`petitRADTRANS.retrieval.retrieval`

Module Contents

Classes

Retrieval

This class implements the retrieval method using petitRADTRANS and pymultinest.

Attributes

`RANK`
`COMM`
`COMM`

petitRADTRANS.retrieval.retrieval.RANK = 0

petitRADTRANS.retrieval.retrieval.COMM

petitRADTRANS.retrieval.retrieval.COMM

class petitRADTRANS.retrieval.retrieval.Retrieval(run_definition, output_dir='', use_MPI=False, sample_spec=False, ultranest=False, bayes_factor_species=None, corner_plot_names=None, short_names=None, pRT_plot_style=True, test_plotting=False)

This class implements the retrieval method using petitRADTRANS and pymultinest. A RetrievalConfig object is passed to this class to describe the retrieval data, parameters and priors. The run() method then uses pymultinest to sample the parameter space, producing posterior distributions for parameters and bayesian evidence for models. Various useful plotting functions have also been included, and can be run once the retrieval is complete.

Args:

run_definitionRetrievalConfig: A RetrievalConfig object that describes the retrieval to be run. This is the user facing class that must be setup for every retrieval.
output_dirStr: The directory in which the output folders should be written
sample_specBool: Produce plots and data files for random samples drawn from the outputs of pymultinest.
ultranestbool: If true, use Ultranest sampling rather than pymultinest. Provides a more accurate evidence estimate, but is significantly slower.
bayes_factor_speciesStr: A pRT species that should be removed to test for the bayesian evidence for its presence.
corner_plot_namesList(Str): List of additional retrieval names that should be included in the corner plot.
short_namesList(Str): For each corner_plot_name, a shorter name to be included when plotting.
pRT_plot_styleBool: Use the petitRADTRANS plotting style as described in plot_style.py. Recommended to turn this parameter to false if you want to use interactive plotting, or if the test_plotting parameter is True.
test_plottingBool: Only use when running locally. A boolean flag that will produce plots for each sample when pymultinest is run.

run(sampling_efficiency=0.8, const_efficiency_mode=False, n_live_points=4000, log_z_convergence=0.5, step_sampler=False, warmstart_max_tau=0.5, n_iter_before_update=50, resume=True, max_iters=0, frac_remain=0.1, importance_nested_sampling=True, Lepsilon=0.3, error_checking=True)

Run mode for the class. Uses pynultinest to sample parameter space and produce standard PMN outputs.

Args:

sampling_efficiencyFloat: pymultinest sampling efficiency. If const efficiency mode is true, should be set to around 0.05. Otherwise, it should be around 0.8 for parameter estimation and 0.3 for evidence comparison.
const_efficiency_modeBool: pymultinest constant efficiency mode
n_live_pointsInt: Number of live points to use in pymultinest, or the minimum number of live points to use for the Ultranest reactive sampler.
log_z_convergencefloat: If ultranest is being used, the convergence criterion on log z.
step_samplerbool: Use a step sampler to improve the efficiency in ultranest.
warmstart_max_taufloat: Warm start allows accelerated computation based on a different but similar UltraNest run.
n_iter_before_updateint: Number of live point replacements before printing an update to a log file.
max_itersint: Maximum number of sampling iterations. If 0, will continue until convergence criteria are satisfied.
frac_remainfloat: Ultranest convergence criterion. Halts integration if live point weights are below the specified value.
Lepsilonfloat: Ultranest convergence criterion. Use with noisy likelihoods. Halts integration if live points are wihin Lepsilon.
resumebool: Continue existing retrieval. If FALSE THIS WILL OVERWRITE YOUR EXISTING RETRIEVAL.
error_checkingbool: Test the model generating function for typical errors. ONLY TURN THIS OFF IF YOU KNOW WHAT YOU’RE DOING!

_run_ultranest(n_live_points, log_z_convergence, step_sampler, warmstart_max_tau, resume, max_iters, frac_remain, Lepsilon)

Run mode for the class. Uses ultranest to sample parameter space and produce standard outputs.

Args:

n_live_pointsInt: The minimum number of live points to use for the Ultranest reactive sampler.
log_z_convergencefloat: The convergence criterion on log z.
step_samplerbool: Use a step sampler to improve the efficiency in ultranest.
max_itersint: Maximum number of sampling iterations. If 0, will continue until convergence criteria are satisfied.
frac_remainfloat: Ultranest convergence criterion. Halts integration if live point weights are below the specified value.
Lepsilonfloat: Ultranest convergence criterion. Use with noisy likelihoods. Halts integration if live points are wihin Lepsilon.
resumebool: Continue existing retrieval. If FALSE THIS WILL OVERWRITE YOUR EXISTING RETRIEVAL.

generate_retrieval_summary(stats=None)

This function produces a human-readable text file describing the retrieval. It includes all the fixed and free parameters, the limits of the priors (if uniform), a description of the data used, and if the retrieval is complete, a summary of the best fit parameters and model evidence.

Args:

statsdict: A Pymultinest stats dictionary, from Analyzer.get_stats(). This contains the evidence and best fit parameters.

setup_data(scaling=10, width=3)

Creates a pRT object for each data set that asks for a unique object. Checks if there are low resolution c-k models from exo-k, and creates them if necessary. The scaling and width parameters adjust the AMR grid as described in RetrievalConfig.setup_pres and models.fixed_length_amr. It is recommended to keep the defaults.

Args:

scalingint: A multiplicative factor that determines the size of the full high resolution pressure grid, which will have length self.p_global.shape[0] * scaling.
widthint: The number of cells in the low pressure grid to replace with the high resolution grid.

_error_check_model_function()

prior(cube, ndim=0, nparams=0): pyMultinest Prior function. Transforms unit hypercube into physical space.

prior_ultranest(cube): pyMultinest Prior function. Transforms unit hypercube into physical space.

log_likelihood(cube, ndim=0, nparam=0, logL_per_datapoint_dict=None)

pyMultiNest required likelihood function.

This function wraps the model computation and log-likelihood calculations for pyMultiNest to sample. If PT_plot_mode is True, it will return only the pressure and temperature arrays rather than the wavelength and flux. If run_mode is ‘evaluate’, it will save the provided sample to the best-fit spectrum file, and add it to the best_fit_specs dictionary. If evaluate_sample_spectra is true, it will store the spectrum in posterior_sample_specs.

Args:

cubenumpy.ndarray: The transformed unit hypercube, providing the parameter values to be passed to the model_generating_function.
ndimint: The number of dimensions of the problem
nparamint: The number of parameters in the fit.
logL_per_datapoint_dictdict: Dictionary with instrument-entries. If provided, log likelihood per datapoint is appended to existing list.

Returns:

log_likelihoodfloat: The (negative) log likelihood of the model given the data.

save_best_fit_outputs(parameters)

static _get_samples(ultranest, names, output_dir=None, ret_names=None)

get_samples(output_dir=None, ret_names=None)

This function looks in the given output directory and finds the post_equal_weights file associated with the current retrieval name.

Args:

output_dirstr: Parent directory of the out_PMN/RETRIEVALNAME_post_equal_weights.dat file
ret_namesList(str): A list of retrieval names to add to the sample and parameter dictionary. Functions the same as setting corner_files during initialisation.

Returns:

sample_dictdict: A dictionary with keys being the name of the retrieval, and values are a numpy ndarray containing the samples in the post_equal_weights file
parameter_dictdict: A dictionary with keys being the name of the retrieval, and values are a list of names of the parameters used in the retrieval. The first name corresponds to the first column of the samples, and so on.

get_max_likelihood_params(best_fit_params, parameters_read)

This function converts the sample from the post_equal_weights file with the maximum log likelihood, and converts it into a dictionary of Parameters that can be used in a model function.

Args:

best_fit_paramsnumpy.ndarray: An array of the best fit parameter values (or any other sample)
parameters_readlist: A list of the free parameter names as read from the output files.

get_median_params(samples, parameters_read, return_array=False)

This function builds a parameter dictionary based on the median value of each parameter. This will update the best_fit_parameter dictionary!

Args:

best_fit_paramsnumpy.ndarray: An array of the best fit parameter values (or any other sample)
parameters_readlist: A list of the free parameter names as read from the output files.

get_full_range_model(parameters, model_generating_function=None, ret_name=None, contribution=False, pRT_object=None, pRT_reference=None)

Retrieve a full wavelength range model based on the given parameters.

Parameters:

parameters (dict): A dictionary containing parameters used to generate the model. model_generating_function (callable, optional): A function to generate the model.

Defaults to None.

ret_name (str, optional): Name of the model to be returned.: TODO: Remove this parameter as it’s currently unused. Defaults to None.
contribution (bool, optional): Return the emission or transmission contribution function.: Defaults to False.
pRT_object (object, optional): RadTrans object for calculating the spectrum.: Defaults to None.
pRT_reference (object, optional): Reference Data object for calculating the spectrum.: Defaults to None.

Returns:

object: The generated full range model.

get_best_fit_model(best_fit_params, parameters_read, ret_name=None, contribution=False, pRT_reference=None, model_generating_function=None, refresh=True, mode='bestfit')

This function uses the best fit parameters to generate a pRT model that spans the entire wavelength range of the retrieval, to be used in plots.

Args:

best_fit_paramsnumpy.ndarray: A numpy array containing the best fit parameters, to be passed to get_max_likelihood_params
parameters_readlist: A list of the free parameters as read from the output files.
ret_namestr: If plotting a fit from a different retrieval, input the retrieval name to be included.
contributionbool: If True, calculate the emission or transmission contribution function as well as the spectrum.
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 pRT_Object and the current set of parameters stored in self.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: If “best_fit”, will use the maximum likelihood parameter values to calculate the best fit model and contribution. If “median”, uses the median parameter values.

Returns:

bf_wlennumpy.ndarray: The wavelength array of the best fit model
bf_spectrumnumpy.ndarray: The emission or transmission spectrum array, with the same shape as bf_wlen

get_mass_fractions(sample, parameters_read=None)

This function returns the mass fraction abundances of each species as a function of pressure

Args:

samplenumpy.ndarray: A sample from the pymultinest output, the abundances returned will be computed for this set of parameters.
parameters_readlist: A list of the free parameters as read from the output files.

Returns:

abundancesdict: A dictionary of abundances. The keys are the species name, the values are the mass fraction abundances at each pressure
MMWnumpy.ndarray: The mean molecular weight at each pressure level in the atmosphere.

get_volume_mixing_ratios(sample, parameters_read=None)

This function returns the VNRs of each species as a function of pressure

Args:

samplenumpy.ndarray: A sample from the pymultinest output, the abundances returned will be computed for this set of parameters.
parameters_readlist: A list of the free parameters as read from the output files.

Returns:

vmrdict: A dictionary of abundances. The keys are the species name, the values are the mass fraction abundances at each pressure
MMWnumpy.ndarray: The mean molecular weight at each pressure level in the atmosphere.

save_volume_mixing_ratios(sample_dict, parameter_dict, rets=None)

Save volume mixing ratios (VMRs) and line absorber species information for specified retrievals.

Parameters: - self: The instance of the class containing the function. - sample_dict (dict): A dictionary mapping retrieval names to lists of samples. - parameter_dict (dict): A dictionary mapping retrieval names to parameter values. - rets (list, optional): List of retrieval names to process. If None, uses the default retrieval name.

Returns: - vmrs (numpy.ndarray): Array containing volume mixing ratios for each sample and species.

The function processes the specified retrievals and saves the corresponding VMRs and line absorber species information to files in the output directory. If ‘rets’ is not provided, the default retrieval name is used. The VMRs are saved in a numpy file, and the line absorber species are saved in a JSON file.

Example usage:: ` sample_dict = {'Retrieval1': [...], 'Retrieval2': [...]} parameter_dict = {'Retrieval1': {...}, 'Retrieval2': {...}} vmrs = save_volume_mixing_ratios(sample_dict, parameter_dict) `

save_mass_fractions(sample_dict, parameter_dict, rets=None)

Save mass fractions and line absorber species information for specified retrievals.

Parameters: - self: The instance of the class containing the function. - sample_dict (dict): A dictionary mapping retrieval names to lists of samples. - parameter_dict (dict): A dictionary mapping retrieval names to parameter values. - rets (list, optional): List of retrieval names to process. If None, uses the default retrieval name.

Returns: - mass_fractions (numpy.ndarray): Array containing mass fractions for each sample and species.

The function processes the specified retrievals and saves the corresponding mass fracs and line absorber species information to files in the output directory. If ‘rets’ is not provided, the default retrieval name is used. The mass fractinos are saved in a numpy file, and the line absorber species are saved in a JSON file.

Example usage:: ` sample_dict = {'Retrieval1': [...], 'Retrieval2': [...]} parameter_dict = {'Retrieval1': {...}, 'Retrieval2': {...}} mass_fractions = save_mass_fractions(sample_dict, parameter_dict) `

get_evidence(ret_name='')

Get the log10 Z and error for the retrieval

This function uses the pymultinest analyzer to get the evidence for the current retrieval_name by default, though any retrieval_name in the out_PMN folder can be passed as an argument - useful for when you’re comparing multiple similar models. This value is also printed in the summary file.

Args:

ret_namestring: The name of the retrieval that prepends all the PMN output files.

static get_best_fit_likelihood(samples)

Get the log likelihood of the best fit model

Args:

samplesnumpy.ndarray: An array of samples and likelihoods taken from a post_equal_weights file

get_best_fit_chi2(samples)

Get the 𝛘^2 of the best fit model - removing normalization term from log L

Args:

samplesnumpy.ndarray: An array of samples and likelihoods taken from a post_equal_weights file

get_log_likelihood_per_datapoint(samples_use, ret_name=None)

get_elpd_per_datapoint(ret_name=None)

get_chi2(sample)

Get the 𝛘^2 of the given sample relative to the data - removing normalization term from log L

Args:

samplenumpy.ndarray: A single sample and likelihood taken from a post_equal_weights file

get_chi2_normalisation(sample)

Get the 𝛘^2 normalization term from log L

Args:

samplenumpy.ndarray: A single sample and likelihood taken from a post_equal_weights file

get_reduced_chi2(sample, subtract_n_parameters=False)

Get the 𝛘^2/DoF of the given model - divide chi^2 by DoF or number of wavelength channels.

Args:

samplenumpy.ndarray: A single sample and likelihoods taken from a post_equal_weights file
subtract_n_parametersbool: If True, divide the Chi2 by the degrees of freedom (n_data - n_parameters). If False, divide only by n_data

get_reduced_chi2_from_model(wlen_model, spectrum_model, subtract_n_parameters=False)

Get the 𝛘^2/DoF of the supplied spectrum - divide chi^2 by DoF

Args:

wlen_modelnp.ndarray: The wavelength grid of the model spectrum in micron.
spectrum_modelnp.ndarray: The model flux in the same units as the data.
subtract_n_parametersbool: If True, divide the Chi2 by the degrees of freedom (n_data - n_parameters). If False, divide only by n_data

get_analyzer(ret_name='')

Get the PMN analyzer from a retrieval run

This function uses gets the PMN analyzer object for the current retrieval_name by default, though any retrieval_name in the out_PMN folder can be passed as an argument - useful for when you’re comparing multiple similar models.

Args:

ret_namestring: The name of the retrieval that prepends all the PMN output files.

build_param_dict(sample, free_param_names)

This function builds a dictionary of parameters that can be passed to the model building functions. It requires a numpy array with the same length as the number of free parameters, and a list of all the parameter names in the order they appear in the array. The returned dictionary will contain all of these parameters, together with the fixed retrieval parameters.

Args:

samplenumpy.ndarray: An array or list of free parameter values
free_param_nameslist(string): A list of names for each of the free parameters.

Returns:

paramsdict: A dictionary of Parameters, with values set to the values in sample.

sample_teff(sample_dict, param_dict, ret_names=None, nsample=None, resolution=40)

This function samples the outputs of a retrieval and computes Teff for each sample. For each sample, a model is computed at low resolution, and integrated to find the total radiant emittance, which is converted into a temperature using the stefan boltzmann law: $j^{star} = sigma T^{4}$. Teff itself is computed using util.calc_teff.

Args:

sample_dictdict: A dictionary, where each key is the name of a retrieval, and the values are the equal weighted samples.
param_dictdict: A dictionary where each key is the name of a retrieval, and the values are the names of the free parameters associated with that retrieval.
ret_namesOptional(list(string)): A list of retrieval names, each should be included in the sample_dict. If left as none, it defaults to only using the current retrieval name.
nsampleOptional(int): The number of times to compute Teff. If left empty, uses the “take_PTs_from” plot_kwarg. Recommended to use ~300 samples, probably more than is set in the kwarg!
resolutionint: The spectra resolution to compute the models at. Typically, this should be very low in order to enable rapid calculation.

Returns:

tdictdict: A dictionary with retrieval names for keys, and the values are the calculated values of Teff for each sample.

plot_all(output_dir=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.

By default, this runs the following functions:

plot_spectra: Plots the best fit spectrum together with the data, with an extra: panel showing the residuals between the model and data.

plot_PT: plots the pressure-temperature profile contours plot_corner : Corner plot based on the posterior sample distributions plot_abundances : Abundance profiles for each line species used.

if contribution = True:

plot_contribution : The emission or transmission contribution function In addition to plotting the contribution function, the contribution will also be overlaid on top of the PT profiles and abundance profiles.

if self.evaluate_sample_spectra = True

plot_sampled : Randomly draws N samples from the posterior distribution, and plots the resulting spectrum overtop the data.

Args:

output_dir: string: Output directory to store the plots. Defaults to selt.output_dir.
ret_nameslist(str): List of retrieval names. Used if multiple retrievals are to be included in a single corner plot.
contributionbool: If true, plot 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 pRT_Object and the current set of parameters stored in self.parameters. This should be the same model function used in the retrieval.
modestr: If ‘bestfit’, consider the maximum likelihood sample for plotting, if median, calculate the model based on the median retrieved parameters.

plot_spectra(samples_use, parameters_read, model_generating_function=None, pRT_reference=None, refresh=True, mode='bestfit', marker_color_type=None, marker_cmap=plt.cm.bwr, marker_label='')

Plot the best fit spectrum, the data from each dataset and the residuals between the two. Saves a file to OUTPUT_DIR/evaluate_RETRIEVAL_NAME/RETRIEVAL_NAME_MODE_spec.pdf

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.
model_generating_functionmethod: A function that will take in the standard ‘model’ arguments (pRT_object, params, pt_plot_mode, AMR, resolution) and will return the wavlength and flux arrays as calculated by petitRadTrans. If no argument is given, it uses the method of the first dataset included in the retrieval.
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 pRT_Object and the current set of parameters stored in self.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: Use ‘bestfit’ (minimum likelihood) parameters, or median parameter values.
marker_color_typestr: Data-attribute to plot as marker colors. Use ‘delta_elpd’, ‘elpd’, or ‘pareto_k’.
marker_cmapmatplotlib colormap: Colormap to use for marker colors.
marker_labelstr: Label to add to colorbar corresponding to marker colors.

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_sampled(samples_use, parameters_read, downsample_factor=None, save_outputs=False, nsample=None, model_generating_function=None, pRT_reference=None, refresh=True)

Plot a set of randomly sampled output spectra for each dataset in the retrieval.

This will save nsample files for each dataset included in the retrieval. Note that if you change the model_resolution of your Data and rerun this function, the files will NOT be updated - if the files exists the function defaults to reading from file rather than recomputing. Delete all of the sample functions and run it again.

Args:

samples_usenp.ndarray: posterior samples from pynmultinest outputs (post_equal_weights)
parameters_readlist(str): list of free parameters as read from the output files.
downsample_factorint: Factor by which to reduce the resolution of the sampled model, for smoother plotting. Defaults to None. A value of None will result in the full resolution spectrum. Note that this factor can only reduce the resolution from the underlying model_resolution of the data.
nsampleint: Number of samples to draw from the posterior distribution. Defaults to the value of self.rd.plot_kwargs[“nsample”].
save_outputsbool: If true, saves each calculated spectrum as a .npy file. The name of the file indicates the index from the post_equal_weights file that was used to generate the sample.
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 pRT_Object and the current set of parameters stored in self.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.

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

Args:

sample_dictnp.ndarray: posterior samples from pynmultinest outputs (post_equal_weights)
parameters_readList: List of free parameters as read from the output file.
contributionbool: Weight the opacity of the pt profile by the emission contribution function, and overplot the contribution curve.
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.
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 pRT_Object and the current set of parameters stored in self.parameters. This should be the same model function used in the retrieval.
modestr: ‘bestfit’ or ‘median’, indicating which set of values should be used to calculate the contribution function.

Returns:

fig : matplotlib.figure ax : matplotlib.axes

plot_corner(sample_dict, parameter_dict, parameters_read, plot_best_fit=True, true_values=None, **kwargs)

Make the corner plots

Args:

sample_dictDict: Dictionary of samples from PMN outputs, with keys being retrieval names
parameter_dictDict: Dictionary of parameters for each of the retrievals to be plotted.
parameters_readList: Used to plot correct parameters, as some in self.parameters are not free, and aren’t included in the PMN outputs
plot_best_fitbool: If true, plot vertical lines to indicate the maximum likelihood parameter values.
true-valuesnp.ndarray: An array of values for each plotted parameter, where a vertical line will be plotted for each value. Can be used to indicate true values if retrieving on synthetic data, or to overplot additional measurements.
kwargsdict: Each kwarg can be one of the kwargs used in corner.corner. These can be used to adjust the title_kwargs,label_kwargs,hist_kwargs, hist2d_kawargs or the contour kwargs. Each kwarg must be a dictionary with the arguments as keys and values as the values.

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

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. This plot indicates the relative contribution from each wavelength and each pressure level in the atmosphere to the spectrum.

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.
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 pRT_Object and the current set of parameters stored in self.parameters. This should be the same model function used in the retrieval.
log_scale_contributionbool: If true, take the log10 of the contribution function to visualise faint features.
n_contour_levelsint: Number of contour levels to pass to the matplotlib contourf function.
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 to calculate the contribution function.

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_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 pRT_Object and the current set of parameters stored in self.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 distribtions to calculate confidence intervales 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

petitRADTRANS.retrieval.retrieval

Module Contents

Classes

Attributes

`petitRADTRANS.retrieval.retrieval`