exosim.tasks.astrosignal.applyAstronomicalSignal#

Classes#

ApplyAstronomicalSignal

This task applies the astronomical signal to the sub-exposures.

Functions#

populate(j0, psf, psf_index, shape_spectral, model, ...)

Creates the variation of the source signal on the focal plane.

Module Contents#

class ApplyAstronomicalSignal[source]#

Bases: exosim.tasks.task.Task

This task applies the astronomical signal to the sub-exposures. To do so, it first convolve the astronomical signal with the instrument line shape (ILS) on the focal plane. This is done by populating the focal plane with the ILS and then weighting the contributions to each pixel. The resulting model is then convolved with the intrapixel response function (IPRF) and downsampled to the sub-exposure time resolution. Then is finally multiplied to the sub-exposure signal.

Returns:

sub-exposure cached signal class

Return type:

Counts

execute()[source]#

Class execution. It runs on call and executes all the task actions returning the outputs. It requires the input with correct keywords

select_chunk_range(chunk, start_t, end_t)[source]#

Selects and adjusts the range of the chunk to be processed based on the given start and end times.

Parameters:

  • chunk (slice) – The original slice object representing the chunk to be processed.

  • start_t (int) – The start time to consider for processing.

  • end_t (int) – The end time to consider for processing.

Returns:

The adjusted slice object representing the new chunk to be processed. Returns None if the chunk is entirely outside the start_t and end_t range.

Return type:

slice

Examples

>>> select_chunk_range(slice(5, 15, 1), 7, 13)
slice(7, 13, 1)
>>> select_chunk_range(slice(5, 15, 1), 16, 20)
None
populate(j0, psf, psf_index, shape_spectral, model, weights)[source]#

Creates the variation of the source signal on the focal plane.

Parameters:
Return type:

tuple[numpy.ndarray, numpy.ndarray]