Adapting Edited Files to comply with PEP8 Standards

Changing Indentation as per PEP8 Guidelines

skimage/filter/__init__.py

@@ -14,28 +14,30 @@
 from ..filters.lpi_filter import inverse, wiener, LPIFilter2D
 from ..filters._gaussian import gaussian
 from ..filters.edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
-                    scharr, hscharr, vscharr, scharr_h, scharr_v,
-                    prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v,
-                    roberts, roberts_positive_diagonal,
-                    roberts_negative_diagonal, roberts_pos_diag,
-                    roberts_neg_diag)
+                             scharr, hscharr, vscharr, scharr_h, scharr_v,
+                             prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v,
+                             roberts, roberts_positive_diagonal,
+                             roberts_negative_diagonal, roberts_pos_diag,
+                             roberts_neg_diag)
 from ..filters._rank_order import rank_order
 from ..filters._gabor import gabor_kernel, gabor
 from ..filters.thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
-                           threshold_isodata)
+                                    threshold_isodata)
 from ..filters import rank
 from ..filters.rank import median
 
 from .._shared.utils import deprecated
 from .. import restoration
 denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\
-                        (restoration.denoise_bilateral)
+    (restoration.denoise_bilateral)
 denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\
-                        (restoration.denoise_tv_bregman)
+    (restoration.denoise_tv_bregman)
 denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
-                        (restoration.denoise_tv_chambolle)
+    (restoration.denoise_tv_chambolle)
 
 # Backward compatibility v<0.11
+
+
 @deprecated('skimage.feature.canny')
 def canny(*args, **kwargs):
     # Hack to avoid circular import

skimage/filters/__init__.py

@@ -16,17 +16,19 @@
 from .._shared.utils import deprecated, copy_func
 from .. import restoration
 denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\
-                        (restoration.denoise_bilateral)
+    (restoration.denoise_bilateral)
 denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\
-                        (restoration.denoise_tv_bregman)
+    (restoration.denoise_tv_bregman)
 denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
-                        (restoration.denoise_tv_chambolle)
+    (restoration.denoise_tv_chambolle)
 gaussian_filter = copy_func(gaussian, name='gaussian_filter')
 gaussian_filter = deprecated('skimage.filters.gaussian')(gaussian_filter)
 gabor_filter = copy_func(gabor, name='gabor_filter')
 gabor_filter = deprecated('skimage.filters.gabor')(gabor_filter)
 
 # Backward compatibility v<0.11
+
+
 @deprecated('skimage.feature.canny')
 def canny(*args, **kwargs):
     # Hack to avoid circular import
@@ -70,5 +72,5 @@ def canny(*args, **kwargs):
            'threshold_otsu',
            'threshold_yen',
            'threshold_isodata',
-           'threshold_li', 
+           'threshold_li',
            'rank']

skimage/filters/_gabor.py

@@ -95,7 +95,7 @@ def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
 
 
 def gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None,
-                 sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0):
+          sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0):
     """Return real and imaginary responses to Gabor filter.
 
     The real and imaginary parts of the Gabor filter kernel are applied to the

skimage/filters/_gaussian.py

@@ -10,7 +10,7 @@
 
 
 def gaussian(image, sigma, output=None, mode='nearest', cval=0,
-                    multichannel=None):
+             multichannel=None):
     """Multi-dimensional Gaussian filter
 
     Parameters

skimage/filters/tests/test_gabor.py

@@ -15,7 +15,7 @@ def test_gabor_kernel_size():
     kernel = gabor_kernel(0, theta=0, sigma_x=sigma_x, sigma_y=sigma_y)
     assert_equal(kernel.shape, (size_y, size_x))
 
-    kernel = gabor_kernel(0, theta=np.pi/2, sigma_x=sigma_x, sigma_y=sigma_y)
+    kernel = gabor_kernel(0, theta=np.pi / 2, sigma_x=sigma_x, sigma_y=sigma_y)
     assert_equal(kernel.shape, (size_x, size_y))
 
 
@@ -39,7 +39,7 @@ def test_gabor_kernel_sum():
     for sigma_x in range(1, 10, 2):
         for sigma_y in range(1, 10, 2):
             for frequency in range(0, 10, 2):
-                kernel = gabor_kernel(frequency+0.1, theta=0,
+                kernel = gabor_kernel(frequency + 0.1, theta=0,
                                       sigma_x=sigma_x, sigma_y=sigma_y)
                 # make sure gaussian distribution is covered nearly 100%
                 assert_almost_equal(np.abs(kernel).sum(), 1, 2)
@@ -50,9 +50,9 @@ def test_gabor_kernel_theta():
         for sigma_y in range(1, 10, 2):
             for frequency in range(0, 10, 2):
                 for theta in range(0, 10, 2):
-                    kernel0 = gabor_kernel(frequency+0.1, theta=theta,
+                    kernel0 = gabor_kernel(frequency + 0.1, theta=theta,
                                            sigma_x=sigma_x, sigma_y=sigma_y)
-                    kernel180 = gabor_kernel(frequency, theta=theta+np.pi,
+                    kernel180 = gabor_kernel(frequency, theta=theta + np.pi,
                                              sigma_x=sigma_x, sigma_y=sigma_y)
 
                     assert_array_almost_equal(np.abs(kernel0),

skimage/filters/tests/test_gaussian.py

@@ -30,23 +30,23 @@ def test_multichannel():
     a = np.zeros((5, 5, 3))
     a[1, 1] = np.arange(1, 4)
     gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect',
-                                     multichannel=True)
+                              multichannel=True)
     # Check that the mean value is conserved in each channel
     # (color channels are not mixed together)
     assert np.allclose([a[..., i].mean() for i in range(3)],
-                        [gaussian_rgb_a[..., i].mean() for i in range(3)])
+                       [gaussian_rgb_a[..., i].mean() for i in range(3)])
     # Test multichannel = None
     with expected_warnings(['multichannel']):
         gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect')
     # Check that the mean value is conserved in each channel
     # (color channels are not mixed together)
     assert np.allclose([a[..., i].mean() for i in range(3)],
-                        [gaussian_rgb_a[..., i].mean() for i in range(3)])
+                       [gaussian_rgb_a[..., i].mean() for i in range(3)])
     # Iterable sigma
     gaussian_rgb_a = gaussian(a, sigma=[1, 2], mode='reflect',
-                                     multichannel=True)
+                              multichannel=True)
     assert np.allclose([a[..., i].mean() for i in range(3)],
-                        [gaussian_rgb_a[..., i].mean() for i in range(3)])
+                       [gaussian_rgb_a[..., i].mean() for i in range(3)])
 
 
 if __name__ == "__main__":

skimage/util/tests/test_apply_parallel.py

@@ -14,8 +14,8 @@ def test_apply_parallel():
     # apply the filter
     expected1 = threshold_adaptive(a, 3)
     result1 = apply_parallel(threshold_adaptive, a, chunks=(6, 6), depth=5,
-                           extra_arguments=(3,),
-                           extra_keywords={'mode': 'reflect'})
+                             extra_arguments=(3,),
+                             extra_keywords={'mode': 'reflect'})
 
     assert_array_almost_equal(result1, expected1)
 
@@ -56,6 +56,6 @@ def wrapped(arr):
     a = np.arange(144).reshape(12, 12).astype(float)
     expected = gaussian(a, 1, mode='nearest')
     result = apply_parallel(wrapped, a, chunks=(6, 6), depth={0: 5, 1: 5},
-                          mode='nearest')
+                            mode='nearest')
 
     assert_array_almost_equal(result, expected)