import numpy as np import matplotlib.pyplot as plt # the random data x = np.random.randn(1000) y = np.random.randn(1000) fig = plt.figure(1, figsize=(5.5,5.5)) from mpl_toolkits.axes_grid import make_axes_locatable axScatter = plt.subplot(111) divider = make_axes_locatable(axScatter) # create a new axes with a height of 1.2 inch above the axScatter axHistx = divider.new_vertical(1.2, pad=0.1, sharex=axScatter) # create a new axes with a width of 1.2 inch on the right side of the # axScatter axHisty = divider.new_horizontal(1.2, pad=0.1, sharey=axScatter) fig.add_axes(axHistx) fig.add_axes(axHisty) # make some labels invisible plt.setp(axHistx.get_xticklabels() + axHisty.get_yticklabels(), visible=False) # the scatter plot: axScatter.scatter(x, y) axScatter.set_aspect(1.) # now determine nice limits by hand: binwidth = 0.25 xymax = np.max( [np.max(np.fabs(x)), np.max(np.fabs(y))] ) lim = ( int(xymax/binwidth) + 1) * binwidth bins = np.arange(-lim, lim + binwidth, binwidth) axHistx.hist(x, bins=bins) axHisty.hist(y, bins=bins, orientation='horizontal') # the xaxis of axHistx and yaxis of axHisty are shared with axScatter, # thus there is no need to manually adjust the xlim and ylim of these # axis. #axHistx.axis["bottom"].major_ticklabels.set_visible(False) for tl in axHistx.get_xticklabels(): tl.set_visible(False) axHistx.set_yticks([0, 50, 100]) #axHisty.axis["left"].major_ticklabels.set_visible(False) for tl in axHisty.get_yticklabels(): tl.set_visible(False) axHisty.set_xticks([0, 50, 100]) plt.draw() plt.show() #plt.savefig("a.pdf")