amoeba2

Submodules

amoeba2.absorption_model

absorption_model.py AbsorptionModel definition

class amoeba2.absorption_model.AbsorptionModel(*args, mol_data: dict | None = None, **kwargs)

Bases: BaseModel

Definition of the AbsorptionModel. SpecData keys must be “absorption_1612”, “absorption_1665”, “absorption_1667”, and “absorption_1720”.

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_tau, prior_log10_depth, ...])	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_absorption`()	Predict the absorption spectra from the model parameters.
`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`([num_gmm_samples, kl_div_threshold])	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model.

add_priors(prior_tau: Iterable[float] = [0.1, 0.1], prior_log10_depth: Iterable[float] = [0.0, 0.25], prior_log10_Tkin: Iterable[float] = [2.0, 1.0], prior_velocity: Iterable[float] = [0.0, 10.0], prior_log10_nth_fwhm_1pc: Iterable[float] = [0.2, 0.1], prior_depth_nth_fwhm_power: Iterable[float] = [0.4, 0.1], prior_baseline_coeffs: dict[str, Iterable[float]] | None = None, ordered: bool = False, mainline_pos_tau: bool = False)

Add priors and deterministics to the model.

Parameters:

prior_taufloat, optional: Prior distribution on optical depth, by default [0.1, 0.1], where tau ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_depthIterable[float], optional: Prior distribution on log10 depth (pc), by default [0.0, 0.25], where log10_depth ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_TkinIterable[float], optional: Prior distribution on log10 kinetic temperature (K), by default [2.0, 1.0], where log10_Tkin ~ Normal(mu=prior[0], sigma=prior[1])
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [0.0, 10.0], where velocity ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_nth_fwhm_1pcIterable[float], optional: Prior distribution on non-thermal line width at 1 pc, by default [0.2, 0.1], where log10_nth_fwhm_1pc ~ Normal(mu=prior[0], sigma=prior[1])
prior_depth_nth_fwhm_powerIterable[float], optional: Prior distribution on depth vs. non-thermal line width power law index, by default [0.4, 0.1], where depth_nth_fwhm_power ~ Normal(mu=prior[0], sigma=prior[1])
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Width of normal prior distribution on the normalized baseline polynomial coefficients. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset, by default None
orderedbool, optional: If True, assume ordered velocities (optically thin assumption), by default False. If True, the prior distribution on the velocity becomes velocity(cloud = n) ~ prior[0] + sum_i(velocity[i < n]) + Gamma(alpha=2.0, beta=1.0/prior[1])
mainline_pos_tau: bool, optional: If True, assume positive main line optical depths, by default False. If True, the prior distribution on the main line (1665 and 1667 MHz) optical depths becomes tau ~ HalfNormal(sigma=prior[1])

predict_absorption() → dict

Predict the absorption spectra from the model parameters.

Returns:

dict: Optical depth spectra for 1612, 1665, 1667, and 1720 MHz transitions

amoeba2.emission_absorption_model

emission_absorption_model.py EmissionAbsorptionModel definition

class amoeba2.emission_absorption_model.EmissionAbsorptionModel(*args, mol_data: dict | None = None, bg_temp: float = 3.77, **kwargs)

Bases: BaseModel

Definition of the TBTauModel. SpecData keys must be “absorption_1612”, “absorption_1665”, “absorption_1667”, “absorption_1720”, “emission_1612”, “emission_1665”, “emission_1667”, and “emission_1720”.

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_N0, ...])	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.
`predict_emission_absorption`()	Predict the emission and absorption spectra from the model parameters.
`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`([num_gmm_samples, kl_div_threshold])	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model.

add_priors(prior_log10_N0: Iterable[float] = [13.0, 1.0], prior_log_boltz_factor: Iterable[float] = [-0.1, 0.1], prior_log10_depth: Iterable[float] = [0.0, 0.25], prior_log10_Tkin: Iterable[float] = [2.0, 1.0], prior_velocity: Iterable[float] = [0.0, 10.0], prior_log10_nth_fwhm_1pc: Iterable[float] = [0.2, 0.1], prior_depth_nth_fwhm_power: Iterable[float] = [0.4, 0.1], prior_baseline_coeffs: dict[str, Iterable[float]] | None = None, ordered: bool = False, mainline_pos_tau: bool = False)

Add priors and deterministics to the model.

Parameters:

prior_log10_N0Iterable[float], optional: Prior distribution on log10 column density (cm-2) in lowest energy state, by default [13.0, 1.0], where log10_N0 ~ Normal(mu=prior[0], sigma=prior[1])
prior_log_boltz_factorIterable[float], optional: Prior distribution on log Boltzmann factor = -h*freq/(k*Tex), by default [-0.1, 0.1], where log_boltz_factor ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_depthIterable[float], optional: Prior distribution on log10 depth (pc), by default [0.0, 0.25], where log10_depth ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_TkinIterable[float], optional: Prior distribution on log10 kinetic temperature (K), by default [2.0, 1.0], where log10_Tkin ~ Normal(mu=prior[0], sigma=prior[1])
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [0.0, 10.0], where velocity ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_nth_fwhm_1pcIterable[float], optional: Prior distribution on non-thermal line width at 1 pc, by default [0.2, 0.1], where log10_nth_fwhm_1pc ~ Normal(mu=prior[0], sigma=prior[1])
prior_depth_nth_fwhm_powerIterable[float], optional: Prior distribution on depth vs. non-thermal line width power law index, by default [0.4, 0.1], where depth_nth_fwhm_power ~ Normal(mu=prior[0], sigma=prior[1])
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Width of normal prior distribution on the normalized baseline polynomial coefficients. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset, by default None
orderedbool, optional: If True, assume ordered velocities (optically thin assumption), by default False. If True, the prior distribution on the velocity becomes velocity(cloud = n) ~ prior[0] + sum_i(velocity[i < n]) + Gamma(alpha=2.0, beta=1.0/prior[1])
mainline_pos_tau: bool, optional: If True, assume positive main line excitation temperatures, by default False. If True, the prior distribution on the Boltzmann factor for the main lines (1665 and 1667) becomes log_boltz_factor ~ HalfNormal(sigma=prior[1])

predict_emission_absorption() → dict

Predict the emission and absorption spectra from the model parameters.

Returns:

dict: Emission spectra (brightness temp; K) for 1612, 1665, 1667, and 1720 MHz transitions
dict: Absorption spectra (1 - exp(-tau)) for 1612, 1665, 1667, and 1720 MHz transitions

amoeba2.physics

physics.py Molecular spectroscopy physics utilities

amoeba2.physics.calc_Tex(freq: float, log_boltz_factor: float) → float

Evaluate the excitation temperature from a given Boltzmann factor.

Parameters:

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

Returns:

float: Excitation temperature

amoeba2.physics.calc_line_profile(velo_axis: Iterable[float], velocity: Iterable[float], fwhm: Iterable[float]) → Iterable[float]

Evaluate the Gaussian line profile, ensuring normalization.

Parameters:

velo_axisIterable[float]: Observed velocity axis (km s-1; length S)
velocityIterable[float]: Cloud center velocities (km s-1; length C)
fwhmIterable[float]: Cloud FWHM line widths (km s-1; length C)

Returns:

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

amoeba2.physics.calc_nonthermal_fwhm(depth: float, nth_fwhm_1pc: float, depth_nth_fwhm_power: float) → float

Calculate the non-thermal line broadening assuming a power-law size-linewidth relationship.

Parameters:

depthfloat: Line-of-sight depth (pc)
nth_fwhm_1pcfloat: Non-thermal broadening at 1 pc (km s-1)
depth_nth_fwhm_powerfloat: Power law index

Returns:

float: Non-thermal FWHM line width (km s-1)

amoeba2.physics.calc_optical_depth(gu: int, gl: int, Nl: Iterable[float], boltz_factor: Iterable[float], line_profile: Iterable[float], freq: float, Aul: float) → Iterable[float]

Evaluate the optical depth spectra, from Mangum & Shirley eq. 29

Parameters:

guint: Upper state degeneracy
glint: Lower state degeneracy
NlIterable[float]: Cloud lower state column densities (cm-2; length C)
boltz_factorIterable[float]: Boltzmann factor = exp(-h*freq/(k*Tex)) (length C)
line_profileIterable[float]: Line profile (km-1 s; shape S x C)
freqfloat: Transition frequency (MHz)
Aulfloat: Transition Einstein A coefficient (s-1)

Returns:

Iterable[float]: Optical depth spectral (shape S x C)

amoeba2.physics.calc_thermal_fwhm(kinetic_temp: float, weight: float = 17.0) → float

Calculate the thermal line broadening assuming a Maxwellian velocity distribution (Condon & Ransom eq. 7.35)

Parameters:

kinetic_tempfloat: Kinetic temperature (K)
weightfloat: Molecular weight (number of protons). By default, 17 (OH molecule)

Returns:

float: Thermal FWHM line width (km s-1)

amoeba2.physics.radiative_transfer(freq: float, tau: Iterable[float], Tex: 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].

Parameters:

freqfloat: Frequency (MHz)
tauIterable[float]: Optical depth spectra (shape S x N)
TexIterable[float]: Cloud excitation temperature (K; length N)
bg_tempfloat: Assumed background temperature

Returns:

Iterable[float]: Predicted emission brightness temperature spectrum (K; length S)

amoeba2.physics.rj_temperature(freq: float, temp: float) → float

Calculate the Rayleigh-Jeans equivalent temperature (AKA the brightness temperature)

Parameters:

freqfloat: Frequency (MHz)
tempfloat: Temperature (K)

Returns:

float: R-J equivalent temperature (K)

amoeba2.utils

utils.py Various utilities.

amoeba2.utils.get_molecule_data(molecule: str | None = 'OH', fmin: float = 1.0, fmax: float = 2.0, rot_state_lower: int | None = 0) → dict

Get molecular transition data from the JPL database. By default fetches data for 1612, 1665, 1667, and 1720 MHz OH hyperfine transitions.

Parameters:

moleculestr, optional: Molecule name, by default “OH”
fminfloat, optional: Minimum frequency (GHz), by default 0.0
fmaxfloat, optional: Maximum frequency (GHz), by default 10000.0
rot_state_lowerOptional[int], optional: For OH, if not None, then limit to this lower rotational state, which is assumed to be the second quantum number returned by JPLSpec, by default 0

Returns:

dict: Molecular transition data, with keys: “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 amoeba2.AbsorptionModel(*args, mol_data: dict | None = None, **kwargs)

Bases: BaseModel

Definition of the AbsorptionModel. SpecData keys must be “absorption_1612”, “absorption_1665”, “absorption_1667”, and “absorption_1720”.

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_tau, prior_log10_depth, ...])	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_absorption`()	Predict the absorption spectra from the model parameters.
`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`([num_gmm_samples, kl_div_threshold])	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model.

add_priors(prior_tau: Iterable[float] = [0.1, 0.1], prior_log10_depth: Iterable[float] = [0.0, 0.25], prior_log10_Tkin: Iterable[float] = [2.0, 1.0], prior_velocity: Iterable[float] = [0.0, 10.0], prior_log10_nth_fwhm_1pc: Iterable[float] = [0.2, 0.1], prior_depth_nth_fwhm_power: Iterable[float] = [0.4, 0.1], prior_baseline_coeffs: dict[str, Iterable[float]] | None = None, ordered: bool = False, mainline_pos_tau: bool = False)

Add priors and deterministics to the model.

Parameters:

prior_taufloat, optional: Prior distribution on optical depth, by default [0.1, 0.1], where tau ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_depthIterable[float], optional: Prior distribution on log10 depth (pc), by default [0.0, 0.25], where log10_depth ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_TkinIterable[float], optional: Prior distribution on log10 kinetic temperature (K), by default [2.0, 1.0], where log10_Tkin ~ Normal(mu=prior[0], sigma=prior[1])
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [0.0, 10.0], where velocity ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_nth_fwhm_1pcIterable[float], optional: Prior distribution on non-thermal line width at 1 pc, by default [0.2, 0.1], where log10_nth_fwhm_1pc ~ Normal(mu=prior[0], sigma=prior[1])
prior_depth_nth_fwhm_powerIterable[float], optional: Prior distribution on depth vs. non-thermal line width power law index, by default [0.4, 0.1], where depth_nth_fwhm_power ~ Normal(mu=prior[0], sigma=prior[1])
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Width of normal prior distribution on the normalized baseline polynomial coefficients. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset, by default None
orderedbool, optional: If True, assume ordered velocities (optically thin assumption), by default False. If True, the prior distribution on the velocity becomes velocity(cloud = n) ~ prior[0] + sum_i(velocity[i < n]) + Gamma(alpha=2.0, beta=1.0/prior[1])
mainline_pos_tau: bool, optional: If True, assume positive main line optical depths, by default False. If True, the prior distribution on the main line (1665 and 1667 MHz) optical depths becomes tau ~ HalfNormal(sigma=prior[1])

predict_absorption() → dict

Predict the absorption spectra from the model parameters.

Returns:

dict: Optical depth spectra for 1612, 1665, 1667, and 1720 MHz transitions

class amoeba2.EmissionAbsorptionModel(*args, mol_data: dict | None = None, bg_temp: float = 3.77, **kwargs)

Bases: BaseModel

Definition of the TBTauModel. SpecData keys must be “absorption_1612”, “absorption_1665”, “absorption_1667”, “absorption_1720”, “emission_1612”, “emission_1665”, “emission_1667”, and “emission_1720”.

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_N0, ...])	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.
`predict_emission_absorption`()	Predict the emission and absorption spectra from the model parameters.
`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`([num_gmm_samples, kl_div_threshold])	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model.

add_priors(prior_log10_N0: Iterable[float] = [13.0, 1.0], prior_log_boltz_factor: Iterable[float] = [-0.1, 0.1], prior_log10_depth: Iterable[float] = [0.0, 0.25], prior_log10_Tkin: Iterable[float] = [2.0, 1.0], prior_velocity: Iterable[float] = [0.0, 10.0], prior_log10_nth_fwhm_1pc: Iterable[float] = [0.2, 0.1], prior_depth_nth_fwhm_power: Iterable[float] = [0.4, 0.1], prior_baseline_coeffs: dict[str, Iterable[float]] | None = None, ordered: bool = False, mainline_pos_tau: bool = False)

Add priors and deterministics to the model.

Parameters:

prior_log10_N0Iterable[float], optional: Prior distribution on log10 column density (cm-2) in lowest energy state, by default [13.0, 1.0], where log10_N0 ~ Normal(mu=prior[0], sigma=prior[1])
prior_log_boltz_factorIterable[float], optional: Prior distribution on log Boltzmann factor = -h*freq/(k*Tex), by default [-0.1, 0.1], where log_boltz_factor ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_depthIterable[float], optional: Prior distribution on log10 depth (pc), by default [0.0, 0.25], where log10_depth ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_TkinIterable[float], optional: Prior distribution on log10 kinetic temperature (K), by default [2.0, 1.0], where log10_Tkin ~ Normal(mu=prior[0], sigma=prior[1])
prior_velocityIterable[float], optional: Prior distribution on centroid velocity (km s-1), by default [0.0, 10.0], where velocity ~ Normal(mu=prior[0], sigma=prior[1])
prior_log10_nth_fwhm_1pcIterable[float], optional: Prior distribution on non-thermal line width at 1 pc, by default [0.2, 0.1], where log10_nth_fwhm_1pc ~ Normal(mu=prior[0], sigma=prior[1])
prior_depth_nth_fwhm_powerIterable[float], optional: Prior distribution on depth vs. non-thermal line width power law index, by default [0.4, 0.1], where depth_nth_fwhm_power ~ Normal(mu=prior[0], sigma=prior[1])
prior_baseline_coeffsOptional[dict[str, Iterable[float]]], optional: Width of normal prior distribution on the normalized baseline polynomial coefficients. Keys are dataset names and values are lists of length baseline_degree+1. If None, use [1.0]*(baseline_degree+1) for each dataset, by default None
orderedbool, optional: If True, assume ordered velocities (optically thin assumption), by default False. If True, the prior distribution on the velocity becomes velocity(cloud = n) ~ prior[0] + sum_i(velocity[i < n]) + Gamma(alpha=2.0, beta=1.0/prior[1])
mainline_pos_tau: bool, optional: If True, assume positive main line excitation temperatures, by default False. If True, the prior distribution on the Boltzmann factor for the main lines (1665 and 1667) becomes log_boltz_factor ~ HalfNormal(sigma=prior[1])

predict_emission_absorption() → dict

Predict the emission and absorption spectra from the model parameters.

Returns:

dict: Emission spectra (brightness temp; K) for 1612, 1665, 1667, and 1720 MHz transitions
dict: Absorption spectra (1 - exp(-tau)) for 1612, 1665, 1667, and 1720 MHz transitions