def make_plot(model):
# Extract model name
model_name = os.path.basename(model).replace('_conv.fits', '').replace('images_total_', '')
fig = plt.figure(figsize=(w, h))
ax = fig.add_axes([0., 0., 1., 1.])
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for spine in ax.spines:
ax.spines[spine].set_visible(False)
flux = pyfits.getdata(model)[:, 0, 0, :]
for iw in range(nw):
ax.text(xmin, ymax - iw * dy - iw * ddy, labels[iw],
va='bottom', ha='left', size=8, color='black')
make_percent_bar(ax, flux[iw, :], groups_short,
[xmin, xmax,
ymax - (iw + 1) * dy - iw * ddy,
ymax - iw * dy - iw * ddy],
light_group_colors, group_colors)
fig.savefig('plots/fractions_%s.eps' % model_name, bbox_inches='tight')
fig.savefig('plots/fractions_%s.png' % model_name, bbox_inches='tight')
# Extract PAH energy
energy = energy[isize, :, :, :]
# Append to lists
energies.append(np.mean(energy) / normalization[isize])
names.append(os.path.basename(model).replace('.rtout', '').replace('energy_%s_' % model_name, '').replace('_', ' ').strip())
# Create list of group numbers
groups = [group(name) for name in names]
# Convert to Numpy arrays and sort
energies, names, groups = np.array(energies), np.array(names), np.array(groups)
key = np.array(zip(groups, energies), dtype=[('groups', int), ('energies', float)])
order = np.argsort(key, order=['groups', 'energies'])
energies, names, groups = energies[order], names[order], groups[order]
# Create pie chart figure
print len(energies)
ax.text(xmin, ymax - isize * dy - isize * ddy + 0.02 / h, labels[isize],
va='bottom', ha='left', size=8, color='black')
make_percent_bar(ax, energies, names,
[xmin, xmax,
ymax - (isize + 1) * dy - isize * ddy,
ymax - isize * dy - isize * ddy],
[light_group_colors[g] for g in groups], [group_colors[g] for g in groups])
fig.savefig('plots/energy_fractions_%s.eps' % model_name)
fig.savefig('plots/energy_fractions_%s.png' % model_name)
