200 Subject: [PATCH 270/586] New translations history.md (Arabic) --- content/ar/history.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/content/ar/history.md b/content/ar/history.md index eafe550ab0..aa669d375b 100644 --- a/content/ar/history.md +++ b/content/ar/history.md @@ -9,12 +9,13 @@ For the in-depth account on milestones in the development of NumPy and related l If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: -[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. ### Historic Documentation -[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From a0075c0821ceaca1b3cedb53030497046cf4f80e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:56 +0200 Subject: [PATCH 271/586] New translations history.md (Japanese) --- content/ja/history.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/content/ja/history.md b/content/ja/history.md index 66ae3a1475..04a5eb6432 100644 --- a/content/ja/history.md +++ b/content/ja/history.md @@ -3,18 +3,23 @@ title: NumPyの歴史 sidebar: false --- -NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 開始当初は資金も少なく、主に大学院生により開発されていました。その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。少数の "野良"学生プログラマーのグループが、すでに確立されていた商用研究ソフトウェアのエコシステムをひっくり返すなんて、想像することすら馬鹿げていました。 商用ソフトは、何百万もの資金と何百人もの優秀なエンジニアに支えられていましたから。それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。現在では、NumPyは科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 このライブラリの開発開始当初は資金も少なく、主に大学院生が開発していましたが、その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。 何百万もの資金調達と何百人もの優秀なエンジニアに支えられている当時の商用研究ソフトウェアのエコシステムを、少数の "野良"学生プログラマーのグループがひっくり返すことができると想像することさえ、当時は馬鹿げていると考えられていました。 それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 現在では、Numpy は科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 NumPy および関連ライブラリの開発におけるマイルストーンの詳細については、 [arxiv.org](arxiv.org/abs/1907.10121) を参照してください。 NumPyのベースとなったNumericとNumarrayライブラリのコピーを入手したい場合は、以下のリンクを参照してください。 -[ _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/) のダウンロード\*\* +[ *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/) のダウンロード** -[\*Numarray \*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/) のダウンロード\*\* +[*Numarray *](https://sourceforge.net/projects/numpy/files/Old%20Numarray/) のダウンロード** -\*これらの古いパッケージはもはや保守されていないことに注意してください。 配列関連の処理をしたい場合は、NumPyを使用するか、NumPyライブラリを利用するために既存のコードをリファクタリングすることを強くお勧めします。

+*これらの古いパッケージはもはや保守されていないことに注意してください。 配列関連の処理をしたい場合は、NumPyを使用するか、NumPyライブラリを利用するために既存のコードをリファクタリングすることを強くお勧めします。

-### 過去の資料 +

+ 過去の資料 +

+ +

+ Numericマニュアルのダウンロード +

-Numericマニュアルのダウンロード From cbf3282cc4c0b6582b47308df2948e0ea7af265a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:57 +0200 Subject: [PATCH 272/586] New translations history.md (Korean) --- content/ko/history.md | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/content/ko/history.md b/content/ko/history.md index eafe550ab0..015495f328 100644 --- a/content/ko/history.md +++ b/content/ko/history.md @@ -1,20 +1,21 @@ --- -title: History of NumPy +title: NumPy의 역사 sidebar: false --- -NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy는 배열 데이터 구조와 이에 대한 빠른 수치적 루틴을 제공하는 Python의 기초적인 라이브러리입니다. 처음 시작했을 때는 라이브러리를 개발할 자금이 거의 없었고, 주로 컴퓨터 공학 교육을 받지 못했고, 교수의 승인조차 받지 못한 대학원생이 이를 제작해나갔습니다. 소규모 "불량" 학생 프로그래머 집단이 이미 잘 정립되었으며 엄청난 자본과 많은 우수한 기술자들이 뒷받침하는 연구 소프트웨어 생태계를 뒤바꾼다고 상상해보세요. 정말 터무니없는 일입니다. 그러나 완전 개방형 도구 속에 감추어졌던 철학적 동기들이, 친근하고 들떴으며 특별한 목표를 가진 공동체와 결합되어, 장기적으로 유의미한 것이 드러났습니다. 오늘날 NumPy는 전 세계의 과학자, 기술자 및 기타 많은 전문가들의 신뢰를 받고 있습니다. 예를 들어, 중력파 분석에 사용되며 출시된 스크립트는 NumPy 패키지를 가져 왔고, M87 블랙홀 시각화 프로젝트에서는 NumPy를 직접 인용하였습니다. -For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). +NumPy 및 관련 라이브러리의 개발 단계에 대한 자세한 설명은 [arxiv.org](https://arxiv.org/abs/1907.10121)를 참고하십시오. -If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: +원본 Numeric 및 Numarray 라이브러리의 사본을 얻으려면 아래 링크를 들어가십시오. -[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* +[*Numeric* 다운로드 페이지](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* +[*Numarray* 다운로드 페이지](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*이런 오래된 배열 패키지는 더 이상 지원되지 않으며, 배열 관련 기능을 이용하기 위해서는 NumPy를 사용하거나 NumPy 라이브러리를 활용하기 위해서는 기존 코드를 리팩토링하는 것이 좋습니다. -### Historic Documentation +### 역사적 문서 + +[*`Numeric'* 메뉴얼 다운로드](static/numeric-manual.pdf) -[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) From 0d6fb83a08da4bfbeb0c291be9a30431eb6052a1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:58 +0200 Subject: [PATCH 273/586] New translations history.md (Russian) --- content/ru/history.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/content/ru/history.md b/content/ru/history.md index eafe550ab0..aa669d375b 100644 --- a/content/ru/history.md +++ b/content/ru/history.md @@ -9,12 +9,13 @@ For the in-depth account on milestones in the development of NumPy and related l If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: -[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. ### Historic Documentation -[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From e209dae09849287efd939ffa1a0566b4f6d57783 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:25:59 +0200 Subject: [PATCH 274/586] New translations history.md (Chinese Simplified) --- content/zh/history.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/content/zh/history.md b/content/zh/history.md index eafe550ab0..aa669d375b 100644 --- a/content/zh/history.md +++ b/content/zh/history.md @@ -9,12 +9,13 @@ For the in-depth account on milestones in the development of NumPy and related l If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: -[Download Page for _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -\*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. ### Historic Documentation -[Download _\`Numeric'_ Manual](static/numeric-manual.pdf) +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From e8a81a8c484731306b6a03f8f64824c418868cf6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:00 +0200 Subject: [PATCH 275/586] New translations history.md (Portuguese, Brazilian) --- content/pt/history.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/content/pt/history.md b/content/pt/history.md index f62ae9b663..2ddc33eb57 100644 --- a/content/pt/history.md +++ b/content/pt/history.md @@ -3,18 +3,19 @@ title: Histórico do NumPy sidebar: false --- -NumPy é uma biblioteca Python fundamental que fornece estruturas de _arrays_ de dados e rotinas numéricas rápidas relacionadas a estas arrays. Quando começou, a biblioteca tinha pouco financiamento e foi escrita principalmente por estudantes de pós-graduação—muitos deles sem educação em ciência da computação e, muitas vezes, sem autorização dos seus orientadores. Imaginar que um pequeno grupo de programadores estudantis "desobedientes" poderiam subverter o já bem estabelecido ecossistema de software de pesquisa - apoiado por milhões em financiamento e muitas centenas de engenheiros altamente qualificados - era absurdo. No entanto, as motivações filosóficas por trás de uma ferramenta totalmente aberta, em combinação com a vibrante, amigável comunidade com foco singular, provaram ser auspiciosas a longo prazo. Hoje em dia, cientistas, engenheiros e muitos outros profissionais ao redor do mundo confiam no NumPy. Por exemplo, os scripts usados e publicados na análise de ondas gravitacionais importam o NumPy, e o projeto de imagem para buraco negro M87 cita diretamente o NumPy. +NumPy é uma biblioteca Python fundamental que fornece estruturas de *arrays* de dados e rotinas numéricas rápidas relacionadas a estas arrays. Quando começou, a biblioteca tinha pouco financiamento e foi escrita principalmente por estudantes de pós-graduação—muitos deles sem educação em ciência da computação e, muitas vezes, sem autorização dos seus orientadores. Imaginar que um pequeno grupo de programadores estudantis "desobedientes" poderiam subverter o já bem estabelecido ecossistema de software de pesquisa - apoiado por milhões em financiamento e muitas centenas de engenheiros altamente qualificados - era absurdo. No entanto, as motivações filosóficas por trás de uma ferramenta totalmente aberta, em combinação com a vibrante, amigável comunidade com foco singular, provaram ser auspiciosas a longo prazo. Hoje em dia, cientistas, engenheiros e muitos outros profissionais ao redor do mundo confiam no NumPy. Por exemplo, os scripts usados e publicados na análise de ondas gravitacionais importam o NumPy, e o projeto de imagem para buraco negro M87 cita diretamente o NumPy. Para um histórico aprofundado dos marcos no desenvolvimento do NumPy e bibliotecas relacionadas, por favor veja [arxiv.org](arxiv.org/abs/1907.10121). Se você quiser obter uma cópia das bibliotecas Numeric e Numarray, siga os links abaixo: -[Página de download para _Numeric_](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)\* +[Página de download para *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Página de download para _Numarray_](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)\* +[Página de download para *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -\*Por favor, note que esses pacotes antigos não são mais mantidos, e os usuários são fortemente aconselhados a usar o NumPy para quaisquer propósitos relacionados a arrays e matrizes ou refatorar qualquer código pré-existente para utilizar a biblioteca do NumPy. +*Por favor, note que esses pacotes antigos não são mais mantidos, e os usuários são fortemente aconselhados a usar o NumPy para quaisquer propósitos relacionados a arrays e matrizes ou refatorar qualquer código pré-existente para utilizar a biblioteca do NumPy. ### Documentação Histórica -[Baixe o manual do _\`Numeric'_](static/numeric-manual.pdf) +[Baixe o manual do *`Numeric'*](static/numeric-manual.pdf) + From f8c90583fbb577afdd8b28f6976c720f964dd1a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:02 +0200 Subject: [PATCH 276/586] New translations config.yaml (Arabic) --- content/ar/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 0ee45e8b33..4aaf75b2e6 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 2.0. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From daed963bed5f7f427090d75a413b2fe3a7847cad Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:04 +0200 Subject: [PATCH 277/586] New translations config.yaml (Russian) --- content/ru/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 0ee45e8b33..4aaf75b2e6 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 2.0. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 53928a4a051cc20989eda5487d429664da4dc213 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:05 +0200 Subject: [PATCH 278/586] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 0ee45e8b33..4aaf75b2e6 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 2.0. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 919376ca91d7432957f484f90b8518cd13b5093d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:06 +0200 Subject: [PATCH 279/586] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/content/es/gethelp.md b/content/es/gethelp.md index cce276b98a..5da0e518f7 100644 --- a/content/es/gethelp.md +++ b/content/es/gethelp.md @@ -1,25 +1,20 @@ --- -title: Get Help +title: Buscar Ayuda sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please -see [Community](/community). +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). -**User questions:** The best way to get help is to post your question to a site -like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or -[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on -these sites, or answer questions directly, but the volume is a little -overwhelming! +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) +Un foro para hacer preguntas de uso, como por ejemplo: "¿Cómo hago X cosa en NumPy?". Por favor [usa la etiqueta `#numpy`](https://stackoverflow.com/help/tagging) *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +Otro foro para hacer preguntas de uso. *** From 07e8af01457c0471811d719913ba26260fbacf4d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:07 +0200 Subject: [PATCH 280/586] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index cce276b98a..da3bff67a5 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -3,14 +3,9 @@ title: Get Help sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please -see [Community](/community). - -**User questions:** The best way to get help is to post your question to a site -like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or -[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on -these sites, or answer questions directly, but the volume is a little -overwhelming! +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From 8150b147de5b7fd70a072147db8e04e2eac0c067 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:08 +0200 Subject: [PATCH 281/586] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md index 7bde760dc4..6105ed9eb4 100644 --- a/content/ja/gethelp.md +++ b/content/ja/gethelp.md @@ -3,20 +3,18 @@ title: サポートを得る方法 sidebar: false --- -**開発関連の問題:** NumPyの開発関連の問題 (例: バグレポート) については、[コミュニティ](/community) のページを参照してください。 +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). -**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大です。 We wish we could keep an eye on -these sites, or answer questions directly, but the volume is a little -overwhelming! +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、[ `#numpy` タグ](https://stackoverflow.com/help/tagging) を使用してください。 [Gitter](https://gitter.im/numpy/numpy) +NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、[ `#numpy` タグ](https://stackoverflow.com/help/tagging) を使用してください。 *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +もう一つの使い方に関する質問の場です。 *** From 7fa394aa59b9abe29b9de77a372e61953596d962 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:09 +0200 Subject: [PATCH 282/586] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md index cce276b98a..f216b532d6 100644 --- a/content/ko/gethelp.md +++ b/content/ko/gethelp.md @@ -1,25 +1,20 @@ --- -title: Get Help +title: 도움 구하기 sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please -see [Community](/community). +**개발 이슈:** NumPy 개발 관련 문제(버그 제보 등)의 경우, [커뮤니티](/community)를 방문해주시기 바랍니다. -**User questions:** The best way to get help is to post your question to a site -like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or -[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on -these sites, or answer questions directly, but the volume is a little -overwhelming! +**사용자 질문:** 도움을 받을 수 있는 가장 좋은 방법은 링크된 사이트에 질문을 게시하는 것입니다. [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) 또는 [Reddit](https://www.reddit.com/r/Numpy/). 저희가 직접 이런 사이트들을 주시하거나 질문에 대해 답해드리고 싶지만, 그러기에는 질문의 양이 너무 많습니다! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) +"How do I do X in NumPy?”와 같이 사용 중 질문을 올리는 포럼입니다. [`#numpy` 태그를 사용](https://stackoverflow.com/help/tagging)해주세요. *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +사용 중 질문을 올리는 또다른 포럼입니다. *** From e875217b1a293fb3df770c296432130766b53cd4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:10 +0200 Subject: [PATCH 283/586] New translations gethelp.md (Russian) --- content/ru/gethelp.md | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/content/ru/gethelp.md b/content/ru/gethelp.md index cce276b98a..da3bff67a5 100644 --- a/content/ru/gethelp.md +++ b/content/ru/gethelp.md @@ -3,14 +3,9 @@ title: Get Help sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please -see [Community](/community). - -**User questions:** The best way to get help is to post your question to a site -like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or -[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on -these sites, or answer questions directly, but the volume is a little -overwhelming! +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From eaaeea3160ab83e69c39a013a30889eca66905ca Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:11 +0200 Subject: [PATCH 284/586] New translations gethelp.md (Chinese Simplified) --- content/zh/gethelp.md | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/content/zh/gethelp.md b/content/zh/gethelp.md index cce276b98a..da3bff67a5 100644 --- a/content/zh/gethelp.md +++ b/content/zh/gethelp.md @@ -3,14 +3,9 @@ title: Get Help sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please -see [Community](/community). - -**User questions:** The best way to get help is to post your question to a site -like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or -[Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on -these sites, or answer questions directly, but the volume is a little -overwhelming! +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From 7855c3ca8eaddbc4b6d1314d3e2d6db6bd14b2c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:12 +0200 Subject: [PATCH 285/586] New translations gethelp.md (Portuguese, Brazilian) --- content/pt/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/gethelp.md b/content/pt/gethelp.md index 047be9368d..f9f0e0322a 100644 --- a/content/pt/gethelp.md +++ b/content/pt/gethelp.md @@ -3,9 +3,9 @@ title: Obter ajuda sidebar: false --- -**Issues sobre desenvolvimento:** Para assuntos relacionados ao desenvolvimento do NumPy (por exemplo, relatórios de bugs), veja a [Comunidade](/community). +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). -**Perguntas de usuários:** A melhor maneira de obter ajuda é postar sua pergunta em um site como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), com milhares de usuários disponíveis para responder. Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso! +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From b98ac6cd48e089c8469b2cf0c719b9f0b4b7b501 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:13 +0200 Subject: [PATCH 286/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index bba5d56bf1..5ee1f4bcbe 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -22,16 +22,16 @@ We strive to: 3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. 4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. 5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - - Violent threats or language directed against another person. - - Sexist, racist, or otherwise discriminatory jokes and language. - - Posting sexually explicit or violent material. - - Posting (or threatening to post) other people’s personally identifying information (“doxing”). - - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - - Personal insults, especially those using racist or sexist terms. - - Unwelcome sexual attention. - - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - - Repeated harassment of others. In general, if someone asks you to stop, then stop. - - Advocating for, or encouraging, any of the above behaviour. + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. ### Diversity Statement @@ -53,9 +53,9 @@ You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@go Currently, the Committee consists of: -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). From a3112e6861b716b7a80e831e3a61fcce5a5c98ac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:15 +0200 Subject: [PATCH 287/586] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index bba5d56bf1..5ee1f4bcbe 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -22,16 +22,16 @@ We strive to: 3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. 4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. 5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - - Violent threats or language directed against another person. - - Sexist, racist, or otherwise discriminatory jokes and language. - - Posting sexually explicit or violent material. - - Posting (or threatening to post) other people’s personally identifying information (“doxing”). - - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - - Personal insults, especially those using racist or sexist terms. - - Unwelcome sexual attention. - - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - - Repeated harassment of others. In general, if someone asks you to stop, then stop. - - Advocating for, or encouraging, any of the above behaviour. + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. ### Diversity Statement @@ -53,9 +53,9 @@ You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@go Currently, the Committee consists of: -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). From eda51d68ed79d79bd579d5212adac5c759eb97b5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:15 +0200 Subject: [PATCH 288/586] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 70 +++++++++++++++++------------------ 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index a26d0fd172..044123f3d1 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -5,78 +5,78 @@ aliases: - /ja/conduct/ --- -### Introduction +### はじめに -This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. +この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、Twitter、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。 この行動規範は、NumPy コミュニティに正式または非公式に参加するすべての人が順守する必要があります。 その他にも、NumPyとの提携・関連するプロジェクト活動においては、特にそれらのプロジェクトを代表する場合、同様の行動規範に従う必要があります。 -This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. +この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。 ### ガイドラインの概要 -We strive to: - -1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. -2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. -3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. -4. Be inquisitive. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. -5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - - 他の人に向けられた暴力的な行為や言葉。 - - 性差別や人種差別、その他の差別的なジョークや言動。 - - 性的または暴力的な内容の投稿。 - - 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 - - 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 - - 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 - - Unwelcome sexual attention. - - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - - Repeated harassment of others. In general, if someone asks you to stop, then stop. - - 上記のいずれかの行動を擁護すること、または奨励すること。 +私たちは下記の内容に真摯に取り組みます。 + +1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、公にすべきではない内容を議論する場合以外、プロジェクトに関連するメッセージを公の場で告知することを選びます。 これは、NumPyに関するヘルプやプロジェクトサポートにも適用されます。公式なサポートだけでなく、NumPyに関する質問に答える場合もです。 これにより、質問に答えた際の意図しない間違いを、より簡単に検出し、訂正できるようになります。 +2. 共感し、歓迎し、友好的で、そして我慢強くありましょう。 私たちは互いに争いを解決し合い、互いの善意を信じ合います。 私たちは時折り不満を感じるかもしれません。 しかしそのような場合も、不満を個人的な攻撃に変えることは許容されません。 人々が不快や脅威を感じるコミュニティは、生産的ではないからです。 +3. 互いに協力し合おう。 私たちの開発成果は他の人々によって利用され、一方で、たちは他の人々の開発成果に依存しているのです。 私たちがプロジェクトために何かを作るとき、私たちはそれがどのように動作するかを他の人に説明する必要があります。 しかし、この作業により、より良いものを作り上げることができるのです。 私たちが下す全ての決断は、ユーザと開発コミュニティに影響を与えうるし、その決断がもたらす結果を私たちは真摯に受け止めます。 +4. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 +5. 使う言葉に注意しましょう。 私たちは、コミュニティにおけるコミュニケーションに注意と敬意を払います。 そして、私たちは自分の言葉に責任を持ちます。 他人に優しくしましょう。 他のコミュニティの参加者を侮辱しないでください。 私たちは、以下のようなハラスメントやその他の排斥行為を許しません。 : + * 他の人に向けられた暴力的な行為や言葉。 + * 性差別や人種差別、その他の差別的なジョークや言動。 + * 性的または暴力的な内容の投稿。 + * 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 + * 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 + * 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 + * 不快な思いをさせる性的な言動。 + * 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。 + * 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。 + * 上記のいずれかの行動を擁護すること、または奨励すること。 ### 多様性に関する声明 -NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. +NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 -No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. +あなたの自己認識や、他者のあなたへの認識は関係ありません。 私たちはあなたを歓迎します。 民族、遺伝、性同一性あるいは関連する表現、言語、国籍、神経学的な差異、生物学的な差異、 政治的信条、職業、人種、宗教、性的指向、社会経済的地位、文化的な差異、技術的な能力。 私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。 -Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). +NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。 ### 報告ガイドライン -私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 Please keep this in mind when deciding on how to respond to a breach of this Code. +私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 -For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. +意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。 NumPy行動規範委員会に問題を報告する場合は、こちらにご連絡下さい: numpy-conduct@googlegroups.com。 現在、行動規範委員会は以下のメンバーで構成されています: -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami -If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). +もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 もしくは、行動規範委員会に報告するのが躊躇われる場合は、こちらからNumFOCUSのシニアスタッフに連絡することも可能です:[conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible) 。 ### インシデント報告の解決 & 行動規範の実施 -本節では、_最も重要な点のみをまとめます。 _詳細については、[NumPy Code of Conduct - How to follow up on a report](report-handling-manual) をご覧ください。 +本節では、_最も重要な点のみをまとめます。 _詳細については、[NumPy Code of Conduct - How to follow up on a report](/report-handling-manual) をご覧ください。 -We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). +私たちはすべての訴えを調査し、対応するようにします。 NumPy行動規範委員会およびNumPy運営委員会(もし関係する場合) は、報告者の身元を保護します。 また(報告者が同意しない限り) 苦情の内容を機密として扱うこととします。 -In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. +もし深刻で明らかな違反の場合、例えば、 個人的な脅し、または暴力的、性差別的または人種差別的な発言などの場合、我々は直ちにNumPyのコミュニケーションの場から発言者を退場させます。詳細についてはマニュアルを参照してください。 -In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: +もし、行動規範に対して明白な違反がみられない場合、受領された行動規範違反報告に対するプロセスは以下の通りです。 1. 報告書の受領を確認 2. 建設的な議論/フィードバック 3. 調停（報告者と報告を受けたものの両方がフィードバックが役に立たなかったと同意した場合に限る） -4. 行動規範委員会による透明性のある決定と執行( [決議](report-handling-manual/#解決方法)を参照) +4. 行動規範委員会による透明性のある決定と執行( [決議](/report-handling-manual/#resolutions)を参照) 行動規範委員会は、可能な限り速やかに対応し、最大で72時間以内に対応する様にします。 -### Endnotes +### 文末脚注: 私たちは下記のドキュメントを作成したグループに感謝します。 内容・発想ともに大いに影響されています。 From 1f7287439b8757fed99d4c32ec976f19986cabda Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:16 +0200 Subject: [PATCH 289/586] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 98 +++++++++++++++++------------------ 1 file changed, 49 insertions(+), 49 deletions(-) diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md index bba5d56bf1..b8dea1fd9d 100644 --- a/content/ko/code-of-conduct.md +++ b/content/ko/code-of-conduct.md @@ -1,83 +1,83 @@ --- -title: NumPy Code of Conduct +title: NumPy 이용 약관 sidebar: false aliases: - /conduct.html --- -### Introduction +### 소개 -This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. +이 이용 약관은 모든 공개 및 비공개 메일링 리스트, 이슈 트래커, 위키, 블로그, 트위터 및 커뮤니티에서 사용하는 기타 커뮤니케이션 채널을 포함하여 NumPy 프로젝트에서 관리하는 모든 공간에 적용됩니다. NumPy 프로젝트는 대면 이벤트를 조직하지 않지만 커뮤니티와 관련된 이벤트에는 이와 유사한 약관이 있어야 합니다. -This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. +이 이용 약관은 NumPy 커뮤니티에 공식적으로 또는 비공식적으로 참여하거나 프로젝트 관련 활동에서, 특히 프로젝트를 대표할 때 어떤 역할에서든 프로젝트와의 관계를 주장하는 모든 사람이 준수해야 합니다. -This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. +이 약관는 철저하거나 완전하지 않습니다. 협력적이고 공유되는 환경과 목표에 대한 우리의 공통적인 이해를 요약하는데 도움이 됩니다. 문자 그대로 뿐만 아니라, 정신에도 이 약관을 따르려 노력해 주시기 바랍니다. 이를 통해 주변 커뮤니티를 더욱 풍요롭게 하는 친근하고 생산적인 환경을 조성해주세요. -### Specific Guidelines +### 구체적인 가이드라인 -We strive to: +우리는 다음을 위해 노력합니다: -1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. -2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. -3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. -4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. -5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - - Violent threats or language directed against another person. - - Sexist, racist, or otherwise discriminatory jokes and language. - - Posting sexually explicit or violent material. - - Posting (or threatening to post) other people’s personally identifying information (“doxing”). - - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - - Personal insults, especially those using racist or sexist terms. - - Unwelcome sexual attention. - - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - - Repeated harassment of others. In general, if someone asks you to stop, then stop. - - Advocating for, or encouraging, any of the above behaviour. +1. 개방적이 되세요. 우리 커뮤니티에 누구든 참여할 수 있습니다. 민감한 내용이 아닌 이상, 프로젝트 관련 메시지에는 공개적인 의사소통 방법을 사용하는 것을 선호합니다. 이는 도움이나 프로젝트 지원과 관련된 메시지에도 적용됩니다. 공개적인 지원 요청은 질문에 대한 대답이 더욱 쉽게 이루어지며, 대답에 대한 우발적인 오류가 보다 쉽게 감지되고 수정될 수 있도록 도와줍니다. +2. 공감하며, 환영하며, 친절하며, 인내심을 가지세요. 우리는 갈등을 해결하기 위해 함께 노력하며, 선의의 의도를 기대합니다. 때로는 모두가 어떤 형태의 좌절을 경험할 수 있지만, 우리는 그것이 개인적인 공격으로 이어지지 않도록 허용하지 않습니다. 사람들이 불편하거나 위협받는 느낌을 가지는 커뮤니티는 생산적이지 않습니다. +3. 협력적이 되세요. 우리의 작업은 다른 사람들에 의해 사용될 것이며, 우리 역시 다른 사람들의 작업에 의존할 것입니다. 프로젝트의 이익을 위해 무언가를 만들 때, 우리는 다른 사람들에게 어떻게 작동하는지 설명할 의향이 있습니다. 이를 통해 다른 사람들이 그 작업을 발전시켜 더욱 좋은 결과물을 만들 수 있도록 도와줄 수 있습니다. 우리가 내린 어떤 결정도 사용자와 동료에게 영향을 미치며, 이러한 결과를 신중하게 고려하여 결정을 내리게 됩니다. +4. 호기심을 가져보세요. 아무도 모든 것을 알수는 없습니다! 미리 질문을 하면 나중에 많은 문제를 예방할 수 있으므로, 우리는 질문하는 것을 장려합니다. 단, 적절한 포럼으로 질문을 옮길 수도 있습니다. 우리는 답변을 빠르고 유용하게 제공하기 위해 최선을 다할 것입니다. +5. 사용하는 말에 신중하게 대해주세요. 우리는 의사소통 시 신중하고 예의 바른 태도를 유지하며, 우리 자신의 언어에 대한 책임을 집니다. 다른 사람들에게 친절하게 대하세요. 다른 참가자들을 모욕하거나 비하하지 마세요. 우리는 괴롭힘 및 다른 형태의 배제 행위를 용인하지 않습니다. 이러한 행위에는 다음과 같은 것들이 포함됩니다: + * 다른 사람을 향한 폭력적인 위협이나 언어. + * 성 차별, 인종 차별 또는 기타 차별적인 농담 및 언어. + * 성적으로 노골적이거나 폭력적인 자료 게시. + * 다른 사람의 개인 식별 정보를 게시(또는 게시하겠다고 위협)("신상 털기"). + * 발신자의 동의 없이 비공개 또는 비공개로 전송된 이메일과 같은 비공개 콘텐츠 또는 IRC 채널 기록과 같은 기록되지 않은 포럼을 공유. + * 개인적인 모욕, 특히 인종차별적이거나 성차별적인 용어를 사용하는 경우. + * 달갑지 않은 성적 관심. + * 과도한 욕설. 욕설은 삼가해주세요; 사람들은 욕에 대한 민감도가 크게 다릅니다. + * 타인에 대한 반복적인 괴롭힘. 일반적으로 누군가가 중지를 요청하면 중지합니다. + * 위의 행동을 옹호하거나 장려하는 행위. -### Diversity Statement +### 다양성 선언 -The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. +NumPy 프로젝트는 모든 사람의 참여를 환영하고 장려합니다. 우리는 모두가 참여하는 것을 즐기는 커뮤니티가 되기 위해 최선을 다하고 있습니다. 항상 개인의 취향을 수용할 수는 없지만 모든 분들께 친절하게 대할 수 있도록 최선을 다하고 있습니다. -No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. +당신이 자신을 어떻게 식별하든 다른 사람들이 당신을 어떻게 인식하든 상관없이 우리는 당신을 환영합니다. 어떤 목록도 포괄적이기를 바랄 수는 없지만 다음과 같은 다양성을 명시적으로 존중합니다: 연령, 문화, 민족, 유전자형, 성 정체성 또는 성 표현, 언어, 출신 국가, 신경형, 표현형, 정치적 신념, 직업, 인종, 종교, 성적 지향, 사회 경제적 지위, 하위 문화 및 기술적 능력 등을 이용 약관과 상충하지 않는 범위 안에서. -Though we welcome people fluent in all languages, NumPy development is conducted in English. +우리는 모든 언어에 능통한 사람들을 환영하지만 NumPy 개발은 영어로 진행됩니다. -Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). +NumPy 커뮤니티의 행동 표준은 위의 이용 약관에 자세히 설명되어 있습니다. 커뮤니티의 참가자는 모든 상호 작용에서 이러한 표준을 유지하고 다른 사람들도 그렇게 하도록 도와야 합니다(다음 섹션 참조). -### Reporting Guidelines +### 신고 지침 -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. +우리는 인터넷 통신이 명백하고 노골적인 남용에서 시작되거나 악화되는 것이 매우 흔한 일이라는 것을 알고 있습니다. 우리는 또한 때때로 사람들이 나쁜 하루를 보내거나 이 이용 약관의 일부 지침을 인식하지 못할 수 있음을 알고 있습니다. 본 약관 위반에 대응하는 방법을 결정할 때 이 점을 염두에 두십시오. -For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. +명백히 의도적인 위반의 경우 이용 약관 위원회(아래 참조) 에 보고하십시오. 의도하지 않은 위반일 가능성이 있는 경우, 그 사람에게 회신하고 이 이용 약관을 지적할 수 있습니다(공개적이든 비공개적이든 가장 적절한 방식으로). 그렇게 하고 싶지 않다면 이용 약관 위원회에 직접 보고하거나 비밀리에 위원회에 조언을 구하십시오. -You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. +numpy-conduct@googlegroups.com 으로 NumPy 윤리 강령 위원회에 문제를 보고할 수 있습니다. -Currently, the Committee consists of: +현재 위원회는 다음과 같이 구성되어 있습니다. -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stéfan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami -If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). +귀하의 보고서가 위원회 구성원과 관련이 있거나 보고서를 처리하는 데 이해 상충이 있다고 생각하는 경우 귀하의 보고서를 고려하지 않을 것입니다. 또는 어떤 이유로든 위원회에 보고하는 것이 불편하다고 느끼면 NumFOCUS 고위 직원에게 연락하셔도 됩니다. [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). -### Incident reporting resolution & Code of Conduct enforcement +### 신고 해결 & 이용약관 강령 -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). +_이 섹션은 가장 중요한 사항을 요약하고 자세한 내용_은 [NumPy 행동 강령 - 보고에 대한 후속 조치 방법](/report-handling-manual)에서 찾을 수 있습니다. -We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). +우리는 모든 불만을 조사하고 대응할 것입니다. NumPy 행동 강령 위원회와 NumPy 운영 위원회(관련된 경우) 는 신고자의 신원을 보호하고 신고 내용을 기밀로 취급합니다(신고자가 달리 동의하지 않는 한). -In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. +심각하고 명백한 위반의 경우, 예: 개인적인 위협이나 폭력적, 성차별적 또는 인종차별적 언어를 사용하는 경우 NumPy 통신 채널에서 발신자를 즉시 연결 해제합니다. 자세한 내용은 설명서를 참조하십시오. -In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: +본 행동 강령의 명백하고 명백한 위반과 관련되지 않은 경우 접수된 행동 강령 위반 보고서에 따라 조치를 취하는 절차는 다음과 같습니다. -1. acknowledge report is received, -2. reasonable discussion/feedback, -3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. +1. 보고서를 수신 확인, +2. 합리적인 토론/피드백, +3. 중재(피드백이 도움이 되지 않은 경우, 신고자와 피신고자 모두 동의하는 경우에만), +4. 행동 강령 위원회의 투명한 결정([해결책](/report-handling-manual/#resolutions) 참조) 을 통한 집행. -The Committee will respond to any report as soon as possible, and at most within 72 hours. +위원회는 가능한 한 빨리, 최대 72시간 이내에 보고에 응답할 것입니다. -### Endnotes +### 끝내며 -We are thankful to the groups behind the following documents, from which we drew content and inspiration: +내용과 영감을 얻은 다음 문서의 배후에 있는 그룹에 감사드립니다. -- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) +- [SciPy 이용 약관](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 17d1cb25d7dc107787fb10a8e5f8c10da17d0fb3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:18 +0200 Subject: [PATCH 290/586] New translations code-of-conduct.md (Russian) --- content/ru/code-of-conduct.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ru/code-of-conduct.md b/content/ru/code-of-conduct.md index bba5d56bf1..5ee1f4bcbe 100644 --- a/content/ru/code-of-conduct.md +++ b/content/ru/code-of-conduct.md @@ -22,16 +22,16 @@ We strive to: 3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. 4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. 5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - - Violent threats or language directed against another person. - - Sexist, racist, or otherwise discriminatory jokes and language. - - Posting sexually explicit or violent material. - - Posting (or threatening to post) other people’s personally identifying information (“doxing”). - - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - - Personal insults, especially those using racist or sexist terms. - - Unwelcome sexual attention. - - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - - Repeated harassment of others. In general, if someone asks you to stop, then stop. - - Advocating for, or encouraging, any of the above behaviour. + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. ### Diversity Statement @@ -53,9 +53,9 @@ You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@go Currently, the Committee consists of: -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). From bff11d2d05b528e6af5e3e2f49e4167686b9d2d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:19 +0200 Subject: [PATCH 291/586] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index bba5d56bf1..5ee1f4bcbe 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -22,16 +22,16 @@ We strive to: 3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. 4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. 5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - - Violent threats or language directed against another person. - - Sexist, racist, or otherwise discriminatory jokes and language. - - Posting sexually explicit or violent material. - - Posting (or threatening to post) other people’s personally identifying information (“doxing”). - - Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - - Personal insults, especially those using racist or sexist terms. - - Unwelcome sexual attention. - - Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - - Repeated harassment of others. In general, if someone asks you to stop, then stop. - - Advocating for, or encouraging, any of the above behaviour. + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. ### Diversity Statement @@ -53,9 +53,9 @@ You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@go Currently, the Committee consists of: -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). From 70c02631a7ea2d00b10b7d772542c907b6360cb6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:19 +0200 Subject: [PATCH 292/586] New translations code-of-conduct.md (Portuguese, Brazilian) --- content/pt/code-of-conduct.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md index 4b9515189f..fe68237a92 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -7,7 +7,7 @@ aliases: ### Introdução -Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, _issue tracker_, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. +Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, *issue tracker*, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. O projeto NumPy não organiza eventos presenciais. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. Este Código de Conduta deve ser honrado por todas as pessoas que participam da comunidade NumPy formal ou informalmente, ou que reivindicam qualquer afiliação com o projeto, em qualquer atividade relacionada ao projeto, especialmente ao representar o projeto, em qualquer função. @@ -22,16 +22,16 @@ Nós nos esforçamos para: 3. Sermos colaborativos. O nosso trabalho será utilizado por outras pessoas e, por sua vez, dependeremos do trabalho dos outros. Quando fazemos algo em benefício do projeto, estamos dispostos a explicar aos outros como esse algo funciona, para que outros possam desenvolver o trabalho e torná-lo ainda melhor. Qualquer decisão que tomemos afetará nossos usuários e os colegas, e levamos essas consequências a sério quando tomamos decisões. 4. Sermos inquisitivos. Ninguém sabe tudo! Fazer perguntas antecipadamente evita muitos problemas mais tarde, por isso encorajamos as perguntas, embora possamos encaminhá-las para um fórum adequado. Vamos nos esforçar para sermos sensíveis e úteis. 5. Termos cuidado com as palavras que escolhemos. Somos cuidadosos e respeitosos na nossa comunicação e assumimos a responsabilidade pelo nosso próprio discurso. Seja gentil com os outros. Não insulte ou deprecie outros participantes. Nós não aceitaremos assédio ou outros comportamentos exclusivos, como: - - Ameaças ou linguagem violenta direcionadas contra outra pessoa. - - Piadas e linguagem sexista, racista ou discriminatória. - - Postagem de material sexualmente explícito ou violento. - - Postar (ou ameaçar postar) informações pessoais de outras pessoas (“doxing”). - - Compartilhar conteúdo privado, como e-mails enviados de maneira privada ou não-pública, ou fóruns não registrados, como histórico de canais IRC, sem o consentimento do remetente. - - Insultos pessoais, especialmente aqueles que utilizam termos racistas ou sexistas. - - Atenção sexual não consentida. - - Profanidade excessiva. Por favor, evite palavrões; as pessoas diferem muito na sua sensibilidade à linguagem. - - Repeated harassment of others. Em geral, se alguém pedir que você pare, então pare. - - Advogar em favor ou encorajar qualquer um dos comportamentos acima. + * Ameaças ou linguagem violenta direcionadas contra outra pessoa. + * Piadas e linguagem sexista, racista ou discriminatória. + * Postagem de material sexualmente explícito ou violento. + * Postar (ou ameaçar postar) informações pessoais de outras pessoas (“doxing”). + * Compartilhar conteúdo privado, como e-mails enviados de maneira privada ou não-pública, ou fóruns não registrados, como histórico de canais IRC, sem o consentimento do remetente. + * Insultos pessoais, especialmente aqueles que utilizam termos racistas ou sexistas. + * Atenção sexual não consentida. + * Profanidade excessiva. Por favor, evite palavrões; as pessoas diferem muito na sua sensibilidade à linguagem. + * Assédio reiterado. Em geral, se alguém pedir que você pare, então pare. + * Advogar em favor ou encorajar qualquer um dos comportamentos acima. ### Declaração de diversidade @@ -43,7 +43,7 @@ Embora sejamos receptivos às pessoas fluentes em todas as línguas, o desenvolv Padrões de comportamento na comunidade NumPy estão detalhados no Código de Conduta acima. Os participantes da nossa comunidade devem se comportar de acordo com esses padrões em todas as suas interações e ajudar os outros a fazê-lo também (veja a próxima seção). -### Reporting Guidelines +### Diretrizes de resposta a incidentes Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Reconhecemos também que, por vezes, as pessoas podem ter um dia ruim, ou não conhecer algumas das orientações deste Código de Conduta. Tenha isto em mente ao decidir como responder a uma violação deste Código. @@ -53,15 +53,15 @@ Você pode relatar problemas ao Comitê do Código de Conduta NumPy em numpy-con Atualmente, o comitê é formato por: -- Stefan van der Walt -- Melissa Weber Mendonça -- Rohit Goswami +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami Se o seu relatório envolve algum membro da comissão, ou se você sentir que existe um conflito de interesses em tratá-lo, então os membros abster-se-ão de considerar o seu relatório. Como alternativa, se por qualquer razão você se sentir desconfortável em fazer um relatório à comissão, então você também pode entrar em contato com a equipe sênior da NumFOCUS em [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). ### Resolução de Incidentes & Aplicação do Código de Conduta -_Esta seção resume os pontos mais importantes, mais detalhes podem ser encontrados em_ [Código de Conduta do NumPy - Como dar seguimento a um relatório](report-handling-manual). +_Esta seção resume os pontos mais importantes, mais detalhes podem ser encontrados em_ [Código de Conduta do NumPy - Como dar seguimento a um relatório](/report-handling-manual). Vamos investigar e responder a todas as queixas. O Comitê do Código de Conduta do NumPy e o Comitê Diretor do NumPy (se envolvido) protegerão a identidade do relatante, e tratarão o conteúdo das reclamações como confidencial (a menos que o relatante aceite o contrário). @@ -72,7 +72,7 @@ Em casos que não envolvam claras violações graves e óbvias deste Código de 1. acusar o recebimento do relato, 2. discussão/feedback razoável, 3. mediação (se o feedback não ajudar e somente se ambos o relatante e relatado concordarem com isso), -4. aplicação de solução via decisão transparente (veja as [Resoluções](report-handling-manual/#resoluções)) do Comitê do Código de Conduta. +4. aplicação de solução via decisão transparente (veja as [Resoluções](/report-handling-manual/#resolutions)) do Comitê do Código de Conduta. O comitê responderá a qualquer relatório o mais rapidamente possível e, no máximo, no prazo de 72 horas. From 56ac1603f9c52de8d0261f96d064d7a375b5e49d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:21 +0200 Subject: [PATCH 293/586] New translations install.md (Spanish) --- content/es/install.md | 147 +++++++++++------------------------------- 1 file changed, 36 insertions(+), 111 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index 236d5dd53a..f64a17841d 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -3,21 +3,13 @@ title: Installing NumPy sidebar: false --- -The only prerequisite for installing NumPy is Python itself. If you don't have -Python yet and want the simplest way to get started, we recommend you use the -[Anaconda Distribution](https://www.anaconda.com/download) - it includes -Python, NumPy, and many other commonly used packages for scientific computing -and data science. +The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. -NumPy can be installed with `conda`, with `pip`, with a package manager on -macOS and Linux, or [from source](https://numpy.org/devdocs/building). -For more detailed instructions, consult our Python and NumPy -installation guide below. +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` -channels: +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: ```bash # Best practice, use an environment rather than install in the base env @@ -36,162 +28,94 @@ If you use `pip`, you can install NumPy with: ```bash pip install numpy ``` +Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. -Also when using pip, it's good practice to use a virtual environment - -see [Reproducible Installs](#reproducible-installs) below for why, and -[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) -for details on using virtual environments. # Python and NumPy installation guide -Installing and managing packages in Python is complicated, there are a -number of alternative solutions for most tasks. This guide tries to give the -reader a sense of the best (or most popular) solutions, and give clear -recommendations. It focuses on users of Python, NumPy, and the PyData (or -numerical computing) stack on common operating systems and hardware. +Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. ## Recommendations -We'll start with recommendations based on the user's experience level and -operating system of interest. If you're in between "beginning" and "advanced", -please go with "beginning" if you want to keep things simple, and with -"advanced" if you want to work according to best practices that go a longer way -in the future. +We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. ### Beginning users On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/download) (it installs all - packages you need and all other tools mentioned below). -- For writing and executing code, use notebooks in - [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for - exploratory and interactive computing, and - [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) - for writing scripts and packages. -- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to - manage your packages and start JupyterLab, Spyder, or Visual Studio Code. +- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + ### Advanced users #### Conda - Install [Miniforge](https://github.com/conda-forge/miniforge). -- Keep the `base` conda environment minimal, and use one or more - [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - to install the package you need for the task or project you're working on. +- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. #### Alternative if you prefer pip/PyPI -For users who know, from personal preference or reading about the main -differences between conda and pip below, they prefer a pip/PyPI-based solution, -we recommend: +For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. -- Install Python from [python.org](https://www.python.org/downloads/), - [Homebrew](https://brew.sh/), or your Linux package manager. -- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool - that provides a dependency resolver and environment management capabilities - in a similar fashion as conda does. ## Python package management -Managing packages is a challenging problem, and, as a result, there are lots of -tools. For web and general purpose Python development there's a whole -[host of tools](https://packaging.python.org/guides/tool-recommendations/) -complementary with pip. For high-performance computing (HPC), -[Spack](https://github.com/spack/spack) is worth considering. For most NumPy -users though, [conda](https://conda.io/en/latest/) and -[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. +Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + ### Pip & conda -The two main tools that install Python packages are `pip` and `conda`. Their -functionality partially overlaps (e.g. both can install `numpy`), however, they -can also work together. We'll discuss the major differences between pip and -conda here - this is important to understand if you want to manage packages -effectively. +The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. -The first difference is that conda is cross-language and it can install Python, -while pip is installed for a particular Python on your system and installs other -packages to that same Python install only. This also means conda can install -non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while -pip can't. +The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. -The second difference is that pip installs from the Python Packaging Index -(PyPI), while conda installs from its own channels (typically "defaults" or -"conda-forge"). PyPI is the largest collection of packages by far, however, all -popular packages are available for conda as well. +The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. -The third difference is that conda is an integrated solution for managing -packages, dependencies and environments, while with pip you may need another -tool (there are many!) for dealing with environments or complex dependencies. +The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. ### Reproducible installs -As libraries get updated, results from running your code can change, or your -code can break completely. It's important to be able to reconstruct the set -of packages and versions you're using. Best practice is to: +As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: 1. use a different environment per project you're working on, -2. record package names and versions using your package installer; - each has its own metadata format for this: +2. record package names and versions using your package installer; each has its own metadata format for this: - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and - [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## NumPy packages & accelerated linear algebra libraries -NumPy doesn't depend on any other Python packages, however, it does depend on an -accelerated linear algebra library - typically -[Intel MKL](https://software.intel.com/en-us/mkl) or -[OpenBLAS](https://www.openblas.net/). Users don't have to worry about -installing those (they're automatically included in all NumPy install methods). -Power users may still want to know the details, because the used BLAS can -affect performance, behavior and size on disk: +NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: -- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. - The OpenBLAS libraries are included in the wheel. This makes the wheel - larger, and if a user installs (for example) SciPy as well, they will now - have two copies of OpenBLAS on disk. +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. -- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a - separate package that will be installed in the users' environment when they - install NumPy. +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When - a user installs NumPy from conda-forge, that BLAS package then gets installed - together with the actual library - this defaults to OpenBLAS, but it can also - be MKL (from the defaults channel), or even - [BLIS](https://github.com/flame/blis) or reference BLAS. +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk - while OpenBLAS is about 30 MB. +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. - MKL is typically a little faster and more robust than OpenBLAS. -Besides install sizes, performance and robustness, there are two more things to -consider: +Besides install sizes, performance and robustness, there are two more things to consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. -- Intel MKL is not open source. For normal use this is not a problem, but if - a user needs to redistribute an application built with NumPy, this could be - an issue. -- Both MKL and OpenBLAS will use multi-threading for function calls like - `np.dot`, with the number of threads being determined by both a build-time - option and an environment variable. Often all CPU cores will be used. This is - sometimes unexpected for users; NumPy itself doesn't auto-parallelize any - function calls. It typically yields better performance, but can also be - harmful - for example when using another level of parallelization with Dask, - scikit-learn or multiprocessing. ## Troubleshooting -If your installation fails with the message below, see Troubleshooting -ImportError. +If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! @@ -199,3 +123,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From 6e6a594684bc5bf368c8db30b43a93e25b3b6712 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:22 +0200 Subject: [PATCH 294/586] New translations install.md (Arabic) --- content/ar/install.md | 147 +++++++++++------------------------------- 1 file changed, 36 insertions(+), 111 deletions(-) diff --git a/content/ar/install.md b/content/ar/install.md index 236d5dd53a..f64a17841d 100644 --- a/content/ar/install.md +++ b/content/ar/install.md @@ -3,21 +3,13 @@ title: Installing NumPy sidebar: false --- -The only prerequisite for installing NumPy is Python itself. If you don't have -Python yet and want the simplest way to get started, we recommend you use the -[Anaconda Distribution](https://www.anaconda.com/download) - it includes -Python, NumPy, and many other commonly used packages for scientific computing -and data science. +The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. -NumPy can be installed with `conda`, with `pip`, with a package manager on -macOS and Linux, or [from source](https://numpy.org/devdocs/building). -For more detailed instructions, consult our Python and NumPy -installation guide below. +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` -channels: +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: ```bash # Best practice, use an environment rather than install in the base env @@ -36,162 +28,94 @@ If you use `pip`, you can install NumPy with: ```bash pip install numpy ``` +Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. -Also when using pip, it's good practice to use a virtual environment - -see [Reproducible Installs](#reproducible-installs) below for why, and -[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) -for details on using virtual environments. # Python and NumPy installation guide -Installing and managing packages in Python is complicated, there are a -number of alternative solutions for most tasks. This guide tries to give the -reader a sense of the best (or most popular) solutions, and give clear -recommendations. It focuses on users of Python, NumPy, and the PyData (or -numerical computing) stack on common operating systems and hardware. +Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. ## Recommendations -We'll start with recommendations based on the user's experience level and -operating system of interest. If you're in between "beginning" and "advanced", -please go with "beginning" if you want to keep things simple, and with -"advanced" if you want to work according to best practices that go a longer way -in the future. +We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. ### Beginning users On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/download) (it installs all - packages you need and all other tools mentioned below). -- For writing and executing code, use notebooks in - [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for - exploratory and interactive computing, and - [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) - for writing scripts and packages. -- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to - manage your packages and start JupyterLab, Spyder, or Visual Studio Code. +- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + ### Advanced users #### Conda - Install [Miniforge](https://github.com/conda-forge/miniforge). -- Keep the `base` conda environment minimal, and use one or more - [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - to install the package you need for the task or project you're working on. +- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. #### Alternative if you prefer pip/PyPI -For users who know, from personal preference or reading about the main -differences between conda and pip below, they prefer a pip/PyPI-based solution, -we recommend: +For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. -- Install Python from [python.org](https://www.python.org/downloads/), - [Homebrew](https://brew.sh/), or your Linux package manager. -- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool - that provides a dependency resolver and environment management capabilities - in a similar fashion as conda does. ## Python package management -Managing packages is a challenging problem, and, as a result, there are lots of -tools. For web and general purpose Python development there's a whole -[host of tools](https://packaging.python.org/guides/tool-recommendations/) -complementary with pip. For high-performance computing (HPC), -[Spack](https://github.com/spack/spack) is worth considering. For most NumPy -users though, [conda](https://conda.io/en/latest/) and -[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. +Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + ### Pip & conda -The two main tools that install Python packages are `pip` and `conda`. Their -functionality partially overlaps (e.g. both can install `numpy`), however, they -can also work together. We'll discuss the major differences between pip and -conda here - this is important to understand if you want to manage packages -effectively. +The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. -The first difference is that conda is cross-language and it can install Python, -while pip is installed for a particular Python on your system and installs other -packages to that same Python install only. This also means conda can install -non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while -pip can't. +The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. -The second difference is that pip installs from the Python Packaging Index -(PyPI), while conda installs from its own channels (typically "defaults" or -"conda-forge"). PyPI is the largest collection of packages by far, however, all -popular packages are available for conda as well. +The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. -The third difference is that conda is an integrated solution for managing -packages, dependencies and environments, while with pip you may need another -tool (there are many!) for dealing with environments or complex dependencies. +The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. ### Reproducible installs -As libraries get updated, results from running your code can change, or your -code can break completely. It's important to be able to reconstruct the set -of packages and versions you're using. Best practice is to: +As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: 1. use a different environment per project you're working on, -2. record package names and versions using your package installer; - each has its own metadata format for this: +2. record package names and versions using your package installer; each has its own metadata format for this: - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and - [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## NumPy packages & accelerated linear algebra libraries -NumPy doesn't depend on any other Python packages, however, it does depend on an -accelerated linear algebra library - typically -[Intel MKL](https://software.intel.com/en-us/mkl) or -[OpenBLAS](https://www.openblas.net/). Users don't have to worry about -installing those (they're automatically included in all NumPy install methods). -Power users may still want to know the details, because the used BLAS can -affect performance, behavior and size on disk: +NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: -- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. - The OpenBLAS libraries are included in the wheel. This makes the wheel - larger, and if a user installs (for example) SciPy as well, they will now - have two copies of OpenBLAS on disk. +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. -- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a - separate package that will be installed in the users' environment when they - install NumPy. +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When - a user installs NumPy from conda-forge, that BLAS package then gets installed - together with the actual library - this defaults to OpenBLAS, but it can also - be MKL (from the defaults channel), or even - [BLIS](https://github.com/flame/blis) or reference BLAS. +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk - while OpenBLAS is about 30 MB. +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. - MKL is typically a little faster and more robust than OpenBLAS. -Besides install sizes, performance and robustness, there are two more things to -consider: +Besides install sizes, performance and robustness, there are two more things to consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. -- Intel MKL is not open source. For normal use this is not a problem, but if - a user needs to redistribute an application built with NumPy, this could be - an issue. -- Both MKL and OpenBLAS will use multi-threading for function calls like - `np.dot`, with the number of threads being determined by both a build-time - option and an environment variable. Often all CPU cores will be used. This is - sometimes unexpected for users; NumPy itself doesn't auto-parallelize any - function calls. It typically yields better performance, but can also be - harmful - for example when using another level of parallelization with Dask, - scikit-learn or multiprocessing. ## Troubleshooting -If your installation fails with the message below, see Troubleshooting -ImportError. +If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! @@ -199,3 +123,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From 82d2519367176b59a0623bfd5881d4232101d4a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:23 +0200 Subject: [PATCH 295/586] New translations install.md (Japanese) --- content/ja/install.md | 75 +++++++++++++------------------------------ 1 file changed, 23 insertions(+), 52 deletions(-) diff --git a/content/ja/install.md b/content/ja/install.md index b503f38663..fad90210ea 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -3,11 +3,9 @@ title: NumPyのインストール sidebar: false --- -NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、[Anaconda Distribution](https://www.anaconda.com/distribution)の使用をおすすめします。これにはPython、NumPy、および科学計算やデータサイエンスでよく使われる様々な多くのパッケージが含まれています。 まずはユーザの経験レベルと、関心のあるOSに基づいた推奨方法から説明していきたいと思います。 PythonやNumPyの経験が「初級」と「上級」の間の方は、シンプルにインストールしたい場合は「初級」を、より長い視点にたったベストプラクティスに沿ってインストールしたい方は「上級」を参照ください。 +NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、[Anaconda Distribution](https://www.anaconda.com/distribution)の使用をおすすめします。これにはPython、NumPy、および科学計算やデータサイエンスでよく使われる様々な多くのパッケージが含まれています。 -NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/building)からインストールすることが出来ます。 詳細な手順については、以下の [Python と Numpyの インストールガイド](#python-numpy-install-guide) を参照してください。 -For more detailed instructions, consult our Python and NumPy -installation guide below. +NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/user/building.html)からインストールすることが出来ます。 詳細な手順については、以下の [Python と Numpyの インストールガイド](#python-numpy-install-guide) を参照してください。 **CONDA** @@ -30,25 +28,18 @@ conda install numpy ```bash pip install numpy ``` - またpipを使う場合、仮想環境を使うことをおすすめします。 [再現可能なインストール](#reproducible-installs)を参照ください。 [こちらの記事](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)では仮想環境を使う詳細について説明されています。 + # PythonとNumPyの インストールガイド -Pythonパッケージのインストールと管理は複雑なので、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 This guide tries to give the -reader a sense of the best (or most popular) solutions, and give clear -recommendations. It focuses on users of Python, NumPy, and the PyData (or -numerical computing) stack on common operating systems and hardware. +Pythonパッケージのインストールと管理は複雑なので、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 ## 推奨方法 -We'll start with recommendations based on the user's experience level and -operating system of interest. If you're in between "beginning" and "advanced", -please go with "beginning" if you want to keep things simple, and with -"advanced" if you want to work according to best practices that go a longer way -in the future. +まずはユーザの経験レベルと、関心のあるOSに基づいた推奨方法から説明していきたいと思います。 PythonやNumPyの経験が「初級」と「上級」の間の方は、シンプルにインストールしたい場合は「初級」を、より長い視点にたったベストプラクティスに沿ってインストールしたい方は「上級」を参照ください。 ### 初級ユーザ @@ -58,6 +49,7 @@ Windows、macOS、Linuxのすべてのユーザー向けには: - コードを書いたり、実行してみましょう。 探索的・対話的コンピューティングには[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html)のノートブックが便利です。 スクリプトやパッケージの作成には[Spyder](https://www.spyder-ide.org/)や[Visual Studio Code](https://code.visualstudio.com/)を利用できます。 - [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) を使ってパッケージを管理し、JupyterLab、Spyder、Visual Studio Codeを使い始められます。 + ### 上級ユーザー #### Conda @@ -68,41 +60,30 @@ Windows、macOS、Linuxのすべてのユーザー向けには: #### pip/PyPI を利用したい場合 個人的な好みや、下記のcondaとpipの違いを理解した上で、pip/PyPIベースの方法を使いたいユーザーには、下記をお勧めします: - - [python.org](https://www.python.org/downloads/)からや、Macを使っている場合は[Homebrew](https://brew.sh/)、 Linuxを使っている場合は、Linuxのパッケージマネージャーを使ってPythonをインストールします。 - 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry.org/) をconda と同様な方法で使用することができます。 + ## Pythonにおけるパッケージ管理 -Managing packages is a challenging problem, and, as a result, there are lots of -tools. パッケージの管理は難しいため、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 For high-performance computing (HPC), -[Spack](https://github.com/spack/spack) is worth considering. For most NumPy -users though, [conda](https://conda.io/en/latest/) and -[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. +パッケージの管理は難しいため、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 + ### Pipとconda -`pip` と `conda` がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも `numpy`をインストールできます)、一緒に動作することもできます。 ここでは、pip とcond の主要な違いについて説明します。 これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。 Their -functionality partially overlaps (e.g. both can install `numpy`), however, they -can also work together. We'll discuss the major differences between pip and -conda here - this is important to understand if you want to manage packages -effectively. +`pip` と `conda` がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも `numpy`をインストールできます)、一緒に動作することもできます。 ここでは、pip とcond の主要な違いについて説明します。 これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。 -最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPIは、最大のパッケージ管理システムですが、すべての代表的なパッケージは、condaにも利用可能です。 This also means conda can install -non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while -pip can't. +2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には "defaults "や "conda-forge "など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。 -2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には "defaults "や "conda-forge "など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。 PyPI is the largest collection of packages by far, however, all -popular packages are available for conda as well. +最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPIは、最大のパッケージ管理システムですが、すべての代表的なパッケージは、condaにも利用可能です。 -3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ for dealing with environments or complex dependencies. +3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ ### 再現可能なインストール -ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: It's important to be able to reconstruct the set -of packages and versions you're using. Best practice is to: +ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: 1. プロジェクトごとに異なる仮想環境を使用して下さい。 2. パッケージインストーラを使用してパッケージ名とバージョンを記録するようにして下さい。 それぞれ、独自のメタデータフォーマットがあります: @@ -110,23 +91,17 @@ of packages and versions you're using. Best practice is to: - pipの場合: [仮想環境](https://docs.python.org/3/tutorial/venv.html) と [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - poetryの場合: [仮想環境とpyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## NumPyパッケージと高速線形代数ライブラリ -NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。 典型的には、[インテル® MKL](https://software.intel.com/en-us/mkl)や[OpenBLAS](https://www.openblas.net/)がこれにあたります。 ユーザーは、これらの線形代数ライブラリのインストールを心配する必要はありません (NumPyのインストール方法に、あらかじめ含まれているためです)。 高度なユーザーは、使用されているBLASがパフォーマンスや、動作、ディスク上のサイズに影響を与えるため、より詳細を知りたがるかもしれません。 Users don't have to worry about -installing those (they're automatically included in all NumPy install methods). -Power users may still want to know the details, because the used BLAS can -affect performance, behavior and size on disk: +NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。 典型的には、[インテル® MKL](https://software.intel.com/en-us/mkl)や[OpenBLAS](https://www.openblas.net/)がこれにあたります。 ユーザーは、これらの線形代数ライブラリのインストールを心配する必要はありません (NumPyのインストール方法に、あらかじめ含まれているためです)。 高度なユーザーは、使用されているBLASがパフォーマンスや、動作、ディスク上のサイズに影響を与えるため、より詳細を知りたがるかもしれません。 - pipでインストールされるPyPI上の NumPy wheelは、OpenBLASを使ってビルドされます。 つまりwheelにはOpenBLASライブラリが含まれています。 そのため、ユーザが（例えば）SciPyも同じようにインストールした場合、ディスク上にOpenBLASのコピーをNumPyのものと2つ持つことになります - The OpenBLAS libraries are included in the wheel. This makes the wheel - larger, and if a user installs (for example) SciPy as well, they will now - have two copies of OpenBLAS on disk. -- condaのデフォルトチャンネルでは、NumPy はインテル® MKLを使ってビルドされます。 MKLはNumPyのインストール時に、独立したパッケージとしてユーザー環境にインストールされます。 MKL is a - separate package that will be installed in the users' environment when they - install NumPy. +- condaのデフォルトチャンネルでは、NumPy はインテル® MKLを使ってビルドされます。 MKLはNumPyのインストール時に、独立したパッケージとしてユーザー環境にインストールされます。 -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. conda-forgeのチャンネルでは、NumPyはダミーの「BLAS」パッケージを使ってビルドされています。 ユーザーがconda-forgeからNumPyをインストールすると、BLASパッケージが実際のライブラリと一緒にインストールされます。 デフォルトはOpenBLASですが、MKL(default チャンネルの場合)や [BLIS](https://github.com/flame/blis)、またはBLASを利用することもできます。 +- conda-forgeのチャンネルでは、NumPyはダミーの「BLAS」パッケージを使ってビルドされています。 ユーザーがconda-forgeからNumPyをインストールすると、BLASパッケージが実際のライブラリと一緒にインストールされます。 デフォルトはOpenBLASですが、MKL(default チャンネルの場合)や [BLIS](https://github.com/flame/blis)、またはBLASを利用することもできます。 - OpenBLASは約30MBですが、MKLパッケージはOpenBLASよりもはるかに大きく、ディスク上の約700MBです。 @@ -134,14 +109,9 @@ affect performance, behavior and size on disk: インストールサイズ、パフォーマンスとロバスト性に加えて、考慮すべき2つの点があります: -- インテル® MKL はオープンソースではありません。 通常の使用では問題ではありませんが、 ユーザーが NumPy で構築されたアプリケーションを再配布する必要がある場合、これは 問題が発生する可能性があります。 For normal use this is not a problem, but if - a user needs to redistribute an application built with NumPy, this could be - an issue. -- MKLとOpenBLASの両方とも、 np.dotのような関数呼び出しにマルチスレッドを使用し、スレッド数はビルド時オプションと環境変数の両方で決定されます。 多くの場合、すべての CPU コアが使用されます。 これにユーザーにとっては予想外のことかもしれません。 NumPy 自体は、関数呼び出しを自動的に並列化しないからです。 自動並列化により、一般にはパフォーマンスが向上しますが、逆にパフォーマンスが悪化する場合もあります。 例えば、Daskやscikit-learn、multiprocessingなど別のレベルの並列化を使用している場合です。 Often all CPU cores will be used. This is - sometimes unexpected for users; NumPy itself doesn't auto-parallelize any - function calls. It typically yields better performance, but can also be - harmful - for example when using another level of parallelization with Dask, - scikit-learn or multiprocessing. +- インテル® MKL はオープンソースではありません。 通常の使用では問題ではありませんが、 ユーザーが NumPy で構築されたアプリケーションを再配布する必要がある場合、これは 問題が発生する可能性があります。 +- MKLとOpenBLASの両方とも、 np.dotのような関数呼び出しにマルチスレッドを使用し、スレッド数はビルド時オプションと環境変数の両方で決定されます。 多くの場合、すべての CPU コアが使用されます。 これにユーザーにとっては予想外のことかもしれません。 NumPy 自体は、関数呼び出しを自動的に並列化しないからです。 自動並列化により、一般にはパフォーマンスが向上しますが、逆にパフォーマンスが悪化する場合もあります。 例えば、Daskやscikit-learn、multiprocessingなど別のレベルの並列化を使用している場合です。 + ## トラブルシューティング @@ -152,3 +122,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From 3ea361d8e81da4a1ffee9428982040b263e042c3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:24 +0200 Subject: [PATCH 296/586] New translations install.md (Korean) --- content/ko/install.md | 213 ++++++++++++++---------------------------- 1 file changed, 69 insertions(+), 144 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 236d5dd53a..c3128cee25 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -1,197 +1,121 @@ --- -title: Installing NumPy +title: NumPy 설치 sidebar: false --- -The only prerequisite for installing NumPy is Python itself. If you don't have -Python yet and want the simplest way to get started, we recommend you use the -[Anaconda Distribution](https://www.anaconda.com/download) - it includes -Python, NumPy, and many other commonly used packages for scientific computing -and data science. +NumPy를 설치하는 유일한 선행 조건은 Python 자체입니다. 만약 아직 Python을 설치하지 않았고 가장 간단한 방법으로 시작하려면, [Anaconda 배포판](https://www.anaconda.com/distribution) 을 사용하길 권장합니다. 이 배포판에는 Python 와 NumPy 및 과학 계산 및 데이터 사이언스에 자주 사용되는 다른 패키지들이 포함되어 있습니다. -NumPy can be installed with `conda`, with `pip`, with a package manager on -macOS and Linux, or [from source](https://numpy.org/devdocs/building). -For more detailed instructions, consult our Python and NumPy -installation guide below. +NumPy 는 `conda` 나 `pip` 를 통해서 사용하여 설치할 수 있고, 또한 macOS 및 Linux의 패키지 관리자나 [원본 코드](https://numpy.org/devdocs/user/building.html) 를 이용하여 설치할 수 있습니다. 더 자세한 설명은 아래의 [Python 및 NumPy 설치 가이드](#python-numpy-install-guide)를 확인해주세요. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` -channels: +`conda`를 사용한다면, NumPy를 `defaults` 또는 `conda-forge` 채널에서 설치할 수 있습니다: ```bash -# Best practice, use an environment rather than install in the base env +# 가장 좋은 방법은 기본 환경 대신에 새로운 환경을 이용하는 것입니다 conda create -n my-env conda activate my-env -# If you want to install from conda-forge +# my-env 에 conda-forge 채널을 더해줍니다 conda config --env --add channels conda-forge -# The actual install command +# my-env 에 NumPy 를 설치합니다 conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +`pip`를 사용한다면, NumPy를 다음과 같이 설치할 수 있습니다: ```bash pip install numpy ``` +또한 `pip`를 사용할 때, 가상 환경을 사용하는 것을 추천합니다. 가상 환경을 사용하는 이유는 [재현 가능한 설치방법들](#reproducible-installs)을 참조해주세요. 가상 환경 사용에 대한 자세한 내용은 [이 가이드](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)에서 확인하실 수 있습니다. -Also when using pip, it's good practice to use a virtual environment - -see [Reproducible Installs](#reproducible-installs) below for why, and -[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) -for details on using virtual environments. -# Python and NumPy installation guide +# Python 및 NumPy 설치 가이드 -Installing and managing packages in Python is complicated, there are a -number of alternative solutions for most tasks. This guide tries to give the -reader a sense of the best (or most popular) solutions, and give clear -recommendations. It focuses on users of Python, NumPy, and the PyData (or -numerical computing) stack on common operating systems and hardware. +Python에서 패키지를 설치하고 관리하는 것은 복잡한 작업이기에, 대부분의 작업에 대해 다양한 대안적인 해결책이 있습니다. 이 가이드는 독자분들에게 가장 좋은 (또는 가장 인기 있는) 해결책을 이해시키고 명확한 권장 사항을 제공하려고 노력합니다. 이 가이드는 일반적인 운영 체제와 하드웨어에서 Python, NumPy 및 PyData (또는 숫자 계산) 사용자에 중점을 둡니다. -## Recommendations +## 권장 사항 -We'll start with recommendations based on the user's experience level and -operating system of interest. If you're in between "beginning" and "advanced", -please go with "beginning" if you want to keep things simple, and with -"advanced" if you want to work according to best practices that go a longer way -in the future. +사용자의 경험과 사용하는 운영체제를 기준으로 추천하는 방식을 먼저 이야기 하겠습니다. 만약 당신이 초심자 또는 숙련자범위에 속해있다면, 간단하게 설치하고 싶다면 초심자로, 추후에 작업을 위해서 보다 구체적인 연습을 하고 싶다면 숙련자 자료를 참고하십시오. -### Beginning users +### 초심자 유저 -On all of Windows, macOS, and Linux: +Windows, macOS, Linux 등 일반적인 운영체제: -- Install [Anaconda](https://www.anaconda.com/download) (it installs all - packages you need and all other tools mentioned below). -- For writing and executing code, use notebooks in - [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for - exploratory and interactive computing, and - [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) - for writing scripts and packages. -- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to - manage your packages and start JupyterLab, Spyder, or Visual Studio Code. +- [Anaconda](https://www.anaconda.com/distribution/) 를 설치하십시오.(당신이 필요로 하는 패키지를 설치하고, 아래에 언급될 다양한 도구들을 제공합니다.) +- 코드를 작성하거나 실행할 때, 데이터를 분석하거나 대화형으로 코드를 작업하는 경우에는 [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) 의 notebooks를 사용하십시오. 그리고 코드를 작성하거나 패키지를 작성할 때는 [Spyder](https://www.spyder-ide.org/)나 [Visual Studio Code](https://code.visualstudio.com/)를 사용하십시오. +- 패키지를 관리하거나 JupyterLab, Spyder, Visual Studio Code 를 사용하는 경우 [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/)를 사용하십시오. -### Advanced users + +### 숙련자 유저 #### Conda -- Install [Miniforge](https://github.com/conda-forge/miniforge). -- Keep the `base` conda environment minimal, and use one or more - [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - to install the package you need for the task or project you're working on. +- [Miniforge](https://github.com/conda-forge/miniforge)를 설치하십시오. +- `base` 라는 이름의 콘다 가상환경은 최소 상태를 유지하고, [콘다 가상환경](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)을 만들어서, 해당 가상환경에 필요한 패키지를 설치하십시오. + +#### Pip/PyPI를 활용하는 경우: -#### Alternative if you prefer pip/PyPI +개인적인 선호나 아래의 conda 와 pip의 차이점을 설명하는 글을 읽은 유저나 또는 pip/PyPI기반의 설치 방법을 선호하는 경우 참고하십시오. +- [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or Linux package manager를 활용해서 Python을 설치하십시오. +- Conda와 동일한 수준의 가상환경 관리와 패키지 의존성을 해결을 도와주는 [Poetry](https://python-poetry.org/)를 유지관리 도구로 사용하십시오. -For users who know, from personal preference or reading about the main -differences between conda and pip below, they prefer a pip/PyPI-based solution, -we recommend: -- Install Python from [python.org](https://www.python.org/downloads/), - [Homebrew](https://brew.sh/), or your Linux package manager. -- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool - that provides a dependency resolver and environment management capabilities - in a similar fashion as conda does. +## Python 패키지 관리 -## Python package management +패키지 관리는 아주 중요하기 때문에, 사용할 수 있는 도구들이 많습니다. 웹 및 범용 Python 개발을 위해 Pip뿐만 아니라 [다양한 도구](https://packaging.python.org/guides/tool-recommendations/)들이 있습니다. 고성능 컴퓨터 (HPC) 를 사용하는 경우 [Spack](https://github.com/spack/spack)를 사용하는 것을 추천합니다. 대부분 Numpy를 사용하는 유저는, [conda](https://conda.io/en/latest/) 와 [pip](https://pip.pypa.io/en/stable/)를 가장 많이 사용합니다. -Managing packages is a challenging problem, and, as a result, there are lots of -tools. For web and general purpose Python development there's a whole -[host of tools](https://packaging.python.org/guides/tool-recommendations/) -complementary with pip. For high-performance computing (HPC), -[Spack](https://github.com/spack/spack) is worth considering. For most NumPy -users though, [conda](https://conda.io/en/latest/) and -[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. ### Pip & conda -The two main tools that install Python packages are `pip` and `conda`. Their -functionality partially overlaps (e.g. both can install `numpy`), however, they -can also work together. We'll discuss the major differences between pip and -conda here - this is important to understand if you want to manage packages -effectively. +Python 패키지를 설치하고 관리하는 주요 툴은 `pip` 과 `conda` 입니다. 그 도구들의 기능은 부분적으로 겹칩지만 (e.g. both can install `numpy`), 같이 쓰일 수도 있습니다. 곧 pip와 conda의 차이점에 대해서 논의해볼 것입니다. - 패키지 관리를 효율적으로 하기 위해서는 차이를 이해하는게 중요합니다. -The first difference is that conda is cross-language and it can install Python, -while pip is installed for a particular Python on your system and installs other -packages to that same Python install only. This also means conda can install -non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while -pip can't. +첫번째 차이점은, conda는 cross-language 를 지원하고, Python을 설치할 수 도 있지만, pip는 특정 Python에만 패키지를 설치하고 관리할 수 있다는 것 입니다. 또한 conda는 non-Python 라이브러리나 도구들을 설치할 수 있지만 (e.g. compilers, CUDA, HDF5), pip는 Python이 필요하기 때문에 설치할 수 없습니다. -The second difference is that pip installs from the Python Packaging Index -(PyPI), while conda installs from its own channels (typically "defaults" or -"conda-forge"). PyPI is the largest collection of packages by far, however, all -popular packages are available for conda as well. +두번째 차이점은 pip는 Python Packaging Index(PyPI) 로 부터 패키지를 다운받아 설치하지만, conda는 conda 만의 채널로 설치합니다 (일반적으로 "defaults" or "conda-forge" 채널을 사용합니다). PyPI 가 가장 큰 패키지 저장소입니다만, 많은 사람들이 사용하는 패키지는 conda에서도 설치할 수 있습니다. -The third difference is that conda is an integrated solution for managing -packages, dependencies and environments, while with pip you may need another -tool (there are many!) for dealing with environments or complex dependencies. +세번째 차이점은 conda는 환경이나 패키지간 의존성을 해결하기 위한 해키지 관리 도구를 제공하지만, pip는 그를 위해서 (아주 많은) 추가적인 도구들이 필요하다는 것 입니다. -### Reproducible installs - -As libraries get updated, results from running your code can change, or your -code can break completely. It's important to be able to reconstruct the set -of packages and versions you're using. Best practice is to: - -1. use a different environment per project you're working on, -2. record package names and versions using your package installer; - each has its own metadata format for this: - - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and - [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) - -## NumPy packages & accelerated linear algebra libraries - -NumPy doesn't depend on any other Python packages, however, it does depend on an -accelerated linear algebra library - typically -[Intel MKL](https://software.intel.com/en-us/mkl) or -[OpenBLAS](https://www.openblas.net/). Users don't have to worry about -installing those (they're automatically included in all NumPy install methods). -Power users may still want to know the details, because the used BLAS can -affect performance, behavior and size on disk: - -- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. - The OpenBLAS libraries are included in the wheel. This makes the wheel - larger, and if a user installs (for example) SciPy as well, they will now - have two copies of OpenBLAS on disk. - -- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a - separate package that will be installed in the users' environment when they - install NumPy. - -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When - a user installs NumPy from conda-forge, that BLAS package then gets installed - together with the actual library - this defaults to OpenBLAS, but it can also - be MKL (from the defaults channel), or even - [BLIS](https://github.com/flame/blis) or reference BLAS. - -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk - while OpenBLAS is about 30 MB. - -- MKL is typically a little faster and more robust than OpenBLAS. - -Besides install sizes, performance and robustness, there are two more things to -consider: - -- Intel MKL is not open source. For normal use this is not a problem, but if - a user needs to redistribute an application built with NumPy, this could be - an issue. -- Both MKL and OpenBLAS will use multi-threading for function calls like - `np.dot`, with the number of threads being determined by both a build-time - option and an environment variable. Often all CPU cores will be used. This is - sometimes unexpected for users; NumPy itself doesn't auto-parallelize any - function calls. It typically yields better performance, but can also be - harmful - for example when using another level of parallelization with Dask, - scikit-learn or multiprocessing. - -## Troubleshooting - -If your installation fails with the message below, see Troubleshooting -ImportError. +### 재현 가능한 설치방법들 + +라이브러리가 업데이트되면, 코드의 실행 결과가 바뀌거나, 코드가 완전히 손상될 수 있습니다. 그러므로 사용중인 패키지 및 버전을 재구성할 수 있도록 하는 것이 중요합니다. 권장되는 방법으로는 + +1. 작업 중인 프로젝트마다 다른 환경을 이용하고, +2. 각각 자체 메타 데이터 형식이 있는 패키지 설치 프로그램을 통해 패키지 이름과 버전을 기록해둡니다. + - Conda: [conda 환경들 과 environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [가상 환경들](https://docs.python.org/3/tutorial/venv.html) 과 [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [가상 환경들 과 pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + + +## NumPy 패키지 & 고속 선형 대수 라이브러리 + +NumPy는 다른 Python 패키지에 의존하지 않습니다. 그러나 고속 선형 대수 라이브러리, 일반적으로 [Inter MKL](https://software.intel.com/en-us/mkl) 또는 [OpenBLAS](https://www.openblas.net/)에 의존하고 있습니다. 사용자는 이를 설치하지 않아도 됩니다 (NumPy 설치 중 설치됩니다). 고급 사용자의 경우 사용한 BLAS가 디스크의 성능, 동작 및 크기에 영향을 끼칠 수 있기 때문에 세부 정보가 필요할 수도 있습니다. + +- Pip가 설치하는 PyPI의 휠 파일에 있는 NumPy의 경우는 OpenBLAS로 빌드되었습니다. OpenBLAS 라이브러리는 휠 파일에 포함되어 있습니다. 이는 휠 파일의 크기를 더 크게 만들고, 사용자가 (예를 들어) SciPy도 설치하게 되면 디스크에 2개의 OpenBLAS 사본을 가지게 됩니다. + +- Conda의 기본 채널 내 NumPy는 Interl MKL로 빌드되었습니다. MKL은 NumPy를 설치할 때 사용자의 환경에 같이 설치되는 분할 패키지입니다. + +- Conda-forge 채널 내 NumPy는 더미 "BLAS" 패키지로 빌드되었습니다. 사용자가 conda-forge에서 NumPy를 설치할 때 해당 BLAS 패키지가 실제 라이브러리와 함께 설치됩니다. 기본값은 OpenBLAS이나, (기본 채널에서는) MKL이 될 수도 있고, 심지어 [BLIS](https://github.com/flame/blis)나 Reference BLAS가 될 수도 있습니다. + +- MKL 패키지가 OpenBLAS에 비해 더욱 큽니다. OpenBLAS가 30MB를 차지하는 반면, MKL 쪽은 700MB에 달하는 디스크 공간을 차지합니다. + +- 보통 MKL이 OpenBLAS보다 더 빠르고 안정적입니다. + +설치 크기, 성능 및 안정성을 제쳐 두더라도, 고려할 사항이 2가지 더 있습니다. + +- Intel MKL은 오픈소스가 아닙니다. 일반적으로 사용할 때는 문제가 되지 않지만, 사용자가 NumPy로 빌드한 애플리케이션을 재배포하는 경우 문제가 될 수 있습니다. +- MKL과 OpenBLAS 모두 `np.dot`과 같이 함수를 호출하는 데 다중 스레드를 사용하며, 스레드의 수는 빌드 시간 설정과 환경 변수에 의해 결정됩니다. 보통은 모든 CPU 코어가 사용됩니다. 이로 인하여 예기치 않은 일이 발생할 수 있습니다. NumPy 자체적으로는 어떤 함수 호출도 병렬화하지 않습니다. 일반적으로 더 나은 성능을 제공해주지만, 예를 들어 Dask, scikit-learn 또는 멀티프로세싱과 함께 다른 수준의 병렬화를 사용하는 경우 좋지 않은 결과를 초래할 수 있습니다. + + +## 트러블슈팅 + +아래와 같은 응답과 함께 설치에 실패한다면, [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html)를 참고하시기 바랍니다. ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! @@ -199,3 +123,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From 8d1f7330ac47b557811e5c6c5b44e088b57d08d2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:25 +0200 Subject: [PATCH 297/586] New translations install.md (Russian) --- content/ru/install.md | 147 +++++++++++------------------------------- 1 file changed, 36 insertions(+), 111 deletions(-) diff --git a/content/ru/install.md b/content/ru/install.md index 236d5dd53a..f64a17841d 100644 --- a/content/ru/install.md +++ b/content/ru/install.md @@ -3,21 +3,13 @@ title: Installing NumPy sidebar: false --- -The only prerequisite for installing NumPy is Python itself. If you don't have -Python yet and want the simplest way to get started, we recommend you use the -[Anaconda Distribution](https://www.anaconda.com/download) - it includes -Python, NumPy, and many other commonly used packages for scientific computing -and data science. +The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. -NumPy can be installed with `conda`, with `pip`, with a package manager on -macOS and Linux, or [from source](https://numpy.org/devdocs/building). -For more detailed instructions, consult our Python and NumPy -installation guide below. +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` -channels: +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: ```bash # Best practice, use an environment rather than install in the base env @@ -36,162 +28,94 @@ If you use `pip`, you can install NumPy with: ```bash pip install numpy ``` +Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. -Also when using pip, it's good practice to use a virtual environment - -see [Reproducible Installs](#reproducible-installs) below for why, and -[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) -for details on using virtual environments. # Python and NumPy installation guide -Installing and managing packages in Python is complicated, there are a -number of alternative solutions for most tasks. This guide tries to give the -reader a sense of the best (or most popular) solutions, and give clear -recommendations. It focuses on users of Python, NumPy, and the PyData (or -numerical computing) stack on common operating systems and hardware. +Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. ## Recommendations -We'll start with recommendations based on the user's experience level and -operating system of interest. If you're in between "beginning" and "advanced", -please go with "beginning" if you want to keep things simple, and with -"advanced" if you want to work according to best practices that go a longer way -in the future. +We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. ### Beginning users On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/download) (it installs all - packages you need and all other tools mentioned below). -- For writing and executing code, use notebooks in - [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for - exploratory and interactive computing, and - [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) - for writing scripts and packages. -- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to - manage your packages and start JupyterLab, Spyder, or Visual Studio Code. +- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + ### Advanced users #### Conda - Install [Miniforge](https://github.com/conda-forge/miniforge). -- Keep the `base` conda environment minimal, and use one or more - [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - to install the package you need for the task or project you're working on. +- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. #### Alternative if you prefer pip/PyPI -For users who know, from personal preference or reading about the main -differences between conda and pip below, they prefer a pip/PyPI-based solution, -we recommend: +For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. -- Install Python from [python.org](https://www.python.org/downloads/), - [Homebrew](https://brew.sh/), or your Linux package manager. -- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool - that provides a dependency resolver and environment management capabilities - in a similar fashion as conda does. ## Python package management -Managing packages is a challenging problem, and, as a result, there are lots of -tools. For web and general purpose Python development there's a whole -[host of tools](https://packaging.python.org/guides/tool-recommendations/) -complementary with pip. For high-performance computing (HPC), -[Spack](https://github.com/spack/spack) is worth considering. For most NumPy -users though, [conda](https://conda.io/en/latest/) and -[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. +Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + ### Pip & conda -The two main tools that install Python packages are `pip` and `conda`. Their -functionality partially overlaps (e.g. both can install `numpy`), however, they -can also work together. We'll discuss the major differences between pip and -conda here - this is important to understand if you want to manage packages -effectively. +The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. -The first difference is that conda is cross-language and it can install Python, -while pip is installed for a particular Python on your system and installs other -packages to that same Python install only. This also means conda can install -non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while -pip can't. +The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. -The second difference is that pip installs from the Python Packaging Index -(PyPI), while conda installs from its own channels (typically "defaults" or -"conda-forge"). PyPI is the largest collection of packages by far, however, all -popular packages are available for conda as well. +The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. -The third difference is that conda is an integrated solution for managing -packages, dependencies and environments, while with pip you may need another -tool (there are many!) for dealing with environments or complex dependencies. +The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. ### Reproducible installs -As libraries get updated, results from running your code can change, or your -code can break completely. It's important to be able to reconstruct the set -of packages and versions you're using. Best practice is to: +As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: 1. use a different environment per project you're working on, -2. record package names and versions using your package installer; - each has its own metadata format for this: +2. record package names and versions using your package installer; each has its own metadata format for this: - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and - [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## NumPy packages & accelerated linear algebra libraries -NumPy doesn't depend on any other Python packages, however, it does depend on an -accelerated linear algebra library - typically -[Intel MKL](https://software.intel.com/en-us/mkl) or -[OpenBLAS](https://www.openblas.net/). Users don't have to worry about -installing those (they're automatically included in all NumPy install methods). -Power users may still want to know the details, because the used BLAS can -affect performance, behavior and size on disk: +NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: -- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. - The OpenBLAS libraries are included in the wheel. This makes the wheel - larger, and if a user installs (for example) SciPy as well, they will now - have two copies of OpenBLAS on disk. +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. -- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a - separate package that will be installed in the users' environment when they - install NumPy. +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When - a user installs NumPy from conda-forge, that BLAS package then gets installed - together with the actual library - this defaults to OpenBLAS, but it can also - be MKL (from the defaults channel), or even - [BLIS](https://github.com/flame/blis) or reference BLAS. +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk - while OpenBLAS is about 30 MB. +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. - MKL is typically a little faster and more robust than OpenBLAS. -Besides install sizes, performance and robustness, there are two more things to -consider: +Besides install sizes, performance and robustness, there are two more things to consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. -- Intel MKL is not open source. For normal use this is not a problem, but if - a user needs to redistribute an application built with NumPy, this could be - an issue. -- Both MKL and OpenBLAS will use multi-threading for function calls like - `np.dot`, with the number of threads being determined by both a build-time - option and an environment variable. Often all CPU cores will be used. This is - sometimes unexpected for users; NumPy itself doesn't auto-parallelize any - function calls. It typically yields better performance, but can also be - harmful - for example when using another level of parallelization with Dask, - scikit-learn or multiprocessing. ## Troubleshooting -If your installation fails with the message below, see Troubleshooting -ImportError. +If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! @@ -199,3 +123,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From bd99f1d3860a47f84483e72f195f238d378515e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:26 +0200 Subject: [PATCH 298/586] New translations install.md (Chinese Simplified) --- content/zh/install.md | 147 +++++++++++------------------------------- 1 file changed, 36 insertions(+), 111 deletions(-) diff --git a/content/zh/install.md b/content/zh/install.md index 236d5dd53a..f64a17841d 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -3,21 +3,13 @@ title: Installing NumPy sidebar: false --- -The only prerequisite for installing NumPy is Python itself. If you don't have -Python yet and want the simplest way to get started, we recommend you use the -[Anaconda Distribution](https://www.anaconda.com/download) - it includes -Python, NumPy, and many other commonly used packages for scientific computing -and data science. +The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. -NumPy can be installed with `conda`, with `pip`, with a package manager on -macOS and Linux, or [from source](https://numpy.org/devdocs/building). -For more detailed instructions, consult our Python and NumPy -installation guide below. +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` -channels: +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: ```bash # Best practice, use an environment rather than install in the base env @@ -36,162 +28,94 @@ If you use `pip`, you can install NumPy with: ```bash pip install numpy ``` +Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. -Also when using pip, it's good practice to use a virtual environment - -see [Reproducible Installs](#reproducible-installs) below for why, and -[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) -for details on using virtual environments. # Python and NumPy installation guide -Installing and managing packages in Python is complicated, there are a -number of alternative solutions for most tasks. This guide tries to give the -reader a sense of the best (or most popular) solutions, and give clear -recommendations. It focuses on users of Python, NumPy, and the PyData (or -numerical computing) stack on common operating systems and hardware. +Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. ## Recommendations -We'll start with recommendations based on the user's experience level and -operating system of interest. If you're in between "beginning" and "advanced", -please go with "beginning" if you want to keep things simple, and with -"advanced" if you want to work according to best practices that go a longer way -in the future. +We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. ### Beginning users On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/download) (it installs all - packages you need and all other tools mentioned below). -- For writing and executing code, use notebooks in - [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for - exploratory and interactive computing, and - [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) - for writing scripts and packages. -- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to - manage your packages and start JupyterLab, Spyder, or Visual Studio Code. +- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + ### Advanced users #### Conda - Install [Miniforge](https://github.com/conda-forge/miniforge). -- Keep the `base` conda environment minimal, and use one or more - [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - to install the package you need for the task or project you're working on. +- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. #### Alternative if you prefer pip/PyPI -For users who know, from personal preference or reading about the main -differences between conda and pip below, they prefer a pip/PyPI-based solution, -we recommend: +For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. -- Install Python from [python.org](https://www.python.org/downloads/), - [Homebrew](https://brew.sh/), or your Linux package manager. -- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool - that provides a dependency resolver and environment management capabilities - in a similar fashion as conda does. ## Python package management -Managing packages is a challenging problem, and, as a result, there are lots of -tools. For web and general purpose Python development there's a whole -[host of tools](https://packaging.python.org/guides/tool-recommendations/) -complementary with pip. For high-performance computing (HPC), -[Spack](https://github.com/spack/spack) is worth considering. For most NumPy -users though, [conda](https://conda.io/en/latest/) and -[pip](https://pip.pypa.io/en/stable/) are the two most popular tools. +Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + ### Pip & conda -The two main tools that install Python packages are `pip` and `conda`. Their -functionality partially overlaps (e.g. both can install `numpy`), however, they -can also work together. We'll discuss the major differences between pip and -conda here - this is important to understand if you want to manage packages -effectively. +The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. -The first difference is that conda is cross-language and it can install Python, -while pip is installed for a particular Python on your system and installs other -packages to that same Python install only. This also means conda can install -non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while -pip can't. +The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. -The second difference is that pip installs from the Python Packaging Index -(PyPI), while conda installs from its own channels (typically "defaults" or -"conda-forge"). PyPI is the largest collection of packages by far, however, all -popular packages are available for conda as well. +The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. -The third difference is that conda is an integrated solution for managing -packages, dependencies and environments, while with pip you may need another -tool (there are many!) for dealing with environments or complex dependencies. +The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. ### Reproducible installs -As libraries get updated, results from running your code can change, or your -code can break completely. It's important to be able to reconstruct the set -of packages and versions you're using. Best practice is to: +As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: 1. use a different environment per project you're working on, -2. record package names and versions using your package installer; - each has its own metadata format for this: +2. record package names and versions using your package installer; each has its own metadata format for this: - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and - [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## NumPy packages & accelerated linear algebra libraries -NumPy doesn't depend on any other Python packages, however, it does depend on an -accelerated linear algebra library - typically -[Intel MKL](https://software.intel.com/en-us/mkl) or -[OpenBLAS](https://www.openblas.net/). Users don't have to worry about -installing those (they're automatically included in all NumPy install methods). -Power users may still want to know the details, because the used BLAS can -affect performance, behavior and size on disk: +NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: -- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. - The OpenBLAS libraries are included in the wheel. This makes the wheel - larger, and if a user installs (for example) SciPy as well, they will now - have two copies of OpenBLAS on disk. +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. -- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a - separate package that will be installed in the users' environment when they - install NumPy. +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When - a user installs NumPy from conda-forge, that BLAS package then gets installed - together with the actual library - this defaults to OpenBLAS, but it can also - be MKL (from the defaults channel), or even - [BLIS](https://github.com/flame/blis) or reference BLAS. +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk - while OpenBLAS is about 30 MB. +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. - MKL is typically a little faster and more robust than OpenBLAS. -Besides install sizes, performance and robustness, there are two more things to -consider: +Besides install sizes, performance and robustness, there are two more things to consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. -- Intel MKL is not open source. For normal use this is not a problem, but if - a user needs to redistribute an application built with NumPy, this could be - an issue. -- Both MKL and OpenBLAS will use multi-threading for function calls like - `np.dot`, with the number of threads being determined by both a build-time - option and an environment variable. Often all CPU cores will be used. This is - sometimes unexpected for users; NumPy itself doesn't auto-parallelize any - function calls. It typically yields better performance, but can also be - harmful - for example when using another level of parallelization with Dask, - scikit-learn or multiprocessing. ## Troubleshooting -If your installation fails with the message below, see Troubleshooting -ImportError. +If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! @@ -199,3 +123,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From 3c2e796ea4019783dcdded54356003ddae3f3bd4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:27 +0200 Subject: [PATCH 299/586] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/content/pt/install.md b/content/pt/install.md index cf3adf3a9a..ff2f33845a 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -5,8 +5,7 @@ sidebar: false O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem o Python e quer começar do jeito mais simples, nós recomendamos que você use a [Distribuição Anaconda](https://www.anaconda.com/distribution) - inclui Python, NumPy e outros pacotes comumente usados para computação científica e ciência de dados. -O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [da fonte](https://numpy.org/devdocs/building). -Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. +O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [da fonte](https://numpy.org/devdocs/user/building.html). Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. **CONDA** @@ -29,14 +28,14 @@ Se você usa o `pip`, você pode instalar o NumPy com: ```bash pip install numpy ``` - Também ao usar o pip, é uma boa prática usar um ambiente virtual - veja em [Instalações Reprodutíveis](#reproducible-installs) abaixo por quê, e [esse guia](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) para detalhes sobre o uso de ambientes virtuais. + # Guia de instalação do Python e do NumPy -Instalar e gerenciar pacotes no Python pode ser complicado. Este guia tenta dar ao leitor um resumo das melhores (ou mais populares) soluções e dar recomendações claras. Ele se concentra em usuários do Python, NumPy e do PyData (ou computação numérica) em sistemas operacionais e hardware comuns. +Instalar e gerenciar pacotes no Python pode ser complicado. Há várias soluções alternativas para a maioria das tarefas. Este guia tenta dar ao leitor um resumo das melhores (ou mais populares) soluções e dar recomendações claras. Ele se concentra em usuários do Python, NumPy e do PyData (ou computação numérica) em sistemas operacionais e hardware comuns. ## Recomendações @@ -50,6 +49,7 @@ Em Windows, macOS e Linux: - Para escrever e executar código, use notebooks no [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) para a computação exploratória e interativa, e o [Spyder](https://www.spyder-ide.org/) ou [Visual Studio Code](https://code.visualstudio.com/) para escrever scripts e pacotes. - Use o [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) para gerenciar seus pacotes e iniciar o JupyterLab, Spyder ou o Visual Studio Code. + ### Usuários avançados #### Conda @@ -60,17 +60,18 @@ Em Windows, macOS e Linux: #### Alternativa se você preferir pip/PyPI Para usuários que preferem uma solução baseada em pip/PyPI, por preferência pessoal ou leitura sobre as principais diferenças entre o conda e o pip, nós recomendamos: - - Instale o Python a partir de, por exemplo, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), ou seu gerenciador de pacotes Linux. - Use [Poetry](https://python-poetry.org/) como a ferramenta mais bem mantida que fornece um resolvedor de dependências e recursos de gerenciamento de ambiente de forma semelhante ao que o conda faz. + ## Gerenciamento de pacotes Python -Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. +Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. + ### Pip & conda -As duas principais ferramentas que instalam pacotes do Python são `pip` e `conda`. Algumas de suas funcionalidades são redundantes (por exemplo, ambos podem instalar o `numpy`). Vamos discutir as principais diferenças entre o pip e o conda aqui - é importante entender isso se você deseja gerenciar pacotes de forma efetiva. +As duas principais ferramentas que instalam pacotes do Python são `pip` e `conda`. Algumas de suas funcionalidades são redundantes (por exemplo, ambos podem instalar o `numpy`). No entanto, elas também podem trabalhar juntas. Vamos discutir as principais diferenças entre o pip e o conda aqui - é importante entender isso se você deseja gerenciar pacotes de forma efetiva. A primeira diferença é que "conda" é multilinguagens e pode instalar o Python, enquanto o pip é instalado em um determinado Python em seu sistema e instala outros pacotes apenas para essa mesma instalação de Python. Isto também significa que o conda pode instalar bibliotecas e ferramentas não-Python das quais você pode precisar (por exemplo, compiladores, CUDA, HDF5), enquanto pip não pode. @@ -82,7 +83,7 @@ A terceira diferença é que o conda é uma solução integrada para gerenciar p ### Instalações reprodutíveis -À medida que as bibliotecas são atualizadas, os resultados obtidos ao executar seu código podem mudar, ou o seu código pode parar de funcionar. É importante poder reconstruir o conjunto de pacotes e versões que você está usando. Best practice is to: +À medida que as bibliotecas são atualizadas, os resultados obtidos ao executar seu código podem mudar, ou o seu código pode parar de funcionar. É importante poder reconstruir o conjunto de pacotes e versões que você está usando. A recomendação é: 1. usar um ambiente diferente para cada projeto em que você trabalha, 2. gravar nomes de pacotes e versões usando seu instalador de pacotes; cada um tem seu próprio formato de metadados para essa tarefa: @@ -90,17 +91,17 @@ A terceira diferença é que o conda é uma solução integrada para gerenciar p - Pip: [ambientes virtuais](https://docs.python.org/3/tutorial/venv.html) e [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [ambientes virtuais e pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## Pacotes NumPy & bibliotecas de álgebra linear aceleradas -No entanto, depende de uma biblioteca de álgebra linear acelerada - tipicamente [Intel MKL](https://software.intel.com/en-us/mkl) ou [OpenBLAS](https://www.openblas.net/). Os usuários não precisam se preocupar com a instalação desses pacotes (eles são incluídos automaticamente em todos os métodos de instalação do NumPy). -No entanto, usuários experientes podem querer saber os detalhes, porque o BLAS usado pode afetar o desempenho, o comportamento e o tamanho em disco: +O NumPy não depende de quaisquer outros pacotes Python. No entanto, depende de uma biblioteca de álgebra linear acelerada - tipicamente [Intel MKL](https://software.intel.com/en-us/mkl) ou [OpenBLAS](https://www.openblas.net/). Os usuários não precisam se preocupar com a instalação desses pacotes (eles são incluídos automaticamente em todos os métodos de instalação do NumPy). No entanto, usuários experientes podem querer saber os detalhes, porque o BLAS usado pode afetar o desempenho, o comportamento e o tamanho em disco: -- As wheels da NumPy no PyPI, que é o que o pip instala, são compiladas com OpenBLAS. - As bibliotecas da OpenBLAS são empacotadas dentro da wheel. Isso faz com que a wheel fique maior, e se um usário também instalar (por exemplo) a SciPy, terá agora duas cópias da OpenBLAS no disco. +- As wheels da NumPy no PyPI, que é o que o pip instala, são compiladas com OpenBLAS. As bibliotecas da OpenBLAS são empacotadas dentro da wheel. Isso faz com que a wheel fique maior, e se um usário também instalar (por exemplo) a SciPy, terá agora duas cópias da OpenBLAS no disco. - No canal defaults do conda, a NumPy é compilada com a Intel MKL. MKL é um pacote separado que será instalado no ambiente do usuário quando instalar a NumPy. -- No canal do conda-Forge, a NumPy é compilada com um pacote "BLAS" fictício. Quando um usuário instala o NumPy do conda-forge, esse pacote BLAS então é instalado juntamente com a biblioteca real - o padrão é OpenBLAS, mas também pode ser MKL (do canal defaults), ou até mesmo [BLIS](https://github.com/flame/blis) ou _reference BLAS_. +- No canal do conda-Forge, a NumPy é compilada com um pacote "BLAS" fictício. Quando um usuário instala o NumPy do conda-forge, esse pacote BLAS então é instalado juntamente com a biblioteca real - o padrão é OpenBLAS, mas também pode ser MKL (do canal defaults), ou até mesmo [BLIS](https://github.com/flame/blis) ou *reference BLAS*. - O pacote MKL é muito maior que o OpenBLAS, ocupa cerca de 700 MB no disco enquanto OpenBLAS ocupa cerca de 30 MB. @@ -111,6 +112,7 @@ Além do tamanho instalado, desempenho e robustez, há mais duas coisas a se con - A Intel MKL não é de código aberto. Para uso normal isto não é um problema, mas se um usuário precisa redistribuir uma aplicação compilada com a NumPy, isso pode ser um problema. - Tanto MKL quanto OpenBLAS usarão multi-threading para chamadas de função como `np.dot`, com o número de threads sendo determinado tanto por uma opção no momento da compilação quanto por uma variável de ambiente. Muitas vezes, todos os núcleos de CPU serão usados. Isto é, às vezes, inesperado para usuários; o NumPy em si não paraleliza automaticamente nenhuma chamada de função. Normalmente, isso produz melhor desempenho, mas também pode ser prejudicial - por exemplo, ao usar outro nível de paralelização com Dask, scikit-learn ou multiprocessamento. + ## Solução de problemas Se sua instalação falhar com a mensagem abaixo, consulte [Solucionando ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). @@ -121,3 +123,4 @@ IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` + From b0844cb035606a95ac6b96d4742a78e494afc9aa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:28 +0200 Subject: [PATCH 300/586] New translations about.md (Spanish) --- content/es/about.md | 65 ++++++++++++++++++++++++--------------------- 1 file changed, 34 insertions(+), 31 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 357bd15fe1..a6e42626dc 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -1,15 +1,16 @@ --- -title: About Us +title: Quiénes Somos sidebar: false --- -NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy es un proyecto de código abierto cuyo objetivo es permitir la computación numérica en Python. Se creó en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será software 100% código abierto, de uso libre para todos. Fue liberado bajo los términos liberales de la [licencia BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). -NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). +NumPy se desarrolla de forma abierta en GitHub, mediante el consenso de las comunidades NumPy y científica de Python en general. Para más información sobre nuestro enfoque de gobernanza, consulte nuestro [Documento de Gobernanza](https://www.numpy.org/devdocs/dev/governance/index.html). -## Steering Council -The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): +## Consejo Directivo + +El Consejo de Dirección de NumPy es el órgano de gobernanza del proyecto. Su papel es el garantizar, a través del trabajo con la comunidad NumPy en general y al servicio de la misma, el bienestar a largo plazo del proyecto, tanto desde el punto de vista técnico como de la comunidad. El Consejo Directivo de NumPy está formado actualmente por los siguientes miembros (en orden alfabético): - Sebastian Berg - Ralf Gommers @@ -21,7 +22,7 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur - Melissa Weber Mendonça - Eric Wieser -Emeritus: +Eméritos: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -32,41 +33,41 @@ Emeritus: - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) -To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. +Para contactar con el Consejo Directivo de NumPy, por favor envía un correo electrónico a numpy-team@googlegroups.com. -## Teams +## Equipos -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
-NumPy currently has the following teams: +La dirección del proyecto NumPy trabaja activamente para diversificar las vías de contribución al proyecto.
NumPy cuenta actualmente con los siguientes equipos: -- development -- documentation -- triage -- website -- survey -- translations -- sprint mentors +- desarrollo +- documentación +- clasificación +- página web +- encuesta +- traducción +- mentores de sprints - optimization -- funding and grants +- financiación y subvenciones -See the [Team](/teams) page for more info. +Visita la página de [Equipos]({{< relref "/teams" >}}) para más información. -## NumFOCUS Subcommittee +## Subcomité NumFOCUS - Charles Harris - Ralf Gommers -- Inessa Pawson +- Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- Miembro externo: Thomas Caswell -## Sponsors +## Patrocinadores -NumPy receives direct funding from the following sources: +NumPy recibe financiación directa de las siguientes fuentes: {{< sponsors >}} -## Institutional Partners -Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: +## Socios institucionales + +Los socios institucionales son organizaciones que apoyan al proyecto empleando a personas que contribuyen a NumPy como parte de su trabajo. Entre los actuales socios institucionales se encuentran: - UC Berkeley (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) @@ -74,14 +75,16 @@ Institutional Partners are organizations that support the project by employing p {{< partners >}} -## Donate -If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. +## Donar + +Si has encontrado NumPy útil en tu trabajo, investigación o empresa, por favor considera una donación al proyecto proporcional a tus recursos. ¡Cualquier cantidad ayuda! Todas las donaciones se utilizarán estrictamente para financiar el desarrollo del software de código abierto, la documentación y la comunidad de NumPy. -NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. +NumPy es un proyecto patrocinado por NumFOCUS, una organización benéfica sin fines de lucro 501(c)(3) de Estados Unidos. NumFOCUS proporciona a NumPy apoyo fiscal, legal y administrativo para ayudar a garantizar el bienestar y la sostenibilidad del proyecto. Visita [numfocus.org](https://numfocus.org) para más información. -Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. +Las donaciones a NumPy son gestionadas por [NumFOCUS](https://numfocus.org). Para los donantes de Estados Unidos, su donación es deducible de impuestos en la medida prevista por la ley. Al igual que con cualquier donación, debes consultar a tu asesor de impuestos sobre tu situación fiscal particular. -NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). +El Consejo Directivo de NumPy tomará las decisiones sobre el mejor uso de los fondos recibidos. Las prioridades técnicas y de infraestructura están documentadas en la [Hoja de Ruta de NumPy](https://www.numpy.org/neps/index.html#roadmap). {{}} + From 2b24975478996763bd14e13f2da8ee3a317e7180 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:29 +0200 Subject: [PATCH 301/586] New translations about.md (Arabic) --- content/ar/about.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 357bd15fe1..243f7083ae 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -7,6 +7,7 @@ NumPy is an open source project that enables numerical computing with Python. It NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + ## Steering Council The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): @@ -36,8 +37,7 @@ To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. ## Teams -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
-NumPy currently has the following teams: +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: - development - documentation @@ -64,6 +64,7 @@ See the [Team](/teams) page for more info. NumPy receives direct funding from the following sources: {{< sponsors >}} + ## Institutional Partners Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: @@ -74,6 +75,7 @@ Institutional Partners are organizations that support the project by employing p {{< partners >}} + ## Donate If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. @@ -85,3 +87,4 @@ Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors i NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{}} + From a25ca34f4cc1ec1865177e4e0752f46ba611a2a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:30 +0200 Subject: [PATCH 302/586] New translations about.md (Japanese) --- content/ja/about.md | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 626a3ff721..6508ca265d 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -3,13 +3,14 @@ title: 私たちについて sidebar: false --- -NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは[修正BSD ライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt) の条項の下で、すべての人が利用可能です。 It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは[修正BSD ライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt) の条項の下で、すべての人が利用可能です。 + +NumPy は 、NumPyコミュニティやより広範な科学計算用Python コミュニティとの合意のもと、GitHub 上でオープンに開発されています。 NumPyのガバナンス方法の詳細については、 [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html) をご覧ください。 -NumPy は 、NumPyコミュニティやより広範な科学計算用Python コミュニティとの合意のもと、GitHub 上でオープンに開発されています。 NumPyのガバナンス方法の詳細については、 [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html) をご覧ください。 For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). ## 運営委員会 -The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): +Numpy運営委員会はこのプロジェクトの管理組織です。 その役割は、Numpy コミュニティと協力し、Numpyのソフトウェアサービスを確実にユーザに提供することです。 ソフトウェアパッケージとコミュニティの両方において、プロジェクトの長期的な持続可能性を保っていきます。 NumPy運営委員会は現在以下のメンバーで構成されています (姓のアルファベット順): - Sebastian Berg - Ralf Gommers @@ -21,7 +22,7 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur - Melissa Weber Mendonça - Eric Wieser -Emeritus: +過去のメンバー - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -40,21 +41,21 @@ Numpy プロジェクトのコアメンバーは、プロジェクトへの貢 - 開発 - ドキュメント -- triage +- トリアージ - ウェブサイト - 調査 - 翻訳 -- sprint mentors +- スプリントのメンター - 最適化 - 資金と助成金 -個々のチームメンバーについては、 [チーム](teams/) のページを参照してください。 +個々のチームメンバーについては、 [チーム](/teams/) のページを参照してください。 ## NumFOCUSサブ委員会 - Charles Harris - Ralf Gommers -- Inessa Pawson +- Melissa Weber Mendonça - Sebastian Berg - 外部メンバー: Thomas Caswell @@ -63,9 +64,10 @@ Numpy プロジェクトのコアメンバーは、プロジェクトへの貢 NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} + ## パートナー団体 -パートナー団体は、NumPyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 Current Institutional Partners include: +パートナー団体は、NumPyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 - カルフォルニア大学 バークレー校 (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) @@ -73,14 +75,16 @@ NumPyは以下の団体から直接資金援助を受けています。 {{< partners >}} + ## 寄付 -NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. +NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 -NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. +NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 -NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 As with any donation, you should consult with your tax advisor about your particular tax situation. +NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 NumPyの運営委員会は、受け取った資金をどのように使えば良いかを検討し、使用する方法について決定します. NumPyに関する技術とインフラの投資の優先順位に関しては、[NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap) に記載されています。 {{}} + From 2ce9b8cb873a89a85d94d129af1eb46e9d3988b8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:31 +0200 Subject: [PATCH 303/586] New translations about.md (Korean) --- content/ko/about.md | 69 +++++++++++++++++++++++---------------------- 1 file changed, 36 insertions(+), 33 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 357bd15fe1..c05b97d1b8 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -1,15 +1,16 @@ --- -title: About Us +title: NumPy 정보 sidebar: false --- -NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy는 Python을 통해 수치적 컴퓨팅을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈 소스 소프트웨어이며 누구나 무료로 사용할 수 있습니다 [수정된 BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt)의 자유로운 조건에 따라 릴리스됩니다. -NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). +NumPy는 NumPy와 더 넓은 과학 Python 커뮤니티의 합의를 통해 GitHub의 공개적으로 개발되었습니다. 거버넌스 접근 방식에 대한 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조하세요. -## Steering Council -The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): +## 운영 위원회 + +NumPy 운영 위원회는 프로젝트를 관리하는 기관입니다. 그 역할은 더 넓은 NumPy 커뮤니티와 협력하고 서비스를 제공함으로써 소프트웨어 패키지와 커뮤니티로서 프로젝트의 장기적인 지속 가능성을 보장하는 것입니다. NumPy 운영 위원회는 현재 다음과 같은 회원들로 구성되어 있습니다. (성씨의 알파벳 순서) - Sebastian Berg - Ralf Gommers @@ -21,67 +22,69 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur - Melissa Weber Mendonça - Eric Wieser -Emeritus: +명예 회원 - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) - Marten van Kerkwijk (2017-2019) -- Travis Oliphant (project founder, 2005-2012) +- Travis Oliphant (프로젝트 설립자, 2005-2012) - Nathaniel Smith (2012-2021) - Julian Taylor (2013-2021) - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) -To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. +NumPy 운영 위원회에 문의하려면, numpy-team@googlegroups.com 주소로 이메일을 보내세요. -## Teams +## 팀 -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
-NumPy currently has the following teams: +NumPy 프로젝트 리더십은 프로젝트에 대한 기여 경로를 다양화하기 위해 적극적으로 노력하고 있습니다.
NumPy에는 현재 다음 팀이 있습니다: -- development -- documentation -- triage -- website -- survey -- translations -- sprint mentors -- optimization -- funding and grants +- 개발 +- 문서 +- 심사 +- 웹사이트 +- 설문조사 +- 번역 +- 스프린트 멘토링 +- 최적화 +- 자원 및 보조금 -See the [Team](/teams) page for more info. +스프린트 멘토링 -## NumFOCUS Subcommittee +## NumFOCUS 소위원회 - Charles Harris - Ralf Gommers - Inessa Pawson - Sebastian Berg -- External member: Thomas Caswell +- 외부 회원: Thomas Caswell -## Sponsors +## 스폰서 -NumPy receives direct funding from the following sources: +NumPy는 다음과 같은 곳들에서 직접적으로 자금을 받습니다. {{< sponsors >}} -## Institutional Partners -Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: +## 기관 파트너 + +기관 파트너는 그들의 업무의 일환으로 NumPy에 기여하는 직원을 고용하여 프로젝트를 지원하는 조직입니다. 현재 기관 파트너는 다음과 같습니다. -- UC Berkeley (Stéfan van der Walt) +- UC 버클리 (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) - NVIDIA (Sebastian Berg) {{< partners >}} -## Donate -If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. +## 후원 -NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. +만약 NumPy가 당신의 업무, 연구 혹은 회사에서 유용하다고 판단된다면 당신의 자원에 맞는 프로젝트에 기여하는 것을 고려해보세요. 그것이 얼마든 도움이 됩니다! 모든 후원은 NumPy의 소프트웨어 개발, 문서 작성과 커뮤니티 운영의 자금으로 엄격하게 사용될 것입니다. -Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. +NumPy는 미국의 501(c)(3) 비영리 단체인 NumFOCUS의 후원 프로젝트입니다. NumFOCUS는 NumPy에 재정적, 법적, 행정적 지원을 제공하고 프로젝트의 건강과 지속 가능성을 보장할 수 있도록 도와줍니다. 더 자세한 정보를 알고싶다면 [numfocus.org](https://numfocus.org)를 방문하세요. -NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). +NumPy에 대한 후원은 [NumFOCUS](https://numfocus.org)가 관리합니다. 미국에 거주하는 후원자의 경우에는, 당신의 후원은 법이 제공하는 한도 내에서 세금 공제를 받을 수 있습니다. 기부와 마찬가지로 특정 세금 상황에 대해서는 세금 전문가와 상담해야합니다. + +NumPy 운영 위원회는 후원받은 후원금을 가장 잘 활용하는 방안을 결정합니다. 기술 및 인프라의 우선 순위는 NumPy [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap)에 문서화되어 있습니다. {{}} + From 794726b41f82fc78b8175ee496c4fd8fbd78f0d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:32 +0200 Subject: [PATCH 304/586] New translations about.md (Russian) --- content/ru/about.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/ru/about.md b/content/ru/about.md index 357bd15fe1..243f7083ae 100644 --- a/content/ru/about.md +++ b/content/ru/about.md @@ -7,6 +7,7 @@ NumPy is an open source project that enables numerical computing with Python. It NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + ## Steering Council The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): @@ -36,8 +37,7 @@ To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. ## Teams -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
-NumPy currently has the following teams: +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: - development - documentation @@ -64,6 +64,7 @@ See the [Team](/teams) page for more info. NumPy receives direct funding from the following sources: {{< sponsors >}} + ## Institutional Partners Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: @@ -74,6 +75,7 @@ Institutional Partners are organizations that support the project by employing p {{< partners >}} + ## Donate If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. @@ -85,3 +87,4 @@ Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors i NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{}} + From 560873cd45430a773deae26a529722e428f5f536 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:33 +0200 Subject: [PATCH 305/586] New translations about.md (Chinese Simplified) --- content/zh/about.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 357bd15fe1..243f7083ae 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -7,6 +7,7 @@ NumPy is an open source project that enables numerical computing with Python. It NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + ## Steering Council The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): @@ -36,8 +37,7 @@ To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. ## Teams -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
-NumPy currently has the following teams: +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: - development - documentation @@ -64,6 +64,7 @@ See the [Team](/teams) page for more info. NumPy receives direct funding from the following sources: {{< sponsors >}} + ## Institutional Partners Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: @@ -74,6 +75,7 @@ Institutional Partners are organizations that support the project by employing p {{< partners >}} + ## Donate If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. @@ -85,3 +87,4 @@ Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors i NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{}} + From a9bcd6841d97922749c01e0ab40f7c3b2586606f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:34 +0200 Subject: [PATCH 306/586] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index 9b0796be05..30e0dbdd13 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -7,9 +7,10 @@ NumPy é um projeto de código aberto que visa possibilitar a computação numé O NumPy é desenvolvido no GitHub, através do consenso da comunidade NumPy e de uma comunidade mais ampla de Python científico. Para obter mais informações sobre nossa abordagem de governança, por favor, consulte nosso [Documento de Governança](https://www.numpy.org/devdocs/dev/governance/index.html). + ## Conselho Diretor (Steering Council) -The NumPy Steering Council is the project's governing body. O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética, pelo sobrenome): +O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. Isso é feito através do trabalho com e para a comunidade NumPy em geral. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética, pelo sobrenome): - Sebastian Berg - Ralf Gommers @@ -21,7 +22,7 @@ The NumPy Steering Council is the project's governing body. O papel do Conselho - Melissa Weber Mendonça - Eric Wieser -Emeritus: +Membros Eméritos: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -34,7 +35,7 @@ Emeritus: Para entrar em contato com o conselho diretor do NumPy, por favor envie um email para numpy-team@googlegroups.com. -## Teams +## Times A liderança do projeto NumPy trabalha ativamente na diversificação dos caminhos possíveis para contribuições.
Atualmente, o NumPy conta com os seguintes times: @@ -48,13 +49,13 @@ A liderança do projeto NumPy trabalha ativamente na diversificação dos caminh - otimização - financiamento e bolsas -Veja a página sobre os [Times](/teams) para mais informações. +Veja a página sobre os [Times]({{< relref "/teams" >}}) para mais informações. ## Subcomitê NumFOCUS - Charles Harris - Ralf Gommers -- Inessa Pawson +- Melissa Weber Mendonça - Sebastian Berg - Membro externo: Thomas Caswell @@ -63,6 +64,7 @@ Veja a página sobre os [Times](/teams) para mais informações. O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} + ## Parceiros Institucionais Os Parceiros Institucionais são organizações que apoiam o projeto, empregando pessoas que contribuem para a NumPy como parte de seu trabalho. Os parceiros institucionais atuais incluem: @@ -73,6 +75,7 @@ Os Parceiros Institucionais são organizações que apoiam o projeto, empregando {{< partners >}} + ## Doações Se você achou o NumPy útil no seu trabalho, pesquisa ou empresa, por favor considere fazer uma doação para o projeto que seja compatível com seus recursos. Qualquer quantidade ajuda! Todas as doações serão utilizadas estritamente para financiar o desenvolvimento do software de código aberto da NumPy, documentação e comunidade. @@ -84,3 +87,4 @@ Doações para o NumPy são gerenciadas pela [NumFOCUS](https://numfocus.org). P O Conselho Diretor da NumPy tomará as decisões sobre a melhor forma de utilizar os fundos recebidos. Prioridades técnicas e de infraestrutura estão documentadas no [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{}} + From b1a5eb0234dc9edd92dbb64a833d16bdd80a35ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:35 +0200 Subject: [PATCH 307/586] New translations privacy.md (Spanish) --- content/es/privacy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/privacy.md b/content/es/privacy.md index 6064e4c4f1..4b62b24be2 100644 --- a/content/es/privacy.md +++ b/content/es/privacy.md @@ -1,8 +1,8 @@ --- -title: Privacy Policy +title: Política de Privacidad sidebar: false --- -**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. +**numpy.org** está operado por [NumFOCUS, Inc.](https://numfocus.org), el patrocinador fiscal del proyecto NumPy. Para ver la Política de Privacidad de este sitio web, por favor dirígete a https://numfocus.org/privacy-policy. -If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. +Si tienes alguna pregunta sobre la política o la recolección, uso y divulgación de datos de NumFOCUS, por favor ponte en contacto con el personal de NumFOCUS en privacy@numfocus.org. From a3e4bfdc3480cd1be7f30fd80132fb7c1a6ddbfc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:37 +0200 Subject: [PATCH 308/586] New translations privacy.md (Japanese) --- content/ja/privacy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/privacy.md b/content/ja/privacy.md index 2d4d2fba91..8cd76d43e4 100644 --- a/content/ja/privacy.md +++ b/content/ja/privacy.md @@ -3,6 +3,6 @@ title: プライバシーポリシー sidebar: false --- -**numpy.org** は、NumPyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policy を参照してください。 For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. +**numpy.org** は、NumPyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policy を参照してください。 ポリシーまたはNumFOCUSのデータ収集、使用、および開示方法についてご質問がある場合は、privacy@numfocus.orgのNumFOCUSスタッフにお問い合わせください。 From f168e0ebb39c7dea4d594e536d4d77688de43b11 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:38 +0200 Subject: [PATCH 309/586] New translations privacy.md (Korean) --- content/ko/privacy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/privacy.md b/content/ko/privacy.md index 6064e4c4f1..b2fc57fcfd 100644 --- a/content/ko/privacy.md +++ b/content/ko/privacy.md @@ -1,8 +1,8 @@ --- -title: Privacy Policy +title: 개인정보 정책 sidebar: false --- -**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. +**numpy.org**는 NumPy 프로젝트의 재정적 후원자인 [NumFOCUS, Inc.](https://numfocus.org)가 관리합니다. 이 웹 사이트에 대한 개인정보 정책을 확인하려면 https://numfocus.org/privacy-policy를 참고하세요. -If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. +NumFOCUS의 데이터 수집, 이용, 공개 정책에 대하여 아무 질문이나 있으시다면, NumFOCUS 스태프인 privacy@numfocus.org로 연락해주세요. From 9cf1af808f203d1b70364ab78cbbe58a649b1584 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:41 +0200 Subject: [PATCH 310/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 167 ++++--------------- 1 file changed, 37 insertions(+), 130 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index 347251d55a..d0be271f50 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -4,162 +4,69 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, the biggest Cricket Festival in India' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" - -> }} -> You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> > {{< /blockquote >}} ## About Cricket -It would be an understatement to state that Indians love cricket. The game is -played in just about every nook and cranny of India, rural or urban, popular -with the young and the old alike, connecting billions in India unlike any other sport. -Cricket enjoys lots of media attention. There is a significant amount of -[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and -fame at stake. Over the last several years, technology has literally been a game -changer. Audiences are spoilt for choice with streaming media, tournaments, -affordable access to mobile based live cricket watching, and more. - -The Indian Premier League (IPL) is a professional Twenty20 cricket -league, founded in 2008. It is one of the most attended cricketing events in -the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) -in 2019. - -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken -by a bowler, the matches won by a cricket team, the number of times a batsman -responds in a certain way to a kind of bowling attack, etc. The capability to -dig into cricketing numbers for both improving performance and studying -the business opportunities, overall market, and economics of cricket via powerful -analytics tools, powered by numerical computing software such as NumPy, is a big -deal. Cricket analytics provides interesting insights into the game and -predictive intelligence regarding game outcomes. - -Today, there are rich and almost infinite troves of cricket game records and -statistics available, e.g., ESPN -cricinfo and -[cricsheet](https://cricsheet.org). These and several such cricket databases -have been used for cricket -analysis -using the latest machine learning and predictive modelling algorithms. -Media and entertainment platforms along with professional sports bodies -associated with the game use technology and analytics for determining key -metrics for improving match winning chances: - -- batting performance moving average, -- score forecasting, -- gaining insights into fitness and performance of a player against different opposition, -- player contribution to wins and losses for making strategic decisions on team composition +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: + +* batting performance moving average, +* score forecasting, +* gaining insights into fitness and performance of a player against different opposition, +* player contribution to wins and losses for making strategic decisions on team composition {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Cricket Pitch, the focal point in the field' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< /figure >}} ### Key Data Analytics Objectives -- Sports data analytics are used not only in cricket but many other - sports for - improving the overall team performance and maximizing winning chances. -- Real-time data analytics can help in gaining insights even during the game - for changing tactics by the team and by associated businesses for economic - benefits and growth. -- Besides historical analysis, predictive models are - harnessed to determine the possible match outcomes that require significant - number crunching and data science know-how, visualization tools and capability - to include newer observations in the analysis. +* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. +* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. +* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'Cricket Pose Estimator' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### The Challenges -- **Data Cleaning and preprocessing** - - IPL has expanded cricket beyond the classic test match format to a much - larger scale. The number of matches played every season across various - formats has increased and so has the data, the algorithms, newer sports data - analysis technologies and simulation models. Cricket data analysis requires - field mapping, player tracking, ball tracking, player shot analysis, and - several other aspects involved in how the ball is delivered, its angle, spin, - velocity, and trajectory. All these factors together have increased the - complexity of data cleaning and preprocessing. - -- **Dynamic Modeling** - - In cricket, just like any other sport, - there can be a large number of variables related to tracking various numbers - of players on the field, their attributes, the ball, and several possibilities - of potential actions. The complexity of data analytics and modeling is - directly proportional to the kind of predictive questions that are put forth - during analysis and are highly dependent on data representation and the - model. Things get even more challenging in terms of computation, data - comparisons when dynamic cricket play predictions are sought such as what - would have happened if the batsman had hit the ball at a different angle or - velocity. - -- **Predictive Analytics Complexity** - - Much of the decision making in cricket is based on questions such as "how - often does a batsman play a certain kind of shot if the ball delivery is of a - particular type", or "how does a bowler change his line and length if the - batsman responds to his delivery in a certain way". - This kind of predictive analytics query requires highly granular dataset - availability and the capability to synthesize data and create generative - models that are highly accurate. +* **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + +* **Dynamic Modeling** + + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + +* **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics -Sports Analytics is a thriving field. Many researchers and companies -[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) -and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, -besides using the latest machine learning and AI techniques. NumPy has been used -for various kinds of cricket related sporting analytics such as: +Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: -- **Statistical Analysis:** NumPy's numerical capabilities help estimate the - statistical significance of observational data or match events in the context - of various player and game tactics, estimating the game outcome by comparison - with a generative or static model. - [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) - and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) - are used for tactical analysis. +* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. -- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. +* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. ## Summary -Sports Analytics is a game changer when it comes to how professional games are -played, especially how strategic decision making happens, which until recently -was primarily done based on “gut feeling" or adherence to past traditions. NumPy -forms a solid foundation for a large set of Python packages which provide higher -level functions related to data analytics, machine learning, and AI algorithms. -These packages are widely deployed to gain real-time insights that help in -decision making for game-changing outcomes, both on field as well as to draw -inferences and drive business around the game of cricket. Finding out the -hidden parameters, patterns, and attributes that lead to the outcome of a -cricket match helps the stakeholders to take notice of game insights that are -otherwise hidden in numbers and statistics. +Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagram showing benefits of using NumPy for cricket analytics' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From 2de2a06580e8468fdcda8d8eec1fa62397a32916 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:42 +0200 Subject: [PATCH 311/586] New translations cricket-analytics.md (Arabic) --- content/ar/case-studies/cricket-analytics.md | 167 ++++--------------- 1 file changed, 37 insertions(+), 130 deletions(-) diff --git a/content/ar/case-studies/cricket-analytics.md b/content/ar/case-studies/cricket-analytics.md index 347251d55a..d0be271f50 100644 --- a/content/ar/case-studies/cricket-analytics.md +++ b/content/ar/case-studies/cricket-analytics.md @@ -4,162 +4,69 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, the biggest Cricket Festival in India' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" - -> }} -> You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> > {{< /blockquote >}} ## About Cricket -It would be an understatement to state that Indians love cricket. The game is -played in just about every nook and cranny of India, rural or urban, popular -with the young and the old alike, connecting billions in India unlike any other sport. -Cricket enjoys lots of media attention. There is a significant amount of -[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and -fame at stake. Over the last several years, technology has literally been a game -changer. Audiences are spoilt for choice with streaming media, tournaments, -affordable access to mobile based live cricket watching, and more. - -The Indian Premier League (IPL) is a professional Twenty20 cricket -league, founded in 2008. It is one of the most attended cricketing events in -the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) -in 2019. - -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken -by a bowler, the matches won by a cricket team, the number of times a batsman -responds in a certain way to a kind of bowling attack, etc. The capability to -dig into cricketing numbers for both improving performance and studying -the business opportunities, overall market, and economics of cricket via powerful -analytics tools, powered by numerical computing software such as NumPy, is a big -deal. Cricket analytics provides interesting insights into the game and -predictive intelligence regarding game outcomes. - -Today, there are rich and almost infinite troves of cricket game records and -statistics available, e.g., ESPN -cricinfo and -[cricsheet](https://cricsheet.org). These and several such cricket databases -have been used for cricket -analysis -using the latest machine learning and predictive modelling algorithms. -Media and entertainment platforms along with professional sports bodies -associated with the game use technology and analytics for determining key -metrics for improving match winning chances: - -- batting performance moving average, -- score forecasting, -- gaining insights into fitness and performance of a player against different opposition, -- player contribution to wins and losses for making strategic decisions on team composition +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: + +* batting performance moving average, +* score forecasting, +* gaining insights into fitness and performance of a player against different opposition, +* player contribution to wins and losses for making strategic decisions on team composition {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Cricket Pitch, the focal point in the field' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< /figure >}} ### Key Data Analytics Objectives -- Sports data analytics are used not only in cricket but many other - sports for - improving the overall team performance and maximizing winning chances. -- Real-time data analytics can help in gaining insights even during the game - for changing tactics by the team and by associated businesses for economic - benefits and growth. -- Besides historical analysis, predictive models are - harnessed to determine the possible match outcomes that require significant - number crunching and data science know-how, visualization tools and capability - to include newer observations in the analysis. +* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. +* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. +* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'Cricket Pose Estimator' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### The Challenges -- **Data Cleaning and preprocessing** - - IPL has expanded cricket beyond the classic test match format to a much - larger scale. The number of matches played every season across various - formats has increased and so has the data, the algorithms, newer sports data - analysis technologies and simulation models. Cricket data analysis requires - field mapping, player tracking, ball tracking, player shot analysis, and - several other aspects involved in how the ball is delivered, its angle, spin, - velocity, and trajectory. All these factors together have increased the - complexity of data cleaning and preprocessing. - -- **Dynamic Modeling** - - In cricket, just like any other sport, - there can be a large number of variables related to tracking various numbers - of players on the field, their attributes, the ball, and several possibilities - of potential actions. The complexity of data analytics and modeling is - directly proportional to the kind of predictive questions that are put forth - during analysis and are highly dependent on data representation and the - model. Things get even more challenging in terms of computation, data - comparisons when dynamic cricket play predictions are sought such as what - would have happened if the batsman had hit the ball at a different angle or - velocity. - -- **Predictive Analytics Complexity** - - Much of the decision making in cricket is based on questions such as "how - often does a batsman play a certain kind of shot if the ball delivery is of a - particular type", or "how does a bowler change his line and length if the - batsman responds to his delivery in a certain way". - This kind of predictive analytics query requires highly granular dataset - availability and the capability to synthesize data and create generative - models that are highly accurate. +* **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + +* **Dynamic Modeling** + + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + +* **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics -Sports Analytics is a thriving field. Many researchers and companies -[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) -and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, -besides using the latest machine learning and AI techniques. NumPy has been used -for various kinds of cricket related sporting analytics such as: +Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: -- **Statistical Analysis:** NumPy's numerical capabilities help estimate the - statistical significance of observational data or match events in the context - of various player and game tactics, estimating the game outcome by comparison - with a generative or static model. - [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) - and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) - are used for tactical analysis. +* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. -- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. +* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. ## Summary -Sports Analytics is a game changer when it comes to how professional games are -played, especially how strategic decision making happens, which until recently -was primarily done based on “gut feeling" or adherence to past traditions. NumPy -forms a solid foundation for a large set of Python packages which provide higher -level functions related to data analytics, machine learning, and AI algorithms. -These packages are widely deployed to gain real-time insights that help in -decision making for game-changing outcomes, both on field as well as to draw -inferences and drive business around the game of cricket. Finding out the -hidden parameters, patterns, and attributes that lead to the outcome of a -cricket match helps the stakeholders to take notice of game insights that are -otherwise hidden in numbers and statistics. +Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagram showing benefits of using NumPy for cricket analytics' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From 39d0a7cbf07e7d79093e381306c084f3a54d538a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:43 +0200 Subject: [PATCH 312/586] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 137 +++++-------------- 1 file changed, 33 insertions(+), 104 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 2bbc4690ee..2e366ad8d1 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -3,141 +3,70 @@ title: "ケーススタディ: クリケット分析、ゲームチェンジャ sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = ' IPLT20、インド最大のクリケットフェスティバル' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="** IPLT20、インド最大のクリケットフェスティバル**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni、 _インディアンチームの元キャプテン、インターナショナル・クリケットプレイヤー、チェンナイ・スーパー・キングスのためにIPLでプレイ_" - -> }} -> You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *世界的なクリケットプレイヤーであり、前世界王者、インドチームで活躍し、IPLにおけるチェンナイスーパー王者となった*" +> }} 観客のために競技しているのではなく、国のために競技しているのです。 +> > {{< /blockquote >}} ## クリケットについて -It would be an understatement to state that Indians love cricket. The game is -played in just about every nook and cranny of India, rural or urban, popular -with the young and the old alike, connecting billions in India unlike any other sport. -Cricket enjoys lots of media attention. There is a significant amount of -[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and -fame at stake. Over the last several years, technology has literally been a game -changer. Audiences are spoilt for choice with streaming media, tournaments, -affordable access to mobile based live cricket watching, and more. - -IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は[67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League)だと評価されています。 - -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken -by a bowler, the matches won by a cricket team, the number of times a batsman -responds in a certain way to a kind of bowling attack, etc. The capability to -dig into cricketing numbers for both improving performance and studying -the business opportunities, overall market, and economics of cricket via powerful -analytics tools, powered by numerical computing software such as NumPy, is a big -deal. Cricket analytics provides interesting insights into the game and -predictive intelligence regarding game outcomes. - -現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : [ESPN cricinfo や](https://stats.espncricinfo.com/ci/engine/stats/index.html) [cricsheet](https://cricsheet.org). These and several such cricket databases -have been used for cricket -analysis -using the latest machine learning and predictive modelling algorithms. -Media and entertainment platforms along with professional sports bodies -associated with the game use technology and analytics for determining key -metrics for improving match winning chances: - -- バッティング成績の移動平均 -- スコア予測 -- プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 -- チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 +インド人はクリケットが大好きだと言っても過言ではないでしょう。 この競技は、他のスポーツと異なり、インドの農村部や都市部を問わず、あらゆる場所でプレイされており、若者から年配の方まで広く人気があり、インドでは何十億人もの人々を結びつける役割を担っています。 クリケットは多くのメディアの注目を集めています。 クリケットは多くのメディアの注目を集め、非常に[多額のお金](https://www.statista.com/topics/4543/indian-premier-league-ipl/)と名声がかかっています。 過去数年間、テクノロジーは文字通りクリケットの試合を変えてきました。 視聴者はストリーミングメディア、トーナメント、モバイルベースの手頃なアクセスによるライブクリケット視聴などを享受しています。 + +インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は[67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League)だと評価されています。 + +クリケットは数のゲームです。 バッツマンによってスコアされたランの数、ボウラーによって取られたウィケットの数、クリケットチームによって獲得した試合の数、バッツマンがボウリング攻撃に特定の方法で応答する回数。 クリケットの数字を掘り下げてパフォーマンスを向上させるとともに、NumPyなどの数値計算ソフトウェアを利用した強力な分析ツールを介して、クリケットのビジネスチャンス、市場全体、経済性を研究することは、大きな意味を持ちます。 クリケット分析は、試合に関する興味深い洞察と、ゲームの結果に関する予測AIを提供します。 + +現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : [ESPN cricinfo や](https://stats.espncricinfo.com/ci/engine/stats/index.html) [cricsheet](https://cricsheet.org). これらのクリケットデータベースは、最新の機械学習と予測モデリングアルゴリズムを使用して、 [クリケット 分析](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) に使用されています。 メディアやプロスポーツ団体のエンターテインメントプラットフォームは、技術や分析を利用し、試合勝率を向上させるために、下記のような要素が主要なメトリックだと考え始めています。 + +* バッティング成績の移動平均 +* スコア予測 +* プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 +* チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = ' フィールドのフォーカルポイントとなるクリケットピッチ' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="クリケット分析にNumPyを使用するメリットを示す図" caption="** 利用されている主なNumPy機能 **" >}} ### データ分析の主要な目標 -- スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 -- リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 -- 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 +* スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 +* リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 +* 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'クリケットの姿勢推定' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### 課題 -- **データのクリーニングと前処理** +* **データのクリーニングと前処理** - IPL has expanded cricket beyond the classic test match format to a much - larger scale. The number of matches played every season across various - formats has increased and so has the data, the algorithms, newer sports data - analysis technologies and simulation models. Cricket data analysis requires - field mapping, player tracking, ball tracking, player shot analysis, and - several other aspects involved in how the ball is delivered, its angle, spin, - velocity, and trajectory. All these factors together have increased the - complexity of data cleaning and preprocessing. + IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 -- **動的モデリング** +* **動的モデリング** - In cricket, just like any other sport, - there can be a large number of variables related to tracking various numbers - of players on the field, their attributes, the ball, and several possibilities - of potential actions. The complexity of data analytics and modeling is - directly proportional to the kind of predictive questions that are put forth - during analysis and are highly dependent on data representation and the - model. Things get even more challenging in terms of computation, data - comparisons when dynamic cricket play predictions are sought such as what - would have happened if the batsman had hit the ball at a different angle or - velocity. + クリケットでは、他のスポーツと同様、フィールド上の選手の様々な数字を追跡するために、関連する変数の数が多くなってしまいがちです。 たとえば、ボールやその属性情報、およびいくつかの行動をとるアクションのいくつかの可能性などの変数です。 データ分析とモデリングの複雑さは、分析中に必要となる予測のための質問の種類に正比例しており、データ表現とモデルにも大きく依存しています。 バッツマンが異なる角度や速度でボールを打った場合に何が起こるのかのような、動的なクリケットのプレーの予測が必要な場合、計算量やデータ比較が更に困難になります。 -- **予測分析の複雑さ** +* **予測分析の複雑さ** クリケットにおいて、意思決定の多くは「ボウラーがある特定のタイプの場合、打者はどのくらいの頻度で特定の種類のショットを打つのか」「バッツマンが特定の方法であるボウラーに反応した場合、ボウラーはどのようにラインと長さを変更するのか 」などの質問に基づいています。 この種の予測分析クエリでは、精度の良いデータセットが利用できることと、データを合成して高精度な生成モデルを作成できることが必要とされます。 - This kind of predictive analytics query requires highly granular dataset - availability and the capability to synthesize data and create generative - models that are highly accurate. ## クリケット解析におけるNumPyの役割 -Sports Analytics is a thriving field. スポーツ分析は現在、非常に盛んな分野です。 多くの研究者や企業は、最新の機械学習やAI技術以外にも、NumPyや、Scikit-learn, SciPy, Matplotlib, Jupyterなどの他のPyDataパッケージを[使っています](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)。 NumPyは以下のように、クリケット関連の様々なスポーツ分析に使用されています。 NumPy has been used -for various kinds of cricket related sporting analytics such as: +スポーツ分析は現在、非常に盛んな分野です。 多くの研究者や企業は、最新の機械学習やAI技術以外にも、NumPyや、Scikit-learn, SciPy, Matplotlib, Jupyterなどの他のPyDataパッケージを[使っています](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)。 NumPyは以下のように、クリケット関連の様々なスポーツ分析に使用されています。 -- **統計分析:** NumPyの数値計算機能は、様々なプレイヤーやゲーム戦術のコンテキストでの観測データで、試合中のイベントの統計的有意性を推定し、生成モデルや静的モデルと比較して試合結果を推定するのに役立ちます。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) と [ビッグデータアプローチ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)が戦術的分析に使用されています。 - [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) - and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) - are used for tactical analysis. +* **統計分析:** NumPyの数値計算機能は、様々なプレイヤーやゲーム戦術のコンテキストでの観測データで、試合中のイベントの統計的有意性を推定し、生成モデルや静的モデルと比較して試合結果を推定するのに役立ちます。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) と [ビッグデータアプローチ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)が戦術的分析に使用されています。 -- **データ可視化:** データのグラフ化・[可視化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) は、さまざまなデータセット間の関係について、有益な洞察を与えてくれます。 +* **データ可視化:** データのグラフ化・[可視化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) は、さまざまなデータセット間の関係について、有益な洞察を与えてくれます。 ## まとめ -Sports Analytics is a game changer when it comes to how professional games are -played, especially how strategic decision making happens, which until recently -was primarily done based on “gut feeling" or adherence to past traditions. NumPy -forms a solid foundation for a large set of Python packages which provide higher -level functions related to data analytics, machine learning, and AI algorithms. -These packages are widely deployed to gain real-time insights that help in -decision making for game-changing outcomes, both on field as well as to draw -inferences and drive business around the game of cricket. Finding out the -hidden parameters, patterns, and attributes that lead to the outcome of a -cricket match helps the stakeholders to take notice of game insights that are -otherwise hidden in numbers and statistics. +スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。 {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'クリケット分析にNumPyを使用するメリットを示す図' -title = ' 利用されている主なNumPy機能 ' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From b734b734d1b7fa8f10386bbef4b09a168fedb5db Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:44 +0200 Subject: [PATCH 313/586] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 199 +++++-------------- 1 file changed, 53 insertions(+), 146 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 347251d55a..7dfb7ea1df 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -1,165 +1,72 @@ --- -title: "Case Study: Cricket Analytics, the game changer!" +title: "사례 연구: 판도를 뒤집은 크리켓 통계!" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, the biggest Cricket Festival in India' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**인도 최대의 크리켓 축제인 IPLT20**" alt="인도 프리미어 리그 크리켓 컵 및 경기장" attr="*(사진 출처: IPLT20 (컵 및 로고) & Akash Yadav (경기장))*" attrlink="https://unsplash.com/@aksh1802" >}} +src = '/images/content_images/cs/ipl-stadium.png' title = '인도 최대의 크리켓 축제인 IPLT20' alt = '인도 프리미어 리그 크리켓 컵 및 경기장' attribution = '(사진 크레딧: IPLT20 (컵 및 로고) & Akash Yadav (경기장))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" - -> }} -> You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> > {{< /blockquote >}} -## About Cricket - -It would be an understatement to state that Indians love cricket. The game is -played in just about every nook and cranny of India, rural or urban, popular -with the young and the old alike, connecting billions in India unlike any other sport. -Cricket enjoys lots of media attention. There is a significant amount of -[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and -fame at stake. Over the last several years, technology has literally been a game -changer. Audiences are spoilt for choice with streaming media, tournaments, -affordable access to mobile based live cricket watching, and more. - -The Indian Premier League (IPL) is a professional Twenty20 cricket -league, founded in 2008. It is one of the most attended cricketing events in -the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) -in 2019. - -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken -by a bowler, the matches won by a cricket team, the number of times a batsman -responds in a certain way to a kind of bowling attack, etc. The capability to -dig into cricketing numbers for both improving performance and studying -the business opportunities, overall market, and economics of cricket via powerful -analytics tools, powered by numerical computing software such as NumPy, is a big -deal. Cricket analytics provides interesting insights into the game and -predictive intelligence regarding game outcomes. - -Today, there are rich and almost infinite troves of cricket game records and -statistics available, e.g., ESPN -cricinfo and -[cricsheet](https://cricsheet.org). These and several such cricket databases -have been used for cricket -analysis -using the latest machine learning and predictive modelling algorithms. -Media and entertainment platforms along with professional sports bodies -associated with the game use technology and analytics for determining key -metrics for improving match winning chances: - -- batting performance moving average, -- score forecasting, -- gaining insights into fitness and performance of a player against different opposition, -- player contribution to wins and losses for making strategic decisions on team composition +## 크리켓이란 + +인도인들이 크리켓과 사랑에 빠졌다고 해도 과언이 아닙니다. 크리켓은 인도의 거의 모든 지역 구석구석에서 시골이든 도시든 상관없이 사랑받고 있습니다. 다른 스포츠와 달리 인도의 수십억 명을 연결하는 매개체 역할을 하는 데다 남녀노소 모두에게 인기가 있습니다. 크리켓은 많은 미디어의 관심을 받고 있기도 합니다. 엄청난 [돈](https://www.statista.com/topics/4543/indian-premier-league-ipl/)과 명성이 달려 있기도 하죠. 최근 몇 년 동안, 기술이 이 분야의 판도를 뒤집어 버렸습니다. 청중들은 스트리밍 미디어, 토너먼트, 모바일 기기를 통해 실시간 크리켓 경기를 저렴하게 볼 수 있습니다. + +인도 프리미어 리그(IPL)는 2008년 설립되어 20개 팀으로 구성된 프로 크리켓 리그입니다. 이는 세계에서 가장 참가자가 많은 크리켓 이벤트 중 하나로, 2019년에 [67억 달러](https://en.wikipedia.org/wiki/Indian_Premier_League)에 달하는 가치로 추산됩니다. + +크리켓은 숫자의 게임이다 - 타자가 득점하고, 위켓이 가져간다. 크리켓 팀이 이긴 경기, 타자가 이긴 횟수 일종의 볼링 공격 등에 특정한 방식으로 반응합니다. NumPy와 같은 수치 컴퓨팅 소프트웨어로 구동되는 강력한 분석 도구를 통해 크리켓의 성능 향상과 사업 기회, 전반적인 시장, 경제학을 연구하기 위한 크리켓 숫자를 파헤칠 수 있는 능력은 큰 일이다. 크리켓 분석은 게임에 대한 흥미로운 통찰력과 게임 결과에 대한 예측 지능을 제공한다. + +오늘날, 크리켓 경기 기록의 풍부하고 거의 무한한 트로브가 있습니다. 이용 가능한 통계, 예를 들어, [ ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) 및 [cricsheet](https://cricsheet.org). 이들 및 몇몇 그러한 크리켓 데이터베이스는 최신 머신 러닝 및 예측 모델링 알고리즘을 이용한 [크리켓 분석](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances)에 사용되어 왔습니다. 게임과 관련된 전문 스포츠 기관과 함께 미디어 및 엔터테인먼트 플랫폼은 경기 승리 기회를 향상시키기 위한 주요 측정 기준을 결정하기 위한 기술 및 분석을 사용합니다. + +* 타격 성적 이동 평균, +* 점수 예측, +* 다른 상대에 맞서 선수의 체력과 경기력에 대한 통찰력을 얻기, +* 팀 구성에 대한 전략적 결정을 내리는 플레이어의 승패 기여도 {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Cricket Pitch, the focal point in the field' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +src = '/images/content_images/cs/cricket-pitch.png' title = '경기장의 중심이 되는 크리켓 피치' alt = '볼러와 배트맨으로 이루어진 크리켓 피치' align = 'center' attribution = '(사진 크레딧: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} -### Key Data Analytics Objectives +### 데이터 분석의 주요 목표 -- Sports data analytics are used not only in cricket but many other - sports for - improving the overall team performance and maximizing winning chances. -- Real-time data analytics can help in gaining insights even during the game - for changing tactics by the team and by associated businesses for economic - benefits and growth. -- Besides historical analysis, predictive models are - harnessed to determine the possible match outcomes that require significant - number crunching and data science know-how, visualization tools and capability - to include newer observations in the analysis. +* 스포츠 데이터는 크리켓에서뿐만 아니라 [다른 스포츠](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)에서도 팀의 전체 역량과 승리 확률을 높이는 데 쓰입니다. +* 실시간 데이터 분석은 경기 중에도 팀과 관련 사업의 변화하는 전략에 대한 통찰력을 확보하여 경제적 이익과 성장을 도모하는 데 도움이 될 수 있습니다. +* 과거 분석 외에도 예측 모델을 활용하여 상당한 수의 크런칭과 데이터 과학 노하우, 시각화 도구 및 분석에 더 새로운 관찰을 포함시킬 수 있는 기능이 필요한 가능한 일치 결과를 결정합니다. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'Cricket Pose Estimator' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = '자세 예측' title = '크리켓 자세 예측' attribution = '(사진 크레딧: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} -### The Challenges - -- **Data Cleaning and preprocessing** - - IPL has expanded cricket beyond the classic test match format to a much - larger scale. The number of matches played every season across various - formats has increased and so has the data, the algorithms, newer sports data - analysis technologies and simulation models. Cricket data analysis requires - field mapping, player tracking, ball tracking, player shot analysis, and - several other aspects involved in how the ball is delivered, its angle, spin, - velocity, and trajectory. All these factors together have increased the - complexity of data cleaning and preprocessing. - -- **Dynamic Modeling** - - In cricket, just like any other sport, - there can be a large number of variables related to tracking various numbers - of players on the field, their attributes, the ball, and several possibilities - of potential actions. The complexity of data analytics and modeling is - directly proportional to the kind of predictive questions that are put forth - during analysis and are highly dependent on data representation and the - model. Things get even more challenging in terms of computation, data - comparisons when dynamic cricket play predictions are sought such as what - would have happened if the batsman had hit the ball at a different angle or - velocity. - -- **Predictive Analytics Complexity** - - Much of the decision making in cricket is based on questions such as "how - often does a batsman play a certain kind of shot if the ball delivery is of a - particular type", or "how does a bowler change his line and length if the - batsman responds to his delivery in a certain way". - This kind of predictive analytics query requires highly granular dataset - availability and the capability to synthesize data and create generative - models that are highly accurate. - -## NumPy’s Role in Cricket Analytics - -Sports Analytics is a thriving field. Many researchers and companies -[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) -and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, -besides using the latest machine learning and AI techniques. NumPy has been used -for various kinds of cricket related sporting analytics such as: - -- **Statistical Analysis:** NumPy's numerical capabilities help estimate the - statistical significance of observational data or match events in the context - of various player and game tactics, estimating the game outcome by comparison - with a generative or static model. - [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) - and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) - are used for tactical analysis. - -- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. - -## Summary - -Sports Analytics is a game changer when it comes to how professional games are -played, especially how strategic decision making happens, which until recently -was primarily done based on “gut feeling" or adherence to past traditions. NumPy -forms a solid foundation for a large set of Python packages which provide higher -level functions related to data analytics, machine learning, and AI algorithms. -These packages are widely deployed to gain real-time insights that help in -decision making for game-changing outcomes, both on field as well as to draw -inferences and drive business around the game of cricket. Finding out the -hidden parameters, patterns, and attributes that lead to the outcome of a -cricket match helps the stakeholders to take notice of game insights that are -otherwise hidden in numbers and statistics. +### 도전 + +* **데이터 정리 및 전처리** + + IPL은 크리켓을 고전적인 테스트 매치 형식에서 훨씬 더 큰 규모로 확대시켰습니다. 매 시즌 다양한 형식으로 열리는 경기의 수가 증가하고 있으며, 데이터, 알고리즘, 최신 스포츠 데이터 분석 기술, 시뮬레이션 모델 또한 증가하고 있습니다. 크리켓 데이터 분석에는 필드 매핑, 플레이어 추적, 공 추적, 플레이어의 타격 분석 및 공이 어떻게 움직이는지에 대한 각도, 스핀, 속도, 궤도 등 다른 많은 종류의 데이터를 필요로 합니다. 이 수많은 인자들은 데이터 정리 및 전처리 과정의 복잡성을 증가시켰습니다. + +* **동적 모델링** + + 크리켓에서는 다른 스포츠와 마찬가지로 다양한 선수의 수, 선수의 속성, 공이나 잠재적 행동의 가능성 등 여러 가능성을 추적할 때 많은 변수가 작용합니다. 데이터 분석 및 모델링의 복잡성은 분석 중 제시되는 예측 질문의 종류에 비례하며, 데이터 표현 및 모델에 크게 의존합니다. 타자가 다른 각도나 속도로 공을 쳤을 때 일어날 일과 같은 동적인 크리켓 경기를 예측할 때, 계산이나 데이터 비교 측면에서 상황이 훨씬 더 어려워집니다. + +* **예측 분석의 복잡성** + + 크리켓에서 의사결정의 상당 부분은 '볼 전달이 특정 유형일 경우 타자가 얼마나 자주 특정 종류의 샷을 하느냐', '배트맨이 특정 방식으로 전달에 반응하면 볼러가 라인과 길이를 어떻게 바꾸느냐' 등의 질문에 따른 것입니다. 이러한 예측 분석 쿼리는 매우 세분화된 데이터셋 가용성과 데이터를 합성하고 정확도가 높은 생성 모델을 만들 수 있는 기능이 필요합니다. + +## 크리켓 분석에서 NumPy의 역할 + +스포츠 분석은 현재 매우 활발한 분야입니다. 많은 연구자들과 기업체에서는 최신 머신러닝 및 AI 기법을 쓰는 대신 [NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)나 Scikit-learn, SciPy, Matplotlib, Jupyter같은 PyData 패키지를 이용합니다. NumPy는 크리켓과 관련된 여러 스포츠 통계에 다음과 같이 쓰였습니다. + +* **통계적 분석:** NumPy의 수치적 기능은 다양한 플레이어 및 게임 전술에서 관찰 데이터 또는 경기의 통계적 중요성을 추정하는 데 도움을 주거나, 생성적 또는 정적 모델과 비교하여 게임 결과를 추정합니다. 전술 분석에는 [인과 분석](https://amplitude.com/blog/2017/01/19/causation-correlation) 및 [빅데이터 접근법](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)이 쓰입니다. + +* **데이터 시각화:** 그래프 그리기 및 [시각화](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b)는 다양한 데이터셋 사이의 관계를 볼 수 있는 유용한 관점을 제공해 줍니다. + +## 요약 + +스포츠 분석은 프로 게임의 판도를 바꿀 것입니다. 특히 최근까지는 주로 "직감"이나 과거부터 내려오던 것을 답습하는 식으로 이뤄진 전략적 의사 결정에 대해서 말입니다. NumPy는 데이터 분석, 기계 학습 및 AI 알고리즘과 관련하여 더욱 높은 수준의 기능을 제공하는 Python 패키지들의 견고한 기반을 제공합니다. 이들 패키지는 크리켓 경기뿐 아니라 크리켓 관련 추론이나 사업을 추진하면서, 판도를 바꿀만한 결정을 이끌어 내는 영감을 실시간으로 제공하는 데 널리 이용되고 있습니다. 크리켓 경기의 결과로 이어지는 숨겨진 매개변수, 패턴이나 속성을 찾는 것은 관계자가 숫자와 통계에 숨겨진 게임을 분석하는 방법을 파악하는 데 도움이 됩니다. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagram showing benefits of using NumPy for cricket analytics' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'NumPy를 크리켓 분석에 적용했을 때의 이익을 보여주는 다이어그램' title = '활용된 주요 NumPy 기능' {{< /figure >}} From cb8da9c5d42f1ecde86fde898beb4f394b48ce64 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:45 +0200 Subject: [PATCH 314/586] New translations cricket-analytics.md (Russian) --- content/ru/case-studies/cricket-analytics.md | 167 ++++--------------- 1 file changed, 37 insertions(+), 130 deletions(-) diff --git a/content/ru/case-studies/cricket-analytics.md b/content/ru/case-studies/cricket-analytics.md index 347251d55a..d0be271f50 100644 --- a/content/ru/case-studies/cricket-analytics.md +++ b/content/ru/case-studies/cricket-analytics.md @@ -4,162 +4,69 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, the biggest Cricket Festival in India' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" - -> }} -> You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> > {{< /blockquote >}} ## About Cricket -It would be an understatement to state that Indians love cricket. The game is -played in just about every nook and cranny of India, rural or urban, popular -with the young and the old alike, connecting billions in India unlike any other sport. -Cricket enjoys lots of media attention. There is a significant amount of -[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and -fame at stake. Over the last several years, technology has literally been a game -changer. Audiences are spoilt for choice with streaming media, tournaments, -affordable access to mobile based live cricket watching, and more. - -The Indian Premier League (IPL) is a professional Twenty20 cricket -league, founded in 2008. It is one of the most attended cricketing events in -the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) -in 2019. - -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken -by a bowler, the matches won by a cricket team, the number of times a batsman -responds in a certain way to a kind of bowling attack, etc. The capability to -dig into cricketing numbers for both improving performance and studying -the business opportunities, overall market, and economics of cricket via powerful -analytics tools, powered by numerical computing software such as NumPy, is a big -deal. Cricket analytics provides interesting insights into the game and -predictive intelligence regarding game outcomes. - -Today, there are rich and almost infinite troves of cricket game records and -statistics available, e.g., ESPN -cricinfo and -[cricsheet](https://cricsheet.org). These and several such cricket databases -have been used for cricket -analysis -using the latest machine learning and predictive modelling algorithms. -Media and entertainment platforms along with professional sports bodies -associated with the game use technology and analytics for determining key -metrics for improving match winning chances: - -- batting performance moving average, -- score forecasting, -- gaining insights into fitness and performance of a player against different opposition, -- player contribution to wins and losses for making strategic decisions on team composition +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: + +* batting performance moving average, +* score forecasting, +* gaining insights into fitness and performance of a player against different opposition, +* player contribution to wins and losses for making strategic decisions on team composition {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Cricket Pitch, the focal point in the field' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< /figure >}} ### Key Data Analytics Objectives -- Sports data analytics are used not only in cricket but many other - sports for - improving the overall team performance and maximizing winning chances. -- Real-time data analytics can help in gaining insights even during the game - for changing tactics by the team and by associated businesses for economic - benefits and growth. -- Besides historical analysis, predictive models are - harnessed to determine the possible match outcomes that require significant - number crunching and data science know-how, visualization tools and capability - to include newer observations in the analysis. +* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. +* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. +* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'Cricket Pose Estimator' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### The Challenges -- **Data Cleaning and preprocessing** - - IPL has expanded cricket beyond the classic test match format to a much - larger scale. The number of matches played every season across various - formats has increased and so has the data, the algorithms, newer sports data - analysis technologies and simulation models. Cricket data analysis requires - field mapping, player tracking, ball tracking, player shot analysis, and - several other aspects involved in how the ball is delivered, its angle, spin, - velocity, and trajectory. All these factors together have increased the - complexity of data cleaning and preprocessing. - -- **Dynamic Modeling** - - In cricket, just like any other sport, - there can be a large number of variables related to tracking various numbers - of players on the field, their attributes, the ball, and several possibilities - of potential actions. The complexity of data analytics and modeling is - directly proportional to the kind of predictive questions that are put forth - during analysis and are highly dependent on data representation and the - model. Things get even more challenging in terms of computation, data - comparisons when dynamic cricket play predictions are sought such as what - would have happened if the batsman had hit the ball at a different angle or - velocity. - -- **Predictive Analytics Complexity** - - Much of the decision making in cricket is based on questions such as "how - often does a batsman play a certain kind of shot if the ball delivery is of a - particular type", or "how does a bowler change his line and length if the - batsman responds to his delivery in a certain way". - This kind of predictive analytics query requires highly granular dataset - availability and the capability to synthesize data and create generative - models that are highly accurate. +* **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + +* **Dynamic Modeling** + + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + +* **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics -Sports Analytics is a thriving field. Many researchers and companies -[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) -and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, -besides using the latest machine learning and AI techniques. NumPy has been used -for various kinds of cricket related sporting analytics such as: +Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: -- **Statistical Analysis:** NumPy's numerical capabilities help estimate the - statistical significance of observational data or match events in the context - of various player and game tactics, estimating the game outcome by comparison - with a generative or static model. - [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) - and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) - are used for tactical analysis. +* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. -- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. +* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. ## Summary -Sports Analytics is a game changer when it comes to how professional games are -played, especially how strategic decision making happens, which until recently -was primarily done based on “gut feeling" or adherence to past traditions. NumPy -forms a solid foundation for a large set of Python packages which provide higher -level functions related to data analytics, machine learning, and AI algorithms. -These packages are widely deployed to gain real-time insights that help in -decision making for game-changing outcomes, both on field as well as to draw -inferences and drive business around the game of cricket. Finding out the -hidden parameters, patterns, and attributes that lead to the outcome of a -cricket match helps the stakeholders to take notice of game insights that are -otherwise hidden in numbers and statistics. +Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagram showing benefits of using NumPy for cricket analytics' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From b560649997863765f1f052288e0f114ad5150763 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:46 +0200 Subject: [PATCH 315/586] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 167 ++++--------------- 1 file changed, 37 insertions(+), 130 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index 347251d55a..d0be271f50 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -4,162 +4,69 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, the biggest Cricket Festival in India' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni, _International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL_" - -> }} -> You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> > {{< /blockquote >}} ## About Cricket -It would be an understatement to state that Indians love cricket. The game is -played in just about every nook and cranny of India, rural or urban, popular -with the young and the old alike, connecting billions in India unlike any other sport. -Cricket enjoys lots of media attention. There is a significant amount of -[money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and -fame at stake. Over the last several years, technology has literally been a game -changer. Audiences are spoilt for choice with streaming media, tournaments, -affordable access to mobile based live cricket watching, and more. - -The Indian Premier League (IPL) is a professional Twenty20 cricket -league, founded in 2008. It is one of the most attended cricketing events in -the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) -in 2019. - -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken -by a bowler, the matches won by a cricket team, the number of times a batsman -responds in a certain way to a kind of bowling attack, etc. The capability to -dig into cricketing numbers for both improving performance and studying -the business opportunities, overall market, and economics of cricket via powerful -analytics tools, powered by numerical computing software such as NumPy, is a big -deal. Cricket analytics provides interesting insights into the game and -predictive intelligence regarding game outcomes. - -Today, there are rich and almost infinite troves of cricket game records and -statistics available, e.g., ESPN -cricinfo and -[cricsheet](https://cricsheet.org). These and several such cricket databases -have been used for cricket -analysis -using the latest machine learning and predictive modelling algorithms. -Media and entertainment platforms along with professional sports bodies -associated with the game use technology and analytics for determining key -metrics for improving match winning chances: - -- batting performance moving average, -- score forecasting, -- gaining insights into fitness and performance of a player against different opposition, -- player contribution to wins and losses for making strategic decisions on team composition +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: + +* batting performance moving average, +* score forecasting, +* gaining insights into fitness and performance of a player against different opposition, +* player contribution to wins and losses for making strategic decisions on team composition {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Cricket Pitch, the focal point in the field' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< /figure >}} ### Key Data Analytics Objectives -- Sports data analytics are used not only in cricket but many other - sports for - improving the overall team performance and maximizing winning chances. -- Real-time data analytics can help in gaining insights even during the game - for changing tactics by the team and by associated businesses for economic - benefits and growth. -- Besides historical analysis, predictive models are - harnessed to determine the possible match outcomes that require significant - number crunching and data science know-how, visualization tools and capability - to include newer observations in the analysis. +* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. +* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. +* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'Cricket Pose Estimator' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### The Challenges -- **Data Cleaning and preprocessing** - - IPL has expanded cricket beyond the classic test match format to a much - larger scale. The number of matches played every season across various - formats has increased and so has the data, the algorithms, newer sports data - analysis technologies and simulation models. Cricket data analysis requires - field mapping, player tracking, ball tracking, player shot analysis, and - several other aspects involved in how the ball is delivered, its angle, spin, - velocity, and trajectory. All these factors together have increased the - complexity of data cleaning and preprocessing. - -- **Dynamic Modeling** - - In cricket, just like any other sport, - there can be a large number of variables related to tracking various numbers - of players on the field, their attributes, the ball, and several possibilities - of potential actions. The complexity of data analytics and modeling is - directly proportional to the kind of predictive questions that are put forth - during analysis and are highly dependent on data representation and the - model. Things get even more challenging in terms of computation, data - comparisons when dynamic cricket play predictions are sought such as what - would have happened if the batsman had hit the ball at a different angle or - velocity. - -- **Predictive Analytics Complexity** - - Much of the decision making in cricket is based on questions such as "how - often does a batsman play a certain kind of shot if the ball delivery is of a - particular type", or "how does a bowler change his line and length if the - batsman responds to his delivery in a certain way". - This kind of predictive analytics query requires highly granular dataset - availability and the capability to synthesize data and create generative - models that are highly accurate. +* **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + +* **Dynamic Modeling** + + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + +* **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics -Sports Analytics is a thriving field. Many researchers and companies -[use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) -and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, -besides using the latest machine learning and AI techniques. NumPy has been used -for various kinds of cricket related sporting analytics such as: +Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: -- **Statistical Analysis:** NumPy's numerical capabilities help estimate the - statistical significance of observational data or match events in the context - of various player and game tactics, estimating the game outcome by comparison - with a generative or static model. - [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) - and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) - are used for tactical analysis. +* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. -- **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. +* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. ## Summary -Sports Analytics is a game changer when it comes to how professional games are -played, especially how strategic decision making happens, which until recently -was primarily done based on “gut feeling" or adherence to past traditions. NumPy -forms a solid foundation for a large set of Python packages which provide higher -level functions related to data analytics, machine learning, and AI algorithms. -These packages are widely deployed to gain real-time insights that help in -decision making for game-changing outcomes, both on field as well as to draw -inferences and drive business around the game of cricket. Finding out the -hidden parameters, patterns, and attributes that lead to the outcome of a -cricket match helps the stakeholders to take notice of game insights that are -otherwise hidden in numbers and statistics. +Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagram showing benefits of using NumPy for cricket analytics' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From da755499dabe0746adb9a1f9cab4aa68c9618c40 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:47 +0200 Subject: [PATCH 316/586] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 77 +++++++------------- 1 file changed, 27 insertions(+), 50 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 54e9bc1fcd..e7140e6c26 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -3,93 +3,70 @@ title: "Estudo de Caso: Análise de Críquete, a revolução!" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, o maior festival de Críquete da Índia' -alt = 'Copa e estádio da Indian Premier League Cricket' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, o maior Festival de Críquete da Índia' alt = 'Taça e estádio de críquete da Premier League indiana' attribution = '(Créditos de imagem: IPLT20 (taça e logo) & Akash Yadav (estádio))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} -{{< blockquote -cite="https://www.scoopwhoop.com/sports/ms-dhoni/" -by="M S Dhoni, _Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL_" - -> }} -> Você não joga para a torcida, joga para o país. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> > {{< /blockquote >}} ## Sobre Críquete -Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. -O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. +Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. -A Primeira Liga Indiana (_Indian Premier League_ - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. +A Primeira Liga Indiana (*Indian Premier League* - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. -perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. +perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. A capacidade de investigar estatísticas do críquete para melhorar a performance dos times e estudar oportunidades de negócios, o mercado em si, e a economia do críquete através de ferramentas de análise poderosas alimentadas por softwares de computação numérica como o NumPy é um grande negócio. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. -Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) e [cricsheet](https://cricsheet.org). Estes e muitos outros bancos de dados de críquete foram usados para [análise de críquete](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) usando os mais modernos algoritmos de aprendizagem de máquina e modelagem preditiva. -Plataformas de mídia e entretenimento, juntamente com entidades de esporte profissionais associadas ao jogo usam tecnologia e análise para determinar métricas chave para melhorar as chances de vitória: +Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) e [cricsheet](https://cricsheet.org). Estes e muitos outros bancos de dados de críquete foram usados para [análise de críquete](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) usando os mais modernos algoritmos de aprendizagem de máquina e modelagem preditiva. Plataformas de mídia e entretenimento, juntamente com entidades de esporte profissionais associadas ao jogo usam tecnologia e análise para determinar métricas chave para melhorar as chances de vitória: -- média móvel do desempenho em rebatidas, -- previsão de pontuação, -- ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, -- contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time +* média móvel do desempenho em rebatidas, +* previsão de pontuação, +* ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, +* contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Pitch de críquete, o ponto focal do campo' -alt = 'Um pitch de críquete com um boleador e batsmen' -align = 'center' -attribution = '(Créditos de imagem: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +src = '/images/content_images/cs/cricket-pitch.png' title = 'Campo de críquete, o ponto focal no campo' alt = 'Campo de críquete com lançador e batedor' align = 'center' attribution = '(Crédito de imagem: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="Diagrama mostrando os benefícios de usar a NumPy para análise de críquete" caption="**Recursos principais da NumPy utilizados**" >}} ### Objetivos Principais da Análise de Dados -- A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. -- A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. -- Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. +* A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. +* A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. +* Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'estimador de postura' -title = 'Estimador de Postura de Críquete' -attribution = '(Créditos de imagem: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'estimador de poses' title = 'Estimador de poses no críquete' attribution = '(Créditos da imagem: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### Desafios -- **Limpeza e pré-processamento de dados** +* **Limpeza e pré-processamento de dados** A IPL expandiu o formato de jogo clássico de cricket para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, as tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados. -- **Modelagem Dinâmica** +* **Modelagem Dinâmica** No críquete, como em qualquer outro esporte, pode haver um grande número de variáveis relacionadas ao rastreamento de vários jogadores no campo, seus atributos, a bola e várias possibilidades de ações em potencial. A complexidade da análise e modelagem de dados é diretamente proporcional ao tipo de questões preditivas que são consideradas durante a análise e são altamente dependentes da representação de dados e do modelo. As coisas são ainda mais desafiadoras em termos de computação e comparações de dados quando previsões dinâmicas de jogo de críquete são desejadas, como o que teria acontecido se o batsman tivesse atingido a bola com um ângulo ou velocidade diferentes. -- **Complexidade da análise preditiva** +* **Complexidade da análise preditiva** - Muito da tomada de decisões em críquete se baseia em questões como "com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo", ou "como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira". - Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos. + Muito da tomada de decisões em críquete se baseia em questões como "com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo", ou "como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira". Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos. ## O papel do NumPy na análise de críquete A análise de dados esportivos é um campo próspero. Muitos pesquisadores e empresas [usam NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) e outros pacotes PyData como Scikit-learn, SciPy, Matplotlib, e Jupyter, além de usar as últimas técnicas de aprendizagem de máquina e IA. O NumPy foi usado para vários tipos de análise esportiva relacionada a críquete, como: -- **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. - [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em _big data_](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. +* **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. -- **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. +* **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. ## Resumo -A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. -Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas. +A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagrama mostrando os benefícios de usar a NumPy para análise de críquete' -title = 'Recursos principais da NumPy utilizados' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagrama mostrando os benefícios do uso do NumPy para análise de críquete' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} From 25dcff67de93c3273d9eef39f834fc96adf54a1e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:48 +0200 Subject: [PATCH 317/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 153 +++++----------------- 1 file changed, 35 insertions(+), 118 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 4ef842d19d..257905de91 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -4,19 +4,12 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analyzing mice hand-movement using DeepLapCut' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> > {{< /blockquote >}} ## About DeepLabCut @@ -26,43 +19,26 @@ by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de L Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Colored dots track the positions of a racehorse’s body part' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 - DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### Key Goals and Results -- **Automation of animal pose analysis for scientific studies:** +* **Automation of animal pose analysis for scientific studies:** - The primary objective of DeepLabCut technology is to measure and track posture - of animals in a diverse settings. This data can be used, for example, in - neuroscience studies to understand how the brain controls movement, or to - elucidate how animals socially interact. Researchers have observed a - [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) - with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second - (FPS). + The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). -- **Creation of an easy-to-use Python toolkit for pose estimation:** +* **Creation of an easy-to-use Python toolkit for pose estimation:** - DeepLabCut wanted to share their animal pose-estimation technology in the form - of an easy to use tool that can be adopted by researchers easily. So they have - created a complete, easy-to-use Python toolbox with project management features - as well. These enable not only automation of pose-estimation but also - managing the project end-to-end by helping the DeepLabCut Toolkit user right - from the dataset collection stage to creating shareable and reusable analysis - pipelines. + DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. Their [toolkit][DLCToolkit] is now available as open source. @@ -74,112 +50,53 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - draw inferences using integrated visualization tools {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pose estimation steps with DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' {{< /figure >}} -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut - ### The Challenges -- **Speed** +* **Speed** - Fast processing of animal behavior videos in order to measure their behavior - and at the same time make scientific experiments more efficient, accurate. - Extracting detailed animal poses for laboratory experiments, without - markers, in dynamically changing backgrounds, can be challenging, both - technically as well as in terms of resource needs and training data required. - Coming up with a tool that is easy to use without the need for skills such - as computer vision expertise that enables scientists to do research in more - real-world contexts, is a non-trivial problem to solve. + Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. -- **Combinatorics** +* **Combinatorics** - Combinatorics involves assembly and integration of movement of multiple - limbs into individual animal behavior. Assembling keypoints and their - connections into individual animal movements and linking them across time - is a complex process that requires heavy-duty numerical analysis, especially - in case of multi-animal movement tracking in experiment videos. + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. -- **Data Processing** +* **Data Processing** - Last but not the least, array manipulation - processing large stacks of - arrays corresponding to various images, target tensors and keypoints is - fairly challenging. + Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. {{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Pose estimation variety and complexity' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## NumPy's Role in meeting Pose Estimation Challenges -NumPy addresses DeepLabCut technology's core need of numerical computations at -high speed for behavioural analytics. Besides NumPy, DeepLabCut employs -various Python software that utilize NumPy at their core, such as -[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), -[matplotlib](https://matplotlib.org), -[Tensorpack](https://github.com/tensorpack/tensorpack), -[imgaug](https://github.com/aleju/imgaug), -[scikit-learn](https://scikit-learn.org/stable/), -[scikit-image](https://scikit-image.org) and -[Tensorflow](https://www.tensorflow.org). - -The following features of NumPy played a key role in addressing the image -processing, combinatorics requirements and need for fast computation in -DeepLabCut pose estimation algorithms: - -- Vectorization -- Masked Array Operations -- Linear Algebra -- Random Sampling -- Reshaping of large arrays - -DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered -by the toolkit. In particular, NumPy is used for sampling distinct frames for -human annotation labeling, and for writing, editing and processing annotation -data. Within TensorFlow the neural network is trained by DeepLabCut technology -over thousands of iterations to predict the ground truth annotations from -frames. For this purpose, target densities (scoremaps) are created to cast pose -estimation as a image-to-image translation problem. To make the neural networks -robust, data augmentation is employed, which requires the calculation of target -scoremaps subject to various geometric and image processing steps. To make -training fast, NumPy’s vectorization capabilities are leveraged. For inference, -the most likely predictions from target scoremaps need to extracted and one -needs to efficiently “link predictions to assemble individual animals”. +NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: + +* Vectorization +* Masked Array Operations +* Linear Algebra +* Random Sampling +* Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCut Workflow' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## Summary -Observing and efficiently describing behavior is a core tenant of modern -ethology, neuroscience, medicine, and technology. -[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) -allows researchers to estimate the pose of the subject, efficiently enabling -them to quantify the behavior. With only a small set of training images, -the DeepLabCut Python toolbox allows training a neural network to within human -level labeling accuracy, thus expanding its application to not only behavior -analysis in the laboratory, but to potentially also in sports, gait analysis, -medicine and rehabilitation studies. Complex combinatorics, data processing -challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy's array manipulation capabilities. +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 38647e9ebf63671097889ed9a38efd58ac434a97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:49 +0200 Subject: [PATCH 318/586] New translations deeplabcut-dnn.md (Arabic) --- content/ar/case-studies/deeplabcut-dnn.md | 153 +++++----------------- 1 file changed, 35 insertions(+), 118 deletions(-) diff --git a/content/ar/case-studies/deeplabcut-dnn.md b/content/ar/case-studies/deeplabcut-dnn.md index 4ef842d19d..257905de91 100644 --- a/content/ar/case-studies/deeplabcut-dnn.md +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -4,19 +4,12 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analyzing mice hand-movement using DeepLapCut' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> > {{< /blockquote >}} ## About DeepLabCut @@ -26,43 +19,26 @@ by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de L Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Colored dots track the positions of a racehorse’s body part' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 - DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### Key Goals and Results -- **Automation of animal pose analysis for scientific studies:** +* **Automation of animal pose analysis for scientific studies:** - The primary objective of DeepLabCut technology is to measure and track posture - of animals in a diverse settings. This data can be used, for example, in - neuroscience studies to understand how the brain controls movement, or to - elucidate how animals socially interact. Researchers have observed a - [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) - with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second - (FPS). + The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). -- **Creation of an easy-to-use Python toolkit for pose estimation:** +* **Creation of an easy-to-use Python toolkit for pose estimation:** - DeepLabCut wanted to share their animal pose-estimation technology in the form - of an easy to use tool that can be adopted by researchers easily. So they have - created a complete, easy-to-use Python toolbox with project management features - as well. These enable not only automation of pose-estimation but also - managing the project end-to-end by helping the DeepLabCut Toolkit user right - from the dataset collection stage to creating shareable and reusable analysis - pipelines. + DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. Their [toolkit][DLCToolkit] is now available as open source. @@ -74,112 +50,53 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - draw inferences using integrated visualization tools {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pose estimation steps with DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' {{< /figure >}} -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut - ### The Challenges -- **Speed** +* **Speed** - Fast processing of animal behavior videos in order to measure their behavior - and at the same time make scientific experiments more efficient, accurate. - Extracting detailed animal poses for laboratory experiments, without - markers, in dynamically changing backgrounds, can be challenging, both - technically as well as in terms of resource needs and training data required. - Coming up with a tool that is easy to use without the need for skills such - as computer vision expertise that enables scientists to do research in more - real-world contexts, is a non-trivial problem to solve. + Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. -- **Combinatorics** +* **Combinatorics** - Combinatorics involves assembly and integration of movement of multiple - limbs into individual animal behavior. Assembling keypoints and their - connections into individual animal movements and linking them across time - is a complex process that requires heavy-duty numerical analysis, especially - in case of multi-animal movement tracking in experiment videos. + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. -- **Data Processing** +* **Data Processing** - Last but not the least, array manipulation - processing large stacks of - arrays corresponding to various images, target tensors and keypoints is - fairly challenging. + Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. {{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Pose estimation variety and complexity' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## NumPy's Role in meeting Pose Estimation Challenges -NumPy addresses DeepLabCut technology's core need of numerical computations at -high speed for behavioural analytics. Besides NumPy, DeepLabCut employs -various Python software that utilize NumPy at their core, such as -[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), -[matplotlib](https://matplotlib.org), -[Tensorpack](https://github.com/tensorpack/tensorpack), -[imgaug](https://github.com/aleju/imgaug), -[scikit-learn](https://scikit-learn.org/stable/), -[scikit-image](https://scikit-image.org) and -[Tensorflow](https://www.tensorflow.org). - -The following features of NumPy played a key role in addressing the image -processing, combinatorics requirements and need for fast computation in -DeepLabCut pose estimation algorithms: - -- Vectorization -- Masked Array Operations -- Linear Algebra -- Random Sampling -- Reshaping of large arrays - -DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered -by the toolkit. In particular, NumPy is used for sampling distinct frames for -human annotation labeling, and for writing, editing and processing annotation -data. Within TensorFlow the neural network is trained by DeepLabCut technology -over thousands of iterations to predict the ground truth annotations from -frames. For this purpose, target densities (scoremaps) are created to cast pose -estimation as a image-to-image translation problem. To make the neural networks -robust, data augmentation is employed, which requires the calculation of target -scoremaps subject to various geometric and image processing steps. To make -training fast, NumPy’s vectorization capabilities are leveraged. For inference, -the most likely predictions from target scoremaps need to extracted and one -needs to efficiently “link predictions to assemble individual animals”. +NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: + +* Vectorization +* Masked Array Operations +* Linear Algebra +* Random Sampling +* Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCut Workflow' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## Summary -Observing and efficiently describing behavior is a core tenant of modern -ethology, neuroscience, medicine, and technology. -[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) -allows researchers to estimate the pose of the subject, efficiently enabling -them to quantify the behavior. With only a small set of training images, -the DeepLabCut Python toolbox allows training a neural network to within human -level labeling accuracy, thus expanding its application to not only behavior -analysis in the laboratory, but to potentially also in sports, gait analysis, -medicine and rehabilitation studies. Complex combinatorics, data processing -challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy's array manipulation capabilities. +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 5665001389459e0d2a5c81c4a5c7b63b3b2afde9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:50 +0200 Subject: [PATCH 319/586] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 144 ++++++---------------- 1 file changed, 40 insertions(+), 104 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 3f2fa6259e..d5db28843a 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -3,67 +3,42 @@ title: "ケーススタディ: DeepLabCut 三次元姿勢推定" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'DeepLapCutを用いたマウスの手の動きの解析' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCutを用いたマウスの手の動きの解析 **" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis、 _准教授、École polytechnology fe’rale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Open Source Software is accelerating Biomedicine. }} -> オープンソースソフトウェアは生体臨床医学を加速させています。 DeepLabCut を使用すると、深層学習を使用して動物の行動を自動的にビデオ解析することができます。 -> {{< /blockquote >}} +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, * ローザンヌ連邦工科大学([EPFL](https://www.epfl.ch/en/))の助教授 *" +> }} オープンソースソフトウェアは生体臨床医学を加速させています。 DeepLabCut を使用すると、深層学習を使用して動物の行動を自動的にビデオ解析することができます。 +> > {{< /blockquote >}} ## DeepLabCut について -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut)は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut)は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 -Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 +神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = '色のついた点は競走馬の体の位置を追跡' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} -DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. +DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 -DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から [チーター][cheetah-movement]のようなより珍しい動物まで、動物の種類を問わず利用できます。 It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. +DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から [チーター][cheetah-movement]のようなより珍しい動物まで、動物の種類を問わず利用できます。 -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 +DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術を使用しています。 これにより必要な学習データの量を大幅に削減し、学習の収束を加速させることができます。 必要に応じて、より高速な推論を提供するさまざまなネットワークアーキテクチャ(MobileNetV2など)を選択することができ、リアルタイムの実験データフィードバックと組み合わせることもできます。 DeepLabCutはもともと[DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれるパフォーマンスのよい人用のポーズ推定アーキテクチャの特徴検出器を使用しており、これが名前の由来になりました。 今ではこのパッケージは大幅に変更され、追加のアーキテクチャ・データの水増し・一通りのユーザー用フロントエンドを含んでいます。 さらに、 大規模な生物学的実験をサポートするため、DeepLabCutはオンライン学習の機能を提供しています。 これにより、動画の時間をこえて学習データを増やすことができ、エッジケースをカバーしたり、特定のコンテキスト内でポーズ推定アルゴリズムを堅牢にしたりできます。 -DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. - -最近、[DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. +最近、[DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 ### 主な目標と結果 -- **科学研究のための動物姿勢解析の自動化:** +* **科学研究のための動物姿勢解析の自動化:** - DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで [10倍のパフォーマンス向上](https://www.biorxiv.org/content/10.1101/457242v1) が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 This data can be used, for example, in - neuroscience studies to understand how the brain controls movement, or to - elucidate how animals socially interact. Researchers have observed a - [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) - with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second - (FPS). + DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで [10倍のパフォーマンス向上](https://www.biorxiv.org/content/10.1101/457242v1) が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 -- **姿勢推定のための使いやすいPythonツールキットの作成:** +* **姿勢推定のための使いやすいPythonツールキットの作成:** - DeepLabCut wanted to share their animal pose-estimation technology in the form - of an easy to use tool that can be adopted by researchers easily. So they have - created a complete, easy-to-use Python toolbox with project management features - as well. These enable not only automation of pose-estimation but also - managing the project end-to-end by helping the DeepLabCut Toolkit user right - from the dataset collection stage to creating shareable and reusable analysis - pipelines. + DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 この[ツールキット][DLCToolkit] はオープンソースとして利用できます。 @@ -75,92 +50,53 @@ DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/190 - 統合された可視化ツールを使用した推論の描画 {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'DeepLabCutによる姿勢推定のステップ' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCutのワークフロー**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} {{< /figure >}} -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut - ### 課題 -- **速度** +* **速度** - 動物行動動画の高速な処理は、動物の行動を測定し、科学実験をより効率的で正確にするために重要です。 動的に変化する背景の中で、マーカーを使用せずに、実験室での実験のために動物の詳細な姿勢を抽出することは、技術的にも、必要なリソース的にも、必要なトレーニングデータの面でも、困難な場合があります。 科学者が、より現実的な状況で研究を行うために、コンピュータビジョンなどの専門知識のスキルを必要とせずに使うことができるツールを開発することは、解決すべき重要な問題です。 - Extracting detailed animal poses for laboratory experiments, without - markers, in dynamically changing backgrounds, can be challenging, both - technically as well as in terms of resource needs and training data required. - Coming up with a tool that is easy to use without the need for skills such - as computer vision expertise that enables scientists to do research in more - real-world contexts, is a non-trivial problem to solve. + 動物行動動画の高速な処理は、動物の行動を測定し、科学実験をより効率的で正確にするために重要です。 動的に変化する背景の中で、マーカーを使用せずに、実験室での実験のために動物の詳細な姿勢を抽出することは、技術的にも、必要なリソース的にも、必要なトレーニングデータの面でも、困難な場合があります。 科学者が、より現実的な状況で研究を行うために、コンピュータビジョンなどの専門知識のスキルを必要とせずに使うことができるツールを開発することは、解決すべき重要な問題です。 -- **組み合わせ問題** +* **組み合わせ問題** - Combinatorics involves assembly and integration of movement of multiple - limbs into individual animal behavior. 組合せ問題とは、複数の四肢の動きを個々の動物行動に統合することを指します。 キーポイントと、その個々の動物行動との関連性を組み合わせ、時間的に結びつけることは、複雑なプロセスであり、非常に膨大な数値解析が必要となります。 特に、実験映像の中で複数の動物の動きを追跡する場合は大変です。 + 組合せ問題とは、複数の四肢の動きを個々の動物行動に統合することを指します。 キーポイントと、その個々の動物行動との関連性を組み合わせ、時間的に結びつけることは、複雑なプロセスであり、非常に膨大な数値解析が必要となります。 特に、実験映像の中で複数の動物の動きを追跡する場合は大変です。 -- **データ処理** +* **データ処理** - 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 + 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = '姿勢推定の多様性と難しさ' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}} +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## 姿勢推定の課題に対応するためのNumPyの役割 -DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術を使用しています。 これにより必要な学習データの量を大幅に削減し、学習の収束を加速させることができます。 必要に応じて、より高速な推論を提供するさまざまなネットワークアーキテクチャ(MobileNetV2など)を選択することができ、リアルタイムの実験データフィードバックと組み合わせることもできます。 DeepLabCutはもともと[DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれるパフォーマンスのよい人用のポーズ推定アーキテクチャの特徴検出器を使用しており、これが名前の由来になりました。 今ではこのパッケージは大幅に変更され、追加のアーキテクチャ・データの水増し・一通りのユーザー用フロントエンドを含んでいます。 さらに、 大規模な生物学的実験をサポートするため、DeepLabCutはオンライン学習の機能を提供しています。 これにより、動画の時間をこえて学習データを増やすことができ、エッジケースをカバーしたり、特定のコンテキスト内でポーズ推定アルゴリズムを堅牢にしたりできます。 NumPy は DeepLabCutにおける、行動分析の高速化のための数値計算の核となっています。 NumPyだけでなく、DeepLabCutは様々なNumPyをベースとしているPythonライブラリを利用しています。 [SciPy](https://www.scipy.org)、[Pandas](https://pandas.pydata.org)、[matplotlib](https://matplotlib.org)、[Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug)、[scikit-learn](https://scikit-learn.org/stable/)、[scikit-image](https://scikit-image.org)、[Tensorflow](https://www.tensorflow.org)などです。 +NumPy は DeepLabCutにおける、行動分析の高速化のための数値計算の核となっています。 NumPyだけでなく、DeepLabCutは様々なNumPyをベースとしているPythonライブラリを利用しています。 [SciPy](https://www.scipy.org)、[Pandas](https://pandas.pydata.org)、[matplotlib](https://matplotlib.org)、[Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug)、[scikit-learn](https://scikit-learn.org/stable/)、[scikit-image](https://scikit-image.org)、[Tensorflow](https://www.tensorflow.org)などです。 以下に挙げるNumPyの特徴が、DeepLabCutの姿勢推定アルゴリズムでの画像処理・組み合わせ処理・高速計算において、重要な役割を果たしました。 -- ベクトル化 -- マスクされた配列操作 -- 線形代数 -- ランダムサンプリング -- 大きな配列の再構成 - -DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 In particular, NumPy is used for sampling distinct frames for -human annotation labeling, and for writing, editing and processing annotation -data. Within TensorFlow the neural network is trained by DeepLabCut technology -over thousands of iterations to predict the ground truth annotations from -frames. For this purpose, target densities (scoremaps) are created to cast pose -estimation as a image-to-image translation problem. To make the neural networks -robust, data augmentation is employed, which requires the calculation of target -scoremaps subject to various geometric and image processing steps. To make -training fast, NumPy’s vectorization capabilities are leveraged. For inference, -the most likely predictions from target scoremaps need to extracted and one -needs to efficiently “link predictions to assemble individual animals”. +* ベクトル化 +* マスクされた配列操作 +* 線形代数 +* ランダムサンプリング +* 大きな配列の再構成 + +DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCutのワークフロー' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## まとめ -Observing and efficiently describing behavior is a core tenant of modern -ethology, neuroscience, medicine, and technology. -行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 With only a small set of training images, -the DeepLabCut Python toolbox allows training a neural network to within human -level labeling accuracy, thus expanding its application to not only behavior -analysis in the laboratory, but to potentially also in sports, gait analysis, -medicine and rehabilitation studies. Complex combinatorics, data processing -challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy's array manipulation capabilities. +行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'NumPyの主要機能' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 36eb16bc576ecca57366f3b6c7aa3c627b91bb5b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:51 +0200 Subject: [PATCH 320/586] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 197 +++++++--------------- 1 file changed, 57 insertions(+), 140 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index 4ef842d19d..217e1565f2 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -1,185 +1,102 @@ --- -title: "Case Study: DeepLabCut 3D Pose Estimation" +title: "사례 연구: DeepLabCut 3D 포즈 추정" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analyzing mice hand-movement using DeepLapCut' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +src = '/images/content_images/cs/mice-hand.gif' title = 'DeepLapCut을 활용한 쥐의 손 움직임 분석' alt = '쥐 손 애니메이션' attribution = '(출처: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} 오픈소스 소프트웨어는 생물 의학의 발전을 가속화하고 있습니다. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다. +> > {{< /blockquote >}} -## About DeepLabCut +## DeepLabCut 소개 -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) 은 전 세계 수백 개 기관의 연구원이 인간 수준의 정확도로 매우 적은 훈련 데이터로 실험실 동물의 행동을 추적할 수 있는 오픈 소스 도구 상자입니다. DeepLabCut 기술을 통해 과학자들은 동물 종과 시간 척도에 걸쳐 운동 제어 및 행동에 대한 과학적 이해를 더 깊이 탐구할 수 있습니다. -Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. +신경 과학, 의학 및 생체 역학을 포함한 여러 연구 분야에서 동물의 움직임을 추적한 데이터를 사용합니다. DeepLabCut은 필름에 기록된 동작을 구문 분석하여 인간과 다른 동물이 하는 일을 이해하는 데 도움을 줍니다. 심층 신경망 기반 데이터 분석과 함께 태깅 및 모니터링의 힘든 작업에 자동화를 사용하는 DeepLabCut은 영장류, 생쥐, 물고기, 파리 등과 같은 동물 관찰과 관련된 과학적 연구를 훨씬 빠르고 정확하게 만듭니다. {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Colored dots track the positions of a racehorse’s body part' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = '경주마 신체 부위의 위치를 추적하는 색 점' alt = '경주마 애니메이션' attribution = '(출처: Mackenzie Mathis)' {{< /figure >}} -DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. +DeeDeepLabCut의 동물 자세 추출을 통한 동물의 비침습적 행동 추적은 생체 역학, 유전학, 행동학 & 신경 과학과 같은 영역에서 과학적 추구에 매우 중요합니다. 동적으로 변화하는 배경에서 마커 없이 비디오에서 동물 포즈를 비침습적으로 측정하는 것은 기술적으로 뿐만 아니라 필요한 리소스 요구 사항 및 필요한 훈련 데이터 측면에서 계산적으로 어려운 일입니다. -DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. +DeepLabCut을 사용하면 연구자가 피험자의 자세를 추정하고 Python 기반 소프트웨어 툴킷을 통해 행동을 정량화할 수 있습니다. DeepLabCut을 사용하여 연구원은 비디오에서 고유한 프레임을 식별하고 맞춤형 GUI를 사용하여 수십 개의 프레임에서 특정 신체 부위에 디지털 레이블을 지정한 다음 DeepLabCut의 딥 러닝 기반 포즈 추정 아키텍처가 나머지 프레임에서 동일한 기능을 선택하는 방법을 학습할 수 있습니다. 그것은 파리와 생쥐와 같은 일반적인 실험실 동물에서 [치타][cheetah-movement]와 같은 좀 더 특이한 동물에 이르기까지 다양한 동물 종에 걸쳐 작동합니다. -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 +DeepLabCut은 [전이 학습](https://arxiv.org/pdf/1909.11229), 이라는 원리를 사용하여 필요한 훈련 데이터의 양을 크게 줄이고 훈련 기간의 수렴 속도를 높입니다. 필요에 따라 사용자는 실시간 실험 피드백과 결합할 수 있는 더 빠른 추론(예: MobileNetV2)을 제공하는 다양한 네트워크 아키텍처를 선택할 수 있습니다. DeepLabCut은 원래 [DeeperCut](https://arxiv.org/abs/1605.03170),이라는 최고 성능의 인간 포즈 추정 아키텍처의 특징 검출기를 사용했습니다. 이 이름에 영감을 받았습니다. 이제 패키지가 추가 아키텍처, 증강 방법 및 전체 프런트 엔드 사용자 경험을 포함하도록 크게 변경되었습니다. 또한 대규모 생물학적 실험을 지원하기 위해 DeepLabCut은 능동적 학습 기능을 제공하므로 사용자는 시간이 지남에 따라 트레이닝 세트를 늘려 엣지 케이스를 다루고 특정 상황 내에서 포즈 추정 알고리즘을 강력하게 만들 수 있습니다. -DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. +최근에는, 영장류의 안면 분석부터 개 자세까지 다양한 종과 실험 조건에 대해 사전 훈련된 모델을 제공하는 [DeepLabCut 모델 동물원](http://www.mousemotorlab.org/dlc-modelzoo) 이 도입되었습니다. 예를 들어 새 데이터의 레이블 지정이나 신경망 교육 없이 클라우드에서 실행할 수 있으며 프로그래밍 경험이 필요하지 않습니다. -Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. +### 주요 목표 및 결과 -### Key Goals and Results +* **과학적 연구를 위한 동물 자세 분석 자동화:** -- **Automation of animal pose analysis for scientific studies:** + DeepLabCut 기술의 주요 목표는 다양한 환경에서 동물의 자세를 측정하고 추적하는 것입니다. 예를 들어, 이 데이터는 뇌가 움직임을 제어하는 ​​방법을 이해하거나 동물이 사회적으로 상호 작용하는 방식을 설명하기 위해 신경 과학 연구에서 사용될 수 있습니다. 연구원들은 DeepLabCut으로 [성능이 10배 향상](https://www.biorxiv.org/content/10.1101/457242v1) 되는 것을 관찰했습니다. 포즈는 최대 1200 초당 프레임 수(FPS)로 오프라인에서 추론할 수 있습니다. - The primary objective of DeepLabCut technology is to measure and track posture - of animals in a diverse settings. This data can be used, for example, in - neuroscience studies to understand how the brain controls movement, or to - elucidate how animals socially interact. Researchers have observed a - [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) - with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second - (FPS). +* **포즈 추정을 위해 사용하기 쉬운 Python 툴킷 생성:** -- **Creation of an easy-to-use Python toolkit for pose estimation:** + DeepLabCut은 연구자들이 쉽게 채택할 수 있는 사용하기 쉬운 도구 형태로 동물 자세 추정 기술을 공유하고 싶었습니다. 그래서 그들은 프로젝트 관리 기능도 포함된 완전하고 사용하기 쉬운 Python 툴킷을 만들었습니다. 이를 통해 포즈 추정의 자동화는 물론 DeepLabCut Toolkit 사용자가 데이터 세트 수집 단계부터 공유 가능하고 재사용 가능한 분석 파이프라인 생성에 이르기까지 프로젝트 전체를 관리할 수 있습니다. - DeepLabCut wanted to share their animal pose-estimation technology in the form - of an easy to use tool that can be adopted by researchers easily. So they have - created a complete, easy-to-use Python toolbox with project management features - as well. These enable not only automation of pose-estimation but also - managing the project end-to-end by helping the DeepLabCut Toolkit user right - from the dataset collection stage to creating shareable and reusable analysis - pipelines. + [툴킷][DLCToolkit]은 현재 오픈 소스로 제공됩니다. - Their [toolkit][DLCToolkit] is now available as open source. + 일반적인 DeepLabCut 작업 흐름에는 다음이 포함됩니다. - A typical DeepLabCut Workflow includes: - - - creation and refining of training sets via active learning - - creation of tailored neural networks for specific animals and scenarios - - code for large-scale inference on videos - - draw inferences using integrated visualization tools + - 능동적 학습을 통한 훈련 세트 생성 및 정제 + - 특정 동물 및 시나리오를 위한 맞춤형 신경망 생성 + - 동영상에 대한 대규모 추론을 위한 코드 + - 통합 시각화 도구를 사용하여 추론 도출 {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pose estimation steps with DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' -{{< /figure >}} - -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut +{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut 워크플로우**" alt="워크플로우" attr="*(출처: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} +{{< figure >}} -### The Challenges +### 도전 -- **Speed** +* **속도** - Fast processing of animal behavior videos in order to measure their behavior - and at the same time make scientific experiments more efficient, accurate. - Extracting detailed animal poses for laboratory experiments, without - markers, in dynamically changing backgrounds, can be challenging, both - technically as well as in terms of resource needs and training data required. - Coming up with a tool that is easy to use without the need for skills such - as computer vision expertise that enables scientists to do research in more - real-world contexts, is a non-trivial problem to solve. + 동물의 행동을 측정하고 동시에 과학 실험을 보다 효율적이고 정확하게 하기 위해 동물 행동 비디오를 빠르게 처리합니다. 동적으로 변화하는 배경에서 마커 없이 실험실 실험을 위해 상세한 동물 포즈를 추출하는 것은 기술적으로 뿐만 아니라 필요한 리소스 요구 사항 및 필요한 교육 데이터 측면에서 어려울 수 있습니다. 컴퓨터 비전 전문 지식과 같은 기술 없이도 사용하기 쉬운 도구를 생각해내어 과학자들이 보다 실제 상황에서 연구를 수행할 수 있도록 하는 것은 해결해야 할 사소한 문제가 아닙니다. -- **Combinatorics** +* **조합론** - Combinatorics involves assembly and integration of movement of multiple - limbs into individual animal behavior. Assembling keypoints and their - connections into individual animal movements and linking them across time - is a complex process that requires heavy-duty numerical analysis, especially - in case of multi-animal movement tracking in experiment videos. + 조합론은 여러 사지의 움직임을 개별 동물 행동으로 조립하고 통합하는 것을 포함합니다. 키포인트와 연결을 개별 동물 움직임으로 조립하고 시간에 따라 연결하는 것은 특히 실험 비디오에서 여러 동물 움직임을 추적하는 경우 강력한 수치 분석이 필요한 복잡한 프로세스입니다. -- **Data Processing** +* **데이터 처리** - Last but not the least, array manipulation - processing large stacks of - arrays corresponding to various images, target tensors and keypoints is - fairly challenging. + 마지막으로 배열 조작 - 다양한 이미지, 대상 텐서 및 키포인트에 해당하는 대규모 배열 스택을 처리하는 것은 상당히 어렵습니다. -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Pose estimation variety and complexity' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용한 주요 NumPy 기능**" >}} +src = '/images/content_images/cs/pose-estimation.png' title = '포즈 추정 변수 및 복잡도' alt = '난점 설명' align = 'center' attribution = '(출처: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} -## NumPy's Role in meeting Pose Estimation Challenges - -NumPy addresses DeepLabCut technology's core need of numerical computations at -high speed for behavioural analytics. Besides NumPy, DeepLabCut employs -various Python software that utilize NumPy at their core, such as -[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), -[matplotlib](https://matplotlib.org), -[Tensorpack](https://github.com/tensorpack/tensorpack), -[imgaug](https://github.com/aleju/imgaug), -[scikit-learn](https://scikit-learn.org/stable/), -[scikit-image](https://scikit-image.org) and -[Tensorflow](https://www.tensorflow.org). - -The following features of NumPy played a key role in addressing the image -processing, combinatorics requirements and need for fast computation in -DeepLabCut pose estimation algorithms: - -- Vectorization -- Masked Array Operations -- Linear Algebra -- Random Sampling -- Reshaping of large arrays - -DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered -by the toolkit. In particular, NumPy is used for sampling distinct frames for -human annotation labeling, and for writing, editing and processing annotation -data. Within TensorFlow the neural network is trained by DeepLabCut technology -over thousands of iterations to predict the ground truth annotations from -frames. For this purpose, target densities (scoremaps) are created to cast pose -estimation as a image-to-image translation problem. To make the neural networks -robust, data augmentation is employed, which requires the calculation of target -scoremaps subject to various geometric and image processing steps. To make -training fast, NumPy’s vectorization capabilities are leveraged. For inference, -the most likely predictions from target scoremaps need to extracted and one -needs to efficiently “link predictions to assemble individual animals”. +## 포즈 추정 문제를 해결하는 NumPy의 역할 + +NumPy는 행동 분석을 위한 고속 수치 계산에 대한 DeepLabCut 기술의 핵심 요구 사항을 해결합니다. NumPy 외에도 DeepLabCut은 [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) 그리고 [Tensorflow](https://www.tensorflow.org) 와 같이 핵심에서 NumPy를 활용하는 다양한 Python 소프트웨어를 사용합니다. + +NumPy의 다음 기능은 이미지 처리, 조합 요구 사항 및 DeepLabCut 포즈 추정 알고리즘의 빠른 계산 요구 사항을 해결하는 데 중요한 역할을 했습니다. + +* 벡터화 +* 마스킹된 어레이 작업 +* 선형 대수학 +* 무작위 샘플링 +* 큰 배열의 재구성 + +DeepLabCut은 툴킷에서 제공하는 워크플로 전체에서 NumPy의 어레이 기능을 활용합니다. 특히, NumPy는 사람의 주석 레이블 지정을 위한 개별 프레임 샘플링과 주석 데이터 작성, 편집 및 처리에 사용됩니다. TensorFlow 내에서 신경망은 DeepLabCut 기술로 수천 번의 반복을 통해 훈련되어 프레임에서 실측 주석을 예측합니다. 이를 위해 이미지 대 이미지 변환 문제로 포즈 추정을 캐스팅하기 위해 목표 밀도(점수 지도)가 생성됩니다. 신경망을 강력하게 만들기 위해 다양한 기하 및 이미지 처리 단계에 따라 대상 스코어맵을 계산해야 하는 데이터 확대가 사용됩니다. 학습을 빠르게 하기 위해 NumPy의 벡터화 기능이 활용됩니다. 추론을 위해 대상 스코어맵에서 가장 가능성이 높은 예측을 추출해야 하며 효율적으로 "개별 동물을 조립하기 위해 예측을 연결"해야 합니다. {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCut Workflow' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut 워크플로우' alt = '워크플로우' attribution = '(출처: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} -## Summary +## 요약 -Observing and efficiently describing behavior is a core tenant of modern -ethology, neuroscience, medicine, and technology. -[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) -allows researchers to estimate the pose of the subject, efficiently enabling -them to quantify the behavior. With only a small set of training images, -the DeepLabCut Python toolbox allows training a neural network to within human -level labeling accuracy, thus expanding its application to not only behavior -analysis in the laboratory, but to potentially also in sports, gait analysis, -medicine and rehabilitation studies. Complex combinatorics, data processing -challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy's array manipulation capabilities. +행동을 관찰하고 효율적으로 설명하는 것은 현대 행동학, 신경과학, 의학 및 기술의 핵심 테넌트입니다. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf)을 사용하면 연구자가 피험자의 자세를 추정하여 행동을 정량화할 수 있습니다. DeepLabCut Python 툴킷은 작은 훈련 이미지 세트만으로 인간 수준의 라벨링 정확도 내에서 신경망을 훈련할 수 있습니다. 따라서 실험실에서의 행동 분석뿐만 아니라 잠재적으로 스포츠, 보행 분석, 의학 및 재활 연구에도 응용 분야를 확장합니다. DeepLabCut 알고리즘이 직면한 복잡한 조합, 데이터 처리 문제는 NumPy의 배열 조작 기능을 사용하여 해결됩니다. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy를 통한 이익' title = '활용된 주요 NumPy 기능' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 0895770549cd0eebda1cd505f43867e2613426de Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:52 +0200 Subject: [PATCH 321/586] New translations deeplabcut-dnn.md (Russian) --- content/ru/case-studies/deeplabcut-dnn.md | 153 +++++----------------- 1 file changed, 35 insertions(+), 118 deletions(-) diff --git a/content/ru/case-studies/deeplabcut-dnn.md b/content/ru/case-studies/deeplabcut-dnn.md index 4ef842d19d..257905de91 100644 --- a/content/ru/case-studies/deeplabcut-dnn.md +++ b/content/ru/case-studies/deeplabcut-dnn.md @@ -4,19 +4,12 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analyzing mice hand-movement using DeepLapCut' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> > {{< /blockquote >}} ## About DeepLabCut @@ -26,43 +19,26 @@ by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de L Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Colored dots track the positions of a racehorse’s body part' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 - DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### Key Goals and Results -- **Automation of animal pose analysis for scientific studies:** +* **Automation of animal pose analysis for scientific studies:** - The primary objective of DeepLabCut technology is to measure and track posture - of animals in a diverse settings. This data can be used, for example, in - neuroscience studies to understand how the brain controls movement, or to - elucidate how animals socially interact. Researchers have observed a - [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) - with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second - (FPS). + The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). -- **Creation of an easy-to-use Python toolkit for pose estimation:** +* **Creation of an easy-to-use Python toolkit for pose estimation:** - DeepLabCut wanted to share their animal pose-estimation technology in the form - of an easy to use tool that can be adopted by researchers easily. So they have - created a complete, easy-to-use Python toolbox with project management features - as well. These enable not only automation of pose-estimation but also - managing the project end-to-end by helping the DeepLabCut Toolkit user right - from the dataset collection stage to creating shareable and reusable analysis - pipelines. + DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. Their [toolkit][DLCToolkit] is now available as open source. @@ -74,112 +50,53 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - draw inferences using integrated visualization tools {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pose estimation steps with DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' {{< /figure >}} -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut - ### The Challenges -- **Speed** +* **Speed** - Fast processing of animal behavior videos in order to measure their behavior - and at the same time make scientific experiments more efficient, accurate. - Extracting detailed animal poses for laboratory experiments, without - markers, in dynamically changing backgrounds, can be challenging, both - technically as well as in terms of resource needs and training data required. - Coming up with a tool that is easy to use without the need for skills such - as computer vision expertise that enables scientists to do research in more - real-world contexts, is a non-trivial problem to solve. + Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. -- **Combinatorics** +* **Combinatorics** - Combinatorics involves assembly and integration of movement of multiple - limbs into individual animal behavior. Assembling keypoints and their - connections into individual animal movements and linking them across time - is a complex process that requires heavy-duty numerical analysis, especially - in case of multi-animal movement tracking in experiment videos. + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. -- **Data Processing** +* **Data Processing** - Last but not the least, array manipulation - processing large stacks of - arrays corresponding to various images, target tensors and keypoints is - fairly challenging. + Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. {{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Pose estimation variety and complexity' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## NumPy's Role in meeting Pose Estimation Challenges -NumPy addresses DeepLabCut technology's core need of numerical computations at -high speed for behavioural analytics. Besides NumPy, DeepLabCut employs -various Python software that utilize NumPy at their core, such as -[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), -[matplotlib](https://matplotlib.org), -[Tensorpack](https://github.com/tensorpack/tensorpack), -[imgaug](https://github.com/aleju/imgaug), -[scikit-learn](https://scikit-learn.org/stable/), -[scikit-image](https://scikit-image.org) and -[Tensorflow](https://www.tensorflow.org). - -The following features of NumPy played a key role in addressing the image -processing, combinatorics requirements and need for fast computation in -DeepLabCut pose estimation algorithms: - -- Vectorization -- Masked Array Operations -- Linear Algebra -- Random Sampling -- Reshaping of large arrays - -DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered -by the toolkit. In particular, NumPy is used for sampling distinct frames for -human annotation labeling, and for writing, editing and processing annotation -data. Within TensorFlow the neural network is trained by DeepLabCut technology -over thousands of iterations to predict the ground truth annotations from -frames. For this purpose, target densities (scoremaps) are created to cast pose -estimation as a image-to-image translation problem. To make the neural networks -robust, data augmentation is employed, which requires the calculation of target -scoremaps subject to various geometric and image processing steps. To make -training fast, NumPy’s vectorization capabilities are leveraged. For inference, -the most likely predictions from target scoremaps need to extracted and one -needs to efficiently “link predictions to assemble individual animals”. +NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: + +* Vectorization +* Masked Array Operations +* Linear Algebra +* Random Sampling +* Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCut Workflow' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## Summary -Observing and efficiently describing behavior is a core tenant of modern -ethology, neuroscience, medicine, and technology. -[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) -allows researchers to estimate the pose of the subject, efficiently enabling -them to quantify the behavior. With only a small set of training images, -the DeepLabCut Python toolbox allows training a neural network to within human -level labeling accuracy, thus expanding its application to not only behavior -analysis in the laboratory, but to potentially also in sports, gait analysis, -medicine and rehabilitation studies. Complex combinatorics, data processing -challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy's array manipulation capabilities. +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From fd632235c8117ab7b80e4147cc34e12f40092ec1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:53 +0200 Subject: [PATCH 322/586] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 153 +++++----------------- 1 file changed, 35 insertions(+), 118 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index 4ef842d19d..257905de91 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -4,19 +4,12 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analyzing mice hand-movement using DeepLapCut' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> > {{< /blockquote >}} ## About DeepLabCut @@ -26,43 +19,26 @@ by="Alexander Mathis, _Assistant Professor, École polytechnique fédérale de L Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Colored dots track the positions of a racehorse’s body part' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 - DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### Key Goals and Results -- **Automation of animal pose analysis for scientific studies:** +* **Automation of animal pose analysis for scientific studies:** - The primary objective of DeepLabCut technology is to measure and track posture - of animals in a diverse settings. This data can be used, for example, in - neuroscience studies to understand how the brain controls movement, or to - elucidate how animals socially interact. Researchers have observed a - [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) - with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second - (FPS). + The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). -- **Creation of an easy-to-use Python toolkit for pose estimation:** +* **Creation of an easy-to-use Python toolkit for pose estimation:** - DeepLabCut wanted to share their animal pose-estimation technology in the form - of an easy to use tool that can be adopted by researchers easily. So they have - created a complete, easy-to-use Python toolbox with project management features - as well. These enable not only automation of pose-estimation but also - managing the project end-to-end by helping the DeepLabCut Toolkit user right - from the dataset collection stage to creating shareable and reusable analysis - pipelines. + DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. Their [toolkit][DLCToolkit] is now available as open source. @@ -74,112 +50,53 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - draw inferences using integrated visualization tools {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pose estimation steps with DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' {{< /figure >}} -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut - ### The Challenges -- **Speed** +* **Speed** - Fast processing of animal behavior videos in order to measure their behavior - and at the same time make scientific experiments more efficient, accurate. - Extracting detailed animal poses for laboratory experiments, without - markers, in dynamically changing backgrounds, can be challenging, both - technically as well as in terms of resource needs and training data required. - Coming up with a tool that is easy to use without the need for skills such - as computer vision expertise that enables scientists to do research in more - real-world contexts, is a non-trivial problem to solve. + Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. -- **Combinatorics** +* **Combinatorics** - Combinatorics involves assembly and integration of movement of multiple - limbs into individual animal behavior. Assembling keypoints and their - connections into individual animal movements and linking them across time - is a complex process that requires heavy-duty numerical analysis, especially - in case of multi-animal movement tracking in experiment videos. + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. -- **Data Processing** +* **Data Processing** - Last but not the least, array manipulation - processing large stacks of - arrays corresponding to various images, target tensors and keypoints is - fairly challenging. + Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. {{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Pose estimation variety and complexity' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## NumPy's Role in meeting Pose Estimation Challenges -NumPy addresses DeepLabCut technology's core need of numerical computations at -high speed for behavioural analytics. Besides NumPy, DeepLabCut employs -various Python software that utilize NumPy at their core, such as -[SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), -[matplotlib](https://matplotlib.org), -[Tensorpack](https://github.com/tensorpack/tensorpack), -[imgaug](https://github.com/aleju/imgaug), -[scikit-learn](https://scikit-learn.org/stable/), -[scikit-image](https://scikit-image.org) and -[Tensorflow](https://www.tensorflow.org). - -The following features of NumPy played a key role in addressing the image -processing, combinatorics requirements and need for fast computation in -DeepLabCut pose estimation algorithms: - -- Vectorization -- Masked Array Operations -- Linear Algebra -- Random Sampling -- Reshaping of large arrays - -DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered -by the toolkit. In particular, NumPy is used for sampling distinct frames for -human annotation labeling, and for writing, editing and processing annotation -data. Within TensorFlow the neural network is trained by DeepLabCut technology -over thousands of iterations to predict the ground truth annotations from -frames. For this purpose, target densities (scoremaps) are created to cast pose -estimation as a image-to-image translation problem. To make the neural networks -robust, data augmentation is employed, which requires the calculation of target -scoremaps subject to various geometric and image processing steps. To make -training fast, NumPy’s vectorization capabilities are leveraged. For inference, -the most likely predictions from target scoremaps need to extracted and one -needs to efficiently “link predictions to assemble individual animals”. +NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: + +* Vectorization +* Masked Array Operations +* Linear Algebra +* Random Sampling +* Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCut Workflow' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## Summary -Observing and efficiently describing behavior is a core tenant of modern -ethology, neuroscience, medicine, and technology. -[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) -allows researchers to estimate the pose of the subject, efficiently enabling -them to quantify the behavior. With only a small set of training images, -the DeepLabCut Python toolbox allows training a neural network to within human -level labeling accuracy, thus expanding its application to not only behavior -analysis in the laboratory, but to potentially also in sports, gait analysis, -medicine and rehabilitation studies. Complex combinatorics, data processing -challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy's array manipulation capabilities. +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 55fcdb011677e9fe4129949d35338024fdb9ec44 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:55 +0200 Subject: [PATCH 323/586] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 93 ++++++++--------------- 1 file changed, 32 insertions(+), 61 deletions(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index a163a46fcb..299c321f1a 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -3,20 +3,13 @@ title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Análise de movimentos de mãos de camundongos usando DeepLapCut' -alt = 'micehandanim' -attribution = '(Fonte: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +src = '/images/content_images/cs/mice-hand.gif' title = 'Analisando o movimento das mãos de ratos usando DeepLapCut' alt = 'micehandanim' attribution = '(Fonte: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} -{{< blockquote -cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" -by="Alexander Mathis, _Professor Assistente, École polytechnique fédérale de Lausanne_ ([EPFL](https://www.epfl.ch/en/))" - -> }} -> Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning. +> > {{< /blockquote >}} ## Sobre o DeepLabCut @@ -26,29 +19,24 @@ by="Alexander Mathis, _Professor Assistente, École polytechnique fédérale de Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos. {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida' -alt = 'horserideranim' -attribution = '(Fonte: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = ‘Pontos coloridos rastreiam as posições de partes do corpo de um cavalo de corrida’ alt = 'horserideranim' attribution = '(Fonte: Mackenzie Mathis)' {{< /figure >}} O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. -A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (_frames_) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. - -[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 +A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. -A DeepLabCut usa um princípio chamado [aprendizado por transferência (_transfer learning_)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. +A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pré-treinados para várias espécies e condições experimentais, desde a análise facial em primatas até à posição de cães. Isso pode ser executado na nuvem, por exemplo, sem qualquer rotulagem de novos dados ou treinamento em rede neural, e não é necessária nenhuma experiência em programação. ### Principais Objetivos e Resultados -- **Automação da análise de poses animais para estudos científicos:** +* **Automação da análise de poses animais para estudos científicos:** O objetivo principal da tecnologia DeepLabCut é medir e rastrear a postura dos animais em várias configurações. Esses dados podem ser usados, por exemplo, em estudos de neurociência para entender como o cérebro controla o movimento, ou para elucidar como os animais interagem socialmente. Pesquisadores observaram que [desempenho é 10 vezes melhor](https://www.biorxiv.org/content/10.1101/457242v1) com o DeepLabCut. Poses podem ser inferidas off-line em até 1200 quadros por segundo (FPS). -- **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** +* **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** DeepLabCut queria compartilhar sua tecnologia de estimativa de poses animal na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. @@ -62,39 +50,25 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab - inferências de desenho usando ferramentas integradas de visualização {{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Passos na estimação de poses com DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Fonte: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**Fluxo de dados DeepLabCut**" alt="workflow" attr="*(Fonte: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} {{< /figure >}} -[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut - ### Desafios -- **Velocidade** +* **Velocidade** - Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. - Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. - Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. + Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. -- **Combinatória** +* **Combinatória** - Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. + Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. -- **Processamento de dados** +* **Processamento de dados** - Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. + Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Estimação de poses e complexidade' -alt = 'challengesfig' -align = 'center' -attribution = '(Fonte: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}} +src = '/images/content_images/cs/pose-estimation.png' title = 'Variedade e complexidade da estimativa de pose' alt = 'challengesfig' align = 'center' attribution = '(Fonte: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## O papel da NumPy nos desafios da estimação de poses @@ -103,29 +77,26 @@ NumPy supre a principal necessidade da tecnologia DeepLabCut de cálculos numér As seguintes características da NumPy desempenharam um papel fundamental para atender às necessidades de processamento de imagens, combinatória e cálculos rápidos nos algoritmos de estimação de pose na DeepLabCut: -- Vetorização -- Operações em arrays com máscaras -- Álgebra linear -- Random Sampling -- Reordenamento de matrizes grandes +* Vetorização +* Operações em arrays com máscaras +* Álgebra linear +* Amostragem aleatória +* Reordenamento de matrizes grandes -A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. Para este propósito, densidades de alvo (_scoremaps_) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. +A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'Fluxo de dados DeepLabCut' -alt = 'workflow' -attribution = '(Fonte: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'Fluxo de trabalho do DeepLabCut' alt = 'workflow' attribution = '(Fonte: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## Resumo -Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. -[DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. +Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Recursos chave do NumPy utilizados' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'benefícios do numpy' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 262ec7dea8d56a17bf31d2e9fe8642b26c8703ac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:56 +0200 Subject: [PATCH 324/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 146 ++++++--------------- 1 file changed, 41 insertions(+), 105 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index bfa12875ff..0087c7b632 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -1,144 +1,80 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "Caso de estudio: La primera imagen de un Agujero Negro" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="imagen de un agujero negro" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" - -> }} -> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> > {{< /blockquote >}} -## A telescope the size of the earth - -The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an -array of eight ground-based radio telescopes forming a computational telescope -the size of the earth, studing the universe with unprecedented -sensitivity and resolution. The huge virtual telescope, which uses a technique -called very-long-baseline interferometry (VLBI), has an angular resolution of -[20 micro-arcseconds][resolution] — enough to read a newspaper in New York -from a sidewalk café in Paris! +## Un telescopio del tamaño de la Tierra -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole - -### Key Goals and Results +El [ telescopio de Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de [20 microsegundos de arco][resolution] — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París! -- **A New View of the Universe:** - The groundwork for the EHT's groundbreaking image had been laid 100 years - earlier when [Sir Arthur Eddington][eddington] yielded the first - observational support of Einstein's theory of general relativity. +### Objetivos clave y resultados -- **The Black Hole:** EHT was trained on a supermassive black hole - approximately 55 million light-years from Earth, lying at the center - of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is - 6.5 billion times the Sun's. It had been studied for - [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before - had a black hole been visually observed. +* **Una nueva vista del universo:** El trabajo preliminar de la innovadora imagen de EHT se había establecido 100 años antes, cuando [Sir Arthur Eddington][eddington] dio el primer apoyo observacional a la teoría de la relatividad general de Einstein. -- **Comparing Observations to Theory:** From Einstein’s general theory of - relativity, scientists expected to find a shadow-like region caused by - gravitational bending and capture of light. Scientists could - use it to measure the black hole's enormous mass. +* **El agujero negro:** EHT se entrenó en un enorme agujero negro aproximadamente a 55 millones de años luz de la tierra, situada en el centro de la galaxia Messier 87 (M87) en el cúmulo de Virgo. Su masa es 6.5 mil millones de veces la del sol. Se había estudiado por [más de 100 años](https://www.jpl.nasa.gov/news/news.php?feature=7385), pero nunca antes se había observado un agujero negro. -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **Comparando las observaciones con la teoría:** A partir de la teoría de la relatividad general de Einstein, los científicos esperaban encontrar una región similar a las sombras causadas por la flexión gravitacional y la captura de la luz. Los científicos pudieron utilizarla para medir la enorme masa del agujero negro. -### The Challenges +### Los desafíos -- **Computational scale** +* **Escala computacional** - EHT poses massive data-processing challenges, including rapid atmospheric - phase fluctuations, large recording bandwidth, and telescopes that are - widely dissimilar and geographically dispersed. + EHT plantea desafíos de procesamiento de datos masivos, incluyendo rápidas fluctuaciones de fase atmosféricas, amplio ancho de banda de grabación, y telescopios que son ampliamente disímiles y geográficamente dispersos. -- **Too much information** +* **Demasiada información** - Each day EHT generates over 350 terabytes of observations, stored on - helium-filled hard drives. Reducing the volume and complexity of this much - data is enormously difficult. + Cada día el EHT genera más de 350 terabytes de observaciones, almacenados en discos duros llenos de helio. Reducir el volumen y complejidad de estos datos es enormemente difícil. -- **Into the unknown** +* **Hacia lo desconocido** - When the goal is to see something never before seen, how can scientists be - confident the image is correct? + Cuando el objetivo es ver algo nunca antes visto, ¿cómo pueden los científicos estar seguros de que la imagen es correcta? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHT Data Processing Pipeline' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="el rol de numpy" caption="**El rol de NumPy en la fotografía del Agujero Negro**" >}} -## NumPy’s Role +## El Rol de NumPy -What if there's a problem with the data? Or perhaps an algorithm relies too -heavily on a particular assumption. Will the image change drastically if a -single parameter is changed? +¿Qué pasa si hay un problema con los datos? O tal vez un algoritmo depende demasiado de una suposición en particular. ¿Cambiará drásticamente la imagen si se cambia un solo parámetro? -The EHT collaboration met these challenges by having independent teams -evaluate the data, using both established and cutting-edge image reconstruction -techniques. When results proved consistent, they were combined to yield the -first-of-a-kind image of the black hole. +La colaboración del EHT respondió a estos desafíos haciendo que los equipos independientes evaluaran los datos, utilizando técnicas de reconstrucción de imágenes ya establecidas y de vanguardia. Cuando los resultados se mostraron consistentes, se combinaron para producir la primera imagen de su tipo de un agujero negro. -Their work illustrates the role the scientific Python ecosystem plays in -advancing science through collaborative data analysis. +Su trabajo ilustra el rol que desempeña el ecosistema científico de Python en el avance de la ciencia a través del análisis de datos colaborativos. {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'The role of NumPy in Black Hole imaging' +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="ventajas de numpy" caption="**Capacidades Clave utilizadas de NumPy**" >}} {{< /figure >}} -For example, the [`eht-imaging`][ehtim] Python package provides tools for -simulating and performing image reconstruction on VLBI data. -NumPy is at the core of array data processing used -in this package, as illustrated by the partial software -dependency chart below. +Por ejemplo, el paquete de Python [`eht-imaging`][ehtim] proporciona herramientas para simular y realizar reconstrucción de imágenes en datos VLBI. NumPy está en el núcleo del procesamiento de datos de matrices utilizados en este paquete, como se muestra a continuación en el gráfico parcial de dependencias de software. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the -data processing pipeline for imaging the black hole. -The standard astronomical file formats and time/coordinate transformations -were handled by [Astropy][astropy], while [Matplotlib][mpl] was used -in visualizing data throughout the analysis pipeline, including the generation -of the final image of the black hole. +Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [Pandas](https://pandas.io), son parte del flujo de procesamiento de datos para fotografiar el agujero negro. Los formatos estándar de archivos astronómicos y transformaciones de tiempo/coordenadas fueron manejados por [Astropy][astropy], mientras que [Matplotlib][mpl] fue utilizado en la visualización de datos a través del flujo de análisis, incluyendo la generación de la imagen final del agujero negro. -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ +## Resumen -## Summary - -The efficient and adaptable n-dimensional array that is NumPy's central feature -enabled researchers to manipulate large numerical datasets, providing a -foundation for the first-ever image of a black hole. A landmark moment in -science, it gives stunning visual evidence of Einstein’s theory. The -achievement encompasses not only technological breakthroughs but also -international collaboration among over 200 scientists and some of the world's -best radio observatories. Innovative algorithms and data processing -techniques, improving upon existing astronomical models, helped unfold a -mystery of the universe. +La matriz n-dimensional eficiente y adaptable que es la característica central de NumPy permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 490f97e487bf192bf551cf69724ee23eeca49eba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:57 +0200 Subject: [PATCH 325/586] New translations blackhole-image.md (Arabic) --- content/ar/case-studies/blackhole-image.md | 130 ++++++--------------- 1 file changed, 33 insertions(+), 97 deletions(-) diff --git a/content/ar/case-studies/blackhole-image.md b/content/ar/case-studies/blackhole-image.md index bfa12875ff..22334ebed0 100644 --- a/content/ar/case-studies/blackhole-image.md +++ b/content/ar/case-studies/blackhole-image.md @@ -4,141 +4,77 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" - -> }} -> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> > {{< /blockquote >}} ## A telescope the size of the earth -The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an -array of eight ground-based radio telescopes forming a computational telescope -the size of the earth, studing the universe with unprecedented -sensitivity and resolution. The huge virtual telescope, which uses a technique -called very-long-baseline interferometry (VLBI), has an angular resolution of -[20 micro-arcseconds][resolution] — enough to read a newspaper in New York -from a sidewalk café in Paris! - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! ### Key Goals and Results -- **A New View of the Universe:** - The groundwork for the EHT's groundbreaking image had been laid 100 years - earlier when [Sir Arthur Eddington][eddington] yielded the first - observational support of Einstein's theory of general relativity. +* **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. -- **The Black Hole:** EHT was trained on a supermassive black hole - approximately 55 million light-years from Earth, lying at the center - of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is - 6.5 billion times the Sun's. It had been studied for - [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before - had a black hole been visually observed. +* **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. -- **Comparing Observations to Theory:** From Einstein’s general theory of - relativity, scientists expected to find a shadow-like region caused by - gravitational bending and capture of light. Scientists could - use it to measure the black hole's enormous mass. - -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. ### The Challenges -- **Computational scale** +* **Computational scale** - EHT poses massive data-processing challenges, including rapid atmospheric - phase fluctuations, large recording bandwidth, and telescopes that are - widely dissimilar and geographically dispersed. + EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. -- **Too much information** +* **Too much information** - Each day EHT generates over 350 terabytes of observations, stored on - helium-filled hard drives. Reducing the volume and complexity of this much - data is enormously difficult. + Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. -- **Into the unknown** +* **Into the unknown** - When the goal is to see something never before seen, how can scientists be - confident the image is correct? + When the goal is to see something never before seen, how can scientists be confident the image is correct? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHT Data Processing Pipeline' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## NumPy’s Role -What if there's a problem with the data? Or perhaps an algorithm relies too -heavily on a particular assumption. Will the image change drastically if a -single parameter is changed? +What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? -The EHT collaboration met these challenges by having independent teams -evaluate the data, using both established and cutting-edge image reconstruction -techniques. When results proved consistent, they were combined to yield the -first-of-a-kind image of the black hole. +The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. -Their work illustrates the role the scientific Python ecosystem plays in -advancing science through collaborative data analysis. +Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'The role of NumPy in Black Hole imaging' +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging' {{< /figure >}} -For example, the [`eht-imaging`][ehtim] Python package provides tools for -simulating and performing image reconstruction on VLBI data. -NumPy is at the core of array data processing used -in this package, as illustrated by the partial software -dependency chart below. +For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the -data processing pipeline for imaging the black hole. -The standard astronomical file formats and time/coordinate transformations -were handled by [Astropy][astropy], while [Matplotlib][mpl] was used -in visualizing data throughout the analysis pipeline, including the generation -of the final image of the black hole. - -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ +Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. ## Summary -The efficient and adaptable n-dimensional array that is NumPy's central feature -enabled researchers to manipulate large numerical datasets, providing a -foundation for the first-ever image of a black hole. A landmark moment in -science, it gives stunning visual evidence of Einstein’s theory. The -achievement encompasses not only technological breakthroughs but also -international collaboration among over 200 scientists and some of the world's -best radio observatories. Innovative algorithms and data processing -techniques, improving upon existing astronomical models, helped unfold a -mystery of the universe. +The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From bbb8e998a6f624d530967547f0278cd641a45c97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:58 +0200 Subject: [PATCH 326/586] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 108 +++++++-------------- 1 file changed, 33 insertions(+), 75 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 163d6347e0..f435ef2b2b 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -1,122 +1,80 @@ --- -title: ケーススタディ：世界初のブラックホール画像 +title: "ケーススタディ：世界初のブラックホール画像" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attrk = 'https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg' +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrk="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg" >}} +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="_カリフォルニア工科大学 計算・数理学部_のKatie Bouman助教授" - -> }} -> M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 -> {{< /blockquote >}} +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, * カリフォルニア工科大学の計算数理科学の助教授 *" +> }} M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 +> > {{< /blockquote >}} ## 地球大の望遠鏡 -[Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です! The huge virtual telescope, which uses a technique -called very-long-baseline interferometry (VLBI), has an angular resolution of -[20 micro-arcseconds][resolution] — enough to read a newspaper in New York -from a sidewalk café in Paris! - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole +[Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です! ### 主な目標と結果 -- **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington]がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 +* **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington]がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 -- **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 [100年以上](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 Its mass is - 6.5 billion times the Sun's. It had been studied for - [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before - had a black hole been visually observed. +* **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 [100年以上](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 -- **観測と理論の比較:** 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 - -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **観測と理論の比較:** 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 ### 課題 -- **大規模な計算** +* **大規模な計算** - EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 + EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 -- **大量のデータ** +* **大量のデータ** - EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 Reducing the volume and complexity of this much - data is enormously difficult. + EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 -- **よくわからないものを観測する** +* **よくわからないものを観測する** - 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? + 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHTのデータ処理パイプライン' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**ブラックホール画像化でNumPyが果たした役割**" >}} ## NumPyが果たした役割 -What if there's a problem with the data? Or perhaps an algorithm relies too -heavily on a particular assumption. データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ +データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ -The EHT collaboration met these challenges by having independent teams -evaluate the data, using both established and cutting-edge image reconstruction -techniques. When results proved consistent, they were combined to yield the -first-of-a-kind image of the black hole. +EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'ブラックホール画像化でNumPyが果たした役割' +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} {{< /figure >}} 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 -NumPy is at the core of array data processing used -in this package, as illustrated by the partial software -dependency chart below. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'NumPyの中心としたehtimのソフトウェア依存図' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the -data processing pipeline for imaging the black hole. NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ - ## まとめ -NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 A landmark moment in -science, it gives stunning visual evidence of Einstein’s theory. The -achievement encompasses not only technological breakthroughs but also -international collaboration among over 200 scientists and some of the world's -best radio observatories. Innovative algorithms and data processing -techniques, improving upon existing astronomical models, helped unfold a -mystery of the universe. +NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = '利用されたNumPyの主要機能' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From d22a40839d663abf84460695a6634c74d5803c49 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:26:59 +0200 Subject: [PATCH 327/586] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 146 ++++++--------------- 1 file changed, 41 insertions(+), 105 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index bfa12875ff..14ddb59afc 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -1,144 +1,80 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "사례 연구: 최초의 블랙홀 사진" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**블랙홀 M87**" alt="블랙홀 사진" attr="*(사진 크레딧: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +src = '/images/content_images/cs/blackhole.jpg' title = '블랙홀 M87' alt = '블랙홀 사진' attribution = '(사진 크레딧: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" - -> }} -> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> > {{< /blockquote >}} -## A telescope the size of the earth - -The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an -array of eight ground-based radio telescopes forming a computational telescope -the size of the earth, studing the universe with unprecedented -sensitivity and resolution. The huge virtual telescope, which uses a technique -called very-long-baseline interferometry (VLBI), has an angular resolution of -[20 micro-arcseconds][resolution] — enough to read a newspaper in New York -from a sidewalk café in Paris! - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole +## 지구 크기의 망원경 -### Key Goals and Results +[사건의 지평선 망원경(EHT)](https://eventhorizontelescope.org)은 8개의 지상 전파 망원경으로 구성된 지구 크기의 전산 망원경으로, 전례없는 감도와 해상도로 우주를 연구하는 데 쓰입니다. 초장기선 간섭 관측법(VLBI)이라는 기술을 사용하는 거대한 가상 망원경의 각해상도는 [20 마이크로각초][resolution]에 달하며 파리의 길거리 카페에서 뉴욕의 신문을 읽기에 충분한 정도입니다! -- **A New View of the Universe:** - The groundwork for the EHT's groundbreaking image had been laid 100 years - earlier when [Sir Arthur Eddington][eddington] yielded the first - observational support of Einstein's theory of general relativity. +### 주요 목표 및 결과 -- **The Black Hole:** EHT was trained on a supermassive black hole - approximately 55 million light-years from Earth, lying at the center - of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is - 6.5 billion times the Sun's. It had been studied for - [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before - had a black hole been visually observed. +* **우주를 보는 새로운 방식:** EHT라는 획기적인 발상의 토대는 [아서 에딩턴 경][eddington]의 관측으로 아인슈타인의 일반 상대성이론이 최초로 관측적 지지를 받았던 시기인 100년 전에 마련되었습니다. -- **Comparing Observations to Theory:** From Einstein’s general theory of - relativity, scientists expected to find a shadow-like region caused by - gravitational bending and capture of light. Scientists could - use it to measure the black hole's enormous mass. +* **블랙홀:** EHT는 처녀자리 은하단의 Messier 87(M87) 은하의 중심부에 있는 초대질량 블랙홀로 훈련되었으며 이는 지구에서 약 5500만 광년 떨어져 있습니다. 이 천체의 질량은 태양의 65억 배입니다. [100년 넘게](https://www.jpl.nasa.gov/news/news.php?feature=7385) 연구되었으나, 블랙홀을 시각적으로 볼 수 있게 구현한 바는 없었습니다. -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **관찰과 이론의 비교:** 아인슈타인의 일반 상대성이론에 따라 과학자들은 중력의 시공간 왜곡이나 빛 흡수에 의해 어둡게 보이는 영역이 나타날 것으로 예측하였습니다. 과학자들은 이를 블랙홀의 엄청난 질량을 재는 데 이용할 수 있었죠. -### The Challenges +### 도전 -- **Computational scale** +* **계산의 규모** - EHT poses massive data-processing challenges, including rapid atmospheric - phase fluctuations, large recording bandwidth, and telescopes that are - widely dissimilar and geographically dispersed. + EHT는 급격한 대기 위상의 변동, 큰 기록 대역폭, 완전히 다르고 지리적으로 분산된 망원경 등의 문제를 포함한 막대한 데이터를 처리해야 하는 문제를 낳습니다. -- **Too much information** +* **지나치게 많은 정보** - Each day EHT generates over 350 terabytes of observations, stored on - helium-filled hard drives. Reducing the volume and complexity of this much - data is enormously difficult. + EHT는 매일 350 테라바이트의 관측 결과를 생성하며, 이 정보는 헬륨으로 채운 하드 드라이브에 저장됩니다. 이토록 많은 데이터의 양과 복잡성을 줄여나가는 것은 지극히 어려운 일입니다. -- **Into the unknown** +* **잘 알지 못함** - When the goal is to see something never before seen, how can scientists be - confident the image is correct? + 만약 목표가 이전에 본 적이 없는 것을 보는 것이라면, 과학자들은 어떻게 이 사진이 옳다고 입증할 수 있을까요? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHT Data Processing Pipeline' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT 데이터 처리 파이프라인' alt = '데이터 파이프라인' align = 'center' attribution = '(다이어그램 크레딧: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} -## NumPy’s Role +## NumPy의 역할 -What if there's a problem with the data? Or perhaps an algorithm relies too -heavily on a particular assumption. Will the image change drastically if a -single parameter is changed? +데이터에 만약 문제가 있다면 어떨까요? 아니면 알고리즘이 특정 가정에 지나치게 의존할 수도 있습니다. 매개변수 하나만 달라져도 사진이 크게 바뀔까요? -The EHT collaboration met these challenges by having independent teams -evaluate the data, using both established and cutting-edge image reconstruction -techniques. When results proved consistent, they were combined to yield the -first-of-a-kind image of the black hole. +EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, 개개의 팀이 데이터를 평가하도록 하여 이런 문제를 해결했습니다. 결과가 일관적이라는 것을 검증한 뒤, 이들을 결합해 최초의 블랙홀 이미지를 만들어내었습니다. -Their work illustrates the role the scientific Python ecosystem plays in -advancing science through collaborative data analysis. +그들의 연구는 협업 데이터 분석을 통해 과학을 발전시키는 과학적인 Python 생태계의 역할을 보여줍니다. {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'The role of NumPy in Black Hole imaging' +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} {{< /figure >}} -For example, the [`eht-imaging`][ehtim] Python package provides tools for -simulating and performing image reconstruction on VLBI data. -NumPy is at the core of array data processing used -in this package, as illustrated by the partial software -dependency chart below. +예를 들어, [`eht-imaging`][ehtim] Python 패키지는 VLBI 데이터를 통해 실험이나 이미지 재구성을 수행할 때 필요한 도구를 제공합니다. NumPy는 아래 소프트웨어 종속성 차트에 나와 있는 것처럼 이 패키지에서 사용되는 배열 데이터 처리의 핵심 역할을 합니다. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'numpy를 강조하는 ehtim의 종속성 지도' title = 'NumPy를 강조하는 ehtim 패키지의 소프트웨어 종속성 차트' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the -data processing pipeline for imaging the black hole. -The standard astronomical file formats and time/coordinate transformations -were handled by [Astropy][astropy], while [Matplotlib][mpl] was used -in visualizing data throughout the analysis pipeline, including the generation -of the final image of the black hole. +NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) 등의 다른 많은 패키지가 블랙홀을 시각화하는 데이터 처리 파이프라인의 일부입니다. 표준 천문 파일 형식과 시간/좌표 변환에는 [Astropy][astropy]가 쓰였고 [Matplotlib][mpl]는 분석 과정 전체에서 블랙홀의 최종 사진을 생성하는 등 데이터를 시각화하는 데 쓰였습니다. -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ +## 요약 -## Summary - -The efficient and adaptable n-dimensional array that is NumPy's central feature -enabled researchers to manipulate large numerical datasets, providing a -foundation for the first-ever image of a black hole. A landmark moment in -science, it gives stunning visual evidence of Einstein’s theory. The -achievement encompasses not only technological breakthroughs but also -international collaboration among over 200 scientists and some of the world's -best radio observatories. Innovative algorithms and data processing -techniques, improving upon existing astronomical models, helped unfold a -mystery of the universe. +NumPy의 핵심 기능인 효율적이고 유용한 n차원 배열은 연구자들이 대규모 수치 데이터셋을 다룰 수 있도록 하여 최초의 블랙홀 사진을 만드는 데 토대를 제공했습니다. 이번 관측은 아인슈타인의 이론에 훌륭한 시각적 증거를 준 관측으로, 과학계에 한 획을 그은 순간이었습니다. 기술적 혁신뿐만 아니라 200명 이상의 과학자와 세계 최고의 전파 관측소 간의 국제 협력도 이루어 냈습니다. 기존의 천문학 모델을 개선한 혁신적인 알고리즘과 데이터 처리 기술이 우주의 비밀을 알아내는 데 도움을 주었습니다. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy를 통한 이익' title = '활용된 주요 NumPy 기능' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://ko.wikipedia.org/wiki/%EC%95%84%EC%84%9C_%EC%8A%A4%ED%83%A0%EB%A6%AC_%EC%97%90%EB%94%A9%ED%84%B4#%EC%9D%BC%EB%B0%98%EC%83%81%EB%8C%80%EC%84%B1%EC%9D%B4%EB%A1%A0%EC%9D%98_%EC%8B%A4%ED%97%98%EC%A0%81_%EA%B2%80%EC%A6%9D + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 383d3481872bea8126ac246a412e9ee7db15c003 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:00 +0200 Subject: [PATCH 328/586] New translations blackhole-image.md (Russian) --- content/ru/case-studies/blackhole-image.md | 130 ++++++--------------- 1 file changed, 33 insertions(+), 97 deletions(-) diff --git a/content/ru/case-studies/blackhole-image.md b/content/ru/case-studies/blackhole-image.md index bfa12875ff..22334ebed0 100644 --- a/content/ru/case-studies/blackhole-image.md +++ b/content/ru/case-studies/blackhole-image.md @@ -4,141 +4,77 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" - -> }} -> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> > {{< /blockquote >}} ## A telescope the size of the earth -The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an -array of eight ground-based radio telescopes forming a computational telescope -the size of the earth, studing the universe with unprecedented -sensitivity and resolution. The huge virtual telescope, which uses a technique -called very-long-baseline interferometry (VLBI), has an angular resolution of -[20 micro-arcseconds][resolution] — enough to read a newspaper in New York -from a sidewalk café in Paris! - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! ### Key Goals and Results -- **A New View of the Universe:** - The groundwork for the EHT's groundbreaking image had been laid 100 years - earlier when [Sir Arthur Eddington][eddington] yielded the first - observational support of Einstein's theory of general relativity. +* **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. -- **The Black Hole:** EHT was trained on a supermassive black hole - approximately 55 million light-years from Earth, lying at the center - of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is - 6.5 billion times the Sun's. It had been studied for - [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before - had a black hole been visually observed. +* **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. -- **Comparing Observations to Theory:** From Einstein’s general theory of - relativity, scientists expected to find a shadow-like region caused by - gravitational bending and capture of light. Scientists could - use it to measure the black hole's enormous mass. - -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. ### The Challenges -- **Computational scale** +* **Computational scale** - EHT poses massive data-processing challenges, including rapid atmospheric - phase fluctuations, large recording bandwidth, and telescopes that are - widely dissimilar and geographically dispersed. + EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. -- **Too much information** +* **Too much information** - Each day EHT generates over 350 terabytes of observations, stored on - helium-filled hard drives. Reducing the volume and complexity of this much - data is enormously difficult. + Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. -- **Into the unknown** +* **Into the unknown** - When the goal is to see something never before seen, how can scientists be - confident the image is correct? + When the goal is to see something never before seen, how can scientists be confident the image is correct? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHT Data Processing Pipeline' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## NumPy’s Role -What if there's a problem with the data? Or perhaps an algorithm relies too -heavily on a particular assumption. Will the image change drastically if a -single parameter is changed? +What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? -The EHT collaboration met these challenges by having independent teams -evaluate the data, using both established and cutting-edge image reconstruction -techniques. When results proved consistent, they were combined to yield the -first-of-a-kind image of the black hole. +The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. -Their work illustrates the role the scientific Python ecosystem plays in -advancing science through collaborative data analysis. +Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'The role of NumPy in Black Hole imaging' +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging' {{< /figure >}} -For example, the [`eht-imaging`][ehtim] Python package provides tools for -simulating and performing image reconstruction on VLBI data. -NumPy is at the core of array data processing used -in this package, as illustrated by the partial software -dependency chart below. +For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the -data processing pipeline for imaging the black hole. -The standard astronomical file formats and time/coordinate transformations -were handled by [Astropy][astropy], while [Matplotlib][mpl] was used -in visualizing data throughout the analysis pipeline, including the generation -of the final image of the black hole. - -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ +Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. ## Summary -The efficient and adaptable n-dimensional array that is NumPy's central feature -enabled researchers to manipulate large numerical datasets, providing a -foundation for the first-ever image of a black hole. A landmark moment in -science, it gives stunning visual evidence of Einstein’s theory. The -achievement encompasses not only technological breakthroughs but also -international collaboration among over 200 scientists and some of the world's -best radio observatories. Innovative algorithms and data processing -techniques, improving upon existing astronomical models, helped unfold a -mystery of the universe. +The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 1dc694d15eca022b1c2bf8113dd7a5352152dafe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:01 +0200 Subject: [PATCH 329/586] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 130 ++++++--------------- 1 file changed, 33 insertions(+), 97 deletions(-) diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md index bfa12875ff..22334ebed0 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -4,141 +4,77 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="Katie Bouman, _Assistant Professor, Computing & Mathematical Sciences, Caltech_" - -> }} -> Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> > {{< /blockquote >}} ## A telescope the size of the earth -The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an -array of eight ground-based radio telescopes forming a computational telescope -the size of the earth, studing the universe with unprecedented -sensitivity and resolution. The huge virtual telescope, which uses a technique -called very-long-baseline interferometry (VLBI), has an angular resolution of -[20 micro-arcseconds][resolution] — enough to read a newspaper in New York -from a sidewalk café in Paris! - -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! ### Key Goals and Results -- **A New View of the Universe:** - The groundwork for the EHT's groundbreaking image had been laid 100 years - earlier when [Sir Arthur Eddington][eddington] yielded the first - observational support of Einstein's theory of general relativity. +* **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. -- **The Black Hole:** EHT was trained on a supermassive black hole - approximately 55 million light-years from Earth, lying at the center - of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is - 6.5 billion times the Sun's. It had been studied for - [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before - had a black hole been visually observed. +* **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. -- **Comparing Observations to Theory:** From Einstein’s general theory of - relativity, scientists expected to find a shadow-like region caused by - gravitational bending and capture of light. Scientists could - use it to measure the black hole's enormous mass. - -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. ### The Challenges -- **Computational scale** +* **Computational scale** - EHT poses massive data-processing challenges, including rapid atmospheric - phase fluctuations, large recording bandwidth, and telescopes that are - widely dissimilar and geographically dispersed. + EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. -- **Too much information** +* **Too much information** - Each day EHT generates over 350 terabytes of observations, stored on - helium-filled hard drives. Reducing the volume and complexity of this much - data is enormously difficult. + Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. -- **Into the unknown** +* **Into the unknown** - When the goal is to see something never before seen, how can scientists be - confident the image is correct? + When the goal is to see something never before seen, how can scientists be confident the image is correct? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHT Data Processing Pipeline' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## NumPy’s Role -What if there's a problem with the data? Or perhaps an algorithm relies too -heavily on a particular assumption. Will the image change drastically if a -single parameter is changed? +What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? -The EHT collaboration met these challenges by having independent teams -evaluate the data, using both established and cutting-edge image reconstruction -techniques. When results proved consistent, they were combined to yield the -first-of-a-kind image of the black hole. +The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. -Their work illustrates the role the scientific Python ecosystem plays in -advancing science through collaborative data analysis. +Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'The role of NumPy in Black Hole imaging' +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging' {{< /figure >}} -For example, the [`eht-imaging`][ehtim] Python package provides tools for -simulating and performing image reconstruction on VLBI data. -NumPy is at the core of array data processing used -in this package, as illustrated by the partial software -dependency chart below. +For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the -data processing pipeline for imaging the black hole. -The standard astronomical file formats and time/coordinate transformations -were handled by [Astropy][astropy], while [Matplotlib][mpl] was used -in visualizing data throughout the analysis pipeline, including the generation -of the final image of the black hole. - -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ +Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. ## Summary -The efficient and adaptable n-dimensional array that is NumPy's central feature -enabled researchers to manipulate large numerical datasets, providing a -foundation for the first-ever image of a black hole. A landmark moment in -science, it gives stunning visual evidence of Einstein’s theory. The -achievement encompasses not only technological breakthroughs but also -international collaboration among over 200 scientists and some of the world's -best radio observatories. Innovative algorithms and data processing -techniques, improving upon existing astronomical models, helped unfold a -mystery of the universe. +The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 584c0f59df9f0d13c9a52dae04d9920b547aae3b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:03 +0200 Subject: [PATCH 330/586] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 80 ++++++++-------------- 1 file changed, 30 insertions(+), 50 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index cb3b12bd4c..3f4031312d 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -3,60 +3,44 @@ title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Créditos: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +src = '/images/content_images/cs/blackhole.jpg' title = 'Buraco Negro M87' alt = 'imagem do buraco negro' attribution = '(Créditos de Imagem: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="Katie Bouman, _Professora Assistente, Ciências da Computação e Matemática, Caltech_" - -> }} -> Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> > {{< /blockquote >}} ## Um telescópio do tamanho da Terra O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um conjunto de oito telescópios em solo formando um telescópio computacional do tamanho da Terra, projetado para estudar o universo com sensibilidade e resolução sem precedentes. O enorme telescópio virtual, que usa uma técnica chamada interferometria de longa linha de base (VLBI), tem uma resolução angular de [20 micro-arcossegundos][resolution] — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris! -[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole - ### Principais Objetivos e Resultados -- **Uma nova visão do universo:** A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. - -- **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. +* **Uma nova visão do universo:**A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. -- **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. +* **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +* **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. ### Desafios -- **Escala computacional** +* **Escala computacional** - O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. + O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. -- **Muitas informações** +* **Muitas informações** - A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. + A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. -- **Em direção ao desconhecido** +* **Em direção ao desconhecido** - Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? + Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'Etapas de Processamento de Dados do EHT' -alt = 'data pipeline' -align = 'center' -attribution = '(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Pipeline de Processamento de Dados' alt = 'pipeline de dados' align = 'center' attribution = '(Créditos do Diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**O papel do NumPy na criação da primeira imagem de um Buraco Negro**" >}} ## O papel do NumPy @@ -67,34 +51,30 @@ A colaboração do EHT venceu esses desafios ao estabelecer equipes independente O trabalho desse grupo ilustra o papel do ecossistema científico do Python no avanço da ciência através da análise de dados colaborativa. {{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'O papel do NumPy na criação da primeira imagem de um Buraco Negro' +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Funcionalidades-chave do NumPy utilizadas**" >}} {{< /figure >}} -Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. -O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. +Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Diagrama de dependência de software do pacote ehtim evidenciando o NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'mapa de dependência ehtim destacando o numpy' title = 'Gráfico de dependência de software do pacote ehtim destacando o NumPy' {{< /figure >}} -[ehtim]: https://github.com/achael/eht-imaging - -Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na _pipeline_ de processamento de dados para criar a imagem do buraco negro. -Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. - -[astropy]: https://www.astropy.org/ -[mpl]: https://matplotlib.org/ +Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. ## Resumo A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo. Eles usaram algoritmos e técnicas de processamento de dados inovadores, que aperfeiçoaram os modelos astronômicos existentes, para ajudar a descobrir um dos mistérios do universo. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Funcionalidades-chave do NumPy utilizadas' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'benefícios do numpy' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 634e98e9fbea97616d35e5736e22fa43f272067e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:04 +0200 Subject: [PATCH 331/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 161 ++++++--------------------- 1 file changed, 37 insertions(+), 124 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 4bdbc88177..08f54b7547 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -4,161 +4,74 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Gravitational Waves' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _LIGO Scientific Collaboration_" >}} -The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} ## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) -Gravitational waves are ripples in the fabric of space and time, generated by -cataclysmic events in the universe such as collision and merging of two black -holes or coalescing binary stars or supernovae. Observing GW can not only help -in studying gravity but also in understanding some of the obscure phenomena in -the distant universe and its impact. - -The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) -was designed to open the field of gravitational-wave astrophysics through the -direct detection of gravitational waves predicted by Einstein’s General Theory -of Relativity. It comprises two widely separated interferometers within the -United States — one in Hanford, Washington and the other in Livingston, -Louisiana — operated in unison to detect gravitational waves. Each of them has -multi-kilometer-scale gravitational wave detectors that use laser -interferometry. The LIGO Scientific Collaboration (LSC), is a group of more -than 1000 scientists from universities around the United States and in 14 -other countries supported by more than 90 universities and research institutes; -approximately 250 students actively contributing to the collaboration. The new -LIGO discovery is the first observation of gravitational waves themselves, -made by measuring the tiny disturbances the waves make to space and time as -they pass through the earth. It has opened up new astrophysical frontiers -that explore the warped side of the universe—objects and phenomena that are -made from warped spacetime. +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. + ### Key Objectives -- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to - detect gravitational waves from some of the most violent and energetic - processes in the Universe, the data LIGO collects may have far-reaching - effects on many areas of physics including gravitation, relativity, - astrophysics, cosmology, particle physics, and nuclear physics. -- Crunch observed data via numerical relativity computations that involves - complex maths in order to discern signal from noise, filter out relevant - signal and statistically estimate significance of observed data -- Data visualization so that the binary / numerical results can be - comprehended. +* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. +* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data +* Data visualization so that the binary / numerical results can be comprehended. + + ### The Challenges -- **Computation** - - Gravitational Waves are hard to detect as they produce a very small effect - and have tiny interaction with matter. Processing and analyzing all of - LIGO's data requires a vast computing infrastructure.After taking care of - noise, which is billions of times of the signal, there is still very - complex relativity equations and huge amounts of data which present a - computational challenge: - [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) - spread on 6 dedicated LIGO clusters - -- **Data Deluge** - - As observational devices become more sensitive and reliable, the challenges - posed by data deluge and finding a needle in a haystack rise multi-fold. - LIGO generates terabytes of data every day! Making sense of this data - requires an enormous effort for each and every detection. For example, the - signals being collected by LIGO must be matched by supercomputers against - hundreds of thousands of templates of possible gravitational-wave signatures. - -- **Visualization** - - Once the obstacles related to understanding Einstein’s equations well - enough to solve them using supercomputers are taken care of, the next big - challenge was making data comprehensible to the human brain. Simulation - modeling as well as signal detection requires effective visualization - techniques. Visualization also plays a role in lending more credibility - to numerical relativity in the eyes of pure science aficionados, who did - not give enough importance to numerical relativity until imaging and - simulations made it easier to comprehend results for a larger audience. - Speed of complex computations and rendering, re-rendering images and - simulations using latest experimental inputs and insights can be a time - consuming activity that challenges researchers in this domain. +* **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + +* **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + +* **Visualization** + + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Estimated gravitational-wave strain amplitude from GW150914' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} ## NumPy’s Role in the Detection of Gravitational Waves -Gravitational waves emitted from the merger cannot be computed using any -technique except brute force numerical relativity using supercomputers. -The amount of data LIGO collects is as incomprehensibly large as gravitational -wave signals are small. - -NumPy, the standard numerical analysis package for Python, was utilized by -the software used for various tasks performed during the GW detection project -at LIGO. NumPy helped in solving complex maths and data manipulation at high -speed. Here are some examples: - -- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch - detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) - (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) -- Data retrieval: Deciding which data can be analyzed, figuring out whether it - contains a signal - needle in a haystack -- Statistical analysis: estimate the statistical significance of observational - data, estimating the signal parameters (e.g. masses of stars, spin velocity, - and distance) by comparison with a model. -- Visualization of data +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: + +* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack +* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* Visualization of data - Time series - Spectrograms -- Compute Correlations -- Key [Software](https://github.com/lscsoft) developed in GW data analysis - such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and - [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for - providing object based interfaces to utilities, tools, and methods for - studying data from gravitational-wave detectors. +* Compute Correlations +* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Dependency graph showing how PyCBC package depends on NumPy' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' {{< /figure >}} ## Summary -GW detection has enabled researchers to discover entirely unexpected phenomena -while providing new insight into many of the most profound astrophysical -phenomena known. Number crunching and data visualization is a crucial step -that helps scientists gain insights into data gathered from the scientific -observations and understand the results. The computations are complex and -cannot be comprehended by humans unless it is visualized using computer -simulations that are fed with the real observed data and analysis. NumPy -along with other Python packages such as matplotlib, pandas, and scikit-learn -is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to -answer complex questions and discover new horizons in our understanding of the -universe. +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From 0f25aa3e07115d05b3d2b66ddb802f07796e2f41 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:05 +0200 Subject: [PATCH 332/586] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 161 ++++++--------------------- 1 file changed, 37 insertions(+), 124 deletions(-) diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md index 4bdbc88177..08f54b7547 100644 --- a/content/ar/case-studies/gw-discov.md +++ b/content/ar/case-studies/gw-discov.md @@ -4,161 +4,74 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Gravitational Waves' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _LIGO Scientific Collaboration_" >}} -The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} ## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) -Gravitational waves are ripples in the fabric of space and time, generated by -cataclysmic events in the universe such as collision and merging of two black -holes or coalescing binary stars or supernovae. Observing GW can not only help -in studying gravity but also in understanding some of the obscure phenomena in -the distant universe and its impact. - -The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) -was designed to open the field of gravitational-wave astrophysics through the -direct detection of gravitational waves predicted by Einstein’s General Theory -of Relativity. It comprises two widely separated interferometers within the -United States — one in Hanford, Washington and the other in Livingston, -Louisiana — operated in unison to detect gravitational waves. Each of them has -multi-kilometer-scale gravitational wave detectors that use laser -interferometry. The LIGO Scientific Collaboration (LSC), is a group of more -than 1000 scientists from universities around the United States and in 14 -other countries supported by more than 90 universities and research institutes; -approximately 250 students actively contributing to the collaboration. The new -LIGO discovery is the first observation of gravitational waves themselves, -made by measuring the tiny disturbances the waves make to space and time as -they pass through the earth. It has opened up new astrophysical frontiers -that explore the warped side of the universe—objects and phenomena that are -made from warped spacetime. +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. + ### Key Objectives -- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to - detect gravitational waves from some of the most violent and energetic - processes in the Universe, the data LIGO collects may have far-reaching - effects on many areas of physics including gravitation, relativity, - astrophysics, cosmology, particle physics, and nuclear physics. -- Crunch observed data via numerical relativity computations that involves - complex maths in order to discern signal from noise, filter out relevant - signal and statistically estimate significance of observed data -- Data visualization so that the binary / numerical results can be - comprehended. +* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. +* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data +* Data visualization so that the binary / numerical results can be comprehended. + + ### The Challenges -- **Computation** - - Gravitational Waves are hard to detect as they produce a very small effect - and have tiny interaction with matter. Processing and analyzing all of - LIGO's data requires a vast computing infrastructure.After taking care of - noise, which is billions of times of the signal, there is still very - complex relativity equations and huge amounts of data which present a - computational challenge: - [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) - spread on 6 dedicated LIGO clusters - -- **Data Deluge** - - As observational devices become more sensitive and reliable, the challenges - posed by data deluge and finding a needle in a haystack rise multi-fold. - LIGO generates terabytes of data every day! Making sense of this data - requires an enormous effort for each and every detection. For example, the - signals being collected by LIGO must be matched by supercomputers against - hundreds of thousands of templates of possible gravitational-wave signatures. - -- **Visualization** - - Once the obstacles related to understanding Einstein’s equations well - enough to solve them using supercomputers are taken care of, the next big - challenge was making data comprehensible to the human brain. Simulation - modeling as well as signal detection requires effective visualization - techniques. Visualization also plays a role in lending more credibility - to numerical relativity in the eyes of pure science aficionados, who did - not give enough importance to numerical relativity until imaging and - simulations made it easier to comprehend results for a larger audience. - Speed of complex computations and rendering, re-rendering images and - simulations using latest experimental inputs and insights can be a time - consuming activity that challenges researchers in this domain. +* **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + +* **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + +* **Visualization** + + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Estimated gravitational-wave strain amplitude from GW150914' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} ## NumPy’s Role in the Detection of Gravitational Waves -Gravitational waves emitted from the merger cannot be computed using any -technique except brute force numerical relativity using supercomputers. -The amount of data LIGO collects is as incomprehensibly large as gravitational -wave signals are small. - -NumPy, the standard numerical analysis package for Python, was utilized by -the software used for various tasks performed during the GW detection project -at LIGO. NumPy helped in solving complex maths and data manipulation at high -speed. Here are some examples: - -- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch - detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) - (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) -- Data retrieval: Deciding which data can be analyzed, figuring out whether it - contains a signal - needle in a haystack -- Statistical analysis: estimate the statistical significance of observational - data, estimating the signal parameters (e.g. masses of stars, spin velocity, - and distance) by comparison with a model. -- Visualization of data +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: + +* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack +* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* Visualization of data - Time series - Spectrograms -- Compute Correlations -- Key [Software](https://github.com/lscsoft) developed in GW data analysis - such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and - [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for - providing object based interfaces to utilities, tools, and methods for - studying data from gravitational-wave detectors. +* Compute Correlations +* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Dependency graph showing how PyCBC package depends on NumPy' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' {{< /figure >}} ## Summary -GW detection has enabled researchers to discover entirely unexpected phenomena -while providing new insight into many of the most profound astrophysical -phenomena known. Number crunching and data visualization is a crucial step -that helps scientists gain insights into data gathered from the scientific -observations and understand the results. The computations are complex and -cannot be comprehended by humans unless it is visualized using computer -simulations that are fed with the real observed data and analysis. NumPy -along with other Python packages such as matplotlib, pandas, and scikit-learn -is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to -answer complex questions and discover new horizons in our understanding of the -universe. +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From 1092bead22e3d6baaa3ebcf5b6f97b2ed9942a25 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:06 +0200 Subject: [PATCH 333/586] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 139 +++++++-------------------- 1 file changed, 37 insertions(+), 102 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 7d7311c141..e7faa49de9 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -3,140 +3,75 @@ title: "ケーススタディ: 重力波の発見" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = '重力波' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**重力波**" alt="binary coalesce black hole generating gravitational waves" attr="*(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _LIGOサイエンティフィック・コラボレーション_" >}} -科学計算のためのPythonエコシステムはLIGOで行われている研究のための重要なインフラです。 -{{< /blockquote >}} +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGOの科学調査協力により*" >}} LIGOで行われる研究には、科学的なPythonエコシステムが重要なインフラとなっています。 {{< /blockquote >}} ## [重力波](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) と [LIGO](https://www.ligo.caltech.edu) について -重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 Observing GW can not only help -in studying gravity but also in understanding some of the obscure phenomena in -the distant universe and its impact. - -\[レーザー干渉計重力波天文台(LIGO)\](https://www. It comprises two widely separated interferometers within the -United States — one in Hanford, Washington and the other in Livingston, -Louisiana — operated in unison to detect gravitational waves. Each of them has -multi-kilometer-scale gravitational wave detectors that use laser -interferometry. The LIGO Scientific Collaboration (LSC), is a group of more -than 1000 scientists from universities around the United States and in 14 -other countries supported by more than 90 universities and research institutes; -approximately 250 students actively contributing to the collaboration. The new -LIGO discovery is the first observation of gravitational waves themselves, -made by measuring the tiny disturbances the waves make to space and time as -they pass through the earth. It has opened up new astrophysical frontiers -that explore the warped side of the universe—objects and phenomena that are -made from warped spacetime. +重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 + +[レーザー干渉計重力波天文台(LIGO)](https://www.ligo.caltech.edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに伴う現象を切り拓くものです。 + ### 主な目的 -- LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 -- Crunch observed data via numerical relativity computations that involves - complex maths in order to discern signal from noise, filter out relevant - signal and statistically estimate significance of observed data -- バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 +* LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 +* 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 +* バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 + + ### 課題 -- **計算** - - Gravitational Waves are hard to detect as they produce a very small effect - and have tiny interaction with matter. Processing and analyzing all of - LIGO's data requires a vast computing infrastructure.After taking care of - noise, which is billions of times of the signal, there is still very - complex relativity equations and huge amounts of data which present a - computational challenge: - [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) - spread on 6 dedicated LIGO clusters - -- **データの氾濫** - - As observational devices become more sensitive and reliable, the challenges - posed by data deluge and finding a needle in a haystack rise multi-fold. - LIGO generates terabytes of data every day! Making sense of this data - requires an enormous effort for each and every detection. For example, the - signals being collected by LIGO must be matched by supercomputers against - hundreds of thousands of templates of possible gravitational-wave signatures. - -- **可視化** - - Once the obstacles related to understanding Einstein’s equations well - enough to solve them using supercomputers are taken care of, the next big - challenge was making data comprehensible to the human brain. Simulation - modeling as well as signal detection requires effective visualization - techniques. Visualization also plays a role in lending more credibility - to numerical relativity in the eyes of pure science aficionados, who did - not give enough importance to numerical relativity until imaging and - simulations made it easier to comprehend results for a larger audience. - Speed of complex computations and rendering, re-rendering images and - simulations using latest experimental inputs and insights can be a time - consuming activity that challenges researchers in this domain. +* **計算** + + 合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 + +* **データの氾濫** + + 観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているのです！ この大量のデータを解釈するには、各検出ごとに多大な労力が必要です。 例えば、LIGOによって収集される信号は、数十万個の重力波シグネチャのテンプレートで構成されており、スーパーコンピュータでしか解析できません。 + +* **可視化** + + アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'GW150914から推定される重力波の歪みの振幅' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**GwPyのNumPy依存グラフ**" >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} ## 重力波の検出におけるNumPyの役割 -合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。 -合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 +ブラックホール合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。 -Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。 NumPy helped in solving complex maths and data manipulation at high -speed. Here are some examples: +Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。 -- [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 -- データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 -- 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 -- データ可視化 +* [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 +* データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 +* 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 +* データ可視化 - 時系列データ - スペクトログラム -- 相関計算 -- 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 +* 相関計算 +* 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'GwPyのNumPy依存グラフ' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'PyCBCのNumPy依存グラフ' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' {{< /figure >}} ## まとめ -GW detection has enabled researchers to discover entirely unexpected phenomena -while providing new insight into many of the most profound astrophysical -phenomena known. Number crunching and data visualization is a crucial step -that helps scientists gain insights into data gathered from the scientific -observations and understand the results. The computations are complex and -cannot be comprehended by humans unless it is visualized using computer -simulations that are fed with the real observed data and analysis. NumPy -along with other Python packages such as matplotlib, pandas, and scikit-learn -is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to -answer complex questions and discover new horizons in our understanding of the -universe. +GW検出により、研究者は完全に予期せぬ現象 を発見することができ、同時に知られている最も深遠な天体物理学 現象の多くに新たな洞察を提供しています。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な現象の疑問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = '利用されたNumPyの主要機能' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From f63132355bbd0b7c0756159a97f9b4ad9786a75f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:07 +0200 Subject: [PATCH 334/586] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 193 ++++++++------------------- 1 file changed, 53 insertions(+), 140 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index 4bdbc88177..e72252a423 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -1,164 +1,77 @@ --- -title: "Case Study: Discovery of Gravitational Waves" +title: "사례 연구: 중력파의 발견" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Gravitational Waves' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**중력파**" alt="이항 결합하며 중력파를 생성하는 블랙홀" attr="*(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +src = '/images/content_images/cs/gw_sxs_image.png' title = '중력파' alt = '이항 결합하며 중력파를 생성하는 블랙홀' attribution = '(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _LIGO Scientific Collaboration_" >}} -The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} -## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) - -Gravitational waves are ripples in the fabric of space and time, generated by -cataclysmic events in the universe such as collision and merging of two black -holes or coalescing binary stars or supernovae. Observing GW can not only help -in studying gravity but also in understanding some of the obscure phenomena in -the distant universe and its impact. - -The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) -was designed to open the field of gravitational-wave astrophysics through the -direct detection of gravitational waves predicted by Einstein’s General Theory -of Relativity. It comprises two widely separated interferometers within the -United States — one in Hanford, Washington and the other in Livingston, -Louisiana — operated in unison to detect gravitational waves. Each of them has -multi-kilometer-scale gravitational wave detectors that use laser -interferometry. The LIGO Scientific Collaboration (LSC), is a group of more -than 1000 scientists from universities around the United States and in 14 -other countries supported by more than 90 universities and research institutes; -approximately 250 students actively contributing to the collaboration. The new -LIGO discovery is the first observation of gravitational waves themselves, -made by measuring the tiny disturbances the waves make to space and time as -they pass through the earth. It has opened up new astrophysical frontiers -that explore the warped side of the universe—objects and phenomena that are -made from warped spacetime. - -### Key Objectives - -- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to - detect gravitational waves from some of the most violent and energetic - processes in the Universe, the data LIGO collects may have far-reaching - effects on many areas of physics including gravitation, relativity, - astrophysics, cosmology, particle physics, and nuclear physics. -- Crunch observed data via numerical relativity computations that involves - complex maths in order to discern signal from noise, filter out relevant - signal and statistically estimate significance of observed data -- Data visualization so that the binary / numerical results can be - comprehended. - -### The Challenges - -- **Computation** - - Gravitational Waves are hard to detect as they produce a very small effect - and have tiny interaction with matter. Processing and analyzing all of - LIGO's data requires a vast computing infrastructure.After taking care of - noise, which is billions of times of the signal, there is still very - complex relativity equations and huge amounts of data which present a - computational challenge: - [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) - spread on 6 dedicated LIGO clusters - -- **Data Deluge** - - As observational devices become more sensitive and reliable, the challenges - posed by data deluge and finding a needle in a haystack rise multi-fold. - LIGO generates terabytes of data every day! Making sense of this data - requires an enormous effort for each and every detection. For example, the - signals being collected by LIGO must be matched by supercomputers against - hundreds of thousands of templates of possible gravitational-wave signatures. - -- **Visualization** - - Once the obstacles related to understanding Einstein’s equations well - enough to solve them using supercomputers are taken care of, the next big - challenge was making data comprehensible to the human brain. Simulation - modeling as well as signal detection requires effective visualization - techniques. Visualization also plays a role in lending more credibility - to numerical relativity in the eyes of pure science aficionados, who did - not give enough importance to numerical relativity until imaging and - simulations made it easier to comprehend results for a larger audience. - Speed of complex computations and rendering, re-rendering images and - simulations using latest experimental inputs and insights can be a time - consuming activity that challenges researchers in this domain. +## [중력파](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) 그리고 [LIGO](https://www.ligo.caltech.edu)에 대해 + +중력파는 '시공간 천막'의 물결이라고 할 수 있으며, 두 블랙홀의 충돌이나 병합, 쌍성의 결합 혹은 초신성과 같이 우주가 대격변하는 사건으로부터 생성됩니다. 중력파를 관측하는 것은 비단 중력 연구에 도움을 줄 뿐만 아니라 먼 우주에서의 모호한 현상들과 이것이 미치는 영향에 대해서도 이해할 수 있게 해 줍니다. + +병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. 그들 각각은 레이저 간섭계를 사용하는 수 킬로미터 규모의 중력파 탐지기를 가지고 있습니다. LIGO Scientific Collaboration(LSC) 은 미국 전역과 90개 이상의 대학 및 연구 기관에서 지원하는 14개국의 대학에서 온 1000명 이상의 과학자 그룹입니다. 약 250명의 학생들이 협력에 적극적으로 기여하고 있습니다. LIGO의 새로운 발견은 중력파가 지구를 통과할 때 공간과 시간에 미치는 미세한 교란을 측정함으로써 중력파 자체를 처음으로 관찰한 것입니다. 그것은 우주의 뒤틀린 측면, 즉 뒤틀린 시공간에서 만들어진 물체와 현상을 탐구하는 새로운 천체물리학의 영역을 열었습니다. + + +### 주요 목표 + +* [임무](https://www.ligo.caltech.edu/page/what-is-ligo)는 우주에서 가장 격렬하고 에너지가 넘치는 일부 과정에서 발생하는 중력파를 감지하는 것이지만, LIGO가 수집하는 데이터는 중력, 상대성 이론, 천체 물리학, 우주론, 입자 물리학 및 핵 물리학을 포함한 많은 물리학 영역에 광범위한 영향을 미칠 수 있습니다. +* 노이즈에서 신호를 식별하고, 관련 신호를 필터링하고, 관찰된 데이터의 유의성을 통계적으로 추정하기 위해 복잡한 수학을 포함하는 수치 상대성 계산을 통해 관측된 데이터를 고속 처리합니다. +* 이진/수치 상의 결과를 이해할 수 있도록 데이터 시각화를 진행합니다. + + + +### 도전 + +* **계산** + + 중력파는 매우 작은 효과를 생성하고 물질과의 상호 작용이 작기 때문에 감지하기 어렵습니다. LIGO의 모든 데이터를 처리하고 분석하려면 방대한 컴퓨팅 인프라가 필요합니다. 신호의 수십억 배에 해당하는 노이즈를 처리한 후에도 여전히 매우 복잡한 상대성 방정식과 엄청난 양의 데이터가 있어 계산상의 어려움이 있습니다. [바이너리 병합 분석에 필요한 O(10^7) CPU 시간](https://youtu.be/7mcHknWWzNI)은 6개의 전용 LIGO 클러스터에 퍼져 있습니다. + +* **데이터 홍수** + + 관찰 장치가 더 민감하고 신뢰할 수 있게 됨에 따라 데이터 폭증과 건초더미에서 바늘 찾기로 인해 제기되는 문제가 여러 배로 증가합니다. LIGO는 매일 테라바이트의 데이터를 생성합니다! 이 데이터를 이해하려면 탐지할 때마다 막대한 노력이 필요합니다. 예를 들어, LIGO가 수집하는 신호는 슈퍼컴퓨터에서 수십만 개의 가능한 중력파 서명 템플릿과 일치해야 합니다. + +* **시각화** + + 아인슈타인의 방정식을 슈퍼컴퓨터를 사용하여 풀 수 있을 만큼 충분히 이해하는 것과 관련된 장애물이 처리되면 다음 큰 과제는 데이터를 인간의 두뇌가 이해할 수 있도록 만드는 것입니다. 시뮬레이션 모델링과 신호 감지에는 효과적인 시각화 기술이 필요합니다. 시각화는 또한 이미징 및 시뮬레이션을 통해 더 많은 청중이 결과를 더 쉽게 이해할 수 있게 될 때까지 수치 상대성에 충분한 중요성을 부여하지 않은 순수 과학 애호가의 눈에 수치 상대성에 더 많은 신뢰성을 제공하는 역할을 합니다. 최신 실험 입력 및 통찰력을 사용하여 복잡한 계산 및 렌더링, 이미지 다시 렌더링 및 시뮬레이션의 속도는 이 영역의 연구자에게 도전이 되는 시간 소모적인 활동이 될 수 있습니다. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Estimated gravitational-wave strain amplitude from GW150914' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**Dependency graph showing how GwPy package depends on NumPy**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} + +## 중력파 검출에서 NumPy의 역할 + +병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. + +Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝트 동안 수행되는 다양한 작업에 사용되는 소프트웨어에서 활용되었습니다. NumPy는 복잡한 수학 및 데이터 조작을 고속으로 해결하는 데 도움이 되었습니다. 몇 가지 예시를 들자면: -## NumPy’s Role in the Detection of Gravitational Waves - -Gravitational waves emitted from the merger cannot be computed using any -technique except brute force numerical relativity using supercomputers. -The amount of data LIGO collects is as incomprehensibly large as gravitational -wave signals are small. - -NumPy, the standard numerical analysis package for Python, was utilized by -the software used for various tasks performed during the GW detection project -at LIGO. NumPy helped in solving complex maths and data manipulation at high -speed. Here are some examples: - -- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch - detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) - (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) -- Data retrieval: Deciding which data can be analyzed, figuring out whether it - contains a signal - needle in a haystack -- Statistical analysis: estimate the statistical significance of observational - data, estimating the signal parameters (e.g. masses of stars, spin velocity, - and distance) by comparison with a model. -- Visualization of data - - Time series - - Spectrograms -- Compute Correlations -- Key [Software](https://github.com/lscsoft) developed in GW data analysis - such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and - [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for - providing object based interfaces to utilities, tools, and methods for - studying data from gravitational-wave detectors. +* [신호 처리](https://www.uv.es/virgogroup/Denoising_ROF.html): 글리치 검출, [잡음 식별 및 데이터 결정](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* 데이터 수집: 어떤 데이터를 분석할 수 있을지 결정하고, 모래 속 바늘과 같이 미미한 신호가 있는지 파악 +* 통계적 분석: 관측 데이터의 통계적 유의성 추정, 모델을 비교하여 신호 매개변수(예: 별의 질량, 회전 속도, 거리 등)를 추정 +* 데이터의 시각화 + - 시계열 데이터 + - 스펙트로그램 +* 상관 분석 연산 +* [GwPy](https://gwpy.github.io/docs/stable/overview.html) 및 [PyCBC](https://pycbc.org)와 같은 중력파 데이터 분석에서 개발된 주요 [소프트웨어](https://github.com/lscsoft)는 후드 아래에서 NumPy 및 AstroPy를 사용하여 중력파 검출기에서 데이터를 연구하기 위한 유틸리티, 도구 및 방법에 객체 기반 인터페이스를 제공합니다. {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy 종속성' title = 'GwPy 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Dependency graph showing how PyCBC package depends on NumPy' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy 종속성' title = 'PyCBC 패키지가 NumPy에 종속하는지를 나타내는 종속성 그래프' {{< /figure >}} -## Summary +## 요약 -GW detection has enabled researchers to discover entirely unexpected phenomena -while providing new insight into many of the most profound astrophysical -phenomena known. Number crunching and data visualization is a crucial step -that helps scientists gain insights into data gathered from the scientific -observations and understand the results. The computations are complex and -cannot be comprehended by humans unless it is visualized using computer -simulations that are fed with the real observed data and analysis. NumPy -along with other Python packages such as matplotlib, pandas, and scikit-learn -is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to -answer complex questions and discover new horizons in our understanding of the -universe. +중력파 검출을 통하여 연구자들은 완전히 예상치 못한 현상을 발견하게 됨으로써, 알려진 것 중 가장 난해한 천체물리학적 현상에 대하여 많은 사람들에게 새로운 통찰을 주었습니다. 숫자 계산 및 데이터 시각화는 과학자가 과학적 관찰에서 수집한 데이터에 대한 통찰력을 얻고 결과를 이해하는 데 도움이 되는 중요한 단계입니다. 계산은 복잡하며 실제 관찰 데이터 및 분석을 제공하는 컴퓨터 시뮬레이션을 사용하여 시각화하지 않는 한 사람이 이해할 수 없습니다. NumPy는 matplotlib, pandas 및 scikit-learn과 같은 다른 Python 패키지와 함께 [연구원](https://www.gw-openscience.org/events/GW150914/)이 복잡한 질문에 답하고 우주에 대한 이해의 새로운 지평을 발견할 수 있도록 합니다. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy를 통한 이익' title = '활용된 주요 NumPy 기능' {{< /figure >}} From c329a5ba0f0a8fa4819103d0a8c9dc18416560b9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:08 +0200 Subject: [PATCH 335/586] New translations gw-discov.md (Russian) --- content/ru/case-studies/gw-discov.md | 161 ++++++--------------------- 1 file changed, 37 insertions(+), 124 deletions(-) diff --git a/content/ru/case-studies/gw-discov.md b/content/ru/case-studies/gw-discov.md index 4bdbc88177..08f54b7547 100644 --- a/content/ru/case-studies/gw-discov.md +++ b/content/ru/case-studies/gw-discov.md @@ -4,161 +4,74 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Gravitational Waves' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _LIGO Scientific Collaboration_" >}} -The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} ## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) -Gravitational waves are ripples in the fabric of space and time, generated by -cataclysmic events in the universe such as collision and merging of two black -holes or coalescing binary stars or supernovae. Observing GW can not only help -in studying gravity but also in understanding some of the obscure phenomena in -the distant universe and its impact. - -The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) -was designed to open the field of gravitational-wave astrophysics through the -direct detection of gravitational waves predicted by Einstein’s General Theory -of Relativity. It comprises two widely separated interferometers within the -United States — one in Hanford, Washington and the other in Livingston, -Louisiana — operated in unison to detect gravitational waves. Each of them has -multi-kilometer-scale gravitational wave detectors that use laser -interferometry. The LIGO Scientific Collaboration (LSC), is a group of more -than 1000 scientists from universities around the United States and in 14 -other countries supported by more than 90 universities and research institutes; -approximately 250 students actively contributing to the collaboration. The new -LIGO discovery is the first observation of gravitational waves themselves, -made by measuring the tiny disturbances the waves make to space and time as -they pass through the earth. It has opened up new astrophysical frontiers -that explore the warped side of the universe—objects and phenomena that are -made from warped spacetime. +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. + ### Key Objectives -- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to - detect gravitational waves from some of the most violent and energetic - processes in the Universe, the data LIGO collects may have far-reaching - effects on many areas of physics including gravitation, relativity, - astrophysics, cosmology, particle physics, and nuclear physics. -- Crunch observed data via numerical relativity computations that involves - complex maths in order to discern signal from noise, filter out relevant - signal and statistically estimate significance of observed data -- Data visualization so that the binary / numerical results can be - comprehended. +* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. +* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data +* Data visualization so that the binary / numerical results can be comprehended. + + ### The Challenges -- **Computation** - - Gravitational Waves are hard to detect as they produce a very small effect - and have tiny interaction with matter. Processing and analyzing all of - LIGO's data requires a vast computing infrastructure.After taking care of - noise, which is billions of times of the signal, there is still very - complex relativity equations and huge amounts of data which present a - computational challenge: - [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) - spread on 6 dedicated LIGO clusters - -- **Data Deluge** - - As observational devices become more sensitive and reliable, the challenges - posed by data deluge and finding a needle in a haystack rise multi-fold. - LIGO generates terabytes of data every day! Making sense of this data - requires an enormous effort for each and every detection. For example, the - signals being collected by LIGO must be matched by supercomputers against - hundreds of thousands of templates of possible gravitational-wave signatures. - -- **Visualization** - - Once the obstacles related to understanding Einstein’s equations well - enough to solve them using supercomputers are taken care of, the next big - challenge was making data comprehensible to the human brain. Simulation - modeling as well as signal detection requires effective visualization - techniques. Visualization also plays a role in lending more credibility - to numerical relativity in the eyes of pure science aficionados, who did - not give enough importance to numerical relativity until imaging and - simulations made it easier to comprehend results for a larger audience. - Speed of complex computations and rendering, re-rendering images and - simulations using latest experimental inputs and insights can be a time - consuming activity that challenges researchers in this domain. +* **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + +* **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + +* **Visualization** + + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Estimated gravitational-wave strain amplitude from GW150914' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} ## NumPy’s Role in the Detection of Gravitational Waves -Gravitational waves emitted from the merger cannot be computed using any -technique except brute force numerical relativity using supercomputers. -The amount of data LIGO collects is as incomprehensibly large as gravitational -wave signals are small. - -NumPy, the standard numerical analysis package for Python, was utilized by -the software used for various tasks performed during the GW detection project -at LIGO. NumPy helped in solving complex maths and data manipulation at high -speed. Here are some examples: - -- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch - detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) - (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) -- Data retrieval: Deciding which data can be analyzed, figuring out whether it - contains a signal - needle in a haystack -- Statistical analysis: estimate the statistical significance of observational - data, estimating the signal parameters (e.g. masses of stars, spin velocity, - and distance) by comparison with a model. -- Visualization of data +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: + +* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack +* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* Visualization of data - Time series - Spectrograms -- Compute Correlations -- Key [Software](https://github.com/lscsoft) developed in GW data analysis - such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and - [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for - providing object based interfaces to utilities, tools, and methods for - studying data from gravitational-wave detectors. +* Compute Correlations +* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Dependency graph showing how PyCBC package depends on NumPy' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' {{< /figure >}} ## Summary -GW detection has enabled researchers to discover entirely unexpected phenomena -while providing new insight into many of the most profound astrophysical -phenomena known. Number crunching and data visualization is a crucial step -that helps scientists gain insights into data gathered from the scientific -observations and understand the results. The computations are complex and -cannot be comprehended by humans unless it is visualized using computer -simulations that are fed with the real observed data and analysis. NumPy -along with other Python packages such as matplotlib, pandas, and scikit-learn -is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to -answer complex questions and discover new horizons in our understanding of the -universe. +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From 194af28249b5b7a669e07b3facc368c3d6b391e7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:10 +0200 Subject: [PATCH 336/586] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 161 ++++++--------------------- 1 file changed, 37 insertions(+), 124 deletions(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index 4bdbc88177..08f54b7547 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -4,161 +4,74 @@ sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Gravitational Waves' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _LIGO Scientific Collaboration_" >}} -The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} ## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) -Gravitational waves are ripples in the fabric of space and time, generated by -cataclysmic events in the universe such as collision and merging of two black -holes or coalescing binary stars or supernovae. Observing GW can not only help -in studying gravity but also in understanding some of the obscure phenomena in -the distant universe and its impact. - -The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) -was designed to open the field of gravitational-wave astrophysics through the -direct detection of gravitational waves predicted by Einstein’s General Theory -of Relativity. It comprises two widely separated interferometers within the -United States — one in Hanford, Washington and the other in Livingston, -Louisiana — operated in unison to detect gravitational waves. Each of them has -multi-kilometer-scale gravitational wave detectors that use laser -interferometry. The LIGO Scientific Collaboration (LSC), is a group of more -than 1000 scientists from universities around the United States and in 14 -other countries supported by more than 90 universities and research institutes; -approximately 250 students actively contributing to the collaboration. The new -LIGO discovery is the first observation of gravitational waves themselves, -made by measuring the tiny disturbances the waves make to space and time as -they pass through the earth. It has opened up new astrophysical frontiers -that explore the warped side of the universe—objects and phenomena that are -made from warped spacetime. +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. + ### Key Objectives -- Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to - detect gravitational waves from some of the most violent and energetic - processes in the Universe, the data LIGO collects may have far-reaching - effects on many areas of physics including gravitation, relativity, - astrophysics, cosmology, particle physics, and nuclear physics. -- Crunch observed data via numerical relativity computations that involves - complex maths in order to discern signal from noise, filter out relevant - signal and statistically estimate significance of observed data -- Data visualization so that the binary / numerical results can be - comprehended. +* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. +* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data +* Data visualization so that the binary / numerical results can be comprehended. + + ### The Challenges -- **Computation** - - Gravitational Waves are hard to detect as they produce a very small effect - and have tiny interaction with matter. Processing and analyzing all of - LIGO's data requires a vast computing infrastructure.After taking care of - noise, which is billions of times of the signal, there is still very - complex relativity equations and huge amounts of data which present a - computational challenge: - [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) - spread on 6 dedicated LIGO clusters - -- **Data Deluge** - - As observational devices become more sensitive and reliable, the challenges - posed by data deluge and finding a needle in a haystack rise multi-fold. - LIGO generates terabytes of data every day! Making sense of this data - requires an enormous effort for each and every detection. For example, the - signals being collected by LIGO must be matched by supercomputers against - hundreds of thousands of templates of possible gravitational-wave signatures. - -- **Visualization** - - Once the obstacles related to understanding Einstein’s equations well - enough to solve them using supercomputers are taken care of, the next big - challenge was making data comprehensible to the human brain. Simulation - modeling as well as signal detection requires effective visualization - techniques. Visualization also plays a role in lending more credibility - to numerical relativity in the eyes of pure science aficionados, who did - not give enough importance to numerical relativity until imaging and - simulations made it easier to comprehend results for a larger audience. - Speed of complex computations and rendering, re-rendering images and - simulations using latest experimental inputs and insights can be a time - consuming activity that challenges researchers in this domain. +* **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + +* **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + +* **Visualization** + + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Estimated gravitational-wave strain amplitude from GW150914' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} ## NumPy’s Role in the Detection of Gravitational Waves -Gravitational waves emitted from the merger cannot be computed using any -technique except brute force numerical relativity using supercomputers. -The amount of data LIGO collects is as incomprehensibly large as gravitational -wave signals are small. - -NumPy, the standard numerical analysis package for Python, was utilized by -the software used for various tasks performed during the GW detection project -at LIGO. NumPy helped in solving complex maths and data manipulation at high -speed. Here are some examples: - -- [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch - detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) - (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) -- Data retrieval: Deciding which data can be analyzed, figuring out whether it - contains a signal - needle in a haystack -- Statistical analysis: estimate the statistical significance of observational - data, estimating the signal parameters (e.g. masses of stars, spin velocity, - and distance) by comparison with a model. -- Visualization of data +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: + +* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack +* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* Visualization of data - Time series - Spectrograms -- Compute Correlations -- Key [Software](https://github.com/lscsoft) developed in GW data analysis - such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and - [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for - providing object based interfaces to utilities, tools, and methods for - studying data from gravitational-wave detectors. +* Compute Correlations +* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Dependency graph showing how PyCBC package depends on NumPy' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' {{< /figure >}} ## Summary -GW detection has enabled researchers to discover entirely unexpected phenomena -while providing new insight into many of the most profound astrophysical -phenomena known. Number crunching and data visualization is a crucial step -that helps scientists gain insights into data gathered from the scientific -observations and understand the results. The computations are complex and -cannot be comprehended by humans unless it is visualized using computer -simulations that are fed with the real observed data and analysis. NumPy -along with other Python packages such as matplotlib, pandas, and scikit-learn -is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to -answer complex questions and discover new horizons in our understanding of the -universe. +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} From c7b99ab95735a33f55fa59873fcdd53bb6bbee6c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 02:27:11 +0200 Subject: [PATCH 337/586] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 79 +++++++++++----------------- 1 file changed, 31 insertions(+), 48 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index db8fd8a87a..bf4be55545 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -3,92 +3,75 @@ title: "Estudo de Caso: Descoberta de Ondas Gravitacionais" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Ondas gravitacionais' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Ondas gravitacionais**" alt="binary coalesce black hole generating gravitational waves" attr="*(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Ondas Gravitacionais' alt = 'buraco negro coalesce binário gerando ondas gravitacionais' attribution = '(Créditos da Imagem: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote -cite="https://www.youtube.com/watch?v=BIvezCVcsYs" -by="David Shoemaker, _Colaborador Científico no LIGO_" >}} -O ecossistema científico Python é uma infraestrutura crítica para a pesquisa feita no LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} ## Sobre [Ondas Gravitacionais](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) e o [LIGO](https://www.ligo.caltech.edu) -Ondas gravitacionais são ondulações no tecido espaço-tempo, gerado por eventos cataclísmicos no universo, como colisão e fusão de dois buracos negros ou a coalescência de estrelas binárias ou supernovas. A observação de ondas gravitacionais pode ajudar não só no estudo da gravidade, mas também no entendimento de alguns dos fenômenos obscuros existentes no universo distante e seu impacto. +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. -O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. ### Objetivos -- Embora sua [missão](https://www.ligo.caltech.edu/page/what-is-ligo) seja detectar ondas gravitacionais de alguns dos processos mais violentos e enérgicos no Universo, os dados que o LIGO coleta podem ter efeitos de grande alcance em muitas áreas da física, incluindo gravitação, relatividade, astrofísica, cosmologia, física de partículas e física nuclear. -- Processar dados observados através de cálculos numéricos de relatividade que envolvem matemática complexa para identificar o sinal e o ruído, filtrar o sinal relevante e estimar estatisticamente o significado dos dados observados. -- Visualização de dados para que os resultados binários/numéricos possam ser compreendidos. +* Embora sua [missão](https://www.ligo.caltech.edu/page/what-is-ligo) seja detectar ondas gravitacionais de alguns dos processos mais violentos e enérgicos no Universo, os dados que o LIGO coleta podem ter efeitos de grande alcance em muitas áreas da física, incluindo gravitação, relatividade, astrofísica, cosmologia, física de partículas e física nuclear. +* Processar dados observados através de cálculos numéricos de relatividade que envolvem matemática complexa para identificar o sinal e o ruído, filtrar o sinal relevante e estimar estatisticamente o significado dos dados observados. +* Visualização de dados para que os resultados binários/numéricos possam ser compreendidos. + + ### Desafios -- **Computação** +* **Computação** - As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. + As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Processar e analisar todos os dados do LIGO requer uma vasta infraestrutura de computação. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. -- **Sobrecarga de dados** +* **Sobrecarga de dados** - À medida que os dispositivos observacionais se tornam mais sensíveis e confiáveis, os desafios criados pela sobrecarga de dados e a procura por uma agulha em um palheiro se tornam muito maiores. - O LIGO gera terabytes de dados todos os dias! Entender esses dados requer um enorme esforço para cada detecção. Por exemplo, os sinais sendo coletados pelo LIGO devem ser combinados por supercomputadores e comparados a centenas de milhares de modelos de possíveis assinaturas de ondas gravitacionais. + À medida que os dispositivos observacionais se tornam mais sensíveis e confiáveis, os desafios criados pela sobrecarga de dados e a procura por uma agulha em um palheiro se tornam muito maiores. O LIGO gera terabytes de dados todos os dias! Entender esses dados requer um enorme esforço para cada detecção. Por exemplo, os sinais sendo coletados pelo LIGO devem ser combinados por supercomputadores e comparados a centenas de milhares de modelos de possíveis assinaturas de ondas gravitacionais. -- **Visualização** +* **Visualização** - Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. - A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. + Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Amplitude estimada da deformação das ondas gravitacionais do evento GW150914' -attribution = '(Créditos do gráfico: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} ## O papel da NumPy na detecção de ondas gravitacionais -Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. -A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. -NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: -- [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) -- Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro -- Análise estatística: estimar o significado estatístico dos dados observados, estimando os parâmetros do sinal (por exemplo, massa de estrelas, velocidade de giro e distância) em comparação com um modelo. -- Visualização de dados +* [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) +* Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro +* Análise estatística: estimar o significado estatístico dos dados observados, estimando os parâmetros do sinal (por exemplo, massa de estrelas, velocidade de giro e distância) em comparação com um modelo. +* Visualização de dados - Séries temporais - Espectrogramas -- Cálculo de correlações -- [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. +* Cálculo de correlações +* [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Grafo de dependências mostrando como o pacote GwPy depended da NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Gráfico de dependência mostrando como o pacote GwPy depende do NumPy' {{< /figure >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Grafo de dependências mostrando como o pacote PyCBC depended da NumPy' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Gráfico de dependência mostrando como o pacote PyCBC depende do NumPy' {{< /figure >}} ## Resumo -A detecção de ondas gravitacionais permitiu que pesquisadores descobrissem fenômenos totalmente inesperados ao mesmo tempo em que proporcionaram novas idéias sobre muitos dos fenômenos mais profundos conhecidos na astrofísica. O processamento e a visualização de dados é um passo crucial que ajuda cientistas a obter informações coletadas de observações científicas e a entender os resultados. Os cálculos são complexos e não podem ser compreendidos por humanos a não ser que sejam visualizados usando simulações de computador que são alimentadas com dados e análises reais observados. A NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Recursos chave da NumPy utilizados' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'benefícios do numpy' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} From 6b5115ddf0f1e16df130e3e7bfb5668df613cf54 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:21 +0200 Subject: [PATCH 338/586] New translations news.md (Spanish) --- content/es/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 7fc9ef2d82..a6f3d0a88a 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -239,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 43ed0b42087aa539c0f4bde7a12db011c485905a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:22 +0200 Subject: [PATCH 339/586] New translations news.md (Arabic) --- content/ar/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 793619c0d1..706b609976 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -239,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From cf1c742dc7d0c22d5896af193d1e68a170a34449 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:23 +0200 Subject: [PATCH 340/586] New translations news.md (Japanese) --- content/ja/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 8d0cfb5b39..3cff82620f 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,18 +1,29 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0 リリース日: 6月16日" +newsHeader: "NumPy 2.0 released!" date: 2023-09-16 --- ### NumPy 2.0 リリース日: 6月16日 -_ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 リリース日: 6月16日 + +_ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + ### NumFOCUSの年末の資金調達 _2023年12月19日_ -- NumFOCUSは、年末キャンペーンでPyCharmチームと協力し、PyCharmライセンスの初回購入に30%の割引を提供しています。 2023年12月23日までのPyCharm購入による1年目の収益は全てNumFOCUSのプログラムに直接寄付されます。 @@ -239,6 +250,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. From 13ff0b4587e4e27dc040bc599a7112e7b4fd083a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:25 +0200 Subject: [PATCH 341/586] New translations news.md (Korean) --- content/ko/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 6f52f07394..447e02eaf7 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,18 +1,29 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 2.0 출시일: 6월 16일" +newsHeader: "NumPy 2.0 released!" date: 2023-09-16 --- ### NumPy 2.0 출시일: 6월 16일 -_2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) - 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 출시일: 6월 16일 + +_2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + ### NumPy 1.26.0 출시 _2023년 12월 19_ -- NumFOCUS에서 연말 캠페인 기간 동안 PyCharm과 협력해 최초 PyCharm 이용자의 라이선스를 30% 할인된 가격에 제공했습니다. 지금부터 2023년 12월 23일까지 PyCharm 구매로 발생한 모든 수익은 NumFOCUS 프로그램으로 직접 전달됩니다. @@ -239,6 +250,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. - NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2024년 1월 2일_. - NumPy 1.26.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023년 1월 2일_. From dd95afd1bd210741632c18c9cce48c0e78531d48 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:26 +0200 Subject: [PATCH 342/586] New translations news.md (Russian) --- content/ru/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 793619c0d1..706b609976 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -239,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From afb32a9cbf149023696e7fd205156a6ba39c8b12 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:27 +0200 Subject: [PATCH 343/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 793619c0d1..706b609976 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -239,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. From 4f590ae2688e48f8042bd98d60eeec2253179395 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:28 +0200 Subject: [PATCH 344/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 085a49d625..d3513d18a6 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,21 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" +newsHeader: "NumPy 2.0 released!" date: 2023-09-16 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** @@ -239,6 +250,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 de novembro de 2023_. From cf6eb0978c7d103860220d82bffb9e7cd1d234f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:38 +0200 Subject: [PATCH 345/586] New translations config.yaml (Arabic) --- content/ar/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 0b4ba6839e607fcf0b117616ca1b670428953fc1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:40 +0200 Subject: [PATCH 346/586] New translations config.yaml (Russian) --- content/ru/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 8642d632aa2227d8759274d05a631fc559854786 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 04:26:40 +0200 Subject: [PATCH 347/586] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 4aaf75b2e6..0ee45e8b33 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.0. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From d6289b8601f9a5df74590da2243b2c2fb82aa33c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 05:29:43 +0200 Subject: [PATCH 348/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 7eabca58e1..6e39c6a8a6 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -112,7 +112,7 @@ params: label: PennyLane - title: Statistical Computing - alttext: A line graph with the line moving up. + alttext: Un gráfico lineal con la línea moviéndose hacia arriba. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - From a9dc70bd3e50e033bf01afe8d3f280feb55091d3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 10:30:28 +0200 Subject: [PATCH 349/586] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index f435ef2b2b..8166212587 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -39,7 +39,7 @@ src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = ' 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHTのデータ処理パイプライン' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**ブラックホール画像化でNumPyが果たした役割**" >}} ## NumPyが果たした役割 @@ -57,7 +57,7 @@ EHTの共同研究では、最先端の画像再構成技術を使用して、 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'NumPyの中心としたehtimのソフトウェア依存図' {{< /figure >}} NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 @@ -67,7 +67,7 @@ NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas. NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 196e9bb368220328bbf69f29d299c32670db2055 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 11:29:04 +0200 Subject: [PATCH 350/586] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 2e366ad8d1..e6b6ee0eff 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/ipl-stadium.png" caption="** IPLT20、インド最大のクリケットフェスティバル**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} -src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' +src = '/images/content_images/cs/ipl-stadium.png' title = ' IPLT20:インド最大のクリケットフェスティバル' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *世界的なクリケットプレイヤーであり、前世界王者、インドチームで活躍し、IPLにおけるチェンナイスーパー王者となった*" @@ -28,7 +28,7 @@ src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest C * チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +src = '/images/content_images/cs/cricket-pitch.png' title = ' フィールドのフォーカルポイントとなるクリケットピッチ' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="クリケット分析にNumPyを使用するメリットを示す図" caption="** 利用されている主なNumPy機能 **" >}} ### データ分析の主要な目標 @@ -38,7 +38,7 @@ src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the * 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'クリケットの姿勢推定' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### 課題 @@ -68,5 +68,5 @@ src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimato スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。 {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'クリケット分析にNumPyを使用するメリットを示す図' title = ' 利用されている主なNumPyの機能 ' {{< /figure >}} From 2e7005f8911b98bed222870ba87ef061fd88fe45 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 11:29:05 +0200 Subject: [PATCH 351/586] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index d5db28843a..e05f244168 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCutを用いたマウスの手の動きの解析 **" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} -src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'DeepLapCutを用いたマウスの手の動きの解析' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' {{< /figure >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, * ローザンヌ連邦工科大学([EPFL](https://www.epfl.ch/en/))の助教授 *" @@ -19,7 +19,7 @@ src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-mov 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 {{< figure >}} -src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = '色のついた点は競走馬の体の位置を追跡している' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 From f8af45ffdd250a6979a2fb47cf0b416ff411e436 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 13:26:04 +0200 Subject: [PATCH 352/586] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index e05f244168..bf650a0409 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -68,7 +68,7 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 {{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}} -src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = '姿勢推定の多様性と複雑性' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## 姿勢推定の課題に対応するためのNumPyの役割 @@ -86,7 +86,7 @@ NumPy は DeepLabCutにおける、行動分析の高速化のための数値計 DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCutのワークフロー' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} ## まとめ @@ -94,7 +94,7 @@ src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Wo 行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From f388b2bd8fc820671d3da55b1d9a47a7414bb5c6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 13:26:05 +0200 Subject: [PATCH 353/586] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index e7faa49de9..01a4bf488a 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**重力波**" alt="binary coalesce black hole generating gravitational waves" attr="*(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} -src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image.png' title = '重力波' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGOの科学調査協力により*" >}} LIGOで行われる研究には、科学的なPythonエコシステムが重要なインフラとなっています。 @@ -59,7 +59,7 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波 * 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 {{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'NumPyに依存しているGwPyの依存グラフ' {{< /figure >}} ---- From c7f7dc86898c8cfe78ecde4b04b81ffccc26c466 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 14:30:31 +0200 Subject: [PATCH 354/586] New translations _index.md (Japanese) --- content/ja/_index.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/ja/_index.md b/content/ja/_index.md index be88f9e642..75f5be2ce6 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -6,44 +6,44 @@ title: null [[item]] type = 'card' -title = 'Powerful N-dimensional arrays' +title = '強力な多次元配列' body = ''' -Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. -''' +NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタンダードとなっています。 [[item]] type = 'card' -title = 'Numerical computing tools' +title = '数値計算ツール群' body = ''' -NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +NumPyは、様々な数学関数、乱数生成器、線形代数ルーチン、フーリエ変換機能などを提供しています。 ''' [[item]] type = 'card' -title = 'Open source' +title = 'オープンソース' body = ''' -Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt)の元で公開されています。NumPyは活発で、互いを尊重し、多様性を認め合う[コミュニティ](/ja/community)によって、 [GitHub](https://github.com/numpy/numpy)上でオープンに開発されています. ''' [[item]] type = 'card' -title = 'Interoperable' +title = '相互運用性' body = ''' -NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +NumPyは、幅広いハードウェアとコンピューティング・プラットフォームをサポートしており、分散処理、GPU、疎行列ライブラリにも対 +応しています。 ''' [[item]] type = 'card' -title = 'Performant' +title = '高いパフォーマンス' body = ''' -The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +NumPyの大部分は最適化されたC言語のコードで構成されています。これによりPythonの柔軟性とコンパイルされたコードの高速性の両方を享受できます。 コンパイルされたコードのスピードでの Python の柔軟性をお楽しみください。 ''' [[item]] type = 'card' -title = 'Easy to use' +title = '使いやすさ' body = ''' -NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +NumPyの抽象化されたシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高めることができます。 ''' -{{< /grid>}} +{{< /grid >}} From 7f54341b86e9a41601c173e8198b6b2880f8c347 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 14:30:32 +0200 Subject: [PATCH 355/586] New translations user-survey-2020.md (Japanese) --- content/ja/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index a6533d3cb8..3c05588349 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -6,7 +6,7 @@ sidebar: false 2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。 {{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 Numpy User survey report, titled 'Numpyコミュニティ調査2020 - 結果'" width="250">}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 Numpy User survey report, titled "Numpyコミュニティ調査2020の結果"' width = '250' {{< /figure >}} 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。 From 0649192233cc447fcfa2249d9503f8deffd518ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 14:30:34 +0200 Subject: [PATCH 356/586] New translations news.md (Japanese) --- content/ja/news.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 3cff82620f..6d3d028c3c 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,28 +1,28 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.0. がリリースされました!" date: 2023-09-16 --- ### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -250,9 +250,9 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From f33e139f1b80a378ab07db1f7630d838c2922795 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 14:30:35 +0200 Subject: [PATCH 357/586] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md index 6105ed9eb4..4349ea996d 100644 --- a/content/ja/gethelp.md +++ b/content/ja/gethelp.md @@ -3,9 +3,9 @@ title: サポートを得る方法 sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**開発関連の問題:** NumPyの開発関連の問題 (例: バグレポート) については、[コミュニティ](/community) のページを参照してください。 -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! +**ユーザーからの質問:** ユーザー視点での質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy) または[Reddit](https://www.reddit.com/r/Numpy/) のようなサイトに質問を投稿することです。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大なのが現実です。 ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From f1e835f9c1c42be16cca87dd847d2942d64689c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 Jun 2024 14:30:37 +0200 Subject: [PATCH 358/586] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 01a4bf488a..f655da4ac7 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -65,7 +65,7 @@ src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy dep ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'NumPyに依存しているPyCBCの依存グラフ' {{< /figure >}} ## まとめ @@ -73,5 +73,5 @@ src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy d GW検出により、研究者は完全に予期せぬ現象 を発見することができ、同時に知られている最も深遠な天体物理学 現象の多くに新たな洞察を提供しています。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な現象の疑問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}} From a97070945f244ca4c2184a981e3f1b19fc8b04ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jul 2024 16:35:34 +0200 Subject: [PATCH 359/586] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 8166212587..2462c7ef63 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -40,7 +40,7 @@ src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = ' {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' title = 'EHTのデータ処理パイプライン' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**ブラックホール画像化でNumPyが果たした役割**" >}} +{{< /figure >}} ## NumPyが果たした役割 @@ -51,7 +51,7 @@ EHTの共同研究では、最先端の画像再構成技術を使用して、 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 {{< figure >}} -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'ブラックホール画像化でNumPyが果たした役割' {{< /figure >}} 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 From 544de76cd9b64ed12892e93b1e976962e228520e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jul 2024 16:40:37 +0200 Subject: [PATCH 360/586] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 2462c7ef63..d6c4ae2ab5 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -3,7 +3,7 @@ title: "ケーススタディ：世界初のブラックホール画像" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrk="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg" >}} +{{< figure >}} src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} From bebf5a6f6591afd50df33ad7daafd65ee9fbddce Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jul 2024 16:54:05 +0200 Subject: [PATCH 361/586] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index d6c4ae2ab5..18335320a5 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -8,7 +8,7 @@ src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = ' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, * カリフォルニア工科大学の計算数理科学の助教授 *" -> }} M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 +> M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 > > {{< /blockquote >}} From ae04a57ee9f9e749c1317e3d11b5e15e744e03f9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jul 2024 17:57:28 +0200 Subject: [PATCH 362/586] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index e6b6ee0eff..c2b2dc7247 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -29,7 +29,7 @@ src = '/images/content_images/cs/ipl-stadium.png' title = ' IPLT20:インド最 {{< figure >}} src = '/images/content_images/cs/cricket-pitch.png' title = ' フィールドのフォーカルポイントとなるクリケットピッチ' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="クリケット分析にNumPyを使用するメリットを示す図" caption="** 利用されている主なNumPy機能 **" >}} +{{< /figure >}} ### データ分析の主要な目標 From dd945bdbfe24a140bed5e2fb4f521c537b88f201 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jul 2024 17:57:29 +0200 Subject: [PATCH 363/586] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index f655da4ac7..b9c7eeb801 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -41,7 +41,7 @@ src = '/images/content_images/cs/gw_sxs_image.png' title = '重力波' alt = 'bi {{< figure >}} {{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**GwPyのNumPy依存グラフ**" >}} -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} +{{< /figure >}} ## 重力波の検出におけるNumPyの役割 @@ -73,5 +73,5 @@ src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'PyCBC-numpy de GW検出により、研究者は完全に予期せぬ現象 を発見することができ、同時に知られている最も深遠な天体物理学 現象の多くに新たな洞察を提供しています。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な現象の疑問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}} From 5b6b51754a9115d776f80bc9745243da3c4facaa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 6 Jul 2024 12:12:52 +0200 Subject: [PATCH 364/586] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index b9c7eeb801..68fbb85371 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -40,7 +40,7 @@ src = '/images/content_images/cs/gw_sxs_image.png' title = '重力波' alt = 'bi アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 {{< figure >}} -{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**GwPyのNumPy依存グラフ**" >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'GW150914から推定される重力波の歪みの振幅' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} ## 重力波の検出におけるNumPyの役割 From 792a36fa4cd71b8ed31e6b4598d76f6c6206fe7c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:19 +0200 Subject: [PATCH 365/586] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index a6f3d0a88a..1e9bb48848 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -250,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. From c7b31749107eed86615a44f6fc1b15550e5c05d6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:20 +0200 Subject: [PATCH 366/586] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index 706b609976..e37178caf8 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -250,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. From a5243a5e48135b9779009389607152822b2a8a21 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:21 +0200 Subject: [PATCH 367/586] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 6d3d028c3c..b77481483a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -250,6 +250,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. From 8d920030e884b53373c80d9807b54bae48640251 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:22 +0200 Subject: [PATCH 368/586] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index 447e02eaf7..e3a37fa837 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -250,6 +250,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. - NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2024년 1월 2일_. From b0e48bfdcbb3de9b50df627befaa0779086488b3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:23 +0200 Subject: [PATCH 369/586] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index 706b609976..e37178caf8 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -250,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. From 742ea879114b5d67259451f6c7bc8387ea9e47b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:24 +0200 Subject: [PATCH 370/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index 706b609976..e37178caf8 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -250,6 +250,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. From 0378d3aaac98a25acb55e26dade1081b8e957e9b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:21:25 +0200 Subject: [PATCH 371/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index d3513d18a6..6d8b142f48 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -250,6 +250,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. From 22d0ea0e74304175640eef763a62d4e21436df5f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jul 2024 21:43:55 +0200 Subject: [PATCH 372/586] New translations news.md (Japanese) --- content/ja/news.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index b77481483a..d6fb198bbe 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,28 +1,28 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0. がリリースされました!" +newsHeader: "NumPy 2.0 released!" date: 2023-09-16 --- ### NumPy 2.0 リリース日: 6月16日 -_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -251,9 +251,9 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From 46007e864658b09d961c4004c2e1b550f8f0140a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 21:22:28 +0200 Subject: [PATCH 373/586] New translations news.md (Japanese) --- content/ja/news.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index d6fb198bbe..b77481483a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,28 +1,28 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.0. がリリースされました!" date: 2023-09-16 --- ### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -251,9 +251,9 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From e6eb4dab614999b1850d3bcb9b00ea700d1b5da1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:21 +0200 Subject: [PATCH 374/586] New translations _index.md (Persian) --- content/fa/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/fa/_index.md diff --git a/content/fa/_index.md b/content/fa/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/fa/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From 6a64af7801310ac433651938a9ea4e13cd8c7348 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:22 +0200 Subject: [PATCH 375/586] New translations user-surveys.md (Persian) --- content/fa/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/fa/user-surveys.md diff --git a/content/fa/user-surveys.md b/content/fa/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/fa/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 75271bdf21eb55c5d522d3f582b5043f33698cca Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:24 +0200 Subject: [PATCH 376/586] New translations 404.md (Persian) --- content/fa/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/fa/404.md diff --git a/content/fa/404.md b/content/fa/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/fa/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 76e3c8373511f89a58f8549958e48756f0afe2de Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:25 +0200 Subject: [PATCH 377/586] New translations arraycomputing.md (Persian) --- content/fa/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/fa/arraycomputing.md diff --git a/content/fa/arraycomputing.md b/content/fa/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/fa/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From c98be78ec894c14f1afcb21f2bcc7dc99ae992c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:26 +0200 Subject: [PATCH 378/586] New translations citing-numpy.md (Persian) --- content/fa/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/fa/citing-numpy.md diff --git a/content/fa/citing-numpy.md b/content/fa/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/fa/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 51498dc1ba5f0f1208035449858b5d444212fac0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:27 +0200 Subject: [PATCH 379/586] New translations user-survey-2020.md (Persian) --- content/fa/user-survey-2020.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 content/fa/user-survey-2020.md diff --git a/content/fa/user-survey-2020.md b/content/fa/user-survey-2020.md new file mode 100644 index 0000000000..b4349bcb7d --- /dev/null +++ b/content/fa/user-survey-2020.md @@ -0,0 +1,18 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. + + +For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. + From 67e4b7e0262bc726dd8397e4dfc56aeaeffcd72e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:28 +0200 Subject: [PATCH 380/586] New translations report-handling-manual.md (Persian) --- content/fa/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/fa/report-handling-manual.md diff --git a/content/fa/report-handling-manual.md b/content/fa/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/fa/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 71de46f1d39882d75e8d0fbe05c770b61b426348 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:29 +0200 Subject: [PATCH 381/586] New translations contribute.md (Persian) --- content/fa/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/fa/contribute.md diff --git a/content/fa/contribute.md b/content/fa/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/fa/contribute.md @@ -0,0 +1,66 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). + +Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). + +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. + +### Writing code + +Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + + +### Reviewing pull requests +The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: +* summarize a long-running discussion +* triage documentation PRs +* test proposed changes + + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. + + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: + +* check if older bugs are still present +* find duplicate issues and link related ones +* add good self-contained reproducers to issues +* label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + + +### Website development + +We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. + + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. + + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. + + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. From 8b5483d7f38475f4cf53d86867e5f7ffca9a16d6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:30 +0200 Subject: [PATCH 382/586] New translations learn.md (Persian) --- content/fa/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/fa/learn.md diff --git a/content/fa/learn.md b/content/fa/learn.md new file mode 100644 index 0000000000..4f9fa53ae3 --- /dev/null +++ b/content/fa/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* + + **Videos** + +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* + +*** + +## NumPy Talks + +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From 88123ffecce5a0b3adf8db48d04d921e5a9b88ed Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:31 +0200 Subject: [PATCH 383/586] New translations community.md (Persian) --- content/fa/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/fa/community.md diff --git a/content/fa/community.md b/content/fa/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/fa/community.md @@ -0,0 +1,66 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + + +## Participate online + +The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ + + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. + + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From 572bcfa632b1f90cc6e9cb62aeb9b9799c8181a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:33 +0200 Subject: [PATCH 384/586] New translations press-kit.md (Persian) --- content/fa/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/fa/press-kit.md diff --git a/content/fa/press-kit.md b/content/fa/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/fa/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From e5dc286b27ede3aa004475f03b251cba22478602 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:34 +0200 Subject: [PATCH 385/586] New translations news.md (Persian) --- content/fa/news.md | 292 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 292 insertions(+) create mode 100644 content/fa/news.md diff --git a/content/fa/news.md b/content/fa/news.md new file mode 100644 index 0000000000..e37178caf8 --- /dev/null +++ b/content/fa/news.md @@ -0,0 +1,292 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 +--- + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +NumPy logo + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 847ee46c345a09f372345926f7b56bcb2a7c2fb6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:35 +0200 Subject: [PATCH 386/586] New translations tabcontents.yaml (Persian) --- content/fa/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/fa/tabcontents.yaml diff --git a/content/fa/tabcontents.yaml b/content/fa/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/fa/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From 1c7f1f3b1cd3e859347673f31f20bb79e2cbf918 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:36 +0200 Subject: [PATCH 387/586] New translations history.md (Persian) --- content/fa/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/fa/history.md diff --git a/content/fa/history.md b/content/fa/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/fa/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From 44247c85744d16c91ce874095685eecabc1cbc0c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:37 +0200 Subject: [PATCH 388/586] New translations config.yaml (Persian) --- content/fa/config.yaml | 140 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/fa/config.yaml diff --git a/content/fa/config.yaml b/content/fa/config.yaml new file mode 100644 index 0000000000..0ee45e8b33 --- /dev/null +++ b/content/fa/config.yaml @@ -0,0 +1,140 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 2.0. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + - + link: https://twitter.com/numpy_team + icon: twitter + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From 9c2e0b9ec4b385c7578df52683152a5195d69cbf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:38 +0200 Subject: [PATCH 389/586] New translations gethelp.md (Persian) --- content/fa/gethelp.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/fa/gethelp.md diff --git a/content/fa/gethelp.md b/content/fa/gethelp.md new file mode 100644 index 0000000000..da3bff67a5 --- /dev/null +++ b/content/fa/gethelp.md @@ -0,0 +1,20 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From f65f8d0f4c42aa6047bf625ed0ac557fba8f6404 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:39 +0200 Subject: [PATCH 390/586] New translations code-of-conduct.md (Persian) --- content/fa/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/fa/code-of-conduct.md diff --git a/content/fa/code-of-conduct.md b/content/fa/code-of-conduct.md new file mode 100644 index 0000000000..5ee1f4bcbe --- /dev/null +++ b/content/fa/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 28dd1b823dffc46160e6d78044a699b5147f91ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:40 +0200 Subject: [PATCH 391/586] New translations install.md (Persian) --- content/fa/install.md | 126 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100644 content/fa/install.md diff --git a/content/fa/install.md b/content/fa/install.md new file mode 100644 index 0000000000..f64a17841d --- /dev/null +++ b/content/fa/install.md @@ -0,0 +1,126 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` +Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. + + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. + + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. + +The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. + +The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. + + +## Troubleshooting + +If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` + From ae75d00eaf90fbc5bc84ffabdee655e8b98b825c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:41 +0200 Subject: [PATCH 392/586] New translations about.md (Persian) --- content/fa/about.md | 90 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/fa/about.md diff --git a/content/fa/about.md b/content/fa/about.md new file mode 100644 index 0000000000..243f7083ae --- /dev/null +++ b/content/fa/about.md @@ -0,0 +1,90 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} + From fea759139b60395135348aa6d3b9ca99b8d1415d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:42 +0200 Subject: [PATCH 393/586] New translations privacy.md (Persian) --- content/fa/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/fa/privacy.md diff --git a/content/fa/privacy.md b/content/fa/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/fa/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 23f8f996a46f314dd1f20da4bf4d1cf5154606ca Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:43 +0200 Subject: [PATCH 394/586] New translations cricket-analytics.md (Persian) --- content/fa/case-studies/cricket-analytics.md | 72 ++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/fa/case-studies/cricket-analytics.md diff --git a/content/fa/case-studies/cricket-analytics.md b/content/fa/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..d0be271f50 --- /dev/null +++ b/content/fa/case-studies/cricket-analytics.md @@ -0,0 +1,72 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: + +* batting performance moving average, +* score forecasting, +* gaining insights into fitness and performance of a player against different opposition, +* player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. +* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. +* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +* **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + +* **Dynamic Modeling** + + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + +* **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: + +* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. + +* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 386b5e5feec78df57d2bceb5f2f4aa90927c13ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:44 +0200 Subject: [PATCH 395/586] New translations deeplabcut-dnn.md (Persian) --- content/fa/case-studies/deeplabcut-dnn.md | 102 ++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 content/fa/case-studies/deeplabcut-dnn.md diff --git a/content/fa/case-studies/deeplabcut-dnn.md b/content/fa/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..257905de91 --- /dev/null +++ b/content/fa/case-studies/deeplabcut-dnn.md @@ -0,0 +1,102 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +* **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). + +* **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +### The Challenges + +* **Speed** + + Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. + +* **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. + +* **Data Processing** + + Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: + +* Vectorization +* Masked Array Operations +* Linear Algebra +* Random Sampling +* Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From d48d4038f1a8ba4743cf130f577e71f202bb77c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:45 +0200 Subject: [PATCH 396/586] New translations blackhole-image.md (Persian) --- content/fa/case-studies/blackhole-image.md | 80 ++++++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 content/fa/case-studies/blackhole-image.md diff --git a/content/fa/case-studies/blackhole-image.md b/content/fa/case-studies/blackhole-image.md new file mode 100644 index 0000000000..22334ebed0 --- /dev/null +++ b/content/fa/case-studies/blackhole-image.md @@ -0,0 +1,80 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! + +### Key Goals and Results + +* **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. + +* **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. + +* **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. + +### The Challenges + +* **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. + +* **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. + +* **Into the unknown** + + When the goal is to see something never before seen, how can scientists be confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? + +The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 41c2c729033aaffb22732b2bfb5503d2b1b7e71b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Jul 2024 22:24:46 +0200 Subject: [PATCH 397/586] New translations gw-discov.md (Persian) --- content/fa/case-studies/gw-discov.md | 77 ++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 content/fa/case-studies/gw-discov.md diff --git a/content/fa/case-studies/gw-discov.md b/content/fa/case-studies/gw-discov.md new file mode 100644 index 0000000000..08f54b7547 --- /dev/null +++ b/content/fa/case-studies/gw-discov.md @@ -0,0 +1,77 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. + + +### Key Objectives + +* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. +* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data +* Data visualization so that the binary / numerical results can be comprehended. + + + +### The Challenges + +* **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + +* **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + +* **Visualization** + + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: + +* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack +* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* Visualization of data + - Time series + - Spectrograms +* Compute Correlations +* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 12befed5d1b86d46f5729f911dd21e871e44d284 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 27 Jul 2024 03:23:45 +0200 Subject: [PATCH 398/586] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index b77481483a..f7672f6a8a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -250,7 +250,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. - NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. From 5b807e9f531623a06aeb59091d71b94cdeaa9ef4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 03:00:32 +0200 Subject: [PATCH 399/586] New translations _index.md (Spanish) --- content/es/_index.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/_index.md b/content/es/_index.md index be88f9e642..e97561ee36 100644 --- a/content/es/_index.md +++ b/content/es/_index.md @@ -5,45 +5,45 @@ title: null {{< grid columns="1 2 2 3" >}} [[item]] -type = 'card' -title = 'Powerful N-dimensional arrays' +type = 'tarjeta' +title = 'Matrices N-dimensionales potentes' body = ''' -Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +Rápida y versátil, la vectorización, indexación y conceptos de broadcasting de NumPy son los estándares de facto en el cálculo de matrices hoy en día. ''' [[item]] type = 'card' -title = 'Numerical computing tools' +title = 'Herramientas de cálculo numérico' body = ''' -NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +NumPy ofrece funciones matemáticas completas, generadores de números aleatorios, rutinas de álgebra lineal, transformadas de Fourier, y más. ''' [[item]] type = 'card' -title = 'Open source' +title = 'Código abierto' body = ''' -Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +Distribuido bajo una [licencia BSD] liberal (https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy es desarrollado y mantenido [públicamente en GitHub](https://github.com/numpy/numpy) por una vibrante, receptiva y diversa [comunidad](/comunidad). ''' [[item]] type = 'card' title = 'Interoperable' body = ''' -NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +NumPy soporta una amplia gama de hardware y plataformas de computación, y funciona bien con librerías distribuidas, de GPU y de matrices dispersas. ''' [[item]] type = 'card' -title = 'Performant' +title = 'Óptimo' body = ''' -The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +El núcleo de NumPy está optimizado adecuadamente con código en C. Disfrute de la flexibilidad de Python con la velocidad del código compilado. ''' [[item]] type = 'card' -title = 'Easy to use' +title = 'Fácil de usar' body = ''' -NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +La sintaxis de alto nivel de NumPy lo hace accesible y productivo para programadores de cualquier formación o nivel de experiencia. ''' {{< /grid>}} From 7f3d337b9b825a69346c155ad893ab403a61403a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 03:00:33 +0200 Subject: [PATCH 400/586] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index d6d502b29a..cabf5f7f96 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -6,7 +6,7 @@ sidebar: false En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. {{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250">}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Página de portada del informe de la encuesta de usuarios de NumPy de 2020, titulado "Encuesta de la Comunidad de NumPy 2020 - resultados"' width = '250' {{< /figure >}} **[Descarga el informe](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver a detalle los resultados de la encuesta. From 767b94d4804458ad54ef860c07e7b61a23248fba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 03:00:34 +0200 Subject: [PATCH 401/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 40 ++++++++++++++-------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 5586668cba..5139f0eac9 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -1,16 +1,16 @@ --- -title: NumPy Code of Conduct - How to follow up on a report -sidebar: false +title: Código de Conducta de NumPy - Cómo hacer el seguimiento de un informe +sidebar: falso --- -This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. +Este es el manual seguido por el Comité de Código de Conducta de NumPy. Se utiliza cuando respondemos a un problema, para asegurarnos de que seamos consistentes y justos. -Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: +Hacer cumplir el [Código de Conducta](/code-of-conduct) impacta a nuestra comunidad hoy y en el futuro. Es una acción que no tomamos a la ligera. Al revisar las medidas de cumplimiento, el Comité de Código de Conducta tendrá en cuenta los siguientes valores y directrices: -* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Actuar de manera personal en lugar de impersonal. El Comité puede involucrar a las partes para que comprendan la situación, respetando la privacidad y cualquier confidencialidad necesaria de los informantes. Sin embargo, a veces es necesario comunicarse directamente con uno o más individuos: el objetivo del Comité es mejorar la salud de nuestra comunidad en lugar de solo producir una decisión formal. +* Enfatizar la empatía hacia los individuos en lugar de juzgar el comportamiento, evitando etiquetas binarias de “bueno” y “malo/malvado”. El acoso directo y claro existe, y lo afrontaremos con firmeza. Pero en muchas circunstancias puede ser complejo resolver estas situaciones, sobre todo aquellas en las que desacuerdos normales conllevan a comportamientos inútiles o perjudiciales para las partes. Comprender el contexto completo y encontrar un camino que vuelva involucrar a todos es difícil, pero es en última instancia lo más productivo para nuestra comunidad. +* Comprendemos que el correo electrónico es un medio difícil y puede generar aislamiento. Recibir críticas por medio de correo electrónico, sin ningún contacto personal, puede ser particularmente doloroso. Esto hace que sea especialmente importante mantener una ambiente de apertura y respeto hacia las opiniones de los demás. También significa que debemos ser transparentes en nuestro actuar, y que debemos hacer todo lo que esté a nuestro alcance para asegurarnos de que todos nuestros miembros sean tratados de manera justa y con compasión. +* La discriminación puede ser sutil e inconsciente. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. * Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. * Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. * Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. @@ -63,23 +63,23 @@ This information will be collected in writing, and whenever possible the group It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. -The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. Dado el caso de que no se pueda establecer una decisión dentro de dicho plazo, el Comité responderá a los informantes con una actualización y duración estimada para la decisión. -## Resolutions +## Resoluciones -The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. +El comité debe llegar a un acuerdo sobre una resolución por consenso. Si el grupo no puede alcanzar un consenso y permanece en un punto muerto durante más de una semana, le trasladará el asunto al Consejo Directivo para que lo resuelva. -Possible responses may include: +Las posibles respuestas pueden incluir: -* Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* No tomar más medidas: + - si determinamos que no se han producido violaciones; + - si el asunto se ha resuelto públicamente mientras que el Comité estaba estudiando las respuestas. +* Coordinación de mediación voluntaria: si todas las partes implicadas están de acuerdo, el Comité podrá facilitar un proceso de mediación como se detalla arriba. +* Recordar públicamente y señalar que algunos comportamientos/acciones/usos de lenguaje, han sido juzgados como inapropiados y por qué, bajo el contexto actual, pueden ser hirientes para algunas personas, solicitando a la comunidad que se autorregule. +* Una reprimenda privada por parte del Comité al individuo(s) involucrado. En este caso, el presidente del grupo entregará esa reprimenda al individuo(s) por correo electrónico, con copia al grupo. +* Una reprimenda pública. En este caso, el presidente del Comité entregará esa reprimenda por el mismo medio por el que se produjo la violación, dentro de los límites de la viabilidad. Por ejemplo, la lista de correo original para una violación de correo electrónico, pero para una discusión en una sala de chat, en la que la persona o el contexto pueden haber desaparecido, puede buscarse el contacto por otros medios. El grupo puede elegir publicar este mensaje en otro lugar con fines de documentación. +* Una solicitud de disculpa pública o privada, asumiendo que el informante esté de acuerdo con esta idea: puede negarse, a su discreción, a continuar contacto con el violador. El presidente entregará esta solicitud. El Comité puede, si así lo desea, adjuntar “cadenas” a esta petición: por ejemplo, el grupo puede solicitar a un violador pedir disculpas para preservar su membresía en una lista de correo. * A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. * A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. From d26f245c39a5c3362047cf1a6008f38719fb18f7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 04:01:56 +0200 Subject: [PATCH 402/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 5139f0eac9..c3ed404296 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -80,16 +80,16 @@ Las posibles respuestas pueden incluir: * Una reprimenda privada por parte del Comité al individuo(s) involucrado. En este caso, el presidente del grupo entregará esa reprimenda al individuo(s) por correo electrónico, con copia al grupo. * Una reprimenda pública. En este caso, el presidente del Comité entregará esa reprimenda por el mismo medio por el que se produjo la violación, dentro de los límites de la viabilidad. Por ejemplo, la lista de correo original para una violación de correo electrónico, pero para una discusión en una sala de chat, en la que la persona o el contexto pueden haber desaparecido, puede buscarse el contacto por otros medios. El grupo puede elegir publicar este mensaje en otro lugar con fines de documentación. * Una solicitud de disculpa pública o privada, asumiendo que el informante esté de acuerdo con esta idea: puede negarse, a su discreción, a continuar contacto con el violador. El presidente entregará esta solicitud. El Comité puede, si así lo desea, adjuntar “cadenas” a esta petición: por ejemplo, el grupo puede solicitar a un violador pedir disculpas para preservar su membresía en una lista de correo. -* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* Un “acuerdo mutuo de suspensión” en el que el Comité solicita al individuo la abstención temporal de participación en la comunidad. Si el individuo decide no aceptar una suspensión temporal voluntariamente, el Comité puede emitir un “período obligatorio de calma”. +* Una prohibición permanente o temporal de algunos o de todos los espacios de NumPy (listas de correo, gitter.im, etc.). El grupo mantendrá los registros de todas esas prohibiciones, para que puedan ser revisadas en el futuro o ser mantenidas en caso contrario. -Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. +Una vez que se acuerda una resolución, pero antes de que se promulgue, el Comité se pondrá en contacto con el informante original y con cualquier otra parte afectada y explicará la resolución propuesta. El Comité preguntará si esta resolución es aceptable y deberá tomar nota de los comentarios para su registro. -Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). +Finalmente, el Comité presentará un informe al Consejo Directivo de NumPy (así como al equipo central de NumPy en caso de una decisión en curso, tal como una prohibición). -The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. +El Comité nunca debatirá públicamente el asunto; todas las declaraciones públicas serán realizadas por el presidente del Comité de Código de Conducta o el Consejo Directivo de NumPy. -## Conflicts of Interest +## Conflictos de intereses -In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. +En caso de cualquier conflicto de intereses, un miembro del Comité deberá notificar inmediatamente a los demás miembros y recusarse en caso de ser necesario. From c2ceda0e71aef7fd27787cafbf104a353d0be7ae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 04:01:57 +0200 Subject: [PATCH 403/586] New translations news.md (Spanish) --- content/es/news.md | 78 +++++++++++++++++++++++----------------------- 1 file changed, 39 insertions(+), 39 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 1e9bb48848..e5c0314bcf 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,26 +1,26 @@ --- -title: News -sidebar: false -newsHeader: "NumPy 2.0 released!" +title: Noticias +sidebar: falso +newsHeader: "Lanzamiento de NumPy 2.0!" date: 2024-06-17 --- -### NumPy 2.0.0 released +### Lanzamiento de NumPy 2.0.0 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. -### NumPy 2.0 release date: June 16 +### Fecha de lanzamiento de NumPy 2.0: 16 de junio -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) @@ -94,48 +94,48 @@ How can we be better when it comes to diversity and inclusion? Read the report a _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. -### NumPy 1.24.0 released +### Lanzamiento de NumPy 1.24.0 -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: +_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento o stacking. +* Nuevas características y correcciones de F2PY. +* Muchas nuevas obsolescencias, revísalas. +* Muchas obsolescencias caducadas, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. +El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. -### Numpy 1.23.0 released +### NumPy 1.23.0 ha sido lanzado -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: +_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Implementation of `loadtxt` in C, greatly improving its performance. -* Exposure of DLPack at the Python level for easy data exchange. -* Changes to the promotion and comparisons of structured dtypes. -* Improvements to f2py. +* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. +* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. +* Cambios a la promoción y comparación de dtypes estructurados. +* Mejoras a f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. +El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. -### NumFOCUS DEI research study: call for participation +### Estudio de investigación NumFOCUS DEI: llamado a participar -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. +_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. -**Interested in sharing your experiences?** +**¿Estás interesado en compartir tus experiencias?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. +Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. -### Numpy 1.22.0 release +### Lanzamiento de NumPy 1.22.0 -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: +_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. -* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. -* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. -* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. -* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. -* A new configurable memory allocator for use by downstream projects. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. Upstream is a moving target, so there will likely be further improvements, but the major work is done. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. +* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). +* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. +* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. +* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. +NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem From 7623d384d7e89698fc913189bdb0f8a052e8ce05 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 05:03:01 +0200 Subject: [PATCH 404/586] New translations news.md (Spanish) --- content/es/news.md | 128 ++++++++++++++++++++++----------------------- 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index e5c0314bcf..42825076d4 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -137,42 +137,42 @@ _31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. -### Advancing an inclusive culture in the scientific Python ecosystem +### Promoviendo una cultura inclusiva en el ecosistema científico de Python -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. +_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). +Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). -This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. +Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. -The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### 2021 NumPy survey +### Encuesta de NumPy de 2021 -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. +_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. -It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. +Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Numpy 1.21.0 release +### Lanzamiento de NumPy 1.21.0 -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: +_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: -- continued SIMD work covering more functions and platforms, -- initial work on the new dtype infrastructure and casting, -- universal2 wheels for Python 3.8 and Python 3.9 on Mac, -- improved documentation, -- improved annotations, -- new `PCG64DXSM` bitgenerator for random numbers. +- trabajo SIMD continuo que cubre más funciones y plataformas, +- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, +- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, +- documentación mejorada, +- anotaciones mejoradas, +- nuevo `PCG64DXSM` generador de bits para números aleatorios. -This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. +Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. -### 2020 NumPy survey results +### Resultados de la encuesta de NumPy de 2020 -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. +_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release @@ -235,58 +235,58 @@ _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17 Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg -_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. +_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. -This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. -More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. +Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. -## Releases +## Lanzamientos -Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. -- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. -- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. +- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. +- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. +- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. +- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. - NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. -- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. -- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. -- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. -- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. -- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. -- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. -- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. -- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. -- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. -- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. -- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. -- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. -- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. -- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. -- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. -- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. -- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. -- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. -- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. -- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. -- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. -- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. -- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. -- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. -- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. -- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. -- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. -- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. -- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. +- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. +- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. +- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. +- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. +- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. +- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. +- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. +- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. +- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. +- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. +- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. +- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. +- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. +- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. +- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. +- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. +- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. +- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. +- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. +- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. +- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. +- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. +- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. +- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. +- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. +- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. +- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. +- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. +- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. +- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. +- . +- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. +- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. From 3f3482701ecb4d26621db0dc348a5bc575139baa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 05:03:02 +0200 Subject: [PATCH 405/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 6e39c6a8a6..a5faa4e0be 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -94,8 +94,8 @@ params: text: "NumPy lleva la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." libraries: - - title: Quantum Computing - alttext: A computer chip. + title: Computación Cuántica + alttext: Un chip para computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - @@ -190,16 +190,16 @@ params: url: https://spacepy.github.io/ label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + title: Psicología Cognitiva + alttext: Una cabeza humana con engranajes. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - - title: Bioinformatics - alttext: A strand of DNA. + title: Bioinformática + alttext: Una hebra de ADN. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -215,8 +215,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: Inferencia Bayesiana + alttext: Un gráfico con una curva en forma de campanas. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - @@ -232,8 +232,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: Análisis Matemático + alttext: Cuatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - @@ -249,8 +249,8 @@ params: url: https://fenicsproject.org/ label: FEniCS - - title: Chemistry - alttext: A test tube. + title: Química + alttext: Un tubo de ensayo. img: /images/content_images/sc_dom_img/chemistry.svg links: - From 37d940acc2ccbbf2916c6c0584449d887877b0e3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 05:03:04 +0200 Subject: [PATCH 406/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 48 +++++++++++++++++------------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index 5ee1f4bcbe..228da2eb4f 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -15,39 +15,39 @@ This code is not exhaustive or complete. It serves to distill our common underst ### Specific Guidelines -We strive to: +Cantera -1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. -2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. -3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. -4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. -5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - * Violent threats or language directed against another person. - * Sexist, racist, or otherwise discriminatory jokes and language. - * Posting sexually explicit or violent material. - * Posting (or threatening to post) other people’s personally identifying information (“doxing”). - * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - * Personal insults, especially those using racist or sexist terms. - * Unwelcome sexual attention. - * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - * Repeated harassment of others. In general, if someone asks you to stop, then stop. - * Advocating for, or encouraging, any of the above behaviour. +1. Ser abiertos. Invitamos a todas las personas a participar en nuestra comunidad. Preferimos utilizar métodos de comunicación públicos para los mensajes relacionados con el proyecto, a menos que se trate de algo delicado. Esto se aplica también a los mensajes de ayuda o soporte relacionados con el proyecto; no sólo es mucho más probable que una solicitud de soporte pública dé lugar a una respuesta a una pregunta, sino que también garantiza que cualquier error involuntario en la respuesta se detecte y corrija más fácilmente. +2. Be empathetic, welcoming, friendly, and patient. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. +3. Ser colaborativos. Nuestro trabajo será utilizado por otras personas, y a su vez dependeremos del trabajo de otros. Cuando hacemos algo en beneficio del proyecto, estamos dispuestos a explicar a otros cómo funciona, de manera que puedan construir sobre este trabajo para hacerlo aún mejor. Cualquier decisión que tomemos afectará a usuarios y colegas, y nos tomamos en serio esas consecuencias a la hora de tomar decisiones. +4. Ser curiosos. ¡Nadie lo sabe todo! Hacer preguntas tempranas evita muchos problemas posteriores, por lo que fomentamos las preguntas, aunque las podamos redirigir al foro adecuado. Nos esforzaremos por ser receptivos y útiles. +5. Ser cuidadosos con las palabras que elegimos. Somos cuidadosos y respetuosos en nuestra comunicación, y asumimos la responsabilidad del lenguaje que utilizamos. Ser amables con los demás. No insultes ni menosprecies a los demás participantes. No aceptaremos el acoso ni otros comportamientos excluyentes, tales como: + * Amenazas o expresiones violentas dirigidas a otra persona. + * Bromas y lenguaje sexista, racista o discriminatorio. + * Publicar material sexualmente explícito o violento. + * Publicar (o amenazar con publicar) información de identificación personal de otras personas ("doxing"). + * Compartir contenido privado, como correos electrónicos enviados de forma privada o no pública, o foros no registrados como el historial de canales IRC, sin el consentimiento del remitente. + * Insultos personales, especialmente aquellos que utilizan términos racistas o sexistas. + * Atención sexual no deseada. + * Uso excesivo de lenguaje inapropiado. Por favor, evite las palabras soeces; las personas difieren mucho en su sensibilidad a las malas palabras. + * Acoso reiterado a los demás. En general, si alguien le pide que se detenga, entonces deténgase. + * Abogar o alentar cualquiera de las conductas anteriormente mencionadas. -### Diversity Statement +### Declaración de Diversidad -The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. +El proyecto NumPy acoge y fomenta la participación de todos. Estamos comprometidos a ser una comunidad de la que todo el mundo disfrute ser parte. Aunque puede que no siempre seamos capaces de satisfacer las preferencias de cada individuo, intentamos al máximo tratar a todos con amabilidad. -No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. +No importa cómo te identifiques o cómo te perciban los demás: te damos la bienvenida. Aunque ninguna lista puede esperar ser exhaustiva, honramos explícitamente la diversidad en: edad, cultura, etnia, genotipo, identidad o expresión de género, lengua, origen nacional, neurotipo, fenotipo, creencias políticas, profesión, raza, religión, orientación sexual, estatus socioeconómico, subcultura y capacidad técnica, en la medida en que éstas no entren en conflicto con este código de conducta. -Though we welcome people fluent in all languages, NumPy development is conducted in English. +Aunque aceptamos a personas con dominio de cualquier idioma, el desarrollo de NumPy se lleva a cabo en inglés. -Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). +Los estándares de comportamiento en la comunidad NumPy se detallan en el Código de Conducta anterior. Los participantes de nuestra comunidad deben respetar estos estándares en todas sus interacciones y ayudar a los demás a hacer lo mismo (véase la siguiente sección). -### Reporting Guidelines +### Directrices Para La Presentación De Informes -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. +Sabemos que es dolorosamente común que la comunicación en Internet comience o se convierta en un abuso evidente y manifiesto. También reconocemos que a veces la gente puede tener un mal día, o no ser consciente de algunas de las directrices de este Código de Conducta. Por favor, tenga esto en cuenta a la hora de decidir cómo responder a una violación de este Código. -For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. +En caso de infracciones claramente intencionadas, informe de éstas al Comité de Código de Conducta (ver más abajo). En caso de infracciones posiblemente involuntarias, puede responder a la persona y señalar este código de conducta (tanto en público como en privado, lo que sea más apropiado). Si prefiere no hacerlo, puede informar directamente al Comité de Código de Conducta o pedirle consejo a éste de forma confidencial. You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. From d34d872d861c4968dc75a0f0bd5246dafd2819d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 06:01:32 +0200 Subject: [PATCH 407/586] New translations install.md (Spanish) --- content/es/install.md | 60 +++++++++++++++++++++---------------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index f64a17841d..161b76adc1 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -55,67 +55,67 @@ On all of Windows, macOS, and Linux: #### Conda - Install [Miniforge](https://github.com/conda-forge/miniforge). -- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. +- Mantenga el entorno conda `base` mínimo, y utilice uno o más [entornos conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) para instalar el paquete que necesite para la tarea o proyecto en que está trabajando. -#### Alternative if you prefer pip/PyPI +#### Alternativa si prefiere pip/PyPI -For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: -- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. -- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. +Para usuarios que conocen, por preferencia personal o leyendo acerca de las diferencias principales entre conda y pip a continuación, y prefieren una solución basada en pip/PyPI, recomendamos: +- Instalar Python desde [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/) o su administrador de paquetes Linux. +- Utilice [Poetry](https://python-poetry.org/) como la herramienta mejor mantenida que proporciona una resolución de dependencias y capacidades de administración de entornos de forma similar a la que lo hace conda. -## Python package management +## Gestión de paquetes de Python -Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. +La gestión de los paquetes es un problema desafiante y, como resultado, hay muchas herramientas. Para desarrollo web y de propósito general en Python existe un completo [conjunto de herramientas ](https://packaging.python.org/guides/tool-recommendations/)complementario a pip. Para computación de alto rendimiento (HPC), [Spack](https://github.com/spack/spack) amerita ser considerado. Sin embargo, para la mayoría de usuarios de NumPy, [conda](https://conda.io/en/latest/) y [pip](https://pip.pypa.io/en/stable/) son las dos herramientas más populares. ### Pip & conda -The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. +Las dos herramientas principales que instalan paquetes de Python son `pip` y `conda`. Sus funcionalidades se traslapan parcialmente (por ejemplo, ambas pueden instalar `numpy`); no obstante, también pueden trabajar conjuntamente. Discutiremos las principales diferencias entre pip y conda aquí - esto es importante comprenderlo si usted desea gestionar paquetes de manera efectiva. -The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. +La primera diferencia radica en que conda es multi-lenguaje y puede instalar Python, mientras que pip es instalado para una versión particular de Python en su sistema e instala paquetes para esa misma versión de Python solamente. Esto también significa que conda puede instalar librerías que no sean de Python y herramientas que usted pueda necesitar (por ejemplo, compiladores, CUDA, HDF5), mientras que pip no. -The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. +La segunda diferencia es que pip instala desde el Índice de Empaquetado de Python (PyPI - Python Packaging Index), mientras que conda instala desde sus propios canales (típicamente "defaults" o "conda-forge"). PyPI es, de lejos, la colección de paquetes más grande; sin embargo, todos los paquetes populares están también disponibles para conda. -The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. +La tercera diferencia consiste en que conda es una solución integrada para gestionar paquetes, dependencias y entornos; mientras que con pip, usted podría necesitar otra herramienta (hay muchas!) para manejar entornos o dependencias complejas. -### Reproducible installs +### Instalaciones reproducibles -As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: +En la medida en que las librerías son actualizadas, los resultados al correr su código pueden cambiar, o su código puede fallar por completo. Es importante que sea capaz de reconstruir el conjunto de paquetes y versiones que usted está utilizando. La mejor práctica es: -1. use a different environment per project you're working on, -2. record package names and versions using your package installer; each has its own metadata format for this: - - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) +1. usar un entorno diferente por cada proyecto en el cual usted esté trabajando, +2. almacenar los nombres de paquetes y versiones utilizando su instalador de paquetes, cada uno de los cuales tiene su propio formato de metadata para esto: + - Conda: [entornos conda y environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [entornos virtuales](https://docs.python.org/3/tutorial/venv.html) y [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [entornos virtuales y pyproject.toml](https://python-poetry.org/docs/basic-usage/) -## NumPy packages & accelerated linear algebra libraries +## Paquetes NumPy & librerías de álgebra lineal aceleradas -NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: +NumPy no depende de ningún otro paquete de Python; sin embargo, sí depende de una librería de álgebra lineal acelerada - típicamente [Intel MKL](https://software.intel.com/en-us/mkl) u [OpenBLAS](https://www.openblas.net/). Los usuarios no tienen que preocuparse por instalar éstas (se incluyen automáticamente en todos los métodos de instalación de NumPy). Los usuarios avanzados podrían querer, de todas maneras, conocer los detalles, ya que la utilización BLAS puede afectar el desempeño, comportamiento y tamaño en disco: -- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. +- Las ruedas NumPy en PyPI, que son las que pip instala, están construidas con OpenBLAS. Las librerías de OpenBLAS están incluidas en la rueda. Esto vuelve a la rueda más grande, y si un usuario instala (por ejemplo) SciPy también, tendrá dos copias de OpenBLAS en disco. -- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. +- En el canal defaults o predeterminado de conda, NumPy está basado en Intel MKL. MKL es un paquete separado que se instalará en el entorno de usuario al instalar NumPy. -- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. +- En el canal conda-forge, Numpy está basado en un paquete "BLAS" ficticio o dummy. Cuando un usuario instala NumPy desde conda-forge, ese paquete BLAS es instalado junto con la librería - éste por defecto es OpenBLAS, pero también puede ser MKL (desde el canal defaults o predeterminado), o incluso [BLIS](https://github.com/flame/blis) o referencia BLAS. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. +- El paquete MKL es mucho más grande que OpenBLAS, de alrededor de 700 MB en disco, mientras que OpenBLAS es aproximadamente de 30MB. -- MKL is typically a little faster and more robust than OpenBLAS. +- MKL es normalmente un poco más rápido y más robusto que OpenBLAS. -Besides install sizes, performance and robustness, there are two more things to consider: +Además del tamaño de instalación, desempeño y robustez, hay dos aspectos más a considerar: -- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. -- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. +- Intel MKL no es de código abierto. Para uso normal esto no es un problema, pero si un usuario necesita redistribuir una aplicación construida con NumPy, esto podría ser un inconveniente. +- MKL y OpenBLAS utilizan funciones multihilo como `np.dot`, siendo el número de hilos determinado tanto por una opción de tiempo de compilación como por una variable de entorno. Todos los núcleos de la CPU usualmente serán utilizados. Esto es en ocasiones inesperado para los usuarios. NumPy en sí mismo no paraleliza automáticamente ninguna llamada a función. Normalmente produce un mejor rendimiento, pero también puede ser perjudicial - por ejemplo cuando se utiliza otro nivel de paralelización con Dask, el aprendizaje de la ciencia o multiprocesamiento. -## Troubleshooting +## Resolución de problemas -If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). +Si su instalación falla con el siguiente mensaje, revise el siguiente enlace [Resolución de problemas ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! From 85f1764b0c66522f2841d33f4ca2e70819e84608 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 06:01:33 +0200 Subject: [PATCH 408/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index d0be271f50..d0eb2488eb 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -49,24 +49,24 @@ src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimato * **Dynamic Modeling** - In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Las cosas se vuelven aún más desafiantes en términos de cálculo y comparación de datos cuando se buscan predicciones dinámicas del juego de críquet, tal como habría sucedido si el bateador hubiera golpeado la bola a un ángulo o velocidad diferente. -* **Predictive Analytics Complexity** +* **Complejidad de Análisis Predictivo** - Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. + Gran parte de la toma de decisiones en el críquet se basa en preguntas como "¿con qué frecuencia un bateador juega un cierto tipo de golpe si la entrega de la pelota es de un tipo particular?" o "¿cómo cambia un lanzador su línea y longitud si el bateador responde a su entrega de una cierta manera?". Este tipo de consulta de análisis predictivo requiere una disponibilidad de un conjunto de datos altamente granular y la capacidad de sintetizar datos y crear modelos generativos que sean altamente precisos. -## NumPy’s Role in Cricket Analytics +## El Papel de NumPy en el Análisis del Críquet -Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: +El análisis deportivo es un campo en desarrollo. Muchos investigadores y compañías [utilizan NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) y otros paquetes de PyData como Scikit-learn, SciPy, Matplotlib y Jupyter, además de utilizar las últimas técnicas de aprendizaje automático e inteligencia artificial. NumPy se ha utilizado para varios tipos de análisis deportivos relacionados con el críquet tales como: -* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. +* **Análisis Estadístico:** Las capacidades numéricas de NumPy ayudan a estimar la significancia estadística de los datos observacionales o de eventos de partidos en el contexto de varias tácticas de jugadores y de juego, estimando el resultado del juego mediante la comparación con un modelo generativo o estático. El [análisis causal](https://amplitude.com/blog/2017/01/19/causation-correlation) y los [enfoques de big data](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) se utilizan para el análisis táctico. -* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. +* **Visualización de Datos:** La creación de gráficos y la [visualización de datos](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) proporcionan información útil sobre la relación entre varios conjuntos de datos. -## Summary +## Resumen -Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. +El análisis deportivo ha revolucionado la forma en que se juegan los partidos profesionales, especialmente en cuanto a la toma de decisiones estratégicas, que hasta hace poco se basaba principalmente en la "intuición" o en la adherencia a tradiciones pasadas. NumPy constituye una base sólida para un gran conjunto de paquetes de Python que brindan funciones de nivel superior relacionadas con análisis de datos, el aprendizaje automático y los algoritmos de IA. Estos paquetes están ampliamente desplegados para obtener información en tiempo real que ayudan en la toma de decisiones para resultados revolucionarios, tanto en el campo como para sacar conclusiones y hacer negocios alrededor del juego del críquet. Encontrar los parámetros ocultos, patrones y atributos que conducen al resultado de un partido de críquet ayuda a los interesados a tomar nota de la información del juego que de otra forma estarían ocultos en números y estadísticas. {{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagrama que muestra los beneficios de usar NumPy para análisis de críquet' title = 'Capacidades claves de NumPy utilizadas' {{< /figure >}} From f4ec22136b206d8211c8940dd6a60159ec3aa98b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 06:01:34 +0200 Subject: [PATCH 409/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 257905de91..1dce212db5 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -1,6 +1,6 @@ --- -title: "Case Study: DeepLabCut 3D Pose Estimation" -sidebar: false +title: "Caso de estudio: Estimación de Postura 3D DeepLabCut" +sidebar: falso --- {{< figure >}} From 5c5fe895d92744e976327037ae6ec90ee741f6ae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Jul 2024 20:52:54 +0200 Subject: [PATCH 410/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 1dce212db5..83372a7354 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -4,7 +4,7 @@ sidebar: falso --- {{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analizar movimiento del ratón usando DeepLapCut' alt = 'micehandanim' attribution = '(Fuente: www.deeplabcut.org )' attributionlink = 'http://www.mousemASElab.org/deeplabcut' {{< /figure >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" From 5dc776c1af44248ce9407902c72e82903cf72c31 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 03:53:17 +0200 Subject: [PATCH 411/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 83372a7354..5f9aa4bfb2 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -7,18 +7,18 @@ sidebar: falso src = '/images/content_images/cs/mice-hand.gif' title = 'Analizar movimiento del ratón usando DeepLapCut' alt = 'micehandanim' attribution = '(Fuente: www.deeplabcut.org )' attributionlink = 'http://www.mousemASElab.org/deeplabcut' {{< /figure >}} -{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" -> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Profesor Asistente, Escuela Politécnica Federal de Lausana* ([EPFL](https://www.epfl.ch/en/))" +> }} El software de código abierto está acelerando la biomedicina. DeepLabCut permite el análisis automatizado de video del comportamiento animal utilizando Aprendizaje Profundo. > > {{< /blockquote >}} -## About DeepLabCut +## Acerca de DeepLabCut -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) es una caja de herramientas de código abierto que permite a los investigadores de cientos de instituciones de todo el mundo rastrear el comportamiento de animales de laboratorio, con muy pocos datos de entrenamiento, con una precisión de nivel humana. Con la tecnología DeepLabCut, los científicos pueden profundizar en la comprensión científica del control motriz y el comportamiento a través de especies animales y escalas de tiempo. -Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. +Muchas áreas de investigación, incluyendo la neurociencia, la medicina y la biomecánica, utilizan datos para rastrear el movimiento animal. DeepLabCut ayuda a entender lo que los humanos y otros animales están haciendo, analizando las acciones que han sido grabadas en la filmación. Utilizando la automatización para tareas laboriosas de etiquetado y monitoreo, junto con el análisis de datos basado en redes neuronales profundas, DeepLabCut realiza estudios científicos que involucran la observación de animales, tales como primates, ratones, peces, moscas, etc. de manera mucho más rápida y precisa. -{{< figure >}} +{{< figura >}} src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' {{< /figure >}} From 1e2b4f741a2005be1f9fac57261ae3151d789849 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 04:53:54 +0200 Subject: [PATCH 412/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 5f9aa4bfb2..1c8e1def7c 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -19,18 +19,18 @@ src = '/images/content_images/cs/mice-hand.gif' title = 'Analizar movimiento del Muchas áreas de investigación, incluyendo la neurociencia, la medicina y la biomecánica, utilizan datos para rastrear el movimiento animal. DeepLabCut ayuda a entender lo que los humanos y otros animales están haciendo, analizando las acciones que han sido grabadas en la filmación. Utilizando la automatización para tareas laboriosas de etiquetado y monitoreo, junto con el análisis de datos basado en redes neuronales profundas, DeepLabCut realiza estudios científicos que involucran la observación de animales, tales como primates, ratones, peces, moscas, etc. de manera mucho más rápida y precisa. {{< figura >}} -src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = 'Puntos de colores rastrean las posiciones de una parte del cuerpo de un caballo de carreras' alt = 'horserideranim' attribution = '(Fuente: Mackenzie Mathis)' {{< /figure >}} -DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. +El rastreo del comportamiento no invasivo de animales de DeepLabCut por medio de la extracción de posturas de animales es crucial para propósitos científicos en dominios tales como la biomecánica, genética, etología y & neurociencia. Medir las poses de animales de manera no invasiva a partir de video - sin marcadores - en fondos que cambian dinámicamente es un desafío computacional, tanto técnicamente como en términos de necesidades de recursos y datos de entrenamiento requeridos. -DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. +DeepLabCut permite a los investigadores estimar la postura del sujeto, permitiéndoles eficientemente cuantificar el comportamiento a través de una caja de herramientas de software basada en Python. Con DeepLabCut, los investigadores pueden identificar fotogramas distintos de videos, etiquetar digitalmente partes específicas del cuerpo en unas pocas docenas de fotogramas con una GUI personalizada, y luego las arquitecturas de estimación de posturas basadas en aprendizaje profundo en DeepLabCut aprenden a identificar esas mismas características en el resto del video y en otros videos similares de animales. Funciona en diferentes especies de animales, desde los animales de laboratorio comunes como moscas y ratones hasta animales más inusuales como los [guepardos][cheetah-movement]. -DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. +DeepLabCut utiliza un principio llamado [aprendizaje por transferencia](https://arxiv.org/pdf/1909.11229), que reduce considerablemente la cantidad de datos de entrenamiento requeridos y acelera la convergencia del período de entrenamiento. Dependiendo de las necesidades, los usuarios pueden seleccionar diferentes arquitecturas de red que proporcionan una inferencia más rápida (por ejemplo MobileNetV2), que también pueden combinarse con retroalimentación experimental en tiempo real. DeepLabCut utilizó originalmente los detectores de características de una arquitectura de estimación de postura humana de alto rendimiento, llamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspiró el nombre. El paquete ahora ha sido significativamente modificado para incluir arquitecturas adicionales, métodos de aumento y una experiencia de usuario completa en el front-end. Además, para apoyar los experimentos biológicos a gran escala, DeepLabCut proporciona capacidades de aprendizaje activo para que los usuarios puedan aumentar el conjunto de entrenamiento a lo largo del tiempo para cubrir casos límite y hacer que su algoritmo de estimación de postura sea robusto dentro de un contexto específico. -Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. +Recientemente, se presentó el [modelo zoo de DeepLabCut](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pre-entrenados para varias especies y condiciones experimentales, desde el análisis facial en primates hasta la postura de perro. Esto se puede ejecutar, por ejemplo, en la nube sin ningún etiquetado de datos nuevos o entrenamiento de redes neuronales, y no es necesaria ninguna experiencia de programación. -### Key Goals and Results +### Objetivos y Resultados Clave * **Automation of animal pose analysis for scientific studies:** From 6210162067cad25698a81ebb84cb47941c1cfce8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 05:54:30 +0200 Subject: [PATCH 413/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 1c8e1def7c..a78bd07567 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -91,10 +91,10 @@ src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Wo ## Summary -Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite a los investigadores estimar la postura del sujeto, permitiéndoles de manera eficiente cuantificar el comportamiento. Con solo un pequeño conjunto de imágenes de entrenamiento, el conjunto de herramientas de Python de DeepLabCut permite entrenar una red neuronal con una precisión de etiquetado a nivel humano, expandiendo así su aplicación no solo al análisis del comportamiento en el laboratorio, sino también potencialmente en deportes, análisis de marcha, medicina y estudios de rehabilitación. Los desafíos de la combinatoria compleja y procesamiento de datos enfrentados por los algoritmos de DeepLabCut se abordan mediante el uso de las capacidades de manipulación de arreglos de NumPy. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de NumPy' title = 'Capacidades claves de NumPy utilizadas' {{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 3423ee24a9f88c345b66406321b90e6856553f35 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 05:54:31 +0200 Subject: [PATCH 414/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 0087c7b632..f76aebc9a5 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -4,11 +4,11 @@ sidebar: false --- {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="imagen de un agujero negro" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} -src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +src = '/images/content_images/cs/blackhole. pg' title = 'Agujero Negro M87' alt = 'Imagen de agujero negro' attribution = '(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)' attributionlink = 'https://www. pl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" -> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> }} Capturar imágenes del Agujero Negro M87 es como intentar ver algo que por definición es imposible de ver. > > {{< /blockquote >}} @@ -39,7 +39,7 @@ El [ telescopio de Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.or Cuando el objetivo es ver algo nunca antes visto, ¿cómo pueden los científicos estar seguros de que la imagen es correcta? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +src = '/images/content_images/cs/dataprocessbh.png' title = 'Flujo de Trabajo de Procesamiento de Datos EHT' alt = 'flujo de datos' align = 'centrar' attribution = '(Créditos del diagrama: The Astrophysical Journal, Colaboración del Telescopio del Horizonte de Sucesos)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="el rol de numpy" caption="**El rol de NumPy en la fotografía del Agujero Negro**" >}} ## El Rol de NumPy @@ -50,14 +50,14 @@ La colaboración del EHT respondió a estos desafíos haciendo que los equipos i Su trabajo ilustra el rol que desempeña el ecosistema científico de Python en el avance de la ciencia a través del análisis de datos colaborativos. -{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'Flujo de Trabajo de Procesamiento de Datos EHT' alt = 'flujo de datos' align = 'centrar' attribution = '(Créditos del diagrama: The Astrophysical Journal, Colaboración del Telescopio del Horizonte de Sucesos)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="ventajas de numpy" caption="**Capacidades Clave utilizadas de NumPy**" >}} {{< /figure >}} Por ejemplo, el paquete de Python [`eht-imaging`][ehtim] proporciona herramientas para simular y realizar reconstrucción de imágenes en datos VLBI. NumPy está en el núcleo del procesamiento de datos de matrices utilizados en este paquete, como se muestra a continuación en el gráfico parcial de dependencias de software. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'mapa de dependencias de ehtim resaltando a numpy' title = 'Gráfico de dependencias de software del paquete ehtim resaltando a NumPy' {{< /figure >}} Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [Pandas](https://pandas.io), son parte del flujo de procesamiento de datos para fotografiar el agujero negro. Los formatos estándar de archivos astronómicos y transformaciones de tiempo/coordenadas fueron manejados por [Astropy][astropy], mientras que [Matplotlib][mpl] fue utilizado en la visualización de datos a través del flujo de análisis, incluyendo la generación de la imagen final del agujero negro. @@ -67,7 +67,7 @@ Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [ La matriz n-dimensional eficiente y adaptable que es la característica central de NumPy permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de numpy' title = 'Capacidades clave de NumPy utilizadas' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From a5d28a634bc06fda5f6b85a3399693b14504ee1d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 05:54:33 +0200 Subject: [PATCH 415/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 08f54b7547..b587d7e2ae 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -1,10 +1,10 @@ --- -title: "Case Study: Discovery of Gravitational Waves" -sidebar: false +title: "Estudio de Caso: Descubrimiento de Ondas Gravitacionales" +sidebar: falso --- {{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image. ng' title = 'Ondas Gravitacionales' alt = 'coalescencia de un agujero negro binario generando ondas gravitacionales' atribución = '(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)' attributionlink = 'https://youtu. e/Zt8Z_uzG71o' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. From b7081e03cd4e2b04bafb5a45af3b7baeb57b4724 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 06:52:58 +0200 Subject: [PATCH 416/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index b587d7e2ae..7ad79c0ff0 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -7,33 +7,33 @@ sidebar: falso src = '/images/content_images/cs/gw_sxs_image. ng' title = 'Ondas Gravitacionales' alt = 'coalescencia de un agujero negro binario generando ondas gravitacionales' atribución = '(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)' attributionlink = 'https://youtu. e/Zt8Z_uzG71o' {{< /figure >}} -{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *Colaboración científica LIGO*" >}} El ecosistema científico de Python es una infraestructura crítica para la investigación realizada en LIGO. {{< /blockquote >}} -## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) +## Acerca de [Ondas Gravitacionales](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) y [LIGO](https://www.ligo.caltech.edu) -Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. +Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por eventos cataclísmicos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de fenómenos más oscuros en el universo distante y su impacto. -The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. +El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](https://www.ligo.caltech.edu) fue diseñado para abrir el campo de la astrofísica de ondas gravitacionales mediante la detección directa de las ondas gravitacionales predichas por la Teoría General de la Relatividad de Einstein. Comprende dos interferómetros ampliamente separados dentro de los Estados Unidos: uno en Hanford, Washington, y el otro en Livingston, Louisiana, operando al unísono para detectar ondas gravitacionales. Cada uno de ellos tiene detectores de ondas gravitacionales de escala de múltiples kilómetros que utilizan interferometría de láser. La Colaboración Científica de LIGO (LSC) es un grupo de más de 1000 científicos de universidades de los Estados Unidos y de otros 14 países, respaldados por más de 90 universidades e institutos de investigación; aproximadamente 250 estudiantes contribuyen activamente a la colaboración. El nuevo descubrimiento de LIGO es la primera observación de ondas gravitacionales, realizada midiendo las diminutas perturbaciones que las ondas generan en el espacio y el tiempo a medida que pasan a través de la Tierra. Ha abierto nuevas fronteras astrofísicas que exploran el lado distorsionado del universo: objetos y fenómenos que se forman a partir del espacio-tiempo deformado. -### Key Objectives +### Objetivos Clave -* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. -* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data -* Data visualization so that the binary / numerical results can be comprehended. +* Aunque su [misión](https://www.ligo.caltech.edu/page/what-is-ligo) es detectar ondas gravitacionales de algunos de los procesos más violentos y energéticos del Universo, los datos que LIGO recopila pueden tener efectos de gran alcance en muchas áreas de la física, incluyendo gravitación, relatividad, astrofísica, cosmología, física de partículas y física nuclear. +* Procesar los datos observados mediante cálculos de relatividad numérica que implican matemáticas complejas para discernir la señal del ruido, filtrar la señal relevante y estimar estadísticamente la significancia de los datos observados +* Visualización de datos para que los resultados binarios/numéricos puedan ser comprendidos. -### The Challenges +### Los desafíos -* **Computation** +* **Cálculo** - Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + Las Olas Gravitacionales son difíciles de detectar, ya que producen un efecto muy pequeño y tienen una diminuta interacción con la materia. Procesar y analizar todos los datos de LIGO requiere una vasta infraestructura informática. Después de ocuparse del ruido, que es miles de millones de veces mayor que la señal, aún quedan ecuaciones de relatividad muy complejas y enormes cantidades de datos que suponen un desafío computacional: [se necesitan aproximadamente O(10^7) horas de CPU para los análisis de fusiones binarias](https://youtu.be/7mcHknWWzNI), distribuidas en 6 clústeres dedicados de LIGO -* **Data Deluge** +* **Avalancha de Datos** - As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + A medida que los dispositivos de observación se vuelven más sensibles y confiables, los desafíos planteados por la avalancha de datos y encontrar una aguja en un pajar aumentan exponencialmente. ¡LIGO genera terabytes de datos cada día! Dar sentido a estos datos requiere de un esfuerzo enorme para cada detección. Por ejemplo, las señales recolectadas por LIGO deben ser comparadas por supercomputadoras contra cientos de miles de plantillas de posibles señales de ondas gravitacionales. * **Visualization** From 7827b3c3124894ad71c1059363c1f5a236e00854 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 19:33:05 +0200 Subject: [PATCH 417/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index c3ed404296..8212d987e6 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -10,7 +10,7 @@ Hacer cumplir el [Código de Conducta](/code-of-conduct) impacta a nuestra comun * Actuar de manera personal en lugar de impersonal. El Comité puede involucrar a las partes para que comprendan la situación, respetando la privacidad y cualquier confidencialidad necesaria de los informantes. Sin embargo, a veces es necesario comunicarse directamente con uno o más individuos: el objetivo del Comité es mejorar la salud de nuestra comunidad en lugar de solo producir una decisión formal. * Enfatizar la empatía hacia los individuos en lugar de juzgar el comportamiento, evitando etiquetas binarias de “bueno” y “malo/malvado”. El acoso directo y claro existe, y lo afrontaremos con firmeza. Pero en muchas circunstancias puede ser complejo resolver estas situaciones, sobre todo aquellas en las que desacuerdos normales conllevan a comportamientos inútiles o perjudiciales para las partes. Comprender el contexto completo y encontrar un camino que vuelva involucrar a todos es difícil, pero es en última instancia lo más productivo para nuestra comunidad. * Comprendemos que el correo electrónico es un medio difícil y puede generar aislamiento. Recibir críticas por medio de correo electrónico, sin ningún contacto personal, puede ser particularmente doloroso. Esto hace que sea especialmente importante mantener una ambiente de apertura y respeto hacia las opiniones de los demás. También significa que debemos ser transparentes en nuestro actuar, y que debemos hacer todo lo que esté a nuestro alcance para asegurarnos de que todos nuestros miembros sean tratados de manera justa y con compasión. -* La discriminación puede ser sutil e inconsciente. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* La discriminación puede ser sutil e inconsciente. Ésta puede manifestarse como injusticia y hostilidad en interacciones que, por todo lo demás, serían normales. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. * Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. * Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. * Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. @@ -22,26 +22,26 @@ Voluntary informal mediation is a tool at our disposal. In contexts such as when * Find a candidate who can serve as a mediator. * Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -* Obtain the agreement of the reported person(s). -* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -* Establish a timeline for mediation to complete, ideally within two weeks. +* Obtener el acuerdo de la persona informante(s). +* Acuerden el mediador: aunque las partes pueden proponer un mediador diferente al candidato sugerido, solo si se llega a un acuerdo común en todos los términos se puede avanzar en el proceso. +* Establezca un marco de tiempo para completar la mediación, idealmente dentro de dos semanas. -The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. +El mediador dialogará con todas las partes y buscará una decisión que sea satisfactoria para todos. Una vez concluido el proceso, el mediador proporcionará un informe (revisado por todas las partes del proceso) al Comité, con recomendaciones sobre pasos a seguir. El comité a su vez evaluará estos resultados (bien se haya logrado una decisión satisfactoria o no) y decidirá sobre cualquier acción que considere necesaria. -## How the Committee will respond to reports +## Cómo responderá el Comité a los informes -When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. +Cuando el Comité (o uno de sus miembros) recibe un informe, primero determinará si el éste se refiere a una violación clara y grave (como se define a continuación). En caso afirmativo, será necesario tomar medidas inmediatas adicionales al proceso de gestión de informe habitual. -## Clear and severe breach actions +## Acciones violatorias claras y severas -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. +Sabemos que es dolorosamente común que la comunicación en Internet comience o se convierta en un abuso evidente y manifiesto. Nos ocuparemos rápidamente de violaciones claras y graves, tales como amenazas personales, lenguaje violento, sexista o racista. -When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: +Cuando un miembro del Comité de Código de Conducta tenga conocimiento de una violación clara y grave, hará lo siguiente: -* Immediately disconnect the originator from all NumPy communication channels. -* Reply to the reporter that their report has been received and that the originator has been disconnected. +* Desconectará inmediatamente al originador de todos los canales de comunicación de NumPy. +* Responderá al informante que su informe ha sido recibido y que el autor ha sido desconectado. * In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. * The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. @@ -61,9 +61,9 @@ The Committee will then review the incident and determine, to the best of their This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). -It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. +Es importante conservar un archivo de todas las actividades de este Comité para asegurar consistencia en el comportamiento y proporcionar memoria institucional para el proyecto. Para ayudar en esto, el canal de discusión por defecto para este Comité será una lista de correo privada accesible a los miembros actuales y futuros del Comité, así como a los miembros del Consejo Directivo con previa solicitud justificada. Si el Comité considera necesario utilizar comunicaciones fuera de la lista (por ejemplo, para una respuesta temprana/rápida), deberá, en todos los casos, resumirlos y documentarlos de vuelta a la lista, de manera que se mantenga un buen registro del proceso. -The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. Dado el caso de que no se pueda establecer una decisión dentro de dicho plazo, el Comité responderá a los informantes con una actualización y duración estimada para la decisión. +El Comité de Código de Conducta debería aspirar a que se acuerde una resolución en el plazo de dos semanas. Dado el caso de que no se pueda establecer una decisión dentro de dicho plazo, el Comité responderá a los informantes con una actualización y duración estimada para la decisión. ## Resoluciones From a686fc2cdcb4806fbe9d3ff6568c750750f3cfcb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 19:33:07 +0200 Subject: [PATCH 418/586] New translations news.md (Spanish) --- content/es/news.md | 52 +++++++++++++++++++++++----------------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 42825076d4..a4073673bd 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -21,18 +21,18 @@ La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python. _23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** - La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. +### Recaudación de fondos de fin de año de NumFOCUS +_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  -### NumPy 1.26.0 released +### NumPy 1.26.0 ha sido lanzado -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -45,9 +45,9 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org is now available in Japanese and Portuguese +### numpy.org ya está disponible en japonés y portugués -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_ 2 de Agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: _Portuguese:_ * Melissa Weber Mendonça (melissawm) @@ -67,18 +67,18 @@ _Japanese:_ The work on the translation infrastructure is supported with funding from CZI. -Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) -### NumPy 1.25.0 released +### NumPy 1.25.0 ha sido lanzado -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: +_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Support for MUSL, there are now MUSL wheels. -* Support for the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum. -* Support for the inplace matrix multiplication (`@=`). +* Soporte para MUSL, ahora hay ruedas MUSL. +* Soporte para el compilador de Fujitsu C/C++. +* Las matrices de objetos ahora están soportadas en einsum. +* Soporte para la multiplicación de matrices in situ (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. +NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. A total of 148 people contributed to this release and 530 pull requests were merged. @@ -179,23 +179,23 @@ _22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociació _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversity in the NumPy project +### Diversidad en el proyecto NumPy -_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). +_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). -### First official NumPy paper published in Nature! +### Primer artículo oficial de NumPy publicado en Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. +_16 de Septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. -### Python 3.9 is coming, when will NumPy release binary wheels? +### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to -- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. +_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de +- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` +- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. ### Numpy 1.19.2 release From 91d2b6ea770e0946cc46065ea4415d0ef1d63b4a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:52 +0200 Subject: [PATCH 419/586] New translations news.md (Spanish) --- content/es/news.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index a4073673bd..db04349a72 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -220,19 +220,19 @@ The logo is a modern take on the old one, with a cleaner design. Thanks to Isabe ### NumPy 1.19.0 release -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. +_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. -### Season of Docs acceptance +### Aceptación a Season of Docs -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 1.18.0 release +### Lanzamiento de NumPy 1.18.0 -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. ### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg From 70a8aae8f493d0eca0d731442f89f4001b20d77d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:53 +0200 Subject: [PATCH 420/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index a5faa4e0be..1aa950a7da 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -128,8 +128,8 @@ params: url: https://seaborn.pydata.org/ label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + title: Procesamiento de Señales + alttext: Un gráfico de barras con valores positivos y negativos. img: /images/content_images/sc_dom_img/signal_processing.svg links: - @@ -143,10 +143,10 @@ params: label: python-control - url: https://hyperspy.org/ - label: HyperSpy + label: HiperSpy - - title: Image Processing - alttext: An photograph of the mountains. + title: Procesamiento de Imágenes + alttext: Una fotografía de las montañas. img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -176,8 +176,8 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomy - alttext: A telescope. + title: Astronomía + alttext: Un telescopio. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - @@ -266,8 +266,8 @@ params: url: https://www.pybamm.org/ label: PyBaMM - - title: Geoscience - alttext: The Earth. + title: Geociencia + alttext: La Tierra. img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -297,8 +297,8 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: Arquitectura e Ingeniería + alttext: Una placa de desarrollo de microprocesadores. img: /images/content_images/sc_dom_img/robotics.svg links: - @@ -306,7 +306,7 @@ params: label: COMPAS - url: https://cityenergyanalyst.com/ - label: City Energy Analyst + label: City Energy Analyst - Analista de Energía de Ciudad - url: https://nortikin.github.io/sverchok/ label: Sverchok From 73cbd915f1f4d30a64761492311fafa1c5907d87 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:55 +0200 Subject: [PATCH 421/586] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/gethelp.md b/content/es/gethelp.md index 5da0e518f7..b875743dea 100644 --- a/content/es/gethelp.md +++ b/content/es/gethelp.md @@ -3,9 +3,9 @@ title: Buscar Ayuda sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**Problemas de desarrollo:** Para asuntos relacionados con el desarrollo de NumPy (por ejemplo, informes de errores), por favor consulte la sección de [Comunidad](/community). -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! +**Preguntas de los usuarios:** La mejor manera de obtener ayuda es publicar tu pregunta en un sitio como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) o [Reddit](https://www.reddit.com/r/Numpy/). Nos gustaría poder estar al tanto de estos sitios, o responder preguntas de manera directa, ¡pero el volumen es algo abrumador! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From b82a7721070ce129b63b31236ddf119e0b63d481 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:56 +0200 Subject: [PATCH 422/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index 228da2eb4f..fb39da53ba 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -1,6 +1,6 @@ --- -title: NumPy Code of Conduct -sidebar: false +title: Código de conducta de NumPy +sidebar: falso aliases: - /conduct.html --- @@ -57,27 +57,27 @@ Currently, the Committee consists of: * Melissa Weber Mendonça * Rohit Goswami -If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). +Si tu informe implica a algún miembro del Comité, o si éste considera que tiene un conflicto de intereses en su tramitación, se abstendrán de examinar tu denuncia. Alternativamente, si por cualquier razón usted se siente incómodo haciendo un informe al Comité, también puede ponerse en contacto con el personal de NumFOCUS senior en [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). -### Incident reporting resolution & Code of Conduct enforcement +### Resolución de reporte de informes & aplicación del Código de Conducta -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). +_Esta sección resume los puntos más importantes, puede encontrar más detalles en el_ [Código de Conducta de NumPy - Cómo hacer seguimiento a un informe](report-handling-manual). -We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). +Investigaremos y responderemos a todas las quejas. El Comité de Código de Conducta de NumPy y el Consejo Directivo de NumPy (en caso de estar involucrado) protegerán la identidad del denunciante y tratarán el contenido de las denuncias con carácter confidencial (a menos que el denunciante acuerde lo contrario). -In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. +En caso de infracciones graves y evidentes, por ejemplo, amenaza personal o lenguaje violento, sexista o racista, desconectaremos inmediatamente al originador de los canales de comunicación de NumPy; por favor consulte el manual para obtener más detalles. -In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: +En casos que no impliquen violaciones claras y graves u obvias de este Código de Conducta, el proceso de actuación sobre cualquier informe de violación del Código de Conducta recibido será: -1. acknowledge report is received, -2. reasonable discussion/feedback, -3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. +1. acusar recibo del informe, +2. una discusión/retroalimentación razonable, +3. mediación (si la retroalimentación no fue útil, y únicamente si tanto el denunciante como el denunciado están de acuerdo con ello), +4. aplicación a través de una decisión transparente (ver [Resoluciones](report-handling-manual/#resolutions)) por parte del Comité de Código de conducta. -The Committee will respond to any report as soon as possible, and at most within 72 hours. +El Comité responderá a cualquier informe lo antes posible y dentro de un plazo máximo de 72 horas. -### Endnotes +### Notas finales -We are thankful to the groups behind the following documents, from which we drew content and inspiration: +Damos las gracias a los grupos que están detrás de los siguientes documentos, de los que hemos sacado contenido e inspiración: - [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 3231cd6c5a6d67b43bbaabc910203f59b522dd49 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:57 +0200 Subject: [PATCH 423/586] New translations install.md (Spanish) --- content/es/install.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index 161b76adc1..ea184ea1ec 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -35,26 +35,26 @@ Also when using pip, it's good practice to use a virtual environment - see [Rep # Python and NumPy installation guide -Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. +Instalar y administrar paquetes en Python es complicado, hay un número de soluciones alternativas para la mayoría de tareas. Esta guía intenta dar al lector una idea de las mejores (o más populares) soluciones y dar recomendaciones claras. Se enfoca en los usuarios de Python, NumPy y del stack de PyData (o computación numérica) en sistemas operativos y hardware comunes. -## Recommendations +## Recomendaciones -We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. +Empezaremos con recomendaciones basadas en el nivel de experiencia del usuario y el sistema operativo de interés. Si se encuentra entre "principiante" y "avanzado", por favor diríjase a "principiante" si quiere mantener las cosas simples, y a "avanzado" si quiere trabajar de acuerdo a las mejores prácticas que le servirán de mucho en el futuro. -### Beginning users +### Usuarios principiantes -On all of Windows, macOS, and Linux: +Tanto en Windows, macOS y Linux: -- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). -- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. -- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. +- Instale [Anaconda](https://www.anaconda.com/download) (esto instala todos los paquetes que necesita y todas las demás herramientas mencionadas a continuación). +- Para escribir y ejecutar código, utilice notebooks en [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) para computación exploratoria e interactiva, y [Spyder](https://www.spyder-ide.org/) o [Visual Studio Code](https://code.visualstudio.com/) para escribir scripts y paquetes. +- Utilice [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) para administrar sus paquetes e iniciar JupyterLab, Spyder o Visual Studio Code. -### Advanced users +### Usuarios avanzados #### Conda -- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Instale [Miniforge](https://github.com/conda-forge/miniforge). - Mantenga el entorno conda `base` mínimo, y utilice uno o más [entornos conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) para instalar el paquete que necesite para la tarea o proyecto en que está trabajando. #### Alternativa si prefiere pip/PyPI @@ -118,7 +118,7 @@ Además del tamaño de instalación, desempeño y robustez, hay dos aspectos má Si su instalación falla con el siguiente mensaje, revise el siguiente enlace [Resolución de problemas ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` -IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! +¡IMPORTANTE: POR FAVOR LEA ESTO COMO SUGERENCIA PARA RESOLVER ESTE PROBLEMA! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. From 8aafeffafd1e95fca0ef86386d55732c0dd49b04 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:58 +0200 Subject: [PATCH 424/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 22 ++++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index d0eb2488eb..541047f40d 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -14,18 +14,18 @@ src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest C ## About Cricket -It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más. -The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. +La Premier League de India (IPL) es una liga profesional de críquet Twenty20, fundada en 2008. Es uno de los eventos de críquet más concurridos en el mundo, valorado en [$6.7 mil millones de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) en 2019. -Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. +El críquet es un juego de números - las carreras anotadas por un bateador, los wickets tomados por un lanzador, los partidos ganados por un equipo de críquet, el número de veces que un bateador responde de cierta manera a un tipo de ataque de lanzamiento, etc. La capacidad de profundizar en los números del críquet tanto para mejorar el rendimiento como para estudiar las oportunidades de negocio, el mercado en general y la economía del cricket mediante potentes herramientas de análisis, impulsadas por software de computación numérica como NumPy, es algo muy importante. El análisis del críquet proporciona ideas interesantes sobre el juego e inteligencia predictiva respecto a los resultados del juego. -Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: +Hoy en día, hay abundantes y casi infinitos tesoros de registros y estadísticas de juegos de críquet disponibles, por ejemplo, en [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) y [cricsheet](https://cricsheet.org). Estas y muchas otras bases de datos de cricket se han utilizado para el [análisis de cricket](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) utilizando los últimos algoritmos de aprendizaje automático y modelación predictiva. Las plataformas de medios y entretenimiento, junto con los organismos deportivos profesionales asociados con el juego, utilizan la tecnología y el análisis para determinar métricas clave que mejoren las posibilidades de ganar los partidos: -* batting performance moving average, -* score forecasting, -* gaining insights into fitness and performance of a player against different opposition, -* player contribution to wins and losses for making strategic decisions on team composition +* promedio móvil del rendimiento de bateo, +* pronóstico del marcador, +* obtener información sobre la condición física y el rendimiento de un jugador contra diferentes oponentes, +* contribución del jugador a las victorias y derrotas para tomar decisiones estratégicas sobre la composición del equipo {{< figure >}} src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' @@ -45,11 +45,11 @@ src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimato * **Data Cleaning and preprocessing** - IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. Todos estos factores juntos han incrementado la complejidad de la limpieza de datos y el preprocesamiento. -* **Dynamic Modeling** +* **Modelación Dinámica** - In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Las cosas se vuelven aún más desafiantes en términos de cálculo y comparación de datos cuando se buscan predicciones dinámicas del juego de críquet, tal como habría sucedido si el bateador hubiera golpeado la bola a un ángulo o velocidad diferente. + En el cricket, al igual que en cualquier otro deporte, puede haber una gran cantidad de variables relacionadas con el seguimiento de varios jugadores en el campo, sus atributos, la pelota y varias posibilidades de acciones potenciales. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Las cosas se vuelven aún más desafiantes en términos de cálculo y comparación de datos cuando se buscan predicciones dinámicas del juego de críquet, tal como habría sucedido si el bateador hubiera golpeado la bola a un ángulo o velocidad diferente. * **Complejidad de Análisis Predictivo** From 01e2053f8fb7d8d94eeef89f1599d1ae1bcaa467 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 21:02:59 +0200 Subject: [PATCH 425/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index a78bd07567..af345eb77f 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -32,17 +32,17 @@ Recientemente, se presentó el [modelo zoo de DeepLabCut](http://www.mousemotorl ### Objetivos y Resultados Clave -* **Automation of animal pose analysis for scientific studies:** +* **Automatización del análisis de la postura animal para estudios científicos:** - The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). + El objetivo principal de la tecnología DeepLabCut es medir y rastrear la postura de los animales en diversos entornos. Estos datos se pueden utilizar, por ejemplo, en estudios de neurociencia para entender cómo el cerebro controla el movimiento, o para dilucidar como interactúan socialmente los animales. Los investigadores han observado un [aumento de rendimiento diez veces mayor](https://www.biorxiv.org/content/10.1101/457242v1) con DeepLabCut. Las posturas se pueden inferir sin conexión hasta a 1200 fotogramas por segundo (FPS). -* **Creation of an easy-to-use Python toolkit for pose estimation:** +* **Creación de un conjunto de herramientas de Python de fácil uso para la estimación de postura:** - DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. + DeepLabCut quería compartir su tecnología de estimación de postura animal en la forma de una herramienta de fácil uso que pueda ser adoptada fácilmente por los investigadores. Así que han creado un conjunto de herramientas de Python completo y de fácil uso, también con características de administración de proyectos. Estas permiten no solo la automatización de la estimación de postura, sino también administrar el proyecto de punta a punta ayudando al usuario del conjunto de herramientas de DeepLabCut desde la etapa de recolección del conjunto de datos para crear flujos de trabajo de análisis compartibles y reutilizables. - Their [toolkit][DLCToolkit] is now available as open source. + Su [conjunto de herramientas][DLCToolkit] ahora está disponible como código abierto. - A typical DeepLabCut Workflow includes: + En flujo de trabajo típico de DeepLabCut incluye: - creation and refining of training sets via active learning - creation of tailored neural networks for specific animals and scenarios @@ -61,11 +61,11 @@ src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose est * **Combinatorics** - Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Ensamblar puntos clave y sus conexiones en movimientos individuales de animales y vincularlos a lo largo del tiempo es un proceso complejo que requiere un análisis numérico intensivo, especialmente en el caso del seguimiento de movimientos de múltiples animales en videos de experimentos. -* **Data Processing** +* **Procesamiento de Datos** - Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. + Por último, pero no menos importante, la manipulación de arreglos - procesamiento de grandes pilas de arreglos correspondientes a varias imágenes, tensores objetivo y puntos clave es bastante desafiante. {{< figure >}} src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' From daeea4eff8800159bbd85deb55d2e4ddfb318745 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Jul 2024 22:04:35 +0200 Subject: [PATCH 426/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index af345eb77f..80b3c3c101 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -68,10 +68,10 @@ src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose est Por último, pero no menos importante, la manipulación de arreglos - procesamiento de grandes pilas de arreglos correspondientes a varias imágenes, tensores objetivo y puntos clave es bastante desafiante. {{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = 'Estimación de variedad y complejidad de postura' alt = 'challengesfig' align = 'centro' atribución = '(Fuente: Mackenzie Mathis)' attributionlink = 'https://www. iorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} -## NumPy's Role in meeting Pose Estimation Challenges +## El Papel de NumPy para afrontar los desafíos de la estimación de postura NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). From 97702b98c98ac9d82dbe2b97092ec2cf8b40374b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Jul 2024 03:26:41 +0200 Subject: [PATCH 427/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 80b3c3c101..dea1684571 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -73,15 +73,15 @@ src = '/images/content_images/cs/pose-estimation.png' title = 'Estimación de va ## El Papel de NumPy para afrontar los desafíos de la estimación de postura -NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). +NumPy aborda la necesidad central de la tecnología de DeepLabCut de realizar cálculos numéricos a alta velocidad para el análisis del comportamiento. Además de NumPy, DeepLabCut emplea varios softwares de Python que utilizan NumPy en su núcleo, como [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) y [Tensorflow](https://www.tensorflow.org). -The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: +Las siguientes características de NumPy jugaron un papel clave en abordar el procesamiento de imágenes, los requisitos de combinatoria y la necesidad de cálculos rápidos en los algoritmos de estimación de posturas de DeepLabCut: -* Vectorization -* Masked Array Operations -* Linear Algebra -* Random Sampling -* Reshaping of large arrays +* Vectorización +* Operaciones con Arreglos Enmascarados +* Álgebra lineal +* Muestreo Aleatorio +* Redimensionamiento de arreglos grandes DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. From ba7ca7ec227d18e98b10010cc8b38b197c9640a7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Jul 2024 03:26:42 +0200 Subject: [PATCH 428/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 7ad79c0ff0..ef8024fa89 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -37,7 +37,7 @@ El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](ht * **Visualization** - Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. {{< figure >}} src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' From 54f4ee2268c182233927a92a1849d3bf56b740e1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Jul 2024 04:26:17 +0200 Subject: [PATCH 429/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index ef8024fa89..a5ecdc3a16 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -40,10 +40,10 @@ El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](ht Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. {{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +src = '/images/content_images/cs/gw_strain_amplitude. ng' alt = 'amplitud de deformación de ondas gravitacionales' title = 'Amplitud de deformación de ondas gravitacionales estimada de GW150914' atribución = '(Créditos del gráfico: Observación de Ondas Gravitacionales de la Fusión de un Agujero Negro Binario, Publicación de ResearchGate)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} -## NumPy’s Role in the Detection of Gravitational Waves +## El Papel de NumPy en la Detección de Ondas Gravitacionales Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. From ab6d237a93e6d7f705cae955ce5f8eac503acdc0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 00:58:25 +0200 Subject: [PATCH 430/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index a5ecdc3a16..2ecfcd7a21 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -45,13 +45,13 @@ src = '/images/content_images/cs/gw_strain_amplitude. ng' alt = 'amplitud de def ## El Papel de NumPy en la Detección de Ondas Gravitacionales -Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. +Las ondas gravitacionales emitidas por la fusión no pueden ser calculadas utilizando ninguna técnica excepto la relatividad numérica por fuerza bruta usando supercomputadoras. La cantidad de datos que LIGO recopila es tan incomprensiblemente grande como pequeñas son las señales de onda gravitacionales. -NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: +NumPy, el paquete de análisis numérico estándar para Python, fue utilizado por el software empleado en varias tareas realizadas durante el proyecto de detección de Ondas Gravitacionales en LIGO. NumPy ayudó a resolver las matemáticas complejas y la manipulación de datos a alta velocidad. Aquí hay algunos ejemplos: -* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) -* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack -* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* [Procesamiento de Señales](https://www.uv.es/virgogroup/Denoising_ROF.html): Detección de fallos, [Identificación de ruido y caracterización de datos](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Recuperación de datos: Decidir qué datos pueden ser analizados, y determinar si estos contienen una señal - aguja en un pajar +* Análisis estadístico: estimar la significancia estadística de los datos observados, estimación de los parámetros de señal (por ejemplo, masas de estrellas, velocidad de giro y distancia) en comparación con un modelo. * Visualization of data - Time series - Spectrograms @@ -70,8 +70,8 @@ src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy d ## Summary -GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. Los cálculos son complejos y no pueden ser comprendidos por humanos, a menos que sean visualizados utilizando simulaciones por computador que se alimenten con datos y análisis reales observados. NumPy, junto con otros paquetes de Python como matplotlib, pandas y scikit-learn, está [permitiendo a los investigadores](https://www.gw-openscience.org/events/GW150914/) responder preguntas complejas y descubrir nuevos horizontes en nuestra comprensión del universo. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'beneficios de NumPy' title = 'Capacidades clave de NumPy utilizadas' {{< /figure >}} From 00818218cf42613a7128475a6582c7ff09f788e6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:07 +0200 Subject: [PATCH 431/586] New translations 404.md (Spanish) --- content/es/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/404.md b/content/es/404.md index b38d73b758..279bf4a482 100644 --- a/content/es/404.md +++ b/content/es/404.md @@ -1,6 +1,6 @@ --- title: 404 -sidebar: false +sidebar: falso --- ¡Oh, oh! Has llegado a un callejón sin salida. From 3d18ef48cc66bd69174cb01b5f65a8d59401c6e0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:08 +0200 Subject: [PATCH 432/586] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 50b9bb2b78..59cbc11840 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -5,13 +5,13 @@ sidebar: false Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Programación de Arreglos con NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). _En formato BibTeX:_ ``` @Article{ harris2020array, - title = {Array programming with {NumPy}}, + title = {Programación de arreglos con {NumPy}}, author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian From 8815f85758a0c3439505c2be515c88de7de56c2c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:09 +0200 Subject: [PATCH 433/586] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index cabf5f7f96..1eaffaff78 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -5,9 +5,9 @@ sidebar: false En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250">}} +{{< figura >}} src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Página de portada del informe de la encuesta de usuarios de NumPy de 2020, titulado "Encuesta de la Comunidad de NumPy 2020 - resultados"' width = '250' -{{< /figure >}} +{{< /figura >}} **[Descarga el informe](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver a detalle los resultados de la encuesta. From eb721a285aaa036affcdcd16bddbc80908c2d722 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:10 +0200 Subject: [PATCH 434/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 8212d987e6..4f6564c096 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -31,7 +31,7 @@ El mediador dialogará con todas las partes y buscará una decisión que sea sat ## Cómo responderá el Comité a los informes -Cuando el Comité (o uno de sus miembros) recibe un informe, primero determinará si el éste se refiere a una violación clara y grave (como se define a continuación). En caso afirmativo, será necesario tomar medidas inmediatas adicionales al proceso de gestión de informe habitual. +Cuando el Comité (o uno de sus miembros) recibe un informe, primero determinará si éste se refiere a una violación clara y grave (como se define a continuación). En caso afirmativo, será necesario tomar medidas inmediatas adicionales al proceso de gestión de informe habitual. ## Acciones violatorias claras y severas From c78a7a3c6b5a119dc1e6f4a972b754e15a8d76d4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:11 +0200 Subject: [PATCH 435/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 75e24296f6..e2b388e7c7 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -7,7 +7,7 @@ sidebar: false Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [propuesta](https://github.com/numpy/numpy/issues) o comenta en una relevante). -Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la communidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). +Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la comunidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). También hacemos _llamadas a la comunidad_ de manera quincenal, cuyos detalles se anuncian en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion). Te invitamos a unirte. Si es la primera vez que contribuyes al código abierto, también te recomendamos encarecidamente que leas [esta guía](https://opensource.guide/how-to-contribute/). @@ -21,7 +21,7 @@ Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development ### Revisando solicitudes de cambios El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: * resumir un debate extenso -* categorizar PRs de documentación +* categorizar documentación de solicitudes de incorporación de cambios * probar los cambios propuestos From 7eaae5ffff85e91f065e9cf968b1b20169211446 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:12 +0200 Subject: [PATCH 436/586] New translations learn.md (Spanish) --- content/es/learn.md | 44 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index 5140f5c664..acb7ca8465 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -19,22 +19,22 @@ Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recom * [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [Lecturas de SciPy](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. -* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) -* [NumPy User Guide](https://numpy.org/devdocs) +* [NumPy: lo absolutamente básico para principiantes](https://numpy.org/devdocs/user/absolute_beginners.html) +* [Tutorial de NumPy *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *por Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [Guía del Usuario de NumPy](https://numpy.org/devdocs) **Libros** -* [Guide to NumPy *by Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Esta es la primera versión gratuita de 2006. Para conseguir la última versión (2015) mira [aquí](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* +* [Guía a NumPy *por Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) Ésta es una versión 1 gratuita de 2006. Para conseguir la última versión (2015) mira [aquí](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [De Python a NumPy *por Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [SciPy Elegante](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *por Juan Nunez-Technesias, Stefan van der Walt, y Harriet Dashnow* También puedes echar un vistazo a esta [lista de Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre el tema "Python+SciPy". La mayoría de esos libros son sobre el "ecosistema SciPy", que tiene NumPy en su núcleo. - **Vídeos** + **Videos** -* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +* [Introducción a la computación numérica con NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** @@ -44,30 +44,30 @@ Pruebe estos recursos avanzados para comprender mejor los conceptos de NumPy com **Tutoriales** -* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* -* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* -* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [100 Ejercicios de NumPy](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* +* [Una Introducción a NumPy y Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *por Stéfan van der Walt* * [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). **Libros** -* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* -* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* -* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* +* [Manual para Ciencia de Datos en Python](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* +* [Python para Análisis de Datos](https://www.amazon.com/Python-Data-Analysis-Wrangling-Ipython/dp/1491957662) *por Wes McKinney* +* [Python Numérico: Aplicaciones en Computación Científica y Ciencia de Datos con NumPy, SciPy y Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *por Robert Johansson* - **Vídeos** + **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* +* [NumPy Avanzado - reglas de broadcasting, strides e indexación avanzada](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunez-Iglesias* *** ## Charlas de NumPy -* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) -* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) -* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) -* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) -* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) +* [El Futuro de la Indexación en NumPy](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) +* [Evolución de la Computación de Arreglos en Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) +* [NumPy: ¿qué ha cambiado y qué va a cambiar?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *por Matti Picus* (2019) +* [Adentro de NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Breve Reseña de Computación de Arreglos en Python](https://www.youtube.com/watch?v=f176j2g2eNc) *por Travis Oliphant* (2019) *** From fdfd6e8a1812ebd938dc2f3122af12452d5a86b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:13 +0200 Subject: [PATCH 437/586] New translations community.md (Spanish) --- content/es/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/community.md b/content/es/community.md index 0e1e53211b..f5918abd76 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -57,7 +57,7 @@ El proyecto NumPy no organiza sus propias conferencias. Las conferencias que tra Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy, en donde puedes aprender cómo contribuir a Numpy o proyectos de código abierto relacionados. -## Únete a la comunidad Numpy +## Únete a la comunidad NumPy Para prosperar, el proyecto NumPy necesita tu experiencia y entusiasmo. ¿No sabes programar? ¡No es un problema! Hay muchas maneras de contribuir a NumPy. From 8ecf1bc5b65f761fd6c6a0fcb5da39dce1ec092c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 03:09:15 +0200 Subject: [PATCH 438/586] New translations news.md (Spanish) --- content/es/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index db04349a72..2a56e2ecf8 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,7 +1,7 @@ --- title: Noticias sidebar: falso -newsHeader: "Lanzamiento de NumPy 2.0!" +newsHeader: "¡NumPy 2.0 ha sido lanzado!" date: 2024-06-17 --- @@ -98,7 +98,7 @@ _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new N _18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento o stacking. +* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. * Nuevas características y correcciones de F2PY. * Muchas nuevas obsolescencias, revísalas. * Muchas obsolescencias caducadas, @@ -128,7 +128,7 @@ Por favor, complete este breve [formulari o de "Interés del Participante"](http _31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. Upstream is a moving target, so there will likely be further improvements, but the major work is done. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. Upstream is a moving target, so there will likely be further improvements, but the major work is done. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. * Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. * NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). * Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. From ee311b02d1a0198ab42b55cea0114aee4e1e4e94 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:38 +0200 Subject: [PATCH 439/586] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 59cbc11840..7255790792 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -1,6 +1,6 @@ --- title: Citando a NumPy -sidebar: false +sidebar: falso --- Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento: From 2e9e747832dd66abcab322d057b7cb4db55fdcf3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:39 +0200 Subject: [PATCH 440/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 4f6564c096..2f1e43eab2 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -10,18 +10,18 @@ Hacer cumplir el [Código de Conducta](/code-of-conduct) impacta a nuestra comun * Actuar de manera personal en lugar de impersonal. El Comité puede involucrar a las partes para que comprendan la situación, respetando la privacidad y cualquier confidencialidad necesaria de los informantes. Sin embargo, a veces es necesario comunicarse directamente con uno o más individuos: el objetivo del Comité es mejorar la salud de nuestra comunidad en lugar de solo producir una decisión formal. * Enfatizar la empatía hacia los individuos en lugar de juzgar el comportamiento, evitando etiquetas binarias de “bueno” y “malo/malvado”. El acoso directo y claro existe, y lo afrontaremos con firmeza. Pero en muchas circunstancias puede ser complejo resolver estas situaciones, sobre todo aquellas en las que desacuerdos normales conllevan a comportamientos inútiles o perjudiciales para las partes. Comprender el contexto completo y encontrar un camino que vuelva involucrar a todos es difícil, pero es en última instancia lo más productivo para nuestra comunidad. * Comprendemos que el correo electrónico es un medio difícil y puede generar aislamiento. Recibir críticas por medio de correo electrónico, sin ningún contacto personal, puede ser particularmente doloroso. Esto hace que sea especialmente importante mantener una ambiente de apertura y respeto hacia las opiniones de los demás. También significa que debemos ser transparentes en nuestro actuar, y que debemos hacer todo lo que esté a nuestro alcance para asegurarnos de que todos nuestros miembros sean tratados de manera justa y con compasión. -* La discriminación puede ser sutil e inconsciente. Ésta puede manifestarse como injusticia y hostilidad en interacciones que, por todo lo demás, serían normales. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* La discriminación puede ser sutil e inconsciente. Ésta puede manifestarse como injusticia y hostilidad en interacciones que, por todo lo demás, serían normales. Sabemos que esto ocurre, y nos ocuparemos de estar pendientes de esto. Nos gustaría mucho saber de usted si cree que ha sido tratado injustamente, y utilizaremos estos procedimientos para asegurarnos de que su queja sea escuchada y atendida. +* Ayudar a aumentar el compromiso con buenas prácticas de discusión: tratar de identificar dónde pudo haberse descompuesto la discusión, y proporcionar información práctica, sugerencias y recursos que puedan conducir a un cambio positivo en estos aspectos. +* Sea consciente de las necesidades de los nuevos miembros: proporcióneles apoyo y consideración explícitos, con el objetivo de aumentar la participación, particularmente de grupos subrepresentados. +* Las personas provienen de orígenes culturales y lingüísticos diversos. Intente identificar cualquier malentendido honesto causado por un hablante no nativo y ayúdele a entender el problema y lo que puede cambiar para evitar causar una ofensa. La discusión compleja en una lengua extranjera puede ser muy intimidante, y queremos aumentar nuestra diversidad también a través de nacionalidades y culturas. -## Mediation +## Mediación -Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: +La mediación informal voluntaria es una herramienta a nuestra disposición. En contextos tales como cuando dos o más partes han escalado hasta el punto de un comportamiento inapropiado (algo tristemente común en el conflicto humano), puede ser útil facilitar un proceso de mediación. Éste es sólo un ejemplo: el Comité puede considerar la mediación en cualquier caso, siendo consciente de que este proceso se entiendo como estrictamente voluntario y que ninguna de las partes puede ser presionada a participar. Si el Comité sugiere la mediación, este debería: -* Find a candidate who can serve as a mediator. -* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Encontrar un candidato que pueda servir de mediador. +* Obtener el acuerdo del informante(s). El informante(s) tienen total libertad para rechazar la propuesta de mediación o para proponer un mediador alternativo. * Obtener el acuerdo de la persona informante(s). * Acuerden el mediador: aunque las partes pueden proponer un mediador diferente al candidato sugerido, solo si se llega a un acuerdo común en todos los términos se puede avanzar en el proceso. * Establezca un marco de tiempo para completar la mediación, idealmente dentro de dos semanas. @@ -63,7 +63,7 @@ This information will be collected in writing, and whenever possible the group Es importante conservar un archivo de todas las actividades de este Comité para asegurar consistencia en el comportamiento y proporcionar memoria institucional para el proyecto. Para ayudar en esto, el canal de discusión por defecto para este Comité será una lista de correo privada accesible a los miembros actuales y futuros del Comité, así como a los miembros del Consejo Directivo con previa solicitud justificada. Si el Comité considera necesario utilizar comunicaciones fuera de la lista (por ejemplo, para una respuesta temprana/rápida), deberá, en todos los casos, resumirlos y documentarlos de vuelta a la lista, de manera que se mantenga un buen registro del proceso. -El Comité de Código de Conducta debería aspirar a que se acuerde una resolución en el plazo de dos semanas. Dado el caso de que no se pueda establecer una decisión dentro de dicho plazo, el Comité responderá a los informantes con una actualización y duración estimada para la decisión. +El Comité de Código de Conducta debería aspirar a que se acuerde una resolución en el plazo de dos semanas. Dado el caso de que no se pueda establecer una decisión dentro de dicho plazo, el Comité responderá al informante(s) con una actualización y duración estimada para la decisión. ## Resoluciones From eee89cd65e7ec030c2f749f659afe2eb396e21a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:41 +0200 Subject: [PATCH 441/586] New translations learn.md (Spanish) --- content/es/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index acb7ca8465..c0d1605eb6 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -15,9 +15,9 @@ Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recom **Tutoriales** -* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [Tutorial NumPy de inicio rápido](https://numpy.org/devdocs/user/quickstart.html) * [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). -* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [NumPy Illustrated: La Guía Visual de NumPy *por Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [Lecturas de SciPy](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. * [NumPy: lo absolutamente básico para principiantes](https://numpy.org/devdocs/user/absolute_beginners.html) * [Tutorial de NumPy *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) From 9772b8c91a205fd913add693bd8148930a42c29e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:42 +0200 Subject: [PATCH 442/586] New translations news.md (Spanish) --- content/es/news.md | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 2a56e2ecf8..b5dfccb13b 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -32,7 +32,7 @@ Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com ### NumPy 1.26.0 ha sido lanzado -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: +_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -47,25 +47,25 @@ Las versiones de Python compatibles con esta versión son 3.9-3.12. ### numpy.org ya está disponible en japonés y portugués -_ 2 de Agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: +_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: -_Portuguese:_ -* Melissa Weber Mendonça (melissawm) -* Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portugués:_ +* Melissa Weber Mendonça (melissawm) +* Precios Ricardo (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japanese:_ +_Japonés:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -The work on the translation infrastructure is supported with funding from CZI. +El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) @@ -188,7 +188,7 @@ _20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y ### Primer artículo oficial de NumPy publicado en Nature! -_16 de Septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. +_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. ### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? @@ -198,27 +198,27 @@ _14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas sema - utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. -### Numpy 1.19.2 release +### Lanzamiento de NumPy 1.19.2 -_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. +_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. -### The inaugural NumPy survey is live! +### La encuesta inaugural de NumPy ya está disponible! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy has a new logo! +### ¡NumPy tiene un nuevo logo! -_Jun 24, 2020_ -- NumPy now has a new logo: +_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: -NumPy logo +Logo de NumPy -The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. +El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. -### NumPy 1.19.0 release +### Lanzamiento de NumPy 1.19.0 _20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. @@ -255,8 +255,8 @@ Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los - NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. - NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. - NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. - NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. - NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. - NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. From c576f95aa3ea082cd19c3f99d6e73fc4dd100010 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:43 +0200 Subject: [PATCH 443/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 1aa950a7da..35920af211 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -34,7 +34,7 @@ params: url: https://github.com/google/jax - title: Xarray - text: Matrices multidimensionales indexadas y etiquetadas para análisis y visualización avanzados + text: Matrices multidimensionales indexadas y etiquetadas para análisis y visualización avanzados. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html @@ -57,35 +57,35 @@ params: alttext: TensorFlow url: https://www.tensorflow.org - - title: MXNet + title: Arrow text: Marco de aprendizaje profundo adecuado para la creación flexible de prototipos de investigación y la producción. img: /images/content_images/arlib/mxnet_logo.png - alttext: MXNet - url: https://mxnet.apache.org/ - - - title: Arrow - text: Plataforma de desarrollo multilenguaje para datos y análisis columnares en memoria. - img: /images/content_images/arlib/arrow.png alttext: arrow - url: https://github.com/apache/arrow + url: https://mxnet.apache.org/ - title: xtensor - text: Matrices multidimensionales con emisión y computación de llamada-bajo-demanda para el análisis numérico. + text: Plataforma de desarrollo multilenguaje para datos y análisis columnares en memoria. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array - text: Manipula datos tipo JSON con modismos similares a los de NumPy. + text: Manipular datos similares a JSON con expresiones similares a NumPy. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray - text: Sistema de backend de Python que desvincula la API de la implementación; unumpy proporciona una API de NumPy. + text: Sistema de backend de Python que desacopla la API de la implementación; unumpy proporciona una API de NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ + - + title: tensorly + text: Aprendizaje tensorial, álgebra y backends para usar de manera fluida NumPy, PyTorch, TensorFlow o CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html scientificdomains: intro: - @@ -159,8 +159,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Graphs and Networks - alttext: A simple graph. + title: Grafos y Redes + alttext: Un simple grafo. img: /images/content_images/sc_dom_img/sd6.svg links: - From bf9b5ee42e0ef00013e0d35b33dca67093c2c27a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:44 +0200 Subject: [PATCH 444/586] New translations history.md (Spanish) --- content/es/history.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/history.md b/content/es/history.md index e4e988a3df..5456c4cf8f 100644 --- a/content/es/history.md +++ b/content/es/history.md @@ -1,6 +1,6 @@ --- title: Historia de NumPy -sidebar: false +sidebar: falso --- NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de matrices y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la biblioteca contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en informática y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amistosa con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados en el análisis de ondas gravitacionales utilizan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. From 986c931a119ac6dd0a8323d530da765d9c18af01 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:45 +0200 Subject: [PATCH 445/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 1205ea6f24..ee89684d99 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -28,7 +28,7 @@ params: features: - title: Primera imagen de un Agujero Negro - text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Event Horizon Telescope producir la primera imagen de un agujero negro + text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. url: /case-studies/blackhole-image @@ -40,7 +40,7 @@ params: url: /case-studies/gw-discov - title: Analíticas Deportivas - text: El uso de Analíticas está cambiando al Cricket, al mejorar el rendimiento de los jugadores y equipos, mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. + text: El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. img: /images/content_images/case_studies/sports.jpg alttext: Bola de Cricket sobre un campo verde. url: /case-studies/cricket-analytics From 0c77fd8261e808844391228ffdebb4fdc66d5708 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:47 +0200 Subject: [PATCH 446/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index fb39da53ba..cb86101d92 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -5,20 +5,20 @@ aliases: - /conduct.html --- -### Introduction +### Introducción -This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. +Este Código de Conducta aplica en todos los espacios gestionados por el proyecto NumPy, incluyendo todas las listas de correo públicas y privadas, gestores de incidencias, wikis, blogs, Twitter y cualquier otro canal de comunicación utilizado por nuestra comunidad. El proyecto NumPy no organiza eventos en persona; sin embargo, los eventos relacionados con nuestra comunidad deben tener un código de conducta similar a este. -This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. +Este Código de Conducta debe ser respetado por todos los que participan en la comunidad NumPy formalmente o informalmente, o reclaman cualquier afiliación con el proyecto, en cualquier actividad relacionada con el proyecto y especialmente cuando representan el proyecto de cualquier manera. -This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. +Este código no es exhaustivo ni completo. Sirve para sintetizar nuestro entendimiento común de un entorno y unos objetivos compartidos y de colaboración. Por favor, intenta seguir este código tanto en el espíritu como en la letra, para crear un ambiente cordial y productivo que enriquezca a la comunidad circundante. -### Specific Guidelines +### Directrices Específicas Cantera 1. Ser abiertos. Invitamos a todas las personas a participar en nuestra comunidad. Preferimos utilizar métodos de comunicación públicos para los mensajes relacionados con el proyecto, a menos que se trate de algo delicado. Esto se aplica también a los mensajes de ayuda o soporte relacionados con el proyecto; no sólo es mucho más probable que una solicitud de soporte pública dé lugar a una respuesta a una pregunta, sino que también garantiza que cualquier error involuntario en la respuesta se detecte y corrija más fácilmente. -2. Be empathetic, welcoming, friendly, and patient. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. +2. Ser empáticos, receptivos, amables y pacientes. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. 3. Ser colaborativos. Nuestro trabajo será utilizado por otras personas, y a su vez dependeremos del trabajo de otros. Cuando hacemos algo en beneficio del proyecto, estamos dispuestos a explicar a otros cómo funciona, de manera que puedan construir sobre este trabajo para hacerlo aún mejor. Cualquier decisión que tomemos afectará a usuarios y colegas, y nos tomamos en serio esas consecuencias a la hora de tomar decisiones. 4. Ser curiosos. ¡Nadie lo sabe todo! Hacer preguntas tempranas evita muchos problemas posteriores, por lo que fomentamos las preguntas, aunque las podamos redirigir al foro adecuado. Nos esforzaremos por ser receptivos y útiles. 5. Ser cuidadosos con las palabras que elegimos. Somos cuidadosos y respetuosos en nuestra comunicación, y asumimos la responsabilidad del lenguaje que utilizamos. Ser amables con los demás. No insultes ni menosprecies a los demás participantes. No aceptaremos el acoso ni otros comportamientos excluyentes, tales como: @@ -49,9 +49,9 @@ Sabemos que es dolorosamente común que la comunicación en Internet comience o En caso de infracciones claramente intencionadas, informe de éstas al Comité de Código de Conducta (ver más abajo). En caso de infracciones posiblemente involuntarias, puede responder a la persona y señalar este código de conducta (tanto en público como en privado, lo que sea más apropiado). Si prefiere no hacerlo, puede informar directamente al Comité de Código de Conducta o pedirle consejo a éste de forma confidencial. -You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. +Puede informar de los problemas al Comité de Código de Conducta de NumPy en numpy-conduct@googlegroups.com. -Currently, the Committee consists of: +Actualmente, el Comité está compuesto por: * Stefan van der Walt * Melissa Weber Mendonça From 1562ba91338261c31933286b68312498dbd10d57 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:48 +0200 Subject: [PATCH 447/586] New translations install.md (Spanish) --- content/es/install.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index ea184ea1ec..b6eb37edcc 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -120,7 +120,6 @@ Si su instalación falla con el siguiente mensaje, revise el siguiente enlace [R ``` ¡IMPORTANTE: POR FAVOR LEA ESTO COMO SUGERENCIA PARA RESOLVER ESTE PROBLEMA! -Importing the numpy c-extensions failed. This error can happen for -different reasons, often due to issues with your setup. +La importación de las extensiones c de numpy falló. Este error puede ocurrir por varias razones, siendo frecuente debido a problemas con su configuración. ``` From 1ede28652d36de03ff61ec2baa21994bd00fdeee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:50 +0200 Subject: [PATCH 448/586] New translations about.md (Spanish) --- content/es/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/about.md b/content/es/about.md index a6e42626dc..c9f873de37 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -46,7 +46,7 @@ La dirección del proyecto NumPy trabaja activamente para diversificar las vías - encuesta - traducción - mentores de sprints -- optimization +- optimización - financiación y subvenciones Visita la página de [Equipos]({{< relref "/teams" >}}) para más información. From a0834bceb19f165ba4303fd2876051cc53a8cebd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:51 +0200 Subject: [PATCH 449/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index 541047f40d..9bffc30c9e 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -1,20 +1,20 @@ --- -title: "Case Study: Cricket Analytics, the game changer!" -sidebar: false +title: "Estudio de caso: Análisis de críquet, ¡el cambio radical!" +sidebar: falso --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' -{{< /figure >}} +{{< figura >}} +figure src="/images/content_images/cs/ipl-stadium.png'title" caption="IPLT20, el festival de críquet más grande en India" alt="Copa y estadio de la Premier League de Críquet de India" attr="(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))" attrlink="https://unsplash.com/@aksh1802' +{{< /figura >}} -{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" -> }} You don't play for the crowd, you play for the country. +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *Jugador Internacional de críquet, ex-capitán del equipo de India, juega para Chennai Super Kings en IPL*" +> }} No juegas para el público, juegas para el país. > > {{< /blockquote >}} -## About Cricket +## Acerca del críquet -It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más. +Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi cada rincón de India, rural o urbano, es popular entre los jóvenes y ancianos por igual, conectando miles de millones en India a diferencia de cualquier otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más. La Premier League de India (IPL) es una liga profesional de críquet Twenty20, fundada en 2008. Es uno de los eventos de críquet más concurridos en el mundo, valorado en [$6.7 mil millones de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) en 2019. @@ -49,7 +49,7 @@ src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimato * **Modelación Dinámica** - En el cricket, al igual que en cualquier otro deporte, puede haber una gran cantidad de variables relacionadas con el seguimiento de varios jugadores en el campo, sus atributos, la pelota y varias posibilidades de acciones potenciales. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Las cosas se vuelven aún más desafiantes en términos de cálculo y comparación de datos cuando se buscan predicciones dinámicas del juego de críquet, tal como habría sucedido si el bateador hubiera golpeado la bola a un ángulo o velocidad diferente. + En el cricket, al igual que en cualquier otro deporte, puede haber una gran cantidad de variables relacionadas con el seguimiento de varios jugadores en el campo, sus atributos, la pelota y varias posibilidades de acciones potenciales. La complejidad del análisis de datos y la modelación es directamente proporcional al tipo de preguntas predictivas que se plantean durante el análisis y depende en gran medida de la representación de los datos y del modelo. Las cosas se vuelven aún más desafiantes en términos de cálculo y comparación de datos cuando se buscan predicciones dinámicas del juego de críquet, tal como habría sucedido si el bateador hubiera golpeado la bola a un ángulo o velocidad diferente. * **Complejidad de Análisis Predictivo** From d8a2b045697bd155cac7381435960b00a75f3594 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:52 +0200 Subject: [PATCH 450/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 24 +++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index dea1684571..e9cce32fdd 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -44,24 +44,24 @@ Recientemente, se presentó el [modelo zoo de DeepLabCut](http://www.mousemotorl En flujo de trabajo típico de DeepLabCut incluye: - - creation and refining of training sets via active learning - - creation of tailored neural networks for specific animals and scenarios - - code for large-scale inference on videos - - draw inferences using integrated visualization tools + - creación y refinamiento de los conjuntos de entrenamiento a través del aprendizaje activo + - creación de redes neuronales a la medida para animales y escenarios específicos + - código para inferencia a gran escala en videos + - graficar las inferencias utilizando herramientas de visualización integradas -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' -{{< /figure >}} +{{< figura >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Postura de estimación de pasos con DeepLabCut' alt = 'dlcsteps' align = 'center' atribución = '(Source: DeepLabCut)' attributionlink = 'https://twitter. om/DeepLabCut/status/1198046918284210176/foto/1' +{{< /figura >}} -### The Challenges +### Los Desafíos -* **Speed** +* **Velocidad** - Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. + Procesamiento rápido de videos de comportamiento animal para medir su comportamiento y al mismo tiempo hacer experimentos científicos más eficientes y precisos. La extracción detallada de posturas del animal para experimentos de laboratorio, sin marcadores, en entornos dinámicamente cambiantes, puede ser un desafío tanto técnico como en términos de recursos necesarios y datos de entrenamiento requeridos. Proponer una herramienta que sea fácil de usar sin necesidad de habilidades como experiencia en visión por computador que permita a los científicos hacer investigaciones en más contextos del mundo real, es un problema que no es trivial de resolver. -* **Combinatorics** +* **Combinatoria** - Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Ensamblar puntos clave y sus conexiones en movimientos individuales de animales y vincularlos a lo largo del tiempo es un proceso complejo que requiere un análisis numérico intensivo, especialmente en el caso del seguimiento de movimientos de múltiples animales en videos de experimentos. + La combinatoria involucra el armado e integración del movimiento de múltiples extremidades en el comportamiento animal individual. Ensamblar puntos clave y sus conexiones en movimientos individuales de animales y vincularlos a lo largo del tiempo es un proceso complejo que requiere un análisis numérico intensivo, especialmente en el caso del seguimiento de movimientos de múltiples animales en videos de experimentos. * **Procesamiento de Datos** From 642c250a755ebca5ea28550ea08363b2fe0540b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 04:16:53 +0200 Subject: [PATCH 451/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 2ecfcd7a21..1e6d278187 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -37,7 +37,7 @@ El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](ht * **Visualization** - Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. La velocidad de los cálculos complejos y la renderización, así como la re-renderización de imágenes y simulaciones utilizando los últimos datos experimentales y conocimientos, puede ser una actividad que consume mucho tiempo y que representa un desafío para los investigadores en este campo. {{< figure >}} src = '/images/content_images/cs/gw_strain_amplitude. ng' alt = 'amplitud de deformación de ondas gravitacionales' title = 'Amplitud de deformación de ondas gravitacionales estimada de GW150914' atribución = '(Créditos del gráfico: Observación de Ondas Gravitacionales de la Fusión de un Agujero Negro Binario, Publicación de ResearchGate)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' @@ -52,25 +52,25 @@ NumPy, el paquete de análisis numérico estándar para Python, fue utilizado po * [Procesamiento de Señales](https://www.uv.es/virgogroup/Denoising_ROF.html): Detección de fallos, [Identificación de ruido y caracterización de datos](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) * Recuperación de datos: Decidir qué datos pueden ser analizados, y determinar si estos contienen una señal - aguja en un pajar * Análisis estadístico: estimar la significancia estadística de los datos observados, estimación de los parámetros de señal (por ejemplo, masas de estrellas, velocidad de giro y distancia) en comparación con un modelo. -* Visualization of data - - Time series - - Spectrograms -* Compute Correlations -* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. +* Visualización de datos + - Series de tiempo + - Espectrogramas +* Cálculo de Correlaciones +* [Software clave](https://github.com/lscsoft) desarrollado en análisis de datos de Ondas Gravitacionales como, tales como [GwPy](https://gwpy.github.io/docs/stable/overview.html) y [PyCBC](https://pycbc.org) utiliza NumPy y AstroPy bajo su cubierta para proporcionar interfaces basadas en objetos a utilidades, herramientas y métodos para el estudio de datos provenientes de detectores de ondas gravitacionales. -{{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' -{{< /figure >}} +{{< figura >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Gráfico de dependencias que muestra cómo depende el paquete GwPy de NumPy' +{{< /figura >}} ---- {{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' -{{< /figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Gráfico de dependencias que muestra cómo el paquete PyCBC depende de NumPy' +{{< /figura >}} -## Summary +## Resumen -GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. Los cálculos son complejos y no pueden ser comprendidos por humanos, a menos que sean visualizados utilizando simulaciones por computador que se alimenten con datos y análisis reales observados. NumPy, junto con otros paquetes de Python como matplotlib, pandas y scikit-learn, está [permitiendo a los investigadores](https://www.gw-openscience.org/events/GW150914/) responder preguntas complejas y descubrir nuevos horizontes en nuestra comprensión del universo. +La detección de ondas gravitacionales ha permitido a los investigadores descubrir fenómenos completamente inesperados, al tiempo que proporciona nuevos conocimientos sobre muchos de los fenómenos astrofísicos más profundos conocidos. El procesamiento de datos y la visualización de datos son pasos cruciales que ayudan a los científicos a obtener información a partir de los datos recopilados en las observaciones científicas y a comprender los resultados. Los cálculos son complejos y no pueden ser comprendidos por humanos, a menos que sean visualizados utilizando simulaciones por computador que se alimenten con datos y análisis reales observados. NumPy, junto con otros paquetes de Python como matplotlib, pandas y scikit-learn, está [permitiendo a los investigadores](https://www.gw-openscience.org/events/GW150914/) responder preguntas complejas y descubrir nuevos horizontes en nuestra comprensión del universo. {{< figure >}} src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'beneficios de NumPy' title = 'Capacidades clave de NumPy utilizadas' From 0dc4112d4326fdcf6dd154e901e2e9ef86e92249 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:33 +0200 Subject: [PATCH 452/586] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/gethelp.md b/content/es/gethelp.md index b875743dea..e05dad4c51 100644 --- a/content/es/gethelp.md +++ b/content/es/gethelp.md @@ -1,6 +1,6 @@ --- title: Buscar Ayuda -sidebar: false +sidebar: falso --- **Problemas de desarrollo:** Para asuntos relacionados con el desarrollo de NumPy (por ejemplo, informes de errores), por favor consulte la sección de [Comunidad](/community). From 5d86b33486cee28dec58de788e3777ad461f7482 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:34 +0200 Subject: [PATCH 453/586] New translations install.md (Spanish) --- content/es/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/install.md b/content/es/install.md index b6eb37edcc..3b5aa5d313 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -120,6 +120,6 @@ Si su instalación falla con el siguiente mensaje, revise el siguiente enlace [R ``` ¡IMPORTANTE: POR FAVOR LEA ESTO COMO SUGERENCIA PARA RESOLVER ESTE PROBLEMA! -La importación de las extensiones c de numpy falló. Este error puede ocurrir por varias razones, siendo frecuente debido a problemas con su configuración. +La importación de las extensiones-c de numpy falló. Este error puede ocurrir por varias razones, siendo frecuente debido a problemas con su configuración. ``` From 22224a3b1a4e6d5d2a9d2bea5bb6f3d14884838c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:35 +0200 Subject: [PATCH 454/586] New translations about.md (Spanish) --- content/es/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/about.md b/content/es/about.md index c9f873de37..7d220d203d 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -62,7 +62,7 @@ Visita la página de [Equipos]({{< relref "/teams" >}}) para más información. ## Patrocinadores NumPy recibe financiación directa de las siguientes fuentes: -{{< sponsors >}} +{{< patrocinadores >}} ## Socios institucionales From 6e6db3907397c55025da664cb6acb4c68ae440dc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:37 +0200 Subject: [PATCH 455/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index 9bffc30c9e..decd6ec5cc 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -28,24 +28,24 @@ Hoy en día, hay abundantes y casi infinitos tesoros de registros y estadística * contribución del jugador a las victorias y derrotas para tomar decisiones estratégicas sobre la composición del equipo {{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +src = '/images/content_images/cs/cricket-pitch.png' title = 'El campo de críquet, el punto focal en el terreno de juego' alt = 'Un campo de cricket con lanzador y bateadores' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< /figure >}} -### Key Data Analytics Objectives +### Objetivos Clave de Análisis de Datos -* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. -* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. -* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. +* El análisis de datos deportivos se utiliza no solo en el críquet, sino en muchos [otros deportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para mejorar el rendimiento general del equipo y maximizar las posibilidades de ganar. +* El análisis de datos en tiempo real puede ayudar a obtener información incluso durante el juego para cambiar tácticas por parte del equipo y de las empresas asociadas para beneficios económicos y crecimiento. +* Además del análisis histórico, se aprovechan los modelos predictivos para determinar los posibles resultados de los partidos, lo cual requiere una cantidad significativa de procesamiento de datos y conocimientos de ciencia de datos, herramientas de visualización y la capacidad de incluir nuevas observaciones en el análisis. {{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'estimador de postura' title = 'Estimador de postura en críquet' attribution = '(Crédito de imagen: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} -### The Challenges +### Los Desafíos -* **Data Cleaning and preprocessing** +* **Limpieza de datos y preprocesamiento** - IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. Todos estos factores juntos han incrementado la complejidad de la limpieza de datos y el preprocesamiento. + La IPL ha expandido el críquet más allá del clásico formato de partido de prueba a una escala mucho más grande. El número de partidos jugados cada temporada a través de varios formatos ha incrementado y así también los datos, los algoritmos, las nuevas tecnologías de análisis de datos deportivos y modelos de simulación. El análisis de datos de críquet requiere mapeo del campo, seguimiento de jugadores, seguimiento de la pelota, análisis de tiros de los jugadores y varios otros aspectos relacionados con cómo se lanza la pelota, su ángulo, giro, velocidad y trayectoria. Todos estos factores juntos han incrementado la complejidad de la limpieza de datos y el preprocesamiento. * **Modelación Dinámica** From 85ba3b35600b80ed5eeba42c3fa513a96a835598 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:38 +0200 Subject: [PATCH 456/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index e9cce32fdd..c673523a32 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -83,7 +83,7 @@ Las siguientes características de NumPy jugaron un papel clave en abordar el pr * Muestreo Aleatorio * Redimensionamiento de arreglos grandes -DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. +DeepLabCut utiliza las capacidades de arreglos de NumPy a lo largo del flujo de trabajo ofrecido por el conjunto de herramientas. En particular, NumPy se utiliza para muestrear diferentes fotogramas para etiquetado de anotaciones humanas, y para escribir, editar y procesar datos de anotación. Dentro de TensorFlow, la red neuronal es entrenada por la tecnología DeepLabCut durante miles de iteraciones para predecir las anotaciones de referencia a partir de fotogramas. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. {{< figure >}} src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' From 5fb42490758a7eaa83686281e0b5e3ae5f1e8676 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:39 +0200 Subject: [PATCH 457/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index f76aebc9a5..759d1d091a 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -3,11 +3,11 @@ title: "Caso de estudio: La primera imagen de un Agujero Negro" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="imagen de un agujero negro" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +{{< figura >}} src = '/images/content_images/cs/blackhole. pg' title = 'Agujero Negro M87' alt = 'Imagen de agujero negro' attribution = '(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)' attributionlink = 'https://www. pl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} -{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Profesora Asistente, Ciencias de la Computación & Matemáticas, Caltech*" > }} Capturar imágenes del Agujero Negro M87 es como intentar ver algo que por definición es imposible de ver. > > {{< /blockquote >}} @@ -50,8 +50,8 @@ La colaboración del EHT respondió a estos desafíos haciendo que los equipos i Su trabajo ilustra el rol que desempeña el ecosistema científico de Python en el avance de la ciencia a través del análisis de datos colaborativos. -src = '/images/content_images/cs/dataprocessbh.png' title = 'Flujo de Trabajo de Procesamiento de Datos EHT' alt = 'flujo de datos' align = 'centrar' attribution = '(Créditos del diagrama: The Astrophysical Journal, Colaboración del Telescopio del Horizonte de Sucesos)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="ventajas de numpy" caption="**Capacidades Clave utilizadas de NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'rol de numpy' title = 'El rol de NumPy en la imagen del agujero negro' {{< /figure >}} Por ejemplo, el paquete de Python [`eht-imaging`][ehtim] proporciona herramientas para simular y realizar reconstrucción de imágenes en datos VLBI. NumPy está en el núcleo del procesamiento de datos de matrices utilizados en este paquete, como se muestra a continuación en el gráfico parcial de dependencias de software. From 2780bcc1d6ac81a7c674a16d87d52281d34e0822 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 05:17:40 +0200 Subject: [PATCH 458/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 1e6d278187..6b34ed3192 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -12,7 +12,7 @@ src = '/images/content_images/cs/gw_sxs_image. ng' title = 'Ondas Gravitacionale ## Acerca de [Ondas Gravitacionales](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) y [LIGO](https://www.ligo.caltech.edu) -Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por eventos cataclísmicos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de fenómenos más oscuros en el universo distante y su impacto. +Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por eventos cataclísmicos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de los fenómenos oscuros en el universo distante y su impacto. El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](https://www.ligo.caltech.edu) fue diseñado para abrir el campo de la astrofísica de ondas gravitacionales mediante la detección directa de las ondas gravitacionales predichas por la Teoría General de la Relatividad de Einstein. Comprende dos interferómetros ampliamente separados dentro de los Estados Unidos: uno en Hanford, Washington, y el otro en Livingston, Louisiana, operando al unísono para detectar ondas gravitacionales. Cada uno de ellos tiene detectores de ondas gravitacionales de escala de múltiples kilómetros que utilizan interferometría de láser. La Colaboración Científica de LIGO (LSC) es un grupo de más de 1000 científicos de universidades de los Estados Unidos y de otros 14 países, respaldados por más de 90 universidades e institutos de investigación; aproximadamente 250 estudiantes contribuyen activamente a la colaboración. El nuevo descubrimiento de LIGO es la primera observación de ondas gravitacionales, realizada midiendo las diminutas perturbaciones que las ondas generan en el espacio y el tiempo a medida que pasan a través de la Tierra. Ha abierto nuevas fronteras astrofísicas que exploran el lado distorsionado del universo: objetos y fenómenos que se forman a partir del espacio-tiempo deformado. @@ -58,15 +58,15 @@ NumPy, el paquete de análisis numérico estándar para Python, fue utilizado po * Cálculo de Correlaciones * [Software clave](https://github.com/lscsoft) desarrollado en análisis de datos de Ondas Gravitacionales como, tales como [GwPy](https://gwpy.github.io/docs/stable/overview.html) y [PyCBC](https://pycbc.org) utiliza NumPy y AstroPy bajo su cubierta para proporcionar interfaces basadas en objetos a utilidades, herramientas y métodos para el estudio de datos provenientes de detectores de ondas gravitacionales. -{{< figura >}} +{{< figure >}} src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Gráfico de dependencias que muestra cómo depende el paquete GwPy de NumPy' -{{< /figura >}} +{{< /figure >}} ---- {{< figure >}} src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Gráfico de dependencias que muestra cómo el paquete PyCBC depende de NumPy' -{{< /figura >}} +{{< /figure >}} ## Resumen From ec1882ef7b91df133697af26b521075956c1e1e7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:42 +0200 Subject: [PATCH 459/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 2f1e43eab2..390f2817ca 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -42,24 +42,24 @@ Cuando un miembro del Comité de Código de Conducta tenga conocimiento de una v * Desconectará inmediatamente al originador de todos los canales de comunicación de NumPy. * Responderá al informante que su informe ha sido recibido y que el autor ha sido desconectado. -* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* En todo caso, el moderador deberá hacer un esfuerzo razonable por contactar al originador, y comunicarle específicamente cómo su lenguaje o sus acciones se constituyeron como "violación clara y grave". El moderador también debe decir que, si el originador cree que esto es injusto o desea reconectarse con NumPy, tiene el derecho a solicitar una revisión, como se indica a continuación, por el Comité de Código de Conducta. El moderador debería copiar esta explicación al Comité de Código de conducta. +* El Comité de Código de conducta revisará y aprobará formalmente todos los casos en los que se haya aplicado este mecanismo, para asegurarse de que no se esté utilizando para controlar desacuerdos ordinarios acalorados. -## Report handling +## Gestión de informes -When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. +Cuando se envíe un informe al Comité, éste responderá inmediatamente al informante confirmando su recepción. Esta respuesta deberá enviarse dentro de un plazo de 72 horas, y el grupo deberá esforzarse por responder en un tiempo inferior. -If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. +Si un informe no contiene suficiente información, el Comité obtendrá todos los datos relevantes antes de actuar. El Comité tiene la facultad de actuar en nombre del Consejo Directivo al contactar cualquier individuo implicado para obtener una descripción más completa de los hechos. -The Committee will then review the incident and determine, to the best of their ability: +El Comité procederá a revisar el incidente y determinará, en la medida de su capacidad: -* What happened. -* Whether this event constitutes a Code of Conduct violation. -* Who are the responsible party(ies). -* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +* Qué sucedió. +* Si este evento constituye una violación del Código de Conducta. +* Quiénes son las parte(s) responsables. +* Si se trata de una situación en progreso y existe una amenaza para la seguridad física de cualquiera. -This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). +Esta información se recopilará por escrito, y siempre que sea posible se registrarán y conservarán las deliberaciones del grupo (por ejemplo, transcripciones de chat, discusiones por correo electrónico, conferencias grabadas, resúmenes de conversaciones de voz, etc.). Es importante conservar un archivo de todas las actividades de este Comité para asegurar consistencia en el comportamiento y proporcionar memoria institucional para el proyecto. Para ayudar en esto, el canal de discusión por defecto para este Comité será una lista de correo privada accesible a los miembros actuales y futuros del Comité, así como a los miembros del Consejo Directivo con previa solicitud justificada. Si el Comité considera necesario utilizar comunicaciones fuera de la lista (por ejemplo, para una respuesta temprana/rápida), deberá, en todos los casos, resumirlos y documentarlos de vuelta a la lista, de manera que se mantenga un buen registro del proceso. From 02ac6bdeeddc55204cf09492e8472dd2ec847d30 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:43 +0200 Subject: [PATCH 460/586] New translations news.md (Spanish) --- content/es/news.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index b5dfccb13b..fb688d7175 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -80,19 +80,19 @@ _17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.2 NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. -A total of 148 people contributed to this release and 530 pull requests were merged. +Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. -The Python versions supported by this release are 3.9-3.11. +Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. -### Fostering an Inclusive Culture: Call for Participation +### Fomentar una Cultura Inclusiva: Convocatoria de Participación -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación -How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). +¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). -### NumPy documentation team leadership transition +### Transición en el liderazgo del equipo de documentación de NumPy -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. +_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. ### Lanzamiento de NumPy 1.24.0 @@ -128,7 +128,7 @@ Por favor, complete este breve [formulari o de "Interés del Participante"](http _31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. Upstream is a moving target, so there will likely be further improvements, but the major work is done. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. * Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. * NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). * Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. @@ -175,10 +175,10 @@ Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de c _22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release +### Lanzamiento de NumPy 1.20.0 -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: +- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. - Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversidad en el proyecto NumPy From ae5d8ed9dd290c326a82366a05431fc6bd1f94e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:44 +0200 Subject: [PATCH 461/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 35920af211..0dba4922c5 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -111,7 +111,7 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing + title: Computación Estadística alttext: Un gráfico lineal con la línea moviéndose hacia arriba. img: /images/content_images/sc_dom_img/statistical_computing.svg links: @@ -283,8 +283,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: Procesamiento Geográfico + alttext: Un mapa. img: /images/content_images/sc_dom_img/GIS.svg links: - From d2da5244657b000cd962e10109f0a946318a00b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:46 +0200 Subject: [PATCH 462/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index cb86101d92..b6073d2ad4 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -80,4 +80,4 @@ El Comité responderá a cualquier informe lo antes posible y dentro de un plazo Damos las gracias a los grupos que están detrás de los siguientes documentos, de los que hemos sacado contenido e inspiración: -- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) +- [El Código de Conducta de SciPy](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 28e07cef7a8d663d0658cf75e15f3f915a1d8dea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:47 +0200 Subject: [PATCH 463/586] New translations install.md (Spanish) --- content/es/install.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index 3b5aa5d313..bc1f2eeaa9 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -1,39 +1,39 @@ --- -title: Installing NumPy +title: Instalando NumPy sidebar: false --- -The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. +El único prerrequisito para instalar NumPy es Python. Si aún no tienes Python y quieres la forma más sencilla de comenzar, te recomendamos que uses la [Distribución Anaconda](https://www.anaconda.com/download) - incluye Python, NumPy y muchos otros paquetes comúnmente utilizados para la computación científica y la ciencia de datos. -NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +NumPy se puede instalar con `conda`, con `pip`, con un gestor de paquetes en macOS y Linux, o [a partir del código fuente](https://numpy.org/devdocs/building). Para instrucciones más detalladas, consulte nuestra [guía de instalación de Python y NumPy](#python-numpy-install-guide) a continuación. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: +Si utiliza `conda`, puede instalar NumPy desde los canales `defaults` o `conda-forge`: ```bash -# Best practice, use an environment rather than install in the base env +# La mejor práctica, utilizar un entorno en lugar de instalar en el entorno base conda create -n my-env conda activate my-env -# If you want to install from conda-forge +# Si desea instalar desde conda-forge conda config --env --add channels conda-forge -# The actual install command +# El comando de instalación conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +Si utiliza `pip`, puede instalar NumPy con: ```bash pip install numpy ``` -Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. +También al utilizar pip, es buena práctica utilizar un entorno virtual - vea [Instalaciones Reproducibles](#reproducible-installs) a continuación para saber por qué, y [esta guía](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) para más detalles sobre el uso de entornos virtuales. -# Python and NumPy installation guide +# Guía de instalación de Python y NumPy Instalar y administrar paquetes en Python es complicado, hay un número de soluciones alternativas para la mayoría de tareas. Esta guía intenta dar al lector una idea de las mejores (o más populares) soluciones y dar recomendaciones claras. Se enfoca en los usuarios de Python, NumPy y del stack de PyData (o computación numérica) en sistemas operativos y hardware comunes. From 6391e532635c009f64c87593122773c4837751b8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:48 +0200 Subject: [PATCH 464/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index c673523a32..2c56362ae7 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -83,15 +83,15 @@ Las siguientes características de NumPy jugaron un papel clave en abordar el pr * Muestreo Aleatorio * Redimensionamiento de arreglos grandes -DeepLabCut utiliza las capacidades de arreglos de NumPy a lo largo del flujo de trabajo ofrecido por el conjunto de herramientas. En particular, NumPy se utiliza para muestrear diferentes fotogramas para etiquetado de anotaciones humanas, y para escribir, editar y procesar datos de anotación. Dentro de TensorFlow, la red neuronal es entrenada por la tecnología DeepLabCut durante miles de iteraciones para predecir las anotaciones de referencia a partir de fotogramas. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. +DeepLabCut utiliza las capacidades de arreglos de NumPy a lo largo del flujo de trabajo ofrecido por el conjunto de herramientas. En particular, NumPy se utiliza para muestrear diferentes fotogramas para etiquetado de anotaciones humanas, y para escribir, editar y procesar datos de anotación. Dentro de TensorFlow, la red neuronal es entrenada por la tecnología DeepLabCut durante miles de iteraciones para predecir las anotaciones de referencia a partir de fotogramas. Para este propósito, se crean densidades objetivo (mapas de puntuación) para plantear la estimación de poses como un problema de traducción de imagen a imagen. Para hacer que las redes neuronales sean robustas, se emplea el aumento de datos, lo que requiere el cálculo de mapas de puntuación objetivo sujetos a varios pasos geométricos y de procesamiento de imágenes. Para hacer que el entrenamiento sea rápido, se aprovechan las capacidades de vectorización de NumPy. Para la inferencia, es necesario extraer las predicciones más probables de los mapas de puntuación objetivo y "vincular eficientemente las predicciones para ensamblar animales individuales". {{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'Flujo de Trabajo de DeepLabCut' alt = 'flujo de trabajo' attribution = '(Fuente: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' {{< /figure >}} -## Summary +## Resumen -Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite a los investigadores estimar la postura del sujeto, permitiéndoles de manera eficiente cuantificar el comportamiento. Con solo un pequeño conjunto de imágenes de entrenamiento, el conjunto de herramientas de Python de DeepLabCut permite entrenar una red neuronal con una precisión de etiquetado a nivel humano, expandiendo así su aplicación no solo al análisis del comportamiento en el laboratorio, sino también potencialmente en deportes, análisis de marcha, medicina y estudios de rehabilitación. Los desafíos de la combinatoria compleja y procesamiento de datos enfrentados por los algoritmos de DeepLabCut se abordan mediante el uso de las capacidades de manipulación de arreglos de NumPy. +Observar y describir eficientemente el comportamiento es un punto central de la etología moderna, neurociencia, medicina y tecnología. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite a los investigadores estimar la postura del sujeto, permitiéndoles de manera eficiente cuantificar el comportamiento. Con solo un pequeño conjunto de imágenes de entrenamiento, el conjunto de herramientas de Python de DeepLabCut permite entrenar una red neuronal con una precisión de etiquetado a nivel humano, expandiendo así su aplicación no solo al análisis del comportamiento en el laboratorio, sino también potencialmente en deportes, análisis de marcha, medicina y estudios de rehabilitación. Los desafíos de la combinatoria compleja y procesamiento de datos enfrentados por los algoritmos de DeepLabCut se abordan mediante el uso de las capacidades de manipulación de arreglos de NumPy. {{< figure >}} src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de NumPy' title = 'Capacidades claves de NumPy utilizadas' From 99f9cde52ebaba6f840d14b38a4a597674fe77a6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Aug 2024 06:17:49 +0200 Subject: [PATCH 465/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 6b34ed3192..b02715660a 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -35,9 +35,9 @@ El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](ht A medida que los dispositivos de observación se vuelven más sensibles y confiables, los desafíos planteados por la avalancha de datos y encontrar una aguja en un pajar aumentan exponencialmente. ¡LIGO genera terabytes de datos cada día! Dar sentido a estos datos requiere de un esfuerzo enorme para cada detección. Por ejemplo, las señales recolectadas por LIGO deben ser comparadas por supercomputadoras contra cientos de miles de plantillas de posibles señales de ondas gravitacionales. -* **Visualization** +* **Visualización** - Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. La velocidad de los cálculos complejos y la renderización, así como la re-renderización de imágenes y simulaciones utilizando los últimos datos experimentales y conocimientos, puede ser una actividad que consume mucho tiempo y que representa un desafío para los investigadores en este campo. + Una vez superados los obstáculos relacionados con comprender suficientemente bien las ecuaciones de Einstein para resolverlas utilizando supercomputadoras, el siguiente gran desafío fue hacer que los datos fueran comprensibles para el cerebro humano. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. La velocidad de los cálculos complejos y la renderización, así como la re-renderización de imágenes y simulaciones utilizando los últimos datos experimentales y conocimientos, puede ser una actividad que consume mucho tiempo y que representa un desafío para los investigadores en este campo. {{< figure >}} src = '/images/content_images/cs/gw_strain_amplitude. ng' alt = 'amplitud de deformación de ondas gravitacionales' title = 'Amplitud de deformación de ondas gravitacionales estimada de GW150914' atribución = '(Créditos del gráfico: Observación de Ondas Gravitacionales de la Fusión de un Agujero Negro Binario, Publicación de ResearchGate)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' From fb24e0209d585da3b2ebf9b6a9896c23ecb5e048 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:28:54 +0200 Subject: [PATCH 466/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index ee89684d99..99894dc262 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -28,7 +28,7 @@ params: features: - title: Primera imagen de un Agujero Negro - text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro + text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Event Horizon Telescope producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. url: /case-studies/blackhole-image @@ -40,7 +40,7 @@ params: url: /case-studies/gw-discov - title: Analíticas Deportivas - text: El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. + text: El uso de Analíticas está cambiando al Cricket, al mejorar el rendimiento de los jugadores y equipos, mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. img: /images/content_images/case_studies/sports.jpg alttext: Bola de Cricket sobre un campo verde. url: /case-studies/cricket-analytics @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From 6eb6d08e29b1a661aca06af23d2429dc87c522be Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:28:55 +0200 Subject: [PATCH 467/586] New translations config.yaml (Arabic) --- content/ar/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 0ee45e8b33..694aba3175 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From a0d06625243cbb3d7fa6afbc8ace2309dc345671 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:28:56 +0200 Subject: [PATCH 468/586] New translations config.yaml (Japanese) --- content/ja/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index ec90ad7023..2a66dc7a9a 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From 942ca6014c3a7729d247fd9f8b18e9e3cf542b37 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:28:57 +0200 Subject: [PATCH 469/586] New translations config.yaml (Korean) --- content/ko/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 9f0b9cfdc3..3fd2c5b61e 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From f54b0aa7dfc536ea1eac7cd59f2d72d9caaa2473 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:28:58 +0200 Subject: [PATCH 470/586] New translations config.yaml (Russian) --- content/ru/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 0ee45e8b33..694aba3175 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From 9b95e0d93b8de3451fdea32c7a738793651e3054 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:28:59 +0200 Subject: [PATCH 471/586] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 0ee45e8b33..694aba3175 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From 50db6498ffbac425555763d70502d0246d5a52c5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:29:00 +0200 Subject: [PATCH 472/586] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index fadb422094..0041d0b2ea 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From 3619ee752cddc25f312213b306e22b1655c6daac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:31:07 +0200 Subject: [PATCH 473/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 99894dc262..83a8745b16 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -28,7 +28,7 @@ params: features: - title: Primera imagen de un Agujero Negro - text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Event Horizon Telescope producir la primera imagen de un agujero negro + text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. url: /case-studies/blackhole-image @@ -40,7 +40,7 @@ params: url: /case-studies/gw-discov - title: Analíticas Deportivas - text: El uso de Analíticas está cambiando al Cricket, al mejorar el rendimiento de los jugadores y equipos, mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. + text: El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. img: /images/content_images/case_studies/sports.jpg alttext: Bola de Cricket sobre un campo verde. url: /case-studies/cricket-analytics From b1770a97e6e1feb06916177f64b7d07b2ec931d1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Aug 2024 21:31:12 +0200 Subject: [PATCH 474/586] New translations config.yaml (Persian) --- content/fa/config.yaml | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/fa/config.yaml b/content/fa/config.yaml index 0ee45e8b33..694aba3175 100644 --- a/content/fa/config.yaml +++ b/content/fa/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" From db028fd9e436d6187c691366f8c25fdfa55fe3a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:37 +0200 Subject: [PATCH 475/586] New translations _index.md (Catalan) --- content/ca/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ca/_index.md diff --git a/content/ca/_index.md b/content/ca/_index.md new file mode 100644 index 0000000000..be88f9e642 --- /dev/null +++ b/content/ca/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +[[item]] +type = 'card' +title = 'Powerful N-dimensional arrays' +body = ''' +Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +''' + +[[item]] +type = 'card' +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' + +[[item]] +type = 'card' +title = 'Open source' +body = ''' +Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). +''' + +[[item]] +type = 'card' +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' + +[[item]] +type = 'card' +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' + +[[item]] +type = 'card' +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' + +{{< /grid>}} From 6bbb5e81913da2f2a14c7690babd9bca50957012 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:38 +0200 Subject: [PATCH 476/586] New translations user-surveys.md (Catalan) --- content/ca/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/ca/user-surveys.md diff --git a/content/ca/user-surveys.md b/content/ca/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/ca/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**2020** The NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results [here](https://numpy.org/user-survey-2020/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From a28a397f506a8ff0426e890225a9c9729c92adba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:39 +0200 Subject: [PATCH 477/586] New translations 404.md (Catalan) --- content/ca/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ca/404.md diff --git a/content/ca/404.md b/content/ca/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/ca/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From f7ea16ed53d3639c66f47703fae9b41dc581c24d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:40 +0200 Subject: [PATCH 478/586] New translations arraycomputing.md (Catalan) --- content/ca/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ca/arraycomputing.md diff --git a/content/ca/arraycomputing.md b/content/ca/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/ca/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 6473de9cdb75d1f68997583a26e3bcbe888c8b96 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:41 +0200 Subject: [PATCH 479/586] New translations citing-numpy.md (Catalan) --- content/ca/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ca/citing-numpy.md diff --git a/content/ca/citing-numpy.md b/content/ca/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/ca/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From fabe893b2cb26d0279a7dd021af9fdd9d5cd0ee8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:42 +0200 Subject: [PATCH 480/586] New translations user-survey-2020.md (Catalan) --- content/ca/user-survey-2020.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 content/ca/user-survey-2020.md diff --git a/content/ca/user-survey-2020.md b/content/ca/user-survey-2020.md new file mode 100644 index 0000000000..b4349bcb7d --- /dev/null +++ b/content/ca/user-survey-2020.md @@ -0,0 +1,18 @@ +--- +title: 2020 NUMPY COMMUNITY SURVEY +sidebar: false +--- + +In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. + +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' width = '250' +{{< /figure >}} + +**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. + + +For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. + +Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. + From b035ae95bab9931a6c1df04154a44c07bbbdd692 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:43 +0200 Subject: [PATCH 481/586] New translations report-handling-manual.md (Catalan) --- content/ca/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/ca/report-handling-manual.md diff --git a/content/ca/report-handling-manual.md b/content/ca/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/ca/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From ebb63571f401a21e199e7d744af260323da10614 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:44 +0200 Subject: [PATCH 482/586] New translations contribute.md (Catalan) --- content/ca/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ca/contribute.md diff --git a/content/ca/contribute.md b/content/ca/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/ca/contribute.md @@ -0,0 +1,66 @@ +--- +title: Contribute to NumPy +sidebar: false +--- + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. + +If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). + +Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). + +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). + +Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. + +### Writing code + +Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + + +### Reviewing pull requests +The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: +* summarize a long-running discussion +* triage documentation PRs +* test proposed changes + + +### Developing educational materials + +NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. + + +### Issue triaging + +The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: + +* check if older bugs are still present +* find duplicate issues and link related ones +* add good self-contained reproducers to issues +* label issues correctly (this requires triage rights -- just ask) + +Please just dive in. + + +### Website development + +We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. + + +### Graphic design + +We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. + + +### Translating website content + +We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. + + +### Community coordination and outreach + +Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. + +### Fundraising + +NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. From 682e94a2ed8564f34bbb1ab1e27dab6825b89aa0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:45 +0200 Subject: [PATCH 483/586] New translations learn.md (Catalan) --- content/ca/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ca/learn.md diff --git a/content/ca/learn.md b/content/ca/learn.md new file mode 100644 index 0000000000..4f9fa53ae3 --- /dev/null +++ b/content/ca/learn.md @@ -0,0 +1,76 @@ +--- +title: Learn +sidebar: false +--- + +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). + +*** + +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + +## Beginners + +There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: + + **Tutorials** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy User Guide](https://numpy.org/devdocs) + + **Books** + +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* + +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. + + **Videos** + +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* + +*** + +## Advanced + +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. + + **Tutorials** + +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + + **Books** + +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* + + **Videos** + +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* + +*** + +## NumPy Talks + +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) + +*** + +## Citing NumPy + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy). From ea862c9bcc6c4df1f320c6facb218b297fd74318 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:46 +0200 Subject: [PATCH 484/586] New translations community.md (Catalan) --- content/ca/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ca/community.md diff --git a/content/ca/community.md b/content/ca/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/ca/community.md @@ -0,0 +1,66 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. + +We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. + + +## Participate online + +The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ + + +### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + +This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. + +On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). + +*** + +### [GitHub issue tracker](https://github.com/numpy/numpy/issues) + +- For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- documentation issues (e.g. "I found this section unclear"); +- and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). + +_Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. + + +## Study Groups and Meetups + +If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). + +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [Twitter](https://twitter.com/numpy_team). + + +## Conferences + +The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) + +Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. + + +## Join the NumPy community + +To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. + +If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. + +Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/). From 0be63d6190ab37291672c960fbaa55837136893e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:48 +0200 Subject: [PATCH 485/586] New translations press-kit.md (Catalan) --- content/ca/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ca/press-kit.md diff --git a/content/ca/press-kit.md b/content/ca/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ca/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From bb905af09309bc0d440aceca4f444343bdfc179a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:49 +0200 Subject: [PATCH 486/586] New translations news.md (Catalan) --- content/ca/news.md | 292 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 292 insertions(+) create mode 100644 content/ca/news.md diff --git a/content/ca/news.md b/content/ca/news.md new file mode 100644 index 0000000000..e37178caf8 --- /dev/null +++ b/content/ca/news.md @@ -0,0 +1,292 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 +--- + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +NumPy logo + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + + + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From c29f3400fa72b70b41e9ab808165b3c59ae1a3d4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:50 +0200 Subject: [PATCH 487/586] New translations tabcontents.yaml (Catalan) --- content/ca/tabcontents.yaml | 373 ++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 content/ca/tabcontents.yaml diff --git a/content/ca/tabcontents.yaml b/content/ca/tabcontents.yaml new file mode 100644 index 0000000000..d74cba9bce --- /dev/null +++ b/content/ca/tabcontents.yaml @@ -0,0 +1,373 @@ +params: + machinelearning: + paras: + - + para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + arraylibraries: + intro: + - + text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. + headers: + - + text: Array Library + - + text: Capabilities & Application areas + libraries: + - + title: Dask + text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - + title: CuPy + text: NumPy-compatible array library for GPU-accelerated computing with Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - + title: JAX + text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://jax.readthedocs.io/ + - + title: Xarray + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - + title: Sparse + text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - + title: PyTorch + text: Deep learning framework that accelerates the path from research prototyping to production deployment. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - + title: TensorFlow + text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - + title: Arrow + text: A cross-language development platform for columnar in-memory data and analytics. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - + title: xtensor + text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - + title: Awkward Array + text: Manipulate JSON-like data with NumPy-like idioms. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - + title: uarray + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - + title: tensorly + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + scientificdomains: + intro: + - + text: Nearly every scientist working in Python draws on the power of NumPy. + - + text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." + libraries: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: http://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://www.statsmodels.org/ + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://seaborn.pydata.org/ + label: Seaborn + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP + - + title: Astronomy + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://sunpy.org/ + label: SunPy + - + url: https://spacepy.github.io/ + label: SpacePy + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - + url: https://www.psychopy.org/ + label: PsychoPy + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC3 + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://www.scipy.org/ + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://www.cvxpy.org/ + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: City Energy Analyst + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + image1: + - + img: /images/content_images/ds-landscape.png + alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + image2: + - + img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + examples: + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" + content: + - + text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). + visualization: + images: + - + url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: A streamplot made in matplotlib + - + url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: A scatter-plot graph made in ggpy + - + url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: A box-plot made in plotly + - + url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: A streamgraph made in altair + - + url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + - + url: https://docs.pyvista.org/ + img: /images/content_images/v_pyvista.png + alttext: A 3D volume rendering made in PyVista. + - + url: https://napari.org + img: /images/content_images/v_napari.png + alttext: A multi-dimensionan image made in napari. + - + url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: A Voronoi diagram made in vispy. + content: + - + text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. + - + text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. From cf5898d21f6a22b00fcb2ab6ead9af1016d34b92 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:51 +0200 Subject: [PATCH 488/586] New translations history.md (Catalan) --- content/ca/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ca/history.md diff --git a/content/ca/history.md b/content/ca/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/ca/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From f904997c85710d090d2296177f33df9c204cb8ee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:52 +0200 Subject: [PATCH 489/586] New translations config.yaml (Catalan) --- content/ca/config.yaml | 137 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 content/ca/config.yaml diff --git a/content/ca/config.yaml b/content/ca/config.yaml new file mode 100644 index 0000000000..694aba3175 --- /dev/null +++ b/content/ca/config.yaml @@ -0,0 +1,137 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + text: NumPy + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: "Latest release: NumPy 2.0. View all releases" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: placeholder + intro: + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser + docslink: Don't forget to check out the docs. + casestudies: + title: CASE STUDIES + features: + - + title: First Image of a Black Hole + text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole + img: /images/content_images/case_studies/blackhole.png + alttext: First image of a black hole. It is an orange circle in a black background. + url: /case-studies/blackhole-image + - + title: Detection of Gravitational Waves + text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Two orbs orbiting each other. They are displacing gravity around them. + url: /case-studies/gw-discov + - + title: Sports Analytics + text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. + img: /images/content_images/case_studies/sports.jpg + alttext: Cricket ball on green field. + url: /case-studies/cricket-analytics + - + title: Pose Estimation using deep learning + text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Cheetah pose analysis + url: /case-studies/deeplabcut-dnn + tabs: + title: ECOSYSTEM + section5: false + navbar: + - + title: Install + url: /install + - + title: Documentation + url: https://numpy.org/doc/stable + - + title: Learn + url: /learn + - + title: Community + url: /community + - + title: About Us + url: /about + - + title: News + url: /news + - + title: Contribute + url: /contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + quicklinks: + column1: + title: "" + links: + - + text: Install + link: /install + - + text: Documentation + link: https://numpy.org/doc/stable + - + text: Learn + link: /learn + - + text: Citing Numpy + link: /citing-numpy + - + text: Roadmap + link: https://numpy.org/neps/roadmap.html + column2: + links: + - + text: About us + link: /about + - + text: Community + link: /community + - + text: User surveys + link: /user-surveys + - + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct + column3: + links: + - + text: Get help + link: /gethelp + - + text: Terms of use + link: /terms + - + text: Privacy + link: /privacy + - + text: Press kit + link: /press-kit From dee6f7d487cbc6b5f7cfa6ae6c26d630ad40d677 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:53 +0200 Subject: [PATCH 490/586] New translations gethelp.md (Catalan) --- content/ca/gethelp.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/ca/gethelp.md diff --git a/content/ca/gethelp.md b/content/ca/gethelp.md new file mode 100644 index 0000000000..da3bff67a5 --- /dev/null +++ b/content/ca/gethelp.md @@ -0,0 +1,20 @@ +--- +title: Get Help +sidebar: false +--- + +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** From 59115b7cfeca1a6b43fc4193614766122baf6548 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:54 +0200 Subject: [PATCH 491/586] New translations code-of-conduct.md (Catalan) --- content/ca/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ca/code-of-conduct.md diff --git a/content/ca/code-of-conduct.md b/content/ca/code-of-conduct.md new file mode 100644 index 0000000000..5ee1f4bcbe --- /dev/null +++ b/content/ca/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From aa249de2eb543efd4688b14d4f3950db29a3fb1d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:56 +0200 Subject: [PATCH 492/586] New translations install.md (Catalan) --- content/ca/install.md | 126 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100644 content/ca/install.md diff --git a/content/ca/install.md b/content/ca/install.md new file mode 100644 index 0000000000..f64a17841d --- /dev/null +++ b/content/ca/install.md @@ -0,0 +1,126 @@ +--- +title: Installing NumPy +sidebar: false +--- + +The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. + +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. + +**CONDA** + +If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: + +```bash +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +# If you want to install from conda-forge +conda config --env --add channels conda-forge +# The actual install command +conda install numpy +``` + +**PIP** + +If you use `pip`, you can install NumPy with: + +```bash +pip install numpy +``` +Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. + + + + +# Python and NumPy installation guide + +Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. + +## Recommendations + +We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. + +### Beginning users + +On all of Windows, macOS, and Linux: + +- Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). +- For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. +- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. + + +### Advanced users + +#### Conda + +- Install [Miniforge](https://github.com/conda-forge/miniforge). +- Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. + +#### Alternative if you prefer pip/PyPI + +For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. + + +## Python package management + +Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. + + +### Pip & conda + +The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. + +The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. + +The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. + +The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. + + + +### Reproducible installs + +As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: + +1. use a different environment per project you're working on, +2. record package names and versions using your package installer; each has its own metadata format for this: + - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) + + + +## NumPy packages & accelerated linear algebra libraries + +NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: + +- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. + +- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. + +- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. + +- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. + +- MKL is typically a little faster and more robust than OpenBLAS. + +Besides install sizes, performance and robustness, there are two more things to consider: + +- Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. +- Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. + + +## Troubleshooting + +If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). + +``` +IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! + +Importing the numpy c-extensions failed. This error can happen for +different reasons, often due to issues with your setup. +``` + From ed299e320cf936b1d52f2116287fceee207d31e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:57 +0200 Subject: [PATCH 493/586] New translations about.md (Catalan) --- content/ca/about.md | 90 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/ca/about.md diff --git a/content/ca/about.md b/content/ca/about.md new file mode 100644 index 0000000000..243f7083ae --- /dev/null +++ b/content/ca/about.md @@ -0,0 +1,90 @@ +--- +title: About Us +sidebar: false +--- + +NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). + +NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). + + +## Steering Council + +The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): + +- Sebastian Berg +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Inessa Pawson +- Matti Picus +- Stéfan van der Walt +- Melissa Weber Mendonça +- Eric Wieser + +Emeritus: + +- Alex Griffing (2015-2017) +- Allan Haldane (2015-2021) +- Marten van Kerkwijk (2017-2019) +- Travis Oliphant (project founder, 2005-2012) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Jaime Fernández del Río (2014-2021) +- Pauli Virtanen (2008-2021) + +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- optimization +- funding and grants + +See the [Team](/teams) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Inessa Pawson +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors + +NumPy receives direct funding from the following sources: +{{< sponsors >}} + + +## Institutional Partners + +Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + +{{< partners >}} + + +## Donate + +If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. + +NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. + +Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. + +NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). + +{{}} + From d0c5a3b504fd2ced8a838461496895aa9e08e89c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:58 +0200 Subject: [PATCH 494/586] New translations privacy.md (Catalan) --- content/ca/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ca/privacy.md diff --git a/content/ca/privacy.md b/content/ca/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ca/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From e491a42c45cc82a6f9848457400a5d28f14d992b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:39:59 +0200 Subject: [PATCH 495/586] New translations cricket-analytics.md (Catalan) --- content/ca/case-studies/cricket-analytics.md | 72 ++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 content/ca/case-studies/cricket-analytics.md diff --git a/content/ca/case-studies/cricket-analytics.md b/content/ca/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..d0be271f50 --- /dev/null +++ b/content/ca/case-studies/cricket-analytics.md @@ -0,0 +1,72 @@ +--- +title: "Case Study: Cricket Analytics, the game changer!" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} + +{{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" +> }} You don't play for the crowd, you play for the country. +> +> {{< /blockquote >}} + +## About Cricket + +It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. + +The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. + +Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. + +Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: + +* batting performance moving average, +* score forecasting, +* gaining insights into fitness and performance of a player against different opposition, +* player contribution to wins and losses for making strategic decisions on team composition + +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} + +### Key Data Analytics Objectives + +* Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. +* Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. +* Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. + +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} + +### The Challenges + +* **Data Cleaning and preprocessing** + + IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. + +* **Dynamic Modeling** + + In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. + +* **Predictive Analytics Complexity** + + Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. + +## NumPy’s Role in Cricket Analytics + +Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: + +* **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. + +* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. + +## Summary + +Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. + +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From af4e6014c79a3c0a5f1423964225f31c7ff1f372 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:40:00 +0200 Subject: [PATCH 496/586] New translations deeplabcut-dnn.md (Catalan) --- content/ca/case-studies/deeplabcut-dnn.md | 102 ++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 content/ca/case-studies/deeplabcut-dnn.md diff --git a/content/ca/case-studies/deeplabcut-dnn.md b/content/ca/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..257905de91 --- /dev/null +++ b/content/ca/case-studies/deeplabcut-dnn.md @@ -0,0 +1,102 @@ +--- +title: "Case Study: DeepLabCut 3D Pose Estimation" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-movement using DeepLapCut' alt = 'micehandanim' attribution = '(Source: www.deeplabcut.org )' attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} + +{{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" +> }} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +> +> {{< /blockquote >}} + +## About DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. + +Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. + +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} + +DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. + +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. + +DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. + +Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. + +### Key Goals and Results + +* **Automation of animal pose analysis for scientific studies:** + + The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). + +* **Creation of an easy-to-use Python toolkit for pose estimation:** + + DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. + + Their [toolkit][DLCToolkit] is now available as open source. + + A typical DeepLabCut Workflow includes: + + - creation and refining of training sets via active learning + - creation of tailored neural networks for specific animals and scenarios + - code for large-scale inference on videos + - draw inferences using integrated visualization tools + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pose estimation steps with DeepLabCut' alt = 'dlcsteps' align = 'center' attribution = '(Source: DeepLabCut)' attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} + +### The Challenges + +* **Speed** + + Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. + +* **Combinatorics** + + Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. + +* **Data Processing** + + Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. + +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' title = 'Pose estimation variety and complexity' alt = 'challengesfig' align = 'center' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} + +## NumPy's Role in meeting Pose Estimation Challenges + +NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). + +The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: + +* Vectorization +* Masked Array Operations +* Linear Algebra +* Random Sampling +* Reshaping of large arrays + +DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. + +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Workflow' alt = 'workflow' attribution = '(Source: Mackenzie Mathis)' attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} + +## Summary + +Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. + +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 774b0efeea42ba61c418ee9ca114f3087e2ded5d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:40:02 +0200 Subject: [PATCH 497/586] New translations blackhole-image.md (Catalan) --- content/ca/case-studies/blackhole-image.md | 80 ++++++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 content/ca/case-studies/blackhole-image.md diff --git a/content/ca/case-studies/blackhole-image.md b/content/ca/case-studies/blackhole-image.md new file mode 100644 index 0000000000..22334ebed0 --- /dev/null +++ b/content/ca/case-studies/blackhole-image.md @@ -0,0 +1,80 @@ +--- +title: "Case Study: First Image of a Black Hole" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} + +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" +> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +> +> {{< /blockquote >}} + +## A telescope the size of the earth + +The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! + +### Key Goals and Results + +* **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. + +* **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. + +* **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. + +### The Challenges + +* **Computational scale** + + EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. + +* **Too much information** + + Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. + +* **Into the unknown** + + When the goal is to see something never before seen, how can scientists be confident the image is correct? + +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} + +## NumPy’s Role + +What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? + +The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. + +Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. + +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} + +For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. + +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} + +Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. + +## Summary + +The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} + +[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole + +[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment + +[ehtim]: https://github.com/achael/eht-imaging + +[astropy]: https://www.astropy.org/ +[mpl]: https://matplotlib.org/ From 9cf413b7bec61e8e19c84cdfaf76176249473bcb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Aug 2024 20:40:03 +0200 Subject: [PATCH 498/586] New translations gw-discov.md (Catalan) --- content/ca/case-studies/gw-discov.md | 77 ++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 content/ca/case-studies/gw-discov.md diff --git a/content/ca/case-studies/gw-discov.md b/content/ca/case-studies/gw-discov.md new file mode 100644 index 0000000000..08f54b7547 --- /dev/null +++ b/content/ca/case-studies/gw-discov.md @@ -0,0 +1,77 @@ +--- +title: "Case Study: Discovery of Gravitational Waves" +sidebar: false +--- + +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Gravitational Waves' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} + +{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +{{< /blockquote >}} + +## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) + +Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. + +The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. + + +### Key Objectives + +* Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. +* Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data +* Data visualization so that the binary / numerical results can be comprehended. + + + +### The Challenges + +* **Computation** + + Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters + +* **Data Deluge** + + As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. + +* **Visualization** + + Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. + +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'Estimated gravitational-wave strain amplitude from GW150914' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} + +## NumPy’s Role in the Detection of Gravitational Waves + +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. + +NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: + +* [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack +* Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. +* Visualization of data + - Time series + - Spectrograms +* Compute Correlations +* Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. + +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} + +---- + +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} + +## Summary + +GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. + +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 883e89f202817cc6062fd86930062bfc1cf3a30c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:03 +0200 Subject: [PATCH 499/586] New translations contribute.md (Catalan) --- content/ca/contribute.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/ca/contribute.md b/content/ca/contribute.md index 6efff53624..0826a76184 100644 --- a/content/ca/contribute.md +++ b/content/ca/contribute.md @@ -17,19 +17,16 @@ Our community aspires to treat everyone equally and to value all contributions. Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. - ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes - ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. - ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: @@ -41,26 +38,28 @@ The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of Please just dive in. - ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. - ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. - ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. - ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. +For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From 13c03e5970d201c8d539a9a7f517c234fd772052 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:04 +0200 Subject: [PATCH 500/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index e2b388e7c7..b7283d8df2 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -17,19 +17,16 @@ Nuestra comunidad aspira a tratar a todos por igual y a valorar todas las contri Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica cómo contribuir al código base.
También revisa nuestro [canal de YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) por consejos adicionales. - ### Revisando solicitudes de cambios El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: * resumir un debate extenso * categorizar documentación de solicitudes de incorporación de cambios * probar los cambios propuestos - ### Creando material educativo La [Guía de usuario](https://numpy.org/devdocs) de NumPy está en proceso de rehabilitación. Necesitamos nuevos tutoriales, instrucciones y explicaciones detalladas, y la página necesita una reestructuración. Las oportunidades no se limitan a escritores. También ejemplos prácticos, notebooks y vídeos. La propuesta [NEP 44 - Reestructuración de la Documentación NumPy](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) expone nuestras ideas -- y tal vez tú puedas tener otras. - ### Clasificación de propuestas El [rastreador de propuestas de NumPy](https://github.com/numpy/numpy/issues) tiene _muchos_ temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes: @@ -41,22 +38,18 @@ El [rastreador de propuestas de NumPy](https://github.com/numpy/numpy/issues) ti Por favor, solo sumérgete. - ### Desarrollo de la Página Web Acabamos de renovar nuestro sitio web, pero aún no hemos terminado. Si te gusta el desarrollo web, estas [incidencias](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) enumeran algunas de nuestras necesidades insatisfechas -- y no dudes en compartir tus propias ideas. - ### Diseño gráfico Apenas podemos empezar a enumerar las aportaciones que puede hacer un diseñador gráfico. Nuestra documentación está sedienta de ilustraciones; nuestro sitio web, en pleno crecimiento, ansía imágenes... las oportunidades abundan. - ### Traduciendo el contenido de la página web Planeamos múltiples traducciones de [numpy.org](https://numpy.org) para hacer a NumPy accesible a los usuarios en su lengua materna. Los traductores voluntarios son el núcleo de este esfuerzo. Consulta [aquí](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para más información; comenta en [este tema de GitHub](https://github.com/numpy/numpy.org/issues/55) para inscribirte. - ### Coordinación y divulgación de la comunidad A través del contacto con la comunidad compartimos nuestro trabajo más ampliamente, y aprendemos en dónde nos estamos quedando cortos. Estamos ansiosos por conseguir más gente involucrada en esfuerzos como nuestra cuenta de [Twitter](https://twitter.com/numpy_team), organizando [code sprints](https://scisprints.github.io/) de NumPy, un boletín y quizás un blog. @@ -64,3 +57,9 @@ A través del contacto con la comunidad compartimos nuestro trabajo más ampliam ### Recaudación de fondos NumPy fue durante muchos años un proyecto voluntario, pero a medida que crecía su importancia se hizo evidente que necesitaríamos apoyo financiero para garantizar su estabilidad y crecimiento. [Esta charla en SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica cuánta diferencia ha supuesto este apoyo. Como todo en el mundo sin ánimo de lucro, estamos constantemente en busca de subvenciones, patrocinios y otros tipos de ayuda. Tenemos varias ideas y, por supuesto, aceptamos más. La recaudación de fondos es una habilidad escasa aquí -- apreciaríamos tu ayuda. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From fc98bf152571090a14feda0b5301d0226f476826 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:05 +0200 Subject: [PATCH 501/586] New translations contribute.md (Arabic) --- content/ar/contribute.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/ar/contribute.md b/content/ar/contribute.md index 6efff53624..0826a76184 100644 --- a/content/ar/contribute.md +++ b/content/ar/contribute.md @@ -17,19 +17,16 @@ Our community aspires to treat everyone equally and to value all contributions. Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. - ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes - ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. - ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: @@ -41,26 +38,28 @@ The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of Please just dive in. - ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. - ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. - ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. - ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. +For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From ff21915f00067e5e1d3ca0d76fd4a5fea08d8abe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:06 +0200 Subject: [PATCH 502/586] New translations contribute.md (Japanese) --- content/ja/contribute.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 90db608852..4b15f93608 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -17,19 +17,16 @@ NumPyプロジェクトを成功させるには、あなたの専門知識とプ プログラマーの方には、こちらの [ガイド](https://numpy.org/devdocs/dev/index.html#development-process-summary)でNumPyのコードに貢献する方法を説明しています。
追加情報に関しては、 こちらの[YouTube チャンネル](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) もご覧ください。 - ### プルリクエストのレビュー NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 * 長期にわたる議論をまとめる * ドキュメントのPRをトリアージする * 提案された変更をテストする - ### 教育用の資料を作成する NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模な再設計中です。 新しいNumPyのWebページは、新しいチュートリアルや、NumPyの使い方、NumPy内部の深い説明など必要としており、サイト全体にも再設計と再構築が必要です。 このウェブサイトの再構築の作業は、ドキュメントを書くだけではありません。 コード例や、ノートブック、ビデオなどの作成も歓迎しています。 [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)に、ウェブサイトの再構築についての詳細が説明されています。 - ### イシューのトリアージ [NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: @@ -41,22 +38,18 @@ NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模 ぜひ、やってみて下さい。 - ### ウェブサイトの開発 私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、この[イシュー](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。 - ### グラフィックデザイン グラフィックデザイナーの方が可能な貢献は、枚挙にいとまがありません。 しかし、私たちのドキュメントは説明のために可視化が重要であり、私たちの拡大しているウェブサイトは良い画像を求めていることから、 貢献する機会が沢山あると言えます。 - ### ウェブサイトの翻訳 私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 - ### コミュニティとの連携とアウトリーチ コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 @@ -64,3 +57,9 @@ NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模 ### 資金調達 NumPyは何年にも渡ってボランティアだけ活動していましたが、その重要性が高まるにつれ、安定性と成長のためには資金面での支援が必要であることがわかってきました。 こちらの[SciPy'19のプレゼン](https://www.youtube.com/watch?v=dBTJD_FDVjU) では、資金的なサポートを受けたことで、どれだけ違いが出たかを説明しています。 他の非営利団体のように、私たちは助成金や、スポンサーシップ、その他の資金支援を常に探しています。 私たちはすでにいくつかの資金調達のアイデアを持っていますが、他にもより多くを資金調達を受けたいと思っています。 資金調達に関する知識は、我々には不足しているスキルです。 是非、あなたのサポートをお待ちしています。 + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From 7233a9333e8ae26967a2ae0fb7852c872a4f9f40 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:07 +0200 Subject: [PATCH 503/586] New translations contribute.md (Korean) --- content/ko/contribute.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 51ce2bd4aa..7ce62c6050 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -17,19 +17,16 @@ NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! 당 프로그래머 여러분, 이 [도움말](https://numpy.org/devdocs/dev/index.html#development-process-summary)에 어떻게 코드베이스에 기여하는지 설명되어 있습니다.
저희의 [유투브 채널](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS)을 통해서 추가적인 정보를 확인 해주세요. - ### Pull Request 리뷰 프로젝트의 열린 풀 요청만 250개가 넘습니다. 즉 많은 잠재적 개선점과 오픈소스 기여자들이 피드백을 기다리고 있다는 것입니다. NumPy를 알고 있는 개발자라면, 코드베이스에 대해 잘 알지 못해도 기여할 수 있습니다. 아래와 같은 기여를 해 보십시오. * 늘어지는 토론 요약 * 문서의 풀 요청 심사 * 제안된 변경 사항 테스트 - ### 교육 자료 개발 NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 재구성되고 있습니다. 현재 새로운 튜토리얼, 방법, 심층적 설명이 필요하고, 사이트의 구조를 다시 짜야 합니다. 글을 쓰는 사람에게만 기회가 주어지는 것은 아닙니다. 코드 예제와 노트북, 동영상 등을 통한 기여도 환영합니다. [NEP 44 — NumPy 문서의 재구성](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)에 사이트 재구성에 대하여 자세한 내용이 설명되어 있습니다. - ### 이슈 확인 [NumPy 이슈 트래커](https://github.com/numpy/numpy/issues)에는 _정말 많은_ 이슈들이 현재 열린 상태로 있습니다. 일부는 더 이상 유효하지 않은 이슈고, 일부는 우선 순위를 지정해야 하며, 일부는 새로운 기여자들이 볼 만한 좋은 이슈가 될 수 있을 것입니다. 아래와 같은 기여를 해 보십시오. @@ -41,22 +38,18 @@ NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 한 번 참여해 보시길 바랍니다. - ### 웹사이트 개발 사이트를 막 뜯어 고친 상태이지만, 아직 끝이라기엔 멀었습니다. 웹 개발을 좋아하신다면, [여기](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign)에서 저희가 이루지 못했던 사항의 목록을 볼 수 있습니다. 자신만의 아이디어를 마음껏 공유해 주십시오. - ### 그래픽 디자인 그래픽 디자이너분들이 할 수 있는 기여의 목록을 여기에 열거하는 건 어렵습니다. 저희 문서에는 일러스트가 많이 부족합니다. 성장하는 사이트에는 이미지가 필요하기 때문에, 기여할 수 있는 기회가 많을 것입니다. - ### 웹사이트 번역 사용자가 모국어로 NumPy를 이용할 수 있도록 [numpy.org](https://numpy.org)의 여러 번역을 계획하고 있습니다. 이를 위해서는 자원봉사자분들의 통역이 필요합니다. 자세한 내용은 [여기](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)를 참고하십시오. [이 GitHub 이슈](https://github.com/numpy/numpy.org/issues/55)에 댓글을 달아 번역에 참여하십시오. - ### 커뮤니티 조직 및 확산 우리는 커뮤니티 연락처를 통해 작업물을 더 널리 공유하고 미흡한 부분을 배워 나갑니다. 우리는 [Twitter](https://twitter.com/numpy_team) 계정, NumPy [코드 스프린트](https://scisprints.github.io/) 개최, 뉴스레터 발행, 그리고 아마 블로그 등을 통해서 더 많은 사람들이 커뮤니티에 참여하기를 간절히 바라고 있습니다. @@ -64,3 +57,9 @@ NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 ### 모금 NumPy는 오랜 기간 동안 자원봉사의 형태로 유지되었으나, 그 중요성이 커짐에 따라 안정성 및 성장을 보장하려면 경제적 지원이 필요함이 분명해졌습니다. 이런 지원이 얼마나 큰 차이를 만들어 냈는지 [SciPy'19 강연](https://www.youtube.com/watch?v=dBTJD_FDVjU)에서 확인하실 수 있습니다. 모든 비영리 조직과 마찬가지로 저희는 지속적으로 보조금, 후원 및 기타 종류의 지원을 끊임없이 찾고 있습니다. 모금을 받을 아이디어가 몇 개 있지만 당연히 더 많은 자금을 받게 된다면 좋을 것입니다. 모금도 정말 희귀한 능력 중 하나입니다 - 도움을 주신다면 감사드리겠습니다. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From a5728ae698b66fc1f508b24a9575f397cc8b7cfa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:09 +0200 Subject: [PATCH 504/586] New translations contribute.md (Russian) --- content/ru/contribute.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/ru/contribute.md b/content/ru/contribute.md index 6efff53624..0826a76184 100644 --- a/content/ru/contribute.md +++ b/content/ru/contribute.md @@ -17,19 +17,16 @@ Our community aspires to treat everyone equally and to value all contributions. Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. - ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes - ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. - ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: @@ -41,26 +38,28 @@ The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of Please just dive in. - ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. - ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. - ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. - ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. +For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From 5f12a0082c30e270f4a95c6a2f12deaf7cc666a1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:10 +0200 Subject: [PATCH 505/586] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 6efff53624..0826a76184 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -17,19 +17,16 @@ Our community aspires to treat everyone equally and to value all contributions. Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. - ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes - ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. - ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: @@ -41,26 +38,28 @@ The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of Please just dive in. - ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. - ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. - ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. - ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. +For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From 804ab99d0f772035e07572f7658bdd24b7432153 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:11 +0200 Subject: [PATCH 506/586] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 65b82636b8..ef45f31c2a 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -17,19 +17,16 @@ Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contrib Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código.
Confira também nosso [canal do YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) para obter informações adicionais. - ### Revisar pull requests O projeto tem mais de 250 pull requests abertos -- o que significa que muitas potenciais melhorias e muitos contribuidores de código aberto estão aguardando feedback. Se você é uma pessoa programadora que conhece o NumPy, você pode ajudar, mesmo que não tenha familiaridade com o código. Você pode: * resumir uma discussão longa * fazer triagem de PRs de documentação * testar alterações propostas - ### Desenvolvimento de materiais educacionais O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. - ### Triagem de Issues O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: @@ -41,22 +38,18 @@ O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um mont Sinta-se à vontade! - ### Desenvolvimento do site Acabamos de renovar o nosso site, mas estamos longe de terminar. Se você adora o desenvolvimento web, estas [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) listam algumas de nossas necessidades não atendidas -- e sinta-se livre para compartilhar suas próprias ideias. - ### Design gráfico Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. - ### Traduzir conteúdo do site Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. - ### Coordenação e promoção na comunidade Através do contato com a comunidade podemos compartilhar nosso trabalho para mais pessoas e descobrir onde precisamos trabalhar mais. Estamos ansiosos para que mais pessoas se envolvam em esforços como nossa conta no [Twitter](https://twitter.com/numpy_team), na organização de [sprints](https://scisprints.github.io/) sobre o NumPy, uma newsletter, e talvez um blog. @@ -64,3 +57,9 @@ Através do contato com a comunidade podemos compartilhar nosso trabalho para ma ### Financiamento O NumPy foi um projeto totalmente voluntário por muitos anos, mas conforme sua importância cresceu, tornou-se clara a necessidade de apoio financeiro para garantir estabilidade e crescimento. [Esta palestra na SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica quanta diferença esse suporte fez. Como todo o mundo das organizações sem fins lucrativos, nós estamos constantemente procurando bolsas, patrocinadores e outros tipos de apoio. Nós temos uma série de ideias e é claro que nós damos as boas-vindas a mais. Habilidade de buscar financiamento é uma habilidade rara aqui -- apreciaríamos a sua ajuda. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From 7759ebf62dfc842fb2a7b0c0f84d3fba5f45e241 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:12:12 +0200 Subject: [PATCH 507/586] New translations contribute.md (Persian) --- content/fa/contribute.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/content/fa/contribute.md b/content/fa/contribute.md index 6efff53624..0826a76184 100644 --- a/content/fa/contribute.md +++ b/content/fa/contribute.md @@ -17,19 +17,16 @@ Our community aspires to treat everyone equally and to value all contributions. Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. - ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes - ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. - ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: @@ -41,26 +38,28 @@ The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of Please just dive in. - ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. - ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. - ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. - ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became clear that to ensure stability and growth we'd need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like all the nonprofit world, we're constantly searching for grants, sponsorships, and other kinds of support. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. +For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + + From 76c3f8a129af79f0d4e72e4a18299be466d9e586 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 12:30:42 +0200 Subject: [PATCH 508/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index b7283d8df2..9ef362edfc 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -7,7 +7,7 @@ sidebar: false Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [propuesta](https://github.com/numpy/numpy/issues) o comenta en una relevante). -Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la comunidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). +Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la communidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). También hacemos _llamadas a la comunidad_ de manera quincenal, cuyos detalles se anuncian en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion). Te invitamos a unirte. Si es la primera vez que contribuyes al código abierto, también te recomendamos encarecidamente que leas [esta guía](https://opensource.guide/how-to-contribute/). @@ -20,7 +20,7 @@ Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development ### Revisando solicitudes de cambios El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: * resumir un debate extenso -* categorizar documentación de solicitudes de incorporación de cambios +* categorizar PRs de documentación * probar los cambios propuestos ### Creando material educativo From 1a3d7a49b3d969138b3f931ee47f8c73504f8a26 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 13:11:12 +0200 Subject: [PATCH 509/586] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index ef45f31c2a..5ba54f9bde 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -58,8 +58,8 @@ Através do contato com a comunidade podemos compartilhar nosso trabalho para ma O NumPy foi um projeto totalmente voluntário por muitos anos, mas conforme sua importância cresceu, tornou-se clara a necessidade de apoio financeiro para garantir estabilidade e crescimento. [Esta palestra na SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica quanta diferença esse suporte fez. Como todo o mundo das organizações sem fins lucrativos, nós estamos constantemente procurando bolsas, patrocinadores e outros tipos de apoio. Nós temos uma série de ideias e é claro que nós damos as boas-vindas a mais. Habilidade de buscar financiamento é uma habilidade rara aqui -- apreciaríamos a sua ajuda. -### Donate +### Doar -If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). +Se você gostaria de contribuir com o NumPy fazendo uma doação, visite [https://numpy.org/pt/about/#doações](https://numpy.org/pt/about/#doa%C3%A7%C3%B5es). From bc0c179afdb72c1f6f700d12f3df9fb8d8f26d42 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Aug 2024 15:06:32 +0200 Subject: [PATCH 510/586] New translations contribute.md (Japanese) --- content/ja/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 4b15f93608..2c7002cb86 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -58,8 +58,8 @@ NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模 NumPyは何年にも渡ってボランティアだけ活動していましたが、その重要性が高まるにつれ、安定性と成長のためには資金面での支援が必要であることがわかってきました。 こちらの[SciPy'19のプレゼン](https://www.youtube.com/watch?v=dBTJD_FDVjU) では、資金的なサポートを受けたことで、どれだけ違いが出たかを説明しています。 他の非営利団体のように、私たちは助成金や、スポンサーシップ、その他の資金支援を常に探しています。 私たちはすでにいくつかの資金調達のアイデアを持っていますが、他にもより多くを資金調達を受けたいと思っています。 資金調達に関する知識は、我々には不足しているスキルです。 是非、あなたのサポートをお待ちしています。 -### Donate +### 寄付 -If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). +寄付をすることでNumpy に貢献したい場合は、 [https://numpy.org/about/#donate](https://numpy.org/about/#donate) をご覧ください。 From 6e5c170c60f5f1ed856aa6934165f0e9ec8797f0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:18 +0200 Subject: [PATCH 511/586] New translations news.md (Catalan) --- content/ca/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ca/news.md b/content/ca/news.md index e37178caf8..b3726008da 100644 --- a/content/ca/news.md +++ b/content/ca/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From c61a2d858c8e7c2b54e4fa72a9f3f71304bb1438 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:20 +0200 Subject: [PATCH 512/586] New translations news.md (Spanish) --- content/es/news.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/content/es/news.md b/content/es/news.md index fb688d7175..e3d476dd41 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -5,6 +5,17 @@ newsHeader: "¡NumPy 2.0 ha sido lanzado!" date: 2024-06-17 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### Lanzamiento de NumPy 2.0.0 _16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: @@ -250,6 +261,7 @@ Puede encontrar más detalles sobre nuestras iniciativas y entregables propuesto Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. - NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. - NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. From f241cb2b43d404b6a791077c1922fd505def71ab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:21 +0200 Subject: [PATCH 513/586] New translations news.md (Arabic) --- content/ar/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index e37178caf8..b3726008da 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From e25741e0c2658c55a916d2284a46d9f69856c5ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:22 +0200 Subject: [PATCH 514/586] New translations news.md (Japanese) --- content/ja/news.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/content/ja/news.md b/content/ja/news.md index f7672f6a8a..58bdfe0a8d 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -5,6 +5,17 @@ newsHeader: "NumPy 2.0. がリリースされました!" date: 2023-09-16 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0 リリース日: 6月16日 _2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 @@ -250,6 +261,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. - NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. From 60ef233a2bc32cd85f977c078984ff4fefa6122f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:24 +0200 Subject: [PATCH 515/586] New translations news.md (Korean) --- content/ko/news.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index e3a37fa837..d1b5c54df6 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,21 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.1 released!" date: 2023-09-16 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0 출시일: 6월 16일 _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. From de7f640f353f6e3aa51fc302ad318856e4aba19e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:25 +0200 Subject: [PATCH 516/586] New translations news.md (Russian) --- content/ru/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index e37178caf8..b3726008da 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From d2920852b3f227027281a15a309513b0b408ede0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:26 +0200 Subject: [PATCH 517/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index e37178caf8..b3726008da 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From eee547d8229762930252807823d714802b79f1b5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:28 +0200 Subject: [PATCH 518/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 6d8b142f48..9c938509f4 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,21 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 2.0 released!" +newsHeader: "NumPy 2.1 released!" date: 2023-09-16 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From 421afb1f8758c64c5fb5d830e8e35d96a93c28fd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:32:29 +0200 Subject: [PATCH 519/586] New translations news.md (Persian) --- content/fa/news.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/content/fa/news.md b/content/fa/news.md index e37178caf8..b3726008da 100644 --- a/content/fa/news.md +++ b/content/fa/news.md @@ -1,10 +1,21 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: @@ -250,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. From 6e39f8c4e2525d54d46312e441717d7ad4109965 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:37:36 +0200 Subject: [PATCH 520/586] New translations news.md (Spanish) --- content/es/news.md | 328 ++++++++++++++++++++++----------------------- 1 file changed, 164 insertions(+), 164 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index e3d476dd41..9759053bf9 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,8 +1,8 @@ --- -title: Noticias -sidebar: falso -newsHeader: "¡NumPy 2.0 ha sido lanzado!" -date: 2024-06-17 +title: News +sidebar: false +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- ### NumPy 2.1.0 released @@ -16,34 +16,34 @@ _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support Python versions 3.10-3.13 are supported by this release. -### Lanzamiento de NumPy 2.0.0 +### NumPy 2.0.0 released -_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -### Fecha de lanzamiento de NumPy 2.0: 16 de junio +### NumPy 2.0 release date: June 16 -_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### Recaudación de fondos de fin de año de NumFOCUS -_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  -### NumPy 1.26.0 ha sido lanzado +### NumPy 1.26.0 released -_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -56,249 +56,249 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org ya está disponible en japonés y portugués +### numpy.org is now available in Japanese and Portuguese -_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: -_Portugués:_ -* Melissa Weber Mendonça (melissawm) -* Precios Ricardo (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japonés:_ +_Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. +The work on the translation infrastructure is supported with funding from CZI. -De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) -### NumPy 1.25.0 ha sido lanzado +### NumPy 1.25.0 released -_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -* Soporte para MUSL, ahora hay ruedas MUSL. -* Soporte para el compilador de Fujitsu C/C++. -* Las matrices de objetos ahora están soportadas en einsum. -* Soporte para la multiplicación de matrices in situ (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. +A total of 148 people contributed to this release and 530 pull requests were merged. -Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. +The Python versions supported by this release are 3.9-3.11. -### Fomentar una Cultura Inclusiva: Convocatoria de Participación +### Fostering an Inclusive Culture: Call for Participation -_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). -### Transición en el liderazgo del equipo de documentación de NumPy +### NumPy documentation team leadership transition -_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. -### Lanzamiento de NumPy 1.24.0 +### NumPy 1.24.0 released -_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. -* Nuevas características y correcciones de F2PY. -* Muchas nuevas obsolescencias, revísalas. -* Muchas obsolescencias caducadas, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. -### NumPy 1.23.0 ha sido lanzado +### Numpy 1.23.0 released -_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. -* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. -* Cambios a la promoción y comparación de dtypes estructurados. -* Mejoras a f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. -### Estudio de investigación NumFOCUS DEI: llamado a participar +### NumFOCUS DEI research study: call for participation -_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. -**¿Estás interesado en compartir tus experiencias?** +**Interested in sharing your experiences?** -Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. -### Lanzamiento de NumPy 1.22.0 +### Numpy 1.22.0 release -_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. -* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. -* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). -* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. -* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. -* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. -NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. -### Promoviendo una cultura inclusiva en el ecosistema científico de Python +### Advancing an inclusive culture in the scientific Python ecosystem -_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. -Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). -Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. -Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### Encuesta de NumPy de 2021 +### 2021 NumPy survey -_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. -Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Lanzamiento de NumPy 1.21.0 +### Numpy 1.21.0 release -_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: -- trabajo SIMD continuo que cubre más funciones y plataformas, -- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, -- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, -- documentación mejorada, -- anotaciones mejoradas, -- nuevo `PCG64DXSM` generador de bits para números aleatorios. +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. -### Resultados de la encuesta de NumPy de 2020 +### 2020 NumPy survey results -_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Lanzamiento de NumPy 1.20.0 +### Numpy 1.20.0 release -_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: -- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. -- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversidad en el proyecto NumPy +### Diversity in the NumPy project -_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). -### Primer artículo oficial de NumPy publicado en Nature! +### First official NumPy paper published in Nature! -_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. -### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? +### Python 3.9 is coming, when will NumPy release binary wheels? -_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de -- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` -- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -### Lanzamiento de NumPy 1.19.2 +### Numpy 1.19.2 release -_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. -### La encuesta inaugural de NumPy ya está disponible! +### The inaugural NumPy survey is live! -_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. -Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### ¡NumPy tiene un nuevo logo! +### NumPy has a new logo! -_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: +_Jun 24, 2020_ -- NumPy now has a new logo: -Logo de NumPy +NumPy logo -El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -### Lanzamiento de NumPy 1.19.0 +### NumPy 1.19.0 release -_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. -### Aceptación a Season of Docs +### Season of Docs acceptance -_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### Lanzamiento de NumPy 1.18.0 +### NumPy 1.18.0 release -_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg +### NumPy receives a grant from the Chan Zuckerberg Initiative -_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. -Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. -Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. -## Lanzamientos +## Releases -Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. -- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. -- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. -- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. -- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. -- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. -- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. -- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. -- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. -- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. -- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. -- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. -- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. -- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. -- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. -- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. -- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. -- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. -- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. -- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. -- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. -- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. -- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. -- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. -- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. -- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. -- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. -- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. -- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. -- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. -- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. -- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. -- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. -- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. -- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. -- . -- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. -- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 029552cd41973ba539e82ed577d873c5b7060320 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Aug 2024 14:37:38 +0200 Subject: [PATCH 521/586] New translations news.md (Japanese) --- content/ja/news.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 58bdfe0a8d..32e1acf5dd 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,7 +1,7 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0. がリリースされました!" +newsHeader: "NumPy 2.1 released!" date: 2023-09-16 --- @@ -18,22 +18,22 @@ Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0 リリース日: 6月16日 -_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -262,10 +262,10 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. -- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From a5dd0fa97e3a34c0666e149f60dcf60f6689c336 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Aug 2024 02:56:55 +0200 Subject: [PATCH 522/586] New translations news.md (Japanese) --- content/ja/news.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 32e1acf5dd..ebfcbc2ffb 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,11 +1,11 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.1 released!" +newsHeader: "NumPy 2.0. がリリースされました!" date: 2023-09-16 --- -### NumPy 2.1.0 released +### NumPy 2.1.0 がリリースされました。 _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: @@ -18,22 +18,22 @@ Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -262,10 +262,10 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. +- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From 271ad0600db9297ea67afa9da71df6ce28e93646 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Aug 2024 04:08:10 +0200 Subject: [PATCH 523/586] New translations news.md (Japanese) --- content/ja/news.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index ebfcbc2ffb..6bd2365263 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -7,13 +7,13 @@ date: 2023-09-16 ### NumPy 2.1.0 がリリースされました。 -_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: +_2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 -- Support for Python 3.13. -- Preliminary support for free threaded Python 3.13. -- Support for the array-api 2023.12 standard. +- Python 3.13 のサポート +- Python 3.13のフリースレッドサポートの事前準備 +- Array-api 2023.12 標準のサポート -Python versions 3.10-3.13 are supported by this release. +Python バージョン 3.10-3.13 か、このリリースでサポートされています。 ### NumPy 2.0 リリース日: 6月16日 @@ -261,7 +261,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. - NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. - NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. From 0a2ca85647e0a1f99efe115aa511fc345ff4f162 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:14 +0200 Subject: [PATCH 524/586] New translations news.md (Catalan) --- content/ca/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ca/news.md b/content/ca/news.md index b3726008da..a511b89bfc 100644 --- a/content/ca/news.md +++ b/content/ca/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From a9accbf402f4d26fea8b1adbfc2a33c96fe6f139 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:15 +0200 Subject: [PATCH 525/586] New translations news.md (Spanish) --- content/es/news.md | 329 +++++++++++++++++++++++---------------------- 1 file changed, 165 insertions(+), 164 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 9759053bf9..0cb10693eb 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,8 +1,8 @@ --- -title: News -sidebar: false -newsHeader: "NumPy 2.1 released!" -date: 2024-08-18 +title: Noticias +sidebar: falso +newsHeader: "¡NumPy 2.0 ha sido lanzado!" +date: 2024-06-17 --- ### NumPy 2.1.0 released @@ -16,34 +16,34 @@ _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support Python versions 3.10-3.13 are supported by this release. -### NumPy 2.0.0 released +### Lanzamiento de NumPy 2.0.0 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. -### NumPy 2.0 release date: June 16 +### Fecha de lanzamiento de NumPy 2.0: 16 de junio -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. +### Recaudación de fondos de fin de año de NumFOCUS +_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  -### NumPy 1.26.0 released +### NumPy 1.26.0 ha sido lanzado -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: +_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -56,249 +56,250 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org is now available in Japanese and Portuguese +### numpy.org ya está disponible en japonés y portugués -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: -_Portuguese:_ -* Melissa Weber Mendonça (melissawm) -* Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portugués:_ +* Melissa Weber Mendonça (melissawm) +* Precios Ricardo (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japanese:_ +_Japonés:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -The work on the translation infrastructure is supported with funding from CZI. +El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. -Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) -### NumPy 1.25.0 released +### NumPy 1.25.0 ha sido lanzado -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: +_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Support for MUSL, there are now MUSL wheels. -* Support for the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum. -* Support for the inplace matrix multiplication (`@=`). +* Soporte para MUSL, ahora hay ruedas MUSL. +* Soporte para el compilador de Fujitsu C/C++. +* Las matrices de objetos ahora están soportadas en einsum. +* Soporte para la multiplicación de matrices in situ (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. +NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. -A total of 148 people contributed to this release and 530 pull requests were merged. +Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. -The Python versions supported by this release are 3.9-3.11. +Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. -### Fostering an Inclusive Culture: Call for Participation +### Fomentar una Cultura Inclusiva: Convocatoria de Participación -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación -How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). +¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). -### NumPy documentation team leadership transition +### Transición en el liderazgo del equipo de documentación de NumPy -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. +_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. -### NumPy 1.24.0 released +### Lanzamiento de NumPy 1.24.0 -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: +_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. +* Nuevas características y correcciones de F2PY. +* Muchas nuevas obsolescencias, revísalas. +* Muchas obsolescencias caducadas, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. +El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. -### Numpy 1.23.0 released +### NumPy 1.23.0 ha sido lanzado -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: +_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Implementation of `loadtxt` in C, greatly improving its performance. -* Exposure of DLPack at the Python level for easy data exchange. -* Changes to the promotion and comparisons of structured dtypes. -* Improvements to f2py. +* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. +* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. +* Cambios a la promoción y comparación de dtypes estructurados. +* Mejoras a f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. +El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. -### NumFOCUS DEI research study: call for participation +### Estudio de investigación NumFOCUS DEI: llamado a participar -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. +_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. -**Interested in sharing your experiences?** +**¿Estás interesado en compartir tus experiencias?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. +Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. -### Numpy 1.22.0 release +### Lanzamiento de NumPy 1.22.0 -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: +_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. -* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. -* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. -* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. -* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. -* A new configurable memory allocator for use by downstream projects. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. +* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). +* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. +* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. +* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. +NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. -### Advancing an inclusive culture in the scientific Python ecosystem +### Promoviendo una cultura inclusiva en el ecosistema científico de Python -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. +_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). +Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). -This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. +Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. -The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### 2021 NumPy survey +### Encuesta de NumPy de 2021 -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. +_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. -It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. +Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Numpy 1.21.0 release +### Lanzamiento de NumPy 1.21.0 -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: +_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: -- continued SIMD work covering more functions and platforms, -- initial work on the new dtype infrastructure and casting, -- universal2 wheels for Python 3.8 and Python 3.9 on Mac, -- improved documentation, -- improved annotations, -- new `PCG64DXSM` bitgenerator for random numbers. +- trabajo SIMD continuo que cubre más funciones y plataformas, +- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, +- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, +- documentación mejorada, +- anotaciones mejoradas, +- nuevo `PCG64DXSM` generador de bits para números aleatorios. -This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. +Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. -### 2020 NumPy survey results +### Resultados de la encuesta de NumPy de 2020 -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. +_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release +### Lanzamiento de NumPy 1.20.0 -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: +- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. +- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversity in the NumPy project +### Diversidad en el proyecto NumPy -_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). +_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). -### First official NumPy paper published in Nature! +### Primer artículo oficial de NumPy publicado en Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. +_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. -### Python 3.9 is coming, when will NumPy release binary wheels? +### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to -- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. +_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de +- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` +- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. -### Numpy 1.19.2 release +### Lanzamiento de NumPy 1.19.2 -_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. +_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. -### The inaugural NumPy survey is live! +### La encuesta inaugural de NumPy ya está disponible! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy has a new logo! +### ¡NumPy tiene un nuevo logo! -_Jun 24, 2020_ -- NumPy now has a new logo: +_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: -NumPy logo +Logo de NumPy -The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. +El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. -### NumPy 1.19.0 release +### Lanzamiento de NumPy 1.19.0 -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. +_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. -### Season of Docs acceptance +### Aceptación a Season of Docs -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 1.18.0 release +### Lanzamiento de NumPy 1.18.0 -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg -_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. +_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. -This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. -More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. +Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. -## Releases +## Lanzamientos -Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. -- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. -- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. -- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. -- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. -- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. -- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. -- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. -- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. -- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. -- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. -- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. -- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. -- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. -- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. -- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. -- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. -- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. -- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. -- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. -- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. -- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. -- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. -- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. -- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. -- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. -- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. -- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. -- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. -- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. -- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. -- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. +- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. +- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. +- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. +- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. +- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. +- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. +- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. +- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. +- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. +- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. +- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. +- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. +- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. +- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. +- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. +- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. +- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. +- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. +- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. +- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. +- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. +- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. +- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. +- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. +- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. +- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. +- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. +- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. +- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. +- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. +- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. +- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. +- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. +- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. +- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. +- . +- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. +- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. From bd18ad5eb7edad8643b4c313dda0636888325523 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:16 +0200 Subject: [PATCH 526/586] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index b3726008da..a511b89bfc 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 3c255ded2d7f334157380fb47f4ef6b771347a50 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:18 +0200 Subject: [PATCH 527/586] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 6bd2365263..401bf277c4 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -261,6 +261,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. - NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. - NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. From ee2e972543787466bd4fdd7c8bd6897e5f342c43 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:19 +0200 Subject: [PATCH 528/586] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index d1b5c54df6..18c5d21123 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -261,6 +261,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 180426c0ee485a4aa7c22733ee79d0ae1150c34c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:20 +0200 Subject: [PATCH 529/586] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index b3726008da..a511b89bfc 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 1db92923acbfa923c48a727e462c5c7fa51bb31d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:21 +0200 Subject: [PATCH 530/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index b3726008da..a511b89bfc 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 431b795192c567303b15f964776ae5e0e268f787 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:23 +0200 Subject: [PATCH 531/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index 9c938509f4..4e452c1c4c 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -261,6 +261,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 0be60ceda1ec246c581a070a8a1c1bb143303a5d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:16:25 +0200 Subject: [PATCH 532/586] New translations news.md (Persian) --- content/fa/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/fa/news.md b/content/fa/news.md index b3726008da..a511b89bfc 100644 --- a/content/fa/news.md +++ b/content/fa/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From e1ac636cdf7722062309009311819bd65e04a1f3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:25:26 +0200 Subject: [PATCH 533/586] New translations news.md (Spanish) --- content/es/news.md | 328 ++++++++++++++++++++++----------------------- 1 file changed, 164 insertions(+), 164 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 0cb10693eb..f4430b278b 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,8 +1,8 @@ --- -title: Noticias -sidebar: falso -newsHeader: "¡NumPy 2.0 ha sido lanzado!" -date: 2024-06-17 +title: News +sidebar: false +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- ### NumPy 2.1.0 released @@ -16,34 +16,34 @@ _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support Python versions 3.10-3.13 are supported by this release. -### Lanzamiento de NumPy 2.0.0 +### NumPy 2.0.0 released -_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -### Fecha de lanzamiento de NumPy 2.0: 16 de junio +### NumPy 2.0 release date: June 16 -_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### Recaudación de fondos de fin de año de NumFOCUS -_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  -### NumPy 1.26.0 ha sido lanzado +### NumPy 1.26.0 released -_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -56,250 +56,250 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org ya está disponible en japonés y portugués +### numpy.org is now available in Japanese and Portuguese -_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: -_Portugués:_ -* Melissa Weber Mendonça (melissawm) -* Precios Ricardo (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japonés:_ +_Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. +The work on the translation infrastructure is supported with funding from CZI. -De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) -### NumPy 1.25.0 ha sido lanzado +### NumPy 1.25.0 released -_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -* Soporte para MUSL, ahora hay ruedas MUSL. -* Soporte para el compilador de Fujitsu C/C++. -* Las matrices de objetos ahora están soportadas en einsum. -* Soporte para la multiplicación de matrices in situ (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. +A total of 148 people contributed to this release and 530 pull requests were merged. -Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. +The Python versions supported by this release are 3.9-3.11. -### Fomentar una Cultura Inclusiva: Convocatoria de Participación +### Fostering an Inclusive Culture: Call for Participation -_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). -### Transición en el liderazgo del equipo de documentación de NumPy +### NumPy documentation team leadership transition -_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. -### Lanzamiento de NumPy 1.24.0 +### NumPy 1.24.0 released -_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. -* Nuevas características y correcciones de F2PY. -* Muchas nuevas obsolescencias, revísalas. -* Muchas obsolescencias caducadas, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. -### NumPy 1.23.0 ha sido lanzado +### Numpy 1.23.0 released -_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. -* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. -* Cambios a la promoción y comparación de dtypes estructurados. -* Mejoras a f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. -### Estudio de investigación NumFOCUS DEI: llamado a participar +### NumFOCUS DEI research study: call for participation -_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. -**¿Estás interesado en compartir tus experiencias?** +**Interested in sharing your experiences?** -Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. -### Lanzamiento de NumPy 1.22.0 +### Numpy 1.22.0 release -_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. -* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. -* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). -* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. -* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. -* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. -NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. -### Promoviendo una cultura inclusiva en el ecosistema científico de Python +### Advancing an inclusive culture in the scientific Python ecosystem -_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. -Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). -Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. -Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### Encuesta de NumPy de 2021 +### 2021 NumPy survey -_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. -Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Lanzamiento de NumPy 1.21.0 +### Numpy 1.21.0 release -_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: -- trabajo SIMD continuo que cubre más funciones y plataformas, -- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, -- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, -- documentación mejorada, -- anotaciones mejoradas, -- nuevo `PCG64DXSM` generador de bits para números aleatorios. +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. -### Resultados de la encuesta de NumPy de 2020 +### 2020 NumPy survey results -_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Lanzamiento de NumPy 1.20.0 +### Numpy 1.20.0 release -_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: -- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. -- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversidad en el proyecto NumPy +### Diversity in the NumPy project -_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). -### Primer artículo oficial de NumPy publicado en Nature! +### First official NumPy paper published in Nature! -_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. -### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? +### Python 3.9 is coming, when will NumPy release binary wheels? -_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de -- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` -- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -### Lanzamiento de NumPy 1.19.2 +### Numpy 1.19.2 release -_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. -### La encuesta inaugural de NumPy ya está disponible! +### The inaugural NumPy survey is live! -_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. -Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### ¡NumPy tiene un nuevo logo! +### NumPy has a new logo! -_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: +_Jun 24, 2020_ -- NumPy now has a new logo: -Logo de NumPy +NumPy logo -El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -### Lanzamiento de NumPy 1.19.0 +### NumPy 1.19.0 release -_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. -### Aceptación a Season of Docs +### Season of Docs acceptance -_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### Lanzamiento de NumPy 1.18.0 +### NumPy 1.18.0 release -_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg +### NumPy receives a grant from the Chan Zuckerberg Initiative -_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. -Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. -Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. -## Lanzamientos +## Releases -Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. -- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. -- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. -- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. -- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. -- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. -- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. -- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. -- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. -- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. -- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. -- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. -- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. -- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. -- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. -- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. -- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. -- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. -- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. -- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. -- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. -- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. -- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. -- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. -- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. -- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. -- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. -- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. -- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. -- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. -- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. -- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. -- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. -- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. -- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. -- . -- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. -- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From 667d21818dd607f90e549da39fdcfdc98d6b048c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 02:25:28 +0200 Subject: [PATCH 534/586] New translations news.md (Japanese) --- content/ja/news.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 401bf277c4..1e821a7539 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,39 +1,39 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0. がリリースされました!" +newsHeader: "NumPy 2.1 released!" date: 2023-09-16 --- -### NumPy 2.1.0 がリリースされました。 +### NumPy 2.1.0 released -_2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: -- Python 3.13 のサポート -- Python 3.13のフリースレッドサポートの事前準備 -- Array-api 2023.12 標準のサポート +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. -Python バージョン 3.10-3.13 か、このリリースでサポートされています。 +Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0 リリース日: 6月16日 -_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -262,11 +262,11 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. -- NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. -- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. -- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From d4d662786a4778433606ff08874086f4150d678e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 17:03:23 +0200 Subject: [PATCH 535/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 4e452c1c4c..2fbdd6b7ab 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -261,7 +261,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. -- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.0.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto de 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 8b9f737f6f54c2c6c1b8b5cc996126619301cbb3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Aug 2024 18:02:53 +0200 Subject: [PATCH 536/586] New translations gethelp.md (Portuguese, Brazilian) --- content/pt/gethelp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/gethelp.md b/content/pt/gethelp.md index f9f0e0322a..d9b02a60ff 100644 --- a/content/pt/gethelp.md +++ b/content/pt/gethelp.md @@ -5,7 +5,7 @@ sidebar: false **Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! +**Perguntas de usuários:** A melhor maneira de obter ajuda é postar sua pergunta em um site como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) ou [Reddit](https://www.reddit.com/r/Numpy/). Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From ed162c26d6c351615f58700a79284541f8d4f42f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 28 Aug 2024 01:34:29 +0200 Subject: [PATCH 537/586] New translations news.md (Spanish) --- content/es/news.md | 330 ++++++++++++++++++++++----------------------- 1 file changed, 165 insertions(+), 165 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index f4430b278b..1fe4f0a23f 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,8 +1,8 @@ --- -title: News -sidebar: false -newsHeader: "NumPy 2.1 released!" -date: 2024-08-18 +title: Noticias +sidebar: falso +newsHeader: "¡NumPy 2.0 ha sido lanzado!" +date: 2024-06-17 --- ### NumPy 2.1.0 released @@ -16,34 +16,34 @@ _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support Python versions 3.10-3.13 are supported by this release. -### NumPy 2.0.0 released +### Lanzamiento de NumPy 2.0.0 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. -### NumPy 2.0 release date: June 16 +### Fecha de lanzamiento de NumPy 2.0: 16 de junio -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. +### Recaudación de fondos de fin de año de NumFOCUS +_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  -### NumPy 1.26.0 released +### NumPy 1.26.0 ha sido lanzado -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: +_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -56,250 +56,250 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org is now available in Japanese and Portuguese +### numpy.org ya está disponible en japonés y portugués -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: -_Portuguese:_ -* Melissa Weber Mendonça (melissawm) -* Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portugués:_ +* Melissa Weber Mendonça (melissawm) +* Precios Ricardo (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japanese:_ +_Japonés:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -The work on the translation infrastructure is supported with funding from CZI. +El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. -Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) -### NumPy 1.25.0 released +### NumPy 1.25.0 ha sido lanzado -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: +_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Support for MUSL, there are now MUSL wheels. -* Support for the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum. -* Support for the inplace matrix multiplication (`@=`). +* Soporte para MUSL, ahora hay ruedas MUSL. +* Soporte para el compilador de Fujitsu C/C++. +* Las matrices de objetos ahora están soportadas en einsum. +* Soporte para la multiplicación de matrices in situ (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. +NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. -A total of 148 people contributed to this release and 530 pull requests were merged. +Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. -The Python versions supported by this release are 3.9-3.11. +Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. -### Fostering an Inclusive Culture: Call for Participation +### Fomentar una Cultura Inclusiva: Convocatoria de Participación -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación -How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). +¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). -### NumPy documentation team leadership transition +### Transición en el liderazgo del equipo de documentación de NumPy -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. +_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. -### NumPy 1.24.0 released +### Lanzamiento de NumPy 1.24.0 -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: +_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. +* Nuevas características y correcciones de F2PY. +* Muchas nuevas obsolescencias, revísalas. +* Muchas obsolescencias caducadas, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. +El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. -### Numpy 1.23.0 released +### NumPy 1.23.0 ha sido lanzado -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: +_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Implementation of `loadtxt` in C, greatly improving its performance. -* Exposure of DLPack at the Python level for easy data exchange. -* Changes to the promotion and comparisons of structured dtypes. -* Improvements to f2py. +* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. +* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. +* Cambios a la promoción y comparación de dtypes estructurados. +* Mejoras a f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. +El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. -### NumFOCUS DEI research study: call for participation +### Estudio de investigación NumFOCUS DEI: llamado a participar -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. +_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. -**Interested in sharing your experiences?** +**¿Estás interesado en compartir tus experiencias?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. +Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. -### Numpy 1.22.0 release +### Lanzamiento de NumPy 1.22.0 -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: +_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. -* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. -* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. -* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. -* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. -* A new configurable memory allocator for use by downstream projects. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. +* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). +* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. +* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. +* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. +NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. -### Advancing an inclusive culture in the scientific Python ecosystem +### Promoviendo una cultura inclusiva en el ecosistema científico de Python -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. +_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). +Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). -This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. +Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. -The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### 2021 NumPy survey +### Encuesta de NumPy de 2021 -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. +_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. -It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. +Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Numpy 1.21.0 release +### Lanzamiento de NumPy 1.21.0 -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: +_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: -- continued SIMD work covering more functions and platforms, -- initial work on the new dtype infrastructure and casting, -- universal2 wheels for Python 3.8 and Python 3.9 on Mac, -- improved documentation, -- improved annotations, -- new `PCG64DXSM` bitgenerator for random numbers. +- trabajo SIMD continuo que cubre más funciones y plataformas, +- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, +- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, +- documentación mejorada, +- anotaciones mejoradas, +- nuevo `PCG64DXSM` generador de bits para números aleatorios. -This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. +Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. -### 2020 NumPy survey results +### Resultados de la encuesta de NumPy de 2020 -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. +_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release +### Lanzamiento de NumPy 1.20.0 -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: +- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. +- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversity in the NumPy project +### Diversidad en el proyecto NumPy -_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). +_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). -### First official NumPy paper published in Nature! +### Primer artículo oficial de NumPy publicado en Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. +_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. -### Python 3.9 is coming, when will NumPy release binary wheels? +### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to -- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. +_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de +- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` +- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. -### Numpy 1.19.2 release +### Lanzamiento de NumPy 1.19.2 -_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. +_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. -### The inaugural NumPy survey is live! +### La encuesta inaugural de NumPy ya está disponible! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy has a new logo! +### ¡NumPy tiene un nuevo logo! -_Jun 24, 2020_ -- NumPy now has a new logo: +_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: -NumPy logo +Logo de NumPy -The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. +El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. -### NumPy 1.19.0 release +### Lanzamiento de NumPy 1.19.0 -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. +_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. -### Season of Docs acceptance +### Aceptación a Season of Docs -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 1.18.0 release +### Lanzamiento de NumPy 1.18.0 -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg -_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. +_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. -This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. -More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. +Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. -## Releases +## Lanzamientos -Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. -- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. -- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. -- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. -- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. -- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. -- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. -- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. -- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. -- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. -- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. -- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. -- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. -- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. -- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. -- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. -- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. -- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. -- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. -- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. -- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. -- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. -- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. -- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. -- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. -- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. -- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. -- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. -- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. -- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. -- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. -- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. -- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. +- NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. +- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. +- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. +- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. +- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. +- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. +- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. +- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. +- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. +- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. +- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. +- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. +- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. +- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. +- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. +- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. +- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. +- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. +- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. +- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. +- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. +- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. +- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. +- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. +- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. +- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. +- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. +- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. +- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. +- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. +- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. +- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. +- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. +- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. +- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. +- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. +- . +- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. +- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. From ca8bc2d013503b87228916d2610e79acaaad4d93 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 31 Aug 2024 02:47:01 +0200 Subject: [PATCH 538/586] New translations news.md (Japanese) --- content/ja/news.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 1e821a7539..37c9acc38c 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,39 +1,39 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.1 released!" +newsHeader: "NumPy 2.0. がリリースされました!" date: 2023-09-16 --- -### NumPy 2.1.0 released +### NumPy 2.1.0 がリリースされました。 -_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: +_2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 -- Support for Python 3.13. -- Preliminary support for free threaded Python 3.13. -- Support for the array-api 2023.12 standard. +- Python 3.13 のサポート +- Python 3.13のフリースレッドサポートの事前準備 +- Array-api 2023.12 標準のサポート -Python versions 3.10-3.13 are supported by this release. +Python バージョン 3.10-3.13 か、このリリースでサポートされています。 ### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -261,12 +261,12 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. -- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 2.0.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _2024年8月26日_. +- NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. +- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. +- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From 5203b5cbb3f1bead226d588b0afdaf7b3a0e0aec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:34 +0200 Subject: [PATCH 539/586] New translations news.md (Catalan) --- content/ca/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ca/news.md b/content/ca/news.md index a511b89bfc..2695eb8ba2 100644 --- a/content/ca/news.md +++ b/content/ca/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From aad447bfcc6cd94f22f76ec5d94cbf6c7cb1946d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:35 +0200 Subject: [PATCH 540/586] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index 1fe4f0a23f..0e810175a7 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -261,6 +261,7 @@ Puede encontrar más detalles sobre nuestras iniciativas y entregables propuesto Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. - NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. From f67b8354a56a542370608b347e346b4ec252c474 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:37 +0200 Subject: [PATCH 541/586] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index a511b89bfc..2695eb8ba2 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From 09814dd861c61f7b5474de04bfd969429a15b518 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:38 +0200 Subject: [PATCH 542/586] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 37c9acc38c..1592c65721 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -261,6 +261,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _2024年8月26日_. - NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. - NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. From a19d9a37b2d84e9e51cfda5b5439569d027ef3f6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:39 +0200 Subject: [PATCH 543/586] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index 18c5d21123..e8e64d3445 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -261,6 +261,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From 1370bf1d93ba13e7c9f44b8cab9c9601b93f8c48 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:40 +0200 Subject: [PATCH 544/586] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index a511b89bfc..2695eb8ba2 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From 57c8954675b97005f089e14ead5025152ddec98f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:42 +0200 Subject: [PATCH 545/586] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index a511b89bfc..2695eb8ba2 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From eb833caf360991b4e1aed8ae461afbc5d82a0bd8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:44 +0200 Subject: [PATCH 546/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index 2fbdd6b7ab..8da54e3f80 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -261,6 +261,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto de 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From 63b2110c4cfb4edfd9b4ca656409931cb77c5a54 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 18:02:45 +0200 Subject: [PATCH 547/586] New translations news.md (Persian) --- content/fa/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/fa/news.md b/content/fa/news.md index a511b89bfc..2695eb8ba2 100644 --- a/content/fa/news.md +++ b/content/fa/news.md @@ -261,6 +261,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. From 1a790e5511982c73a516e328f43e4dc531240c4a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 19:07:24 +0200 Subject: [PATCH 548/586] New translations news.md (Spanish) --- content/es/news.md | 330 ++++++++++++++++++++++----------------------- 1 file changed, 165 insertions(+), 165 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 0e810175a7..255ca3e026 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,8 +1,8 @@ --- -title: Noticias -sidebar: falso -newsHeader: "¡NumPy 2.0 ha sido lanzado!" -date: 2024-06-17 +title: News +sidebar: false +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- ### NumPy 2.1.0 released @@ -16,34 +16,34 @@ _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support Python versions 3.10-3.13 are supported by this release. -### Lanzamiento de NumPy 2.0.0 +### NumPy 2.0.0 released -_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -### Fecha de lanzamiento de NumPy 2.0: 16 de junio +### NumPy 2.0 release date: June 16 -_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### Recaudación de fondos de fin de año de NumFOCUS -_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  -### NumPy 1.26.0 ha sido lanzado +### NumPy 1.26.0 released -_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -56,251 +56,251 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org ya está disponible en japonés y portugués +### numpy.org is now available in Japanese and Portuguese -_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: -_Portugués:_ -* Melissa Weber Mendonça (melissawm) -* Precios Ricardo (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japonés:_ +_Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. +The work on the translation infrastructure is supported with funding from CZI. -De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) -### NumPy 1.25.0 ha sido lanzado +### NumPy 1.25.0 released -_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -* Soporte para MUSL, ahora hay ruedas MUSL. -* Soporte para el compilador de Fujitsu C/C++. -* Las matrices de objetos ahora están soportadas en einsum. -* Soporte para la multiplicación de matrices in situ (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. +A total of 148 people contributed to this release and 530 pull requests were merged. -Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. +The Python versions supported by this release are 3.9-3.11. -### Fomentar una Cultura Inclusiva: Convocatoria de Participación +### Fostering an Inclusive Culture: Call for Participation -_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). -### Transición en el liderazgo del equipo de documentación de NumPy +### NumPy documentation team leadership transition -_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. -### Lanzamiento de NumPy 1.24.0 +### NumPy 1.24.0 released -_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. -* Nuevas características y correcciones de F2PY. -* Muchas nuevas obsolescencias, revísalas. -* Muchas obsolescencias caducadas, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. -### NumPy 1.23.0 ha sido lanzado +### Numpy 1.23.0 released -_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. -* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. -* Cambios a la promoción y comparación de dtypes estructurados. -* Mejoras a f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. -### Estudio de investigación NumFOCUS DEI: llamado a participar +### NumFOCUS DEI research study: call for participation -_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. -**¿Estás interesado en compartir tus experiencias?** +**Interested in sharing your experiences?** -Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. -### Lanzamiento de NumPy 1.22.0 +### Numpy 1.22.0 release -_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. -* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. -* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). -* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. -* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. -* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. -NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. -### Promoviendo una cultura inclusiva en el ecosistema científico de Python +### Advancing an inclusive culture in the scientific Python ecosystem -_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. -Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). -Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. -Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### Encuesta de NumPy de 2021 +### 2021 NumPy survey -_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. -Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Lanzamiento de NumPy 1.21.0 +### Numpy 1.21.0 release -_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: -- trabajo SIMD continuo que cubre más funciones y plataformas, -- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, -- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, -- documentación mejorada, -- anotaciones mejoradas, -- nuevo `PCG64DXSM` generador de bits para números aleatorios. +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. -### Resultados de la encuesta de NumPy de 2020 +### 2020 NumPy survey results -_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Lanzamiento de NumPy 1.20.0 +### Numpy 1.20.0 release -_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: -- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. -- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversidad en el proyecto NumPy +### Diversity in the NumPy project -_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). -### Primer artículo oficial de NumPy publicado en Nature! +### First official NumPy paper published in Nature! -_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. -### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? +### Python 3.9 is coming, when will NumPy release binary wheels? -_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de -- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` -- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -### Lanzamiento de NumPy 1.19.2 +### Numpy 1.19.2 release -_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. -### La encuesta inaugural de NumPy ya está disponible! +### The inaugural NumPy survey is live! -_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. -Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### ¡NumPy tiene un nuevo logo! +### NumPy has a new logo! -_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: +_Jun 24, 2020_ -- NumPy now has a new logo: -Logo de NumPy +NumPy logo -El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -### Lanzamiento de NumPy 1.19.0 +### NumPy 1.19.0 release -_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. -### Aceptación a Season of Docs +### Season of Docs acceptance -_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### Lanzamiento de NumPy 1.18.0 +### NumPy 1.18.0 release -_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg +### NumPy receives a grant from the Chan Zuckerberg Initiative -_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. -Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. -Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. -## Lanzamientos +## Releases -Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. -- NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. -- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. -- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. -- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. -- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. -- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. -- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. -- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. -- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. -- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. -- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. -- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. -- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. -- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. -- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. -- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. -- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. -- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. -- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. -- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. -- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. -- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. -- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. -- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. -- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. -- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. -- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. -- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. -- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. -- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. -- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. -- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. -- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. -- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. -- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. -- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. -- . -- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. -- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. From d7e730b2a784b2406e0bc88bd19b8d723d4726ed Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 19:07:26 +0200 Subject: [PATCH 549/586] New translations news.md (Japanese) --- content/ja/news.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 1592c65721..4e1f22ab0e 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,39 +1,39 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0. がリリースされました!" +newsHeader: "NumPy 2.1 released!" date: 2023-09-16 --- -### NumPy 2.1.0 がリリースされました。 +### NumPy 2.1.0 released -_2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: -- Python 3.13 のサポート -- Python 3.13のフリースレッドサポートの事前準備 -- Array-api 2023.12 標準のサポート +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. -Python バージョン 3.10-3.13 か、このリリースでサポートされています。 +Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0 リリース日: 6月16日 -_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -262,12 +262,12 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. -- NumPy 2.0.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _2024年8月26日_. -- NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. -- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. -- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From ccd7c69bee598c6353d2ea92b4d217a355cc8b2c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 19:07:30 +0200 Subject: [PATCH 550/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 8da54e3f80..0a734e5ed0 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -262,7 +262,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. -- NumPy 2.0.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto de 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From 718a56a2bcf7a237975aa66223cbb4df1407d17e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 3 Sep 2024 19:10:12 +0200 Subject: [PATCH 551/586] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 0a734e5ed0..b3cf0dd539 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -261,8 +261,8 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. -- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. -- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 de setembro de 2024_. +- NumPy 2.0.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto de 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. From acc6e9f4874e8b91b6abaf8d99326f7bd5893db7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 01:29:18 +0200 Subject: [PATCH 552/586] New translations news.md (Japanese) --- content/ja/news.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 4e1f22ab0e..b25ddbc716 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,39 +1,39 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.1 released!" +newsHeader: "NumPy 2.0. がリリースされました!" date: 2023-09-16 --- -### NumPy 2.1.0 released +### NumPy 2.1.0 がリリースされました。 -_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: +_2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 -- Support for Python 3.13. -- Preliminary support for free threaded Python 3.13. -- Support for the array-api 2023.12 standard. +- Python 3.13 のサポート +- Python 3.13のフリースレッドサポートの事前準備 +- Array-api 2023.12 標準のサポート -Python versions 3.10-3.13 are supported by this release. +Python バージョン 3.10-3.13 か、このリリースでサポートされています。 ### NumPy 2.0 リリース日: 6月16日 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 ### NumPy 2.0 リリース日: 6月16日 _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 @@ -261,13 +261,13 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. -- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. -- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 2.1.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _2024年9月3日_. +- NumPy 2.0.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _2024年8月26日_. +- NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. +- NumPy 2.0.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _2024年7月21日_. +- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. From 4d8e7d44b02cd6752e3e7685fc7ed298b93695f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 05:02:39 +0200 Subject: [PATCH 553/586] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md index b473f9330f..967bc5e115 100644 --- a/content/es/arraycomputing.md +++ b/content/es/arraycomputing.md @@ -1,21 +1,21 @@ --- -title: Cómputo Matricial +title: Computación con Arreglos sidebar: false --- -*El cómputo matricial es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y aplicaciones de analíticas, como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática, el aprendizaje automático, la IA y entre otras.* +*La computación con arreglos es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y aplicaciones de analíticas, tales como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática, el aprendizaje automático, la inteligencia artificial, entre muchas otras.* -La manipulación y transformación de datos a gran escala depende de una computación matricial eficiente y de alto rendimiento. El lenguaje de elección para la analítica de datos, el aprendizaje automático y el cómputo numérico productivo es **Python.** +La manipulación y transformación de datos a gran escala depende de una computación con arreglos eficiente y de alto rendimiento. El lenguaje de elección para la analítica de datos, el aprendizaje automático y el cómputo numérico productivo es **Python.** -**Num**erical **Py**thon o NumPy es la biblioteca estándar de-facto del lenguaje de programación Python que soporta conjuntos y matrices multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos conjuntos. +**Num**erical **Py**thon o NumPy es la biblioteca estándar de-facto del lenguaje de programación Python que soporta arreglos y matrices multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos arreglos. -Tras el lanzamiento de NumPy en 2006, Pandas apareció en el panorama en 2008, y no fue hasta hace un par de años que aparecieron sucesivamente varias bibliotecas de cómputo matricial, poblando este escenario. Muchas de estas nuevas bibliotecas imitan las características y capacidades de NumPy, y contienen nuevos algoritmos y características orientadas a las aplicaciones de aprendizaje automático e inteligencia artificial. +Tras el lanzamiento de NumPy en 2006, Pandas apareció en el panorama en 2008, y no fue hasta hace un par de años que aparecieron sucesivamente varias bibliotecas de computación con arreglos, poblando este escenario. Muchas de estas nuevas bibliotecas imitan las características y capacidades de NumPy, y contienen nuevos algoritmos y características orientadas a las aplicaciones de aprendizaje automático e inteligencia artificial. arraycl + title="Panorama de la Computación con Arreglos" /> -El **cómputo matricial** está basado en los **conjuntos** como estructura de datos. *Los conjuntos* se utilizan para organizar grandes cantidades de datos de manera que un conjunto de valores relacionados pueda ordenarse, buscarse, manipularse matemáticamente y transformarse con facilidad y rapidez. +La **computación con arreglos** está basada en los **arreglos** como estructura de datos. *Los arreglos* son utilizados para organizar grandes cantidades de datos de manera que un conjunto de valores relacionados pueda ordenarse, buscarse, manipularse matemáticamente y transformarse con facilidad y rapidez. -La computación matricial es *única* ya que implica operar sobre todo el conjunto de datos *al mismo tiempo*. Esto significa que cualquier operación de conjuntos se aplica a un conjunto completo de valores de una sola vez. Este enfoque vectorial proporciona velocidad y simplicidad, al permitir a los programadores codificar y trabajar sobre los datos agregados, sin tener que utilizar bucles de instrucciones escalares individuales. +La computación con arreglos es *única* ya que implica operar sobre todos los datos del arreglo *al mismo tiempo*. Esto significa que cualquier operación de arreglos se aplica a un conjunto completo de valores de una sola vez. Este enfoque vectorial proporciona velocidad y simplicidad, al permitir a los programadores codificar y operar sobre los datos agregados, sin tener que utilizar bucles de instrucciones escalares individuales. From 6f4f2ed5b0442b47b470b3d8592fe35d88c02d4b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 05:02:41 +0200 Subject: [PATCH 554/586] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 7255790792..9ba1ad4b92 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -5,13 +5,13 @@ sidebar: falso Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Programación de Arreglos con NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _ Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). _En formato BibTeX:_ ``` @Article{ harris2020array, - title = {Programación de arreglos con {NumPy}}, + title = { Array programming with {NumPy}}, author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian From 19415c8c644f613eed179e3ea96faada54c88cc3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 05:02:42 +0200 Subject: [PATCH 555/586] New translations about.md (Spanish) --- content/es/about.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 7d220d203d..e35f788c3d 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -3,14 +3,14 @@ title: Quiénes Somos sidebar: false --- -NumPy es un proyecto de código abierto cuyo objetivo es permitir la computación numérica en Python. Se creó en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será software 100% código abierto, de uso libre para todos. Fue liberado bajo los términos liberales de la [licencia BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy es un proyecto de código abierto cuyo objetivo es permitir la computación numérica en Python. Fue creado en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será un software 100% de código abierto y de uso libre para todos. Fue liberado bajo los términos liberales de la [licencia BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). -NumPy se desarrolla de forma abierta en GitHub, mediante el consenso de las comunidades NumPy y científica de Python en general. Para más información sobre nuestro enfoque de gobernanza, consulte nuestro [Documento de Gobernanza](https://www.numpy.org/devdocs/dev/governance/index.html). +NumPy es desarrollado de forma abierta en GitHub, mediante el consenso de las comunidades NumPy y Python científico en general. Para más información sobre nuestro enfoque de gobernanza, por favor consulta nuestro [Documento de Gobernanza](https://www.numpy.org/devdocs/dev/governance/index.html). ## Consejo Directivo -El Consejo de Dirección de NumPy es el órgano de gobernanza del proyecto. Su papel es el garantizar, a través del trabajo con la comunidad NumPy en general y al servicio de la misma, el bienestar a largo plazo del proyecto, tanto desde el punto de vista técnico como de la comunidad. El Consejo Directivo de NumPy está formado actualmente por los siguientes miembros (en orden alfabético): +El Consejo de Dirección de NumPy es el órgano de gobernanza del proyecto. Su papel es garantizar, a través del trabajo con la comunidad NumPy en general y al servicio de la misma, el bienestar a largo plazo del proyecto, tanto desde el punto de vista técnico como de la comunidad. El Consejo Directivo de NumPy está formado actualmente por los siguientes miembros (en orden alfabético): - Sebastian Berg - Ralf Gommers @@ -49,13 +49,13 @@ La dirección del proyecto NumPy trabaja activamente para diversificar las vías - optimización - financiación y subvenciones -Visita la página de [Equipos]({{< relref "/teams" >}}) para más información. +Visita la página de [Equipos](/teams) para más información. ## Subcomité NumFOCUS - Charles Harris - Ralf Gommers -- Melissa Weber Mendonça +- Inessa Pawson - Sebastian Berg - Miembro externo: Thomas Caswell From 818d16d915651c791f23faa94ff9fa7c6889098d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 15:56:37 +0200 Subject: [PATCH 556/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index b6073d2ad4..bed87302d2 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -1,13 +1,13 @@ --- title: Código de conducta de NumPy -sidebar: falso +sidebar: false aliases: - /conduct.html --- ### Introducción -Este Código de Conducta aplica en todos los espacios gestionados por el proyecto NumPy, incluyendo todas las listas de correo públicas y privadas, gestores de incidencias, wikis, blogs, Twitter y cualquier otro canal de comunicación utilizado por nuestra comunidad. El proyecto NumPy no organiza eventos en persona; sin embargo, los eventos relacionados con nuestra comunidad deben tener un código de conducta similar a este. +Este Código de Conducta aplica en todos los espacios gestionados por el proyecto NumPy, incluyendo todas las listas de correo públicas y privadas, gestores de incidencias, wikis, blogs, X (antes llamado Twitter) y cualquier otro canal de comunicación utilizado por nuestra comunidad. El proyecto NumPy no organiza eventos en persona; sin embargo, los eventos relacionados con nuestra comunidad deben tener un código de conducta similar a este. Este Código de Conducta debe ser respetado por todos los que participan en la comunidad NumPy formalmente o informalmente, o reclaman cualquier afiliación con el proyecto, en cualquier actividad relacionada con el proyecto y especialmente cuando representan el proyecto de cualquier manera. @@ -15,10 +15,10 @@ Este código no es exhaustivo ni completo. Sirve para sintetizar nuestro entendi ### Directrices Específicas -Cantera +Nos esforzamos por: 1. Ser abiertos. Invitamos a todas las personas a participar en nuestra comunidad. Preferimos utilizar métodos de comunicación públicos para los mensajes relacionados con el proyecto, a menos que se trate de algo delicado. Esto se aplica también a los mensajes de ayuda o soporte relacionados con el proyecto; no sólo es mucho más probable que una solicitud de soporte pública dé lugar a una respuesta a una pregunta, sino que también garantiza que cualquier error involuntario en la respuesta se detecte y corrija más fácilmente. -2. Ser empáticos, receptivos, amables y pacientes. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. +2. Ser empáticos, cordiales, amables y pacientes. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. 3. Ser colaborativos. Nuestro trabajo será utilizado por otras personas, y a su vez dependeremos del trabajo de otros. Cuando hacemos algo en beneficio del proyecto, estamos dispuestos a explicar a otros cómo funciona, de manera que puedan construir sobre este trabajo para hacerlo aún mejor. Cualquier decisión que tomemos afectará a usuarios y colegas, y nos tomamos en serio esas consecuencias a la hora de tomar decisiones. 4. Ser curiosos. ¡Nadie lo sabe todo! Hacer preguntas tempranas evita muchos problemas posteriores, por lo que fomentamos las preguntas, aunque las podamos redirigir al foro adecuado. Nos esforzaremos por ser receptivos y útiles. 5. Ser cuidadosos con las palabras que elegimos. Somos cuidadosos y respetuosos en nuestra comunicación, y asumimos la responsabilidad del lenguaje que utilizamos. Ser amables con los demás. No insultes ni menosprecies a los demás participantes. No aceptaremos el acoso ni otros comportamientos excluyentes, tales como: From 03faba4ff5b9fc9f689494b1e3710e5f5610dc34 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 16:52:59 +0200 Subject: [PATCH 557/586] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index bed87302d2..7245fe918a 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -47,7 +47,7 @@ Los estándares de comportamiento en la comunidad NumPy se detallan en el Códig Sabemos que es dolorosamente común que la comunicación en Internet comience o se convierta en un abuso evidente y manifiesto. También reconocemos que a veces la gente puede tener un mal día, o no ser consciente de algunas de las directrices de este Código de Conducta. Por favor, tenga esto en cuenta a la hora de decidir cómo responder a una violación de este Código. -En caso de infracciones claramente intencionadas, informe de éstas al Comité de Código de Conducta (ver más abajo). En caso de infracciones posiblemente involuntarias, puede responder a la persona y señalar este código de conducta (tanto en público como en privado, lo que sea más apropiado). Si prefiere no hacerlo, puede informar directamente al Comité de Código de Conducta o pedirle consejo a éste de forma confidencial. +En caso de infracciones claramente intencionadas, informe de éstas al Comité de Código de Conducta (ver más abajo). En caso de infracciones posiblemente involuntarias, puede responder a la persona y señalar este código de conducta (tanto en público como en privado, lo que sea más apropiado). Si prefiere no hacerlo, por favor siéntase libre de informar directamente al Comité de Código de Conducta o de pedirle consejo a éste de forma confidencial. Puede informar de los problemas al Comité de Código de Conducta de NumPy en numpy-conduct@googlegroups.com. @@ -57,7 +57,7 @@ Actualmente, el Comité está compuesto por: * Melissa Weber Mendonça * Rohit Goswami -Si tu informe implica a algún miembro del Comité, o si éste considera que tiene un conflicto de intereses en su tramitación, se abstendrán de examinar tu denuncia. Alternativamente, si por cualquier razón usted se siente incómodo haciendo un informe al Comité, también puede ponerse en contacto con el personal de NumFOCUS senior en [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). +Si tu informe implica a algún miembro del Comité, o si éste considera que tiene un conflicto de intereses en su tramitación, se abstendrán de examinar tu denuncia. Alternativamente, si por cualquier razón usted se siente incómodo haciendo un informe al Comité, también puede ponerse en contacto con el personal senior de NumFOCUS en [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). ### Resolución de reporte de informes & aplicación del Código de Conducta From 4b1739268d9391a8198349b42d70cdf9bea6b73e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 16:53:00 +0200 Subject: [PATCH 558/586] New translations community.md (Spanish) --- content/es/community.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index f5918abd76..f96474c060 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -15,7 +15,7 @@ Las siguientes son formas de relacionarse directamente con el proyecto y la comu ### [Lista de correo de NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) -Este es el foro principal para discusiones más extensas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones sobre el proyecto. Aquí también se hacen los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o conferencias. +Este es el foro principal para discusiones más extensas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones sobre el proyecto. Aquí también se realizan los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o charlas en conferencias. En esta lista, por favor, utiliza el botón de envío inferior, responde a la lista (en lugar de a otro remitente) y no respondas a los resúmenes. El archivo de consulta de esta lista está disponible [aquí](https://mail.python.org/archives/list/numpy-discussion@python.org/). @@ -40,7 +40,7 @@ Una sala de chat en tiempo real para hacer preguntas sobre las _contribuciones_ Si desea encontrar un grupo de estudio o reunión local para aprender más sobre NumPy y el ecosistema más amplio de paquetes de Python para ciencia de datos y computación científica, te recomendamos que explores los [PyData meetups](https://www.meetup.com/pro/pydata/) (más de 150 reuniones, más de 100,000 miembros). -NumPy también organiza ocasionalmente sprints presenciales para su equipo y colaboradores interesados. Estos normalmente se planifican con varios meses de anticipación y se anunciarán en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion) y en [Twitter](https://twitter.com/numpy_team). +NumPy también organiza ocasionalmente sprints presenciales para su equipo y colaboradores interesados. Estos normalmente se planifican con varios meses de anticipación y se anunciarán en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion) y en [X (antes conocido como Twitter)](https://twitter.com/numpy_team). ## Conferencias @@ -61,6 +61,6 @@ Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy, e Para prosperar, el proyecto NumPy necesita tu experiencia y entusiasmo. ¿No sabes programar? ¡No es un problema! Hay muchas maneras de contribuir a NumPy. -Si te interesa colaborar en NumPy (¡yupi!) te recomendamos que visites nuestra página [Contribuir](/contribute). +Si te interesa colaborar en NumPy (¡hurra!) te recomendamos que visites nuestra página [Contribuir](/contribute). No dudes en pasar a saludarnos en uno de nuestros encuentros de la comunidad. Para enterarte del próximo, consulta nuestro calendario de eventos [aquí](https://scientific-python.org/calendars/). From 849f00f59a707d86dbb13881dc3acf401ce583a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 4 Sep 2024 16:53:02 +0200 Subject: [PATCH 559/586] New translations config.yaml (Spanish) --- content/es/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 83a8745b16..d937607b49 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -1,6 +1,6 @@ languageName: Inglés params: - description: '¿Por qué NumPy? Potentes matrices n-dimensionales. Herramientas de cálculo numérico. Interoperable. Rendimiento. Código abierto.' + description: '¿Por qué NumPy? Potentes arreglos n-dimensionales. Herramientas de cálculo numérico. Interoperabilidad. Rendimiento. Código abierto.' navbarlogo: image: logo.svg text: NumPy @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: El paquete fundamental para la computación científica con Python #Button text - buttontext: "Última versión: NumPy 1.25. Ver todas las versiones" + buttontext: "Última versión: NumPy 2.0. Ver todas las versiones" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) @@ -67,7 +67,7 @@ params: title: Comunidad url: /community - - title: Acerca de nosotros + title: Quiénes somos url: /about - title: Noticias From 4605eb1a46ffe4863a1d5fe03a49580924ebc192 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 5 Sep 2024 04:20:31 +0200 Subject: [PATCH 560/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 9ef362edfc..e6c877df9e 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -7,7 +7,7 @@ sidebar: false Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [propuesta](https://github.com/numpy/numpy/issues) o comenta en una relevante). -Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la communidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). +Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la comunidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). También hacemos _llamadas a la comunidad_ de manera quincenal, cuyos detalles se anuncian en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion). Te invitamos a unirte. Si es la primera vez que contribuyes al código abierto, también te recomendamos encarecidamente que leas [esta guía](https://opensource.guide/how-to-contribute/). @@ -20,7 +20,7 @@ Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development ### Revisando solicitudes de cambios El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: * resumir un debate extenso -* categorizar PRs de documentación +* categorizar documentación de solicitudes de incorporación de cambios * probar los cambios propuestos ### Creando material educativo @@ -52,7 +52,7 @@ Planeamos múltiples traducciones de [numpy.org](https://numpy.org) para hacer a ### Coordinación y divulgación de la comunidad -A través del contacto con la comunidad compartimos nuestro trabajo más ampliamente, y aprendemos en dónde nos estamos quedando cortos. Estamos ansiosos por conseguir más gente involucrada en esfuerzos como nuestra cuenta de [Twitter](https://twitter.com/numpy_team), organizando [code sprints](https://scisprints.github.io/) de NumPy, un boletín y quizás un blog. +A través del contacto con la comunidad compartimos nuestro trabajo más ampliamente, y aprendemos en dónde nos estamos quedando cortos. Estamos ansiosos por conseguir más gente involucrada en esfuerzos como nuestra cuenta de [X (antes llamado Twitter)](https://twitter.com/numpy_team), organizando [code sprints](https://scisprints.github.io/) de NumPy, un boletín y quizás un blog. ### Recaudación de fondos From 6acb96e0d3ecc71a4c290a1c872bf7c24631dcd5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 5 Sep 2024 05:21:31 +0200 Subject: [PATCH 561/586] New translations learn.md (Spanish) --- content/es/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/learn.md b/content/es/learn.md index c0d1605eb6..2abe55fd7b 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -18,7 +18,7 @@ Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recom * [Tutorial NumPy de inicio rápido](https://numpy.org/devdocs/user/quickstart.html) * [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: La Guía Visual de NumPy *por Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [Lecturas de SciPy](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. +* [Lecturas de Python Científico](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. * [NumPy: lo absolutamente básico para principiantes](https://numpy.org/devdocs/user/absolute_beginners.html) * [Tutorial de NumPy *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *por Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) From 2115e16c64568871e2201f1704368e34e5a0bde5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 5 Sep 2024 05:21:32 +0200 Subject: [PATCH 562/586] New translations history.md (Spanish) --- content/es/history.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/history.md b/content/es/history.md index 5456c4cf8f..fd4000b630 100644 --- a/content/es/history.md +++ b/content/es/history.md @@ -3,7 +3,7 @@ title: Historia de NumPy sidebar: falso --- -NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de matrices y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la biblioteca contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en informática y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amistosa con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados en el análisis de ondas gravitacionales utilizan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. +NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de arreglos y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la biblioteca contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en ciencias de la computación y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amigable con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados usados en el análisis de ondas gravitacionales importan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. Para conocer en profundidad los hitos en el desarrollo de NumPy y las bibliotecas relacionadas, consulta [arxiv.org](arxiv.org/abs/1907.10121). @@ -13,7 +13,7 @@ Si deseas obtener una copia de las bibliotecas originales Numeric y Numarray, si [Página de Descarga de *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -*Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con matrices o que refactoricen cualquier código preexistente para utilizar la biblioteca NumPy. +*Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con arreglos o que refactoricen cualquier código preexistente para utilizar la biblioteca NumPy. ### Documentación Histórica From 3af4c1eb6b81d17833b96be201aa8927c73d7c65 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 5 Sep 2024 05:21:34 +0200 Subject: [PATCH 563/586] New translations install.md (Spanish) --- content/es/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/install.md b/content/es/install.md index bc1f2eeaa9..2c709bd013 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -97,7 +97,7 @@ En la medida en que las librerías son actualizadas, los resultados al correr su NumPy no depende de ningún otro paquete de Python; sin embargo, sí depende de una librería de álgebra lineal acelerada - típicamente [Intel MKL](https://software.intel.com/en-us/mkl) u [OpenBLAS](https://www.openblas.net/). Los usuarios no tienen que preocuparse por instalar éstas (se incluyen automáticamente en todos los métodos de instalación de NumPy). Los usuarios avanzados podrían querer, de todas maneras, conocer los detalles, ya que la utilización BLAS puede afectar el desempeño, comportamiento y tamaño en disco: -- Las ruedas NumPy en PyPI, que son las que pip instala, están construidas con OpenBLAS. Las librerías de OpenBLAS están incluidas en la rueda. Esto vuelve a la rueda más grande, y si un usuario instala (por ejemplo) SciPy también, tendrá dos copias de OpenBLAS en disco. +- Las ruedas NumPy en PyPI, que son los que pip instala, están construidas con OpenBLAS. Las librerías de OpenBLAS están incluidas en la rueda. Esto vuelve a la rueda más grande, y si un usuario instala (por ejemplo) SciPy también, tendrá dos copias de OpenBLAS en disco. - En el canal defaults o predeterminado de conda, NumPy está basado en Intel MKL. MKL es un paquete separado que se instalará en el entorno de usuario al instalar NumPy. From 8a03e0c02c016c98d2df779e28de358b7e5f3df2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Sep 2024 03:55:02 +0200 Subject: [PATCH 564/586] New translations news.md (Spanish) --- content/es/news.md | 328 ++++++++++++++++++++++----------------------- 1 file changed, 164 insertions(+), 164 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 255ca3e026..875751d3cd 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,8 +1,8 @@ --- -title: News +title: Noticias sidebar: false -newsHeader: "NumPy 2.1 released!" -date: 2024-08-18 +newsHeader: "¡NumPy 2.0 ha sido lanzado!" +date: 2024-06-17 --- ### NumPy 2.1.0 released @@ -16,34 +16,34 @@ _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support Python versions 3.10-3.13 are supported by this release. -### NumPy 2.0.0 released +### Lanzamiento de NumPy 2.0.0 -_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: +_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. +La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. -### NumPy 2.0 release date: June 16 +### Fecha de lanzamiento de NumPy 2.0: 16 de junio -_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** +_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** -- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### NumFOCUS end of the year fundraiser -_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. +### Recaudación de fondos de fin de año de NumFOCUS +_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  -### NumPy 1.26.0 released +### NumPy 1.26.0 ha sido lanzado -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: +_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. @@ -56,251 +56,251 @@ La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org is now available in Japanese and Portuguese +### numpy.org ya está disponible en japonés y portugués -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: -_Portuguese:_ -* Melissa Weber Mendonça (melissawm) -* Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portugués:_ +* Melissa Weber Mendonça (melissawm) +* Precios Ricardo (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japanese:_ +_Japonés:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -The work on the translation infrastructure is supported with funding from CZI. +El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. -Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) +De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) -### NumPy 1.25.0 released +### NumPy 1.25.0 ha sido lanzado -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: +_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Support for MUSL, there are now MUSL wheels. -* Support for the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum. -* Support for the inplace matrix multiplication (`@=`). +* Soporte para MUSL, ahora hay ruedas MUSL. +* Soporte para el compilador de Fujitsu C/C++. +* Las matrices de objetos ahora están soportadas en einsum. +* Soporte para la multiplicación de matrices in situ (`@=`). -The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. +NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. -A total of 148 people contributed to this release and 530 pull requests were merged. +Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. -The Python versions supported by this release are 3.9-3.11. +Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. -### Fostering an Inclusive Culture: Call for Participation +### Fomentar una Cultura Inclusiva: Convocatoria de Participación -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación -How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). +¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). -### NumPy documentation team leadership transition +### Transición en el liderazgo del equipo de documentación de NumPy -_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. +_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. -### NumPy 1.24.0 released +### Lanzamiento de NumPy 1.24.0 -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: +_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. +* Nuevas características y correcciones de F2PY. +* Muchas nuevas obsolescencias, revísalas. +* Muchas obsolescencias caducadas, -The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. +El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. -### Numpy 1.23.0 released +### NumPy 1.23.0 ha sido lanzado -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: +_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Implementation of `loadtxt` in C, greatly improving its performance. -* Exposure of DLPack at the Python level for easy data exchange. -* Changes to the promotion and comparisons of structured dtypes. -* Improvements to f2py. +* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. +* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. +* Cambios a la promoción y comparación de dtypes estructurados. +* Mejoras a f2py. -The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. +El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. -### NumFOCUS DEI research study: call for participation +### Estudio de investigación NumFOCUS DEI: llamado a participar -_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. +_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. -**Interested in sharing your experiences?** +**¿Estás interesado en compartir tus experiencias?** -Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. +Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. -### Numpy 1.22.0 release +### Lanzamiento de NumPy 1.22.0 -_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: +_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. -* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. -* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. -* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. -* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. -* A new configurable memory allocator for use by downstream projects. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. +* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). +* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. +* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. +* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. +NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. -### Advancing an inclusive culture in the scientific Python ecosystem +### Promoviendo una cultura inclusiva en el ecosistema científico de Python -_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. +_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. -As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). +Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). -This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. +Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. -The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### 2021 NumPy survey +### Encuesta de NumPy de 2021 -_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. +_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. -It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. +Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Numpy 1.21.0 release +### Lanzamiento de NumPy 1.21.0 -_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: +_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: -- continued SIMD work covering more functions and platforms, -- initial work on the new dtype infrastructure and casting, -- universal2 wheels for Python 3.8 and Python 3.9 on Mac, -- improved documentation, -- improved annotations, -- new `PCG64DXSM` bitgenerator for random numbers. +- trabajo SIMD continuo que cubre más funciones y plataformas, +- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, +- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, +- documentación mejorada, +- anotaciones mejoradas, +- nuevo `PCG64DXSM` generador de bits para números aleatorios. -This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. +Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. -### 2020 NumPy survey results +### Resultados de la encuesta de NumPy de 2020 -_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. +_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release +### Lanzamiento de NumPy 1.20.0 -_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: +- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. +- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversity in the NumPy project +### Diversidad en el proyecto NumPy -_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). +_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). -### First official NumPy paper published in Nature! +### Primer artículo oficial de NumPy publicado en Nature! -_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. +_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. -### Python 3.9 is coming, when will NumPy release binary wheels? +### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? -_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to -- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` -- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. +_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de +- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` +- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. -### Numpy 1.19.2 release +### Lanzamiento de NumPy 1.19.2 -_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. +_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. -### The inaugural NumPy survey is live! +### La encuesta inaugural de NumPy ya está disponible! -_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. +_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy has a new logo! +### ¡NumPy tiene un nuevo logo! -_Jun 24, 2020_ -- NumPy now has a new logo: +_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: -NumPy logo +Logo de NumPy -The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. +El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. -### NumPy 1.19.0 release +### Lanzamiento de NumPy 1.19.0 -_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. +_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. -### Season of Docs acceptance +### Aceptación a Season of Docs -_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 1.18.0 release +### Lanzamiento de NumPy 1.18.0 -_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. +_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg -_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. +_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. -This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. +Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. -More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. +Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. -## Releases +## Lanzamientos -Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. -- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. -- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. -- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. -- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. -- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. -- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. -- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. -- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. -- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. -- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. -- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. -- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. -- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. -- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. -- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. -- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. -- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. -- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. -- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. -- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. -- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. -- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. -- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. -- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. -- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. -- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. -- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. -- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. -- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. -- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. -- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. -- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. -- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. -- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_. +- NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. +- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. +- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. +- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. +- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. +- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. +- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. +- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. +- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. +- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. +- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. +- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. +- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. +- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. +- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. +- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. +- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. +- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. +- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. +- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. +- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. +- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. +- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. +- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. +- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. +- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. +- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. +- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. +- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. +- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. +- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. +- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. +- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. +- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. +- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. +- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. +- . +- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. +- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. From 0771716ddefc108a37222668d60b0807046fe684 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Sep 2024 04:52:42 +0200 Subject: [PATCH 565/586] New translations news.md (Spanish) --- content/es/news.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 875751d3cd..e631741f0c 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -18,28 +18,28 @@ Python versions 3.10-3.13 are supported by this release. ### Lanzamiento de NumPy 2.0.0 -_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características emocionantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían ocurrir en un lanzamiento menor regular, incluyendo una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que pueden no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: +_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características interesantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían producirse en un lanzamiento menor regular, como una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que podrían no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: - La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Problema de anuncio para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. ### Fecha de lanzamiento de NumPy 2.0: 16 de junio -_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puede, por favor verifique si su código funciona con NumPy `2.0.0rc2`. **Por favor, vea lo siguiente para más detalles:** +_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puedes, por favor verifica que tu código funciona con NumPy `2.0.0rc2`. **Por favor, revisa lo siguiente para más detalles:** - La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Anuncio de actualización de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### Recaudación de fondos de fin de año de NumFOCUS _19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. -Utilice una URL única que permita rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  +Utiliza una URL única que te permitirá rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  ### NumPy 1.26.0 ha sido lanzado @@ -86,7 +86,7 @@ _17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.2 * Soporte para MUSL, ahora hay ruedas MUSL. * Soporte para el compilador de Fujitsu C/C++. -* Las matrices de objetos ahora están soportadas en einsum. +* Los arreglos de objetos ahora están soportadas en einsum. * Soporte para la multiplicación de matrices in situ (`@=`). NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. @@ -133,13 +133,13 @@ _13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.o **¿Estás interesado en compartir tus experiencias?** -Por favor, complete este breve [formulari o de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Su participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. +Por favor, completa este breve [formulario de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Tu participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. ### Lanzamiento de NumPy 1.22.0 _31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. El repositorio principal o upstream es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el trabajo principal ya está hecho. +* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. Esta es probablemente la mejora más visible para el usuario en esta versión. * Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. * NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). * Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. @@ -154,9 +154,9 @@ _31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerber Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). -Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una imagen más clara tanto de lo que sí y como de lo que no funciona en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. +Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una idea más clara tanto de lo que funciona y lo que no en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. -Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puede leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puedes leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### Encuesta de NumPy de 2021 From 0d5dac8c8ecec74de0ad62cca6af55b79309a7d1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Sep 2024 05:53:00 +0200 Subject: [PATCH 566/586] New translations news.md (Spanish) --- content/es/news.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index e631741f0c..5b0fd5615a 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -5,15 +5,15 @@ newsHeader: "¡NumPy 2.0 ha sido lanzado!" date: 2024-06-17 --- -### NumPy 2.1.0 released +### Lanzamiento de NumPy 2.1.0 -_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: +_18 de agosto 2024_ -- NumPy 2.1.0 proporciona soporte para Python 3.13 y abandona el soporte para Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son: -- Support for Python 3.13. -- Preliminary support for free threaded Python 3.13. -- Support for the array-api 2023.12 standard. +- Soporte para Python 3.13. +- Soporte preliminar para Python 3.13 de hilos libres. +- Compatibilidad con la norma array-api 2023.12. -Python versions 3.10-3.13 are supported by this release. +Esta versión es compatible con las versiones 3.10-3.13 de Python. ### Lanzamiento de NumPy 2.0.0 @@ -173,7 +173,7 @@ _23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.2 - trabajo SIMD continuo que cubre más funciones y plataformas, - trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, -- ruedas universal2 para Python 3.8 y Python 3.9 en Mac, +- universal2 wheels para Python 3.8 y Python 3.9 en Mac, - documentación mejorada, - anotaciones mejoradas, - nuevo `PCG64DXSM` generador de bits para números aleatorios. @@ -188,7 +188,7 @@ _22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociació ### Lanzamiento de NumPy 1.20.0 -_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más emocionantes son: +_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más importantes son: - Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. - Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). @@ -261,8 +261,8 @@ Puede encontrar más detalles sobre nuestras iniciativas y entregables propuesto Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. -- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. -- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.1 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 de septiembre 2024_. +- NumPy 2.0.2 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto 2024_. - NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. - NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. - NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. @@ -301,6 +301,6 @@ Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los - NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. - NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. - NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. -- . +- NumPy 1.16.0 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. - NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. From b9815a6a7a31ed40f1424ab524a3cc8c7ad6a72c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Sep 2024 05:53:03 +0200 Subject: [PATCH 567/586] New translations privacy.md (Spanish) --- content/es/privacy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/privacy.md b/content/es/privacy.md index 4b62b24be2..ab27618f84 100644 --- a/content/es/privacy.md +++ b/content/es/privacy.md @@ -5,4 +5,4 @@ sidebar: false **numpy.org** está operado por [NumFOCUS, Inc.](https://numfocus.org), el patrocinador fiscal del proyecto NumPy. Para ver la Política de Privacidad de este sitio web, por favor dirígete a https://numfocus.org/privacy-policy. -Si tienes alguna pregunta sobre la política o la recolección, uso y divulgación de datos de NumFOCUS, por favor ponte en contacto con el personal de NumFOCUS en privacy@numfocus.org. +Si tienes alguna pregunta sobre la política o la recolección, uso y prácticas de divulgación de datos de NumFOCUS, por favor ponte en contacto con el personal de NumFOCUS en privacy@numfocus.org. From bc60b902b882e8bb9bf89fba1e00cb0ebeb36139 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Sep 2024 15:38:03 +0200 Subject: [PATCH 568/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 0dba4922c5..d608565b42 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -2,27 +2,27 @@ params: machinelearning: paras: - - para1: NumPy constituye la base de potentes bibliotecas de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series temporales y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión por ordenador y procesamiento del lenguaje natural. [MXNet](https://github.com/apache/incubator-mxnet) es otro paquete de IA que proporciona modelos y plantillas para el aprendizaje profundo. - para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. + para1: NumPy constituye la base de potentes bibliotecas de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. [MXNet](https://github.com/apache/incubator-mxnet) es otro paquete de IA que proporciona modelos y plantillas para el aprendizaje profundo. + para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. arraylibraries: intro: - - text: La API de NumPy es el punto de partida cuando se escriben bibliotecas para explotar hardware innovador, crear tipos de matrices especializadas o añadir capacidades más allá de lo que NumPy proporciona. + text: La API de NumPy es el punto de partida cuando se escriben bibliotecas para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. headers: - - text: Biblioteca de matrices + text: Biblioteca de arreglos - text: Capacidades y áreas de aplicación libraries: - title: Dask - text: Matrices distribuidas y paralelismo avanzado para análisis, que permiten un rendimiento a escala. + text: Arreglos distribuidos y paralelismo avanzado para análisis, que permiten un rendimiento a escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy - text: Biblioteca de matrices compatible con NumPy para cálculo acelerado en la GPU con Python. + text: Biblioteca de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org @@ -34,13 +34,13 @@ params: url: https://github.com/google/jax - title: Xarray - text: Matrices multidimensionales indexadas y etiquetadas para análisis y visualización avanzados. + text: Arreglos multidimensionales indexados y etiquetados para análisis y visualización avanzados. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse - text: Biblioteca de matrices dispersas compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. + text: Biblioteca de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ @@ -64,7 +64,7 @@ params: url: https://mxnet.apache.org/ - title: xtensor - text: Plataforma de desarrollo multilenguaje para datos y análisis columnares en memoria. + text: Matrices multidimensionales con difusión y computación perezosa para análisis numérico. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python @@ -91,7 +91,7 @@ params: - text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. - - text: "NumPy lleva la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." + text: "NumPy aporta la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." libraries: - title: Computación Cuántica From 90a15e2413271032959bad938b4a99981f8e4c61 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Sep 2024 16:37:35 +0200 Subject: [PATCH 569/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index d608565b42..b30afc410e 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -160,7 +160,7 @@ params: label: Mahotas - title: Grafos y Redes - alttext: Un simple grafo. + alttext: Un grafo simple. img: /images/content_images/sc_dom_img/sd6.svg links: - @@ -319,7 +319,7 @@ params: image2: - img: /images/content_images/data-science.png - alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Informática" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". + alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Ciencias de la Computación" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". examples: - text: "Extraer, Transformar, Cargar: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" @@ -328,7 +328,7 @@ params: - text: "Modelado y evaluación: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Informes en paneles: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "Informes en un panel de control: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)). @@ -370,4 +370,4 @@ params: - text: NumPy es un componente esencial en el floreciente [panorama de visualización de Python](https://pyviz.org/overviews/index.html), que incluye [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), y [PyVista](https://github.com/pyvista/pyvista), por nombrar algunos. - - text: El procesamiento acelerado de matrices de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. + text: El procesamiento acelerado de arreglos de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. From 225afb7af701102b1a6801f33dd1334afc0ee2f9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Sep 2024 16:37:37 +0200 Subject: [PATCH 570/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index 390f2817ca..f7ef3a03ed 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -1,24 +1,24 @@ --- title: Código de Conducta de NumPy - Cómo hacer el seguimiento de un informe -sidebar: falso +sidebar: false --- -Este es el manual seguido por el Comité de Código de Conducta de NumPy. Se utiliza cuando respondemos a un problema, para asegurarnos de que seamos consistentes y justos. +Este es el manual que sigue el Comité de Código de Conducta de NumPy. Se utiliza cuando respondemos a un problema, para asegurarnos de que seamos consistentes y justos. Hacer cumplir el [Código de Conducta](/code-of-conduct) impacta a nuestra comunidad hoy y en el futuro. Es una acción que no tomamos a la ligera. Al revisar las medidas de cumplimiento, el Comité de Código de Conducta tendrá en cuenta los siguientes valores y directrices: -* Actuar de manera personal en lugar de impersonal. El Comité puede involucrar a las partes para que comprendan la situación, respetando la privacidad y cualquier confidencialidad necesaria de los informantes. Sin embargo, a veces es necesario comunicarse directamente con uno o más individuos: el objetivo del Comité es mejorar la salud de nuestra comunidad en lugar de solo producir una decisión formal. -* Enfatizar la empatía hacia los individuos en lugar de juzgar el comportamiento, evitando etiquetas binarias de “bueno” y “malo/malvado”. El acoso directo y claro existe, y lo afrontaremos con firmeza. Pero en muchas circunstancias puede ser complejo resolver estas situaciones, sobre todo aquellas en las que desacuerdos normales conllevan a comportamientos inútiles o perjudiciales para las partes. Comprender el contexto completo y encontrar un camino que vuelva involucrar a todos es difícil, pero es en última instancia lo más productivo para nuestra comunidad. -* Comprendemos que el correo electrónico es un medio difícil y puede generar aislamiento. Recibir críticas por medio de correo electrónico, sin ningún contacto personal, puede ser particularmente doloroso. Esto hace que sea especialmente importante mantener una ambiente de apertura y respeto hacia las opiniones de los demás. También significa que debemos ser transparentes en nuestro actuar, y que debemos hacer todo lo que esté a nuestro alcance para asegurarnos de que todos nuestros miembros sean tratados de manera justa y con compasión. +* Actuar de manera personal en lugar de impersonal. El Comité puede involucrar a las partes para que comprendan la situación, respetando al mismo tiempo la privacidad y, en su caso, la confidencialidad de los informadores. Sin embargo, a veces es necesario comunicarse directamente con uno o más individuos: el objetivo del Comité es mejorar la salud de nuestra comunidad en lugar de solo producir una decisión formal. +* Enfatizar la empatía hacia los individuos en lugar de juzgar el comportamiento, evitando etiquetas binarias de “bueno” y “malo/malvado”. Existen agresiones y acosos manifiestos y claros, y los abordaremos con firmeza. Pero en muchas circunstancias puede ser complejo resolver estas situaciones, sobre todo aquellas en las que desacuerdos normales se convierten en comportamientos inútiles o perjudiciales para las partes. Comprender el contexto completo y encontrar un camino que vuelva involucrar a todos es difícil, pero es en última instancia lo más productivo para nuestra comunidad. +* Comprendemos que el correo electrónico es un medio difícil y puede aislarnos. Recibir críticas por medio de correo electrónico, sin ningún contacto personal, puede ser particularmente doloroso. Esto hace que sea especialmente importante mantener una ambiente de apertura y respeto hacia las opiniones de los demás. También significa que debemos ser transparentes en nuestro actuar, y que haremos todo lo que esté a nuestro alcance para asegurarnos de que todos nuestros miembros reciban un trato justo y comprensivo. * La discriminación puede ser sutil e inconsciente. Ésta puede manifestarse como injusticia y hostilidad en interacciones que, por todo lo demás, serían normales. Sabemos que esto ocurre, y nos ocuparemos de estar pendientes de esto. Nos gustaría mucho saber de usted si cree que ha sido tratado injustamente, y utilizaremos estos procedimientos para asegurarnos de que su queja sea escuchada y atendida. -* Ayudar a aumentar el compromiso con buenas prácticas de discusión: tratar de identificar dónde pudo haberse descompuesto la discusión, y proporcionar información práctica, sugerencias y recursos que puedan conducir a un cambio positivo en estos aspectos. +* Ayude a aumentar el compromiso con buenas prácticas de debate: trate de identificar los puntos en los que el debate puede haberse interrumpido y proporcione información práctica, sugerencias y recursos que puedan conducir a un cambio positivo en estos aspectos. * Sea consciente de las necesidades de los nuevos miembros: proporcióneles apoyo y consideración explícitos, con el objetivo de aumentar la participación, particularmente de grupos subrepresentados. -* Las personas provienen de orígenes culturales y lingüísticos diversos. Intente identificar cualquier malentendido honesto causado por un hablante no nativo y ayúdele a entender el problema y lo que puede cambiar para evitar causar una ofensa. La discusión compleja en una lengua extranjera puede ser muy intimidante, y queremos aumentar nuestra diversidad también a través de nacionalidades y culturas. +* Las personas provienen de entornos culturales y lingüísticos diferentes. Intente identificar cualquier malentendido honesto causado por un hablante no nativo y ayúdele a entender el problema y lo que puede cambiar para evitar causar una ofensa. La discusión compleja en una lengua extranjera puede ser muy intimidante, y queremos aumentar nuestra diversidad también a través de nacionalidades y culturas. ## Mediación -La mediación informal voluntaria es una herramienta a nuestra disposición. En contextos tales como cuando dos o más partes han escalado hasta el punto de un comportamiento inapropiado (algo tristemente común en el conflicto humano), puede ser útil facilitar un proceso de mediación. Éste es sólo un ejemplo: el Comité puede considerar la mediación en cualquier caso, siendo consciente de que este proceso se entiendo como estrictamente voluntario y que ninguna de las partes puede ser presionada a participar. Si el Comité sugiere la mediación, este debería: +La mediación informal voluntaria es una herramienta a nuestra disposición. En contextos tales como cuando dos o más partes han escalado hasta el punto de un comportamiento inapropiado (algo tristemente común en el conflicto humano), puede ser útil facilitar un proceso de mediación. Éste es sólo un ejemplo: el Comité puede considerar la mediación en cualquier caso, siendo consciente de que este proceso se entiende como estrictamente voluntario y que ninguna de las partes puede ser presionada a participar. Si el Comité sugiere la mediación, este debería: * Encontrar un candidato que pueda servir de mediador. * Obtener el acuerdo del informante(s). El informante(s) tienen total libertad para rechazar la propuesta de mediación o para proponer un mediador alternativo. @@ -59,7 +59,7 @@ El Comité procederá a revisar el incidente y determinará, en la medida de su * Quiénes son las parte(s) responsables. * Si se trata de una situación en progreso y existe una amenaza para la seguridad física de cualquiera. -Esta información se recopilará por escrito, y siempre que sea posible se registrarán y conservarán las deliberaciones del grupo (por ejemplo, transcripciones de chat, discusiones por correo electrónico, conferencias grabadas, resúmenes de conversaciones de voz, etc.). +Esta información se recopilará por escrito, y siempre que sea posible se registrarán y conservarán las deliberaciones del grupo (por ejemplo, transcripciones de chat, discusiones por correo electrónico, conferencias telefónicas grabadas, resúmenes de conversaciones de voz, etc.). Es importante conservar un archivo de todas las actividades de este Comité para asegurar consistencia en el comportamiento y proporcionar memoria institucional para el proyecto. Para ayudar en esto, el canal de discusión por defecto para este Comité será una lista de correo privada accesible a los miembros actuales y futuros del Comité, así como a los miembros del Consejo Directivo con previa solicitud justificada. Si el Comité considera necesario utilizar comunicaciones fuera de la lista (por ejemplo, para una respuesta temprana/rápida), deberá, en todos los casos, resumirlos y documentarlos de vuelta a la lista, de manera que se mantenga un buen registro del proceso. @@ -77,10 +77,10 @@ Las posibles respuestas pueden incluir: - si el asunto se ha resuelto públicamente mientras que el Comité estaba estudiando las respuestas. * Coordinación de mediación voluntaria: si todas las partes implicadas están de acuerdo, el Comité podrá facilitar un proceso de mediación como se detalla arriba. * Recordar públicamente y señalar que algunos comportamientos/acciones/usos de lenguaje, han sido juzgados como inapropiados y por qué, bajo el contexto actual, pueden ser hirientes para algunas personas, solicitando a la comunidad que se autorregule. -* Una reprimenda privada por parte del Comité al individuo(s) involucrado. En este caso, el presidente del grupo entregará esa reprimenda al individuo(s) por correo electrónico, con copia al grupo. -* Una reprimenda pública. En este caso, el presidente del Comité entregará esa reprimenda por el mismo medio por el que se produjo la violación, dentro de los límites de la viabilidad. Por ejemplo, la lista de correo original para una violación de correo electrónico, pero para una discusión en una sala de chat, en la que la persona o el contexto pueden haber desaparecido, puede buscarse el contacto por otros medios. El grupo puede elegir publicar este mensaje en otro lugar con fines de documentación. -* Una solicitud de disculpa pública o privada, asumiendo que el informante esté de acuerdo con esta idea: puede negarse, a su discreción, a continuar contacto con el violador. El presidente entregará esta solicitud. El Comité puede, si así lo desea, adjuntar “cadenas” a esta petición: por ejemplo, el grupo puede solicitar a un violador pedir disculpas para preservar su membresía en una lista de correo. -* Un “acuerdo mutuo de suspensión” en el que el Comité solicita al individuo la abstención temporal de participación en la comunidad. Si el individuo decide no aceptar una suspensión temporal voluntariamente, el Comité puede emitir un “período obligatorio de calma”. +* Una amonestación privada por parte del Comité al individuo(s) involucrado(s). En este caso, el presidente del grupo entregará esa amonestación al (los) individuo(s) por correo electrónico, con copia al grupo. +* Una amonestación pública. En este caso, el presidente del Comité entregará la amonestación por el mismo medio por el que se produjo la violación, dentro de los límites de lo posible. Por ejemplo, la lista de correo original para una violación de correo electrónico, pero para una discusión en una sala de chat, en la que la persona o el contexto pueden haber desaparecido, puede buscarse el contacto por otros medios. El grupo puede elegir publicar este mensaje en otro lugar con fines de documentación. +* Una solicitud de disculpa pública o privada, asumiendo que el informante esté de acuerdo con esta idea: puede negarse, a su discreción, a continuar contacto con el infractor. El presidente entregará esta solicitud. El Comité puede, si así lo desea, adjuntar “condiciones” a esta petición: por ejemplo, el grupo puede solicitar a un infractor pedir disculpas para preservar su membresía en una lista de correo. +* Un “acuerdo mutuo de suspensión” en el que el Comité solicita al individuo la abstención temporal de participación en la comunidad. Si el individuo decide no aceptar una suspensión temporal voluntariamente, el Comité puede emitir un “período obligatorio de reflexión”. * Una prohibición permanente o temporal de algunos o de todos los espacios de NumPy (listas de correo, gitter.im, etc.). El grupo mantendrá los registros de todas esas prohibiciones, para que puedan ser revisadas en el futuro o ser mantenidas en caso contrario. Una vez que se acuerda una resolución, pero antes de que se promulgue, el Comité se pondrá en contacto con el informante original y con cualquier otra parte afectada y explicará la resolución propuesta. El Comité preguntará si esta resolución es aceptable y deberá tomar nota de los comentarios para su registro. From 554d01d6e1aa2a7d1c5207687883ce25dbf2c0c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Sep 2024 17:36:54 +0200 Subject: [PATCH 571/586] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md index f7ef3a03ed..aab614b2e3 100644 --- a/content/es/report-handling-manual.md +++ b/content/es/report-handling-manual.md @@ -92,4 +92,4 @@ El Comité nunca debatirá públicamente el asunto; todas las declaraciones púb ## Conflictos de intereses -En caso de cualquier conflicto de intereses, un miembro del Comité deberá notificar inmediatamente a los demás miembros y recusarse en caso de ser necesario. +En caso de cualquier conflicto de intereses, el miembro del Comité deberá notificarlo inmediatamente a los demás miembros y excusarse en caso de ser necesario. From e0c5aaee14b30b07e88223f58b1a6592b6af7b43 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Sep 2024 04:27:06 +0200 Subject: [PATCH 572/586] New translations news.md (Spanish) --- content/es/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/news.md b/content/es/news.md index 5b0fd5615a..d9ea652f53 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -211,7 +211,7 @@ _14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas sema ### Lanzamiento de NumPy 1.19.2 -_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las ruedas aarch64 están construidas con el último lanzamiento de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. +_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las wheels para aarch64 están construidas con la última versión de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. ### La encuesta inaugural de NumPy ya está disponible! From a942a3e9695580566d84633f8b51a761c991ff25 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Sep 2024 05:24:03 +0200 Subject: [PATCH 573/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index b30afc410e..43abcde2ee 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -2,7 +2,7 @@ params: machinelearning: paras: - - para1: NumPy constituye la base de potentes bibliotecas de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. [MXNet](https://github.com/apache/incubator-mxnet) es otro paquete de IA que proporciona modelos y plantillas para el aprendizaje profundo. + para1: NumPy constituye la base de potentes bibliotecas de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. arraylibraries: intro: From 0872efff4cea1ae17b3d1b06414cbc825a9652c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 03:28:38 +0200 Subject: [PATCH 574/586] New translations contribute.md (Spanish) --- content/es/contribute.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index e6c877df9e..dae661e9e3 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -32,9 +32,9 @@ La [Guía de usuario](https://numpy.org/devdocs) de NumPy está en proceso de re El [rastreador de propuestas de NumPy](https://github.com/numpy/numpy/issues) tiene _muchos_ temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes: * revisar si errores antiguos siguen presentes -* encontrar propuestas duplicadas, y enlazar las relacionadas -* añadir buenos reproductores autónomos de las incidencias -* etiquetar correctamente las propuestas (para ello es necesario tener derechos de categorización, solo necesitas preguntar) +* encontrar problemas duplicados, y enlazar las relacionadas +* añadir la forma de reproducir siempre el mismo problema +* etiquetar correctamente los problemas (para ello es necesario tener derechos de categorización, solo necesitas preguntar) Por favor, solo sumérgete. @@ -58,8 +58,8 @@ A través del contacto con la comunidad compartimos nuestro trabajo más ampliam NumPy fue durante muchos años un proyecto voluntario, pero a medida que crecía su importancia se hizo evidente que necesitaríamos apoyo financiero para garantizar su estabilidad y crecimiento. [Esta charla en SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica cuánta diferencia ha supuesto este apoyo. Como todo en el mundo sin ánimo de lucro, estamos constantemente en busca de subvenciones, patrocinios y otros tipos de ayuda. Tenemos varias ideas y, por supuesto, aceptamos más. La recaudación de fondos es una habilidad escasa aquí -- apreciaríamos tu ayuda. -### Donate +### Donar -If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). +Si deseas contribuir a NumPy haciendo una donación, visita https://numpy.org/about/#donate. From 345f9536ef88f8f1728cd10e76b845969dddd87e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 03:28:39 +0200 Subject: [PATCH 575/586] New translations history.md (Spanish) --- content/es/history.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/history.md b/content/es/history.md index fd4000b630..455aecff89 100644 --- a/content/es/history.md +++ b/content/es/history.md @@ -3,17 +3,17 @@ title: Historia de NumPy sidebar: falso --- -NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de arreglos y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la biblioteca contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en ciencias de la computación y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amigable con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados usados en el análisis de ondas gravitacionales importan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. +NumPy es una librería fundamental de Python que proporciona estructuras de datos de arreglos y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la librería contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en ciencias de la computación y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amigable con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados usados en el análisis de ondas gravitacionales importan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. -Para conocer en profundidad los hitos en el desarrollo de NumPy y las bibliotecas relacionadas, consulta [arxiv.org](arxiv.org/abs/1907.10121). +Para conocer en profundidad los hitos en el desarrollo de NumPy y las librerías relacionadas, consulta [arxiv.org](arxiv.org/abs/1907.10121). -Si deseas obtener una copia de las bibliotecas originales Numeric y Numarray, sigue los siguientes enlaces: +Si deseas obtener una copia de las librerías originales Numeric y Numarray, sigue los siguientes enlaces: [Página de Descarga de *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* [Página de Descarga de *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -*Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con arreglos o que refactoricen cualquier código preexistente para utilizar la biblioteca NumPy. +*Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con arreglos o que refactoricen cualquier código preexistente para utilizar la librería NumPy. ### Documentación Histórica From 220568aff89de20ff99050890c13bb618a6d03bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 04:25:09 +0200 Subject: [PATCH 576/586] New translations news.md (Spanish) --- content/es/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index d9ea652f53..1e34cd9fcf 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -7,7 +7,7 @@ date: 2024-06-17 ### Lanzamiento de NumPy 2.1.0 -_18 de agosto 2024_ -- NumPy 2.1.0 proporciona soporte para Python 3.13 y abandona el soporte para Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son: +_18 de agosto 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son: - Soporte para Python 3.13. - Soporte preliminar para Python 3.13 de hilos libres. @@ -20,11 +20,11 @@ Esta versión es compatible con las versiones 3.10-3.13 de Python. _16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características interesantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían producirse en un lanzamiento menor regular, como una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que podrían no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- La [Guía de migración de NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [notas de lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) - Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -La publicación ["NumPy 2.0: un hito evolutivo"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. +La publicación ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. ### Fecha de lanzamiento de NumPy 2.0: 16 de junio From 52e71942b47169e57e799ae5284e15f85e771474 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 04:25:11 +0200 Subject: [PATCH 577/586] New translations learn.md (Spanish) --- content/es/learn.md | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index 2abe55fd7b..016e78cfd1 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -15,26 +15,26 @@ Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recom **Tutoriales** -* [Tutorial NumPy de inicio rápido](https://numpy.org/devdocs/user/quickstart.html) -* [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). +* [Tutorial de inicio rápido de NumPy](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: La Guía Visual de NumPy *por Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [Lecturas de Python Científico](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. -* [NumPy: lo absolutamente básico para principiantes](https://numpy.org/devdocs/user/absolute_beginners.html) -* [Tutorial de NumPy *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [Scientific Python Lectures](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. +* [NumPy: the absolute basics for beginners.](https://numpy.org/devdocs/user/absolute_beginners.html) +* [NumPy tutorial *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *por Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) -* [Guía del Usuario de NumPy](https://numpy.org/devdocs) +* [NumPy User Guide.](https://numpy.org/devdocs) **Libros** -* [Guía a NumPy *por Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) Ésta es una versión 1 gratuita de 2006. Para conseguir la última versión (2015) mira [aquí](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -* [De Python a NumPy *por Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -* [SciPy Elegante](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *por Juan Nunez-Technesias, Stefan van der Walt, y Harriet Dashnow* +* [Guide to NumPy *por Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) Ésta es una versión 1 gratuita de 2006. Para conseguir la última versión (2015) mira [aquí](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [From Python to NumPy *por Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *por Juan Nunez-Technesias, Stefan van der Walt, y Harriet Dashnow* También puedes echar un vistazo a esta [lista de Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre el tema "Python+SciPy". La mayoría de esos libros son sobre el "ecosistema SciPy", que tiene NumPy en su núcleo. **Videos** -* [Introducción a la computación numérica con NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** @@ -44,30 +44,30 @@ Pruebe estos recursos avanzados para comprender mejor los conceptos de NumPy com **Tutoriales** -* [100 Ejercicios de NumPy](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* -* [Una Introducción a NumPy y Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *por Stéfan van der Walt* * [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). **Libros** -* [Manual para Ciencia de Datos en Python](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* -* [Python para Análisis de Datos](https://www.amazon.com/Python-Data-Analysis-Wrangling-Ipython/dp/1491957662) *por Wes McKinney* -* [Python Numérico: Aplicaciones en Computación Científica y Ciencia de Datos con NumPy, SciPy y Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *por Robert Johansson* +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Ipython/dp/1491957662) *por Wes McKinney* +* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *por Robert Johansson* **Videos** -* [NumPy Avanzado - reglas de broadcasting, strides e indexación avanzada](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunez-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunez-Iglesias* *** ## Charlas de NumPy -* [El Futuro de la Indexación en NumPy](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) -* [Evolución de la Computación de Arreglos en Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) -* [NumPy: ¿qué ha cambiado y qué va a cambiar?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *por Matti Picus* (2019) -* [Adentro de NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) -* [Breve Reseña de Computación de Arreglos en Python](https://www.youtube.com/watch?v=f176j2g2eNc) *por Travis Oliphant* (2019) +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *por Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *por Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *por Travis Oliphant* (2019) *** From d8e8d33fefa231c1ff3ffadbeb5d890fef81d792 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 04:25:12 +0200 Subject: [PATCH 578/586] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index 1eaffaff78..b246779380 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -5,7 +5,7 @@ sidebar: false En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. -{{< figura >}} +{{< figure >}} src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = 'Página de portada del informe de la encuesta de usuarios de NumPy de 2020, titulado "Encuesta de la Comunidad de NumPy 2020 - resultados"' width = '250' {{< /figura >}} From 7adf788ce6c96ce6a7deb1e71a8ad1b219fd2299 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 04:25:13 +0200 Subject: [PATCH 579/586] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 43abcde2ee..fa5776380a 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -2,15 +2,15 @@ params: machinelearning: paras: - - para1: NumPy constituye la base de potentes bibliotecas de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. + para1: NumPy constituye la base de potentes librerías de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. arraylibraries: intro: - - text: La API de NumPy es el punto de partida cuando se escriben bibliotecas para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. + text: La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. headers: - - text: Biblioteca de arreglos + text: Librería de arreglos - text: Capacidades y áreas de aplicación libraries: @@ -22,16 +22,16 @@ params: url: https://dask.org/ - title: CuPy - text: Biblioteca de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python. + text: Librería de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python. img: /images/content_images/arlib/cupy.png alttext: CuPy - url: https://cupy.chainer.org + url: https://cupy.dev - title: JAX text: "Transformaciones componibles de programas NumPy: diferenciar, vectorizar, compilación justo-a-tiempo a GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://github.com/google/jax + url: https://jax.readthedocs.io/ - title: Xarray text: Arreglos multidimensionales indexados y etiquetados para análisis y visualización avanzados. @@ -40,7 +40,7 @@ params: url: https://xarray.pydata.org/en/stable/index.html - title: Sparse - text: Biblioteca de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. + text: Librería de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ @@ -58,13 +58,13 @@ params: url: https://www.tensorflow.org - title: Arrow - text: Marco de aprendizaje profundo adecuado para la creación flexible de prototipos de investigación y la producción. - img: /images/content_images/arlib/mxnet_logo.png + text: Plataforma de desarrollo multilingüe para datos y análisis columnares en memoria. + img: /images/content_images/arlib/arrow.png alttext: arrow - url: https://mxnet.apache.org/ + url: https://arrow.apache.org/ - title: xtensor - text: Matrices multidimensionales con difusión y computación perezosa para análisis numérico. + text: Arreglos multidimensionales con difusión y computación perezosa para análisis numérico. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python From 82dafcddacbd2256e85b24b509a933c119ba2e65 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 05:21:13 +0200 Subject: [PATCH 580/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 759d1d091a..4b67fed57e 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -14,33 +14,33 @@ src = '/images/content_images/cs/blackhole. pg' title = 'Agujero Negro M87' alt ## Un telescopio del tamaño de la Tierra -El [ telescopio de Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de [20 microsegundos de arco][resolution] — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París! +El [ Telescopio Event Horizon (EHT) ](https://eventhorizontelescope.org), es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la Tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de [20 microsegundos de arco][resolution] — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París! ### Objetivos clave y resultados * **Una nueva vista del universo:** El trabajo preliminar de la innovadora imagen de EHT se había establecido 100 años antes, cuando [Sir Arthur Eddington][eddington] dio el primer apoyo observacional a la teoría de la relatividad general de Einstein. -* **El agujero negro:** EHT se entrenó en un enorme agujero negro aproximadamente a 55 millones de años luz de la tierra, situada en el centro de la galaxia Messier 87 (M87) en el cúmulo de Virgo. Su masa es 6.5 mil millones de veces la del sol. Se había estudiado por [más de 100 años](https://www.jpl.nasa.gov/news/news.php?feature=7385), pero nunca antes se había observado un agujero negro. +* **El agujero negro:** EHT se entrenó en un enorme agujero negro supermasivo aproximadamente a 55 millones de años luz de la Tierra, situado en el centro de la galaxia Messier 87 (M87) en el cúmulo de galaxias Virgo. Su masa es 6.5 mil millones de veces la del sol. Se había estudiado por [más de 100 años](https://www.jpl.nasa.gov/news/news.php?feature=7385), pero nunca antes se había observado un agujero negro. -* **Comparando las observaciones con la teoría:** A partir de la teoría de la relatividad general de Einstein, los científicos esperaban encontrar una región similar a las sombras causadas por la flexión gravitacional y la captura de la luz. Los científicos pudieron utilizarla para medir la enorme masa del agujero negro. +* **Comparando las observaciones con la teoría:** A partir de la teoría general de la relatividad de Einstein, los científicos esperaban encontrar una región similar a una sombra causada por la flexión gravitacional y la captura de la luz. Los científicos pudieron utilizarla para medir la enorme masa del agujero negro. ### Los desafíos * **Escala computacional** - EHT plantea desafíos de procesamiento de datos masivos, incluyendo rápidas fluctuaciones de fase atmosféricas, amplio ancho de banda de grabación, y telescopios que son ampliamente disímiles y geográficamente dispersos. + EHT plantea enormes desafíos de procesamiento de datos, incluyendo las rápidas fluctuaciones de fase atmosféricas, amplio ancho de banda de grabación, y telescopios que son ampliamente disímiles y geográficamente dispersos. * **Demasiada información** - Cada día el EHT genera más de 350 terabytes de observaciones, almacenados en discos duros llenos de helio. Reducir el volumen y complejidad de estos datos es enormemente difícil. + Cada día, el EHT genera más de 350 terabytes de observaciones, almacenados en discos duros llenos de helio. Reducir el volumen y complejidad de estos datos es enormemente difícil. * **Hacia lo desconocido** Cuando el objetivo es ver algo nunca antes visto, ¿cómo pueden los científicos estar seguros de que la imagen es correcta? {{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' title = 'Flujo de Trabajo de Procesamiento de Datos EHT' alt = 'flujo de datos' align = 'centrar' attribution = '(Créditos del diagrama: The Astrophysical Journal, Colaboración del Telescopio del Horizonte de Sucesos)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="el rol de numpy" caption="**El rol de NumPy en la fotografía del Agujero Negro**" >}} +src = '/images/content_images/cs/dataprocessbh.png' title = 'Flujo de Trabajo de Procesamiento de Datos EHT' alt = 'flujo de datos' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} ## El Rol de NumPy From 5e568c3afd67fc33f52ec09f3f8a4bd86be334b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 22:14:52 +0200 Subject: [PATCH 581/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 4b67fed57e..8d63d7b5b2 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -64,7 +64,7 @@ Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [ ## Resumen -La matriz n-dimensional eficiente y adaptable que es la característica central de NumPy permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo. +El eficiente y adaptable arreglo n-dimensional que es la característica central de NumPy, permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo. {{< figure >}} src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de numpy' title = 'Capacidades clave de NumPy utilizadas' From 0bd500a1ff1fb352bd6ff58e60ab7038d71e8639 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 23:16:14 +0200 Subject: [PATCH 582/586] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 8d63d7b5b2..1866d88b36 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -3,7 +3,7 @@ title: "Caso de estudio: La primera imagen de un Agujero Negro" sidebar: false --- -{{< figura >}} +{{< figure >}} src = '/images/content_images/cs/blackhole. pg' title = 'Agujero Negro M87' alt = 'Imagen de agujero negro' attribution = '(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)' attributionlink = 'https://www. pl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} From 5e742e404be05d7f2089c493ea71dbad99a5398e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 23:16:15 +0200 Subject: [PATCH 583/586] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index decd6ec5cc..b814167dd3 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -1,11 +1,11 @@ --- title: "Estudio de caso: Análisis de críquet, ¡el cambio radical!" -sidebar: falso +sidebar: false --- -{{< figura >}} +{{< figure >}} figure src="/images/content_images/cs/ipl-stadium.png'title" caption="IPLT20, el festival de críquet más grande en India" alt="Copa y estadio de la Premier League de Críquet de India" attr="(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))" attrlink="https://unsplash.com/@aksh1802' -{{< /figura >}} +{{< /figure >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *Jugador Internacional de críquet, ex-capitán del equipo de India, juega para Chennai Super Kings en IPL*" > }} No juegas para el público, juegas para el país. @@ -14,16 +14,16 @@ figure src="/images/content_images/cs/ipl-stadium.png'title" caption="IPLT20, el ## Acerca del críquet -Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi cada rincón de India, rural o urbano, es popular entre los jóvenes y ancianos por igual, conectando miles de millones en India a diferencia de cualquier otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más. +Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi todos los rincones de la India, rurales o urbanos, es popular entre los jóvenes y ancianos por igual, conectando miles de millones de personas en India como ningún otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más. -La Premier League de India (IPL) es una liga profesional de críquet Twenty20, fundada en 2008. Es uno de los eventos de críquet más concurridos en el mundo, valorado en [$6.7 mil millones de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) en 2019. +La Indian Premier League (IPL) es una liga profesional de críquet Twenty20, fundada en 2008. Es uno de los eventos de críquet más concurridos en el mundo, valorado en [$6.7 mil millones de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) en 2019. El críquet es un juego de números - las carreras anotadas por un bateador, los wickets tomados por un lanzador, los partidos ganados por un equipo de críquet, el número de veces que un bateador responde de cierta manera a un tipo de ataque de lanzamiento, etc. La capacidad de profundizar en los números del críquet tanto para mejorar el rendimiento como para estudiar las oportunidades de negocio, el mercado en general y la economía del cricket mediante potentes herramientas de análisis, impulsadas por software de computación numérica como NumPy, es algo muy importante. El análisis del críquet proporciona ideas interesantes sobre el juego e inteligencia predictiva respecto a los resultados del juego. Hoy en día, hay abundantes y casi infinitos tesoros de registros y estadísticas de juegos de críquet disponibles, por ejemplo, en [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) y [cricsheet](https://cricsheet.org). Estas y muchas otras bases de datos de cricket se han utilizado para el [análisis de cricket](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) utilizando los últimos algoritmos de aprendizaje automático y modelación predictiva. Las plataformas de medios y entretenimiento, junto con los organismos deportivos profesionales asociados con el juego, utilizan la tecnología y el análisis para determinar métricas clave que mejoren las posibilidades de ganar los partidos: * promedio móvil del rendimiento de bateo, -* pronóstico del marcador, +* previsión del marcador, * obtener información sobre la condición física y el rendimiento de un jugador contra diferentes oponentes, * contribución del jugador a las victorias y derrotas para tomar decisiones estratégicas sobre la composición del equipo From d2514f95eda615427ee1a741315caa01f8ffae21 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 13 Sep 2024 23:16:16 +0200 Subject: [PATCH 584/586] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 2c56362ae7..19327beda4 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -1,10 +1,10 @@ --- -title: "Caso de estudio: Estimación de Postura 3D DeepLabCut" -sidebar: falso +title: "Caso de estudio: DeepLabCut Estimación de Postura 3D" +sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' title = 'Analizar movimiento del ratón usando DeepLapCut' alt = 'micehandanim' attribution = '(Fuente: www.deeplabcut.org )' attributionlink = 'http://www.mousemASElab.org/deeplabcut' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analizar movimiento de las manos de los ratones usando DeepLapCut' alt = 'micehandanim' attribution = '(Fuente: www.deeplabcut.org )' attributionlink = 'http://www.mousemASElab.org/deeplabcut' {{< /figure >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Profesor Asistente, Escuela Politécnica Federal de Lausana* ([EPFL](https://www.epfl.ch/en/))" @@ -14,11 +14,11 @@ src = '/images/content_images/cs/mice-hand.gif' title = 'Analizar movimiento del ## Acerca de DeepLabCut -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) es una caja de herramientas de código abierto que permite a los investigadores de cientos de instituciones de todo el mundo rastrear el comportamiento de animales de laboratorio, con muy pocos datos de entrenamiento, con una precisión de nivel humana. Con la tecnología DeepLabCut, los científicos pueden profundizar en la comprensión científica del control motriz y el comportamiento a través de especies animales y escalas de tiempo. +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) es una caja de herramientas de código abierto que permite a los investigadores de cientos de instituciones de todo el mundo rastrear el comportamiento de animales de laboratorio, con muy pocos datos de entrenamiento, con una precisión de nivel humana. Con la tecnología DeepLabCut, los científicos pueden profundizar en la comprensión científica del control motriz y el comportamiento a través de especies animales y escalas temporales. Muchas áreas de investigación, incluyendo la neurociencia, la medicina y la biomecánica, utilizan datos para rastrear el movimiento animal. DeepLabCut ayuda a entender lo que los humanos y otros animales están haciendo, analizando las acciones que han sido grabadas en la filmación. Utilizando la automatización para tareas laboriosas de etiquetado y monitoreo, junto con el análisis de datos basado en redes neuronales profundas, DeepLabCut realiza estudios científicos que involucran la observación de animales, tales como primates, ratones, peces, moscas, etc. de manera mucho más rápida y precisa. -{{< figura >}} +{{< figure >}} src = '/images/content_images/cs/race-horse.gif' title = 'Puntos de colores rastrean las posiciones de una parte del cuerpo de un caballo de carreras' alt = 'horserideranim' attribution = '(Fuente: Mackenzie Mathis)' {{< /figure >}} @@ -34,7 +34,7 @@ Recientemente, se presentó el [modelo zoo de DeepLabCut](http://www.mousemotorl * **Automatización del análisis de la postura animal para estudios científicos:** - El objetivo principal de la tecnología DeepLabCut es medir y rastrear la postura de los animales en diversos entornos. Estos datos se pueden utilizar, por ejemplo, en estudios de neurociencia para entender cómo el cerebro controla el movimiento, o para dilucidar como interactúan socialmente los animales. Los investigadores han observado un [aumento de rendimiento diez veces mayor](https://www.biorxiv.org/content/10.1101/457242v1) con DeepLabCut. Las posturas se pueden inferir sin conexión hasta a 1200 fotogramas por segundo (FPS). + El objetivo principal de la tecnología DeepLabCut es medir y rastrear la postura de los animales en diversos entornos. Estos datos se pueden utilizar, por ejemplo, en estudios de neurociencia para entender cómo el cerebro controla el movimiento, o para aclarar como interactúan socialmente los animales. Los investigadores han observado un [aumento de rendimiento diez veces mayor](https://www.biorxiv.org/content/10.1101/457242v1) con DeepLabCut. Las posturas se pueden inferir sin conexión hasta a 1200 fotogramas por segundo (FPS). * **Creación de un conjunto de herramientas de Python de fácil uso para la estimación de postura:** @@ -49,9 +49,9 @@ Recientemente, se presentó el [modelo zoo de DeepLabCut](http://www.mousemotorl - código para inferencia a gran escala en videos - graficar las inferencias utilizando herramientas de visualización integradas -{{< figura >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Postura de estimación de pasos con DeepLabCut' alt = 'dlcsteps' align = 'center' atribución = '(Source: DeepLabCut)' attributionlink = 'https://twitter. om/DeepLabCut/status/1198046918284210176/foto/1' -{{< /figura >}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Pasos de estimación de la postura con DeepLabCut' alt = 'dlcsteps' align = 'center' atribución = '(Source: DeepLabCut)' attributionlink = 'https://twitter. om/DeepLabCut/status/1198046918284210176/foto/1' +{{< /figure >}} ### Los Desafíos @@ -68,7 +68,7 @@ src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' title = 'Postura Por último, pero no menos importante, la manipulación de arreglos - procesamiento de grandes pilas de arreglos correspondientes a varias imágenes, tensores objetivo y puntos clave es bastante desafiante. {{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' title = 'Estimación de variedad y complejidad de postura' alt = 'challengesfig' align = 'centro' atribución = '(Fuente: Mackenzie Mathis)' attributionlink = 'https://www. iorxiv.org/content/10.1101/476531v1.full.pdf' +src = '/images/content_images/cs/pose-estimation.png' title = 'Estimación de variedad y complejidad de postura' alt = 'challengesfig' align = 'center' atribución = '(Fuente: Mackenzie Mathis)' attributionlink = 'https://www. iorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## El Papel de NumPy para afrontar los desafíos de la estimación de postura @@ -94,7 +94,7 @@ src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'Flujo de Trab Observar y describir eficientemente el comportamiento es un punto central de la etología moderna, neurociencia, medicina y tecnología. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite a los investigadores estimar la postura del sujeto, permitiéndoles de manera eficiente cuantificar el comportamiento. Con solo un pequeño conjunto de imágenes de entrenamiento, el conjunto de herramientas de Python de DeepLabCut permite entrenar una red neuronal con una precisión de etiquetado a nivel humano, expandiendo así su aplicación no solo al análisis del comportamiento en el laboratorio, sino también potencialmente en deportes, análisis de marcha, medicina y estudios de rehabilitación. Los desafíos de la combinatoria compleja y procesamiento de datos enfrentados por los algoritmos de DeepLabCut se abordan mediante el uso de las capacidades de manipulación de arreglos de NumPy. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de NumPy' title = 'Capacidades claves de NumPy utilizadas' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de NumPy' title = 'Capacidades claves utilizadas de NumPy' {{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 8fd4f42f953b267f27cd702320aee5ec1ed0d86d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Sep 2024 04:40:35 +0200 Subject: [PATCH 585/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index b02715660a..3b764efcac 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -1,10 +1,10 @@ --- title: "Estudio de Caso: Descubrimiento de Ondas Gravitacionales" -sidebar: falso +sidebar: false --- {{< figure >}} -src = '/images/content_images/cs/gw_sxs_image. ng' title = 'Ondas Gravitacionales' alt = 'coalescencia de un agujero negro binario generando ondas gravitacionales' atribución = '(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)' attributionlink = 'https://youtu. e/Zt8Z_uzG71o' +src = '/images/content_images/cs/gw_sxs_image. ng' title = 'Ondas Gravitacionales' alt = 'coalescencia de un agujero negro binario generando ondas gravitacionales' attribution= '(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)' attributionlink = 'https://youtu. e/Zt8Z_uzG71o' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *Colaboración científica LIGO*" >}} El ecosistema científico de Python es una infraestructura crítica para la investigación realizada en LIGO. From ebc4b2c2e5b7e351bb034a0be6bc8275a3c63387 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Sep 2024 05:41:49 +0200 Subject: [PATCH 586/586] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 3b764efcac..7adf756eb8 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -14,13 +14,13 @@ src = '/images/content_images/cs/gw_sxs_image. ng' title = 'Ondas Gravitacionale Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por eventos cataclísmicos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de los fenómenos oscuros en el universo distante y su impacto. -El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](https://www.ligo.caltech.edu) fue diseñado para abrir el campo de la astrofísica de ondas gravitacionales mediante la detección directa de las ondas gravitacionales predichas por la Teoría General de la Relatividad de Einstein. Comprende dos interferómetros ampliamente separados dentro de los Estados Unidos: uno en Hanford, Washington, y el otro en Livingston, Louisiana, operando al unísono para detectar ondas gravitacionales. Cada uno de ellos tiene detectores de ondas gravitacionales de escala de múltiples kilómetros que utilizan interferometría de láser. La Colaboración Científica de LIGO (LSC) es un grupo de más de 1000 científicos de universidades de los Estados Unidos y de otros 14 países, respaldados por más de 90 universidades e institutos de investigación; aproximadamente 250 estudiantes contribuyen activamente a la colaboración. El nuevo descubrimiento de LIGO es la primera observación de ondas gravitacionales, realizada midiendo las diminutas perturbaciones que las ondas generan en el espacio y el tiempo a medida que pasan a través de la Tierra. Ha abierto nuevas fronteras astrofísicas que exploran el lado distorsionado del universo: objetos y fenómenos que se forman a partir del espacio-tiempo deformado. +El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](https://www.ligo.caltech.edu) fue diseñado para abrir el campo de la astrofísica de ondas gravitacionales mediante la detección directa de las ondas gravitacionales predichas por la Teoría General de la Relatividad de Einstein. Comprende dos interferómetros ampliamente separados dentro de los Estados Unidos: uno en Hanford, Washington, y el otro en Livingston, Louisiana, operando al unísono para detectar ondas gravitacionales. Cada uno de ellos tiene detectores de ondas gravitacionales de escala de múltiples kilómetros que utilizan interferometría de láser. La Colaboración Científica de LIGO (LSC) es un grupo de más de 1000 científicos de universidades de los Estados Unidos y de otros 14 países, respaldados por más de 90 universidades e institutos de investigación; aproximadamente 250 estudiantes contribuyen activamente a la colaboración. El nuevo descubrimiento de LIGO es la primera observación de ondas gravitacionales, realizada midiendo las diminutas perturbaciones que las ondas generan en el espacio y el tiempo a medida que pasan a través de la Tierra. Ha abierto nuevas fronteras astrofísicas que exploran el lado deformado del universo: objetos y fenómenos que están hechos de espaciotiempo deformado. ### Objetivos Clave * Aunque su [misión](https://www.ligo.caltech.edu/page/what-is-ligo) es detectar ondas gravitacionales de algunos de los procesos más violentos y energéticos del Universo, los datos que LIGO recopila pueden tener efectos de gran alcance en muchas áreas de la física, incluyendo gravitación, relatividad, astrofísica, cosmología, física de partículas y física nuclear. -* Procesar los datos observados mediante cálculos de relatividad numérica que implican matemáticas complejas para discernir la señal del ruido, filtrar la señal relevante y estimar estadísticamente la significancia de los datos observados +* Desmenuzar los datos observados mediante cálculos numéricos de relatividad que implican matemáticas complejas para discernir la señal del ruido, filtrar la señal relevante y estimar estadísticamente la importancia de los datos observados * Visualización de datos para que los resultados binarios/numéricos puedan ser comprendidos. @@ -33,7 +33,7 @@ El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](ht * **Avalancha de Datos** - A medida que los dispositivos de observación se vuelven más sensibles y confiables, los desafíos planteados por la avalancha de datos y encontrar una aguja en un pajar aumentan exponencialmente. ¡LIGO genera terabytes de datos cada día! Dar sentido a estos datos requiere de un esfuerzo enorme para cada detección. Por ejemplo, las señales recolectadas por LIGO deben ser comparadas por supercomputadoras contra cientos de miles de plantillas de posibles señales de ondas gravitacionales. + A medida que los dispositivos de observación se vuelven más sensibles y confiables, los desafíos planteados por la avalancha de datos y encontrar una aguja en un pajar aumentan exponencialmente. ¡LIGO genera terabytes de datos cada día! Dar sentido a estos datos requiere de un esfuerzo enorme para todas y cada una de las detecciones. Por ejemplo, las señales recolectadas por LIGO deben ser comparadas por supercomputadoras contra cientos de miles de plantillas de posibles señales de ondas gravitacionales. * **Visualización**