pygsti.extras.paritybenchmarking.disturbancecalc.ProfileLikelihoodPlot

class ProfileLikelihoodPlot(profile_likelihood, mode='auto-cr', maxiters=20, search_tol=0.1, reltol=1e-05, abstol=1e-05, log10_alpha_values=None, num_auto_pts=10, verbosity=1)

Bases: object

Creates a plot of the profile log-likelihood.

Parameters:

• profile_likelihood (ProfileLikelihood) – The profile likelihood to plot

• mode ({"auto-cr", "auto-fullrange", "manual"}) – How to decide what domain/range to plot. “auto-cr” plots the region of the profile likelihood relevant to finding a confidence region. “auto-fullrange” plots the entire range of log-likelihood values, from the maximum to the amount it is reduced when the residual-TVD reaches 0. “manual” lets the user specify the log10(alpha) values to use (given in the log10_alpha_values argument).

• maxiters (int, optional) – The maximum number of alternating-minimization iterations to allow before giving up and deeming the final result “ok”.

• search_tol (float, optional) – The tolerance on the log-likelihood used when trying to locate a (residualTVD, logL) pair with a particular logL.

• reltol (float, optional) – The relative tolerance used to within profile likelihood.

• abstol (float, optional) – The absolute tolerance used to within profile likelihood.

• log10_alpha_values (list, optional) – A list of log10(alpha) values to use to determing the (x,y)=(residualTVD, logL) points to plot when mode == “manual”.

• num_auto_pts (int, optional) – The number of points to include in the plot when mode is “auto-cr” or “auto-fullrange”.

• verbosity (int, optional) – Sets the level of detail for messages printed to the console (higher = more detail).

Methods

__init__(profile_likelihood[, mode, ...])	Creates a plot of the profile log-likelihood.
make_plot([xlim, ylim, figsize, title])	Creates the plot figure using matplotlib.

make_plot(xlim=None, ylim=None, figsize=(10, 7), title=None)

Creates the plot figure using matplotlib. Arguments are familiar plot variables.