DOC: update example of histogram2d to doctest format.

1 files changed, 30 insertions, 31 deletions

diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index c4cbaa9f1..4021a1b3c 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -587,49 +587,48 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None):
     --------
     2D-histogram with variable bin width:
 
-    import matplotlib as ml
-    import matplotlib.pyplot as plt
-    import numpy as np
+    >>> import matplotlib as mpl
+    >>> import matplotlib.pyplot as plt
 
     # First we define the bin edges                                                
-    xedges = [0, 1, 1.5, 3, 5]
-    yedges = [0, 2, 3, 4, 6]
+    >>> xedges = [0, 1, 1.5, 3, 5]
+    >>> yedges = [0, 2, 3, 4, 6]
 
     # Next we create a histogram H with random bin content                         
-    x = np.random.normal(3, 1, 100)
-    y = np.random.normal(1, 1, 100)
-    H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))
+    >>> x = np.random.normal(3, 1, 100)
+    >>> y = np.random.normal(1, 1, 100)
+    >>> H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))
 
     # Or we fill the histogram H with a determined bin content                     
-    H = np.ones((4, 4)).cumsum().reshape(4, 4)
-    print H[::-1]  # This shows the bin content in the order as plotted            
+    >>> H = np.ones((4, 4)).cumsum().reshape(4, 4)
+    >>> print H[::-1]  # This shows the bin content in the order as plotted
 
-    fig = plt.figure(figsize=(7, 3))
+    >>> fig = plt.figure(figsize=(7, 3))
     # Imshow can only do an equidistant representation of bins                     
-    ax = fig.add_subplot(131)
-    ax.set_title('imshow:\nequidistant')
-    im = plt.imshow(H, interpolation='nearest', origin='low',
-                    extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
+    >>> ax = fig.add_subplot(131)
+    >>> ax.set_title('imshow:\nequidistant')
+    >>> im = plt.imshow(H, interpolation='nearest', origin='low',
+                        extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
 
     # pcolormesh can displaying exact bin edges                                    
-    ax = fig.add_subplot(132)
-    ax.set_title('pcolormesh:\nexact bin edges')
-    X, Y = np.meshgrid(xedges, yedges)
-    ax.pcolormesh(X, Y, H)
-    ax.set_aspect('equal')
+    >>> ax = fig.add_subplot(132)
+    >>> ax.set_title('pcolormesh:\nexact bin edges')
+    >>> X, Y = np.meshgrid(xedges, yedges)
+    >>> ax.pcolormesh(X, Y, H)
+    >>> ax.set_aspect('equal')
     
     # NonUniformImage displays exact bin edges with interpolation                  
-    ax = fig.add_subplot(133)
-    ax.set_title('NonUniformImage:\ninterpolated')
-    im = ml.image.NonUniformImage(ax, interpolation='bilinear')
-    xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])
-    ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])
-    im.set_data(xcenters, ycenters, H)
-    ax.images.append(im)
-    ax.set_xlim(xedges[0], xedges[-1])
-    ax.set_ylim(yedges[0], yedges[-1])
-    ax.set_aspect('equal')
-    plt.show()
+    >>> ax = fig.add_subplot(133)
+    >>> ax.set_title('NonUniformImage:\ninterpolated')
+    >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')
+    >>> xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])
+    >>> ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])
+    >>> im.set_data(xcenters, ycenters, H)
+    >>> ax.images.append(im)
+    >>> ax.set_xlim(xedges[0], xedges[-1])
+    >>> ax.set_ylim(yedges[0], yedges[-1])
+    >>> ax.set_aspect('equal')
+    >>> plt.show()
     """
     from numpy import histogramdd