tardis.energy_input.main_gamma_ray_loop module

tardis.energy_input.main_gamma_ray_loop.calculate_electron_number_density(simulation_state: SimulationState, ejecta_volume: ndarray[tuple[Any, ...], dtype[float64]], effective_time_array: ndarray[tuple[Any, ...], dtype[float64]], legacy: bool = False, legacy_atom_data: AtomData | None = None) → ndarray[tuple[Any, ...], dtype[float64]][source]

Calculate the time-dependent electron number density.

Parameters:

simulation_stateSimulationState: State containing the ejecta geometry and composition.
ejecta_volumenumpy.ndarray: Shell volumes in cubic centimeters at the simulation-state time.
effective_time_arraynumpy.ndarray: Effective times in seconds at which to evaluate the density.
legacybool, optional: If True, calculate the elemental number density through the legacy simulation-state interface.
legacy_atom_dataAtomData or None, optional: Atomic data supplying elemental masses for the legacy calculation. Required when legacy is True.

Returns:

numpy.ndarray: Electron number density in inverse cubic centimeters, indexed by shell and effective time.

Raises:

tardis.energy_input.main_gamma_ray_loop.get_effective_time_array(time_start: float, time_end: float, time_space: str, time_steps: int) → tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]][source]

Create time-bin boundaries and representative effective times.

Parameters:

Returns:

timesnumpy.ndarray: Time-bin boundaries in days. The array has time_steps + 1 entries.
effective_time_arraynumpy.ndarray: Representative time of each bin in days. Logarithmic bins use the geometric mean and linear bins use the arithmetic mean.

Raises:

tardis.energy_input.main_gamma_ray_loop.get_packet_properties(number_of_shells: int, times: ndarray[tuple[Any, ...], dtype[float64]], time_steps: int, packets: list[GXPacket]) → tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]][source]

Bin packet frequencies and energies by shell and time.

Parameters:

number_of_shellsint: Number of ejecta shells.
timesnumpy.ndarray: Time-bin boundaries in the same units as each packet’s current time.
time_stepsint: Number of time bins.
packetslist of GXPacket: Gamma-ray packets to bin by shell and time.

Returns:

packets_nu_cmf_arraynumpy.ndarray: Sum of comoving-frame frequencies in each shell and time bin.
packets_nu_rf_arraynumpy.ndarray: Sum of rest-frame frequencies in each shell and time bin.
packets_energy_cmf_arraynumpy.ndarray: Sum of comoving-frame energies in each shell and time bin.
packets_energy_rf_arraynumpy.ndarray: Sum of rest-frame energies in each shell and time bin.
packets_positron_energy_arraynumpy.ndarray: Sum of positron energies in each shell and time bin.

tardis.energy_input.main_gamma_ray_loop.run_gamma_ray_loop(simulation_state: SimulationState, legacy_isotope_decacy_df: DataFrame, cumulative_decays_df: DataFrame, number_of_packets: int, times: ndarray[tuple[Any, ...], dtype[float64]], effective_time_array: ndarray[tuple[Any, ...], dtype[float64]], seed: int, positronium_fraction: float, spectrum_bins: int, grey_opacity: float, photoabsorption_opacity: str = 'tardis', pair_creation_opacity: str = 'tardis', legacy: bool = False, legacy_atom_data: AtomData | None = None) → tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame][source]

Propagate gamma-ray packets through homologously expanding ejecta.

Parameters:

simulation_stateSimulationState: State containing the ejecta geometry, density, and composition.
legacy_isotope_decacy_dfpandas.DataFrame: Radioactive-decay transition data used to compute packet energies and isotope-specific positron fractions.
cumulative_decays_dfpd.DataFrame: Time-dependent radioactive-decay data from which packets are sampled.
number_of_packetsint: Number of Monte Carlo packets to propagate.
timesnumpy.ndarray: Time-bin boundaries in days.
effective_time_arraynumpy.ndarray: Representative time of each time bin in days.
seedint: Seed for the random number generator.
positronium_fractionfloat: Fraction of positrons that form positronium.
spectrum_binsint: Number of logarithmically spaced escaping-spectrum energy bins.
grey_opacityfloat: Grey opacity in square centimeters per gram. A negative value enables the detailed interaction opacities.
photoabsorption_opacity{“kasen”, “tardis”}, optional: Photoabsorption opacity prescription used when grey_opacity is negative.
pair_creation_opacity{“artis”, “tardis”}, optional: Pair-creation opacity prescription used when grey_opacity is negative.
legacybool, optional: Whether to use the legacy elemental-density and packet-energy calculations.
legacy_atom_dataAtomData or None, optional: Atomic data used by the legacy elemental-density calculation. Required when legacy is True.

Returns:

escape_energypandas.DataFrame: Escaping spectral luminosity, indexed by energy in keV with time-bin columns in seconds.
escape_energy_cosipandas.DataFrame: Escaping photon rate per energy bin, indexed by energy in keV with time-bin columns in seconds.
packets_df_escapedpandas.DataFrame: Final packet diagnostics, including status, frequencies, energies, and shell number.
gamma_ray_deposited_energypandas.DataFrame: Gamma-ray energy deposited in each shell and time bin, in ergs.
total_deposited_energypandas.DataFrame: Gamma-ray plus positron energy deposition rate in each shell and time bin, in ergs per second.
positron_energy_dfpandas.DataFrame: Positron energy deposited in each shell and time bin, in ergs.