mirage_darks

Create Linearized Dark files for MIRAGE using the Darks from the JWST/NIRCam Cryo-Vacuum Campaigns (ISIM/CV)

Author: Arsh Nadkarni (UArizona)

Source: Bryan Hilbert (STScI)

Process:

• Get the essential Darks from FENRIR: Copy the required darks from /surtrdata/External/ISIMCV3_unzipped. For example, if you want to get AX Darks, say cp -r /surtrdata/External/ISIMCV3_unzipped/NRCNRCAX*.fits AX/. This will copy the AX Darks from FENRIR to a directory AX on your local machine. Create a directory AX_Darks inside AX to copy only the .fits dark files.

• Copy .fits Darks to their sub-directories: Use the copy_darks_to_subdirectory.sh code to copy the .fits dark files in AX to a sub-directory AX_Darks which will host all the .fits Dark files. This bash file should be inside the A1 directory. Then, from the command-line: bash copy_darks_to_subdirectory.sh. You will see all required darks in the AX_Darks directory subsequently.

• Sort the Darks based on the ISIM/CVX performance: Manually sort the acquired dark files based on your ISIM/CVX needs.

• Convert from Raw to DMS Format: Use convert_to_DMS.py, which calls sci2ssb.py and nircam2ssb.py First, update the glob call in convert_to_DMS.py so that it is pointing to your files (AX_Darks\*.fits). Then from the command-line:python convert_to_DMS.py. You will see all required darks in the AX_DMS_Darks directory subsequently.

• Tweak the DMS Darks to be in the Level1B format (this will save disk space by removing unnecessary extensions): You can either:

  • A) make convert_to_Level1b.py executable, and then use a separate call for each uncal file: > ./convert_to_Level1b.py your_dark_file_uncal.fits
  • B) update the glob call in run_conversion.py to point to your DMS uncal files (AX_DMS_Darks\*uncal.fits), and run: > python run_conversion.py
    You will see all required darks in the AX_DMS_Darks directory subsequently. Now, move the Level-1B Darks to another directory in AX, say AX_Level1b_Darks using mv *level1b_uncal.fits /AX_Level1b_Darks.

• Run MIRAGE's dark_prep step on the DMS Darks in order to Linearize: Create a MIRAGE yaml file for each Level1b dark - easiest is probably to use the attached yaml file as a template and update the file name in the 'dark:' field, as well as the value in 'array_name:', which should be 'NRCXX_FULL' where XX is the detector number (e.g. 'A1'). For the A5 and B5 detectors, you may also need to update the filter value to be something in the LW channel. I don't think dark_prep looks at this info, but if you get an error, that's one thing to do to solve it. Update the glob call in create_linearized_darks.py to use your yaml files. E.g yaml_files = sorted(glob('/home/anadkarni/mirage_darks/A1/A1_yaml_files/*.yaml')). Then call create_linearized_darks.py from the command-line: python create_linearized_darks.py. You will see all required Linearized Darks in the mirage_darks directory subsequently. Now, move the Linearized Darks to another directory in AX, say AX_Linearized_Darks using mv *_uncal.fits AX/AX_Linearized_Darks.

• Finally, put the resulting Linearized Darks into the proper sub-directory in your MIRAGE_DATA location: E.g. /home/anadkarni/JWST_Programs/mirage_reference_files/mirage_data/nircam/darks/linearized/<detector_name>/

The list of Darks to be converted from ISIM/CV3 can be found in: darks_notes_CV3.txt