diff options
| -rw-r--r-- | numpy/lib/twodim_base.py | 23 |
1 files changed, 21 insertions, 2 deletions
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index ce147965c..a8ae3dba0 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -547,7 +547,7 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): An array of values ``w_i`` weighing each sample ``(x_i, y_i)``. Weights are normalized to 1 if `normed` is True. If `normed` is False, the values of the returned histogram are equal to the sum of the weights - belonging to the samples falling into each bin. +l belonging to the samples falling into each bin. Returns ------- @@ -584,12 +584,14 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): 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 now use the Matplotlib to visualize this 2-dimensional histogram: + 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 @@ -599,6 +601,23 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): <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() + + + For interpolated visulization matplotlib provides the NonUniformImage. See + http://matplotlib.org/examples/pylab_examples/image_nonuniform.html """ from numpy import histogramdd |