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Merge pull request #766 from jarrodmillman/pre-commit-6.19.24
Update pre-commit repos
2 parents 9ee405f + 923134b commit a01e644

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.git-blame-ignore-revs

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.pre-commit-config.yaml

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args: [--no-pycodestyle, --max-line-length=88]
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: f8a3f8c471fb698229face5ed7640a64900b781e # frozen: v0.4.4
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rev: 02609a0003fd4903bd7f43852e5dfc82242a96db # frozen: v0.4.9
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hooks:
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- id: ruff
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args: ["--fix", "--show-fixes", "--exit-non-zero-on-fix"]
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- id: ruff-format
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- repo: https://github.com/codespell-project/codespell
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rev: "6e41aba91fb32e9feb741a6258eefeb9c6e4a482" # frozen: v2.2.6
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rev: "193cd7d27cd571f79358af09a8fb8997e54f8fff" # frozen: v2.3.0
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hooks:
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- id: codespell
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args: ["-w", "-L", "ans,nd,sav,mke,ags,mot,coo,whos,manuel"]

advanced/mathematical_optimization/index.rst

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See also :func:`scipy.optimize.approx_fprime` to find your errors.
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Synthetic exercices
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Synthetic exercises
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-------------------
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.. |flat_min_0| image:: auto_examples/images/sphx_glr_plot_exercise_flat_minimum_001.png
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:target: auto_examples/plot_exercise_ill_conditioned.html
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:align: right
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.. topic:: **Exercice: A simple (?) quadratic function**
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.. topic:: **Exercise: A simple (?) quadratic function**
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:class: green
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Optimize the following function, using K[0] as a starting point::
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Time your approach. Find the fastest approach. Why is BFGS not
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working well?
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.. topic:: **Exercice: A locally flat minimum**
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.. topic:: **Exercise: A locally flat minimum**
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:class: green
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Consider the function `exp(-1/(.1*x**2 + y**2)`. This function admits

intro/numpy/operations.rst

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The remainder of this chapter is not necessary to follow the rest of
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the intro part. But be sure to come back and finish this chapter, as
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well as to do some more :ref:`exercices <numpy_exercises>`.
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well as to do some more :ref:`exercises <numpy_exercises>`.

packages/statistics/examples/plot_wage_data.py

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This example uses seaborn to quickly plot various factors relating wages,
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experience and eduction.
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experience, and education.
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Seaborn (https://seaborn.pydata.org) is a library that combines
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visualization and statistical fits to show trends in data.

packages/statistics/index.rst

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A regression capturing the relation between one variable and another, eg
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wage and eduction, can be plotted using :func:`seaborn.lmplot`::
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wage, and education, can be plotted using :func:`seaborn.lmplot`::
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>>> seaborn.lmplot(y='WAGE', x='EDUCATION', data=data) # doctest: +SKIP
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