def plot_one_slice_of_ninja_star_samples_2(samples, output_image_path_generator, dpi=100):
import ninja_star_distribution
pylab.hold(True)
print "Computing the original pdf values."
M = 4.0
mesh_x,mesh_y = np.mgrid[-M:M:.01, -M:M:.01]
z = ninja_star_distribution.mesh_pdf(mesh_x, mesh_y)
print "Generating the nice plots."
model_pdf_values_plot_handle = plt.pcolor(mesh_x, mesh_y, z)
#plt.winter()
plt.pink()
#d = plt.colorbar(model_pdf_value_plot_handle, orientation='horizontal')
for c in np.arange(samples.shape[0]):
x = samples[c,:,0]
y = samples[c,:,1]
scatter_handle = pylab.scatter(x, y)
pylab.draw()
pylab.savefig(output_image_path_generator(c), dpi=dpi)
print "Wrote " + output_image_path_generator(c)
del(scatter_handle)
pylab.close()
def plot_one_slice_of_ninja_star_samples(samples_slice, output_image_path, dpi=100, omit_ninja_star_from_plots = False):
"""
Samples should be of size (M, d).
You would generally pick either one chain alone
or one time slice from a set of chains.
This plotting function doesn't pretend to be
a very general method. It assumes that the samples
are 2-dimensional and that [-4.0, 4.0]^2 is the
best choice of window to plot the samples.
"""
import ninja_star_distribution
pylab.hold(True)
x = samples_slice[:,0]
y = samples_slice[:,1]
# TODO : pick better color for the sample dots
pylab.scatter(x, y)
# TODO : stamp the KL divergence on the plots
M = 4.0
if not omit_ninja_star_from_plots:
print "Computing the original pdf values."
mesh_x,mesh_y = np.mgrid[-M:M:.01, -M:M:.01]
z = ninja_star_distribution.mesh_pdf(mesh_x, mesh_y)
print "Generating the nice plots."
model_pdf_values_plot_handle = plt.pcolor(mesh_x, mesh_y, z)
#plt.winter()
plt.pink()
#d = plt.colorbar(model_pdf_value_plot_handle, orientation='horizontal')
pylab.axes([-M, M, -M, M])
count_of_points_outside = np.count_nonzero( (x < M) + (M < x) + (y < M) + (M < y) )
pylab.text(0.0, 0.0,"%d points outside" % count_of_points_outside,fontsize=12, transform = pylab.gca().transAxes)
pylab.draw()
#pylab.savefig(output_image_path)
pylab.savefig(output_image_path, dpi=dpi)
pylab.close()
