if not opts.projection:
# Add scale bar, 1/4 width of the plot
ax.plot([0.0625, 0.3125], [0.0625, 0.0625],
color='black', linewidth=1, transform=ax.transAxes)
ax.text(0.0625, 0.0625, '{0:g} Mpc'.format(0.5 * opts.max_distance),
fontsize=8, transform=ax.transAxes, verticalalignment='bottom')
# Create marginal distance plot.
progress.update(-1, 'Plotting distance')
gs1 = gridspec.GridSpecFromSubplotSpec(5, 5, gs[0, 1])
ax = fig.add_subplot(gs1[1:-1, 1:-1])
# Plot marginal distance distribution, integrated over the whole sky.
d = np.linspace(0, opts.max_distance)
ax.fill_between(d, marginal_pdf(d, prob, mu, sigma, norm),
alpha=0.5, color=colors[0])
# Plot conditional distance distribution at true position
# and mark true distance.
for (ra, dec, dist), color in zip(opts.radecdist, colors[1:]):
theta = 0.5*np.pi - np.deg2rad(dec)
phi = np.deg2rad(ra)
ipix = hp.ang2pix(nside, theta, phi)
ax.fill_between(d, scipy.stats.norm(
mu[ipix], sigma[ipix]).pdf(d) * norm[ipix] * np.square(d),
alpha=0.5, color=color)
ax.axvline(dist, color='black', linewidth=0.5)
ax.plot(
[dist], [-0.15], marker=truth_marker, markeredgecolor=color,
markerfacecolor='none', markeredgewidth=1, clip_on=False,
ax.text(0.0625,
0.0625,
'{0:d} Mpc'.format(int(np.round(0.5 * max_distance))),
fontsize=8,
transform=ax.transAxes,
verticalalignment='bottom')
# Create marginal distance plot.
progress.update(-1, 'Plotting distance')
gs1 = gridspec.GridSpecFromSubplotSpec(5, 5, gs[0, 1])
ax = fig.add_subplot(gs1[1:-1, 1:-1])
# Plot marginal distance distribution, integrated over the whole sky.
d = np.linspace(0, max_distance)
ax.fill_between(d,
marginal_pdf(d, prob, mu, sigma, norm),
alpha=0.5,
color=colors[0])
# Plot conditional distance distribution at true position
# and mark true distance.
for (ra, dec, dist), color in zip(opts.radecdist, colors[1:]):
theta = 0.5 * np.pi - np.deg2rad(dec)
phi = np.deg2rad(ra)
ipix = hp.ang2pix(nside, theta, phi)
ax.fill_between(d,
scipy.stats.norm(mu[ipix], sigma[ipix]).pdf(d) *
norm[ipix] * np.square(d),
alpha=0.5,
color=color)
ax.axvline(dist, color='black', linewidth=0.5)
if not opts.projection:
# Add scale bar, 1/4 width of the plot
ax.plot([0.0625, 0.3125], [0.0625, 0.0625],
color='black', linewidth=1, transform=ax.transAxes)
ax.text(0.0625, 0.0625,
'{0:d} Mpc'.format(int(np.round(0.5 * max_distance))),
fontsize=8, transform=ax.transAxes, verticalalignment='bottom')
# Create marginal distance plot.
progress.update(-1, 'Plotting distance')
gs1 = gridspec.GridSpecFromSubplotSpec(5, 5, gs[0, 1])
ax = fig.add_subplot(gs1[1:-1, 1:-1])
# Plot marginal distance distribution, integrated over the whole sky.
d = np.linspace(0, max_distance)
ax.fill_between(d, marginal_pdf(d, prob, mu, sigma, norm),
alpha=0.5, color=colors[0])
# Plot conditional distance distribution at true position
# and mark true distance.
for (ra, dec, dist), color in zip(opts.radecdist, colors[1:]):
theta = 0.5*np.pi - np.deg2rad(dec)
phi = np.deg2rad(ra)
ipix = hp.ang2pix(nside, theta, phi)
ax.fill_between(d, scipy.stats.norm(
mu[ipix], sigma[ipix]).pdf(d) * norm[ipix] * np.square(d),
alpha=0.5, color=color)
ax.axvline(dist, color='black', linewidth=0.5)
ax.plot(
[dist], [-0.15], marker=truth_marker, markeredgecolor=color,
markerfacecolor='none', markeredgewidth=1, clip_on=False,
[0.0625, 0.3125], [0.0625, 0.0625],
color='black', linewidth=1, transform=ax.transAxes)
ax.text(
0.0625, 0.0625,
'{0:d} Mpc'.format(int(np.round(0.5 * max_distance))),
fontsize=8, transform=ax.transAxes, verticalalignment='bottom')
# Create marginal distance plot.
progress.update(-1, 'Plotting distance')
gs1 = gridspec.GridSpecFromSubplotSpec(5, 5, gs[0, 1])
ax = fig.add_subplot(gs1[1:-1, 1:-1])
# Plot marginal distance distribution, integrated over the whole sky.
d = np.linspace(0, max_distance)
ax.fill_between(
d, marginal_pdf(d, prob, mu, sigma, norm), alpha=0.5, color=colors[0])
# Plot conditional distance distribution at true position
# and mark true distance.
for (ra, dec, dist), color in zip(opts.radecdist, colors[1:]):
theta = 0.5*np.pi - np.deg2rad(dec)
phi = np.deg2rad(ra)
ipix = hp.ang2pix(nside, theta, phi)
ax.fill_between(d, scipy.stats.norm(
mu[ipix], sigma[ipix]).pdf(d) * norm[ipix] * np.square(d),
alpha=0.5, color=color)
ax.axvline(dist, color='black', linewidth=0.5)
ax.plot(
[dist], [-0.15], marker=truth_marker, markeredgecolor=color,
markerfacecolor='none', markeredgewidth=1, clip_on=False,
transform=transforms.blended_transform_factory(