petitRADTRANS.radtrans

Stores the Radtrans object.

Classes

Radtrans

Calculate spectra using a given set of opacities.

Module Contents

class petitRADTRANS.radtrans.Radtrans(pressures: numpy.typing.NDArray[numpy.floating] = None, wavelength_boundaries: numpy.typing.NDArray[numpy.floating] = None, line_species: list[str] = None, gas_continuum_contributors: list[str] = None, rayleigh_species: list[str] = None, cloud_species: list[str] = None, line_opacity_mode: str = 'c-k', line_by_line_opacity_sampling: int = 1, scattering_in_emission: bool = False, emission_angle_grid: numpy.typing.NDArray[numpy.floating] = None, anisotropic_cloud_scattering: bool = 'auto', path_input_data: str = None)

Calculate spectra using a given set of opacities.

Args:

pressures (Optional):

(bar) Array defining the pressure grid to be used for the spectral calculations.

wavelength_boundaries (Optional):

List containing left and right border of wavelength region to be considered, in micron. If nothing else is specified, it will be equal to [0.05, 300], hence using the full petitRADTRANS wavelength range (0.11 to 250 microns for 'c-k' mode, 0.3 to 30 microns for the 'lbl' mode). The larger the range the longer the computation time.

line_species (Optional):

List of strings, denoting which line absorber species to include.

gas_continuum_contributors (Optional):

List of strings, denoting which continuum absorber species to include.

rayleigh_species (Optional):

List of strings, denoting which Rayleigh scattering species to include.

cloud_species (Optional):

List of strings, denoting which cloud opacity species to include.

line_opacity_mode (Optional[string]):

If equal to 'c-k': use low-resolution mode, at \(\\lambda/\\Delta \\lambda = 1000\), with the correlated-k assumption. if equal to 'lbl': use high-resolution mode, at \(\\lambda/\\Delta \\lambda = 10^6\), with a line-by-line treatment.

line_by_line_opacity_sampling (Optional[int]):

If mode = 'lbl', then this will only load every line_by_line_opacity_sampling-nth point of the high-resolution opacities. This can be used to save time and RAM. The user should verify whether this leads to solutions which are identical to the results of the non down-sampled \(10^6\) resolution.

scattering_in_emission (Optional[bool]):

Will be False by default. If True scattering will be included in the emission spectral calculations. Note that this increases the runtime of pRT!

emission_angle_grid (Optional):

Array defining the cosines of the angle grid to be used for the emission spectrum calculations, and their weights. The array is of shape (2, n_angles), with emission_angle_grid[0] being the cosines of the angles, and emission_angle_grid[1] being the weights. A dictionary of array with keys ‘cos_angles’ and ‘weights’ can also be used. If None, a default set of values and weights are used.

anisotropic_cloud_scattering (Optional[bool, str]):

If True, anisotropic cloud scattering opacities are used for the spectral calculations. If False, isotropic cloud scattering opacities are used for the spectral calculations. If ‘auto’ (recommended), anisotropic_cloud_scattering is set to True for emission spectrum calculations, and to False for transmission spectrum calculations.

path_input_data (Optional[str]):

Path to the input_data folder, containing the files to be loaded by petitRADTRANS.

__dat_opacity_files_warning_message = Multiline-String

Show Value

"""loading opacities from .dat files is discouraged, the HDF5 format offer better performances at for a lower memory usage

Converting petitRADTRANS .dat opacity files into HDF5 can be done by executing:
>>> from petitRADTRANS.__file_conversion import convert_all
>>> convert_all()

Alternatively, the petitRADTRANS HDF5 files can be downloaded (see https://petitradtrans.readthedocs.io/en/latest/content/available_opacities.html)"""

__line_opacity_property_setting_warning_message = Multiline-String

Show Value

"""setting a Radtrans line opacity property should be avoided
These properties are loaded from the opacity data in the input_data directory and are inter-dependent (they need to be updated for consistency)
It is recommended to create a new Radtrans instance instead"""

__property_setting_warning_message = Multiline-String

Show Value

"""setting a Radtrans property directly is not recommended
Create a new Radtrans instance (recommended) or re-do all the setup steps necessary for the modification to be taken into account"""

_frequency_grid_misalignment_tolerance: float = 0.002

_pressures

_line_opacity_mode = 'c-k'

_line_by_line_opacity_sampling = 1

_scattering_in_emission = False

_anisotropic_cloud_scattering = 'auto'

_path_input_data = None

__scattering_in_transmission = False

__sum_opacities = False

__absorber_present

__use_smart_clear_opacities = False

_lines_loaded_opacities

_cias_loaded_opacities

_clouds_loaded_opacities

_emission_angle_grid

__getattr__(name)

Override of the object base __getattr__ method, in order to hint towards pRT3 names when pRT2 names are used.

property anisotropic_cloud_scattering

property cias_loaded_opacities

property clouds_loaded_opacities

property cloud_species

property emission_angle_grid

property frequencies

property frequency_bins_edges

property gas_continuum_contributors

property line_by_line_opacity_sampling

property lines_loaded_opacities

property line_opacity_mode

property line_species

property path_input_data

property pressures: numpy.typing.NDArray[numpy.floating]

property rayleigh_species

property scattering_in_emission

property wavelength_boundaries

static __check_anisotropic_cloud_scattering(mode)

static __check_line_opacity_mode(mode)

static __check_input_data_file_existence(path)

static __check_path_input_data(path)

static __check_pressures(pressures)

static __check_return_clear_spectrum_relevance(return_clear_spectrum, clouds_have_effect, opaque_cloud_top_pressure, cloud_fraction)

static __check_wavelength_boundaries(boundaries)

__clouds_have_effect(mass_fractions, cloud_fraction)

Check if the clouds have any effect, i.e. if the cloud species MMR is greater than 0.

Args:

mass_fractions: atmospheric mass mixing ratios cloud_fraction: cloud coverage fraction

static __compute_additional_continuum_opacities(frequencies, pressures, additional_absorption_opacities_function, additional_scattering_opacities_function, cloud_photosphere_median_optical_depth)

static __compute_cloud_continuum_opacities(frequencies, frequency_bins_edges, pressures, temperatures, mass_fractions, mean_molar_masses, reference_gravity, scattering_in_transmission, sum_opacities, opaque_cloud_top_pressure, power_law_opacity_350nm, power_law_opacity_coefficient, clouds_have_effect, cloud_species, clouds_particles_porosity_factor, clouds_loaded_opacities, anisotropic_cloud_scattering, cloud_particles_mean_radii, cloud_particle_radius_distribution_std, cloud_f_sed, eddy_diffusion_coefficients, cloud_particles_radius_distribution, cloud_hansen_a, cloud_hansen_b, cloud_particle_number_density_grid, cloud_photosphere_median_optical_depth, complete_coverage_clouds, return_cloud_contribution, target_particle_radii, target_pressure)

static __compute_continuum_opacities(frequencies, frequency_bins_edges, pressures, temperatures, mass_fractions, mean_molar_masses, cias_loaded_opacities, gas_continuum_contributors, gray_opacity)

static __compute_continuum_scattering_opacities(frequencies, pressures, temperatures, mass_fractions, mean_molar_masses, rayleigh_species, haze_factor)

static __compute_combined_opacities(opacities, continuum_opacities, line_species, line_species_mass_fractions, line_opacity_mode, sum_opacities, g_gauss, weights_gauss)

static __get_base_species_mass_fractions(mass_fractions)

static __get_cia_contributors(gas_continuum_contributors)

static __get_base_opacities(use_smart_clear_opacities, complete_coverage_clouds, opacities, continuum_opacities_scattering, cloud_anisotropic_scattering_opacities=None, cloud_absorption_opacities=None)

Return the opacities that should be returned when not using cloud coverage fraction.

static __get_cia_aliases(name: str) → str

static __get_frequency_grids_intersection_indices(frequencies: numpy.typing.NDArray[numpy.floating], file_frequencies: numpy.typing.NDArray[numpy.floating], tolerate_grid_misalignment: bool = False) → tuple[numpy.typing.NDArray[numpy.bool_], numpy.typing.NDArray[numpy.bool_]]

Get the indices to fill frequencies from a file within the Radtrans frequency grid.

For example, if the Radtrans frequency grid is [0.1, …, 0.3, …, 3] and some loaded opacity frequency grid is in the interval [0.3, …, 3, …, 28], then the output indices will be the indices corresponding to [0.3, …, 3] on the Radtrans frequency grid, and on the file frequency grid.

Args:

frequencies: the Radtrans frequency grid file_frequencies: the file frequency grid tolerate_grid_misalignment: if True, do not raise an error if the intersection indices sizes differ by at most 1.

Returns:

The indices of the intersection between both grids, for the Radtrans grid and the file grid0

static __get_line_opacity_file(path_input_data, species, category)

static __get_non_cia_gas_continuum_contributions()

static __get_opacity_sources_dict()

static __init_clouds_particles_porosity_factor(clouds_particles_porosity_factor, cloud_species)

__init_spectral_function(is_emission, reference_gravity, complete_coverage_clouds, return_clear_spectrum, mass_fractions, cloud_fraction, opaque_cloud_top_pressure)

Initializations and checks common to calculate_flux and calculate_transit_radii.

__set_use_smart_clear_opacities(cloud_fraction, complete_coverage_clouds, return_clear_spectrum)

__set_sum_opacities(emission)

_calculate_flux(temperatures, reference_gravity, opacities, continuum_opacities_scattering, emission_geometry, star_irradiation_cos_angle, stellar_intensity, reflectances, emissivities, cloud_f_sed, photospheric_cloud_optical_depths, cloud_photosphere_wavelength_boundaries, cloud_anisotropic_scattering_opacities, cloud_absorption_opacities, adaptive_feautrier_iterations, return_contribution=False, return_rosseland_opacities=False)

Calculate the flux. TODO complete docstring

Args:

temperatures: reference_gravity: opacities: continuum_opacities_scattering: emission_geometry: star_irradiation_cos_angle: stellar_intensity: reflectances: emissivities: cloud_f_sed: photospheric_cloud_optical_depths: cloud_photosphere_wavelength_boundaries: cloud_anisotropic_scattering_opacities: cloud_absorption_opacities: adaptive_feautrier_iterations: return_contribution: return_rosseland_opacities:

Returns:

_calculate_opacities(temperatures, mass_fractions, mean_molar_masses, reference_gravity, opaque_cloud_top_pressure=None, cloud_particles_mean_radii=None, cloud_particle_radius_distribution_std=None, cloud_particles_radius_distribution='lognormal', cloud_particle_number_density_grid=None, cloud_hansen_a=None, cloud_hansen_b=None, clouds_particles_porosity_factor=None, cloud_f_sed=None, eddy_diffusion_coefficients=None, haze_factor=1.0, power_law_opacity_350nm=None, power_law_opacity_coefficient=None, gray_opacity=None, cloud_photosphere_median_optical_depth=None, cloud_fraction=1.0, complete_coverage_clouds=None, return_cloud_contribution=False, additional_absorption_opacities_function=None, additional_scattering_opacities_function=None, target_particle_radii=None, target_pressure=None)

Combine total line opacities, according to mass fractions (abundances), also add continuum opacities, i.e. clouds, CIA… TODO complete docstring

Args:

temperatures: mass_fractions: mean_molar_masses: reference_gravity: cloud_particle_radius_distribution_std: cloud_f_sed: eddy_diffusion_coefficients: cloud_particles_mean_radii: cloud_particles_radius_distribution: cloud_hansen_a: cloud_hansen_b: cloud_particle_number_density_grid: return_cloud_contribution: additional_absorption_opacities_function: additional_scattering_opacities_function:

Returns:

_calculate_transit_radii(temperatures, mean_molar_masses, reference_gravity, reference_pressure, planet_radius, variable_gravity, opacities, continuum_opacities_scattering, copy_opacities, return_contributions)

Calculate the transit radii. TODO complete docstring

Args:

temperatures: mean_molar_masses: reference_gravity: reference_pressure: planet_radius: variable_gravity: opacities: continuum_opacities_scattering: copy_opacities:

Returns:

static _combine_ck_opacities(opacities, g_gauss, weights_gauss)

static _combine_opacities(line_species_mass_fractions, opacities, continuum_opacities)

static _compute_cia_opacities(cia_dicts, mass_fractions, pressures, temperatures, frequencies, mean_molar_masses)

Wrapper to _interpolate_cia, calculating each collision’s combined mass fraction.

static _compute_ck_flux(frequencies, temperatures, weights_gauss, emission_cos_angle_grid, emission_cos_angle_grid_weights, optical_depths, return_contribution)

static _integrate_cloud_opacities_from_dn_dr(dn_dr, atmosphere_densities, clouds_particles_densities, cloud_particles_radii_bins, cloud_particles_radii, clouds_absorption_opacities, clouds_scattering_opacities, clouds_particles_asymmetry_parameters, pressures, target_particle_radii, target_pressure)

Shared integration backend for both cloud opacity methods. Operates purely on dn/dr and handles all integration logic.

static _compute_cloud_log_normal_particles_distribution_opacities(atmosphere_densities, clouds_particles_densities, clouds_mass_fractions, cloud_particles_mean_radii, cloud_particles_distribution_std, cloud_particles_radii_bins, cloud_particles_radii, clouds_absorption_opacities, clouds_scattering_opacities, clouds_particles_asymmetry_parameters, pressures, target_particle_radii, target_pressure)

This function reimplements calc_cloud_opas from fortran_radtrans_core.f90. For some reason it runs faster in python than in fortran, so we’ll use this from now on. This function integrates the cloud opacity through the different layers of the atmosphere to get the total optical depth, scattering and anisotropic fraction. # TODO optical depth or opacity? # TODO complete docstring and vectorize on/off code

Author: Francois Rozet

Args:

atmosphere_densities:

Density of the atmosphere at each of its layer

clouds_particles_densities:

Density of each cloud particles

clouds_mass_fractions:

Mass fractions of each cloud at each atmospheric layer

cloud_particles_mean_radii:

Mean radius of each cloud particles at each atmospheric layer

cloud_particles_distribution_std:

Standard deviation of the log-normal cloud particles distribution

cloud_particles_radii_bins:

Bins of the particles cloud radii grid

cloud_particles_radii:

Particles cloud radii grid

clouds_absorption_opacities:

Cloud absorption opacities (radius grid, wavelength grid, clouds)

clouds_scattering_opacities:

Cloud scattering opacities (radius grid, wavelength grid, clouds)

clouds_particles_asymmetry_parameters:

Cloud particles asymmetry parameters (radius grid, wavelength grid, clouds)

Returns:

static _compute_cloud_opacities_using_particle_size_grid(atmosphere_densities, clouds_mass_fractions, cloud_particle_number_density_grid, clouds_particles_densities, cloud_particles_radii_bins, cloud_particles_radii, clouds_absorption_opacities, clouds_scattering_opacities, clouds_particles_asymmetry_parameters, pressures, target_particle_radii, target_pressure)

# TODO vectorize on/off code Args:

atmosphere_densities:

Density of the atmosphere at each of its layer

clouds_mass_fractions:

Mass fractions of each cloud at each atmospheric layer

cloud_particle_number_density_grid:

Output from cloud lognormal EXPERIMENTAL

clouds_particles_densities:

Density of each cloud particles

cloud_particles_radii_bins:

Bins of the particles cloud radii grid

cloud_particles_radii:

Particles cloud radii grid

clouds_absorption_opacities:

Cloud absorption opacities (radius grid, wavelength grid, clouds)

clouds_scattering_opacities:

Cloud scattering opacities (radius grid, wavelength grid, clouds)

clouds_particles_asymmetry_parameters:

Cloud particles asymmetry parameters (radius grid, wavelength grid, clouds)

pressures:

Pressure array

target_particle_radii:

Target particle radii array

target_pressure:

Target pressure array

Returns:

static _compute_cloud_opacities(pressures, temperatures, frequency_bins_edges, cloud_species_mass_fractions, mean_molar_masses, reference_gravity, cloud_particle_radius_distribution_std, clouds_loaded_opacities, sum_opacities, anisotropic_cloud_scattering, cloud_f_sed=None, eddy_diffusion_coefficients=None, cloud_particles_mean_radii=None, cloud_particles_radius_distribution='lognormal', cloud_hansen_a=None, cloud_hansen_b=None, cloud_particle_number_density_grid=None, clouds_particles_porosity_factor=None, photospheric_cloud_optical_depths=None, continuum_opacities=None, return_cloud_contribution=False, target_particle_radii=None, target_pressure=None)

Calculate cloud opacities for a defined atmospheric structure. # TODO complete docstring

Args:

temperatures: cloud_species_mass_fractions: mean_molar_masses: reference_gravity: cloud_particle_radius_distribution_std: cloud_f_sed: eddy_diffusion_coefficients: cloud_particles_mean_radii: cloud_particles_radius_distribution: cloud_hansen_a: cloud_hansen_b: cloud_particle_number_density_grid: return_cloud_contribution:

Returns:

static _compute_species_optical_depths(reference_gravity, pressures, cloud_opacities)

Calculate the optical depth of the clouds as function of frequency and pressure. The array with the optical depths is set to the tau_cloud attribute. The optical depth is calculated from the top of the atmosphere (i.e. the smallest pressure). Therefore, below the cloud base, the optical depth is constant and equal to the value at the cloud base. # TODO complete docstring

Args:

reference_gravity (float):

Surface gravity in cgs. Vertically constant for emission spectra.

static _compute_feautrier_radiative_transfer(frequency_bins_edges, temperatures, weights_gauss, emission_cos_angle_grid, emission_cos_angle_grid_weights, optical_depths, photon_destruction_probabilities, emission_geometry, stellar_intensity, star_irradiation_cos_angle, reflectances, emissivities, adaptive_feautrier_iterations, return_contribution)

static _compute_h_minus_free_free_xsec(wavelengths, temperatures, electron_partial_pressure)

Calculate the H- free-free cross-section in units of cm^2 per H per e- pressure (in cgs). Source: “The Observation and Analysis of Stellar Photospheres” by David F. Gray, p. 156 # TODO complete docstring Args:

wavelengths: (angstrom) temperatures: electron_partial_pressure:

Returns:

static _compute_h_minus_bound_free_xsec(wavelengths_bin_edges)

Calculate the H- bound-free cross-section in units of cm^2 per H-, as defined on page 155 of “The Observation and Analysis of Stellar Photospheres” by David F. Gray # TODO complete docstring Args:

wavelengths_bin_edges: (angstrom)

Returns:

static _compute_h_minus_opacities(mass_fractions, pressures, temperatures, frequencies, frequency_bins_edges, mean_molar_masses, **kwargs)

Calculate the H- opacity.

static _compute_non_cia_gas_induced_continuum_opacities(gas_continuum_contributors, mass_fractions, pressures, temperatures, frequencies, frequency_bins_edges, mean_molar_masses, **kwargs)

static _compute_optical_depths(pressures, reference_gravity, opacities, continuum_opacities_scattering, scattering_in_emission)

static _compute_optical_depths_wrapper(pressures, reference_gravity, opacities, continuum_opacities_scattering, scattering_in_emission, sum_opacities, photospheric_cloud_optical_depths=None, absorber_present=True, **custom_cloud_parameters)

static _compute_optical_depths_with_photospheric_cloud(pressures, reference_gravity, opacities, continuum_opacities_scattering, frequencies, weights_gauss, cloud_wavelengths, cloud_f_sed, cloud_anisotropic_scattering_opacities, cloud_absorption_opacities, photospheric_cloud_optical_depths, scattering_in_emission)

static _compute_power_law_opacities(power_law_opacity_350nm, power_law_opacity_coefficient, frequencies, n_layers)

static _compute_radius_hydrostatic_equilibrium(pressures, temperatures, mean_molar_masses, reference_gravity, reference_pressure, planet_radius, variable_gravity=True)

static _compute_rayleigh_scattering_opacities(rayleigh_species, pressures, temperatures, mass_fractions, mean_molar_masses, frequencies, haze_factor=1.0)

Add Rayleigh scattering opacities to scattering continuum opacities.

Args:

temperatures: temperatures in each atmospheric layer mass_fractions: dictionary of the Rayleigh scattering species mass fractions

static _compute_rosseland_opacities(frequency_bins_edges, temperatures, weights_gauss, opacities, continuum_opacities_scattering, scattering_in_emission)

static _compute_transit_radii(opacities, continuum_opacities_scattering, pressures, temperatures, weights_gauss, mean_molar_masses, reference_gravity, reference_pressure, planet_radius, variable_gravity, summed_species_opacities, copy_opacities)

Calculate the planetary transmission spectrum. # TODO complete docstring Args:

opacities: continuum_opacities_scattering: pressures: temperatures: weights_gauss: mean_molar_masses:

Mean molecular weight in units of amu. (1-d numpy array, same length as pressure array).

reference_gravity (float):

Atmospheric gravitational acceleration at reference pressure and radius in units of dyne/cm^2

reference_pressure (float):

Reference pressure in bar.

planet_radius (float):

Planet radius in cm.

variable_gravity (bool):

If True, gravity in the atmosphere will vary proportional to 1/r^2, where r is the planet radius.

summed_species_opacities (bool):

If True, it is assumed that the given opacities contains the summed opacities of all species. This is the case in line-by-line.

copy_opacities (bool):

If True, a new array will be created for the transparencies, distinct from the opacities. This increases memory usage.

Returns:

• transmission radius in cm (1-d numpy array, as many elements as wavelengths)

• planet radius as function of atmospheric pressure (1-d numpy array, as many elements as atmospheric

layers)

static _compute_transmission_spectrum_contribution(transit_radii, pressures, temperatures, mean_molar_masses, reference_gravity, reference_pressure, planet_radius, weights_gauss, opacities, continuum_opacities_scattering, scattering_in_transmission, variable_gravity)

static _handle_grid_misalignment(indices: numpy.typing.NDArray[numpy.bool_], frequencies_reference: numpy.typing.NDArray[numpy.floating], frequencies_test: numpy.typing.NDArray[numpy.floating]) → numpy.typing.NDArray[numpy.bool_]

static _init_cia_loaded_opacities(cia_contributors)

_init_frequency_grid()

Initialize the Radtrans frequency grid, used to calculate the spectra. The frequency grid comes from the requested opacity files, in the following priority order:

1. lines,

2. CIA,

3. clouds.

If not opacities are provided, the mean of the wavelength boundaries is used. The frequency grid in that case has only 1 element.

Returns:

frequencies:

(Hz) frequencies (center of bin) of the line opacities, also use for spectral calculations, of size N

frequency_bins_edges:

(Hz) edges of the frequencies bins, of size N+1 for correlated-k only, number of points used to sample the g-space (1 in the case lbl is used)

static _init_frequency_grid_from_frequency_grid(frequency_grid, wavelength_boundaries, sampling=1)

_init_frequency_grid_from_lines()

static _interpolate_cia(collision_dict, combined_mass_fractions, pressures, temperatures, frequencies, mean_molar_masses)

Interpolate CIA cross-sections onto the Radtrans (wavelength, temperature) grid and convert it into opacities.

Args:

combined_mass_fractions: combined mass fractions of the colliding species

e.g., for H2-He and an atmosphere with H2 and He MMR of respectively 0.74 and 0.24, combined_mas_fractions = 0.74 * 0.24 combined_mas_fractions is divided by the combined weight (e.g. for H2 and He, 2 * 4 AMU^2), so there is no units issue.

Returns:

A (wavelength, temperature) array containing the CIA opacities.

static _interpolate_species_opacities(pressures, temperatures, n_g, n_frequencies, line_opacities_grid, line_opacities_temperature_pressure_grid, line_opacities_temperature_grid_size, line_opacities_pressure_grid_size)

static compute_bins_edges(middle_bin_points: numpy.typing.NDArray[numpy.floating]) → numpy.typing.NDArray[numpy.floating]

Calculate bin edges for middle bin points.

Args:

middle_bin_points: array of size N containing the middle bin points to calculate the bin edges of

Returns:

array of size N+1 containing the bins edges

calculate_contribution_spectra(mode, opaque_cloud_top_pressure=None, power_law_opacity_350nm=None, power_law_opacity_coefficient=None, gray_opacity=None, cloud_photosphere_median_optical_depth=None, additional_absorption_opacities_function=None, additional_scattering_opacities_function=None, stellar_intensities=None, **kwargs)

calculate_flux(temperatures: numpy.typing.NDArray[numpy.floating], mass_fractions: dict[str, numpy.typing.NDArray[numpy.floating]], mean_molar_masses: numpy.typing.NDArray[numpy.floating], reference_gravity: float, planet_radius: float = None, opaque_cloud_top_pressure: float = None, cloud_particles_mean_radii: dict[str, numpy.typing.NDArray[numpy.floating]] = None, cloud_particle_radius_distribution_std: float = None, cloud_particles_radius_distribution: str = 'lognormal', cloud_hansen_a: dict[str, numpy.typing.NDArray[numpy.floating]] = None, cloud_hansen_b: dict[str, numpy.typing.NDArray[numpy.floating]] = None, cloud_particle_number_density_grid: dict[str, numpy.typing.NDArray[numpy.floating]] = None, clouds_particles_porosity_factor: dict[str, float] = None, cloud_f_sed: float = None, eddy_diffusion_coefficients: numpy.typing.NDArray[numpy.floating] = None, haze_factor: float = 1.0, power_law_opacity_350nm: float = None, power_law_opacity_coefficient: float = None, gray_opacity: float = None, cloud_photosphere_wavelength_boundaries: numpy.typing.NDArray[numpy.floating] = None, cloud_photosphere_median_optical_depth: float = None, cloud_fraction: float = 1.0, complete_coverage_clouds: list[str] = None, irradiation_geometry: str = 'dayside_ave', emission_geometry: str = None, stellar_intensities: numpy.typing.NDArray[numpy.floating] = None, star_effective_temperature: float = None, star_radius: float = None, orbit_semi_major_axis: float = None, star_irradiation_angle: float = 0.0, reflectances: numpy.typing.NDArray[numpy.floating] = None, emissivities: numpy.typing.NDArray[numpy.floating] = None, additional_absorption_opacities_function: Callable = None, additional_scattering_opacities_function: Callable = None, adaptive_feautrier_iterations: bool = False, frequencies_to_wavelengths: bool = True, return_contribution: bool = False, return_clear_spectrum: bool = False, return_photosphere_radius: bool = False, return_rosseland_optical_depths: bool = False, return_cloud_contribution: bool = False, return_opacities: bool = False, return_abundances: bool = False, return_particles_radii_bins: bool = False, return_particles_radii: bool = False, target_particle_radii: numpy.typing.NDArray[numpy.floating] = None, target_pressure: numpy.typing.NDArray[numpy.floating] = None) → tuple[numpy.typing.NDArray[numpy.floating], numpy.typing.NDArray[numpy.floating], dict[str, Any]]

Method to calculate the atmosphere’s emitted flux (emission spectrum).

Args:

temperatures:

The atmospheric temperature in K, at each atmospheric layer (1-d numpy array, same length as pressure array).

mass_fractions:

Dictionary of mass fractions for all atmospheric absorbers. Dictionary keys are the species names. Every mass fraction array has same length as pressure array.

mean_molar_masses:

The atmospheric mean molecular weight in amu, at each atmospheric layer (1-d numpy array, same length as pressure array).

reference_gravity (float):

Surface gravity in cgs. Vertically constant for emission spectra.

planet_radius: planet radius at maximum pressure in cm. Only used to calculate the planet’s changing

Photospheric radius as function of wavelength, if return_photosphere_radius is True.

opaque_cloud_top_pressure (Optional[float]):

Pressure, in bar, where opaque cloud deck is added to the absorption opacity.

cloud_particles_mean_radii (Optional):

Dictionary of mean particle radii for all cloud species. Dictionary keys are the cloud species names. Every radius array has same length as pressure array.

cloud_particle_radius_distribution_std (Optional[float]):

Width of the log-normal cloud particle size distribution

cloud_particles_radius_distribution (Optional[string]):

The cloud particle size distribution to use. Can be either ‘lognormal’ (default) or ‘hansen’. If hansen, the cloud_hansen_b parameters must be used.

cloud_hansen_a (Optional[dict]):

A dictionary of the ‘a’ parameter values for each included cloud species and for each atmospheric layer, formatted as the kzz argument. Equivalent to cloud_particles_mean_radii. If cloud_hansen_a is not included and dist is “hansen”, then it will be computed using Kzz and fsed (recommended).

cloud_hansen_b (Optional[dict]):

A dictionary of the ‘b’ parameter values for each included cloud species and for each atmospheric layer, formatted as the kzz argument. This is the width of the hansen distribution normalized by the particle area (1/cloud_hansen_a^2)

cloud_particle_number_density_grid (Optional[dict]):

EXPERIMENTAL

clouds_particles_porosity_factor (Optional[dict]):

A dictionary of porosity factors depending on the cloud species. This can be useful when opacities are calculated using the Distribution of Hollow Spheres (DHS) method.

cloud_f_sed (Optional[float]):

Cloud settling parameter.

eddy_diffusion_coefficients (Optional[float]):

The atmospheric eddy diffusion coefficient in cgs (i.e. \(\rm cm^2/s\)), at each atmospheric layer (1-d numpy array, same length as pressure array).

haze_factor (Optional[float]):

Scalar factor, increasing the gas Rayleigh scattering cross-section.

power_law_opacity_350nm (Optional[float]):

Scattering opacity at 0.35 micron, in cgs units (cm^2/g).

power_law_opacity_coefficient (Optional[float]):

Has to be given if kappa_zero is defined, this is the wavelength powerlaw index of the parametrized scattering opacity.

gray_opacity (Optional[float]):

Gray opacity value, to be added to the opacity at all pressures and wavelengths (units \(\rm cm^2/g\))

cloud_photosphere_median_optical_depth (Optional[float]):

Median optical depth (across wavelength_boundaries) of the clouds from the top of the atmosphere down to the gas-only photosphere. This parameter can be used for enforcing the presence of clouds in the photospheric region.

cloud_photosphere_wavelength_boundaries (Optional[NDArray]):

Min and max wavelength boundaries when calculating the median of the photospheric cloud optical depth. By default, the whole spectral range is used.

cloud_fraction:

Mix a column without cloud with a column with cloud to the requested fraction (0 = clear, 1 = full cloud cover). By default, assume a full cloud cover.

complete_coverage_clouds:

List of clouds that have full coverage, regardless of the value of cloud_fraction.

irradiation_geometry (Optional[string]):

if equal to 'dayside_ave': use the dayside average geometry. If equal to 'planetary_ave': use the planetary average geometry. If equal to 'non-isotropic': use the non-isotropic geometry.

emission_geometry (Optional[string]):

Deprecated, same as irradiation_geometry.

stellar_intensities (Optional[array]):

The stellar intensity to use. If None, it will be calculated using a PHOENIX model.

star_effective_temperature (Optional[float]):

The temperature of the host star in K, used only if the scattering is considered. If not specified, the direct light contribution is not calculated.

star_radius (Optional[float]):

The radius of the star in cm. If specified, used to scale the to scale the stellar flux, otherwise it uses PHOENIX radius.

orbit_semi_major_axis (Optional[float]):

The distance of the planet from the star. Used to scale the stellar flux when the scattering of the direct light is considered.

star_irradiation_angle (Optional[float]):

Inclination angle of the direct light with respect to the normal to the atmosphere. Used only in the non-isotropic geometry scenario.

reflectances (Optional):

Reflectances of the surface (layer with the highest pressure).

emissivities (Optional):

Emissivities of the surface (layer with the highest pressure).

additional_absorption_opacities_function (Optional[function]):

A python function that takes wavelength arrays in microns and pressure arrays in bars as input, and returns an absorption opacity matrix in units of cm^2/g, in the shape of number of wavelength points x number of pressure points. This opacity will then be added to the atmospheric absorption opacity. This must not be used to add atomic / molecular line opacities in low-resolution mode (c-k), because line opacities require a proper correlated-k treatment. It may be used to add simple cloud absorption laws, for example, which have opacities that vary only slowly with wavelength, such that the current model resolution is sufficient to resolve any variations.

additional_scattering_opacities_function (Optional[function]):

A python function that takes wavelength arrays in microns and pressure arrays in bars as input, and returns an isotropic scattering opacity matrix in units of cm^2/g, in the shape of number of wavelength points x number of pressure points. This opacity will then be added to the atmospheric absorption opacity. It may be used to add simple cloud absorption laws, for example, which have opacities that vary only slowly with wavelength, such that the current model resolution is sufficient to resolve any variations.

adaptive_feautrier_iterations (Optional[bool]):

If True, adapt the number of iterations of the Feautrier scattering-method (scattering_in_emission=True) based on the photon destruction probability.

frequencies_to_wavelengths (Optional[bool]):

if True, convert the frequencies (Hz) output to wavelengths (cm), and the flux per frequency output (erg.s-1.cm-2/Hz) to flux per wavelength (erg.s-1.cm-2/cm)

return_contribution (Optional[bool]):

If True, calculate the emission contribution function.

return_clear_spectrum (Optional[bool]):

If True, return the clear spectrum in addition to a cloudy spectrum.

return_photosphere_radius (Optional[bool]):

If True, calculate and return the photosphere radius.

return_rosseland_optical_depths (Optional[bool]):

If True, calculate and return the Rosseland opacities and optical depths.

return_cloud_contribution (Optional[bool]):

If True, calculate the cloud contribution.

return_opacities (Optional[bool]):

If True, return the absorption opacities and scattering opacities for species and clouds, as well as optical depths

return_abundances (Optional[bool]):

If True, return the mass fractions.

return_particles_radii_bins (Optional[bool]):

If True, return the particle radii bins.

return_particles_radii (Optional[bool]):

If True, return the particle radii.

target_particle_radii (Optional[npt.NDArray[np.floating]]):

# TODO complete docstring

target_pressure (Optional[npt.NDArray[np.floating]]):

# TODO complete docstring

calculate_photosphere_radius(temperatures: numpy.typing.NDArray[numpy.floating], mean_molar_masses: numpy.typing.NDArray[numpy.floating], reference_gravity: float, planet_radius: float, opacities: numpy.typing.NDArray[numpy.floating], continuum_opacities_scattering: numpy.typing.NDArray[numpy.floating], cloud_f_sed: float, cloud_photosphere_median_optical_depth: float, cloud_anisotropic_scattering_opacities: numpy.typing.NDArray[numpy.floating], cloud_absorption_opacities: numpy.typing.NDArray[numpy.floating], optical_depths: numpy.typing.NDArray[numpy.floating] = None, cloud_photosphere_wavelength_boundaries: numpy.typing.NDArray[numpy.floating] = None) → numpy.typing.NDArray[numpy.floating]

Calculate the photosphere radius. TODO complete docstring Args:

temperatures: mean_molar_masses: reference_gravity: planet_radius: opacities: continuum_opacities_scattering: cloud_f_sed: cloud_photosphere_wavelength_boundaries: cloud_photosphere_median_optical_depth: cloud_anisotropic_scattering_opacities: cloud_absorption_opacities: optical_depths:

Returns:

calculate_rosseland_planck_opacities(temperatures: numpy.typing.NDArray[numpy.floating], mass_fractions: dict[str, numpy.typing.NDArray[numpy.floating]], mean_molar_masses: numpy.typing.NDArray[numpy.floating], reference_gravity: float, opaque_cloud_top_pressure: float = None, cloud_particles_mean_radii: dict[str, numpy.typing.NDArray[numpy.floating]] = None, cloud_particle_radius_distribution_std: float = None, cloud_particles_radius_distribution: str = 'lognormal', cloud_hansen_a: float = None, cloud_hansen_b: float = None, clouds_particles_porosity_factor: dict[str, float] = None, cloud_f_sed: float = None, eddy_diffusion_coefficients: float = None, haze_factor: float = 1.0, power_law_opacity_350nm: float = None, power_law_opacity_coefficient: float = None, gray_opacity: float = None) → tuple[numpy.typing.NDArray[numpy.floating], numpy.typing.NDArray[numpy.floating], dict[str, Any]]

Method to calculate the atmosphere’s Rosseland and Planck mean opacities.

Args:

temperatures:

the atmospheric temperature in K, at each atmospheric layer (1-d numpy array, same length as pressure array).

mass_fractions:

dictionary of mass fractions for all atmospheric absorbers. Dictionary keys are the species names. Every mass fraction array has same length as pressure array.

mean_molar_masses:

the atmospheric mean molecular weight in amu, at each atmospheric layer (1-d numpy array, same length as pressure array).

reference_gravity (float):

Surface gravity in cgs. Vertically constant for emission spectra.

opaque_cloud_top_pressure (Optional[float]):

Pressure, in bar, where opaque cloud deck is added to the absorption opacity.

cloud_particles_mean_radii (Optional):

dictionary of mean particle radii for all cloud species. Dictionary keys are the cloud species names. Every radius array has same length as pressure array.

cloud_particle_radius_distribution_std (Optional[float]):

width of the log-normal cloud particle size distribution

cloud_particles_radius_distribution (Optional[string]):

The cloud particle size distribution to use. Can be either ‘lognormal’ (default) or ‘hansen’. If hansen, the cloud_hansen_b parameters must be used.

cloud_hansen_a (Optional[dict]):

A dictionary of the ‘a’ parameter values for each included cloud species and for each atmospheric layer, formatted as the kzz argument. Equivalent to radius arg. If cloud_hansen_a is not included and dist is “hansen”, then it will be computed using Kzz and fsed (recommended).

cloud_hansen_b (Optional[dict]):

A dictionary of the ‘b’ parameter values for each included cloud species and for each atmospheric layer, formatted as the kzz argument. This is the width of the hansen distribution normalized by the particle area (1/cloud_hansen_a^2)

clouds_particles_porosity_factor (Optional[dict]):

A dictionary containing the particle porosity factor for each cloud species.

cloud_f_sed (Optional[float]):

cloud settling parameter

eddy_diffusion_coefficients (Optional):

the atmospheric eddy diffusion coefficient in cgs (i.e. \(\rm cm^2/s\)), at each atmospheric layer (1-d numpy array, same length as pressure array).

haze_factor (Optional[float]):

Scalar factor, increasing the gas Rayleigh scattering cross-section.

power_law_opacity_350nm (Optional[float]):

Scattering opacity at 0.35 micron, in cgs units (cm^2/g).

power_law_opacity_coefficient (Optional[float]):

Has to be given if kappa_zero is defined, this is the wavelength powerlaw index of the parametrized scattering opacity.

gray_opacity (Optional[float]):

Gray opacity value, to be added to the opacity at all pressures and wavelengths (units \(\rm cm^2/g\))

calculate_transit_radii(temperatures: numpy.typing.NDArray[numpy.floating], mass_fractions: dict[str, numpy.typing.NDArray[numpy.floating]], mean_molar_masses: numpy.typing.NDArray[numpy.floating], reference_gravity: float, reference_pressure: float, planet_radius: float, variable_gravity: bool = True, opaque_cloud_top_pressure: float = None, cloud_particles_mean_radii: dict[str, numpy.typing.NDArray[numpy.floating]] = None, cloud_particle_radius_distribution_std: float = None, cloud_particles_radius_distribution: str = 'lognormal', cloud_hansen_a: float = None, cloud_hansen_b: float = None, cloud_particle_number_density_grid: dict[str, numpy.typing.NDArray[numpy.floating]] = None, clouds_particles_porosity_factor: dict[str, float] = None, cloud_f_sed: float = None, eddy_diffusion_coefficients: float = None, haze_factor: float = 1.0, power_law_opacity_350nm: float = None, power_law_opacity_coefficient: float = None, gray_opacity: float = None, cloud_fraction: float = 1.0, complete_coverage_clouds: list[str] = None, additional_absorption_opacities_function: Callable = None, additional_scattering_opacities_function: Callable = None, frequencies_to_wavelengths: bool = True, return_contribution: bool = False, return_clear_spectrum: bool = False, return_cloud_contribution: bool = False, return_radius_hydrostatic_equilibrium: bool = False, return_opacities: bool = False, return_abundances: bool = False, return_particles_radii_bins: bool = False, return_particles_radii: bool = False, return_particles_densities: bool = False, target_particle_radii: numpy.typing.NDArray[numpy.floating] = None, target_pressure: numpy.typing.NDArray[numpy.floating] = None) → tuple[numpy.typing.NDArray[numpy.floating], numpy.typing.NDArray[numpy.floating], dict[str, Any]]

Method to calculate the atmosphere’s transmission radius (for the transmission spectrum).

Args:

temperatures:

the atmospheric temperature in K, at each atmospheric layer (1-d numpy array, same length as pressure array).

mass_fractions:

dictionary of mass fractions for all atmospheric absorbers. Dictionary keys are the species names. Every mass fraction array has same length as pressure array.

mean_molar_masses:

the atmospheric mean molecular weight in amu, at each atmospheric layer (1-d numpy array, same length as pressure array).

reference_gravity (float):

Surface gravity in cgs at reference radius and pressure.

reference_pressure (float):

Reference pressure P0 in bar where R(P=P0) = R_pl, where R_pl is the reference radius (parameter of this method), and g(P=P0) = gravity, where gravity is the reference gravity (parameter of this method)

planet_radius (float):

Reference radius R_pl, in cm.

variable_gravity (Optional[bool]):

Standard is True. If False the gravity will be constant as a function of pressure, during the transmission radius calculation.

opaque_cloud_top_pressure (Optional[float]):

Pressure, in bar, where opaque cloud deck is added to the absorption opacity.

cloud_particles_mean_radii (Optional):

dictionary of mean particle radii for all cloud species. Dictionary keys are the cloud species names. Every radius array has same length as pressure array.

cloud_particle_radius_distribution_std (Optional[float]):

width of the log-normal cloud particle size distribution

cloud_particles_radius_distribution (Optional[string]):

The cloud particle size distribution to use. Can be either ‘lognormal’ (default) or ‘hansen’. If hansen, the cloud_hansen_b parameters must be used.

cloud_hansen_a (Optional[dict]):

A dictionary of the ‘a’ parameter values for each included cloud species and for each atmospheric layer, formatted as the kzz argument. Equivalent to radius arg. If cloud_hansen_a is not included and dist is “hansen”, then it will be computed using Kzz and fsed (recommended).

cloud_hansen_b (Optional[dict]):

A dictionary of the ‘b’ parameter values for each included cloud species and for each atmospheric layer, formatted as the kzz argument. This is the width of the hansen distribution normalized by the particle area (1/cloud_hansen_a^2)

cloud_particle_number_density_grid (Optional[dict]):

EXPERIMENTAL

clouds_particles_porosity_factor (Optional[dict]):

A dictionary of porosity factors depending on the cloud species. This can be useful when opacities are calculated using the Distribution of Hollow Spheres (DHS) method.

cloud_f_sed (Optional[float]):

cloud settling parameter

eddy_diffusion_coefficients (Optional):

the atmospheric eddy diffusion coefficient in cgs (i.e. \(\rm cm^2/s\)), at each atmospheric layer (1-d numpy array, same length as pressure array).

haze_factor (Optional[float]):

Scalar factor, increasing the gas Rayleigh scattering cross-section.

power_law_opacity_350nm (Optional[float]):

Scattering opacity at 0.35 micron, in cgs units (cm^2/g).

power_law_opacity_coefficient (Optional[float]):

Has to be given if kappa_zero is defined, this is the wavelength powerlaw index of the parametrized scattering opacity.

gray_opacity (Optional[float]):

Gray opacity value, to be added to the opacity at all pressures and wavelengths (units \(\rm cm^2/g\))

cloud_fraction:

Mix a column without cloud with a column with cloud to the requested fraction (0 = clear, 1 = full cloud cover). By default, assume a full cloud cover.

complete_coverage_clouds:

List of clouds that have full coverage, regardless of the value of cloud_fraction.

additional_absorption_opacities_function (Optional[function]):

A python function that takes wavelength arrays in microns and pressure arrays in bars as input, and returns an absorption opacity matrix in units of cm^2/g, in the shape of number of wavelength points x number of pressure points. This opacity will then be added to the atmospheric absorption opacity. This must not be used to add atomic / molecular line opacities in low-resolution mode (c-k), because line opacities require a proper correlated-k treatment. It may be used to add simple cloud absorption laws, for example, which have opacities that vary only slowly with wavelength, such that the current model resolution is sufficient to resolve any variations.

additional_scattering_opacities_function (Optional[function]):

A python function that takes wavelength arrays in microns and pressure arrays in bars as input, and returns an isotropic scattering opacity matrix in units of cm^2/g, in the shape of number of wavelength points x number of pressure points. This opacity will then be added to the atmospheric absorption opacity. It may be used to add simple cloud absorption laws, for example, which have opacities that vary only slowly with wavelength, such that the current model resolution is sufficient to resolve any variations.

frequencies_to_wavelengths (Optional[bool]):

If True, convert the frequencies (Hz) output to wavelengths (cm).

return_contribution (Optional[bool]):

If True the transmission and emission contribution function is calculated.

return_clear_spectrum (Optional[bool]):

If True, return the clear spectrum in addition to a cloudy spectrum.

return_cloud_contribution (Optional[bool]):

If True, calculate and return the cloud contribution.

return_radius_hydrostatic_equilibrium (Optional[bool]):

If True, return the radius at hydrostatic equilibrium of the planet.

return_opacities (Optional[bool]):

If True, return the absorption opacities and scattering opacities.

return_abundances (Optional[bool]):

If True, return the mass fractions.

return_particles_radii_bins (Optional[bool]):

If True, return the particles radii bins.

return_particles_radii (Optional[bool]):

If True, return the particles radii.

return_particles_densities (Optional[bool]):

If True, return the particles densities.

target_particle_radii (Optional[npt.NDArray[np.floating]]):

# TODO complete docstring

target_pressure (Optional[npt.NDArray[np.floating]]):

# TODO complete docstring

static compute_pressure_hydrostatic_equilibrium(mean_molar_masses, reference_gravity, planet_radius, p0, temperature, radii, rk4=True)

static compute_star_spectrum(star_effective_temperature, orbit_semi_major_axis, frequencies, star_radius=None)

Method to get the PHOENIX spectrum of the star and rebin it to the wavelength points. If t_star is not explicitly written, the spectrum will be 0. If the distance is not explicitly written, the code will raise an error and break to urge the user to specify the value.

Args:

star_effective_temperature (float):

the stellar temperature in K.

orbit_semi_major_axis (float):

the semi-major axis of the planet in cm.

frequencies (float):

the frequencies on which to interpolate the spectrum in Hz.

star_radius (float):

if specified, uses this radius in cm to scale the flux, otherwise it uses PHOENIX radius.

get_wavelengths()

Convert the frequency grid into wavelengths in centimeter.

load_all_opacities()

load_cia_opacities(path_input_data)

static load_cia_opacity(file_path_hdf5, return_radtrans_opacities=True)

Load a CIA opacity file.

load_cloud_opacities(path_input_data)

Load the cloud opacities.

Args:

path_input_data: path to petitRADTRANS input_data directory

static load_cloud_opacity(file_path_hdf5, return_radtrans_opacities=True)

Load a cloud opacity file.

classmethod load_hdf5_ktables(file_path_hdf5, frequencies, g_size, temperature_pressure_grid_size, return_radtrans_opacities=True)

Load k-coefficient tables in HDF5 format, based on the ExoMol setup.

static load_hdf5_line_opacity_table(file_path_hdf5, frequencies, line_by_line_opacity_sampling=1, return_radtrans_opacities=True)

Load opacities (cm2.g-1) tables in HDF5 format, based on petitRADTRANS pseudo-ExoMol setup.

load_line_opacities(path_input_data)

Load the line opacities for spectral calculation. The default pressure-temperature grid is a log-uniform (10, 13) grid.

Args:

path_input_data: path to petitRADTRANS input_data directory

static load_line_opacities_pressure_temperature_grid(hdf5_file)

Load line opacities temperature grids.

static rebin_star_spectrum(star_spectrum, star_wavelengths, wavelengths)