DOC: Fix Sphinx errors.

doc/examples/plot_corner.py

@@ -11,7 +11,6 @@
 
 """
 
-import numpy as np
 from matplotlib import pyplot as plt
 
 from skimage import data

doc/examples/plot_random_walker_segmentation.py

@@ -19,7 +19,6 @@
 .. [1] *Random walks for image segmentation*, Leo Grady, IEEE Trans. Pattern
        Anal. Mach. Intell. 2006 Nov; 28(11):1768-83
 """
-print __doc__
 
 import numpy as np
 from scipy import ndimage

skimage/exposure/_adapthist.py

@@ -259,6 +259,8 @@ def map_histogram(hist, min_val, max_val, n_pixels):
 
     It does so by cumulating the input histogram.
 
+    Parameters
+    ----------
     hist : ndarray
         Clipped histogram.
     min_val : int
@@ -301,12 +303,11 @@ def interpolate(image, xslice, yslice,
     out : ndarray
         Original image with the subregion replaced.
 
-    Note
-    ----
-    This function calculates the new greylevel assignments of pixels
-    within a submatrix of the image.
-    This is done by a bilinear interpolation between four different
-    mappings in order to eliminate boundary artifacts.
+    Notes
+    -----
+    This function calculates the new greylevel assignments of pixels within
+    a submatrix of the image. This is done by a bilinear interpolation between
+    four different mappings in order to eliminate boundary artifacts.
     """
     norm = xslice.size * yslice.size  # Normalization factor
     # interpolation weight matrices

skimage/feature/_daisy.py

@@ -53,30 +53,35 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
         the spatial smoothing of the center histogram and the last sigma value
         defines the spatial smoothing of the outermost ring. Specifying sigmas
         overrides the following parameter.
-           ``rings = len(sigmas)-1``
+
+            ``rings = len(sigmas) - 1``
 
     ring_radii : 1D array of int, optional
         Radius (in pixels) for each ring. Specifying ring_radii overrides the
         following two parameters.
-          | ``rings = len(ring_radii)``
-          | ``radius = ring_radii[-1]``
+
+            ``rings = len(ring_radii)``
+            ``radius = ring_radii[-1]``
 
         If both sigmas and ring_radii are given, they must satisfy the
         following predicate since no radius is needed for the center
         histogram.
-            ``len(ring_radii) == len(sigmas)+1``
+
+            ``len(ring_radii) == len(sigmas) + 1``
+
     visualize : bool, optional
         Generate a visualization of the DAISY descriptors
 
-
     Returns
     -------
     descs : array
         Grid of DAISY descriptors for the given image as an array
         dimensionality  (P, Q, R) where
-          | ``P = ceil((M-radius*2)/step)``
-          | ``Q = ceil((N-radius*2)/step)``
-          | ``R = (rings*histograms + 1)*orientations``
+
+            ``P = ceil((M - radius*2) / step)``
+            ``Q = ceil((N - radius*2) / step)``
+            ``R = (rings * histograms + 1) * orientations``
+
     descs_img : (M, N, 3) array (only if visualize==True)
         Visualization of the DAISY descriptors.
 

skimage/feature/corner.py

@@ -140,7 +140,7 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1):
     ..[2] http://en.wikipedia.org/wiki/Corner_detection
 
     Examples
-    -------
+    --------
     >>> from skimage.feature import corner_harris, corner_peaks
     >>> square = np.zeros([10, 10])
     >>> square[2:8, 2:8] = 1
@@ -210,7 +210,7 @@ def corner_shi_tomasi(image, sigma=1):
     ..[2] http://en.wikipedia.org/wiki/Corner_detection
 
     Examples
-    -------
+    --------
     >>> from skimage.feature import corner_shi_tomasi, corner_peaks
     >>> square = np.zeros([10, 10])
     >>> square[2:8, 2:8] = 1
@@ -277,7 +277,7 @@ def corner_foerstner(image, sigma=1):
     ..[2] http://en.wikipedia.org/wiki/Corner_detection
 
     Examples
-    -------
+    --------
     >>> from skimage.feature import corner_foerstner, corner_peaks
     >>> square = np.zeros([10, 10])
     >>> square[2:8, 2:8] = 1

skimage/feature/corner_cy.pyx

@@ -34,7 +34,7 @@ def corner_moravec(image, Py_ssize_t window_size=1):
     ..[2] http://en.wikipedia.org/wiki/Corner_detection
 
     Examples
-    -------
+    --------
     >>> from skimage.feature import moravec, peak_local_max
     >>> square = np.zeros([7, 7])
     >>> square[3, 3] = 1

skimage/feature/peak.py

@@ -50,9 +50,10 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
     Returns
     -------
     output : (N, 2) array or ndarray of bools
-        If `indices = True`  : (row, column) coordinates of peaks.
-        If `indices = False` : Boolean array shaped like `image`,
-            with peaks represented by True values.
+
+        * If `indices = True`  : (row, column) coordinates of peaks.
+        * If `indices = False` : Boolean array shaped like `image`, with peaks
+          represented by True values.
 
     Notes
     -----

skimage/filter/_denoise.py

@@ -32,7 +32,7 @@ def _denoise_tv_chambolle_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
     Rudin, Osher and Fatemi algorithm.
 
     Examples
-    ---------
+    --------
     >>> x, y, z = np.ogrid[0:40, 0:40, 0:40]
     >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
     >>> mask = mask.astype(np.float)
@@ -123,7 +123,7 @@ def _denoise_tv_chambolle_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
            Springer, 2004, 20, 89-97.
 
     Examples
-    ---------
+    --------
     >>> from skimage import color, data
     >>> lena = color.rgb2gray(data.lena())
     >>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
@@ -221,7 +221,7 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200,
            Springer, 2004, 20, 89-97.
 
     Examples
-    ---------
+    --------
     2D example on Lena image:
 
     >>> from skimage import color, data

skimage/filter/rank/rank.pyx

@@ -648,12 +648,12 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
     References
     ----------
     .. [Hashimoto12] N. Hashimoto et al. Referenceless image quality evaluation
-    for whole slide imaging. J Pathol Inform 2012;3:9.
+                     for whole slide imaging. J Pathol Inform 2012;3:9.
 
     Returns
     -------
     out : uint8 array or uint16 array (same as input image)
-        The image noise .
+        The image noise.
 
     """
 

skimage/segmentation/random_walker_segmentation.py

@@ -30,8 +30,7 @@
 
 
 def _make_graph_edges_3d(n_x, n_y, n_z):
-    """
-    Returns a list of edges for a 3D image.
+    """Returns a list of edges for a 3D image.
 
     Parameters
     ----------
@@ -45,9 +44,12 @@ def _make_graph_edges_3d(n_x, n_y, n_z):
     Returns
     -------
     edges : (2, N) ndarray
-        with the total number of edges N = n_x * n_y * (nz - 1) +
-                                           n_x * (n_y - 1) * nz +
-                                           (n_x - 1) * n_y * nz
+        with the total number of edges::
+
+            N = n_x * n_y * (nz - 1) +
+                n_x * (n_y - 1) * nz +
+                (n_x - 1) * n_y * nz
+
         Graph edges with each column describing a node-id pair.
     """
     vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z))
@@ -200,6 +202,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
     mode : {'bf', 'cg_mg', 'cg'} (default: 'bf')
         Mode for solving the linear system in the random walker
         algorithm.
+
         - 'bf' (brute force, default): an LU factorization of the Laplacian is
           computed. This is fast for small images (<1024x1024), but very slow
           (due to the memory cost) and memory-consuming for big images (in 3-D
@@ -214,6 +217,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
           requires that the pyamg module (http://code.google.com/p/pyamg/) is
           installed. For images of size > 512x512, this is the recommended
           (fastest) mode.
+
     tol : float
         tolerance to achieve when solving the linear system, in
         cg' and 'cg_mg' modes.
@@ -237,12 +241,12 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
     Returns
     -------
     output : ndarray
-        If `return_full_prob` is False, array of ints of same shape as `data`,
-        in which each pixel has been labeled according to the marker that
-        reached the pixel first by anisotropic diffusion.
-        If `return_full_prob` is True, array of floats of shape
-        `(nlabels, data.shape)`. `output[label_nb, i, j]` is the probability
-        that label `label_nb` reaches the pixel `(i, j)` first.
+        * If `return_full_prob` is False, array of ints of same shape as
+          `data`, in which each pixel has been labeled according to the marker
+          that reached the pixel first by anisotropic diffusion.
+        * If `return_full_prob` is True, array of floats of shape
+          `(nlabels, data.shape)`. `output[label_nb, i, j]` is the probability
+          that label `label_nb` reaches the pixel `(i, j)` first.
 
     See also
     --------