petitRADTRANS.retrieval.plotting

petitRADTRANS.retrieval.plotting#

Classes#

RetrievalPlotter

Plotting companion for Retrieval.

Module Contents#

class petitRADTRANS.retrieval.plotting.RetrievalPlotter(retrieval, reference_data_name=None)#

Plotting companion for Retrieval.

The plotter owns the plotting-related configuration that used to live in the retrieval configuration object. These attributes can be adjusted after construction through retrieval.plotter and are used by the plotting and model-evaluation helpers throughout the retrieval module.

Attributes:

spec_xlabelstr: Label for the wavelength axis in spectrum plots.
spec_ylabelstr: Label for the flux axis in spectrum plots.
y_axis_scalingfloat: Multiplicative scaling applied when plotting spectra and contribution-like outputs.
xscalestr: X-axis scaling used in spectral plots.
yscalestr: Y-axis scaling used in spectral plots.
resolutionfloat: Plotting resolution used when rebinned observational data are shown.
nsampleint | float: Default number of posterior samples used in sampled plotting utilities.
temp_limitslist[float] | tuple[float, float] | None: Optional x-axis limits for pressure-temperature plots.
press_limitslist[float] | tuple[float, float] | None: Optional y-axis limits for pressure-temperature plots.
flux_limlist[float] | tuple[float, float] | None: Optional y-axis limits for spectral plots.
wavelength_limlist[float] | tuple[float, float] | None: Optional x-axis limits for spectral plots.
reference_data_namestr | None: Name of the dataset used as the default plotting reference. If not provided at construction time, it defaults to the first key in retrieval.configuration.data.

_local_attributes#

__getattr__(name)#

__setattr__(name, value)#

get_reference_data_name()#

get_reference_data()#

get_reference_radtrans_name()#

get_reference_radtrans_data()#

_is_plotting_root()#

_synchronize()#

static _get_clipped_contribution_output(auxiliary_outputs)#

plot_abundances(samples_use, parameters_read, species_to_plot=None, contribution=False, refresh=True, model_generating_function=None, prt_reference=None, mode='bestfit', sample_posteriors=False, volume_mixing_ratio=False)#

Plot the abundance profiles in mass fractions or volume mixing ratios as a function of pressure.

Args:

samples_usenumpy.ndarray: An array of the samples from the post_equal_weights file, used to find the best fit sample
parameters_readlist: A list of the free parameters as read from the output files.
species_to_plotlist: A list of which molecular species to include in the plot.
contributionbool: If true, overplot the emission or transmission contribution function.
prt_referencestr: If specified, the pRT object of the data with name pRT_reference will be used for plotting, instead of generating a new pRT object at R = 1000.
model_generating_function(callable, optional):: A function that returns the wavelength and spectrum, and takes a Radtrans object and the current set of parameters stored in self.configuration.parameters. This should be the same model function used in the retrieval.
refreshbool: If True (default value) the .npy files in the evaluate_[retrieval_name] folder will be replaced by recalculating the best fit model. This is useful if plotting intermediate results from a retrieval that is still running. If False no new spectrum will be calculated and the plot will be generated from the .npy files in the evaluate_[retrieval_name] folder.
modestr: ‘bestfit’ or ‘median’, indicating which set of values should be used for plotting the abundances.
sample_posteriorsbool: If true, sample the posterior distributions to calculate confidence intervals for the retrieved abundance profiles.
volume_mixing_ratiobool: If true, plot in units of volume mixing ratio (number fraction) instead of mass fractions.

Returns:

figmatplotlib.figure: The matplotlib figure, containing the data, best fit spectrum and residuals.
axmatplotlib.axes: The upper pane of the plot, containing the best fit spectrum and data.
ax_rmatplotlib.axes: The lower pane of the plot, containing the residuals between the fit and the data.

plot_all(output_directory=None, ret_names=None, contribution=False, model_generating_function=None, prt_reference=None, mode='bestfit')#: Produces plots for the best fit spectrum, a sample of 100 output spectra, the best fit PT profile and a corner plot for parameters specified in the run definition.

plot_contribution(samples_use, parameters_read, model_generating_function=None, prt_reference=None, log_scale_contribution=False, n_contour_levels=30, refresh=True, mode='bestfit')#: Plot the contribution function of the bestfit or median model from a retrieval.

plot_corner(sample_dict, parameter_dict, parameters_read, plot_best_fit=True, true_values=None, **kwargs)#: Make the corner plots.

plot_data(yscale='linear')#: Plot the data used in the retrieval.

plot_pt(sample_dict, parameters_read, contribution=False, refresh=False, model_generating_function=None, prt_reference=None, mode='bestfit')#: Plot the PT profile with error contours

plot_sampled(samples_use, parameters_read, downsample_factor=None, save_outputs=True, nsample=None, model_generating_function=None, prt_reference=None, refresh=True, figsize=(16, 10))#: Plot a set of randomly sampled output spectra for each dataset in the retrieval.

plot_spectra(samples_use, parameters_read, model_generating_function=None, prt_reference=None, refresh=True, mode='bestfit', marker_color_type=None, marker_cmap=None, marker_label='', only_save_best_fit_spectra=False, figsize=(16, 10))#: Plot the best fit spectrum, the data from each dataset and the residuals between the two.