bhilbert4 / dark_prep

Dark current preparation for NIRCam Data Simulator

This repository contains code that can be used to prepare an input dark current exposure for use in the NIRCam Data Simulator.

The input dark current exposure will be reorganized into the requested readout pattern (if possible). If the input is not a linearized exposure, then it will be run through the initial stages of the JWST calibration pipeline in order to linearize the data. This includes superbias subtraction and reference pixel subtraction, followed by the linearization step.

The signal associated with the superbias and reference pixels is saved along side the linearized dark ramp such that it can be added back in later, if the user requests a raw output ramp from the NIRCam Data Simulator.

Dependencies:

If the:

Inst: use_JWST_pipeline

input is set to true, then the JWST calibration pipeline is needed.

Output:

The linearized dark current and zeroth frame as saved to a fits file that uses the name from the Output:file entry in the input yaml file and ending with '_linearizedDark.fits'.

These are also available as self.linDark and self.zeroModel

To use:

python dark_prep.py myinputs.yaml

or:

from dark_prep.scripts import dark_prep dark = dark_prep.DarkPrep() dark.paramfile = 'myinputs.yaml' dark.run()