Move away from random.random in favor of random.rand

skimage/color/tests/test_colorconv.py

@@ -225,7 +225,7 @@ class TestColorconv(TestCase):
         assert_equal(g.shape, (1, 1))
 
     def test_rgb2grey_on_grey(self):
-        rgb2grey(np.random.random((5, 5)))
+        rgb2grey(np.random.rand(5, 5))
 
     # test matrices for xyz2lab and lab2xyz generated using http://www.easyrgb.com/index.php?X=CALC
     # Note: easyrgb website displays xyz*100
@@ -351,7 +351,7 @@ def test_gray2rgb():
 
 
 def test_gray2rgb_rgb():
-    x = np.random.random((5, 5, 4))
+    x = np.random.rand(5, 5, 4)
     y = gray2rgb(x)
     assert_equal(x, y)
 

skimage/feature/brief.py

@@ -71,7 +71,7 @@ class BRIEF(DescriptorExtractor):
            [0, 0, 1, 1, 1, 1, 0, 0],
            [0, 0, 1, 1, 1, 1, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
+           [0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.int32)
     >>> square2 = np.zeros((9, 9), dtype=np.int32)
     >>> square2[2:7, 2:7] = 1
     >>> square2
@@ -83,7 +83,7 @@ class BRIEF(DescriptorExtractor):
            [0, 0, 1, 1, 1, 1, 1, 0, 0],
            [0, 0, 1, 1, 1, 1, 1, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
+           [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.int32)
     >>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1)
     >>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
     >>> extractor = BRIEF(patch_size=5)

skimage/feature/tests/test_censure.py

@@ -10,8 +10,8 @@ np.random.seed(0)
 
 def test_censure_on_rectangular_images():
     """Censure feature detector should work on 2D image of any shape."""
-    rect_image = np.random.random((300, 200))
-    square_image = np.random.random((200, 200))
+    rect_image = np.random.rand(300, 200)
+    square_image = np.random.rand(200, 200)
     CENSURE().detect((square_image))
     CENSURE().detect((rect_image))
 

skimage/feature/tests/test_peak.py

@@ -5,6 +5,9 @@ import scipy.ndimage
 from skimage.feature import peak
 
 
+np.random.seed(21)
+
+
 def test_trivial_case():
     trivial = np.zeros((25, 25))
     peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True)
@@ -17,7 +20,7 @@ def test_noisy_peaks():
     peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
 
     # image with noise of amplitude 0.8 and peaks of amplitude 1
-    image = 0.8 * np.random.random((20, 20))
+    image = 0.8 * np.random.rand(20, 20)
     for r, c in peak_locations:
         image[r, c] = 1
 
@@ -80,7 +83,6 @@ def test_num_peaks():
 
 
 def test_reorder_labels():
-    np.random.seed(21)
     image = np.random.uniform(size=(40, 60))
     i, j = np.mgrid[0:40, 0:60]
     labels = 1 + (i >= 20) + (j >= 30) * 2
@@ -100,7 +102,6 @@ def test_reorder_labels():
 
 
 def test_indices_with_labels():
-    np.random.seed(21)
     image = np.random.uniform(size=(40, 60))
     i, j = np.mgrid[0:40, 0:60]
     labels = 1 + (i >= 20) + (j >= 30) * 2
@@ -233,7 +234,6 @@ def test_adjacent_different_objects():
 
 
 def test_four_quadrants():
-    np.random.seed(21)
     image = np.random.uniform(size=(40, 60))
     i, j = np.mgrid[0:40, 0:60]
     labels = 1 + (i >= 20) + (j >= 30) * 2
@@ -255,7 +255,6 @@ def test_disk():
     '''regression test of img-1194, footprint = [1]
     Test peak.peak_local_max when every point is a local maximum
     '''
-    np.random.seed(31)
     image = np.random.uniform(size=(10, 20))
     footprint = np.array([[1]])
     result = peak.peak_local_max(image, labels=np.ones((10, 20)),

skimage/feature/tests/test_texture.py

@@ -199,7 +199,7 @@ class TestLBP():
         np.random.seed(13141516)
 
         # Create random image with known variance.
-        image = np.random.random((500, 500))
+        image = np.random.rand(500, 500)
         target_std = 0.3
         image = image / image.std() * target_std
 

skimage/filter/_canny.py

@@ -112,7 +112,7 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
     >>> # Generate noisy image of a square
     >>> im = np.zeros((256, 256))
     >>> im[64:-64, 64:-64] = 1
-    >>> im += 0.2 * np.random.random(im.shape)
+    >>> im += 0.2 * np.random.rand(*im.shape)
     >>> # First trial with the Canny filter, with the default smoothing
     >>> edges1 = filter.canny(im)
     >>> # Increase the smoothing for better results

skimage/filter/rank/tests/test_rank.py

@@ -46,7 +46,7 @@ def test_random_sizes():
 def test_compare_with_cmorph_dilate():
     # compare the result of maximum filter with dilate
 
-    image = (np.random.random((100, 100)) * 256).astype(np.uint8)
+    image = (np.random.rand(100, 100) * 256).astype(np.uint8)
     out = np.empty_like(image)
     mask = np.ones(image.shape, dtype=np.uint8)
 
@@ -60,7 +60,7 @@ def test_compare_with_cmorph_dilate():
 def test_compare_with_cmorph_erode():
     # compare the result of maximum filter with erode
 
-    image = (np.random.random((100, 100)) * 256).astype(np.uint8)
+    image = (np.random.rand(100, 100) * 256).astype(np.uint8)
     out = np.empty_like(image)
     mask = np.ones(image.shape, dtype=np.uint8)
 
@@ -145,7 +145,7 @@ def test_inplace_output():
     # rank filters are not supposed to filter inplace
 
     selem = disk(20)
-    image = (np.random.random((500, 500)) * 256).astype(np.uint8)
+    image = (np.random.rand(500, 500) * 256).astype(np.uint8)
     out = image
     assert_raises(NotImplementedError, rank.mean, image, selem, out=out)
 

skimage/graph/tests/test_mcp.py

@@ -5,6 +5,7 @@ from numpy.testing import (assert_array_equal,
 
 import skimage.graph.mcp as mcp
 
+np.random.seed(0)
 a = np.ones((8, 8), dtype=np.float32)
 a[1:-1, 1] = 0
 a[1, 1:-1] = 0
@@ -133,15 +134,14 @@ def test_crashing():
 
 def _test_random(shape):
     # Just tests for crashing -- not for correctness.
-    np.random.seed(0)
-    a = np.random.random(shape).astype(np.float32)
+    a = np.random.rand(*shape).astype(np.float32)
     starts = [[0] * len(shape), [-1] * len(shape),
-              (np.random.random(len(shape)) * shape).astype(int)]
-    ends = [(np.random.random(len(shape)) * shape).astype(int)
+              (np.random.rand(len(shape)) * shape).astype(int)]
+    ends = [(np.random.rand(len(shape)) * shape).astype(int)
             for i in range(4)]
     m = mcp.MCP(a, fully_connected=True)
     costs, offsets = m.find_costs(starts)
-    for point in [(np.random.random(len(shape)) * shape).astype(int)
+    for point in [(np.random.rand(len(shape)) * shape).astype(int)
                   for i in range(4)]:
         m.traceback(point)
     m._reset()

skimage/io/_io.py

@@ -193,7 +193,7 @@ def show():
     >>> import skimage.io as io
 
     >>> for i in range(4):
-    ...     io.imshow(np.random.random((50, 50)))
+    ...     io.imshow(np.random.rand(50, 50))
     >>> io.show() # doctest: +SKIP
 
     '''

skimage/io/tests/test_freeimage.py

@@ -64,7 +64,7 @@ def test_imread_uint16_big_endian():
 @skipif(not FI_available)
 def test_write_multipage():
     shape = (64, 64, 64)
-    x = np.ones(shape, dtype=np.uint8) * np.random.random(shape) * 255
+    x = np.ones(shape, dtype=np.uint8) * np.random.rand(*shape) * 255
     x = x.astype(np.uint8)
     f = NamedTemporaryFile(suffix='.tif')
     fname = f.name
@@ -93,7 +93,7 @@ class TestSave:
             ]:
             tests = [(d, f) for d in dtype for f in format]
             for d, f in tests:
-                x = np.ones(shape, dtype=d) * np.random.random(shape)
+                x = np.ones(shape, dtype=d) * np.random.rand(*shape)
                 if not np.issubdtype(d, float):
                     x = (x * 255).astype(d)
                 yield self.roundtrip, d, x, f

skimage/io/tests/test_imread.py

@@ -68,7 +68,7 @@ class TestSave:
     def test_imsave_roundtrip(self):
         dtype = np.uint8
         for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]:
-            x = np.ones(shape, dtype=dtype) * np.random.random(shape)
+            x = np.ones(shape, dtype=dtype) * np.random.rand(*shape)
 
             if np.issubdtype(dtype, float):
                 yield self.roundtrip, x, 255

skimage/io/tests/test_pil.py

@@ -126,7 +126,7 @@ class TestSave:
     def test_imsave_roundtrip(self):
         for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]:
             for dtype in (np.uint8, np.uint16, np.float32, np.float64):
-                x = np.ones(shape, dtype=dtype) * np.random.random(shape)
+                x = np.ones(shape, dtype=dtype) * np.random.rand(*shape)
 
                 if np.issubdtype(dtype, float):
                     yield self.roundtrip, dtype, x, 255

skimage/io/tests/test_plugin_util.py

@@ -8,10 +8,10 @@ np.random.seed(0)
 
 class TestPrepareForDisplay:
     def test_basic(self):
-        prepare_for_display(np.random.random((10, 10)))
+        prepare_for_display(np.random.rand(10, 10))
 
     def test_dtype(self):
-        x = prepare_for_display(np.random.random((10, 15)))
+        x = prepare_for_display(np.random.rand(10, 15))
         assert x.dtype == np.dtype(np.uint8)
 
     def test_grey(self):
@@ -21,18 +21,18 @@ class TestPrepareForDisplay:
         assert x[3, 2, 0] == 255
 
     def test_colour(self):
-        prepare_for_display(np.random.random((10, 10, 3)))
+        prepare_for_display(np.random.rand(10, 10, 3))
 
     def test_alpha(self):
-        prepare_for_display(np.random.random((10, 10, 4)))
+        prepare_for_display(np.random.rand(10, 10, 4))
 
     @raises(ValueError)
     def test_wrong_dimensionality(self):
-        prepare_for_display(np.random.random((10, 10, 1, 1)))
+        prepare_for_display(np.random.rand(10, 10, 1, 1))
 
     @raises(ValueError)
     def test_wrong_depth(self):
-        prepare_for_display(np.random.random((10, 10, 5)))
+        prepare_for_display(np.random.rand(10, 10, 5))
 
 
 class TestWindowManager:

skimage/io/tests/test_simpleitk.py

@@ -88,7 +88,7 @@ class TestSave:
     def test_imsave_roundtrip(self):
         for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]:
             for dtype in (np.uint8, np.uint16, np.float32, np.float64):
-                x = np.ones(shape, dtype=dtype) * np.random.random(shape)
+                x = np.ones(shape, dtype=dtype) * np.random.rand(*shape)
 
                 if np.issubdtype(dtype, float):
                     yield self.roundtrip, dtype, x

skimage/io/tests/test_tifffile.py

@@ -51,7 +51,7 @@ class TestSave:
     def test_imsave_roundtrip(self):
         for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]:
             for dtype in (np.uint8, np.uint16, np.float32, np.float64):
-                x = np.ones(shape, dtype=dtype) * np.random.random(shape)
+                x = np.ones(shape, dtype=dtype) * np.random.rand(*shape)
 
                 if not np.issubdtype(dtype, float):
                     x = (x * 255).astype(dtype)

skimage/measure/fit.py

@@ -586,7 +586,8 @@ def ransac(data, model_class, min_samples, residual_threshold,
     Robustly estimate geometric transformation:
 
     >>> from skimage.transform import SimilarityTransform
-    >>> src = 100 * np.random.random((50, 2))
+    >>> np.random.seed(0)
+    >>> src = 100 * np.random.rand(50, 2)
     >>> model0 = SimilarityTransform(scale=0.5, rotation=1,
     ...                              translation=(10, 20))
     >>> dst = model0(src)

skimage/measure/tests/test_structural_similarity.py

@@ -9,8 +9,8 @@ np.random.seed(1234)
 
 def test_ssim_patch_range():
     N = 51
-    X = (np.random.random((N, N)) * 255).astype(np.uint8)
-    Y = (np.random.random((N, N)) * 255).astype(np.uint8)
+    X = (np.random.rand(N, N) * 255).astype(np.uint8)
+    Y = (np.random.rand(N, N) * 255).astype(np.uint8)
 
     assert(ssim(X, Y, win_size=N) < 0.1)
     assert_equal(ssim(X, X, win_size=N), 1)
@@ -18,8 +18,8 @@ def test_ssim_patch_range():
 
 def test_ssim_image():
     N = 100
-    X = (np.random.random((N, N)) * 255).astype(np.uint8)
-    Y = (np.random.random((N, N)) * 255).astype(np.uint8)
+    X = (np.random.rand(N, N) * 255).astype(np.uint8)
+    Y = (np.random.rand(N, N) * 255).astype(np.uint8)
 
     S0 = ssim(X, X, win_size=3)
     assert_equal(S0, 1)
@@ -31,8 +31,8 @@ def test_ssim_image():
 # NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6)
 def test_ssim_grad():
     N = 30
-    X = np.random.random((N, N)) * 255
-    Y = np.random.random((N, N)) * 255
+    X = np.random.rand(N, N) * 255
+    Y = np.random.rand(N, N) * 255
 
     f = ssim(X, Y, dynamic_range=255)
     g = ssim(X, Y, dynamic_range=255, gradient=True)
@@ -44,8 +44,8 @@ def test_ssim_grad():
 
 def test_ssim_dtype():
     N = 30
-    X = np.random.random((N, N))
-    Y = np.random.random((N, N))
+    X = np.random.rand(N, N)
+    Y = np.random.rand(N, N)
 
     S1 = ssim(X, Y)
 

skimage/morphology/tests/test_ccomp.py

@@ -27,7 +27,7 @@ class TestConnectedComponents:
         assert self.x[0, 2] == 3
 
     def test_random(self):
-        x = (np.random.random((20, 30)) * 5).astype(np.int)
+        x = (np.random.rand(20, 30) * 5).astype(np.int)
 
         with catch_warnings():
             labels = label(x)

skimage/restoration/tests/test_denoise.py

@@ -15,7 +15,7 @@ def test_denoise_tv_chambolle_2d():
     # lena image
     img = lena_gray
     # add noise to lena
-    img += 0.5 * img.std() * np.random.random(img.shape)
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
     # clip noise so that it does not exceed allowed range for float images.
     img = np.clip(img, 0, 1)
     # denoise
@@ -57,7 +57,7 @@ def test_denoise_tv_chambolle_3d():
     mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
     mask = 100 * mask.astype(np.float)
     mask += 60
-    mask += 20 * np.random.random(mask.shape)
+    mask += 20 * np.random.rand(*mask.shape)
     mask[mask < 0] = 0
     mask[mask > 255] = 255
     res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=100)
@@ -66,13 +66,13 @@ def test_denoise_tv_chambolle_3d():
 
     # test wrong number of dimensions
     assert_raises(ValueError, restoration.denoise_tv_chambolle,
-                  np.random.random((8, 8, 8, 8)))
+                  np.random.rand(8, 8, 8, 8))
 
 
 def test_denoise_tv_bregman_2d():
     img = lena_gray
     # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
     img = np.clip(img, 0, 1)
 
     out1 = restoration.denoise_tv_bregman(img, weight=10)
@@ -98,7 +98,7 @@ def test_denoise_tv_bregman_float_result_range():
 def test_denoise_tv_bregman_3d():
     img = lena
     # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
     img = np.clip(img, 0, 1)
 
     out1 = restoration.denoise_tv_bregman(img, weight=10)
@@ -112,7 +112,7 @@ def test_denoise_tv_bregman_3d():
 def test_denoise_bilateral_2d():
     img = lena_gray
     # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
     img = np.clip(img, 0, 1)
 
     out1 = restoration.denoise_bilateral(img, sigma_range=0.1,
@@ -128,7 +128,7 @@ def test_denoise_bilateral_2d():
 def test_denoise_bilateral_3d():
     img = lena
     # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
     img = np.clip(img, 0, 1)
 
     out1 = restoration.denoise_bilateral(img, sigma_range=0.1,

skimage/segmentation/random_walker_segmentation.py

@@ -316,7 +316,8 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
 
     Examples
     --------
-    >>> a = np.zeros((10, 10)) + 0.2 * np.random.random((10, 10))
+    >>> np.random.seed(0)
+    >>> a = np.zeros((10, 10)) + 0.2 * np.random.rand(10, 10)
     >>> a[5:8, 5:8] += 1
     >>> b = np.zeros_like(a)
     >>> b[3, 3] = 1  # Marker for first phase
@@ -331,7 +332,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
            [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
            [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
 
     """
     # Parse input data

skimage/transform/tests/test_integral.py

@@ -4,14 +4,14 @@ from numpy.testing import assert_equal
 from skimage.transform import integral_image, integrate
 
 np.random.seed(0)
-x = (np.random.random((50, 50)) * 255).astype(np.uint8)
+x = (np.random.rand(50, 50) * 255).astype(np.uint8)
 s = integral_image(x)
 
 
 def test_validity():
     y = np.arange(12).reshape((4, 3))
 
-    y = (np.random.random((50, 50)) * 255).astype(np.uint8)
+    y = (np.random.rand(50, 50) * 255).astype(np.uint8)
     assert_equal(integral_image(y)[-1, -1],
                  y.sum())
 

skimage/transform/tests/test_warps.py

@@ -252,7 +252,7 @@ def test_inverse():
 
 
 def test_slow_warp_nonint_oshape():
-    image = np.random.random((5, 5))
+    image = np.random.rand(5, 5)
 
     assert_raises(ValueError, warp, image, lambda xy: xy,
                   output_shape=(13.1, 19.5))