StepSkydip

class sofia_redux.instruments.hawc.steps.stepskydip.StepSkydip[source]

Bases: StepMOParent

Produce diagnostic plots from skydip data.

This step uses demodulated data taken in sky dip mode to produce a plot of averaged raw data vs. elevation. If a skydip fit was produced by the scan map algorithm, it is also stored as a plot.

This step should be run as the final step in the skydip recipe. Pipeline steps for this mode should be run in this order:

  • sofia_redux.instruments.hawc.steps.StepCheckhead

  • sofia_redux.instruments.hawc.steps.StepScanMap

  • sofia_redux.instruments.hawc.steps.StepFluxjump

  • sofia_redux.instruments.hawc.steps.StepPrepare

  • sofia_redux.instruments.hawc.steps.StepDemodulate

  • sofia_redux.instruments.hawc.steps.StepDmdPlot

  • sofia_redux.instruments.hawc.steps.StepDmdCut

  • sofia_redux.instruments.hawc.steps.StepSkydip

This step produces two PNG images, saved to the same directory and base name as the input data. The output data is otherwise identical to the input data.

Methods Summary

run()

Run the data reduction algorithm.

setup()

Set parameters and metadata for the pipeline step.

Methods Documentation

run()[source]

Run the data reduction algorithm.

Because this step is multi-in, multi-out (MIMO), self.datain must be a list of DataFits objects. The output is also a list of DataFits objects, stored in self.dataout.

The process is:

  1. Plot elevation vs. averaged raw data.

  2. Read in a scan map skydip fit (‘tmp*.dat’ in the same directory as the input data).

  3. Plot scan map fit data.

setup()[source]

Set parameters and metadata for the pipeline step.

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

Parameters defined for this step are:

indatastr

Column name for the data to be displayed (vs. elevation). Either ‘R array AVG’ or ‘T array AVG’ is recommended.