Merge pull request #1918 from ahojnnes/examples

Several fixes

doc/examples/color_exposure/plot_adapt_rgb.py

@@ -49,7 +49,8 @@ image = data.astronaut()
 
 fig = plt.figure(figsize=(14, 7))
 ax_each = fig.add_subplot(121, adjustable='box-forced')
-ax_hsv = fig.add_subplot(122, sharex=ax_each, sharey=ax_each, adjustable='box-forced')
+ax_hsv = fig.add_subplot(122, sharex=ax_each, sharey=ax_each,
+                         adjustable='box-forced')
 
 # We use 1 - sobel_each(image)
 # but this will not work if image is not normalized
@@ -107,7 +108,8 @@ def sobel_gray(image):
     return filters.sobel(image)
 
 fig = plt.figure(figsize=(7, 7))
-ax = fig.add_subplot(111, sharex=ax_each, sharey=ax_each, adjustable='box-forced')
+ax = fig.add_subplot(111, sharex=ax_each, sharey=ax_each,
+                     adjustable='box-forced')
 
 # We use 1 - sobel_gray(image)
 # but this will not work if image is not normalized

doc/examples/edges/plot_active_contours.py

@@ -28,7 +28,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from skimage.color import rgb2gray
 from skimage import data
-from skimage.filters import gaussian_filter
+from skimage.filters import gaussian
 from skimage.segmentation import active_contour
 
 # Test scipy version, since active contour is only possible
@@ -52,7 +52,7 @@ if not new_scipy:
           '0.14.0 and above.')
 
 if new_scipy:
-    snake = active_contour(gaussian_filter(img, 3),
+    snake = active_contour(gaussian(img, 3),
                            init, alpha=0.015, beta=10, gamma=0.001)
 
     fig = plt.figure(figsize=(7, 7))
@@ -80,7 +80,7 @@ y = np.linspace(136, 50, 100)
 init = np.array([x, y]).T
 
 if new_scipy:
-    snake = active_contour(gaussian_filter(img, 1), init, bc='fixed',
+    snake = active_contour(gaussian(img, 1), init, bc='fixed',
                            alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1)
 
     fig = plt.figure(figsize=(9, 5))

doc/examples/edges/plot_circular_elliptical_hough_transform.py

@@ -138,7 +138,9 @@ image_rgb[cy, cx] = (0, 0, 255)
 edges = color.gray2rgb(edges)
 edges[cy, cx] = (250, 0, 0)
 
-fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
+fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True,
+                                sharey=True,
+                                subplot_kw={'adjustable':'box-forced'})
 
 ax1.set_title('Original picture')
 ax1.imshow(image_rgb)

doc/examples/filters/plot_rank_mean.py

@@ -26,7 +26,7 @@ from skimage.morphology import disk
 from skimage.filters import rank
 
 
-image = (data.coins()).astype(np.uint16) * 16
+image = data.coins()
 selem = disk(20)
 
 percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
@@ -46,5 +46,4 @@ for n in range(0, len(imgs)):
     ax[n].set_adjustable('box-forced')
     ax[n].axis('off')
 
-
 plt.show()

doc/examples/transform/plot_ransac.py

@@ -10,7 +10,7 @@ the RANSAC algorithm.
 import numpy as np
 from matplotlib import pyplot as plt
 
-from skimage.measure import LineModel, ransac
+from skimage.measure import LineModelND, ransac
 
 
 np.random.seed(seed=1)
@@ -32,11 +32,11 @@ data[::2] += 5 * noise[::2]
 data[::4] += 20 * noise[::4]
 
 # fit line using all data
-model = LineModel()
+model = LineModelND()
 model.estimate(data)
 
 # robustly fit line only using inlier data with RANSAC algorithm
-model_robust, inliers = ransac(data, LineModel, min_samples=2,
+model_robust, inliers = ransac(data, LineModelND, min_samples=2,
                                residual_threshold=1, max_trials=1000)
 outliers = inliers == False
 

doc/examples/transform/plot_swirl.py

@@ -72,9 +72,11 @@ from skimage.transform import swirl
 
 
 image = data.checkerboard()
-swirled = swirl(image, rotation=0, strength=10, radius=120, order=2)
+swirled = swirl(image, rotation=0, strength=10, radius=120)
 
-fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 3), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
+fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3),
+                               sharex=True, sharey=True,
+                               subplot_kw={'adjustable':'box-forced'})
 
 ax0.imshow(image, cmap=plt.cm.gray, interpolation='none')
 ax0.axis('off')

doc/examples/xx_applications/plot_morphology.py

@@ -25,14 +25,16 @@ To get started, let's load an image using ``io.imread``. Note that morphology
 functions only work on gray-scale or binary images, so we set ``as_grey=True``.
 """
 
+import os
 import matplotlib.pyplot as plt
 from skimage.data import data_dir
 from skimage.util import img_as_ubyte
 from skimage import io
 
-phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
+orig_phantom = img_as_ubyte(io.imread(os.path.join(data_dir, "phantom.png"),
+                                      as_grey=True))
 fig, ax = plt.subplots()
-ax.imshow(phantom, cmap=plt.cm.gray)
+ax.imshow(orig_phantom, cmap=plt.cm.gray)
 
 """
 .. image:: PLOT2RST.current_figure
@@ -42,7 +44,8 @@ Let's also define a convenience function for plotting comparisons:
 
 def plot_comparison(original, filtered, filter_name):
 
-    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True)
+    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True,
+                                   sharey=True)
     ax1.imshow(original, cmap=plt.cm.gray)
     ax1.set_title('original')
     ax1.axis('off')
@@ -68,8 +71,8 @@ from skimage.morphology import black_tophat, skeletonize, convex_hull_image
 from skimage.morphology import disk
 
 selem = disk(6)
-eroded = erosion(phantom, selem)
-plot_comparison(phantom, eroded, 'erosion')
+eroded = erosion(orig_phantom, selem)
+plot_comparison(orig_phantom, eroded, 'erosion')
 
 """
 .. image:: PLOT2RST.current_figure
@@ -88,8 +91,8 @@ pixels in the neighborhood centered at (i, j)*. Dilation enlarges bright
 regions and shrinks dark regions.
 """
 
-dilated = dilation(phantom, selem)
-plot_comparison(phantom, dilated, 'dilation')
+dilated = dilation(orig_phantom, selem)
+plot_comparison(orig_phantom, dilated, 'dilation')
 
 """
 .. image:: PLOT2RST.current_figure
@@ -108,8 +111,8 @@ dilation*. Opening can remove small bright spots (i.e. "salt") and connect
 small dark cracks.
 """
 
-opened = opening(phantom, selem)
-plot_comparison(phantom, opened, 'opening')
+opened = opening(orig_phantom, selem)
+plot_comparison(orig_phantom, opened, 'opening')
 
 """
 .. image:: PLOT2RST.current_figure
@@ -134,7 +137,7 @@ small bright cracks.
 To illustrate this more clearly, let's add a small crack to the white border:
 """
 
-phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
+phantom = orig_phantom.copy()
 phantom[10:30, 200:210] = 0
 
 closed = closing(phantom, selem)
@@ -161,7 +164,7 @@ that are smaller than the structuring element.
 To make things interesting, we'll add bright and dark spots to the image:
 """
 
-phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
+phantom = orig_phantom.copy()
 phantom[340:350, 200:210] = 255
 phantom[100:110, 200:210] = 0
 
@@ -215,10 +218,9 @@ on binary images only.
 
 """
 
-from skimage import img_as_bool
-horse = ~img_as_bool(io.imread(data_dir+'/horse.png', as_grey=True))
+horse = io.imread(os.path.join(data_dir, "horse.png"), as_grey=True)
 
-sk = skeletonize(horse)
+sk = skeletonize(horse == 0)
 plot_comparison(horse, sk, 'skeletonize')
 
 """
@@ -237,7 +239,7 @@ that this is also performed on binary images.
 
 """
 
-hull1 = convex_hull_image(horse)
+hull1 = convex_hull_image(horse == 0)
 plot_comparison(horse, hull1, 'convex hull')
 
 """
@@ -252,11 +254,11 @@ enclose that grain:
 
 import numpy as np
 
-horse2 = np.copy(horse)
-horse2[45:50, 75:80] = 1
+horse_mask = horse == 0
+horse_mask[45:50, 75:80] = 1
 
-hull2 = convex_hull_image(horse2)
-plot_comparison(horse2, hull2, 'convex hull')
+hull2 = convex_hull_image(horse_mask)
+plot_comparison(horse_mask, hull2, 'convex hull')
 
 """
 .. image:: PLOT2RST.current_figure

skimage/_shared/version_requirements.py

@@ -57,8 +57,8 @@ def is_installed(name, version=None):
     out : bool
         True if `name` is installed matching the optional version.
 
-    Note
-    ----
+    Notes
+    -----
     Original Copyright (C) 2009-2011 Pierre Raybaut
     Licensed under the terms of the MIT License.
     """

skimage/exposure/_adapthist.py

@@ -25,7 +25,7 @@ NR_OF_GREY = 2 ** 14  # number of grayscale levels to use in CLAHE algorithm
 
 
 @adapt_rgb(hsv_value)
-def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
+def equalize_adapthist(image, ntiles_x=None, ntiles_y=None, clip_limit=0.01,
                        nbins=256, kernel_size=None):
     """Contrast Limited Adaptive Histogram Equalization (CLAHE).
 
@@ -76,10 +76,12 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
     image = img_as_uint(image)
     image = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1))
 
-    if kernel_size is None:
+    if ntiles_x is not None or ntiles_y is not None:
         warn('`ntiles_*` have been deprecated in favor of '
              '`kernel_size`.  The `ntiles_*` keyword arguments '
              'will be removed in v0.14', skimage_deprecation)
+
+    if kernel_size is None:
         ntiles_x = ntiles_x or 8
         ntiles_y = ntiles_y or 8
         kernel_size = (np.round(image.shape[0] / ntiles_y),

skimage/exposure/tests/test_exposure.py

@@ -211,11 +211,13 @@ def test_adapthist_grayscale():
     img = skimage.img_as_float(data.astronaut())
     img = rgb2gray(img)
     img = np.dstack((img, img, img))
-    with expected_warnings(['precision loss|non-contiguous input', 
+    with expected_warnings(['precision loss|non-contiguous input',
                             'deprecated']):
         adapted_old = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.001,
                                                   nbins=128)
-        adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01, nbins=128)
+    with expected_warnings(['precision loss|non-contiguous input']):
+        adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51),
+                                              clip_limit=0.01, nbins=128)
     assert img.shape == adapted.shape
     assert_almost_equal(peak_snr(img, adapted),  102.078, 3)
     assert_almost_equal(norm_brightness_err(img, adapted), 0.0529, 3)
@@ -230,7 +232,7 @@ def test_adapthist_color():
         warnings.simplefilter('always')
         hist, bin_centers = exposure.histogram(img)
         assert len(w) > 0
-    with expected_warnings(['precision loss', 'deprecated']):
+    with expected_warnings(['precision loss']):
         adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
 
     assert_almost_equal = np.testing.assert_almost_equal
@@ -249,7 +251,7 @@ def test_adapthist_alpha():
     img = skimage.img_as_float(data.astronaut())
     alpha = np.ones((img.shape[0], img.shape[1]), dtype=float)
     img = np.dstack((img, alpha))
-    with expected_warnings(['precision loss', 'deprecated']):
+    with expected_warnings(['precision loss']):
         adapted = exposure.equalize_adapthist(img)
     assert adapted.shape != img.shape
     img = img[:, :, :3]

skimage/external/tifffile/tifffile.py

@@ -2208,7 +2208,7 @@ class TiffSequence(object):
 
     The data shape and dtype of all files must match.
 
-    Properties
+    Attributes
     ----------
     files : list
         List of file names.
@@ -3104,8 +3104,8 @@ def _replace_by(module_function, package=None, warn=False):
     func : function
         Wrapped function, hopefully calling a function in another module.
 
-    Example
-    -------
+    Examples
+    --------
     >>> @_replace_by('_tifffile.decodepackbits')
     ... def decodepackbits(encoded):
     ...     raise NotImplementedError

skimage/novice/__init__.py

@@ -19,8 +19,8 @@ the normal, array-oriented image functions used by scikit-image.
     instead of row, column.
 
 
-Example
--------
+Examples
+--------
 We can create a Picture object open opening an image file:
 
 >>> from skimage import novice

skimage/restoration/_denoise.py

@@ -55,8 +55,8 @@ def denoise_bilateral(image, win_size=5, sigma_range=None, sigma_spatial=1,
     ----------
     .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf
 
-    Example
-    -------
+    Examples
+    --------
     >>> from skimage import data, img_as_float
     >>> astro = img_as_float(data.astronaut())
     >>> astro = astro[220:300, 220:320]

skimage/restoration/inpaint.py

@@ -92,8 +92,8 @@ def inpaint_biharmonic(img, mask, multichannel=False):
     out : (M[, N[, ..., P]][, C]) ndarray
         Input image with masked pixels inpainted.
 
-    Example
-    -------
+    Examples
+    --------
     >>> img = np.tile(np.square(np.linspace(0, 1, 5)), (5, 1))
     >>> mask = np.zeros_like(img)
     >>> mask[2, 2:] = 1
@@ -111,11 +111,11 @@ def inpaint_biharmonic(img, mask, multichannel=False):
 
     if img.ndim < 1:
         raise ValueError('Input array has to be at least 1D')
-    
+
     img_baseshape = img.shape[:-1] if multichannel else img.shape
     if img_baseshape != mask.shape:
         raise ValueError('Input arrays have to be the same shape')
-    
+
     if np.ma.isMaskedArray(img):
         raise TypeError('Masked arrays are not supported')
 

skimage/transform/_warps.py

@@ -50,8 +50,8 @@ def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
         Whether to keep the original range of values. Otherwise, the input
         image is converted according to the conventions of `img_as_float`.
 
-    Note
-    ----
+    Notes
+    -----
     Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
     pixels are duplicated during the reflection.  As an example, if an array
     has values [0, 1, 2] and was padded to the right by four values using

skimage/transform/_warps_cy.pyx

@@ -77,8 +77,8 @@ def _warp_fast(cnp.ndarray image, cnp.ndarray H, output_shape=None,
         Used in conjunction with mode 'C' (constant), the value
         outside the image boundaries.
 
-    Note
-    ----
+    Notes
+    -----
     Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
     pixels are duplicated during the reflection.  As an example, if an array
     has values [0, 1, 2] and was padded to the right by four values using