matplotlib
diff --git a/‎examples/mplot3d/hist3d_demo.py
Copy file name to clipboardExpand all lines: examples/mplot3d/hist3d_demo.py
+9-3Lines changed: 9 additions & 3 deletions b/‎examples/mplot3d/hist3d_demo.py
Copy file name to clipboardExpand all lines: examples/mplot3d/hist3d_demo.py
+9-3Lines changed: 9 additions & 3 deletions
@@ -1,5 +1,6 @@
-# Note: np.meshgrid produces arrays in (ny, nx), so flatten is called with 'F'.
-# For numpy >= 1.7, this can be avoided by calling meshgrid with indexing='ij'.
+'''
+Demo of a histogram for 2 dimensional data as a bar graph in 3D.
+'''
 
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
@@ -8,13 +9,18 @@
 fig = plt.figure()
 ax = fig.add_subplot(111, projection='3d')
 x, y = np.random.rand(2, 100) * 4
-hist, xedges, yedges = np.histogram2d(x, y, bins=4)
+hist, xedges, yedges = np.histogram2d(x, y, bins=4, range=[[0, 4], [0, 4]])
 
+# Construct arrays for the anchor positions of the 16 bars.
+# Note: np.meshgrid gives arrays in (ny, nx) so we use 'F' to flatten xpos,
+# ypos in column-major order. For numpy >= 1.7, we could instead call meshgrid
+# with indexing='ij'.
 xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)
 xpos = xpos.flatten('F')
 ypos = ypos.flatten('F')
 zpos = np.zeros_like(xpos)
 
+# Construct arrays with the dimensions for the 16 bars.
 dx = 0.5 * np.ones_like(zpos)
 dy = dx.copy()
 dz = hist.flatten()