Final review of the content

content/en/about.md

@@ -5,7 +5,7 @@ sidebar: false
 
 _Some information about the NumPy project and community_
 
-NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on early work of the Numerical and Numarray libraries. NumPy will always be 100% open-source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt).
+NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numerical and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/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).
 
@@ -60,11 +60,11 @@ Institutional Partners are organizations that support the project by employing p
 
 ## Donate
 
-If you have found NumPy to be useful in your work, research or company, please consider making 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.
+If you have found NumPy to be useful in your work, research, or company, please consider making 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 adviser about your particular tax situation.
+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).
 {{< numfocus >}}

content/en/arraycomputing.md

@@ -6,11 +6,11 @@ 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.*
+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.**
+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,
@@ -30,10 +30,10 @@ pack newer algorithms and features geared towards machine learning and artificia
 
 **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.
+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
+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.

content/en/case-studies/blackhole-image.md

@@ -29,7 +29,7 @@ from a sidewalk café in Paris!
 * **A New View of the Universe:**
   The EHT is an exciting new tool for studying the most extreme objects in the
   universe. The EHT's groundbreaking image was published 100 years
-  after [Sir Arthur Eddington's expidition][eddington] yielded the first 
+  after [Sir Arthur Eddington's experiment][eddington] yielded the first 
   observational evidence in support of Einstein's theory of general relativity.
 
 * **Investigating Black Holes:**
@@ -44,7 +44,7 @@ from a sidewalk café in Paris!
 
 * **Comparing Observations to Theory:**
   Based on Einstein’s general theory of relativity, scientists expected 
-  see a dark region similar to a shadow, caused by the gravitational bending
+  to see a dark region similar to a shadow, caused by the gravitational bending
   and capture of light by the event horizon. By studying this shadow
   scientists could measure the enormous mass of M87’s central supermassive
   black hole.
@@ -94,7 +94,7 @@ techniques to verify that the resulting images were consistent.
 Results from these independent teams of researchers were combined to yield the
 first-of-a-kind image of the black hole.
 This approach is a powerful example of the importance of reproducibility and
-collaboration to modern scientific discovery, and illustrates the role that
+collaboration to modern scientific discovery and illustrates the role that
 the scientific Python ecosystem plays in supporting scientific advancement
 through collaborative data analysis.
 
@@ -125,7 +125,7 @@ of the final image of the black hole.
 
 NumPy enabled researchers to manipulate large numerical datasets through its
 efficient and generic n-dimensional array, providing a foundation for the
-software used to generated the first ever image of
+software used to generate the first ever image of
 a black hole. The direct imaging of a black hole is
 a major scientific accomplishment providing stunning, visual evidence of Einstein’s
 general theory of relativity. This achievement encompasses not only

content/en/case-studies/cricket-analytics.md

@@ -34,7 +34,7 @@ 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
+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.
@@ -72,7 +72,7 @@ metrics for improving match winning chances:
   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
+  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.
 
@@ -90,28 +90,28 @@ metrics for improving match winning chances:
   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
+  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
+  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
+  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
+  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
+  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".
@@ -135,20 +135,19 @@ for various kinds of cricket related sporting analytics such as:
   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)
-  provides 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) provides 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
+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.
+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
+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.
 

content/en/case-studies/gw-discov.md

@@ -22,8 +22,8 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig
 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
+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
@@ -56,7 +56,7 @@ made from warped spacetime.
 * **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
+    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
@@ -89,7 +89,7 @@ made from warped spacetime.
 
 {{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**Estimated gravitational-wave strain amplitude from GW150914**" attr="(**Graph Credits:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}}
 
-## NumPy’s Role in the detection of Gravitational Waves
+## 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.
@@ -103,7 +103,7 @@ 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 )
+  (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
@@ -116,7 +116,7 @@ speed.  Here are some examples:
 * 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
+  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" class="fig-center" alt="gwpy-numpy depgraph" caption="**Dependency graph showing how GwPy package depends on NumPy**" >}}
@@ -134,7 +134,7 @@ 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
+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.

content/en/citing-numpy.md

@@ -6,7 +6,7 @@ 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 papers:
 
 * Travis E, Oliphant. _A guide to NumPy_, USA: Trelgol Publishing, (2006).
-* Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. _The NumPy Array: A Structure for Efficient Numerical Computation_, Computing in Science & Engineering, 13, 22-30 (2011),[ DOI:10.1109/MCSE.2011.37](http://dx.doi.org/10.1109/MCSE.2011.37) ([publisher link](http://scitation.aip.org/content/aip/journal/cise/13/2/10.1109/MCSE.2011.37))
+* Stéfan van der Walt, S. Chris Colbert, and Gaël Varoquaux. _The NumPy Array: A Structure for Efficient Numerical Computation_, Computing in Science & Engineering, 13, 22-30 (2011),[ DOI:10.1109/MCSE.2011.37](http://dx.doi.org/10.1109/MCSE.2011.37) ([publisher link](http://scitation.aip.org/content/aip/journal/cise/13/2/10.1109/MCSE.2011.37))
 
 _In BibTeX format:_
 

content/en/code-of-conduct.md

@@ -19,7 +19,7 @@ We strive to:
 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:
+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.