Add MyPy type checking

.pre-commit-config.yaml

@@ -38,6 +38,38 @@ repos:
         args: ["--fix", "--show-fixes", "--exit-non-zero-on-fix"]
       - id: ruff-format
 
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: "v1.10.0"
+    hooks:
+      - id: mypy
+        additional_dependencies:
+          - types-aiofiles
+          - types-requests
+          - pandas-stubs
+          - types-pillow
+          - matplotlib
+        exclude: |
+          (?x)(
+            ^build/
+            | ^pyximages/
+            | conf\.py$
+            | .*/setup.*\.py$
+            | .*/demo.py$
+            | .*/auto_examples/
+            | advanced/mathematical_optimization/examples/plot_gradient_descent\.py$
+            | advanced/mathematical_optimization/examples/helper/compare_optimizers\.py$
+            | advanced/advanced_numpy/examples/view-colors\.py$
+            | advanced/advanced_numpy/examples/stride-diagonals\.py$
+            | advanced/interfacing_with_c/cython_numpy/test_cos_doubles\.py$
+            | advanced/interfacing_with_c/numpy_shared/test_cos_doubles\.py$
+            | advanced/interfacing_with_c/swig.*\.py$
+            | advanced/advanced_numpy/examples/myobject_test\.py$
+            | advanced/interfacing_with_c/numpy_shared/test_cos_doubles\.py$
+            | advanced/interfacing_with_c/numpy_c_api/test_cos_module_np\.py$
+            | intro/numpy/solutions/2_a_call_fortran\.py$
+            | advanced/advanced_numpy/examples/mandelplot\.py$
+          )
+
   - repo: https://github.com/codespell-project/codespell
     rev: 193cd7d27cd571f79358af09a8fb8997e54f8fff # frozen: v2.3.0
     hooks:

advanced/advanced_numpy/examples/pilbuffer-answer.py

@@ -7,26 +7,21 @@
 """
 
 import numpy as np
-import Image
+from PIL import Image
 
 # Let's make a sample image, RGBA format
 
-x = np.zeros((200, 200, 4), dtype=np.int8)
+x = np.zeros((200, 200, 4), dtype=np.uint8)
 
-x[:, :, 0] = 254  # red
+x[:, :, 0] = 255  # red
 x[:, :, 3] = 255  # opaque
 
 data = x.view(np.int32)  # Check that you understand why this is OK!
 
 img = Image.frombuffer("RGBA", (200, 200), data)
 img.save("test.png")
 
-#
 # Modify the original data, and save again.
-#
-# It turns out that PIL, which knows next to nothing about NumPy,
-# happily shares the same data.
-#
 
-x[:, :, 1] = 254
+x[:, :, 1] = 255
 img.save("test2.png")

advanced/advanced_numpy/examples/pilbuffer.py

@@ -1,21 +1,23 @@
 """
-Exercise: using the buffer protocole
-====================================
+Exercise: using the buffer protocol
+===================================
 
-Skeletton of the code to do an exercise using the buffer protocole.
+Skeleton of the code to do an exercise using the buffer protocole.
 """
 
 import numpy as np
-import Image
+from PIL import Image
 
 # Let's make a sample image, RGBA format
 
-x = np.zeros((200, 200, 4), dtype=np.int8)
+x = np.zeros((200, 200, 4), dtype=np.uint8)
 
-# TODO: fill `x` with fully opaque red [255, 0, 0, 255]
+# TODO: fill `data` with fully opaque red [255, 0, 0, 255]
 
-# TODO: RGBA images consist of 32-bit integers whose bytes are [RR,GG,BB,AA]
-#       How to get that from ``x``?
+# TODO: `x` is an array of bytes (8-bit integers)
+#       What we need for PIL to understand this data is RGBA array,
+#       where each element is a 32-bit integer, with bytes [RR,GG,BB,AA].
+#       How do we convert `x` to such an array, called `data`?
 
 data = ...
 

advanced/advanced_numpy/examples/plots/plot_maskedstats.py

@@ -10,7 +10,7 @@
 import matplotlib.pyplot as plt
 
 data = np.loadtxt("../../../../data/populations.txt")
-populations = np.ma.masked_array(data[:, 1:])
+populations = np.ma.masked_array(data[:, 1:])  # type: ignore[var-annotated]
 year = data[:, 0]
 
 bad_years = ((year >= 1903) & (year <= 1910)) | ((year >= 1917) & (year <= 1918))

advanced/debugging/to_debug_solution.py

@@ -57,11 +57,7 @@ def compare_optimizers(optimizers):
     """
     random_a = -1.3 + rng.random(size=100)
     random_b = 0.3 + rng.random(size=100)
-    param_grid = product(FUNCTIONS, random_a, random_b)
-    values = []
-    for value in param_grid:
-        values.append(value)
-    param_grid = values
+    param_grid = list(product(FUNCTIONS, random_a, random_b))
     print("Benching 1D root-finder optimizers from scipy.optimize:")
     for optimizer in OPTIMIZERS:
         print(

advanced/debugging/wiener_filtering.py

@@ -46,10 +46,10 @@ def iterated_wiener(noisy_img, size=3):
 face = sp.datasets.face(gray=True)
 noisy_face = face + 20 * rng.integers(3, size=face.shape) - 30
 
-plt.matshow(face[cut], cmap=plt.cm.gray)
-plt.matshow(noisy_face[cut], cmap=plt.cm.gray)
+plt.matshow(face[cut], cmap="gray")
+plt.matshow(noisy_face[cut], cmap="gray")
 
 denoised_face = iterated_wiener(noisy_face)
-plt.matshow(denoised_face[cut], cmap=plt.cm.gray)
+plt.matshow(denoised_face[cut], cmap="gray")
 
 plt.show()

advanced/image_processing/examples/plot_GMM.py

@@ -47,7 +47,7 @@
 plt.text(0.57, 0.8, "histogram", fontsize=20, transform=plt.gca().transAxes)
 plt.yticks([])
 plt.subplot(133)
-plt.imshow(binary_img, cmap=plt.cm.gray, interpolation="nearest")
+plt.imshow(binary_img, cmap="gray", interpolation="nearest")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)

advanced/image_processing/examples/plot_block_mean.py

@@ -19,7 +19,7 @@
 block_mean.shape = (sx // 4, sy // 6)
 
 plt.figure(figsize=(5, 5))
-plt.imshow(block_mean, cmap=plt.cm.gray)
+plt.imshow(block_mean, cmap="gray")
 plt.axis("off")
 
 plt.show()

advanced/image_processing/examples/plot_blur.py

@@ -15,13 +15,13 @@
 
 plt.figure(figsize=(9, 3))
 plt.subplot(131)
-plt.imshow(blurred_face, cmap=plt.cm.gray)
+plt.imshow(blurred_face, cmap="gray")
 plt.axis("off")
 plt.subplot(132)
-plt.imshow(very_blurred, cmap=plt.cm.gray)
+plt.imshow(very_blurred, cmap="gray")
 plt.axis("off")
 plt.subplot(133)
-plt.imshow(local_mean, cmap=plt.cm.gray)
+plt.imshow(local_mean, cmap="gray")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0, hspace=0.0, top=0.99, bottom=0.01, left=0.01, right=0.99)

advanced/image_processing/examples/plot_clean_morpho.py

@@ -36,16 +36,16 @@
 l = 128
 
 plt.subplot(141)
-plt.imshow(binary_img[:l, :l], cmap=plt.cm.gray)
+plt.imshow(binary_img[:l, :l], cmap="gray")
 plt.axis("off")
 plt.subplot(142)
-plt.imshow(open_img[:l, :l], cmap=plt.cm.gray)
+plt.imshow(open_img[:l, :l], cmap="gray")
 plt.axis("off")
 plt.subplot(143)
-plt.imshow(close_img[:l, :l], cmap=plt.cm.gray)
+plt.imshow(close_img[:l, :l], cmap="gray")
 plt.axis("off")
 plt.subplot(144)
-plt.imshow(mask[:l, :l], cmap=plt.cm.gray)
+plt.imshow(mask[:l, :l], cmap="gray")
 plt.contour(close_img[:l, :l], [0.5], linewidths=2, colors="r")
 plt.axis("off")
 

advanced/image_processing/examples/plot_denoising.py

@@ -34,7 +34,7 @@
 plt.axis("off")
 plt.title("Median filter", fontsize=20)
 plt.subplot(144)
-plt.imshow(np.abs(im - im_med), cmap=plt.cm.hot, interpolation="nearest")
+plt.imshow(np.abs(im - im_med), cmap="hot", interpolation="nearest")
 plt.axis("off")
 plt.title("Error", fontsize=20)
 

advanced/image_processing/examples/plot_display_face.py

@@ -13,14 +13,14 @@
 plt.figure(figsize=(10, 3.6))
 
 plt.subplot(131)
-plt.imshow(f, cmap=plt.cm.gray)
+plt.imshow(f, cmap="gray")
 
 plt.subplot(132)
-plt.imshow(f, cmap=plt.cm.gray, vmin=30, vmax=200)
+plt.imshow(f, cmap="gray", vmin=30, vmax=200)
 plt.axis("off")
 
 plt.subplot(133)
-plt.imshow(f, cmap=plt.cm.gray)
+plt.imshow(f, cmap="gray")
 plt.contour(f, [50, 200])
 plt.axis("off")
 

advanced/image_processing/examples/plot_face_denoise.py

@@ -23,15 +23,15 @@
 plt.figure(figsize=(12, 2.8))
 
 plt.subplot(131)
-plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.imshow(noisy, cmap="gray", vmin=40, vmax=220)
 plt.axis("off")
 plt.title("noisy", fontsize=20)
 plt.subplot(132)
-plt.imshow(gauss_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.imshow(gauss_denoised, cmap="gray", vmin=40, vmax=220)
 plt.axis("off")
 plt.title("Gaussian filter", fontsize=20)
 plt.subplot(133)
-plt.imshow(med_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.imshow(med_denoised, cmap="gray", vmin=40, vmax=220)
 plt.axis("off")
 plt.title("Median filter", fontsize=20)
 

advanced/image_processing/examples/plot_face_tv_denoise.py

@@ -24,17 +24,17 @@
 plt.figure(figsize=(12, 2.8))
 
 plt.subplot(131)
-plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.imshow(noisy, cmap="gray", vmin=40, vmax=220)
 plt.axis("off")
 plt.title("noisy", fontsize=20)
 plt.subplot(132)
-plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.imshow(tv_denoised, cmap="gray", vmin=40, vmax=220)
 plt.axis("off")
 plt.title("TV denoising", fontsize=20)
 
 tv_denoised = denoise_tv_chambolle(noisy, weight=50)
 plt.subplot(133)
-plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.imshow(tv_denoised, cmap="gray", vmin=40, vmax=220)
 plt.axis("off")
 plt.title("(more) TV denoising", fontsize=20)
 

advanced/image_processing/examples/plot_find_edges.py

@@ -23,7 +23,7 @@
 
 plt.figure(figsize=(16, 5))
 plt.subplot(141)
-plt.imshow(im, cmap=plt.cm.gray)
+plt.imshow(im, cmap="gray")
 plt.axis("off")
 plt.title("square", fontsize=20)
 plt.subplot(142)

advanced/image_processing/examples/plot_find_object.py

@@ -35,7 +35,7 @@
 roi = im[slice_x, slice_y]
 
 plt.figure(figsize=(4, 2))
-plt.axes([0, 0, 1, 1])
+plt.axes((0, 0, 1, 1))
 plt.imshow(roi)
 plt.axis("off")
 

advanced/image_processing/examples/plot_geom_face.py

@@ -23,19 +23,19 @@
 
 
 plt.subplot(151)
-plt.imshow(face, cmap=plt.cm.gray)
+plt.imshow(face, cmap="gray")
 plt.axis("off")
 plt.subplot(152)
-plt.imshow(crop_face, cmap=plt.cm.gray)
+plt.imshow(crop_face, cmap="gray")
 plt.axis("off")
 plt.subplot(153)
-plt.imshow(flip_ud_face, cmap=plt.cm.gray)
+plt.imshow(flip_ud_face, cmap="gray")
 plt.axis("off")
 plt.subplot(154)
-plt.imshow(rotate_face, cmap=plt.cm.gray)
+plt.imshow(rotate_face, cmap="gray")
 plt.axis("off")
 plt.subplot(155)
-plt.imshow(rotate_face_noreshape, cmap=plt.cm.gray)
+plt.imshow(rotate_face_noreshape, cmap="gray")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)

advanced/image_processing/examples/plot_granulo.py

@@ -44,7 +44,7 @@ def granulometry(data, sizes=None):
 plt.figure(figsize=(6, 2.2))
 
 plt.subplot(121)
-plt.imshow(mask, cmap=plt.cm.gray)
+plt.imshow(mask, cmap="gray")
 opened = sp.ndimage.binary_opening(mask, structure=disk_structure(10))
 opened_more = sp.ndimage.binary_opening(mask, structure=disk_structure(14))
 plt.contour(opened, [0.5], colors="b", linewidths=2)

advanced/image_processing/examples/plot_greyscale_dilation.py

@@ -23,16 +23,16 @@
 
 plt.figure(figsize=(12.5, 3))
 plt.subplot(141)
-plt.imshow(im, interpolation="nearest", cmap=plt.cm.nipy_spectral)
+plt.imshow(im, interpolation="nearest", cmap="nipy_spectral")
 plt.axis("off")
 plt.subplot(142)
-plt.imshow(bigger_points, interpolation="nearest", cmap=plt.cm.nipy_spectral)
+plt.imshow(bigger_points, interpolation="nearest", cmap="nipy_spectral")
 plt.axis("off")
 plt.subplot(143)
-plt.imshow(dist, interpolation="nearest", cmap=plt.cm.nipy_spectral)
+plt.imshow(dist, interpolation="nearest", cmap="nipy_spectral")
 plt.axis("off")
 plt.subplot(144)
-plt.imshow(dilate_dist, interpolation="nearest", cmap=plt.cm.nipy_spectral)
+plt.imshow(dilate_dist, interpolation="nearest", cmap="nipy_spectral")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0, hspace=0.02, top=0.99, bottom=0.01, left=0.01, right=0.99)

advanced/image_processing/examples/plot_histo_segmentation.py

@@ -39,7 +39,7 @@
 plt.text(0.57, 0.8, "histogram", fontsize=20, transform=plt.gca().transAxes)
 plt.yticks([])
 plt.subplot(133)
-plt.imshow(binary_img, cmap=plt.cm.gray, interpolation="nearest")
+plt.imshow(binary_img, cmap="gray", interpolation="nearest")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)

advanced/image_processing/examples/plot_interpolation_face.py

@@ -13,11 +13,11 @@
 plt.figure(figsize=(8, 4))
 
 plt.subplot(1, 2, 1)
-plt.imshow(f[320:340, 510:530], cmap=plt.cm.gray)
+plt.imshow(f[320:340, 510:530], cmap="gray")
 plt.axis("off")
 
 plt.subplot(1, 2, 2)
-plt.imshow(f[320:340, 510:530], cmap=plt.cm.gray, interpolation="nearest")
+plt.imshow(f[320:340, 510:530], cmap="gray", interpolation="nearest")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0.02, hspace=0.02, top=1, bottom=0, left=0, right=1)

advanced/image_processing/examples/plot_measure_data.py

@@ -33,10 +33,10 @@
 plt.figure(figsize=(6, 3))
 
 plt.subplot(121)
-plt.imshow(label_im, cmap=plt.cm.nipy_spectral)
+plt.imshow(label_im, cmap="nipy_spectral")
 plt.axis("off")
 plt.subplot(122)
-plt.imshow(label_clean, vmax=nb_labels, cmap=plt.cm.nipy_spectral)
+plt.imshow(label_clean, vmax=nb_labels, cmap="nipy_spectral")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0.01, hspace=0.01, top=1, bottom=0, left=0, right=1)

advanced/image_processing/examples/plot_numpy_array.py

@@ -22,8 +22,8 @@
 face[range(400), range(400)] = 255
 
 plt.figure(figsize=(3, 3))
-plt.axes([0, 0, 1, 1])
-plt.imshow(face, cmap=plt.cm.gray)
+plt.axes((0, 0, 1, 1))
+plt.imshow(face, cmap="gray")
 plt.axis("off")
 
 plt.show()

advanced/image_processing/examples/plot_propagation.py

@@ -22,13 +22,13 @@
 
 plt.figure(figsize=(9.5, 3))
 plt.subplot(131)
-plt.imshow(square, cmap=plt.cm.gray, interpolation="nearest")
+plt.imshow(square, cmap="gray", interpolation="nearest")
 plt.axis("off")
 plt.subplot(132)
-plt.imshow(open_square, cmap=plt.cm.gray, interpolation="nearest")
+plt.imshow(open_square, cmap="gray", interpolation="nearest")
 plt.axis("off")
 plt.subplot(133)
-plt.imshow(reconstruction, cmap=plt.cm.gray, interpolation="nearest")
+plt.imshow(reconstruction, cmap="gray", interpolation="nearest")
 plt.axis("off")
 
 plt.subplots_adjust(wspace=0, hspace=0.02, top=0.99, bottom=0.01, left=0.01, right=0.99)