petitRADTRANS.retrieval.plotting ================================ .. py:module:: petitRADTRANS.retrieval.plotting Classes ------- .. autoapisummary:: petitRADTRANS.retrieval.plotting.RetrievalPlotter Module Contents --------------- .. py:class:: RetrievalPlotter(retrieval, reference_data_name=None) Plotting companion for :class:`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_xlabel : str Label for the wavelength axis in spectrum plots. spec_ylabel : str Label for the flux axis in spectrum plots. y_axis_scaling : float Multiplicative scaling applied when plotting spectra and contribution-like outputs. xscale : str X-axis scaling used in spectral plots. yscale : str Y-axis scaling used in spectral plots. resolution : float Plotting resolution used when rebinned observational data are shown. nsample : int | float Default number of posterior samples used in sampled plotting utilities. temp_limits : list[float] | tuple[float, float] | None Optional x-axis limits for pressure-temperature plots. press_limits : list[float] | tuple[float, float] | None Optional y-axis limits for pressure-temperature plots. flux_lim : list[float] | tuple[float, float] | None Optional y-axis limits for spectral plots. wavelength_lim : list[float] | tuple[float, float] | None Optional x-axis limits for spectral plots. reference_data_name : str | 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``. .. py:attribute:: _local_attributes .. py:method:: __getattr__(name) .. py:method:: __setattr__(name, value) .. py:method:: get_reference_data_name() .. py:method:: get_reference_data() .. py:method:: get_reference_radtrans_name() .. py:method:: get_reference_radtrans_data() .. py:method:: _is_plotting_root() .. py:method:: _synchronize() .. py:method:: _get_clipped_contribution_output(auxiliary_outputs) :staticmethod: .. py:method:: 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_use : numpy.ndarray An array of the samples from the post_equal_weights file, used to find the best fit sample parameters_read : list A list of the free parameters as read from the output files. species_to_plot : list A list of which molecular species to include in the plot. contribution : bool If true, overplot the emission or transmission contribution function. prt_reference : str 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. refresh : bool 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. mode : str 'bestfit' or 'median', indicating which set of values should be used for plotting the abundances. sample_posteriors : bool If true, sample the posterior distributions to calculate confidence intervals for the retrieved abundance profiles. volume_mixing_ratio : bool If true, plot in units of volume mixing ratio (number fraction) instead of mass fractions. Returns: fig : matplotlib.figure The matplotlib figure, containing the data, best fit spectrum and residuals. ax : matplotlib.axes The upper pane of the plot, containing the best fit spectrum and data. ax_r : matplotlib.axes The lower pane of the plot, containing the residuals between the fit and the data. .. py:method:: 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. .. py:method:: 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. .. py:method:: plot_corner(sample_dict, parameter_dict, parameters_read, plot_best_fit=True, true_values=None, **kwargs) Make the corner plots. .. py:method:: plot_data(yscale='linear') Plot the data used in the retrieval. .. py:method:: 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 .. py:method:: 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. .. py:method:: 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.