==============
Explain the DQ
==============
.. image:: https://img.shields.io/pypi/v/explaintheDQ.svg
:target: https://pypi.python.org/pypi/explaintheDQ
.. image:: https://img.shields.io/travis/eas342/explaintheDQ.svg
:target: https://travis-ci.com/eas342/explaintheDQ
.. image:: https://readthedocs.org/projects/explaintheDQ/badge/?version=latest
:target: https://explaintheDQ.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
This is a simple package that helps explain a DQ value for the DQ extension of JWST data.
* Free software: MIT license
* Documentation: https://explaintheDQ.readthedocs.io.
The Problem
-----------
.. image:: images/example_SCI_ext.png
.. image:: images/example_DQ_ext.png
"SCI": Science extension of a ::code::`_rate.fits` image.
"DQ": Data Quality (DQ) extension of a ::code:`_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.
.. code-block:: python
import explaintheDQ
explaintheDQ.DQtab(1049603)
.. code-block:: console
Name Flag Bit Description
---------------- ----- --- -------------------------------------------
DO_NOT_USE True 0 Bad pixel. Do not use.
SATURATED False 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 False 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 True 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 False 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 NaN in the rate image's SCI extension has the following characteristics.
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
=======
History
=======
0.1.0 (2023-11-02)
------------------
* First release on PyPI.
Raw data
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"description": "==============\nExplain the DQ\n==============\n\n\n.. image:: https://img.shields.io/pypi/v/explaintheDQ.svg\n :target: https://pypi.python.org/pypi/explaintheDQ\n\n.. image:: https://img.shields.io/travis/eas342/explaintheDQ.svg\n :target: https://travis-ci.com/eas342/explaintheDQ\n\n.. image:: https://readthedocs.org/projects/explaintheDQ/badge/?version=latest\n :target: https://explaintheDQ.readthedocs.io/en/latest/?version=latest\n :alt: Documentation Status\n\n\n\nThis is a simple package that helps explain a DQ value for the DQ extension of JWST data.\n\n\n* Free software: MIT license\n* Documentation: https://explaintheDQ.readthedocs.io.\n\n\nThe Problem\n-----------\n\n.. image:: images/example_SCI_ext.png\n\n.. image:: images/example_DQ_ext.png\n \n\n\"SCI\": Science extension of a ::code::`_rate.fits` image.\n\"DQ\": Data Quality (DQ) extension of a ::code:`_rate.fits` image.\n\nExample images\n\nThis 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:\nhttps://jwst-pipeline.readthedocs.io/en/latest/jwst/references_general/references_general.html#data-quality-flags\n\nbut what does 1049603 mean?\n\n\nWhat this Package Does\n-----------------------\nThis is a bear-bones package to break down the DQ number.\n\n.. code-block:: python\n\n import explaintheDQ\n explaintheDQ.DQtab(1049603)\n\n.. code-block:: console\n \n Name Flag Bit Description \n ---------------- ----- --- -------------------------------------------\n DO_NOT_USE True 0 Bad pixel. Do not use.\n SATURATED False 1 Pixel saturated during exposure\n JUMP_DET False 2 Jump detected during exposure\n DROPOUT False 3 Data lost in transmission\n OUTLIER False 4 Flagged by outlier detection\n PERSISTENCE False 5 High persistence\n AD_FLOOR False 6 Below A/D floor\n CHARGELOSS False 7 Charge Migration\n UNRELIABLE_ERROR False 8 Uncertainty exceeds quoted error\n NON_SCIENCE False 9 Pixel not on science portion of detector\n DEAD False 10 Dead pixel\n HOT False 11 Hot pixel\n WARM False 12 Warm pixel\n LOW_QE False 13 Low quantum efficiency\n RC False 14 RC pixel\n TELEGRAPH True 15 Telegraph pixel\n NONLINEAR False 16 Pixel highly nonlinear\n BAD_REF_PIXEL False 17 Reference pixel cannot be used\n NO_FLAT_FIELD False 18 Flat field cannot be measured\n NO_GAIN_VALUE False 19 Gain cannot be measured\n NO_LIN_CORR False 20 Linearity correction not available\n NO_SAT_CHECK False 21 Saturation check not available\n UNRELIABLE_BIAS False 22 Bias variance large\n UNRELIABLE_DARK False 23 Dark variance large\n UNRELIABLE_SLOPE False 24 Slope variance large (i.e., noisy pixel)\n UNRELIABLE_FLAT False 25 Flat variance large\n OPEN False 26 Open pixel (counts move to adjacent pixels)\n ADJ_OPEN False 27 Adjacent to open pixel\n UNRELIABLE_RESET False 28 Sensitive to reset anomaly\n MSA_FAILED_OPEN False 29 Pixel sees light from failed-open shutter\n OTHER_BAD_PIXEL False 30 A catch-all flag\n REFERENCE_PIXEL False 31 Pixel is a reference pixel\n\n\n\nSo the pixel that is NaN in the rate image's SCI extension has the following characteristics.\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.0 (2023-11-02)\n------------------\n\n* First release on PyPI.\n",
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