reduced_predictors_subset1 = pca.transform(predictors, startdate=startdate, enddate=enddate, months=[6,7,8]) reduced_predictors_subset2 = pca.transform(predictors, startdate=startdate, enddate=enddate, months=[12,1,2]) id = 20 for pred in reduced_predictors: ranges = zip(np.min(pred, axis=0), np.max(pred, axis=0)) tree = BinaryTree(pred, ranges=ranges, maxdepth=10) tree_subset1 = BinaryTree(reduced_predictors_subset1[id-20], ranges=ranges, maxdepth=10) tree_subset2 = BinaryTree(reduced_predictors_subset2[id-20], ranges=ranges, maxdepth=10) print "this location is at ", locations[id-20] full_volume = tree.volume() subset1_volume = tree_subset1.volume() subset2_volume = tree_subset2.volume() volume_overlap = full_volume - tree_subset1.volume_diff(tree_subset2) fig = plt.fig = plt.figure(figsize=(8,8)) ax = fig.add_subplot(111) #vmin = 0 #vmax = tree.max_leaves() #print "max leaves = ", vmax #mappable = ScalarMappable(cmap='Greys') #mappable.set_array(np.arange(vmin,vmax,0.1)) #mappable.set_clim((vmin,vmax)) #tree.plot_density(ax, mappable, 10)
