Superbias Subtraction

The superbias subtraction step removes the fixed detector bias from a science data set by subtracting a superbias reference image. This superbias is subtracted from every group in every integration of the science ramp data. Any NaN’s present in the superbias image are set to zero before being subtracted from the science data. The superbias correction should apply to subarray exposures. See Kevin Volk’s presentation at the 5/31/2016 JWST Cal WG meeting.

For more details on this step refer to the JWST Science Pipelines Documentation at http://ssb.stsci.edu/doc/jwst_git/docs/superbias/html/

Test Requirements

This step requires verification only. The outcome of this step depends almost exclusively on the reference file used. Darks should have enough S/N for all possible ramps for full frame and subarrays. The Guiders should be excluded from this test. Guiders don’t have darks from CV3 because of a large chamber background (5 to 10 ADU/second vs. ~0.01 ADU/second dark current expected).

Requirement	Fulfilled by
Check the bias is correctly subtracted.	test_superbias_subtraction
Check that the PIXELDQ array of the science exposure is correctly combined with the DQ array.	test_pixeldq_propagation

Test Data

Todo

Determine test data including at least one subarray case.

Test Procedure

To run these tests the config.json should contain the "superbias" section for example:

{
    "superbias": [
        "superbias/jw82600004001_02101_00001_nrcb1_dqinitstep_saturationstep.fits"
    ]
}

Using the above config.json simply run:

test_pipeline --config config.json

Reference/API

caltest.test_caldetector1.test_superbias Module

Functions

extract_subarray(array, hdul)
fits_output(fits_input)
fits_superbias(fits_output)
normaltest(a[, axis, nan_policy])	Test whether a sample differs from a normal distribution.
sigma_clipped_stats(data[, mask, …])	Calculate sigma-clipped statistics on the provided data.
test_pixeldq_propagation(fits_input, …)
test_superbias_residuals(fits_output, fits_input)
test_superbias_step(fits_input)	Make sure the DQInitStep runs without error.
test_superbias_subtraction(fits_input, …)
translate_dq(ref_hdul)