diff --git a/doc/examples/plot_windowed_histogram.py b/doc/examples/plot_windowed_histogram.py
index 2cc77139..1c57721b 100644
--- a/doc/examples/plot_windowed_histogram.py
+++ b/doc/examples/plot_windowed_histogram.py
@@ -57,7 +57,7 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
 
     # Reshape coin histogram to (1,1,N) for broadcast when we want to use it in
     # arithmetic operations with the windowed histograms from the image
-    reference_hist = reference_hist.reshape((1,1) + reference_hist.shape)
+    reference_hist = reference_hist.reshape((1, 1) + reference_hist.shape)
 
     # Compute Chi squared distance metric: sum((X-Y)^2 / (X+Y));
     # a measure of distance between histograms
@@ -66,7 +66,7 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
     num = (X-Y)*(X-Y)
     denom = X+Y
     frac = num / denom
-    frac[denom==0] = 0
+    frac[denom == 0] = 0
     chi_sqr = np.sum(frac, axis=2) * 0.5
 
     # Generate a similarity measure. It needs to be low when distance is high
@@ -80,17 +80,18 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
 # Load the `skimage.data.coins` image
 img = img_as_ubyte(data.coins())
 
-# Quantize to 16 levels of grayscale; this way the output image will have a
+# Quantize to 16 levels of greyscale; this way the output image will have a
 # 16-dimensional feature vector per pixel
 quantized_img = img//16
 
 # Select the coin from the 4th column, second row.
 # Co-ordinate ordering: [x1,y1,x2,y2]
-coin_coords = [184,100,228,148]   # 44 x 44 region
-coin = quantized_img[coin_coords[1]:coin_coords[3], coin_coords[0]:coin_coords[2]]
+coin_coords = [184, 100, 228, 148]   # 44 x 44 region
+coin = quantized_img[coin_coords[1]:coin_coords[3],
+                     coin_coords[0]:coin_coords[2]]
 
 # Compute coin histogram and normalize
-coin_hist, _ = np.histogram(coin.flatten(), bins=16, range=(0,16))
+coin_hist, _ = np.histogram(coin.flatten(), bins=16, range=(0, 16))
 coin_hist = coin_hist.astype(float) / np.sum(coin_hist)
 
 
@@ -114,7 +115,6 @@ rotated_similarity = windowed_histogram_similarity(quantized_rotated_image,
                                                    coin_hist.shape[0])
 
 
-
 # Plot it all
 fig, axes = plt.subplots(nrows=5, figsize=(6, 18))
 ax0, ax1, ax2, ax3, ax4 = axes
diff --git a/skimage/filter/rank/bilateral.py b/skimage/filter/rank/bilateral.py
index 73147c7f..aeb318d1 100644
--- a/skimage/filter/rank/bilateral.py
+++ b/skimage/filter/rank/bilateral.py
@@ -158,8 +158,9 @@ def pop_bilateral(image, selem, out=None, mask=None, shift_x=False,
     return _apply(bilateral_cy._pop, image, selem, out=out,
                   mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1)
 
+
 def sum_bilateral(image, selem, out=None, mask=None, shift_x=False,
-                   shift_y=False, s0=10, s1=10):
+                  shift_y=False, s0=10, s1=10):
     """Apply a flat kernel bilateral filter.
 
     This is an edge-preserving and noise reducing denoising filter. It averages
diff --git a/skimage/filter/rank/bilateral_cy.pyx b/skimage/filter/rank/bilateral_cy.pyx
index 20a97788..b4e22d3f 100644
--- a/skimage/filter/rank/bilateral_cy.pyx
+++ b/skimage/filter/rank/bilateral_cy.pyx
@@ -51,6 +51,7 @@ cdef inline void _kernel_pop(dtype_t_out* out, Py_ssize_t odepth,
     else:
         out[0] = <dtype_t_out>0
 
+
 cdef inline void _kernel_sum(dtype_t_out* out, Py_ssize_t odepth,
                              Py_ssize_t* histo,
                              double pop, dtype_t g,
@@ -96,6 +97,7 @@ def _pop(dtype_t[:, ::1] image,
     _core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out,
           shift_x, shift_y, 0, 0, s0, s1, max_bin)
 
+
 def _sum(dtype_t[:, ::1] image,
          char[:, ::1] selem,
          char[:, ::1] mask,
diff --git a/skimage/filter/rank/core_cy.pyx b/skimage/filter/rank/core_cy.pyx
index c1216c0d..4bd21df0 100644
--- a/skimage/filter/rank/core_cy.pyx
+++ b/skimage/filter/rank/core_cy.pyx
@@ -42,8 +42,8 @@ cdef inline char is_in_mask(Py_ssize_t rows, Py_ssize_t cols,
             return 0
 
 
-cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype_t,
-                	        Py_ssize_t, Py_ssize_t, double,
+cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double,
+                            dtype_t, Py_ssize_t, Py_ssize_t, double,
                             double, Py_ssize_t, Py_ssize_t),
                 dtype_t[:, ::1] image,
                 char[:, ::1] selem,
@@ -173,8 +173,8 @@ cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype
                 if is_in_mask(rows, cols, rr, cc, mask_data):
                     histogram_decrement(histo, &pop, image[rr, cc])
 
-            kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin,
-                   p0, p1, s0, s1)
+            kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin,
+                   mid_bin, p0, p1, s0, s1)
 
         r += 1  # pass to the next row
         if r >= rows:
@@ -193,8 +193,8 @@ cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype
             if is_in_mask(rows, cols, rr, cc, mask_data):
                 histogram_decrement(histo, &pop, image[rr, cc])
 
-        kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin,
-               p0, p1, s0, s1)
+        kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin,
+               mid_bin, p0, p1, s0, s1)
 
         # ---> east to west
         for c in range(cols - 2, -1, -1):
@@ -210,8 +210,8 @@ cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype
                 if is_in_mask(rows, cols, rr, cc, mask_data):
                     histogram_decrement(histo, &pop, image[rr, cc])
 
-            kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin,
-                   p0, p1, s0, s1)
+            kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin,
+                   mid_bin, p0, p1, s0, s1)
 
         r += 1  # pass to the next row
         if r >= rows:
diff --git a/skimage/filter/rank/generic.py b/skimage/filter/rank/generic.py
index 018d7c0b..f2e75051 100644
--- a/skimage/filter/rank/generic.py
+++ b/skimage/filter/rank/generic.py
@@ -68,7 +68,8 @@ def _handle_input(image, selem, out, mask, out_dtype=None, pixel_size=1):
     return image, selem, out, mask, max_bin
 
 
-def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None):
+def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y,
+                            out_dtype=None):
 
     image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask,
                                                      out_dtype)
@@ -79,10 +80,12 @@ def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out
     return out.reshape(out.shape[:2])
 
 
-def _apply_vector_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None, pixel_size=1):
+def _apply_vector_per_pixel(func, image, selem, out, mask, shift_x, shift_y,
+                            out_dtype=None, pixel_size=1):
 
     image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask,
-                                                     out_dtype, pixel_size=pixel_size)
+                                                     out_dtype,
+                                                     pixel_size=pixel_size)
 
     func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask,
          out=out, max_bin=max_bin)
@@ -128,7 +131,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._autolevel, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -169,7 +173,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._bottomhat, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -207,7 +212,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._equalize, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -245,7 +251,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._gradient, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -292,7 +299,8 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._maximum, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -330,7 +338,8 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._mean, image, selem, out=out,
-                  mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
@@ -369,7 +378,8 @@ def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
     """
 
     return _apply_scalar_per_pixel(generic_cy._subtract_mean, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -407,7 +417,8 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._median, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -454,7 +465,8 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._minimum, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -494,7 +506,8 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._modal, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
@@ -537,7 +550,8 @@ def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
     """
 
     return _apply_scalar_per_pixel(generic_cy._enhance_contrast, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -586,7 +600,8 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._pop, image, selem, out=out,
-                  mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   mask=mask, shift_x=shift_x,
+                                   shift_y=shift_y)
 
 
 def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -635,7 +650,8 @@ def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._sum, image, selem, out=out,
-                  mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   mask=mask, shift_x=shift_x,
+                                   shift_y=shift_y)
 
 
 def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -684,7 +700,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._threshold, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -725,7 +742,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._tophat, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def noise_filter(image, selem, out=None, mask=None, shift_x=False,
@@ -775,8 +793,9 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
     selem_cpy = selem.copy()
     selem_cpy[centre_r, centre_c] = 0
 
-    return _apply_scalar_per_pixel(generic_cy._noise_filter, image, selem_cpy, out=out,
-                  mask=mask, shift_x=shift_x, shift_y=shift_y)
+    return _apply_scalar_per_pixel(generic_cy._noise_filter, image, selem_cpy,
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y)
 
 
 def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -821,8 +840,9 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._entropy, image, selem,
-                  out=out, mask=mask, shift_x=shift_x, shift_y=shift_y,
-                  out_dtype=np.double)
+                                   out=out, mask=mask,
+                                   shift_x=shift_x, shift_y=shift_y,
+                                   out_dtype=np.double)
 
 
 def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -865,10 +885,12 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     """
 
     return _apply_scalar_per_pixel(generic_cy._otsu, image, selem, out=out,
-                  mask=mask, shift_x=shift_x, shift_y=shift_y)
+                                   mask=mask, shift_x=shift_x,
+                                   shift_y=shift_y)
 
 
-def windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None):
+def windowed_histogram(image, selem, out=None, mask=None,
+                       shift_x=False, shift_y=False, n_bins=None):
     """Normalized sliding window histogram
 
     Parameters
@@ -887,18 +909,18 @@ def windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y
         to the structuring element sizes (center must be inside the given
         structuring element).
     n_bins : int or None
-        The number of histogram bins. Will default to `image.max() + 1`
+        The number of histogram bins. Will default to ``image.max() + 1``
         if None is passed.
 
     Returns
     -------
     out : 3-D array with float dtype of dimensions (H,W,N), where (H,W) are
-        the dimensions of the input image and N is n_bins or image.max()+1
-        if no value is provided as a parameter. Effectively, each pixel
-        is a N-D feature vector that is the histogram. The sum of the
-        elements in the feature vector will be 1, unless no pixels in the
-        window were covered by both selem and mask, in which case all
-        elements will be 0.
+        the dimensions of the input image and N is n_bins or
+        ``image.max() + 1`` if no value is provided as a parameter.
+        Effectively, each pixel is a N-D feature vector that is the histogram.
+        The sum of the elements in the feature vector will be 1, unless no
+        pixels in the window were covered by both selem and mask, in which
+        case all elements will be 0.
 
     Examples
     --------
diff --git a/skimage/filter/rank/generic_cy.pyx b/skimage/filter/rank/generic_cy.pyx
index bc54855f..98c8cd8a 100644
--- a/skimage/filter/rank/generic_cy.pyx
+++ b/skimage/filter/rank/generic_cy.pyx
@@ -407,7 +407,6 @@ cdef inline void _kernel_win_hist(dtype_t_out* out, Py_ssize_t odepth,
             out[i] = <dtype_t_out>0
 
 
-
 def _autolevel(dtype_t[:, ::1] image,
                char[:, ::1] selem,
                char[:, ::1] mask,
@@ -527,6 +526,7 @@ def _pop(dtype_t[:, ::1] image,
     _core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out,
           shift_x, shift_y, 0, 0, 0, 0, max_bin)
 
+
 def _sum(dtype_t[:, ::1] image,
          char[:, ::1] selem,
          char[:, ::1] mask,
diff --git a/skimage/filter/rank/percentile_cy.pyx b/skimage/filter/rank/percentile_cy.pyx
index 6d54999f..5ff584d3 100644
--- a/skimage/filter/rank/percentile_cy.pyx
+++ b/skimage/filter/rank/percentile_cy.pyx
@@ -285,6 +285,7 @@ def _mean(dtype_t[:, ::1] image,
     _core(_kernel_mean[dtype_t_out, dtype_t], image, selem, mask, out,
           shift_x, shift_y, p0, p1, 0, 0, max_bin)
 
+
 def _sum(dtype_t[:, ::1] image,
          char[:, ::1] selem,
          char[:, ::1] mask,
@@ -295,6 +296,7 @@ def _sum(dtype_t[:, ::1] image,
     _core(_kernel_sum[dtype_t_out, dtype_t], image, selem, mask, out,
           shift_x, shift_y, p0, p1, 0, 0, max_bin)
 
+
 def _subtract_mean(dtype_t[:, ::1] image,
                    char[:, ::1] selem,
                    char[:, ::1] mask,