def figure(self):
fig, ax = self._new_plot()
opt = self.plot_options
summary = []
# Best-fit point
best_fit_x = stats.best_fit(self.chisq, self.xdata)
best_fit_y = stats.best_fit(self.chisq, self.ydata)
summary.append("Best-fit point (x,y): {}, {}".format(best_fit_x, best_fit_y))
if opt.show_best_fit:
pm.plot_data(best_fit_x, best_fit_y, schemes.best_fit)
# Posterior mean
posterior_mean_x = stats.posterior_mean(self.posterior, self.xdata)
posterior_mean_y = stats.posterior_mean(self.posterior, self.ydata)
summary.append("Posterior mean (x,y): {}, {}".format(posterior_mean_x, posterior_mean_y))
if opt.show_posterior_mean:
pm.plot_data(posterior_mean_x, posterior_mean_y, schemes.posterior_mean)
# Profile likelihood
prof_data = two_dim.profile_like(
self.xdata,
self.ydata,
self.chisq,
nbins=opt.nbins,
bin_limits=opt.bin_limits)
if opt.show_prof_like:
pm.plot_image(
prof_data.prof_like,
opt.bin_limits,
opt.plot_limits,
schemes.prof_like)
levels = [two_dim.critical_prof_like(aa) for aa in opt.alpha]
if opt.show_conf_intervals:
pm.plot_contour(
prof_data.prof_like,
levels,
schemes.prof_like,
bin_limits=opt.bin_limits)
# Add legend
pm.legend(opt.leg_title, opt.leg_position)
return self.plot_data(figure=fig, summary=summary)
def figure(self):
fig, ax = self._new_plot()
opt = self.plot_options
summary = []
# Best-fit point
best_fit_x = stats.best_fit(self.chisq, self.xdata)
best_fit_y = stats.best_fit(self.chisq, self.ydata)
summary.append("Best-fit point (x,y): {}, {}".format(
best_fit_x, best_fit_y))
if opt.show_best_fit:
pm.plot_data(best_fit_x, best_fit_y, schemes.best_fit)
# Posterior mean
posterior_mean_x = stats.posterior_mean(self.posterior, self.xdata)
posterior_mean_y = stats.posterior_mean(self.posterior, self.ydata)
summary.append("Posterior mean (x,y): {}, {}".format(
posterior_mean_x, posterior_mean_y))
if opt.show_posterior_mean:
pm.plot_data(posterior_mean_x, posterior_mean_y,
schemes.posterior_mean)
# Profile likelihood
prof_data = two_dim.profile_like(self.xdata,
self.ydata,
self.chisq,
nbins=opt.nbins,
bin_limits=opt.bin_limits)
if opt.show_prof_like:
pm.plot_image(prof_data.prof_like, opt.bin_limits, opt.plot_limits,
schemes.prof_like)
levels = [two_dim.critical_prof_like(aa) for aa in opt.alpha]
if opt.show_conf_intervals:
pm.plot_contour(prof_data.prof_like,
levels,
schemes.prof_like,
bin_limits=opt.bin_limits)
# Add legend
pm.legend(opt.leg_title, opt.leg_position)
return self.plot_data(figure=fig, summary=summary)
def figure(self):
fig, ax = self._new_plot()
opt = self.plot_options
summary = []
# Best-fit point
best_fit_x = stats.best_fit(self.chisq, self.xdata)
best_fit_y = stats.best_fit(self.chisq, self.ydata)
summary.append("Best-fit point (x,y): {}, {}".format(
best_fit_x, best_fit_y))
if opt.show_best_fit:
pm.plot_data(best_fit_x, best_fit_y, schemes.best_fit)
# Posterior mean
posterior_mean_x = stats.posterior_mean(self.posterior, self.xdata)
posterior_mean_y = stats.posterior_mean(self.posterior, self.ydata)
summary.append("Posterior mean (x,y): {}, {}".format(
posterior_mean_x, posterior_mean_y))
if opt.show_posterior_mean:
pm.plot_data(posterior_mean_x, posterior_mean_y,
schemes.posterior_mean)
# Plot scatter of points.
sc = plt.scatter(self.xdata,
self.ydata,
s=schemes.scatter.size,
c=self.zdata,
marker=schemes.scatter.symbol,
cmap=schemes.scatter.colour_map,
norm=None,
vmin=None,
vmax=None,
alpha=0.5,
linewidths=None,
verts=None)
# Plot a colour bar
cb = plt.colorbar(sc, orientation='horizontal', shrink=0.5)
# Colour bar label
cb.ax.set_xlabel(opt.zlabel)
# Set reasonable number of ticks
cb.locator = MaxNLocator(4)
cb.update_ticks()
# Credible regions
pdf_data = two_dim.posterior_pdf(self.xdata,
self.ydata,
self.posterior,
nbins=opt.nbins,
bin_limits=opt.bin_limits)
levels = [
two_dim.critical_density(pdf_data.pdf, aa) for aa in opt.alpha
]
# Make sure pdf is correctly normalised
pdf = pdf_data.pdf
pdf = pdf / pdf.sum()
if opt.show_credible_regions:
pm.plot_contour(pdf,
levels,
schemes.posterior,
bin_limits=opt.bin_limits)
# Confidence interval
prof_data = two_dim.profile_like(self.xdata,
self.ydata,
self.chisq,
nbins=opt.nbins,
bin_limits=opt.bin_limits)
levels = [two_dim.critical_prof_like(aa) for aa in opt.alpha]
if opt.show_conf_intervals:
pm.plot_contour(prof_data.prof_like,
levels,
schemes.prof_like,
bin_limits=opt.bin_limits)
# Add legend
pm.legend(opt.leg_title, opt.leg_position)
return self.plot_data(figure=fig, summary=summary)
def figure(self):
fig, ax = self._new_plot()
opt = self.plot_options
summary = []
# Best-fit point
best_fit_x = stats.best_fit(self.chisq, self.xdata)
best_fit_y = stats.best_fit(self.chisq, self.ydata)
summary.append("Best-fit point (x,y): {}, {}".format(best_fit_x, best_fit_y))
if opt.show_best_fit:
pm.plot_data(best_fit_x, best_fit_y, schemes.best_fit)
# Posterior mean
posterior_mean_x = stats.posterior_mean(self.posterior, self.xdata)
posterior_mean_y = stats.posterior_mean(self.posterior, self.ydata)
summary.append("Posterior mean (x,y): {}, {}".format(posterior_mean_x, posterior_mean_y))
if opt.show_posterior_mean:
pm.plot_data(posterior_mean_x, posterior_mean_y, schemes.posterior_mean)
# Plot scatter of points.
sc = plt.scatter(
self.xdata,
self.ydata,
s=schemes.scatter.size,
c=self.zdata,
marker=schemes.scatter.symbol,
cmap=schemes.scatter.colour_map,
norm=None,
vmin=None,
vmax=None,
alpha=0.5,
linewidths=None,
verts=None)
# Plot a colour bar
cb = plt.colorbar(sc, orientation='horizontal', shrink=0.5)
# Colour bar label
cb.ax.set_xlabel(opt.zlabel)
# Set reasonable number of ticks
cb.locator = MaxNLocator(4)
cb.update_ticks()
# Credible regions
pdf_data = two_dim.posterior_pdf(
self.xdata,
self.ydata,
self.posterior,
nbins=opt.nbins,
bin_limits=opt.bin_limits)
levels = [two_dim.critical_density(pdf_data.pdf, aa) for aa in opt.alpha]
# Make sure pdf is correctly normalised
pdf = pdf_data.pdf
pdf = pdf / pdf.sum()
if opt.show_credible_regions:
pm.plot_contour(
pdf,
levels,
schemes.posterior,
bin_limits=opt.bin_limits)
# Confidence interval
prof_data = two_dim.profile_like(
self.xdata,
self.ydata,
self.chisq,
nbins=opt.nbins,
bin_limits=opt.bin_limits)
levels = [two_dim.critical_prof_like(aa) for aa in opt.alpha]
if opt.show_conf_intervals:
pm.plot_contour(
prof_data.prof_like,
levels,
schemes.prof_like,
bin_limits=opt.bin_limits)
# Add legend
pm.legend(opt.leg_title, opt.leg_position)
return self.plot_data(figure=fig, summary=summary)
