PEP8: fix indentation

skimage/filter/rank/generic.py

@@ -249,8 +249,8 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
     Note
     ----
-    * the lower algorithm complexity makes the rank.maximum() more efficient for
-      larger images and structuring elements
+    * the lower algorithm complexity makes the rank.maximum() more efficient
+    for larger images and structuring elements
 
     """
 
@@ -299,7 +299,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
 
 def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
-                     shift_y=False):
+                  shift_y=False):
     """Return image subtracted from its local mean.
 
     Parameters
@@ -439,7 +439,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
 
 def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
-                    shift_y=False):
+                     shift_y=False):
     """Enhance an image replacing each pixel by the local maximum if pixel
     greylevel is closest to maximimum than local minimum OR local minimum
     otherwise.

skimage/filter/thresholding.py

@@ -1,4 +1,4 @@
-__all__ = ['threshold_adaptive', 'threshold_otsu', 'threshold_yen'] 
+__all__ = ['threshold_adaptive', 'threshold_otsu', 'threshold_yen']
 
 import numpy as np
 import scipy.ndimage
@@ -65,7 +65,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
     thresh_image = np.zeros(image.shape, 'double')
     if method == 'generic':
         scipy.ndimage.generic_filter(image, param, block_size,
-            output=thresh_image, mode=mode)
+                                     output=thresh_image, mode=mode)
     elif method == 'gaussian':
         if param is None:
             # automatically determine sigma which covers > 99% of distribution
@@ -73,17 +73,17 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
         else:
             sigma = param
         scipy.ndimage.gaussian_filter(image, sigma, output=thresh_image,
-            mode=mode)
+                                      mode=mode)
     elif method == 'mean':
         mask = 1. / block_size * np.ones((block_size,))
         # separation of filters to speedup convolution
         scipy.ndimage.convolve1d(image, mask, axis=0, output=thresh_image,
-            mode=mode)
+                                 mode=mode)
         scipy.ndimage.convolve1d(thresh_image, mask, axis=1,
-            output=thresh_image, mode=mode)
+                                 output=thresh_image, mode=mode)
     elif method == 'median':
         scipy.ndimage.median_filter(image, block_size, output=thresh_image,
-            mode=mode)
+                                    mode=mode)
 
     return image > (thresh_image - offset)
 
@@ -146,7 +146,7 @@ def threshold_yen(image, nbins=256):
     nbins : int, optional
         Number of bins used to calculate histogram. This value is ignored for
         integer arrays.
-    
+
     Returns
     -------
     threshold : float
@@ -155,11 +155,11 @@ def threshold_yen(image, nbins=256):
 
     References
     ----------
-    .. [1] Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion 
-           for Automatic Multilevel Thresholding" IEEE Trans. on Image 
+    .. [1] Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion
+           for Automatic Multilevel Thresholding" IEEE Trans. on Image
            Processing, 4(3): 370-378
-    .. [2] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding 
-           Techniques and Quantitative Performance Evaluation" Journal of 
+    .. [2] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
+           Techniques and Quantitative Performance Evaluation" Journal of
            Electronic Imaging, 13(1): 146-165,
            http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf
     .. [3] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold

skimage/measure/find_contours.py

@@ -116,7 +116,7 @@ def find_contours(array, level,
         raise ValueError('Parameters "fully_connected" and'
         ' "positive_orientation" must be either "high" or "low".')
     point_list = _find_contours.iterate_and_store(array, level,
-        fully_connected == 'high')
+                                                  fully_connected == 'high')
     contours = _assemble_contours(_take_2(point_list))
     if positive_orientation == 'high':
         contours = [c[::-1] for c in contours]