petitRADTRANS.retrieval.retrieval

Module Contents

Classes

Retrieval

This class implements the retrieval method using petitRADTRANS and pymultinest.

class petitRADTRANS.retrieval.retrieval.Retrieval(run_definition, output_dir='', test_plotting=False, sample_spec=False, ultranest=False, sampling_efficiency=None, const_efficiency_mode=None, n_live_points=None, resume=None, bayes_factor_species=None, corner_plot_names=None, short_names=None, pRT_plot_style=True)

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

test_plottingBool

Only use when running locally. A boolean flag that will produce plots for each sample when pymultinest is run.

sample_specBool

Produce plots and data files for 100 randomly sampled outputs from pymultinest.

ultranestbool

If true, use Ultranest sampling rather than pymultinest. This is still a work in progress, so use with caution!

bayes_factor_speciesStr

A pRT species that should be removed to test for the bayesian evidence for it’s 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.

run(self, sampling_efficiency=0.8, const_efficiency_mode=True, n_live_points=4000, log_z_convergence=0.5, step_sampler=False, warmstart_max_tau=0.5, resume=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.

resumebool

Continue existing retrieval. If FALSE THIS WILL OVERWRITE YOUR EXISTING RETRIEVAL.

_run_ultranest(self, n_live_points=4000, log_z_convergence=0.5, step_sampler=True, warmstart_max_tau=0.5, resume=True)

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.

resumebool

Continue existing retrieval. If FALSE THIS WILL OVERWRITE YOUR EXISTING RETRIEVAL.

generate_retrieval_summary(self, stats=None)

This function produces a human-readable text file describing the retrieval. It includes all of 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(self, 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.

prior(self, cube, ndim=0, nparams=0)

pyMultinest Prior function. Transforms unit hypercube into physical space.

prior_ultranest(self, cube)

pyMultinest Prior function. Transforms unit hypercube into physical space.

log_likelihood(self, cube, ndim=0, nparam=0)

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 the calculate only the pressure and temperature arrays rather than the wavlength 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.

Returns:
log_likelihoodfloat

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

get_samples(self, output_dir=None, ret_names=[])

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_best_fit_params(self, 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 parameters as read from the output files.

get_best_fit_model(self, best_fit_params, parameters_read, model_generating_func=None, ret_name=None)

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_best_fit_params

parameters_readlist

A list of the free parameters as read from the output files.

model_generating_funmethod

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 dataset given in the take_PTs_from kwarg.

ret_namestr

If plotting a fit from a different retrieval, input the retrieval name to be included.

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_abundances(self, sample, parameters_read=None)

This function returns the 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.

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_evidence(self, 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 of the PMN output files.

get_analyzer(self, ret_name='')

Get the PMN analyer 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 of the PMN output files.

plot_all(self, output_dir=None, ret_names=[])

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_spectra(self, samples_use, parameters_read, model_generating_func=None)

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/best_fit_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_funmethod

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.

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(self, samples_use, parameters_read, downsample_factor=None)

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)

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.

plot_PT(self, sample_dict, parameters_read)

Plot the PT profile with error contours

Args:
samples_usenp.ndarray

posterior samples from pynmultinest outputs (post_equal_weights)

parameters_readList

Used to plot correct parameters, as some in self.parameters are not free, and aren’t included in the PMN outputs

Returns:

fig : matplotlib.figure ax : matplotlib.axes

plot_corner(self, sample_dict, parameter_dict, parameters_read, **kwargs)

Make the corner plots

Args:
samples_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

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.