exosim.tasks.detector.addGainDrift

Classes

AddGainDrift

It adds a gain noise map to the array.

Module Contents

class AddGainDrift

Bases: exosim.tasks.task.Task

It adds a gain noise map to the array.

The gain Drift is modeled as a polynomial model for a spectral and time dependent modulation.

Notes

This is a default class with standardised inputs and outputs. The user can load this class and overwrite the “model” method to implement a custom Task to replace this.

execute()

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

model(subexposures, parameters, output=None)

Apply a gain drift model to the subexposures.

This method models the gain drift as a polynomial trend based on time and wavelength.

Parameters:

• subexposures (Signal) – Sub-exposures cached signal.

• parameters (dict) –

  A dictionary containing parameters for the noise model. Expected keys and sub-keys include: - ‘readout’: dict with ‘readout_frequency’ - ‘detector’: dict with keys ‘gain_w0’, ‘gain_f0’, ‘gain_coeff_order_t’,

  ’gain_coeff_t_min’, ‘gain_coeff_t_max’, ‘gain_coeff_order_w’, ‘gain_coeff_w_min’, and ‘gain_coeff_w_max’.

• integration_times (np.ndarray) – Sub-exposures integration times.

• output (Optional[Output]) – Output file to write information, defaults to None.

Returns:

This method does not return a value but updates the subexposures dataset with applied gain noise.

Return type:

None

Notes

The method computes Brownian noise based on the frequency parameters in ‘parameters’ and applies a polynomial trend to it. The trend’s coefficients are randomly generated within specified ranges. The resulting gain noise is then rebinned and applied to the subexposures.