StepFlat

class sofia_redux.instruments.hawc.steps.stepflat.StepFlat

Bases: StepLoadAux

Correct for flat response for chop/nod data.

This pipeline step corrects the flux data for instrumental response, using a flat file made from a reference sky calibration and an internal calibrator file. Flats are made by sofia_redux.instruments.hawc.steps.StepMkflat. Input for this step is the demodulated data, produced by sofia_redux.instruments.hawc.steps.StepDemodulate and sofia_redux.instruments.hawc.steps.StepDmdCut.

In the output from this step, the R array and T array columns of the input demodulated data and their corresponding variances are converted to images and their columns removed from the table. In addition, separate bad pixel masks for the R and T array are copied from the flat file and appended to the output object. Pixels with a value of zero in the mask are good, any other value is bad.

Notes

For the data \(d\) and flat image \(f\) with variances \(\sigma^2\) and \(\sigma_f^2\) respectively, the correction is applied as

\[d' = f d\]

and propagated to the variance as

\[\sigma'^2 = f^2 \sigma^2 + d^2 \sigma_f^2 .\]

Methods Summary

checksize(datashape, flatshape)	Validate data and flux shapes.
flatfield(imgin, varin, flat, flatvar)	Flat field an array.
loadauxname([auxpar, data, multi])	Search for files matching auxfile.
loadflat()	Load the flat images.
run()	Run the data reduction algorithm.
setup()	Set parameters and metadata for the pipeline step.

Methods Documentation

checksize(datashape, flatshape)

Validate data and flux shapes.

Parameters:

datashapetuple

Data shape.

flatshapetuple

Flat image shape.

Raises:

ValueError

If the flat does not match the data.

flatfield(imgin, varin, flat, flatvar)

Flat field an array.

The flux image is multiplied by the flat data.

Parameters:

imginarray-like

Input flux array.

varinarray-like

Input variance array.

flatarray-like

Flat array matching imgin.

flatvararray-like

Variance array matching imgin.

Returns:

imgoutarray-like

Flat fielded image.

varoutarray-like

Updated variance.

loadauxname(auxpar='', data=None, multi=False)

Search for files matching auxfile.

Overrides the default function in order to make flat path relative to data output directory if necessary.

Parameters:

auxparstr, optional

A name for the aux file parameter to use. This allows loadauxfiles to be used multiple times in a given pipe step (for example for darks and flats). Default value is self.auxpar which is set by loadauxsetup().

dataDataFits or DataText, optional

A data object to match the auxiliary file to. If no data is specified, self.datain is used (for Multi Input steps self.datain[0]).

multibool, optional

If set, a list of file names is returned instead of a single file name.

Returns:

str or list of str

The matching auxiliary file(s).

loadflat()

Load the flat images.

The data is stored in self.flats.

run()

Run the data reduction algorithm.

Because this step is single-in, single-out (SISO), self.datain must be a DataFits object. The output is also a DataFits object, stored in self.dataout.

The process is:

1. Read data from the flat file.

2. Apply the flat to the R and T arrays.

3. Propagate the variance on the flux.

4. Store the data as images in the output object.

setup()

Set parameters and metadata for the pipeline step.

Output files have PRODTYPE = ‘flat’, and are named with the step abbreviation ‘FLA’.

Parameters defined for this step are:

labmodebool

If True, flat correction will be skipped.

flatfilestr

File name glob to match flat files. Default is ‘flats/OFT.fits’ to match flat files in a folder named flats, in the same directory as the input file.

flatfitkeyslist of str

Keys that need to match between flat and data file.

bkupflatstr

File name glob to match a set of backup files. These are used only if the files specified by flatfile are not found.