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NIRLIN

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NIRLIN - NIR LINearization

Introduction:

NIRLIN should be used to linearize all science data. This version uses three coefficients to correct for non-linearity in the NIRI detector: an exposure time correction, a counts squared term, and a counts cubed term. These coefficients are dependent on the read mode and detector ROI. We have currently derived coefficients for the following configurations:

Read Mode ROI Well Depth dt c2 c3
Low RN 1024 shallow 1.266 7.39e-06 1.94e-10
Medium RN 1024 shallow 0.094 3.43e-06 4.81e-10
Medium RN 256 shallow 0.0103 6.82e-06 2.13e-10
High RN 1024 shallow 0.0097 3.04e-06 4.64e-10
High RN 1024 deep 0.0077 3.58e-06 1.82e-10

Note: We have not yet quantified the effect of linearizing flat fields.

USAGE:

NAME: nirlin.py - NIR linearization

SYNOPSIS: nirlin.py [options] infile

DESCRIPTION: Run on raw or nprepared Gemini NIRI data. This script calculates and applies a per-pixel linearity correction based on the counts in the pixel, the exposure time, the read mode, the bias level, and the ROI. Pixels over the maximum correctable value are set to BADVAL unless given the force flag. Note that you may use the glob expansion in the infile; however, any pattern matching characters (*,?) must either be quoted or excaped with a backslash.

OPTIONS:

-b : value to assign to uncorrectable pixels [0]

-f : force correction on all pixels

-0 <file> : write output to <file> [l<inputfile>]. If no .fits is included this is assumed to be a directory.

-v : verbose debugging output

VERSION: 2019 Aug 29

REQUIREMENTS:

Download the latest version of nirlin.py here.

Download the associated README file here.

DETAILS:


Any questions, comments or suggestions for improvements are welcome, and should be sent to Andrew Stephens (astephens at gemini dot edu).


Gemini Observatory Participants