Update twodim_base.py

1 files changed, 44 insertions, 33 deletions

diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index 9e3377411..17d20e4df 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -584,40 +584,51 @@ l        belonging to the samples falling into each bin.
 
     Examples
     --------
-    2D-histogram with fixed bin width:
-
-    >>> x, y = np.random.randn(2, 100)
-    >>> H, xedges, yedges = np.histogram2d(x, y, bins=(5, 8))
-    >>> H.shape, xedges.shape, yedges.shape
-    ((5, 8), (6,), (9,))
-
-    We can use imshow from Matplotlib to visualize this 2D-histogram:
-
-    >>> extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
-    >>> import matplotlib.pyplot as plt
-    >>> plt.imshow(H, extent=extent, interpolation='nearest')
-    <matplotlib.image.AxesImage object at ...>
-    >>> plt.colorbar()
-    <matplotlib.colorbar.Colorbar instance at ...>
-    >>> plt.show()
-
-
-    2D-histogram with variable bin size:
-
-    >>>import numpy as np, matplotlib.pyplot as plt
-    x = np.random.normal(3, 2, 1000)
-    y = np.random.normal(3, 1, 1000)
-    yedges=xedges=[0,2,3,4,6]
-    H, yedges, xedges = np.histogram2d(y,x, bins=(yedges,xedges))
-    extent = [xedges[0], xedges[-1], yedges[-1], yedges[0]]
-    X,Y = np.meshgrid(xedges, yedges)
-    plt.pcolormesh(X, Y,H)
-    plt.colorbar()
-    plt.show()
-
+    2D-histogram with variable bin width:
+
+    import matplotlib as ml
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    # First we define the bin edges                                                
+    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))
+
+    # 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            
+
+    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]])
+
+    # 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')
     
-    For interpolated visulization matplotlib provides the NonUniformImage. See
-    http://matplotlib.org/examples/pylab_examples/image_nonuniform.html
+    # 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()
     """
     from numpy import histogramdd