tardis.opacities.macro_atom.macroatom_line_transitions module

tardis.opacities.macro_atom.macroatom_line_transitions.line_transition_emission_down(line_f_uls: ndarray, line_nus: ndarray, energies_upper: ndarray, energies_lower: ndarray, beta_sobolevs: DataFrame, transition_a_i_l_u_array: ndarray, line_ids: ndarray) → tuple[DataFrame, DataFrame]

Calculate emission down transition probabilities for line transitions.

This function computes the probability of emission down transitions for atomic line transitions using oscillator strengths, frequencies, energy differences, and Sobolev optical depths.

Parameters:

line_f_ulsnp.ndarray

Oscillator strengths for the line transitions.

line_nusnp.ndarray

Frequencies of the line transitions in Hz.

energies_uppernp.ndarray

Energy values of the upper levels in the transitions.

energies_lowernp.ndarray

Energy values of the lower levels in the transitions.

beta_sobolevspd.DataFrame

Sobolev escape probabilities for the transitions.

transition_a_i_l_u_arraynp.ndarray

Array containing atomic number, ion number, lower level, and upper level indices for each transition. Shape should be (n_transitions, 4).

line_idsnp.ndarray

Unique identifiers for each line transition.

Returns:

p_emission_downpd.DataFrame

DataFrame containing the calculated emission down probabilities with source information. Contains columns for the probability values and ‘source’ tuples.

emission_down_metadatapd.DataFrame

DataFrame containing metadata for the transitions including transition line IDs, source and destination level information, transition type, and transition line indices.

Notes

The emission down probability is calculated using the formula: P = 2 * nu^2 * f_ul / c^2 * beta * (E_upper - E_lower)

The function uses MacroAtomTransitionType to mark the transition type.

tardis.opacities.macro_atom.macroatom_line_transitions.line_transition_internal_down(line_f_uls: ndarray, line_nus: ndarray, energies_lower: ndarray, beta_sobolevs: DataFrame, transition_a_i_l_u_array: ndarray, line_ids: ndarray) → tuple[DataFrame, DataFrame]

Calculate internal downward transition probabilities for line transitions.

This function computes the probability of internal downward transitions in macro atoms for line transitions, based on oscillator strengths, frequencies, and other atomic parameters.

Parameters:

line_f_ulsnp.ndarray

Oscillator strengths for line transitions.

line_nusnp.ndarray

Frequencies of line transitions.

energies_lowernp.ndarray

Energies of the lower levels involved in transitions.

beta_sobolevspd.DataFrame

Sobolev beta factors for the transitions.

transition_a_i_l_u_arraynp.ndarray

Array containing atomic number, ion number, lower level, and upper level indices for each transition. Shape should be (n_transitions, 4).

line_idsnp.ndarray

Identifiers for each line transition.

Returns:

p_internal_downpd.DataFrame

DataFrame containing the calculated internal downward transition probabilities with source information.

internal_down_metadatapd.DataFrame

DataFrame containing metadata for the transitions including transition line IDs, source and destination level information, transition type, and transition line indices.

Notes

The internal downward transition probability is calculated using the formula: p = 2 * nu^2 * f_ul / c^2 * beta * E_lower

The function uses MacroAtomTransitionType to mark the transition type.

tardis.opacities.macro_atom.macroatom_line_transitions.line_transition_internal_up(line_f_lus: ndarray, line_nus: ndarray, energies_lower: ndarray, mean_intensities_blue_wing: DataFrame, beta_sobolevs: DataFrame, stimulated_emission_factors: ndarray, transition_a_i_l_u_array: ndarray, line_ids: ndarray) → tuple[DataFrame, DataFrame]

Calculate internal upward transition probabilities for line transitions in macro atoms.

This function computes the probability of internal upward transitions between energy levels in macro atoms due to line transitions. It calculates the transition probabilities and creates metadata for tracking the transitions.

Parameters:

line_f_lusnp.ndarray

Oscillator strengths for line transitions from lower to upper levels.

line_nusnp.ndarray

Frequencies of the line transitions in Hz.

energies_lowernp.ndarray

Energy values of the lower levels in the transitions.

mean_intensities_blue_wingpd.DataFrame

Mean radiation field intensities at the blue wing of the lines.

beta_sobolevspd.DataFrame

Sobolev escape probabilities for the line transitions.

stimulated_emission_factorspd.DataFrame

Factors accounting for stimulated emission in the transitions.

transition_a_i_l_u_arraynp.ndarray

2D array containing atomic number, ion number, lower level, and upper level indices for each transition. Shape should be (n_transitions, 4).

line_idsnp.ndarray

Unique identifiers for each line transition.

Returns:

p_internal_uppd.DataFrame

DataFrame containing the calculated internal upward transition probabilities with source information, indexed by transition.

internal_up_metadatapd.DataFrame

DataFrame containing metadata for the transitions including transition line IDs, source and destination level information, transition type, and line indices.

Notes

The transition probability is calculated using the formula: P = (f_lu / (h * nu)) * stimulated_emission_factor * mean_intensity * beta * E_lower

The function uses MacroAtomTransitionType to mark the transition type.

tardis.opacities.macro_atom.macroatom_line_transitions.probability_emission_down(beta_sobolevs: DataFrame, line_nus: ndarray, line_f_uls: ndarray, energies_upper: ndarray, energies_lower: ndarray) → DataFrame

Calculate emission down transition probabilities.

This function computes the probability of emission down transitions for atomic line transitions. The calculation considers oscillator strengths, line frequencies, and the energy difference between upper and lower levels.

Parameters:

beta_sobolevspd.DataFrame

Sobolev escape probabilities for the line transitions.

line_nusnp.ndarray

Frequencies of the line transitions in Hz.

line_f_ulsnp.ndarray

Oscillator strengths for line transitions from upper to lower levels.

energies_uppernp.ndarray

Energy values of the upper levels in the transitions.

energies_lowernp.ndarray

Energy values of the lower levels in the transitions.

Returns:

pd.DataFrame

DataFrame containing the calculated emission down transition probabilities.

tardis.opacities.macro_atom.macroatom_line_transitions.probability_internal_down(beta_sobolevs: DataFrame, line_nus: ndarray, line_f_uls: ndarray, energies_lower: ndarray) → DataFrame

Calculate internal downward transition probabilities.

This function computes the probability of internal downward transitions between energy levels in macro atoms. The calculation is based on oscillator strengths, line frequencies, and lower level energies.

Parameters:

beta_sobolevspd.DataFrame

Sobolev escape probabilities for the line transitions.

line_nusnp.ndarray

Frequencies of the line transitions in Hz.

line_f_ulsnp.ndarray

Oscillator strengths for line transitions from upper to lower levels.

energies_lowernp.ndarray

Energy values of the lower levels in the transitions.

Returns:

pd.DataFrame

DataFrame containing the calculated internal downward transition probabilities.

tardis.opacities.macro_atom.macroatom_line_transitions.probability_internal_up(beta_sobolevs: DataFrame, line_nus: ndarray, line_f_lus: ndarray, stimulated_emission_factors: ndarray, mean_intensities_blue_wing: DataFrame, energies_lower: ndarray) → DataFrame

Calculate internal upward transition probabilities.

This function computes the probability of internal upward transitions between energy levels in macro atoms. The calculation involves oscillator strengths, stimulated emission factors, mean radiation field intensities, and energy values.

Parameters:

beta_sobolevspd.DataFrame

Sobolev escape probabilities for the line transitions.

line_nusnp.ndarray

Frequencies of the line transitions in Hz.

line_f_lusnp.ndarray

Oscillator strengths for line transitions from lower to upper levels.

stimulated_emission_factorsnp.ndarray

Factors accounting for stimulated emission in the transitions.

mean_intensities_blue_wingpd.DataFrame

Mean radiation field intensities at the blue wing of the lines.

energies_lowernp.ndarray

Energy values of the lower levels in the transitions.

Returns:

pd.DataFrame

DataFrame containing the calculated internal upward transition probabilities.