PEP8

doc/examples/plot_join_segmentations.py

@@ -60,4 +60,3 @@
     ax.axis('off')
 plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
 plt.show()
-

doc/examples/plot_marching_cubes.py

@@ -22,7 +22,6 @@
 """
 import numpy as np
 import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
 from mpl_toolkits.mplot3d.art3d import Poly3DCollection
 
 from skimage import measure

skimage/__init__.py

@@ -91,6 +91,7 @@ def _test(verbose=False):
 class _Log(Warning):
     pass
 
+
 class _FakeLog(object):
     def __init__(self, name):
         """

skimage/data/__init__.py

@@ -200,4 +200,3 @@ def coffee():
 
     """
     return load("coffee.png")
-

skimage/exposure/exposure.py

@@ -287,7 +287,7 @@ def adjust_log(image, gain=1, inv=False):
     inv : float
         If True, it performs inverse logarithmic correction,
         else correction will be logarithmic. Defaults to False.
-    
+
     Returns
     -------
     out : ndarray

skimage/filter/__init__.py

@@ -3,7 +3,7 @@
 from ._gaussian import gaussian_filter
 from ._canny import canny
 from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
-                    hprewitt, vprewitt, roberts , roberts_positive_diagonal,
+                    hprewitt, vprewitt, roberts, roberts_positive_diagonal,
                     roberts_negative_diagonal)
 from ._denoise import denoise_tv_chambolle, tv_denoise
 from ._denoise_cy import denoise_bilateral, denoise_tv_bregman

skimage/filter/_denoise.py

@@ -250,7 +250,7 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200,
             out = np.zeros_like(im)
             for c in range(im.shape[2]):
                 out[..., c] = _denoise_tv_chambolle_2d(im[..., c], weight, eps,
-                                             n_iter_max)
+                                                       n_iter_max)
         else:
             out = _denoise_tv_chambolle_3d(im, weight, eps, n_iter_max)
     else:

skimage/filter/_rank_order.py

@@ -8,7 +8,7 @@
 All rights reserved.
 Original author: Lee Kamentstky
 """
-import numpy
+import numpy as np
 
 
 def rank_order(image):
@@ -47,14 +47,14 @@ def rank_order(image):
     (array([0, 1, 2, 1], dtype=uint32), array([-1. ,  2.5,  3.1]))
     """
     flat_image = image.ravel()
-    sort_order = flat_image.argsort().astype(numpy.uint32)
+    sort_order = flat_image.argsort().astype(np.uint32)
     flat_image = flat_image[sort_order]
-    sort_rank = numpy.zeros_like(sort_order)
+    sort_rank = np.zeros_like(sort_order)
     is_different = flat_image[:-1] != flat_image[1:]
-    numpy.cumsum(is_different, out=sort_rank[1:])
-    original_values = numpy.zeros((sort_rank[-1] + 1,), image.dtype)
+    np.cumsum(is_different, out=sort_rank[1:])
+    original_values = np.zeros((sort_rank[-1] + 1,), image.dtype)
     original_values[0] = flat_image[0]
     original_values[1:] = flat_image[1:][is_different]
-    int_image = numpy.zeros_like(sort_order)
+    int_image = np.zeros_like(sort_order)
     int_image[sort_order] = sort_rank
     return (int_image.reshape(image.shape), original_values)

skimage/filter/edges.py

@@ -31,8 +31,8 @@
                              [-1,-1,-1]]) / 3.0
 VPREWITT_WEIGHTS = HPREWITT_WEIGHTS.T
 
-ROBERTS_PD_WEIGHTS = np.array([[ 1, 0],
-                               [ 0, -1]], dtype=np.double)
+ROBERTS_PD_WEIGHTS = np.array([[1, 0],
+                               [0, -1]], dtype=np.double)
 ROBERTS_ND_WEIGHTS = np.array([[0, 1],
                                [-1, 0]], dtype=np.double)
 
@@ -346,7 +346,7 @@ def roberts(image, mask=None):
     """Find the edge magnitude using Roberts' cross operator.
 
     Parameters
-	----------
+    ----------
     image : 2-D array
         Image to process.
     mask : 2-D array, optional

skimage/morphology/convex_hull.py

@@ -44,7 +44,7 @@ def convex_hull_image(image):
                                                  (-0.5, 0.5, 0, 0))):
         coords_corners[i * N:(i + 1) * N] = coords + [x_offset, y_offset]
 
-    # repeated coordinates can *sometimes* cause problems in 
+    # repeated coordinates can *sometimes* cause problems in
     # scipy.spatial.Delaunay, so we remove them.
     coords = unique_rows(coords_corners)
 

skimage/segmentation/_join.py

@@ -94,7 +94,7 @@ def relabel_sequential(label_field, offset=1):
     Examples
     --------
     >>> from skimage.segmentation import relabel_sequential
-    >>> label_field = array([1, 1, 5, 5, 8, 99, 42])
+    >>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])
     >>> relab, fw, inv = relabel_sequential(label_field)
     >>> relab
     array([1, 1, 2, 2, 3, 5, 4])
@@ -117,7 +117,7 @@ def relabel_sequential(label_field, offset=1):
     labels = np.unique(label_field)
     labels0 = labels[labels != 0]
     m = labels.max()
-    if m == len(labels0): # nothing to do, already 1...n labels
+    if m == len(labels0):  # nothing to do, already 1...n labels
         return label_field, labels, labels
     forward_map = np.zeros(m+1, int)
     forward_map[labels0] = np.arange(offset, offset + len(labels0) + 1)
@@ -127,4 +127,3 @@ def relabel_sequential(label_field, offset=1):
     inverse_map[(offset - 1):] = labels
     relabeled = forward_map[label_field]
     return relabeled, forward_map, inverse_map
-

viewer_examples/plugins/watershed_demo.py

@@ -7,6 +7,7 @@
 from skimage.viewer.widgets import history
 from skimage.viewer.plugins.labelplugin import LabelPainter
 
+
 class OKCancelButtons(history.OKCancelButtons):
 
     def update_original_image(self):