soxs_recipe - IN PROGRESS

Input

Method

Output

QC Metrics

class soxs_mbias(log, settings=False, inputFrames=[], verbose=False, overwrite=False)

The soxs_mbias recipe is used to generate a master-bias frame from a set of input raw bias frames. The recipe is used only for the UV-VIS arm as NIR frames have bias (and dark current) removed by subtracting an off-frame of equal expsoure length.

Key Arguments

• log – logger

• settings – the settings dictionary

• inputFrames – input fits frames. Can be a directory, a set-of-files (SOF) file or a list of fits frame paths.

• verbose – verbose. True or False. Default False

• overwrite – overwrite the prodcut file if it already exists. Default False

Usage

from soxspipe.recipes import soxs_mbias
mbiasFrame = soxs_mbias(
    log=log,
    settings=settings,
    inputFrames=fileList
).produce_product()

Todo

• add a tutorial about soxs_mbias to documentation

verify_input_frames()

verify the input frame match those required by the soxs_mbias recipe

If the fits files conform to required input for the recipe everything will pass silently, otherwise an exception shall be raised.

produce_product()

generate a master bias frame

Return:

• productPath – the path to the master bias frame

qc_bias_structure(combined_bias_mean)

calculate the structure of the bias

Key Arguments:

• combined_bias_mean – the mbias frame

Return:

• structx – slope of BIAS in X direction

• structx – slope of BIAS in Y direction

Usage:

structx, structy = self.qc_bias_structure(combined_bias_mean)

qc_periodic_pattern_noise(frames)

calculate the periodic pattern noise based on the raw input bias frames

A 2D FFT is applied to each of the raw bias frames and the standard deviation and median absolute deviation calcualted for each result. The maximum std/mad is then added as the ppnmax QC in the master bias frame header.

Key Arguments:

• frames – the raw bias frames (imageFileCollection)

Return:

- ``ppnmax``

Usage:

self.qc_periodic_pattern_noise(frames=self.inputFrames)