mappable = ScalarMappable(cmap="Blues")
mappable.set_array(np.arange(vmin, vmax, 0.1))
mappable.set_clim((vmin, vmax))
id = 20
for pred in reduced_predictors:
tree = BinaryTree(pred, maxdepth=10)
fig = plt.fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
# ax.scatter(np.array(tree.samples)[:,0], np.array(tree.samples)[:,1], s=1, alpha=0.4, color='black')
values = np.ma.masked_less(predictand_data[:, id], 0.1)
# ax.scatter(np.array(tree.samples)[:,0], np.array(tree.samples)[:,1], c='grey', s=5, alpha=0.3)
# ax.scatter(np.array(tree.samples)[:,0], np.array(tree.samples)[:,1], c=values, s=values, alpha=0.7)
tree.plot_density(ax, mappable)
plt.show()
id += 1
# fig = plt.fig = plt.figure(figsize=(10,10))
# ax = fig.add_subplot(111)
# ax.scatter(reduced_predictors[:,0], reduced_predictors[:,1], s=1, alpha=0.4, color='black')
# fig.show()
# for location in locations:
# print location
# pcas = functions.pca_fit(predictors, locations, 1)
# for location_string, pca in pcas.items():
# print location_string
# print pca.n_components_
