Merge pull request #721 from jarrodmillman/pre-commit-update

Update pre-commit repos

.git-blame-ignore-revs

@@ -2,3 +2,4 @@
 87566f51031ddd54e2f3020bb62d7bf85289e408
 7cb7b365c51ab20d92745e5eefb95690e3ba2e14
 67c4efab4e3ec1dc16c18b9ab8c5cd5f375040e0
+8d00b416689c428b2bafdaad61ed3ec660ca06c2

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: f71fa2c1f9cf5cb705f73dffe4b21f7c61470ba9 # v4.4.0
+    rev: c4a0b883114b00d8d76b479c820ce7950211c99b # frozen: v4.5.0
     hooks:
       - id: check-added-large-files
       - id: check-ast
@@ -19,36 +19,36 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: efd8b1e16f05132acf6edcf2827eeab21e0e00db # frozen: v3.0.0
+    rev: ffb6a759a979008c0e6dff86e39f4745a2d9eac4 # frozen: v3.1.0
     hooks:
       - id: prettier
         files: \.(md|rst|yml|yaml)
         args: [--prose-wrap=preserve]
 
   - repo: https://github.com/psf/black
-    rev: 193ee766ca496871f93621d6b58d57a6564ff81b # frozen: 23.7.0
+    rev: 6fdf8a4af28071ed1d079c01122b34c5d587207a # frozen: 24.2.0
     hooks:
       - id: black
 
   - repo: https://github.com/adamchainz/blacken-docs
-    rev: 2e74dc4c30c336176e76e0426c66f09a4e79df9c # frozen: 1.15.0
+    rev: 960ead214cd1184149d366c6d27ca6c369ce46b6 # frozen: 1.16.0
     hooks:
       - id: blacken-docs
 
   - repo: https://github.com/MarcoGorelli/cython-lint
-    rev: 0d4704ff7d75ad487dcde6947c628be824060184 # frozen: v0.15.0
+    rev: 582556b052c898dc484a981bea48de3284a4b8ee # frozen: v0.16.0
     hooks:
       - id: cython-lint
         args: [--no-pycodestyle, --max-line-length=88]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: ade96e24aa3fa43c9d9b50d6cccae93d82765f40 # frozen: v0.0.282
+    rev: cc575a3e5800b42ffbb5182b53d87203b2c4fc26 # frozen: v0.2.2
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
 
   - repo: https://github.com/codespell-project/codespell
-    rev: "355e50e14fd03fe83e4ed9aa0489824b150b3b58" # frozen: v2.2.5
+    rev: "6e41aba91fb32e9feb741a6258eefeb9c6e4a482" # frozen: v2.2.6
     hooks:
       - id: codespell
-        args: ["-w", "-L", "ans,nd,sav,mke,ags,mot,coo,whos"]
+        args: ["-w", "-L", "ans,nd,sav,mke,ags,mot,coo,whos,manuel"]

advanced/advanced_numpy/examples/plots/plot_maskedstats.py

@@ -5,6 +5,7 @@
 Plot a masked statistics
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

advanced/debugging/to_debug.py

@@ -12,6 +12,7 @@
                 ridder:   778394 total function calls
                 bisect:  2148380 total function calls
 """
+
 from itertools import product
 
 import numpy as np

advanced/debugging/to_debug_solution.py

@@ -12,6 +12,7 @@
                 ridder:   778394 total function calls
                 bisect:  2148380 total function calls
 """
+
 from itertools import product
 
 import numpy as np

advanced/image_processing/examples/plot_display_face.py

@@ -4,6 +4,7 @@
 
 An example that displays a raccoon face with matplotlib.
 """
+
 import scipy as sp
 import matplotlib.pyplot as plt
 

advanced/mathematical_optimization/examples/helper/compare_optimizers.py

@@ -4,6 +4,7 @@
 
 Comparison of optimizers on various problems.
 """
+
 import functools
 import pickle
 import sys

advanced/mathematical_optimization/examples/helper/cost_functions.py

@@ -4,6 +4,7 @@
 
 Example cost functions or objective functions to optimize.
 """
+
 import numpy as np
 
 ###############################################################################

advanced/mathematical_optimization/examples/plot_constraints.py

@@ -4,6 +4,7 @@
 
 A small figure explaining optimization with constraints
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy as sp

advanced/mathematical_optimization/examples/plot_exercise_ill_conditioned.py

@@ -7,6 +7,7 @@
 first term in simply 2 * K.T @ K. Thus the conditioning of the
 problem can be judged from looking at the conditioning of K.
 """
+
 import time
 
 import numpy as np

advanced/mathematical_optimization/examples/plot_gradient_descent.py

@@ -5,6 +5,7 @@
 An example demoing gradient descent by creating figures that trace the
 evolution of the optimizer.
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy as sp

advanced/mathematical_optimization/examples/plot_noisy.py

@@ -4,6 +4,7 @@
 
 Draws a figure explaining noisy vs non-noisy optimization
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

advanced/mathematical_optimization/examples/plot_non_bounds_constraints.py

@@ -5,6 +5,7 @@
 An example showing how to do optimization with general constraints using
 SLSQP and cobyla.
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy as sp

advanced/mathematical_optimization/examples/plot_smooth.py

@@ -4,6 +4,7 @@
 
 Draws a figure to explain smooth versus non smooth optimization.
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

advanced/scipy_sparse/examples/direct_solve.py

@@ -6,6 +6,7 @@
 to CSR format and solve A x = b for x:and solve a linear system with a
 direct solver.
 """
+
 import numpy as np
 import scipy as sp
 import matplotlib.pyplot as plt

conf.py

@@ -248,7 +248,7 @@ def __init__(self, **options):
         }%
         \makeatother%
         \newpage\newpage
-    """
+    """,
     # 'tableofcontents': '\\pagestyle{normal}\\pagenumbering{arabic} %\\tableofcontents',
 }
 

intro/language/oop.rst

@@ -5,7 +5,7 @@ Python supports object-oriented programming (OOP). The goals of OOP are:
 
     * to organize the code, and
 
-    * to re-use code in similar contexts.
+    * to reuse code in similar contexts.
 
 
 Here is a small example: we create a Student *class*, which is an object
@@ -52,6 +52,6 @@ Thanks to classes and object-oriented programming, we can organize code
 with different classes corresponding to different objects we encounter
 (an Experiment class, an Image class, a Flow class, etc.), with their own
 methods and attributes. Then we can use inheritance to consider
-variations around a base class and **re-use** code. Ex : from a Flow
+variations around a base class and **reuse** code. Ex : from a Flow
 base class, we can create derived StokesFlow, TurbulentFlow,
 PotentialFlow, etc.

intro/numpy/examples/plot_basic1dplot.py

@@ -5,6 +5,7 @@
 Plot a basic 1D figure
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_basic2dplot.py

@@ -5,6 +5,7 @@
 Plot a basic 2D figure
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_chebyfit.py

@@ -5,6 +5,7 @@
 Plot noisy data and their polynomial fit in a Chebyshev basis
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_distances.py

@@ -5,6 +5,7 @@
 Plot distances in a grid
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_elephant.py

@@ -5,6 +5,7 @@
 Read and write images
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_mandelbrot.py

@@ -5,6 +5,7 @@
 Compute the Mandelbrot fractal and plot it
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 from numpy import newaxis

intro/numpy/examples/plot_polyfit.py

@@ -5,6 +5,7 @@
 Plot noisy data and their polynomial fit
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_populations.py

@@ -5,6 +5,7 @@
 Plot populations of hares, lynxes, and carrots
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/examples/plot_randomwalk.py

@@ -6,6 +6,7 @@
 together with the theoretical result
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/numpy/solutions/2_4_mandelbrot.py

@@ -1,6 +1,7 @@
 """
 Compute the Mandelbrot fractal
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 from numpy import newaxis

intro/scipy/image_processing/image_processing.rst

@@ -16,7 +16,7 @@ Changing orientation, resolution, .. ::
     >>> # Load an image
     >>> face = sp.datasets.face(gray=True)
 
-    >>> # Shift, roate and zoom it
+    >>> # Shift, rotate and zoom it
     >>> shifted_face = sp.ndimage.shift(face, (50, 50))
     >>> shifted_face2 = sp.ndimage.shift(face, (50, 50), mode='nearest')
     >>> rotated_face = sp.ndimage.rotate(face, 30)

intro/scipy/solutions/test_dir_sort.py

@@ -1,6 +1,7 @@
 """
 Test the dir_sort logic.
 """
+
 import dir_sort
 
 

intro/scipy/summary-exercises/examples/plot_gumbell_wind_speed_prediction.py

@@ -4,6 +4,7 @@
 
 Generate the exercise results on the Gumbell distribution
 """
+
 import numpy as np
 import scipy as sp
 import matplotlib.pyplot as plt

intro/scipy/summary-exercises/examples/plot_optimize_lidar_complex_data_fit.py

@@ -4,6 +4,7 @@
 
 Generate a chart of the data fitted by Gaussian curve
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy as sp

intro/scipy/summary-exercises/examples/plot_optimize_lidar_data.py

@@ -4,6 +4,7 @@
 
 Generate a chart of the data recorded by the lidar system
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

intro/scipy/summary-exercises/examples/plot_optimize_lidar_data_fit.py

@@ -4,6 +4,7 @@
 
 Generate a chart of the data fitted by Gaussian curve
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy as sp

intro/scipy/summary-exercises/examples/plot_sprog_annual_maxima.py

@@ -4,6 +4,7 @@
 
 Generate the exercise results on the Gumbell distribution
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

packages/scikit-learn/index.rst

@@ -1073,7 +1073,7 @@ Model Selection via Validation
 Cross-validation
 ----------------
 
-Cross-validation consists in repetively splitting the data in pairs of
+Cross-validation consists in repeatedly splitting the data in pairs of
 train and test sets, called 'folds'. Scikit-learn comes with a function
 to automatically compute score on all these folds. Here we do
 :class:`~sklearn.model_selection.KFold` with k=5. ::

packages/statistics/examples/plot_airfare.py

@@ -27,7 +27,7 @@
     # Download the file if it is not present
     r = requests.get(
         "https://users.stat.ufl.edu/~winner/data/airq4.dat",
-        verify=False  # Wouldn't normally do this, but this site's certificate
+        verify=False,  # Wouldn't normally do this, but this site's certificate
         # is not yet distributed
     )
     with open("airfares.txt", "wb") as f:

packages/statistics/examples/plot_iris_analysis.py

@@ -10,6 +10,7 @@
   of a continuous confound
 
 """
+
 import matplotlib.pyplot as plt
 
 import pandas

packages/statistics/examples/plot_regression.py

@@ -5,6 +5,7 @@
 Fit a simple linear regression using 'statsmodels', compute corresponding
 p-values.
 """
+
 # Original author: Thomas Haslwanter
 
 import numpy as np

packages/statistics/examples/plot_regression_3d.py

@@ -7,6 +7,7 @@
 
 Also shows how to make 3d plots.
 """
+
 # Original author: Thomas Haslwanter
 
 import numpy as np

packages/statistics/examples/solutions/plot_brain_size.py

@@ -10,6 +10,7 @@
 data type, statsmodels is able to automatically infer this.
 
 """
+
 import pandas
 from statsmodels.formula.api import ols
 

packages/statistics/index.rst

@@ -811,7 +811,7 @@ Categorical variables can be plotted as the hue::
    .. tip::
 
      To switch back to seaborn settings, or understand better styling in
-     seaborn, see the `relevent section of the seaborn documentation
+     seaborn, see the `relevant section of the seaborn documentation
      <https://seaborn.pydata.org/tutorial/aesthetics.html>`_.