bayes_hfs

Submodules

bayes_hfs.hfs_model

hfs_model.py HFSModel definition

class bayes_hfs.hfs_model.HFSModel(mol_data: dict, *args, bg_temp: float = 2.7, Beff: float = 1.0, Feff: float = 1.0, **kwargs)

Bases: BaseModel

Definition of the HFSModel.

Attributes:

baseline_deterministics: Get the deterministic baseline parameter names.
baseline_freeRVs: Get the free baseline parameter names.
cloud_deterministics: Get the deterministic cloud parameter names.
cloud_freeRVs: Get the free cloud parameter names.
hyper_deterministics: Get the deterministic hyper parameter names.
hyper_freeRVs: Get the free hyper parameter names.
labeller: Get the arviz labeller.
unique_solution: Check if posterior samples suggest a unique solution.

Methods

`add_baseline_priors`([prior_baseline_coeffs])	Add baseline priors to the model.
`add_likelihood`()	Add likelihood to the model.
`add_priors`([prior_log10_Ntot, prior_fwhm2, ...])	Add priors and deterministics to the model
`bic`([chain, solution])	Calculate the Bayesian information criterion at the mean point estimate.
`fit`([n, draws, rel_tolerance, ...])	Approximate posterior distribution using Variational Inference (VI).
`graph`()	Generate visualization of the model graph.
`mean_lnlike`([chain, solution])	Evaluate mean log-likelihood over posterior samples.
`null_bic`()	Evaluate the Bayesian Information Criterion for the null hypothesis (baseline only, no clouds)
`predict_baseline`([baseline_params])	Predict the un-normalized baseline model.
`reset_results`()	Reset results and convergence checks.
`sample`([init, n_init, chains, init_kwargs, ...])	Sample posterior distribution using MCMC.
`sample_posterior_predictive`([solution, thin])	Generate posterior predictive samples
`sample_prior_predictive`([samples])	Generate prior predictive samples
`sample_smc`(**kwargs)	Sample posterior distribution using Sequential Monte Carlo.
`solve`(**kwargs)	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model. SpecData key must be “observation”.

add_priors(prior_log10_Ntot: Iterable[float] = [13.5, 0.25], prior_fwhm2: float = 1.0, prior_velocity: Iterable[float] = [-10.0, 10.0], prior_log10_Tex_CTEX: Iterable[float] = [0.75, 0.1], assume_CTEX: bool = True, prior_log10_CTEX_variance: float = [-4.0, 1.0], clip_weights: float | None = 1e-09, clip_tau: float | None = -10.0, prior_fwhm_L: float | None = None, prior_baseline_coeffs: dict[str, Iterable[float]] | None = None)

Add priors and deterministics to the model

Parameters:

prior_log10_NtotIterable[float], optional: Prior distribution on log10 total column density (cm-2), by default [13.5, 0.5], where log10_Ntot ~ Normal(mu=prior[0], sigma=prior[1])
prior_fwhm2float, optional: Prior distribution on FWHM^2 (km2 s-2), by default 1.0, where fwhm2 ~ prior * ChiSquared(nu=1) i.e., half-normal on FWHM
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [-10.0, 10.0], where velocity_norm ~ Beta(alpha=2.0, beta=2.0) velocity ~ prior[0] + (prior[1] - prior[0]) * velocity_norm
prior_log10_Tex_CTEXIterable[float], optional: Prior distribution on log10 CTEX excitation temperature (K), by default [0.75, 0.25], where log10_Tex_CTEX ~ Normal(mu=prior[0], sigma=prior[1])
assume_CTEXbool, optional: Assume that every transition has the same excitation temperature, by default True.
prior_log10_CTEX_varianceIterable[float], optional: Prior distribution on the log10_variance of departures from CTEX, by default [-4.0, 1.0], where log10_CTEX_variance ~ prior[0] + HalfNormal(sigma=prior[1]) stat_weights ~ Dirichlet(a=len(states)*CTEX_stat_weights/CTEX_variance) where CTEX_stat_weights are derived from Tex
clip_weightsOptional[float], optional: Clip weights between [clip, 1.0-clip], by default 1.0e-9
clip_tauOptional[float], optional: Clip masers by truncating optical depths below this value, by default -10.0
prior_fwhm_LOptional[float], optional: Prior distribution on the pseudo-Voight Lorentzian profile line width (km/s), by default None, where fwhm_L ~ HalfNormal(sigma=prior_fwhm_L) If None, the line profile is assumed Gaussian.
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Prior distribution on the normalized baseline polynomial coefficients, by default None. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset.

bayes_hfs.hfs_ratio_model

hfs_ratio_model.py HFSRatioModel definition

class bayes_hfs.hfs_ratio_model.HFSRatioModel(mol1_data: dict, mol2_data: dict, mol_keys: dict, *args, bg_temp: float = 2.7, Beff: float = 1.0, Feff: float = 1.0, **kwargs)

Bases: BaseModel

Definition of the HFSRatioModel.

Attributes:

baseline_deterministics: Get the deterministic baseline parameter names.
baseline_freeRVs: Get the free baseline parameter names.
cloud_deterministics: Get the deterministic cloud parameter names.
cloud_freeRVs: Get the free cloud parameter names.
hyper_deterministics: Get the deterministic hyper parameter names.
hyper_freeRVs: Get the free hyper parameter names.
labeller: Get the arviz labeller.
unique_solution: Check if posterior samples suggest a unique solution.

Methods

`add_baseline_priors`([prior_baseline_coeffs])	Add baseline priors to the model.
`add_likelihood`()	Add likelihood to the model.
`add_priors`([prior_log10_Ntot1, prior_ratio, ...])	Add priors and deterministics to the model
`bic`([chain, solution])	Calculate the Bayesian information criterion at the mean point estimate.
`fit`([n, draws, rel_tolerance, ...])	Approximate posterior distribution using Variational Inference (VI).
`graph`()	Generate visualization of the model graph.
`mean_lnlike`([chain, solution])	Evaluate mean log-likelihood over posterior samples.
`null_bic`()	Evaluate the Bayesian Information Criterion for the null hypothesis (baseline only, no clouds)
`predict_baseline`([baseline_params])	Predict the un-normalized baseline model.
`reset_results`()	Reset results and convergence checks.
`sample`([init, n_init, chains, init_kwargs, ...])	Sample posterior distribution using MCMC.
`sample_posterior_predictive`([solution, thin])	Generate posterior predictive samples
`sample_prior_predictive`([samples])	Generate prior predictive samples
`sample_smc`(**kwargs)	Sample posterior distribution using Sequential Monte Carlo.
`solve`(**kwargs)	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model. SpecData key must be “observation”.

add_priors(prior_log10_Ntot1: Iterable[float] = [13.5, 0.5], prior_ratio: float = 0.1, prior_fwhm2: float = 1.0, prior_velocity: Iterable[float] = [-10.0, 10.0], prior_log10_Tex_CTEX: Iterable[float] = [0.75, 0.25], assume_CTEX1: bool = True, assume_CTEX2: bool = True, prior_log10_CTEX_variance: float = [-4.0, 1.0], clip_weights: float | None = 1e-09, clip_tau: float | None = -10.0, prior_fwhm_L: float | None = None, prior_baseline_coeffs: dict[str, Iterable[float]] | None = None)

Add priors and deterministics to the model

Parameters:

prior_log10_Ntot1Iterable[float], optional: Prior distribution on log10 total column density (cm-2) of the first species, by default [13.5, 0.5], where log10_Ntot1 ~ Normal(mu=prior[0], sigma=prior[1])
prior_ratiofloat, optional: Prior distribution on the ratio Ntot2/Ntot1, by default 0.1, where ratio ~ HalfNormal(sigma=prior)
prior_fwhm2float, optional: Prior distribution on FWHM^2 (km2 s-2), by default 1.0, where fwhm2 ~ prior * ChiSquared(nu=1) i.e., half-normal on FWHM
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [-10.0, 10.0], where velocity_norm ~ Beta(alpha=2.0, beta=2.0) velocity ~ prior[0] + (prior[1] - prior[0]) * velocity_norm
prior_log10_Tex_CTEXIterable[float], optional: Prior distribution on log10 CTEX excitation temperature (K), by default [0.75, 0.25], where log10_Tex_CTEX ~ Normal(mu=prior[0], sigma=prior[1])
assume_CTEX1bool, optional: Assume that every transition of the first species has the same excitation temperature, by default True.
assume_CTEX2bool, optional: Assume that every transition of the second species has the same excitation temperature, by default True.
prior_log10_CTEX_varianceIterable[float], optional: Prior distribution on the log10_variance of departures from CTEX, by default [-6.0, 1.0], where log10_CTEX_variance ~ prior[0] + HalfNormal(sigma=prior[1]) stat_weights ~ Dirichlet(a=len(states)*CTEX_stat_weights/CTEX_variance) where CTEX_stat_weights are derived from Tex
clip_weightsOptional[float], optional: Clip weights between [clip, 1.0-clip], by default 1.0e-9
clip_tauOptional[float], optional: Clip masers by truncating optical depths below this value, by default -10.0
prior_fwhm_LOptional[float], optional: Prior distribution on the pseudo-Voight Lorentzian profile line width (km/s), by default None, where fwhm_L ~ HalfNormal(sigma=prior_fwhm_L) If None, the line profile is assumed Gaussian.
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Prior distribution on the normalized baseline polynomial coefficients, by default None. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset.

bayes_hfs.physics

physics.py Hyperfine physics

bayes_hfs.physics.calc_TR(freq: float, boltz_factor: float) → float

Evaluate the radiation temperature (AKA Rayleigh-Jeans equivalent temperature, AKA brightness temperature). Note that we do not assume the R-J limit here.

Parameters:

freqfloat: frequency (MHz)
boltz_factorfloat: Boltzmann factor

Returns:

float: Radiation temperature (AKA brightness temperature, K)

bayes_hfs.physics.calc_Tex(freq: float, boltz_factor: float) → float

Evaluate the excitation temperature from a given Boltzmann factor.

Parameters:

freqfloat: Frequency (MHz)
boltz_factorfloat: Boltzmann factor = exp(-h*freq/(k*Tex))

Returns:

float: Excitation temperature

bayes_hfs.physics.calc_boltz_factor(freq: float, Tex: float) → float

Evaluate the Boltzmann factor from a given excitation temperature. B = Nu*gl/(Nl*gu) = exp(-h*freq/(k*Tex))

Parameters:

freqfloat: Frequency (MHz)
Texfloat: Excitation temperature (K)

Returns:

float: Boltzmann factor

bayes_hfs.physics.calc_frequency(freq: float, velocity: float) → float

Apply the Doppler equation to calculate the frequency in the same frame as the velocity.

Parameters:

freqfloat: Frequencies. Output has same units.
velocityfloat: Velocity (km/s)

Returns:

float: Radio-defined Doppler frequency

bayes_hfs.physics.calc_fwhm_freq(freq: float, fwhm: float) → float

Calculate the FWHM line width in frequency units

Parameters:

freqfloat: Frequencies. Output has same units.
fwhmfloat: FWHM line width (km/s) in velocity units (length C)

Returns:

float: FWHM line width (MHz) in frequency units (shape C x N)

bayes_hfs.physics.calc_optical_depth(freq: float, gl: float, gu: float, Nl: float, Nu: float, Aul: float, line_profile: float) → float

Evaluate the total optical depth assuming a homogeneous medium. This is the integral of the absorption coefficient (Condon & Ransom eq. 7.55)

Parameters:

freq: float: Transition frequency (MHz)
gl: float: Lower state degeneracy
gufloat: Upper state degeneracy
Nlfloat: Lower state column density (cm-2)
Nufloat: Upper state column density (cm-2)
Aulfloat: Einstein A coefficient (s-1)
line_profilefloat: Line profile (MHz-1). Pass 1.0 to get the integrated optical depth.

Returns:

float: Total optical depth

bayes_hfs.physics.calc_pseudo_voigt(freq_axis: Iterable[float], frequency: Iterable[float], fwhm: Iterable[float], fwhm_L: Iterable[float]) → Iterable[float]

Evaluate a pseudo Voight profile in order to aid in posterior exploration of the parameter space. This parameterization includes a latent variable fwhm_L, which can be conditioned on zero to analyze the posterior. We also consider the spectral channelization. We do not perform a full boxcar convolution, rather we approximate the convolution by assuming an equivalent FWHM for the boxcar kernel of 4 ln(2) / pi * channel_width ~= 0.88 * channel_width

Parameters:

freq_axisIterable[float]: Observed frequency axis (MHz length S)
frequencyIterable[float]: Cloud center frequency (MHz length C x N)
fwhmIterable[float]: Cloud FWHM line widths (MHz length C x N)
fwhm_LIterable[float]: Latent pseudo-Voigt profile Lorentzian FWHM (MHz length C x N)

Returns:

Iterable[float]: Line profile (MHz-1; shape S x C x N)

bayes_hfs.physics.calc_stat_weight(g: float, E: float, Tex: float) → float

Evaluate the statistical weight for a given excitation temperature.

Parameters:

gfloat: Degeneracy
Efloat: Energy relative to ground state / k_B (K)
Texfloat: Excitation temperature (K)

Returns:

float: statistical weight g * exp(-E/(k*Tex))

bayes_hfs.physics.gaussian(x: float, center: float, fwhm: float) → float

Evaluate a normalized Gaussian function

Parameters:

xfloat: Position at which to evaluate
centerfloat: Gaussian centroid
fwhmfloat: Gaussian FWHM line width

Returns:

float: Gaussian evaluated at x

bayes_hfs.physics.lorentzian(x: float, center: float, fwhm: float) → float

Evaluate a normalized Lorentzian function

Parameters:

xfloat: Position at which to evaluate
centerfloat: Centroid
fwhmfloat: FWHM

Returns:

float: Lorentzian evaluated at x

bayes_hfs.physics.predict_tau_spectra(mol_data: dict, freq_axis: Iterable[float], tau: Iterable[float], velocity: Iterable[float], fwhm: Iterable[float], fwhm_L: float) → Iterable[float]

Predict the optical depth spectra from model parameters.

Parameters:

mol_datadict: Dictionary of molecular data returned by utils.get_molecule_data mol_data[‘freq’] contains C-length array of transition frequencies
freq_axisIterable[float]: Observed frequency axis (MHz length S)
tauIterable[float]: Total optical depth of each transition (shape C x N)
velocityIterable[float]: Velocity (km s-1) (length N)
fwhmIterable[float]: FWHM line width (km s-1) (length N)
fwhm_Lfloat: Latent pseudo-Voigt profile Lorentzian FWHM (km s-1)

Returns:

Iterable[float]: Predicted optical depth spectra (shape S x C x N)

bayes_hfs.physics.radiative_transfer(freq_axis: Iterable[float], tau: Iterable[float], TR: Iterable[float], bg_temp: float) → Iterable[float]

Evaluate the radiative transfer to predict the emission spectrum. The emission spectrum is ON - OFF, where ON includes the attenuated emission of the background and the clouds, and the OFF is the emission of the background. Order of N clouds is assumed to be [nearest, …, farthest]. The emission spectum is the Rayleigh-Jeans equivalent temperature, AKA the brightness temperature.

Parameters:

freq_axisfloat: Frequency axis (MHz) (length S)
tauIterable[float]: Optical depth spectra (shape S x C x N)
TRIterable[float]: Radiation temperature; shape: C x N)
bg_tempfloat: Assumed background temperature

Returns:

Iterable[float]: Predicted emission Rayleigh-Jeans equivalent temperature (AKA brightness temperature) spectrum (K) (length S)

bayes_hfs.utils

utils.py Hyperfine utilities

bayes_hfs.utils.get_molecule_data(molecule: str, fmin: float = 0.0, fmax: float = 10000.0) → dict

Get molecular transition data from the JPL database. Lifted heavily from pyspeckit’s get_molecular_parameters.

Parameters:

moleculestr: Molecule name
fminfloat, optional: Minimum frequency (GHz), by default 0.0
fmaxfloat, optional: Maximum frequency (GHz), by default 10000.0

Returns:

astropy.table.Table: CDMS data
astropy.table.Table: CDMS metadata

Raises:

ValueError: Molecule not found in CDMS database

bayes_hfs.utils.supplement_molecule_data(mol_data: Table, mol_metadata: Table) → dict

Supplement molecular transition data. Lifted heavily from pyspeckit’s get_molecular_parameters. The user must first prune the table to only those transitions they wish to model, and they must manually add the lower state degeneracy (GLO) for each transition (e.g., mol_data[‘GLO’] = 2*mol_data[‘F1l’] + 1).

Parameters:

mol_dataastropy.table.Table: Molecule data returned by get_molecule_data

Returns:

dict: Molecular transition data, with keys: “mol_weight” (float) : Molecular weight (Daltons) “freq” (Iterable[float]) : Rest frequencies (MHz) “Aul” (Iterable[float]) : Einstein A coefficients (s-1) “degu” (Iterable[float]) : Upper state degeneracies “Eu” (Iterable[float]) : Upper state energies (erg) “relative_int” (Iterable[float]) : Relative intensities “log10_Q_terms” (Iterable[float]) : Polynomial coefficients for logQ vs. logT (K)

Raises:

ValueError: Molecule not found in JPLSpec database

Module contents

class bayes_hfs.HFSModel(mol_data: dict, *args, bg_temp: float = 2.7, Beff: float = 1.0, Feff: float = 1.0, **kwargs)

Bases: BaseModel

Definition of the HFSModel.

Attributes:

baseline_deterministics: Get the deterministic baseline parameter names.
baseline_freeRVs: Get the free baseline parameter names.
cloud_deterministics: Get the deterministic cloud parameter names.
cloud_freeRVs: Get the free cloud parameter names.
hyper_deterministics: Get the deterministic hyper parameter names.
hyper_freeRVs: Get the free hyper parameter names.
labeller: Get the arviz labeller.
unique_solution: Check if posterior samples suggest a unique solution.

Methods

`add_baseline_priors`([prior_baseline_coeffs])	Add baseline priors to the model.
`add_likelihood`()	Add likelihood to the model.
`add_priors`([prior_log10_Ntot, prior_fwhm2, ...])	Add priors and deterministics to the model
`bic`([chain, solution])	Calculate the Bayesian information criterion at the mean point estimate.
`fit`([n, draws, rel_tolerance, ...])	Approximate posterior distribution using Variational Inference (VI).
`graph`()	Generate visualization of the model graph.
`mean_lnlike`([chain, solution])	Evaluate mean log-likelihood over posterior samples.
`null_bic`()	Evaluate the Bayesian Information Criterion for the null hypothesis (baseline only, no clouds)
`predict_baseline`([baseline_params])	Predict the un-normalized baseline model.
`reset_results`()	Reset results and convergence checks.
`sample`([init, n_init, chains, init_kwargs, ...])	Sample posterior distribution using MCMC.
`sample_posterior_predictive`([solution, thin])	Generate posterior predictive samples
`sample_prior_predictive`([samples])	Generate prior predictive samples
`sample_smc`(**kwargs)	Sample posterior distribution using Sequential Monte Carlo.
`solve`(**kwargs)	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model. SpecData key must be “observation”.

add_priors(prior_log10_Ntot: Iterable[float] = [13.5, 0.25], prior_fwhm2: float = 1.0, prior_velocity: Iterable[float] = [-10.0, 10.0], prior_log10_Tex_CTEX: Iterable[float] = [0.75, 0.1], assume_CTEX: bool = True, prior_log10_CTEX_variance: float = [-4.0, 1.0], clip_weights: float | None = 1e-09, clip_tau: float | None = -10.0, prior_fwhm_L: float | None = None, prior_baseline_coeffs: dict[str, Iterable[float]] | None = None)

Add priors and deterministics to the model

Parameters:

prior_log10_NtotIterable[float], optional: Prior distribution on log10 total column density (cm-2), by default [13.5, 0.5], where log10_Ntot ~ Normal(mu=prior[0], sigma=prior[1])
prior_fwhm2float, optional: Prior distribution on FWHM^2 (km2 s-2), by default 1.0, where fwhm2 ~ prior * ChiSquared(nu=1) i.e., half-normal on FWHM
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [-10.0, 10.0], where velocity_norm ~ Beta(alpha=2.0, beta=2.0) velocity ~ prior[0] + (prior[1] - prior[0]) * velocity_norm
prior_log10_Tex_CTEXIterable[float], optional: Prior distribution on log10 CTEX excitation temperature (K), by default [0.75, 0.25], where log10_Tex_CTEX ~ Normal(mu=prior[0], sigma=prior[1])
assume_CTEXbool, optional: Assume that every transition has the same excitation temperature, by default True.
prior_log10_CTEX_varianceIterable[float], optional: Prior distribution on the log10_variance of departures from CTEX, by default [-4.0, 1.0], where log10_CTEX_variance ~ prior[0] + HalfNormal(sigma=prior[1]) stat_weights ~ Dirichlet(a=len(states)*CTEX_stat_weights/CTEX_variance) where CTEX_stat_weights are derived from Tex
clip_weightsOptional[float], optional: Clip weights between [clip, 1.0-clip], by default 1.0e-9
clip_tauOptional[float], optional: Clip masers by truncating optical depths below this value, by default -10.0
prior_fwhm_LOptional[float], optional: Prior distribution on the pseudo-Voight Lorentzian profile line width (km/s), by default None, where fwhm_L ~ HalfNormal(sigma=prior_fwhm_L) If None, the line profile is assumed Gaussian.
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Prior distribution on the normalized baseline polynomial coefficients, by default None. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset.

class bayes_hfs.HFSRatioModel(mol1_data: dict, mol2_data: dict, mol_keys: dict, *args, bg_temp: float = 2.7, Beff: float = 1.0, Feff: float = 1.0, **kwargs)

Bases: BaseModel

Definition of the HFSRatioModel.

Attributes:

baseline_deterministics: Get the deterministic baseline parameter names.
baseline_freeRVs: Get the free baseline parameter names.
cloud_deterministics: Get the deterministic cloud parameter names.
cloud_freeRVs: Get the free cloud parameter names.
hyper_deterministics: Get the deterministic hyper parameter names.
hyper_freeRVs: Get the free hyper parameter names.
labeller: Get the arviz labeller.
unique_solution: Check if posterior samples suggest a unique solution.

Methods

`add_baseline_priors`([prior_baseline_coeffs])	Add baseline priors to the model.
`add_likelihood`()	Add likelihood to the model.
`add_priors`([prior_log10_Ntot1, prior_ratio, ...])	Add priors and deterministics to the model
`bic`([chain, solution])	Calculate the Bayesian information criterion at the mean point estimate.
`fit`([n, draws, rel_tolerance, ...])	Approximate posterior distribution using Variational Inference (VI).
`graph`()	Generate visualization of the model graph.
`mean_lnlike`([chain, solution])	Evaluate mean log-likelihood over posterior samples.
`null_bic`()	Evaluate the Bayesian Information Criterion for the null hypothesis (baseline only, no clouds)
`predict_baseline`([baseline_params])	Predict the un-normalized baseline model.
`reset_results`()	Reset results and convergence checks.
`sample`([init, n_init, chains, init_kwargs, ...])	Sample posterior distribution using MCMC.
`sample_posterior_predictive`([solution, thin])	Generate posterior predictive samples
`sample_prior_predictive`([samples])	Generate prior predictive samples
`sample_smc`(**kwargs)	Sample posterior distribution using Sequential Monte Carlo.
`solve`(**kwargs)	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model. SpecData key must be “observation”.

add_priors(prior_log10_Ntot1: Iterable[float] = [13.5, 0.5], prior_ratio: float = 0.1, prior_fwhm2: float = 1.0, prior_velocity: Iterable[float] = [-10.0, 10.0], prior_log10_Tex_CTEX: Iterable[float] = [0.75, 0.25], assume_CTEX1: bool = True, assume_CTEX2: bool = True, prior_log10_CTEX_variance: float = [-4.0, 1.0], clip_weights: float | None = 1e-09, clip_tau: float | None = -10.0, prior_fwhm_L: float | None = None, prior_baseline_coeffs: dict[str, Iterable[float]] | None = None)

Add priors and deterministics to the model

Parameters:

prior_log10_Ntot1Iterable[float], optional: Prior distribution on log10 total column density (cm-2) of the first species, by default [13.5, 0.5], where log10_Ntot1 ~ Normal(mu=prior[0], sigma=prior[1])
prior_ratiofloat, optional: Prior distribution on the ratio Ntot2/Ntot1, by default 0.1, where ratio ~ HalfNormal(sigma=prior)
prior_fwhm2float, optional: Prior distribution on FWHM^2 (km2 s-2), by default 1.0, where fwhm2 ~ prior * ChiSquared(nu=1) i.e., half-normal on FWHM
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [-10.0, 10.0], where velocity_norm ~ Beta(alpha=2.0, beta=2.0) velocity ~ prior[0] + (prior[1] - prior[0]) * velocity_norm
prior_log10_Tex_CTEXIterable[float], optional: Prior distribution on log10 CTEX excitation temperature (K), by default [0.75, 0.25], where log10_Tex_CTEX ~ Normal(mu=prior[0], sigma=prior[1])
assume_CTEX1bool, optional: Assume that every transition of the first species has the same excitation temperature, by default True.
assume_CTEX2bool, optional: Assume that every transition of the second species has the same excitation temperature, by default True.
prior_log10_CTEX_varianceIterable[float], optional: Prior distribution on the log10_variance of departures from CTEX, by default [-6.0, 1.0], where log10_CTEX_variance ~ prior[0] + HalfNormal(sigma=prior[1]) stat_weights ~ Dirichlet(a=len(states)*CTEX_stat_weights/CTEX_variance) where CTEX_stat_weights are derived from Tex
clip_weightsOptional[float], optional: Clip weights between [clip, 1.0-clip], by default 1.0e-9
clip_tauOptional[float], optional: Clip masers by truncating optical depths below this value, by default -10.0
prior_fwhm_LOptional[float], optional: Prior distribution on the pseudo-Voight Lorentzian profile line width (km/s), by default None, where fwhm_L ~ HalfNormal(sigma=prior_fwhm_L) If None, the line profile is assumed Gaussian.
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Prior distribution on the normalized baseline polynomial coefficients, by default None. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset.

bayes_hfs.get_molecule_data(molecule: str, fmin: float = 0.0, fmax: float = 10000.0) → dict

Get molecular transition data from the JPL database. Lifted heavily from pyspeckit’s get_molecular_parameters.

Parameters:

moleculestr: Molecule name
fminfloat, optional: Minimum frequency (GHz), by default 0.0
fmaxfloat, optional: Maximum frequency (GHz), by default 10000.0

Returns:

astropy.table.Table: CDMS data
astropy.table.Table: CDMS metadata

Raises:

ValueError: Molecule not found in CDMS database

bayes_hfs.supplement_molecule_data(mol_data: Table, mol_metadata: Table) → dict

Supplement molecular transition data. Lifted heavily from pyspeckit’s get_molecular_parameters. The user must first prune the table to only those transitions they wish to model, and they must manually add the lower state degeneracy (GLO) for each transition (e.g., mol_data[‘GLO’] = 2*mol_data[‘F1l’] + 1).

Parameters:

mol_dataastropy.table.Table: Molecule data returned by get_molecule_data

Returns:

dict: Molecular transition data, with keys: “mol_weight” (float) : Molecular weight (Daltons) “freq” (Iterable[float]) : Rest frequencies (MHz) “Aul” (Iterable[float]) : Einstein A coefficients (s-1) “degu” (Iterable[float]) : Upper state degeneracies “Eu” (Iterable[float]) : Upper state energies (erg) “relative_int” (Iterable[float]) : Relative intensities “log10_Q_terms” (Iterable[float]) : Polynomial coefficients for logQ vs. logT (K)

Raises:

ValueError: Molecule not found in JPLSpec database