tardis.opacities.macro_atom.base module¶

class tardis.opacities.macro_atom.base.NonMarkovChainTransitionProbabilities(plasma_parent)[source]¶

Bases: TransitionProbabilities

outputs = ('non_markov_transition_probabilities',)¶

class tardis.opacities.macro_atom.base.TransitionProbabilities(plasma_parent)[source]¶

Bases: ProcessingPlasmaProperty

Attributes:

transition_probabilitiesPandas DataFrame, dtype float

calculate(atomic_data, beta_sobolev, j_blues, stimulated_emission_factor, tau_sobolevs)[source]¶

calculate_transition_probabilities(macro_atom_data, beta_sobolev, j_blues, stimulated_emission_factor)[source]¶

initialize_macro_atom_transition_type_filters(atomic_data, macro_atom_data)[source]¶

outputs = ('transition_probabilities',)¶

prepare_transition_probabilities(macro_atom_data, beta_sobolev, j_blues, stimulated_emission_factor)[source]¶

tardis.opacities.macro_atom.base.calculate_transition_probabilities(atomic_data, beta_sobolev, j_blues, stimulated_emission_factor, tau_sobolevs, transition_probability_coef, block_references, normalize=True)[source]¶

Computes transition probabilities and provides them as a pd.DataFrame

Parameters:

atomic_datatardis.io.atom_data.AtomData: Atomic Data
beta_sobolevpd.DataFrame: Beta Sobolevs
j_bluespd.DataFrame: mean intensity
stimulated_emission_factornp.ndarray: Stimulated Emission Factors
tau_sobolevpd.DataFrame: Expansion Optical Depths
transition_probability_coefnp.ndarray: Reshaped macro atom transition probabilities
block_referencesnp.ndarray: macro atom block references
normalizebool: Whether or not to normalize the transition probabilities to unity

Returns:

pd.DataFrame: transition probabilities

tardis.opacities.macro_atom.base.calculate_transition_probability(macro_atom_data, beta_sobolev, j_blues, stimulated_emission_factor, transition_probability_coef, block_references, normalize)[source]¶

Calculate the transition probabilities using optimized functions Parameters ———- macro_atom_data : pd.DataFrame

Macro Atom Data

beta_sobolevpd.DataFrame: Beta Sobolevs
j_bluespd.DataFrame: mean intensity
stimulated_emission_factornp.ndarray: Stimulated Emission Factors
transition_probability_coefnp.ndarray: Reshaped macro atom transition probabilities
block_referencesnp.ndarray: macro atom block references
normalizebool: Whether or not to normalize the transition probabilities to unity

Returns:

np.ndarray: transition probabilities

tardis.opacities.macro_atom.base.get_macro_atom_data(atomic_data)[source]¶

Get the macro atom data from the atomic data

Parameters:

atomic_datatardis.io.atom_data.AtomData: Atomic Data

Returns:

pd.DataFrame: The macro atom data in the plasma

tardis.opacities.macro_atom.base.get_transition_probability_coefs(macro_atom_data)[source]¶

Coefficients of the transition probabilities

Parameters:

macro_atom_datapd.DataFrame: Macro Atom Data

Returns:

np.ndarray: Reshaped macro atom transition probabilities

tardis.opacities.macro_atom.base.initialize_macro_atom_transition_type_filters(atomic_data, macro_atom_data)[source]¶

Get the filters and block references from the macro atom

Parameters:

atomic_datatardis.io.atom_data.AtomData: Atomic Data
macro_atom_datapd.DataFrame: Macro Atom Data

Returns:

np.ndarray: Mask where the transition type is 1
np.ndarray: index of lines at these locations
pd.ndarray: macro atom block references

tardis.opacities.macro_atom.base.initialize_transition_probabilities(atomic_data)[source]¶

Convienience Function for initializing the transition probabilities

Parameters:

atomic_datatardis.io.atom_data.AtomData: Atomic Data

Returns:

dict

“transition_probability_coef”np.ndarray: Reshaped macro atom transition probabilities
“block_references”: np.ndarray: macro atom block references