autogalaxy.plot.MLEPlotter

class MLEPlotter

Bases: SamplesPlotter

Methods

close
corner_cornerpy	Plots a corner plot via the visualization library corner.py.
log_likelihood_vs_iteration	Plot the log likelihood of a model fit as a function of iteration number.
log_plot_exception	Plotting the results of a dynesty model-fit before they have converged on an accurate estimate of the posterior can lead the dynesty plotting routines to raise a ValueError.
subplot_parameters	Plots a subplot of every parameter against iteration number.

Attributes

log_posterior_list
model

subplot_parameters(use_log_y=False, use_last_50_percent=False, **kwargs)

Plots a subplot of every parameter against iteration number.

The subplot extends over all free parameters in the model-fit, with the number of parameters per subplot given by the total number of free parameters in the model-fit.

This often produces a large dynamic range in the y-axis. Plotting the y-axis on a log-scale or only plotting the last 50% of samples can make the plot easier to inspect.

Parameters:

• use_log_y (bool) – If True, the y-axis is plotted on a log-scale.

• use_last_50_percent (bool) – If True, only the last 50% of samples are plotted.

• kwargs – Additional key word arguments can be passed to the plt.subplots method.

log_likelihood_vs_iteration(use_log_y=False, use_last_50_percent=False, **kwargs)

Plot the log likelihood of a model fit as a function of iteration number.

For a maximum likelihood estimate, the log likelihood should increase with iteration number.

This often produces a large dynamic range in the y-axis. Plotting the y-axis on a log-scale or only plotting the last 50% of samples can make the plot easier to inspect.

Parameters:

use_log_y (bool) – If True, the y-axis is plotted on a log-scale.