Explain the DQ

This is a simple package that helps explain a DQ value for the DQ extension of JWST data.

• Free software: MIT license
• Source code: https://github.com/eas342/explaintheDQ
• Documentation: https://explaintheDQ.readthedocs.io.

The Problem

"SCI": Science extension of a _rate.fits image. "DQ": Data Quality (DQ) extension of a _rate.fits image.

Example images

This image has a some strange blocks of pixels in a 9x9 grid. If you open the DQ extension of the data, the DQ values in the pixels are marked at 3 and also as 1049603. But what does that mean? The bits are explained here: https://jwst-pipeline.readthedocs.io/en/latest/jwst/references_general/references_general.html#data-quality-flags

but what does 1049603 mean?

What this Package Does

This is a bear-bones package to break down the DQ number.

import explaintheDQ
explaintheDQ.DQtab(1049603)

      DO_NOT_USE  True   0                      Bad pixel. Do not use.
       SATURATED  True   1             Pixel saturated during exposure
        JUMP_DET False   2               Jump detected during exposure
         DROPOUT False   3                   Data lost in transmission
         OUTLIER False   4                Flagged by outlier detection
     PERSISTENCE False   5                            High persistence
        AD_FLOOR False   6                             Below A/D floor
      CHARGELOSS False   7                            Charge Migration
UNRELIABLE_ERROR False   8            Uncertainty exceeds quoted error
     NON_SCIENCE False   9    Pixel not on science portion of detector
            DEAD  True  10                                  Dead pixel
             HOT False  11                                   Hot pixel
            WARM False  12                                  Warm pixel
          LOW_QE False  13                      Low quantum efficiency
              RC False  14                                    RC pixel
       TELEGRAPH False  15                             Telegraph pixel
       NONLINEAR False  16                      Pixel highly nonlinear
   BAD_REF_PIXEL False  17              Reference pixel cannot be used
   NO_FLAT_FIELD False  18               Flat field cannot be measured
   NO_GAIN_VALUE False  19                     Gain cannot be measured
     NO_LIN_CORR  True  20          Linearity correction not available
    NO_SAT_CHECK False  21              Saturation check not available
 UNRELIABLE_BIAS False  22                         Bias variance large
 UNRELIABLE_DARK False  23                         Dark variance large
UNRELIABLE_SLOPE False  24    Slope variance large (i.e., noisy pixel)
 UNRELIABLE_FLAT False  25                         Flat variance large
            OPEN False  26 Open pixel (counts move to adjacent pixels)
        ADJ_OPEN False  27                      Adjacent to open pixel
UNRELIABLE_RESET False  28                  Sensitive to reset anomaly
 MSA_FAILED_OPEN False  29   Pixel sees light from failed-open shutter
 OTHER_BAD_PIXEL False  30                            A catch-all flag
 REFERENCE_PIXEL False  31                  Pixel is a reference pixel

So the pixel that is a NaN in the middle is marked as not to be used, saturated, dead and having no linearity correction available.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.