petitRADTRANS.retrieval.data

Classes

Data	This class stores the spectral data to be retrieved from a single instrument or observation.
PhotometryData	Data class for photometric observations.
TimeSeriesData	Data class for photometric observations.

Module Contents

class petitRADTRANS.retrieval.data.Data(name: str, path_to_observations: str | None = None, data_resolution: float | None = None, model_resolution: float | None = None, system_distance: float | None = None, external_radtrans_reference: object | None = None, model_generating_function: Callable[Ellipsis, tuple[jax.typing.ArrayLike, jax.typing.ArrayLike, jax.typing.ArrayLike]] | None = None, wavelength_boundaries: tuple[float, float] | None = None, scale_flux: bool = False, scale_uncertainties: bool = False, fit_flux_offset: bool = False, fit_instrumental_resolution: bool = False, subtract_continuum: bool = False, wavelength_bin_widths: jax.typing.ArrayLike | None = None, line_opacity_mode: str = 'c-k', radtrans_grid: bool = False, radtrans_object: object = None, wavelengths: jax.typing.ArrayLike | None = None, spectrum: jax.typing.ArrayLike | None = None, uncertainties: jax.typing.ArrayLike | None = None, covariance: jax.typing.ArrayLike | None = None, fit_covariance: bool = False, covariance_mode: str = 'none', global_covariance_kernel: str = 'squared_exponential', local_covariance_kernel: str = 'squared_exponential', n_local_covariance_kernels: int = 0, covariance_jitter: float = 0.0, mask: jax.typing.ArrayLike | None = None)

This class stores the spectral data to be retrieved from a single instrument or observation.

Each dataset is associated with an instance of petitRadTrans and an atmospheric model. The pRT instance can be overwritten, and associated with an existing pRT instance with the external_pRT_reference parameter. This setup allows for joint or independent retrievals on multiple datasets. Args:

namestr

Identifier for this data set.

path_to_observationsstr

Path to observations file, including filename. This can be a txt or dat file containing the wavelength, flux, transit depth and error, or a fits file containing the wavelength, spectrum and covariance matrix. Alternatively, the data information can be directly given by the wavelengths, spectrum, uncertainties, and mask attributes.

data_resolutionfloat or jnp.ndarray

Spectral resolution of the instrument. Optional, allows convolution of model to instrumental line width. If the data_resolution is an array, the resolution can vary as as a function of wavelength. The array should have the same shape as the input wavelength array, and should specify the spectral resolution at each wavelength bin.

model_resolutionfloat

Will be None by default. The resolution of the c-k opacity tables in pRT. This will generate a new c-k table using exo-k. The default (and maximum) correlated k resolution in pRT is \(\\lambda/\\Delta \\lambda > 1000\) (R=500). Lowering the resolution will speed up the computation. If integer positive value, and if opacities == 'lbl' is True, then this will sample the high-resolution opacities at the specified resolution. This may be desired in the case where medium-resolution spectra are required with a \(\\lambda/\\Delta \\lambda > 1000\), but much smaller than \(10^6\), which is the resolution of the lbl mode. In this case it may make sense to carry out the calculations with line_by_line_opacity_sampling = 10e5, for example, and then re-binning to the final desired resolution: this may save time! The user should verify whether this leads to solutions which are identical to the re-binned results of the fiducial \(10^6\) resolution. If not, this parameter must not be used. Note the difference between this parameter and the line_by_line_opacity_sampling parameter in the RadTrans class - the actual desired resolution should be set here.

system_distancefloat

The distance to the object in CGS units. Defaults to a 10pc normalized distance.

external_radtrans_referenceobject

An existing RadTrans object. Leave as none unless you’re sure of what you’re doing.

model_generating_functionmethod

A function, typically defined in run_definition.py that returns the model wavelength and spectrum (emission or transmission). This is the function that contains the physics of the model, and calls pRT in order to compute the spectrum.

wavelength_boundariestuple,list

Set the wavelength range of the pRT object. Defaults to a range +/-5% greater than that of the data. Must at least be equal to the range of the data.

scale_fluxbool

Turn on or off scaling the data by a constant factor. Set to True if scaling the data during the retrieval.

scale_uncertainties:

Turn on or off scaling the uncertainties by a constant factor. Set to True if scaling the uncertainties during the retrieval.

fit_flux_offset:

Turn on or off fitting a flux offset. Set to True if fitting a flux offset during the retrieval.

wavelength_bin_widthsnumpy.ndarray

Set the wavelength bin width to bin the Radtrans object to the data. Defaults to the data bins.

photometrybool

Set to True if using photometric data.

photometric_transformation_functionmethod

Transform the photometry (account for filter transmission etc.). This function must take in the wavelength and flux of a spectrum, and output a single photometric point (and optionally flux error).

photometric_bin_edgesTuple, numpy.ndarray

The edges of the photometric bin in micron. [low,high]

line_opacity_modestr

Should the retrieval be run using correlated-k opacities (default, ‘c-k’), or line by line (‘lbl’) opacities? If ‘lbl’ is selected, it is HIGHLY recommended to set the model_resolution parameter. In general, ‘c-k’ mode is recommended for retrievals of everything other than high-resolution (R>40000) spectra.

radtrans_grid: bool

Set to true if data has been binned to a pRT c-k grid.

radtrans_object:

An instance of Radtrans object to be used to generate model spectra in retrievals.

wavelengths:

(um) Wavelengths of the data.

spectrum:

Spectrum of the data.

uncertainties:

Uncertainties of the data, in the same units as the spectrum.

covariance:

Covariance matrix of the data, in the same units as the spectrum squared.

fit_covariance:

If True, construct a fitted covariance matrix during retrieval using GP-like kernel terms. This is currently supported for 1D spectroscopic datasets only.

covariance_mode:

Covariance model to use. Supported values are 'none', 'local', 'global', and 'global_local'.

global_covariance_kernel:

Global kernel name used when covariance_mode includes a global term.

local_covariance_kernel:

Local kernel name used when covariance_mode includes local terms.

n_local_covariance_kernels:

Number of local covariance kernels to include when using 'global_local' mode.

covariance_jitter:

Small diagonal stabilization term added to the fitted covariance matrix.

mask:

Mask of the data.

resolving_power_str = '.R'

name: str

path_to_observations: str | None = None

radtrans_object: object = None

wavelengths: jax.typing.ArrayLike | None = None

spectrum: jax.typing.ArrayLike | None = None

uncertainties: jax.typing.ArrayLike | None = None

mask: jax.typing.ArrayLike

system_distance: float

data_resolution: float | None = None

data_resolution_array_model: jax.typing.ArrayLike | None = None

model_resolution: float | None = None

external_radtrans_reference: object = None

model_generating_function: Callable = None

line_opacity_mode: str = 'c-k'

covariance: jax.typing.ArrayLike | None = None

inv_cov: jax.typing.ArrayLike | None = None

covariance_cholesky_factor: jax.typing.ArrayLike | None = None

log_covariance_determinant: float | None = None

fit_covariance: bool = False

covariance_mode: str = 'none'

global_covariance_kernel: str = 'squared_exponential'

local_covariance_kernel: str = 'squared_exponential'

n_local_covariance_kernels: int = 0

covariance_jitter: float

scale_flux: bool = False

scale_uncertainties: bool = False

fit_flux_offset: bool = False

fit_instrumental_resolution: bool = False

subtract_continuum: bool = False

scale_factor: float = 1.0

offset: float = 0.0

bval: float

wavelength_boundaries: tuple[float, float] | None = None

wavelength_bin_widths: jax.typing.ArrayLike | float | None = None

radtrans_grid = False

loadtxt(path: str, delimiter: str = ',', comments: str = '#') → None

Read a TXT or DAT file containing a header above 3 or 4 data columns representing a spectrum. Headers should start with the symbol given in the ‘comment’ argument. The 4 possible data columns are, from left to right:

1. The wavelengths in microns,

2. [Optional] The wavelengths bin widths, in microns.

3. The flux or transit depths,

4. The error on each data point.

Alternatively, the wavelength column Checks will be performed to determine the correct delimiter, but the recommended format is to use a CSV file with columns for wavelength, flux and error.

Args:

pathstr

Directory and filename of the data.

delimiterstring, int

The string used to separate values. By default, commas act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field.

commentsstring

The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded

load_x1d_fits(path)

Load in a x1d fits file as produced by the STSci JWST pipeline. Expects units of Jy for the flux and micron for the wavelength.

Args:

pathstr

Directory and filename of the data.

loadfits(path) → None

Load a Radtrans-formatted fits file. Must include extension SPECTRUM with fields WAVELENGTH, FLUX and COVARIANCE (or ERROR).

Args:

pathstr

Directory and filename of the data.

initialise_data_resolution(wavelengths_model: jax.typing.ArrayLike) → None

update_bins(wavelengths: jax.typing.ArrayLike) → None

Update the wavelength bin widths based on the provided wavelength array.

This method computes the bin width for each wavelength bin as the difference between consecutive wavelength values. The last bin width is set equal to the second-to-last bin width to maintain array length consistency.

Args:

wavelengthsArrayLike

Array of wavelength bin centers or edges.

scale_to_distance(new_distance: float) → float

Update the distance variable in the data class.

This will rescale the flux to the new distance.

Args:

new_distancefloat

The distance to the object in CGS units.

line_b_uncertainty_scaling(parameters)

This function implements the 10^b scaling from Line 2015, which allows for us to account for underestimated uncertainties:

We modify the standard error on the data point by the factor 10^b to account for underestimated uncertainties and/or unknown missing forward model physics (Foreman-Mackey et al. 2013, Hogg et al. 2010, Tremain et al. 2002), e.g., imperfect fits. This results in a more generous estimate of the parameter uncertainties. Note that this is similar to inflating the error bars post-facto in order to achieve reduced chi-squares of unity, except that this approach is more formal because uncertainties in this parameter are properly marginalized into the other relevant parameters. Generally, the factor 10^b takes on values that fall between the minimum and maximum of the square of the data uncertainties.

Args:

parameters: Dict

Dictionary of Parameters, should contain key ‘uncertianty_scaling_b’. This can be done for all data sets, or specified with a tag at the end of the key to apply different factors to different datasets.

Returns:

b: float

10**b error bar scaling factor.

_validate_covariance_configuration() → None

to_observation_state(model_contract: str = 'legacy') → petitRADTRANS.retrieval.runtime.ObservationState

_cache_covariance_factorization() → None

refresh_covariance_bookkeeping() → None

to_likelihood_state(n_free_parameters: int = 0, parameter_names: tuple[str, Ellipsis] = ()) → petitRADTRANS.retrieval.runtime.LikelihoodState

apply_flux_scaling(input_spectrum, parameters_dict: dict) → jax.typing.ArrayLike

Applies multiplicative flux scaling based on parameters in parameters_dict.

offset_flux(input_spectrum, parameters_dict: dict) → jax.typing.ArrayLike

Applies additive flux offset based on parameters in parameters_dict.

scale_and_inflate_uncertainties(parameters_dict: dict) → tuple[jax.typing.ArrayLike, jax.typing.ArrayLike]

Applies scaling to self.uncertainties or self.covariance. Returns a tuple: (scaled_uncertainties, scaled_covariance).

class petitRADTRANS.retrieval.data.PhotometryData(name, photometric_transformation_function, model_generating_function, photometric_bin_edges=None, wavelength_boundaries=None, path_to_observations=None, data_resolution=None, model_resolution=None, system_distance=None, external_radtrans_reference=None, scale_flux=False, scale_uncertainties=False, fit_flux_offset=False, line_opacity_mode='c-k', radtrans_object=None, wavelengths=None)

Bases: Data

Data class for photometric observations. Handles single or multiple photometric bands.

photometric_transformation_function

photometric_bin_edges

wavelength_boundaries = None

wavelength_bin_widths = None

_build_photometry_metadata() → dict[str, object]

to_observation_state(model_contract: str = 'legacy') → petitRADTRANS.retrieval.runtime.ObservationState

to_likelihood_state(n_free_parameters: int = 0, parameter_names: tuple[str, Ellipsis] = ()) → petitRADTRANS.retrieval.runtime.LikelihoodState

class petitRADTRANS.retrieval.data.TimeSeriesData(name, path_to_observations=None, filename_list=None, observation_times=None, data_resolution=None, model_resolution=None, system_distance=None, external_radtrans_reference=None, model_generating_function=None, wavelength_boundaries=None, scale_flux=False, scale_uncertainties=False, fit_flux_offset=False, line_opacity_mode='c-k', radtrans_grid=False, concatenate_flux_epochs_variability=False, atmospheric_column_flux_mixer=None, variability_atmospheric_column_model_flux_return_mode=False, radtrans_object=None, spectrum=None, wavelengths=None, wavelength_bin_widths=None, uncertainties=None, covariance=None, mask=None)

Bases: Data

Data class for photometric observations. Handles single or multiple photometric bands.

filename_list = None

concatenate_flux_epochs_variability = False

atmospheric_column_flux_mixer = None

variability_atmospheric_column_model_flux_return_mode = False

time_series_config = None

static _collapse_shared_epoch_axis(values)

to_observation_state(model_contract: str = 'legacy') → petitRADTRANS.retrieval.runtime.ObservationState

load_single_spectrum_txt(delimiter=',', comments='#')

loadtxt(path, delimiter=',', comments='#')

This function reads in a .txt or .dat file containing the spectrum. Headers should be commented out with ‘#’, the first column must be the wavelength in micron, the second column the flux or transit depth, and the final column must be the error on each data point. Checks will be performed to determine the correct delimiter, but the recommended format is to use a csv file with columns for wavelength, flux and error.

Args:

pathstr

Directory and filename of the data.

delimiterstring, int

The string used to separate values. By default, commas act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field.

commentsstring

The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded

initialise_data_resolution(wavelengths_model: jax.typing.ArrayLike) → None