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nircam_calib's Introduction

NIRCam Calibration (nircam_calib)

This is a collection of NIRCam software for:

  • creating reference files
  • pipeline testing
  • commissioning
  • general tools
  • training notebooks

Contributing

If you want to suggest changes for anything in these repos, do the following:

  1. Clone the repo:
    • using SSH (git clone [email protected]:spacetelescope/nircam_calib.git)
    • using HTTPS (git clone https://github.com/spacetelescope/nircam_calib.git)
  2. Change to nircam_calib directory (cd nircam_calib)
  3. Install (python setup.py install)
  4. Create your own branch (git checkout -b my-new-reffile-feature)
  5. Commit your changes (git commit -am 'Added some feature to reference file scripts')
  6. Push to the branch (git push origin my-new-reffile-feature)
  7. Create a new Pull Request

See the following for a standard package template for repositories under the "spacetelescope" organization:

Issues

If you find any problems with the scripts in these folders, do the following:

  1. Click on the "Issues" tab at the top of the page
  2. Click the green "New Issue" button on the right side of the Issues screen
  3. Fill out the title and description of the issue (don't assign anyone or tag it with a label if you aren't sure)
  4. Click "Submit New Issue"

Collaborators on the repository will be notified of the new issue ticket and can respond or provide comments inside the ticket. Submitting issues through GitHub helps track any problems and work being done on the repository.

nircam_calib's People

Contributors

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nircam_calib's Issues

Gain calculator plug-in

This module should be able to plug in to mkrefs and will be used to create gain reference files.

Inputs for Persistence Reference Files

Need to get persistence maps from Jarron to go into the initial delivery of persistence map reference files.

Also need to turn hard saturation maps into persistence saturation reference files.

Dark Current

Need to determine if darks should be mean of observed darks, or zeros with only hot pixels populated.

Appropriate reference files must be created for full frames and subarrays.

Reading the source catalog

I was looking at the jwst_pipeline_walkthrough.ipynb notebook (which is great!) and in section 11, you read in the source catalog using astropy.io.ascii.read:

# below is an example source catalog product
from astropy.io import ascii
catalog = ascii.read("mosaic_A1-A4_obs1-8_cat.ecsv")

I just wanted to point out that the preferred way to read in an ECSV file is to use astropy.table.Table (or astropy.table.QTable). That will preserve the metadata stored in the table, e.g. units on column values (e.g. flux) and you will also get back any serialized astropy objects stored in the table (e.g. Time objects or SkyCoord objects (instead of separate columns for RA and Dec):

from astropy.tables import Table
catalog = Table.read('mosaic_A1-A4_obs1-8_cat.ecsv')

CC: @hcferguson

Update pipeline notebook with new pipeline names

For @aliciacanipe
e.g. sloper pipeline is now calwebb_detector1.

Also would be helpful to show how to override individual step values when calling a pipeline. Here's an example I put together the other day after talking with Howard and James.

from jwst.pipeline import calwebb_detector1
m = calwebb_detector1.Detector1Pipeline(config_file='calwebb_detector1.cfg')
m.saturation.override_saturation = 'mysatfile.fits'
m.superbias.override_superbias = 'mysuperbias.fits'
m.refpix.odd_even_rows = False
m.group_scale.skip = True
m.ipc.skip = True
m.dark_current.skip = True
m.persistence.skip = True
m.output_file = outfile
m.run('myrawfile_uncal.fits')

Pipeline calls: cfg files and call vs run

James Davies tells me that the only way to use config files (or more accurately, the only way that the pipeline pays attention to config files) is when you call the pipeline from the command line (strun) or when you use the call method. The run method just looks for default values and values set by the user, but ignores cfg files.

I've only looked at the level 3 pipeline notebook, but in there the config files are used with run, which we saw at a recent hack day didn't work well.

We should check the existing notebooks and make updates anywhere they are needed.

Update the help

Tyler Desjardins mentions that we should consider moving emails from help[at]stsci.edu to point to the web portal where possible and appropriate. For HST (or any non-JWST), it is https://hsthelp.stsci.edu . For JWST, it is https://jwsthelp.stsci.edu . Please update info in setup.py, setup.cfg, documentation, etc as appropriate.

Please close this issue if it is irrelevant to your repository. This is an automated issue. If this is opened in error, please let pllim know!

xref spacetelescope/hstcal#317

Data Validation Tool Enhancements

Some future enhancements for the GSEG Data Validation Notebook:

  • Verify header APERNAME based on APT Subarray (currently APERNAME is only checked to be consistent with DETECTOR, but not with the APT file, so this has to be checked manually)
  • Modify adjust_exptype() to work with more than EXP_TYPE=NRC_IMAGE and NRC_GRISM (e.g. NRC_TSIMAGE, NRC_TSGRISM)
  • Include checks for dark.fits file for files with EXP_TYPE=NRC_DARK, rather than assuming all uncal files have corresponding rate files
  • Fix EFFEXPTM estimate calculation, which is giving wrong answers for some files
  • Remove assumption that all detectors will be used for a given observation (currently missing files have to be checked for manually because of this)
  • Remove dependence on MAST Portal (astroquery, read access to MAST directories)?
  • Clean up unnecessary messages in output (i.e. only show warnings and mismatches), and save output to a text file
  • Check TFRAME values to make sure they are computed for the correct number of outputs (see JIRA ticket JWSTDMS-19)
  • enhancement (nice to have): check for more than just *rate.fits files (e.g., exposures with NINTS>1 will also have a *rateints.fits file, data products are here: https://jwst-pipeline.readthedocs.io/en/latest/jwst/data_products/product_types.html)
  • Check that any associations (ASNTABLE) exist and that its members are assigned the correct designation

No setup scripts

I don't see any way to install this package. This issue is just a friendly reminder that it needs to be done.

SSB_proc

Create the code that will take a list of file base names and required SSB calibration pipeline steps, and generate strun commands to convert the input base files to the proper data reduction state.

Need to update for python 3

Some of the older functions and modules need to be updated for python 3. mkref.py is one that I came across. I'm sure there are others.

Version of gain reference files to use

Need to decide which gain values we are going to use, U of A's or ours? The values are different by ~20%.

The chosen gain values need to be packaged up as reference files and delivered. Also, there are several dependencies with their own deadlines.

Gain values will impact full well values in electrons, which are present in JDox.
Gain values are used in the production of the flux calibration reference files.
The flux calibration reference files include filter-dependent zeropoints, which are present on JDox.

Deadline for all reference files is the end of January.
JDox deadline is ?

Flat field reference file updates

Karl still has not delivered the F070W flat (and maybe also the F090W) flat. He was having trouble removing all of the illumination pattern effects and wanted to keep working on it.

Also, a check of the other flat field reference files in CRDS show that the error and DQ extensions that Karl provided have been zeroed out. Most likely this happened when converting Karl's files to SSB format and the extensions were never checked. These files will have to be remade at some point.

Deadline for everything is 31 January 2018.

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