cosmectics (flake8)

skimage/transform/_hough_transform.pyx

@@ -21,8 +21,8 @@ cdef inline Py_ssize_t round(double r):
 
 
 def hough_circle(cnp.ndarray img,
-                  cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius,
-                  char normalize=True):
+                 cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius,
+                 char normalize=True):
     """Perform a circular Hough transform.
 
     Parameters
@@ -112,7 +112,8 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
     -----
     The origin is the top left corner of the original image.
     X and Y axis are horizontal and vertical edges respectively.
-    The distance is the minimal algebraic distance from the origin to the detected line.
+    The distance is the minimal algebraic distance from the origin
+    to the detected line.
 
     Examples
     --------
@@ -163,7 +164,7 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
 
     # finally, run the transform
     cdef Py_ssize_t nidxs, nthetas, i, j, x, y, accum_idx
-    nidxs = y_idxs.shape[0] # x and y are the same shape
+    nidxs = y_idxs.shape[0]  # x and y are the same shape
     nthetas = theta.shape[0]
     for i in range(nidxs):
         x = x_idxs[i]
@@ -175,8 +176,8 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
 
 
 def probabilistic_hough_line(cnp.ndarray img, int threshold=10,
-                         int line_length=50, int line_gap=10,
-                         cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
+                             int line_length=50, int line_gap=10,
+                             cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
     """Return lines from a progressive probabilistic line Hough transform.
 
     Parameters

skimage/transform/hough_transform.py

@@ -1,7 +1,5 @@
 __all__ = ['hough_line_peaks']
 
-from itertools import izip as zip
-
 import numpy as np
 from scipy import ndimage
 from skimage import measure, morphology
@@ -10,30 +8,35 @@ from skimage import measure, morphology
 from ._hough_transform import hough_line, probabilistic_hough_line
 from skimage._shared.utils import deprecated
 
+
 @deprecated('hough_line')
 def hough(img, theta=None):
     return hough_line(img, theta)
 
+
 @deprecated('probabilistic_hough')
 def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
                         theta=None):
     return probabilistic_hough_line(img, threshold=threshold,
-                                    line_length=line_length, line_gap=line_gap, theta=theta)
+                                    line_length=line_length, line_gap=line_gap,
+                                    theta=theta)
+
 
 @deprecated('hough_peaks')
 def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
                 threshold=None, num_peaks=np.inf):
     return hough_line_peaks(hspace, angles, dists, min_distance, min_angle,
-                threshold, num_peaks)
+                            threshold, num_peaks)
 
-def hough_line_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
-                threshold=None, num_peaks=np.inf):
+
+def hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10,
+                     threshold=None, num_peaks=np.inf):
     """Return peaks in hough transform.
 
-    Identifies most prominent lines separated by a certain angle and distance in
-    a hough transform. Non-maximum suppression with different sizes is applied
-    separately in the first (distances) and second (angles) dimension of the
-    hough space to identify peaks.
+    Identifies most prominent lines separated by a certain angle and distance
+    in a hough transform. Non-maximum suppression with different sizes is
+    applied separately in the first (distances) and second (angles) dimension
+    of the hough space to identify peaks.
 
     Parameters
     ----------

skimage/transform/tests/test_hough_transform.py

@@ -2,7 +2,6 @@ import numpy as np
 from numpy.testing import *
 
 import skimage.transform as tf
-import skimage.transform.hough_transform as ht
 from skimage.draw import circle_perimeter, line
 
 
@@ -50,7 +49,7 @@ def test_probabilistic_hough():
     # as mentioned in article of Galambos et al
     theta = np.linspace(0, np.pi, 45)
     lines = tf.probabilistic_hough_line(img, threshold=10, line_length=10,
-                                line_gap=1, theta=theta)
+                                        line_gap=1, theta=theta)
     # sort the lines according to the x-axis
     sorted_lines = []
     for line in lines:
@@ -110,7 +109,7 @@ def test_hough_line_peaks_num():
     img[:, 40] = True
     hspace, angles, dists = tf.hough_line(img)
     assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=0,
-                              min_angle=0, num_peaks=1)[0]) == 1
+                                   min_angle=0, num_peaks=1)[0]) == 1
 
 
 def test_hough_circle():