magrittetorch.model.sources.lineproducingspecies

Contains all species which produce lines.

class magrittetorch.model.sources.lineproducingspecies.LineProducingSpecies(params: Parameters, dataCollection: DataCollection, lineproducingspeciesindex: int)

compute_line_cooling(current_level_pops: Tensor, device: device, indices: Tensor | None = None) → Tensor

Computes the line cooling rate for each point, based on the given level populations

TODO: test whether memory error might occur for NLTE models with many levels; if so, consider adding memory management

Parameters:

• current_level_pops (torch.Tensor) – The current level populations. Has dimensions [parameters.npoints, linedata.nlev].

• device (torch.device) – The device on which to compute.

• indices (Optional[torch.Tensor], optional) – The indices of the points to consider. Defaults to None.

Returns:

The line cooling rate. Has dimensions [parameters.npoints] and units W/m^3

Return type:

torch.Tensor

line_opacities: InferredTensorPerNLTEIter

Line opacities for this species, for all positions and all line transitions; dtype = Types.FrequencyInfo, dims = [Parameters.npoints, Linedata.nrad], units = units.Hz*units.m**-1

linedata: Linedata

Line data for this species

linequadrature: LineQuadrature

Line quadrature for this species

population: InferredTensor

Level populations of this species; dtype = Types.LevelPopsInfo, dims = [Parameters.npoints, Linedata.nlev], units = units.m**-3

population_tot: InferredTensor

Number density of this species; dtype = Types.LevelPopsInfo, dims = [Parameters.npoints], units = units.m**-3

sorted_linefreqs: InferredTensor

Frequencies of the lines, sorted in ascending order; dtype = Types.FrequencyInfo, dims = [Linedata.nrad], units = units.Hz

sorted_linewidths: InferredTensorPerNLTEIter

Widths of the lines, sorted in according to the line frequencies; dtype = Types.FrequencyInfo, dims = [Parameters.npoints, Linedata.nrad], units = units.Hz