MIN: fix unused import

doc/examples/plot_contours.py

@@ -18,7 +18,6 @@ Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage import data
 from skimage import measure
 
 

doc/examples/plot_denoise.py

@@ -28,7 +28,7 @@ pixels based on their spatial closeness and radiometric similarity.
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage import data, color, img_as_float
+from skimage import data, img_as_float
 from skimage.filter import denoise_tv_chambolle, denoise_bilateral
 
 

doc/examples/plot_ssim.py

@@ -24,7 +24,7 @@ but with very different mean structural similarity indices.
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage import data, color, io, exposure, img_as_float
+from skimage import data, img_as_float
 from skimage.measure import structural_similarity as ssim
 
 
@@ -34,6 +34,7 @@ rows, cols = img.shape
 noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
 noise[np.random.random(size=noise.shape) > 0.5] *= -1
 
+
 def mse(x, y):
     return np.linalg.norm(x - y)
 

skimage/io/_plugins/imread_plugin.py

@@ -1,6 +1,5 @@
 __all__ = ['imread', 'imsave']
 
-import numpy as np
 from skimage.utils.dtype import convert
 
 try:

skimage/morphology/misc.py

@@ -34,7 +34,6 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
     Examples
     --------
     >>> from skimage import morphology
-    >>> from scipy import ndimage as nd
     >>> a = np.array([[0, 0, 0, 1, 0],
     ...               [1, 1, 1, 0, 0],
     ...               [1, 1, 1, 0, 1]], bool)
@@ -55,7 +54,7 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
     # Should use `issubdtype` for bool below, but there's a bug in numpy 1.7
     if not (ar.dtype == bool or np.issubdtype(ar.dtype, int)):
         raise TypeError("Only bool or integer image types are supported. "
-                         "Got %s." % ar.dtype)
+                        "Got %s." % ar.dtype)
 
     if in_place:
         out = ar

skimage/transform/_warps.py

@@ -1,7 +1,6 @@
 import numpy as np
 from scipy import ndimage
-from ._geometric import (warp, SimilarityTransform, AffineTransform,
-                         ProjectiveTransform)
+from ._geometric import (warp, SimilarityTransform, AffineTransform)
 
 
 def resize(image, output_shape, order=1, mode='constant', cval=0.):
@@ -67,7 +66,7 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.):
         out = ndimage.map_coordinates(image, coord_map, order=order, mode=mode,
                                       cval=cval)
 
-    else: # 2-dimensional interpolation
+    else:  # 2-dimensional interpolation
 
         # 3 control points necessary to estimate exact AffineTransform
         src_corners = np.array([[1, 1], [1, rows], [cols, rows]]) - 1
@@ -116,7 +115,7 @@ def rescale(image, scale, order=1, mode='constant', cval=0.):
     Examples
     --------
     >>> from skimage import data
-    >>> from skimage.transform import resize
+    >>> from skimage.transform import rescale
     >>> image = data.camera()
     >>> rescale(image, 0.1).shape
     (51, 51)