Use isort (take 2)

.pre-commit-config.yaml

@@ -38,6 +38,11 @@ repos:
         args: ["--fix", "--show-fixes", "--exit-non-zero-on-fix"]
       - id: ruff-format
 
+  - repo: https://github.com/pycqa/isort
+    rev: c235f5e450b4b84e58d114ed4c589cbf454175a3 # frozen: 5.13.2
+    hooks:
+      - id: isort
+
   - repo: https://github.com/codespell-project/codespell
     rev: "6e41aba91fb32e9feb741a6258eefeb9c6e4a482" # frozen: v2.2.6
     hooks:

advanced/advanced_numpy/examples/mandel-answer.pyx

@@ -42,11 +42,8 @@ cdef void mandel_single_point(double complex *z_in,
 # Pulls definitions from the NumPy C headers.
 # -------------------------------------------
 
-from numpy cimport import_array, import_ufunc
-from numpy cimport (PyUFunc_FromFuncAndData,
-                    PyUFuncGenericFunction)
-from numpy cimport NPY_CDOUBLE
-from numpy cimport PyUFunc_DD_D
+from numpy cimport (NPY_CDOUBLE, PyUFunc_DD_D, PyUFunc_FromFuncAndData,
+                    PyUFuncGenericFunction, import_array, import_ufunc)
 
 # Required module initialization
 # ------------------------------

advanced/advanced_numpy/examples/mandel.pyx

@@ -61,31 +61,17 @@ cdef void mandel_single_point(double complex *z_in,
 # Pulls definitions from the NumPy C headers.
 # -------------------------------------------
 
-from numpy cimport import_array, import_ufunc
-from numpy cimport (PyUFunc_FromFuncAndData,
-                    PyUFuncGenericFunction)
-from numpy cimport NPY_CDOUBLE, NP_DOUBLE, NPY_LONG
+from numpy cimport (NP_DOUBLE, NPY_CDOUBLE, NPY_LONG, PyUFunc_d_d, PyUFunc_D_D,
+                    PyUFunc_dd_d, PyUFunc_DD_D, PyUFunc_f_f, PyUFunc_F_F,
+                    PyUFunc_f_f_As_d_d, PyUFunc_F_F_As_D_D, PyUFunc_ff_f,
+                    PyUFunc_FF_F, PyUFunc_ff_f_As_dd_d, PyUFunc_FF_F_As_DD_D,
+                    PyUFunc_FromFuncAndData, PyUFunc_g_g, PyUFunc_G_G,
+                    PyUFunc_gg_g, PyUFunc_GG_G, PyUFuncGenericFunction,
+                    import_array, import_ufunc)
 
 # Import all pre-defined loop functions
 # you won't need all of them - keep the relevant ones
 
-from numpy cimport (
-    PyUFunc_f_f_As_d_d,
-    PyUFunc_d_d,
-    PyUFunc_f_f,
-    PyUFunc_g_g,
-    PyUFunc_F_F_As_D_D,
-    PyUFunc_F_F,
-    PyUFunc_D_D,
-    PyUFunc_G_G,
-    PyUFunc_ff_f_As_dd_d,
-    PyUFunc_ff_f,
-    PyUFunc_dd_d,
-    PyUFunc_gg_g,
-    PyUFunc_FF_F_As_DD_D,
-    PyUFunc_DD_D,
-    PyUFunc_FF_F,
-    PyUFunc_GG_G)
 
 
 # Required module initialization

advanced/advanced_numpy/examples/mandelplot.py

@@ -6,9 +6,10 @@
 
 """
 
-import numpy as np
 import mandel
 
+import numpy as np
+
 x = np.linspace(-1.7, 0.6, 1000)
 y = np.linspace(-1.4, 1.4, 1000)
 c = x[None, :] + 1j * y[:, None]

advanced/advanced_numpy/examples/pilbuffer-answer.py

@@ -6,9 +6,10 @@
 the buffer interface.
 """
 
-import numpy as np
 import Image
 
+import numpy as np
+
 # Let's make a sample image, RGBA format
 
 x = np.zeros((200, 200, 4), dtype=np.int8)

advanced/advanced_numpy/examples/pilbuffer.py

@@ -5,9 +5,10 @@
 Skeletton of the code to do an exercise using the buffer protocole.
 """
 
-import numpy as np
 import Image
 
+import numpy as np
+
 # Let's make a sample image, RGBA format
 
 x = np.zeros((200, 200, 4), dtype=np.int8)

advanced/advanced_numpy/examples/setup.py

@@ -6,10 +6,11 @@
 C sources.
 """
 
+from distutils.sysconfig import get_python_inc
 import os
-import sys
 from os.path import join
-from distutils.sysconfig import get_python_inc
+import sys
+
 import numpy
 from numpy.distutils.misc_util import get_numpy_include_dirs
 

advanced/advanced_numpy/examples/setup_myobject.py

@@ -6,7 +6,7 @@
 
 """
 
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
 
 setup(
     name="myobject",

advanced/advanced_numpy/examples/wavreader.py

@@ -6,6 +6,7 @@
 """
 
 import sys
+
 import numpy as np
 
 wav_header_dtype = np.dtype(

advanced/image_processing/examples/plot_face.py

@@ -5,9 +5,10 @@
 Small example to plot a raccoon face.
 """
 
-import scipy as sp
 import imageio.v3 as iio
 
+import scipy as sp
+
 f = sp.datasets.face()
 iio.imwrite("face.png", f)  # uses the Image module (PIL)
 

advanced/image_processing/examples/plot_face_tv_denoise.py

@@ -8,7 +8,6 @@
 import numpy as np
 import scipy as sp
 import matplotlib.pyplot as plt
-
 from skimage.restoration import denoise_tv_chambolle
 
 rng = np.random.default_rng(27446968)

advanced/image_processing/examples/plot_spectral_clustering.py

@@ -7,9 +7,8 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-
-from sklearn.feature_extraction import image
 from sklearn.cluster import spectral_clustering
+from sklearn.feature_extraction import image
 
 ################################################################################
 l = 100

advanced/image_processing/examples/plot_watershed_segmentation.py

@@ -6,10 +6,10 @@
 """
 
 import numpy as np
-from skimage.segmentation import watershed
-from skimage.feature import peak_local_max
-import matplotlib.pyplot as plt
 import scipy as sp
+import matplotlib.pyplot as plt
+from skimage.feature import peak_local_max
+from skimage.segmentation import watershed
 
 # Generate an initial image with two overlapping circles
 x, y = np.indices((80, 80))

advanced/interfacing_with_c/ctypes/cos_module.py

@@ -1,11 +1,11 @@
 """Example of wrapping cos function from math.h using ctypes."""
 
 import ctypes
+# OSX or linux
+from ctypes.util import find_library
 
 # find and load the library
 
-# OSX or linux
-from ctypes.util import find_library
 
 libm = ctypes.cdll.LoadLibrary(find_library("m"))
 

advanced/interfacing_with_c/ctypes_numpy/cos_doubles.py

@@ -1,9 +1,10 @@
 """Example of wrapping a C library function that accepts a C double array as
 input using the numpy.ctypeslib."""
 
+from ctypes import c_int
+
 import numpy as np
 import numpy.ctypeslib as npct
-from ctypes import c_int
 
 # input type for the cos_doubles function
 # must be a double array, with single dimension that is contiguous

advanced/interfacing_with_c/cython/setup.py

@@ -1,4 +1,5 @@
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
+
 from Cython.Distutils import build_ext
 
 setup(

advanced/interfacing_with_c/cython_numpy/setup.py

@@ -1,7 +1,9 @@
-from distutils.core import setup, Extension
-import numpy
+from distutils.core import Extension, setup
+
 from Cython.Distutils import build_ext
 
+import numpy
+
 setup(
     cmdclass={"build_ext": build_ext},
     ext_modules=[

advanced/interfacing_with_c/cython_simple/cos_module.pyx

@@ -2,5 +2,6 @@
 
 from libc.math cimport cos
 
+
 def cos_func(arg):
     return cos(arg)

advanced/interfacing_with_c/cython_simple/setup.py

@@ -1,4 +1,5 @@
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
+
 from Cython.Distutils import build_ext
 
 setup(

advanced/interfacing_with_c/numpy_c_api/setup.py

@@ -1,4 +1,5 @@
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
+
 import numpy
 
 # define the extension module

advanced/interfacing_with_c/numpy_c_api/test_cos_module_np.py

@@ -1,4 +1,5 @@
 import cos_module_np
+
 import numpy as np
 import matplotlib.pyplot as plt
 

advanced/interfacing_with_c/numpy_shared/test_cos_doubles.py

@@ -1,6 +1,7 @@
+import cos_doubles
+
 import numpy as np
 import matplotlib.pyplot as plt
-import cos_doubles
 
 x = np.arange(0, 2 * np.pi, 0.1)
 y = np.empty_like(x)

advanced/interfacing_with_c/python_c_api/setup.py

@@ -1,4 +1,4 @@
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
 
 # define the extension module
 cos_module = Extension("cos_module", sources=["cos_module.c"])

advanced/interfacing_with_c/swig/setup.py

@@ -1,3 +1,3 @@
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
 
 setup(ext_modules=[Extension("_cos_module", sources=["cos_module.c", "cos_module.i"])])

advanced/interfacing_with_c/swig_numpy/setup.py

@@ -1,4 +1,5 @@
-from distutils.core import setup, Extension
+from distutils.core import Extension, setup
+
 import numpy
 
 setup(

advanced/mathematical_optimization/examples/helper/compare_optimizers.py

@@ -9,20 +9,14 @@
 import pickle
 import sys
 
+from cost_functions import (CountingFunction, LoggingFunction, mk_gauss,
+                            mk_quad, rosenbrock, rosenbrock_hessian,
+                            rosenbrock_prime)
+
 import numpy as np
 import scipy as sp
 from joblib import Memory
 
-from cost_functions import (
-    mk_quad,
-    mk_gauss,
-    rosenbrock,
-    rosenbrock_prime,
-    rosenbrock_hessian,
-    LoggingFunction,
-    CountingFunction,
-)
-
 
 def my_partial(**kwargs):
     function = sp.optimize.minimize

advanced/mathematical_optimization/examples/plot_1d_optim.py

@@ -6,8 +6,8 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 import scipy as sp
+import matplotlib.pyplot as plt
 
 x = np.linspace(-1, 3, 100)
 x_0 = np.exp(-1)

advanced/mathematical_optimization/examples/plot_constraints.py

@@ -6,8 +6,8 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 import scipy as sp
+import matplotlib.pyplot as plt
 
 x, y = np.mgrid[-2.9:5.8:0.05, -2.5:5:0.05]
 x = x.T

advanced/mathematical_optimization/examples/plot_gradient_descent.py

@@ -6,23 +6,17 @@
 evolution of the optimizer.
 """
 
+import os
+import sys
+
 import numpy as np
-import matplotlib.pyplot as plt
 import scipy as sp
-
-import sys
-import os
+import matplotlib.pyplot as plt
 
 sys.path.append(os.path.abspath("helper"))
-from cost_functions import (
-    mk_quad,
-    mk_gauss,
-    rosenbrock,
-    rosenbrock_prime,
-    rosenbrock_hessian,
-    LoggingFunction,
-    CountingFunction,
-)
+from cost_functions import (CountingFunction, LoggingFunction, mk_gauss,
+                            mk_quad, rosenbrock, rosenbrock_hessian,
+                            rosenbrock_prime)
 
 x_min, x_max = -1, 2
 y_min, y_max = 2.25 / 3 * x_min - 0.2, 2.25 / 3 * x_max - 0.2

advanced/mathematical_optimization/examples/plot_non_bounds_constraints.py

@@ -7,8 +7,8 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 import scipy as sp
+import matplotlib.pyplot as plt
 
 x, y = np.mgrid[-2.03:4.2:0.04, -1.6:3.2:0.04]
 x = x.T

advanced/optimizing/demo.py

@@ -1,10 +1,10 @@
 # For this example to run, you also need the 'ica.py' file
 
+from ica import fastica
+
 import numpy as np
 import scipy as sp
 
-from ica import fastica
-
 
 # @profile  # uncomment this line to run with line_profiler
 def test():

advanced/optimizing/demo_opt.py

@@ -1,10 +1,10 @@
 # For this example to run, you also need the 'ica.py' file
 
+from ica import fastica
+
 import numpy as np
 import scipy as sp
 
-from ica import fastica
-
 
 def test():
     rng = np.random.default_rng()

advanced/optimizing/ica.py

@@ -3,9 +3,9 @@
 # License: GPL unless permission obtained otherwise
 # Look at algorithms in Tables 8.3 and 8.4 page 196 in the book: Independent Component Analysis, by Aapo et al.
 
-import numpy as np
 import types
 
+import numpy as np
 
 __all__ = ["fastica"]