species.data package
Contents
species.data package#
Submodules#
species.data.allers2013 module#
Module for adding young, M- and L-type dwarf spectra from Allers & Liu (2013) to the database. All spectra are also available in the SpeX Prism Library Analysis Toolkit.
- species.data.allers2013.add_allers2013(input_path: str, database: h5py._hl.files.File) None [source]#
Function for adding the spectra of young, M- and L-type dwarfs from Allers & Liu (2013) to the database.
- Parameters
input_path (str) – Path of the data folder.
database (h5py._hl.files.File) – The HDF5 database.
- Returns
None
- Return type
NoneType
species.data.bonnefoy2014 module#
Module for adding young, M- and L-type dwarf spectra from Bonnefoy et al. (2014) to the database.
- species.data.bonnefoy2014.add_bonnefoy2014(input_path: str, database: h5py._hl.files.File) None [source]#
Function for adding the SINFONI spectra of young, M- and L-type dwarfs from Bonnefoy et al. (2014) to the database.
- Parameters
input_path (str) – Path of the data folder.
database (h5py._hl.files.File) – The HDF5 database.
- Returns
None
- Return type
NoneType
species.data.companions module#
Module for extracting data of directly imaged planets and brown dwarfs.
- species.data.companions.companion_spectra(input_path: str, comp_name: str, verbose: bool = True) Optional[Dict[str, Tuple[str, Optional[str], float]]] [source]#
Function for getting available spectra of directly imaged planets and brown dwarfs.
- Parameters
input_path (str) – Path of the data folder.
comp_name (str) – Companion name for which the spectra will be returned.
verbose (bool) – Print details on the companion data that are added to the database.
- Returns
Dictionary with the spectra of
comp_name
. ANone
will be returned if there are not any spectra available.- Return type
dict, None
- species.data.companions.get_data() Dict[str, Dict[str, Union[str, bool, Tuple[float, float], Dict[str, Union[Tuple[float, float], List[Tuple[float, float]]]]]]] [source]#
Function for extracting a dictionary with the distances (pc) and apparent magnitudes of directly imaged planets and brown dwarfs. These data can be added to the database with
add_companion()
.- Returns
Dictionary with the parallaxes and apparent magnitudes of directly imaged companions. Distances are mainly from the Gaia Early Data Release 3 DR2.
- Return type
dict
- species.data.companions.get_spec_data() Dict[str, Dict[str, Tuple[str, Optional[str], float, str]]] [source]#
Function for extracting a dictionary with the spectra of directly imaged planets. These data can be added to the database with
add_companion()
.- Returns
Dictionary with the spectrum, optional covariances, spectral resolution, and filename.
- Return type
dict
species.data.database module#
Module with functionalities for reading and writing of data.
- class species.data.database.Database[source]#
Bases:
object
Class with reading and writing functionalities for the HDF5 database.
- Returns
None
- Return type
NoneType
- add_calibration(tag: str, filename: Optional[str] = None, data: Optional[numpy.ndarray] = None, units: Optional[Dict[str, str]] = None, scaling: Optional[Tuple[float, float]] = None) None [source]#
Function for adding a calibration spectrum to the database.
- Parameters
tag (str) – Tag name in the database.
filename (str, None) – Name of the file that contains the calibration spectrum. The file could be either a plain text file, in which the first column contains the wavelength (um), the second column the flux density (W m-2 um-1), and the third column the uncertainty (W m-2 um-1). Or, a FITS file can be provided in which the data is stored as a 2D array in the primary HDU. The
data
argument is not used if set toNone
.data (np.ndarray, None) – Spectrum stored as 3D array with shape
(n_wavelength, 3)
. The first column should contain the wavelength (um), the second column the flux density (W m-2 um-1), and the third column the error (W m-2 um-1). Thefilename
argument is used if set toNone
.units (dict, None) – Dictionary with the wavelength and flux units, e.g.
{'wavelength': 'angstrom', 'flux': 'w m-2'}
. The default units (um and W m-2 um-1) are used if set toNone
.scaling (tuple(float, float), None) – Scaling for the wavelength and flux as
(scaling_wavelength, scaling_flux)
. Not used if set toNone
.
- Returns
None
- Return type
NoneType
- add_companion(name: Union[str, None, List[str]] = None, verbose: bool = True) None [source]#
Function for adding the magnitudes and spectra of directly imaged planets and brown dwarfs from
get_data
andget_comp_spec()
to the database.- Parameters
name (str, list(str), None) – Name or list with names of the directly imaged planets and brown dwarfs (e.g.
'HR 8799 b'
or['HR 8799 b', '51 Eri b', 'PZ Tel B']
). All the available companion data are added if the argument is set toNone
.verbose (bool) – Print details on the companion data that are added to the database.
- Returns
None
- Return type
NoneType
- add_comparison(tag: str, goodness_of_fit: numpy.ndarray, flux_scaling: numpy.ndarray, model_param: List[str], coord_points: List[numpy.ndarray], object_name: str, spec_name: List[str], model: str, scale_spec: List[str], extra_scaling: Optional[numpy.ndarray]) None [source]#
- Parameters
tag (str) – Database tag where the results will be stored.
goodness_of_fit (np.ndarray) – Array with the goodness-of-fit values.
flux_scaling (np.ndarray) – Array with the best-fit scaling values to match the model spectra with the data.
model_param (list(str)) – List with the names of the model parameters.
coord_points (list(np.ndarray)) – List with 1D arrays of the model grid points, in the same order as
model_param
.object_name (str) – Object name as stored in the database with
add_object()
oradd_companion()
.spec_name (list(str)) – List with spectrum names that are stored at the object data of
object_name
.model (str) – Atmospheric model grid that is used for the comparison.
scale_spec (list(str)) – List with spectrum names to which an additional scaling has been applied.
extra_scaling (np.ndarray. None) – Array with extra scalings that have been applied to the spectra of
scale_spec
. The argument can be set toNone
if no extra scalings have been applied.
- Returns
None
- Return type
NoneType
- add_dust() None [source]#
Function for adding optical constants of MgSiO3 and Fe, and MgSiO3 cross sections for a log-normal and power-law size distribution to the database. The optical constants have been compiled by Mollière et al. (2019) for petitRADTRANS from the following sources:
- MgSiO3, crystalline
Scott & Duley (1996), ApJS, 105, 401
Jäger et al. (1998), A&A, 339, 904
- MgSiO3, amorphous
Jäger et al. (2003), A&A, 408, 193
- Fe, crystalline
Henning & Stognienko (1996), A&A, 311, 291
- Fe, amorphous
Pollack et al. (1994), ApJ, 421, 615
- Returns
None
- Return type
NoneType
- add_empirical(tag: str, names: List[str], sptypes: List[str], goodness_of_fit: List[float], flux_scaling: List[numpy.ndarray], av_ext: List[float], rad_vel: List[float], object_name: str, spec_name: List[str], spec_library: str) None [source]#
- Parameters
tag (str) – Database tag where the results will be stored.
names (list(str)) – Array with the names of the empirical spectra.
sptypes (list(str)) – Array with the spectral types of
names
.goodness_of_fit (list(float)) – Array with the goodness-of-fit values.
flux_scaling (list(np.ndarray)) – List with arrays with the best-fit scaling values to match the library spectra with the data. The size of each array is equal to the number of spectra that are provided as argument of
spec_name
.av_ext (list(float)) – Array with the visual extinction \(A_V\).
rad_vel (list(float)) – Array with the radial velocities (km s-1).
object_name (str) – Object name as stored in the database with
add_object()
oradd_companion()
.spec_name (list(str)) – List with spectrum names that are stored at the object data of
object_name
.spec_library (str) – Name of the spectral library that was used for the empirical comparison.
- Returns
None
- Return type
NoneType
- add_filter(filter_name: str, filename: Optional[str] = None, detector_type: str = 'photon', verbose: bool = True) None [source]#
Function for adding a filter profile to the database, either from the SVO Filter profile Service or from an input file. Additional filters that are automatically added are Magellan/VisAO.rp, Magellan/VisAO.ip, Magellan/VisAO.zp, Magellan/VisAO.Ys, ALMA/band6, and ALMA/band7.
- Parameters
filter_name (str) – Filter name from the SVO Filter Profile Service (e.g., ‘Paranal/NACO.Lp’) or a user-defined name if a
filename
is specified.filename (str) – Filename of the filter profile. The first column should contain the wavelength (um) and the second column the fractional transmission. The profile is downloaded from the SVO Filter Profile Service if the argument of
filename
is set toNone
.detector_type (str) – The detector type (‘photon’ or ‘energy’). The argument is only used if a
filename
is provided. Otherwise, for filters that are fetched from the SVO website, the detector type is read from the SVO data. The detector type determines if a wavelength factor is included in the integral for the synthetic photometry.verbose (bool) – Print details on the companion data that are added to the database.
- Returns
None
- Return type
NoneType
- add_isochrones(model: str, filename: Optional[str] = None, tag: Optional[str] = None) None [source]#
Function for adding isochrone data to the database.
- Parameters
model (str) – Evolutionary model (‘ames’, ‘bt-settl’, ‘sonora’, ‘baraffe’, or ‘saumon2008’). For ‘ames’, ‘bt-settl’, ‘sonora’, and ‘saumon2008’, the isochrones will be automatically downloaded and added to the database. For ‘baraffe’, the isochrone data can be downloaded from https://phoenix.ens-lyon.fr/Grids/ and manually added by setting the
filename
andtag
arguments.filename (str, None) – Filename with the isochrone data. Only required with
model='baraffe'
and can be set toNone
otherwise.tag (str) – Database tag name where the isochrone that will be stored. Only required with
model='baraffe'
and can be set toNone
otherwise.
- Returns
None
- Return type
NoneType
- add_model(model: str, wavel_range: Optional[Tuple[float, float]] = None, spec_res: Optional[float] = None, teff_range: Optional[Tuple[float, float]] = None) None [source]#
Function for adding a grid of model spectra to the database. All spectra have been resampled to logarithmically-spaced wavelengths. The spectral resolution is returned with the
get_spec_res()
method ofReadModel
, but is typically of the order of several thousand. It should be noted that the original spectra were often calculated with a constant step size in wavenumber, so the original spectral resolution decreased from short to long wavelengths.- Parameters
model (str) – Model name (‘ames-cond’, ‘ames-dusty’, ‘atmo’, ‘bt-settl’, ‘bt-settl-cifist’, ‘bt-nextgen’, ‘drift-phoenix’, ‘petitcode-cool-clear’, ‘petitcode-cool-cloudy’, ‘petitcode-hot-clear’, ‘petitcode-hot-cloudy’, ‘exo-rem’, ‘blackbody’, bt-cond’, ‘bt-cond-feh, ‘morley-2012’, ‘sonora-cholla’, ‘sonora-bobcat’, ‘sonora-bobcat-co’, ‘koester-wd’).
wavel_range (tuple(float, float), None) – Wavelength range (um) for adding a subset of the spectra. The full wavelength range is used if the argument is set to
None
.spec_res (float, None) – Spectral resolution to which the spectra will be resampled. This parameter is optional since the spectra have already been resampled to a lower, constant resolution (typically \(R = 5000\)). The argument is only used if
wavel_range
is notNone
.teff_range (tuple(float, float), None) – Effective temperature range (K) for adding a subset of the model grid. The full parameter grid will be added if the argument is set to
None
.
- Returns
None
- Return type
NoneType
- add_object(object_name: str, parallax: Optional[Tuple[float, float]] = None, distance: Optional[Tuple[float, float]] = None, app_mag: Optional[Dict[str, Union[Tuple[float, float], List[Tuple[float, float]]]]] = None, flux_density: Optional[Dict[str, Tuple[float, float]]] = None, spectrum: Optional[Dict[str, Tuple[str, Optional[str], Optional[float]]]] = None, deredden: Union[Dict[str, float], float] = None, verbose: bool = True) None [source]#
Function for adding the photometry and/or spectra of an object to the database.
- Parameters
object_name (str) – Object name that will be used as label in the database.
parallax (tuple(float, float), None) – Parallax and uncertainty (mas). Not stored if the argument is set to
None
.distance (tuple(float, float), None) – Distance and uncertainty (pc). Not stored if the argument is set to
None
. This parameter is deprecated and will be removed in a future release. Please use theparallax
parameter instead.app_mag (dict, None) – Dictionary with the filter names, apparent magnitudes, and uncertainties. For example,
{'Paranal/NACO.Lp': (15., 0.2), 'Paranal/NACO.Mp': (13., 0.3)}
. For the use of duplicate filter names, the magnitudes have to be provided in a list, for example{'Paranal/NACO.Lp': [(15., 0.2), (14.5, 0.5)], 'Paranal/NACO.Mp': (13., 0.3)}
. No photometry is stored if the argument is set toNone
.flux_density (dict, None) – Dictionary with filter names, flux densities (W m-2 um-1), and uncertainties (W m-1 um-1). For example,
{'Paranal/NACO.Lp': (1e-15, 1e-16)}
. Currently, the use of duplicate filters is not implemented. The use ofapp_mag
is preferred overflux_density
because withflux_density
only fluxes are stored while withapp_mag
both magnitudes and fluxes. However,flux_density
can be used in case the magnitudes and/or filter profiles are not available. In that case, the fluxes can still be selected withinc_phot
inFitModel
. The argument offlux_density
is ignored if set toNone
.spectrum (dict, None) – Dictionary with the spectrum, optional covariance matrix, and spectral resolution for each instrument. The input data can either have a FITS or ASCII format. The spectra should have 3 columns with wavelength (um), flux (W m-2 um-1), and uncertainty (W m-2 um-1). The covariance matrix should be 2D with the same number of wavelength points as the spectrum. For example,
{'SPHERE': ('spectrum.dat', 'covariance.fits', 50.)}
. No covariance data is stored if set toNone
, for example,{'SPHERE': ('spectrum.dat', None, 50.)}
. Thespectrum
parameter is ignored if set toNone
. For GRAVITY data, the same FITS file can be provided as spectrum and covariance matrix.deredden (dict, float, None) – Dictionary with
spectrum
andapp_mag
names that will be dereddened with the provided \(A_V\). For example,deredden={'SPHERE': 1.5, 'Keck/NIRC2.J': 1.5}
will deredden the provided spectrum named ‘SPHERE’ and the Keck/NIRC2 J-band photometry with a visual extinction of 1.5. For photometric fluxes, the filter-averaged extinction is used for the dereddening.verbose (bool) – Print details on the object data that are added to the database.
- Returns
None
- Return type
NoneType
- add_photometry(phot_library: str) None [source]#
- Parameters
phot_library (str) – Photometric library (‘vlm-plx’ or ‘leggett’).
- Returns
None
- Return type
NoneType
- add_retrieval(tag: str, output_folder: str, inc_teff: bool = False) None [source]#
Function for adding the output data from the atmospheric retrieval with
AtmosphericRetrieval
to the database.- Parameters
tag (str) – Database tag to store the posterior samples.
output_folder (str) – Output folder that was used for the output files by
MultiNest
.inc_teff (bool) – Calculate \(T_\mathrm{eff}\) for each sample by integrating the model spectrum from 0.5 to 50 um. The \(T_\mathrm{eff}\) samples are added to the array with samples that are stored in the database. The computation time for adding \(T_\mathrm{eff}\) will be long because the spectra need to be calculated and integrated for all samples.
- Returns
None
- Return type
NoneType
- add_samples(sampler: str, samples: numpy.ndarray, ln_prob: numpy.ndarray, tag: str, modelpar: List[str], ln_evidence: Optional[Tuple[float, float]] = None, mean_accept: Optional[float] = None, spectrum: Tuple[str, str] = None, parallax: Optional[float] = None, spec_labels: Optional[List[str]] = None, attr_dict: Optional[Dict] = None)[source]#
This function stores the posterior samples from classes such as
FitModel
in the database, including some additional attributes.- Parameters
sampler (str) – Sampler (‘emcee’, ‘multinest’, or ‘ultranest’).
samples (np.ndarray) – Samples of the posterior.
ln_prob (np.ndarray) – Log posterior for each sample.
tag (str) – Database tag.
modelpar (list(str)) – List with the model parameter names.
ln_evidence (tuple(float, float)) – Log evidence and uncertainty. Set to
None
whensampler
is ‘emcee’.mean_accept (float, None) – Mean acceptance fraction. Set to
None
whensampler
is ‘multinest’ or ‘ultranest’.spectrum (tuple(str, str)) – Tuple with the spectrum type (‘model’ or ‘calibration’) and spectrum name (e.g. ‘drift-phoenix’ or ‘evolution’).
parallax (float, None) – Parallax (mas) of the object. Not used if the argument is set to
None
.spec_labels (list(str), None) – List with the spectrum labels that are used for fitting an additional scaling parameter. Not used if set to
None
.attr_dict (dict, None) – Dictionary with data that will be stored as attributes of the dataset with samples.
- Returns
None
- Return type
NoneType
- add_spectra(spec_library: str, sptypes: Optional[List[str]] = None) None [source]#
- Parameters
spec_library (str) – Spectral library (‘irtf’, ‘spex’, ‘kesseli+2017’, ‘bonnefoy+2014’, ‘allers+2013’).
sptypes (list(str)) – Spectral types (‘F’, ‘G’, ‘K’, ‘M’, ‘L’, ‘T’). Currently only implemented for ‘irtf’.
- Returns
None
- Return type
NoneType
- add_spectrum(spec_library: str, sptypes: Optional[List[str]] = None) None [source]#
DEPRECATION: This method is deprecated and will be removed in a future release. Please use the
add_spectra()
method instead.- Parameters
spec_library (str) – Spectral library (‘irtf’, ‘spex’, ‘kesseli+2017’, ‘bonnefoy+2014’, ‘allers+2013’).
sptypes (list(str)) – Spectral types (‘F’, ‘G’, ‘K’, ‘M’, ‘L’, ‘T’). Currently only implemented for ‘irtf’.
- Returns
None
- Return type
NoneType
- static available_models() Dict [source]#
Function for printing an overview of the available model grids that can be downloaded and added to the database with
add_model
.- Returns
Dictionary with the details on the model grids. The dictionary is created from the
model_data.json
file in thespecies.data
folder.- Return type
dict
- delete_data(data_set: str) None [source]#
Function for deleting a dataset from the HDF5 database.
- Parameters
data_set (str) – Group or dataset path in the HDF5 database. The content and structure of the database can be shown with
list_content()
. That could help to determine which argument should be provided as argument ofdata_set
. For example,data_set="models/drift-phoenix"
will remove the model spectra of DRIFT-PHOENIX.- Returns
None
- Return type
NoneType
- get_compare_sample(tag: str) Dict[str, float] [source]#
Function for extracting the sample parameters with the highest posterior probability.
- Parameters
tag (str) – Database tag where the results from
compare_model()
are stored.- Returns
Dictionary with the best-fit parameters.
- Return type
dict
- get_evidence(tag: str) Tuple[float, float] [source]#
Function for returning the log-evidence (i.e. marginalized likelihood) that was computed by the nested sampling algorithm when using
FitModel
orAtmosphericRetrieval
.- Parameters
tag (str) – Database tag with the posterior samples.
- Returns
float – Log-evidence.
float – Uncertainty on the log-evidence.
- get_mcmc_photometry(tag: str, filter_name: str, burnin: Optional[int] = None, phot_type: str = 'magnitude') numpy.ndarray [source]#
Function for calculating synthetic magnitudes or fluxes from the posterior samples.
- Parameters
tag (str) – Database tag with the posterior samples.
filter_name (str) – Filter name for which the synthetic photometry will be computed.
burnin (int, None) – Number of burnin steps to remove. No burnin is removed if the argument is set to
None
. Is only applied on posterior distributions that have been sampled withemcee
.phot_type (str) – Photometry type (‘magnitude’ or ‘flux’).
- Returns
Synthetic magnitudes or fluxes (W m-2 um-1).
- Return type
np.ndarray
- get_mcmc_spectra(tag: str, random: int, burnin: Optional[int] = None, wavel_range: Optional[Union[Tuple[float, float], str]] = None, spec_res: Optional[float] = None, wavel_resample: Optional[numpy.ndarray] = None) Union[List[species.core.box.ModelBox], List[species.core.box.SpectrumBox]] [source]#
Function for drawing random spectra from the sampled posterior distributions.
- Parameters
tag (str) – Database tag with the posterior samples.
random (int) – Number of random samples.
burnin (int, None) – Number of burnin steps to remove. No burnin is removed if the argument is set to
None
. Is only applied on posterior distributions that have been sampled withemcee
.wavel_range (tuple(float, float), str, None) – Wavelength range (um) or filter name. Full spectrum is used if set to
None
.spec_res (float, None) – Spectral resolution that is used for the smoothing with a Gaussian kernel. No smoothing is applied if the argument set to
None
.wavel_resample (np.ndarray, None) – Wavelength points (um) to which the model spectrum will be resampled. The resampling is applied after the optional smoothing to the resolution of
spec_res
.
- Returns
List with
ModelBox
objects.- Return type
- get_median_sample(tag: str, burnin: Optional[int] = None) Dict[str, float] [source]#
Function for extracting the median parameter values from the posterior samples.
- Parameters
tag (str) – Database tag with the posterior results.
burnin (int, None) – Number of burnin steps to remove. No burnin is removed if the argument is set to
None
. Is only applied on posterior distributions that have been sampled withemcee
.
- Returns
Median parameter values of the posterior distribution.
- Return type
dict
- get_object(object_name: str, inc_phot: Union[bool, List[str]] = True, inc_spec: Union[bool, List[str]] = True) species.core.box.ObjectBox [source]#
Function for extracting the photometric and/or spectroscopic data of an object from the database. The spectroscopic data contains optionally the covariance matrix and its inverse.
- Parameters
object_name (str) – Object name in the database.
inc_phot (bool, list(str)) – Include photometric data. If a boolean, either all (
True
) or none (False
) of the data are selected. If a list, a subset of filter names (as stored in the database) can be provided.inc_spec (bool, list(str)) – Include spectroscopic data. If a boolean, either all (
True
) or none (False
) of the data are selected. If a list, a subset of spectrum names (as stored in the database withadd_object()
) can be provided.
- Returns
Box with the object’s data.
- Return type
- get_probable_sample(tag: str, burnin: Optional[int] = None) Dict[str, float] [source]#
Function for extracting the sample parameters with the highest posterior probability.
- Parameters
tag (str) – Database tag with the posterior results.
burnin (int, None) – Number of burnin steps to remove. No burnin is removed if the argument is set to
None
. Is only applied on posterior distributions that have been sampled withemcee
.
- Returns
Parameters and values for the sample with the maximum posterior probability.
- Return type
dict
- get_pt_profiles(tag: str, random: Optional[int] = None, out_file: Optional[str] = None) Tuple[numpy.ndarray, numpy.ndarray] [source]#
Function for returning the pressure-temperature profiles from the posterior of the atmospheric retrieval with
petitRADTRANS
. The data can also optionally be written to an output file.- Parameters
tag (str) – Database tag with the posterior samples from the atmospheric retrieval with
AtmosphericRetrieval
.random (int, None) – Number of random samples that will be used for the P-T profiles. All samples will be selected if set to
None
.out_file (str, None) – Output file to store the P-T profiles. The data will be stored in a FITS file if the argument of
out_file
ends with .fits. Otherwise, the data will be written to a text file. The data has two dimensions with the first column containing the pressures (bar) and the remaining columns the temperature profiles (K). The data will not be written to a file if the argument is set toNone
.
- Returns
np.ndarray – Array (1D) with the pressures (bar).
np.ndarray – Array (2D) with the temperature profiles (K). The shape of the array is (n_pressures, n_samples).
- static get_retrieval_spectra(tag: str, random: Optional[int], wavel_range: Union[Tuple[float, float], str] = None, spec_res: Optional[float] = None) Tuple[List[species.core.box.ModelBox], species.read.read_radtrans.ReadRadtrans] [source]#
Function for extracting random spectra from the posterior distribution that was sampled with
AtmosphericRetrieval
.- Parameters
tag (str) – Database tag with the posterior samples.
random (int, None) – Number of randomly selected samples. All samples are used if set to
None
.wavel_range (tuple(float, float), str, None) – Wavelength range (um) or filter name. The wavelength range from the retrieval is adopted (i.e. the``wavel_range`` parameter of
AtmosphericRetrieval
) when set toNone
. It is mandatory to set the argument toNone
in case thelog_tau_cloud
parameter has been used with the retrieval.spec_res (float, None) – Spectral resolution that is used for the smoothing with a Gaussian kernel. No smoothing is applied when the argument is set to
None
.
- Returns
list(box.ModelBox) – Boxes with the randomly sampled spectra.
read_radtrans.Radtrans – Instance of
ReadRadtrans
.
- get_retrieval_teff(tag: str, random: int = 100) Tuple[numpy.ndarray, numpy.ndarray] [source]#
Function for calculating \(T_\mathrm{eff}\) and \(L_\mathrm{bol}\) from randomly drawn samples of the posterior distribution that is estimated with
AtmosphericRetrieval
. This requires the recalculation of the spectra across a broad wavelength range (0.5-50 um).- Parameters
tag (str) – Database tag with the posterior samples.
random (int) – Number of randomly selected samples.
- Returns
np.ndarray – Array with \(T_\mathrm{eff}\) samples.
np.ndarray – Array with \(\log(L/L_\mathrm{sun})\) samples.
- get_samples(tag: str, burnin: Optional[int] = None, random: Optional[int] = None, json_file: Optional[str] = None) species.core.box.SamplesBox [source]#
- Parameters
tag (str) – Database tag with the samples.
burnin (int, None) – Number of burnin steps to remove. No burnin is removed if the argument is set to
None
. Is only applied on posterior distributions that have been sampled withemcee
.random (int, None) – Number of random samples to select. All samples (with the burnin excluded) are selected if set to
None
.json_file (str, None) – JSON file to store the posterior samples. The data will not be written if the argument is set to
None
.
- Returns
Box with the posterior samples.
- Return type
- static list_companions() List[str] [source]#
Function for printing an overview of the companion data that are stored in the database. It will return a list with all the companion names. Each name can be used as input for
ReadObject
.- Returns
List with the object names that are stored in the database.
- Return type
list(str)
- list_content() None [source]#
Function for listing the content of the HDF5 database. The database structure will be descended while printing the paths of all the groups and datasets, as well as the dataset attributes.
- Returns
None
- Return type
NoneType
- petitcode_param(tag: str, sample_type: str = 'median', json_file: Optional[str] = None) Dict[str, float] [source]#
Function for converting the median are maximum likelihood posterior parameters of
petitRADTRANS
into a dictionary of input parameters forpetitCODE
.- Parameters
tag (str) – Database tag with the posterior samples.
sample_type (str) – Sample type that will be selected from the posterior (‘median’ or ‘probable’). Either the median or maximum likelihood parameters are used.
json_file (str, None) – JSON file to store the posterior samples. The data will not be written if the argument is set to
None
.
- Returns
Dictionary with parameters for
petitCODE
.- Return type
dict
species.data.dust module#
Module for optical constants of dust grains.
- species.data.dust.add_cross_sections(input_path: str, database: h5py._hl.files.File) None [source]#
Function for adding the extinction cross section of crystalline MgSiO3 for a log-normal and power-law size distribution to the database.
- Parameters
input_path (str) – Folder where the data is located.
database (h5py._hl.files.File) – Database.
- Returns
NoneType
- Return type
None
- species.data.dust.add_optical_constants(input_path: str, database: h5py._hl.files.File) None [source]#
Function for adding the optical constants of crystalline and amorphous MgSiO3 and Fe to the database.
- Parameters
input_path (str) – Folder where the data is located.
database (h5py._hl.files.File) – Database.
- Returns
NoneType
- Return type
None
species.data.filters module#
Module for downloading filter data from the website of the SVO Filter Profile Service.
- species.data.filters.download_filter(filter_id: str) Tuple[Optional[numpy.ndarray], Optional[numpy.ndarray], Optional[str]] [source]#
Function for downloading filter profile data from the SVO Filter Profile Service.
- Parameters
filter_id (str) – Filter name as listed on the website of the SVO Filter Profile Service (see http://svo2.cab.inta-csic.es/svo/theory/fps/).
- Returns
np.ndarray – Wavelength (um).
np.ndarray – Fractional transmission.
str – Detector type (‘energy’ or ‘photon’).
species.data.irtf module#
Module for adding the IRTF Spectral Library to the database.
- species.data.irtf.add_irtf(input_path: str, database: h5py._hl.files.File, sptypes: Optional[List[str]] = None) None [source]#
Function for adding the IRTF Spectral Library to the database.
- Parameters
input_path (str) – Path of the data folder.
database (h5py._hl.files.File) – Database.
sptypes (list(str), None) – List with the spectral types (‘F’, ‘G’, ‘K’, ‘M’, ‘L’, ‘T’). All spectral types are included if set to
None
.
- Returns
None
- Return type
NoneType
species.data.isochrones module#
Module for isochrone data from evolutionary models.
- species.data.isochrones.add_ames(database, input_path)[source]#
Function for adding the AMES-Cond and AMES-Dusty isochrone data to the database.
- Parameters
database (h5py._hl.files.File) – Database.
input_path (str) – Folder where the data is located.
- Returns
None
- Return type
NoneType
- species.data.isochrones.add_baraffe(database, tag, file_name)[source]#
Function for adding the Baraffe et al. (2003) isochrone data to the database. Any of the isochrones from https://phoenix.ens-lyon.fr/Grids/ can be used as input.
- Parameters
database (h5py._hl.files.File) – Database.
tag (str) – Tag name in the database.
file_name (str) – Filename with the isochrones data.
- Returns
None
- Return type
NoneType
- species.data.isochrones.add_btsettl(database, input_path)[source]#
Function for adding the BT-Settl isochrone data to the database.
- Parameters
database (h5py._hl.files.File) – Database.
input_path (str) – Folder where the data is located.
- Returns
None
- Return type
NoneType
- species.data.isochrones.add_marleau(database, tag, file_name)[source]#
Function for adding the Marleau et al. isochrone data to the database. The isochrone data can be requested from Gabriel Marleau.
https://ui.adsabs.harvard.edu/abs/2019A%26A…624A..20M/abstract
- Parameters
database (h5py._hl.files.File) – Database.
tag (str) – Tag name in the database.
file_name (str) – Filename with the isochrones data.
- Returns
None
- Return type
NoneType
- species.data.isochrones.add_saumon(database, input_path)[source]#
Function for adding the Saumon & Marley (2008) isochrone data to the database.
- Parameters
database (h5py._hl.files.File) – Database.
input_path (str) – Folder where the data is located.
- Returns
None
- Return type
NoneType
- species.data.isochrones.add_sonora(database, input_path)[source]#
Function for adding the Sonora Bobcat isochrone data to the database.
- Parameters
database (h5py._hl.files.File) – Database.
input_path (str) – Folder where the data is located.
- Returns
None
- Return type
NoneType
species.data.kesseli2017 module#
Module for adding O5 through L3 SDSS stellar spectra from Kesseli et al. (2017) to the database.
- species.data.kesseli2017.add_kesseli2017(input_path: str, database: h5py._hl.files.File) None [source]#
Function for adding the SDSS stellar spectra from Kesseli et al. (2017) to the database.
- Parameters
input_path (str) – Path of the data folder.
database (h5py._hl.files.File) – The HDF5 database.
- Returns
None
- Return type
NoneType
species.data.leggett module#
Text
species.data.model_spectra module#
Module for adding a grid of model spectra to the database.
- species.data.model_spectra.add_model_grid(model_name: str, input_path: str, database: h5py._hl.files.File, wavel_range: Optional[Tuple[float, float]], teff_range: Optional[Tuple[float, float]], spec_res: Optional[float]) None [source]#
Function for adding a grid of model spectra to the database. The original spectra had been resampled to logarithmically- spaced wavelengths, so at a constant resolution, \(\lambda/\Delta\lambda\). This function downloads the model grid, unpacks the tar file, and adds the spectra and parameters to the database.
- Parameters
model_name (str) – Name of the model grid.
input_path (str) – Folder where the data is located.
database (h5py._hl.files.File) – Database.
wavel_range (tuple(float, float), None) – Wavelength range (um). The original wavelength points are used if set to
None
.teff_range (tuple(float, float), None) – Effective temperature range (K). All temperatures are selected if set to
None
.spec_res (float, None) – Spectral resolution for resampling. Not used if
wavel_range
is set toNone
and/orspec_res
is set toNone
- Returns
None
- Return type
NoneType
species.data.spex module#
Module for adding the SpeX Prism Spectral Libraries to the database.
species.data.vega module#
Text
species.data.vlm_plx module#
Module for the photometric data and parallaxes from the Database of Ultracool Parallaxes.