exosim.tools.darkCurrentMap

Classes

DarkCurrentMap

Produces the channel dark current map

Module Contents

class DarkCurrentMap

Bases: exosim.tasks.task.Task

Produces the channel dark current map

Returns:

channel dark current map

Return type:

Signal

Raises:

TypeError: – if the output is not a Signal class

Notes

This is a default class with standardized 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(parameters, time)

Parameters:

• parameters (dict) – dictionary contained the sources parameters. This is usually parsed from LoadOptions

• time (Quantity) – time grid.

Returns:

dark current efficiency map

Return type:

Signal

compute_dc_mean(detector)

Computes the mean of the dark current (dc_mean) from the log-normal distributon.

The probability density function for the log-normal distributon is:

\[pdf(x) =\]

rac{1}{sigma x sqrt{2pi}}

expleft(-

rac{(log(x) - mu)^2}{2 sigma^2} ight)

The mean of the pdf can be computed as:

\[mean = \exp(\mu +\]

rac{s^2}{2})

where s is the pdf standard deviation, computed by taking the sqrt of the variance, defined as:

\[var = \left(\exp(\sigma^2) - 1\]

ight) exp(2 mu + sigma^2)

detector: dict

Dictionary for the detector. This is usually parsed from LoadOptions

None

Updates the detector dictionary with the value of dc_mean