Merge branch 'pr/531'

skimage/exposure/exposure.py

@@ -14,6 +14,7 @@ __all__ = ['histogram', 'cumulative_distribution', 'equalize',
 def histogram(image, nbins=256):
     """Return histogram of image.
 
+
     Unlike `numpy.histogram`, this function returns the centers of bins and
     does not rebin integer arrays. For integer arrays, each integer value has
     its own bin, which improves speed and intensity-resolution.
@@ -32,6 +33,14 @@ def histogram(image, nbins=256):
         The values of the histogram.
     bin_centers : array
         The values at the center of the bins.
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> hist = histogram(data.camera())
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(hist[1], hist[0])  # doctest: +ELLIPSIS
+    [...]
     """
 
     # For integer types, histogramming with bincount is more efficient.

skimage/filter/ctmf.py

@@ -42,6 +42,13 @@ def median_filter(image, radius=2, mask=None, percent=50):
         not overlap the mask, the filtered result is underfined, but
         in practice, it will be the lowest value in the valid area.
 
+    Examples
+    --------
+    >>> a = np.ones((5, 5))
+    >>> a[2, 2] = 10 # introduce outlier
+    >>> b = median_filter(a)
+    >>> b[2, 2] # the median filter is good at removing outliers
+    1.0
     '''
 
     if image.ndim != 2:

skimage/measure/find_contours.py

@@ -95,6 +95,17 @@ def find_contours(array, level,
            Resolution 3D Surface Construction Algorithm. Computer Graphics
            (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
 
+    Examples
+    --------
+    >>> a = np.zeros((3, 3))
+    >>> a[0, 0] = 1
+    >>> a
+    array([[ 1.,  0.,  0.],
+           [ 0.,  0.,  0.],
+           [ 0.,  0.,  0.]])
+    >>> find_contours(a, 0.5)
+    [array([[ 0. ,  0.5],
+           [ 0.5,  0. ]])]
     """
     array = np.asarray(array, dtype=np.double)
     if array.ndim != 2:

skimage/morphology/greyreconstruct.py

@@ -60,13 +60,15 @@ def reconstruction(seed, mask, method='dilation', selem=None, offset=None):
     >>> import numpy as np
     >>> from skimage.morphology import reconstruction
 
-    First, we create a sinusoidal mask image w/ peaks at middle and ends.
+    First, we create a sinusoidal mask image with peaks at middle and ends.
+
     >>> x = np.linspace(0, 4 * np.pi)
     >>> y_mask = np.cos(x)
 
     Then, we create a seed image initialized to the minimum mask value (for
     reconstruction by dilation, min-intensity values don't spread) and add
     "seeds" to the left and right peak, but at a fraction of peak value (1).
+
     >>> y_seed = y_mask.min() * np.ones_like(x)
     >>> y_seed[0] = 0.5
     >>> y_seed[-1] = 0