NIRSpec Pipe Testing Tool (we affectionately call it NPTT)

Please note, the source code contained in this repository is designed for use ONLY by STScI staff, and on the institute's servers. If you do not have active STScI credentials, you will not be able to run NPTT.

Latest NPTT working version is tag 3.0.2

What is a Pytest

Simply put, a Pytest is a pass or fail Python test. For instance, with the WCS step, we have Python scripts (which we are calling auxiliary code within the frame of the testing tool) that compares the pipeline result with a verified "truth" file (or benchmark), and calculates the difference. The Pytest is to assert if that difference is less than or equal to an X threshold value. Hence, a failed test means that the condition was not met. If an error should occur with the code, the test will be flagged as an error, and it will specify the routine where it occured.

Possible Outcomes of the Pytest

• Passed = the assertion was true, so the test condition was met.
• Failed = the assertion was false, the test condition was NOT met (there will be an AssertionError on-screen and in the html file, with a clear NPTT customized message of what happened).
• Skipped = the test was skipped (there will be a message on the html report and on-screen explaining why the test was skipped).
• Error = this is a coding error, a bug in either the pipeline or the NPTT code. Please contact the NIRSpec pipeline testing lead to determine how to proceed.

Useful links

• NPTT Documentation: https://innerspace.stsci.edu/pages/viewpage.action?pageId=123011558

• Testing data sets: https://innerspace.stsci.edu/display/JWST/NIRSpec+Pipeline+Testing+Build+7.1+part+2

• SCSB GitHub repository: https://github.com/spacetelescope/jwst

• Pipeline Documentation: http://jwst-pipeline.readthedocs.io

• Validation Notebooks https://github.com/spacetelescope/jwst_validation_notebooks

Quick Start Guide

IMPORTANT:

1. This guide assumes that Conda has been installed. If you have not yet done so, please follow the instructions at: https://astroconda.readthedocs.io/en/latest/ Please use the latest python version (check for the pipeline's site for the version minimum supported)

2. Keyword check for running the Stage 1 pipeline, i.e. calwebb_detector1. When you follow this guide from the beginning with any data set, the script that checks the keywords is automatically run and your data is ready for ingestion in the detector1 pipeline. Nonetheless, if you need/want to check keywords, run the following from the command line:

nptt_hdr_keywd_check file_rate.fits -m=bots -u

where the -u flag is to update the file rather than creating a new one with the updated header, and the -m flag sets the mode used, which can be any of these values: FS, MOS, IFU, BOTS, dark, image, confirm, taconfirm, wata, msata, focus, mimf.

3. Keyword check for running the stage2 pipeline, i.e. spec2 or image2. If, however, you already have a _rate.fits file, i.e. the final output of cal_detector1, and you need/want to check keywords, run the following from the command line:

nptt_level2b_hdr_keywd_check file_rate.fits MODE -u

where MODE is any value from FS, MOS, IFU, BOTS, dark, image, confirm, taconfirm, wata, msata, focus, and mimf, and the -u flag is to update the file rather than creating a new one with the updated header.

THREE THINGS BEFORE STARTING

I.- You may want to clean your PYTHONPATH so that you do not get mysterious failures. To do this simply type the following command in the terminal:

unset PYTHONPATH

You can do this every time you run the pytests, or when you start getting strange failures. Another option is not to define PYTHONPATH at all in the .profile (or equivalent: .bashrc, .bash_profile, .cshrc, .login, etc.) file.

II.- If you work outside the internal network, i.e. in the visitors network or at home, you also want to set the environment variables listed at: https://innerspace.stsci.edu/pages/viewpage.action?pageId=123011558 Set these environment variables via terminal or add them to your .profile (or equivalent) file. These changes will not affect your work while working with the internal network at ST.

III.- A brief description of what each pipeline step does, as well as a brief description of all the pytests implemented in the tool, the tests that are still in concept phase, and the tests that are still needed, can be found in the Confluence space for NPTT. You can get to it from the main Confluence page at NIRSpec/NIRSpec JWST Pipeline/NIRSpec Calibration Pipeline Testing Tool (NPTT), or by clicking in the following link: https://confluence.stsci.edu/pages/viewpage.action?pageId=123011558

QUICK START GUIDE

STEP 1. Create the conda environment for testing and get the configuration files.

1.a. Conda environment for this testing campaign:

• Please go to the pipeline's developers repository and follow the instructions for the latest GitHub tag in the "Installing From Github" section at https://github.com/spacetelescope/jwst. The most stable release candidate will be listed in the top line under the section "Software vs DMS build version map".

• If you are a developer, please follow the instructions in the section "Installing for Developers" at https://github.com/spacetelescope/jwst.

1.b. Configuration files corresponding to this build. Create a directory (e.g. build_XX_cfg_files) somewhere in your testing working space, and cd into it. Now type the following command within the conda environment you just created (see step 2).

collect_pipeline_cfgs .

STEP 2. Activate the conda environment for testing the pipeline, if you have not already done so, e.g. type:

source activate your_newly_created_environment

If the above command does not work try:

conda activate your_newly_created_environment

From here on, every step of this guide should happen within the conda testing environment.

To list and/or remove old environments:

• If you forget what did you name your new environment type:

conda env list

this will list all environments you have created.

• If you want to remove an old testing environment type:

conda env remove -n name_of_your_old_environment

STEP 3. Install NPTT. There are three ways to install the tool:

• Option A. For non-developers and without NPTT source code. For the latest stable tag type:

pip install git+https://github.com/spacetelescope/nirspec_pipe_testing_tool@tag

where "tag" at the end represents the latest stable version of NPTT (flight versions after commissioning are tags 2.0.0 and up). To see what is the latest tag please go at the top of this file and look for the section named "Latest NPTT working version".

For development versions and/or the most recent code, in the terminal type:

pip install git+https://github.com/spacetelescope/nirspec_pipe_testing_tool@master

This will install the latest version of NPTT and all necessary dependencies to run the tool. However, this will not install the pipeline, NPTT will assume you already have installed the pipeline version you need.

• Option B. For non-developers and with the NPTT source code. After you clone NPTT, go into the directory, then type:

pip install .

• Option C. For developers and with the NPTT source code. After you clone NPTT, go into the directory, then type the same command as with Option B with an additional -e flag at the end of the command.

NOTE: You can install the latest pipeline version, but this will replace any existing version of the pipeline. Hence, you most likely want to create a new conda environment, install NPTT, and then type the command:

pip install -e ".[pipeline]"

Should I clone or fork the repo? If you are considering becoming an NPTT code contributor please choose fork the repository, otherwise choose clone.

IF YOU WANT THE SOURCE CODE

Clone or fork the NPTT repository. If you are planing to contribute with code to NPTT, fork the repo, otherwise choose to clone it. To do this click at the top right of this page, in the dropdown button that says clone or download, then copy the ulr that appears there. Now, within the conda testing environment, go to or create the directory where you want the NPTT to "live" in. However, make sure that the configuration files directory is, at least, at the top level of the directory tree where the NPTT will live, e.g. the cfg_files directory and the nirspec_pipe_testing_tool directory can be at the same level, but the cfg_files directory cannot be inside the nirspec_pipe_testing_tool directory because the .cfg files will be picked up by Git, and will be recognized as changes to the repo. Remember you are in the GitHub repository page so go all the way to the top of the page, click on the green button and copy the ulr that appears there.

git clone the_ulr_you_copied

After this is done, you should see a full copy of the NPTT in your directory.

Updating NPTT

• If you are not a developer and do not have the source code, simply run again the command:

pip install git+https://github.com/spacetelescope/nirspec_pipe_testing_tool@version

where version is either master for the most recent code, or the latest stable tag (the latest tag is always uodated at the top of this file in the section called Latest NPTT working version is tag).

• If you are not a developer but have the source code, in the terminal go to where you placed the nirspec_pipe_testing_tool directory. Then, use the following commands to update the code:

git pull
pip install .

• If you are a developer and have already forked the repository, in the terminal go to where you placed the nirspec_pipe_testing_tool directory. Then, use the following commands to update the code:

git pull
pip install -e .

• Note that if you had changed anything or written script(s) in the tool's directory tree, git will not let you continue until you commit the changes or move the script(s) to another directory.

STEP 4. Prepare the data to run through the pipeline. To do this:

4.a. Copy the test data you will use from the NIRSpec vault directory. Go to the directory where you want to have the testing data, and from there type:

cp -r /nirspec_vault/the_data_you_want_to_copy .

Benchmark data to test NPTT

You can start with the FS benchmark data to make sure you are doing the right thing. To get the data go to the nirspec_vault and look for the directory

/pipe_testing_tool/NPTT_FS_benchmark_run

There you will find a FS raw file, a NPTT_config_NRS1.cfg file, and a directory called NRS1_results, which contains all the final fits products from calwebb_detector1 with it's corresponding log file, the all the intermediary fits products obtained from running calwebb_spec2, and all the plots created with the NPTT. You can use the NPTT_config_NRS1.cfg (just make sure you change the output directory) to make sure you obtain the same results from the NPTT run. Alternatively, you can create your NPTT_config_NRS1.cfg by running the described in step 5 of this guide.

4.b. In the directory where you copied the test data, you will need to run a script PER fits file you want to test. Do not worry, this will only be done once. This script will create a new subdirectory with the necessary input file to run the SCSB script that converts raw data into uncal type files. You can choose to either keep this subdirectory, or tell the script to remove it after the operation is done. In the terminal type:

nptt_prepare_data2run fits_file.fits MODE -rm

where the MODE is expected to be one of: FS, MOS, IFU, BOTS, dark, image, confirm, taconfirm, wata, msata, focus, mimf, or MOS_sim (use this last one only for MOS simulations, simulations for other modes should use the corresponding mode). This command will update the uncal keyword header without creating a new file, and will also keep the subdirectory. To remove it, simply add -rm at the end. To save the keyword changes in a new fits file (instead of updating), add the flag the -nf. The new uncal fits file is now ready for pipeline ingest.

This module can also be called from a script in the following way:

# import the tool
import nirspec_pipe_testing_tool as nptt

# set the variables
fits_file = 'blah.fits'
mode = 'FS'
rm_prep_data = True
only_update = True

# run the module
nptt.utils.prepare_data2run.prep_data2run(fits_file, mode, rm_prep_data, only_update)

4.c. Optional. Check the file header. If you want to see the header of any file, you can use the another script in the utils directory of the NPTT. If you just want to see on-screen the header, go where your fits file "lives" and type:

nptt_read_hdr fits_file.fits -s

This command will show the main header. To save the header to a text file add a -s at the end. If you want to see/save a different extension add at the end -e=1 for extension 1, and so on.

This module can also be called from a script in the following way:

# set the variables
fits_file = 'blah.fits'
save_txt = True
ext_number = 1

# run the module
nptt.utils.read_hdr.read_hdr(fits_file_name, save_txt, ext_number)

4.d. Now, the data is ready to be ran through cal_detector1. Please go ahead with the next step of this guide to do that.

STEP 5. Set the NPTT configuration file. This is the file that controls all the input that the tool needs. To create NPTT_config.cfg, run the following command:

nptt_mk_npttconfig_file output_directory input_file mode_used raw_data_root_file

where output_directory is the path where you want to save all the NPTT outputs and pipeline products, input_file is the basename of the count rate file (e.g. the final product of calwebb_detector1), mode_used is the instrument mode used (e.g. FS), and raw_data_root_file is the basename of the raw data file used to create the uncal input file for calwebb_detector1.

As an additional check, you can open the file and see if:

• All the paths point to the right places. The files can be located anywhere, but both, the pipeline and the tool, will run faster if the files are local on your computer.
• The input file for the NPTT is the final output file from calwebb_detector1.
• The adequate mode for the data to be tested is set correctly, choices are: FS, IFU, MOS, BOTS, dark, or MOS_sim.
• The variable change_filter_opaque should be set to False unless you want to change the FILTER keyword back to OPAQUE (still present for historic reasons).
• The variable raw_data_root_file should be the name of the raw file you downloaded from the NIRSpec vault; for ground observations it starts with NRS. If you are running simulations then you can look into the ESA_Int_products directory and see what is the name of the directory that corresponds to your data, copy that name and add .fits to the end, e.g. for my simulation file F170LP-G235M_MOS_observation-6-c0e0_001_DN_NRS1.fits go into the nirspec_vault directory and then go to /prelaunch_data/testing_sets/b7.1_pipeline_testing/test_data_suite/simulations/ESA_Int_products, then set raw_data_root_file = F170LP-G235M_MOS_observation-6-c0e0_001.fits
• The steps that you want to be ran or not are set to True or False.
• In the bottom part of the file, all the additional arguments for the NPTT are correct, e.g. threshold values, figure switches, and additional fits files.

STEP 6. Run the stage 1 pipeline, calwebb_detector1. The final output of this is the level 2 data required to run the NPTT. In a terminal, please make sure that the testing conda environment is active, and that you are in the directory where your NPTT_config.cfg lives. There are two ways to run the stage 1 pipeline:

1. Automatically done by adding a flag to the command of step 9. This command will run both the stage 1 pipeline and the spec2 and/or spec3. To do this you will need the name of the fits file created in step 4b. With this information, in the terminal type:

nptt_run name_of_the_html_report NPTT_config.cfg -d1=jwdata0010010_11010_0001_NRS1_uncal.fits

if you do this you can skip directly to step 10.

OR

2. You manually run the pipeline stages from the terminal. Just follow the instructions at: https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/main.html

The detector 1 pipeline is currently being tested through unit tests and regression tests that run automatically in SCSB. Hence, NPTT does not contain any tests for the detector 1 pipeline.

---

NOTE FOR SIMULATIONS:

If you are working with simulations you may need to convert the count rate map (file.crm) to an STScI pipeline-ingestible file (with all the keyword header modifications). In order to do this run the module crm2STpipeline: To run this type:

nptt_crm2STpipeline file.fits MODE -r -p=my_proposal -t=my_target -n=new_file -s=200a1

where MODE is FS, MOS, IFU, BOTS, or dark. The input file for this module generally has a .crm or .cts suffix. The output files of this script can be directly ingested into the cal_spec2 pipeline, no need to run cal_dedector1. The flag -r is used only for IFU data, when needing to add the reference pixels. The other three flags are to modify the keyword values to match IPS information: the flag -p is to modify the proposal title header keyword, the -t flag is to modify the target name header keyword, the -n flag is to create a new file with updated header, and the -s flag is to force the script to use this specific subarray (and to set other associated parameters automatically).

This module can also be called from a script in the following way:

# set the variables
ips_file = '/path_to_crm_file/crm.fits'
mode_used = 'MOS'  # One of FS, MOS, IFU, or BOTS
add_ref_pix = False  # Add the reference pixels for IFU - old simulations (<2020) needed this
proposal_title = 'some cool title'
target_name = 'some target name'
subarray = 'FULL-FRAME'  # name of the subarray to use
new_file = False  # create a new file with the modified/fixed header
msa_metafile = 'V962150_msa.fits'
output_dir = None  # path to place the output file - if None output will be in same dir as input

# create the pipeline-ready count rate file
stsci_pipe_ready_file = nptt.utils.crm2STpipeline.crm2pipe(ips_file, mode_used, 
                                                           add_ref_pix,
                                                           proposal_title, 
                                                           target_name, 
                                                           subarray=subarray, 
                                                           new_file=new_file, msa_metafile=msa_metafile,
                                                           output_dir=output_dir, 
                                                           verbose=False)

The conversion from simulations.erm to simulations.crm, can be done with the script called ESAsim_erm2crm.py in the utils directory of NPTT. However, this script does not run within NPTT because you need to have created/installed the NIRspec Instrument Performance Simulator (IPS) environment. If you need to convert a .erm file into a .crm, either contact the simulations lead and ask them to do this for you, or ask them to give you instructions on installing/creating the IPS environment so you can run the script yourself.

---

---

NOTE FOR MOS DATA:

If you are working with MOS data, you may need to create the shutter configuration file to be able to process the data through the cal_spec2 stage. To create the shutter configuration file you need the .msa.fits files from APT, or for simulations, you need the nod .csv files. Once you have those files you can use the module create_metafile for MOS data, simulations, or to fix an old shutter configuration file (to update from format of build 7.3).

Use this command to create a new shutter configuration file:

nptt_create_metafile /path_to_file/blah.msa.fits

To fix an old shutter configuration file use:

nptt_create_metafile /path_to_file/blah_metafile_msa.fits -f

To create new shutter configuration file for simulations and/or dithers:

nptt_create_metafile /path_to_file/blah.msa.fits -d=obs1.csv,obs2.csv,obs3.csv

Note that for the simulations, the nod files are in a list separated by commas without spaces.

In all cases the module create_metafile will output a file called blah_metafile_msa.fits.

This module can also be called from a script in the following way:

# to create a shutter configuration file for the pipeline
config_binary_file = 'CB10-GD-B.msa.fits'
fix_old_config_file = False
targ_file_list = 'obs1.csv, obs1.csv'   # list of dither files

# to fix an old shutter configuration file for the pipeline
config_binary_file = 'V9621500100101_metafile_msa.fits'
fix_old_config_file = False
targ_file_list = nod1.csv,nod2.csv,nod3.csv
shutters_in_slitlet = 3
operability_ref = None
flat_metafile = False  # set to true if this is a metafile for a flat
verbose = False

# run the module
nptt.calwebb_spec2_pytests.auxiliary_code.create_metafile.run_create_metafile(config_binary_file, 
                                    fix_old_config_file, targ_file_list, shutters_in_slitlet, 
                                    operability_ref=operability_ref, flat_metafile=flat_metafile, 
                                    verbose=verbose)

---

STEP 7. Fix the pointing keywords in the count rate file. This will only be possible if you have the APT file that corresponds to your testing data. Skip this step if you do not have he corresponding APT file for your data set. NPTT used default dummy values so the pipeline will not break, but the spec3 pipeline may get wrong results unless these dummy values are replaced.

If you do have the corresponding APT files for your data set, you will manually need to get the following information from the APT file: the target's RA, DEC, V2, and V3, as well as the aperture position angle. Sample values for these quantities are: ra_targ = 53.16199112, dec_targ = -27.79127312,
v2_targ = 393.86285, v3_targ = -424.00329, and aper_angle = 45.0.

Please note that there is a known bug in the extract_1d step, which can be minimized by setting the aperture angle to 45.0. But this fix only works for for NON-MOS data.

To fix the keywords use the following command from the terminal:

nptt_fix_pointing blah.fits 53.16199112 -27.79127312 393.86285 -424.00329 45.0

If the data is IFU add the flag -ifu at the end of the command. The output will be the updated file.

To create a new updated file add flag -nf to the above command.

This module can also be called from a script in the following way:

# set the variables
input_fits_file = 'blah.fits'
ra_targ = 53.16199112
dec_targ = -27.79127312
v2_targ = 393.86285
v3_targ = -424.00329
aper_angle = 45.0
ifu_used = True

# run the module
nptt.utils.fix_pointing.fix_header_pointing(input_file, ra_targ, dec_targ, v2_targ, v3_targ, apa, ifu=ifu_used)

STEP 8. Optional. Test to run NPTT. To ensure that everything is in order, and to see what pytests will be executed and in which order, in the terminal type go to the top level directory where NPTT lives, then type:

cd nirspec_pipe_testing_tool/calwebb_spec2_pytests
pytest --collect-only

STEP 9. Do the first NPTT run. Go back to the output directory. As an output of the testing tool you will see an html file, called report.html, and an intermediary product text file name map will appear. The output fits files of intermediary products will also be saved in the output directory. In the terminal type:

nptt_run name_of_the_html_report NPTT_config.cfg

The spec3 pipeline can also be run within NPTT. It will be automatically run if the variable associations in the NPTT_config.cfg file is set to True, otherwise NPTT will stop processing the file and exit gracefully. An .html report will be written independently for each pipeline.

This module can also be called from a script in the following way:

# set the variables
report_name = 'my_report'
config_file = 'NPTT_config_NRS2.cfg'
quiet = False   # this flag will show progress on-screen

# run the module
nptt.utils.run_nptt.run_nptt(report_name, config_file, quiet)

TO RUN A SINGLE PIPELINE STEP:

-> If you are running a single pipeline step (or only the corresponding pytest), NPTT will create a log file specifically named with the step you are studying. At the end of the run you will see 2 log files, one from the pipeline and one from NPTT. This will not overwrite the full pipeline run log files.

TO RUN A FEW PIPELINE STEPS:

-> To only run a few pipeline steps you need to: a) Make sure that the variable run_calwebb_spec2 in the NPTT_config.cfg file is set to False (if True, the pipeline will run in full and we have no control of individual steps). Another option is skip and this is to skip pipeline running and testing of the spec2 pipeline and go straight to spec3. Similarly, the variable run_calwebb_spec3 can also be set to True, False, or skip, to only run the spec2 pipeline. Each pipeline will produce its own report. b) Turn off (i.e. set to False) the steps you do not want to run in the NPTT_config.cfg file, which are located in the section run_pipe_steps of the file.

TO RUN A FEW PYTEST:

-> To run a few pytest you need to select which pytest to run (i.e. set to True) in the NPTT_config.cfg file, which are located in the section run_pytest of the file.

-> To only run pytest and skip running the pipeline entirely: a) Make sure that the variable run_calwebb_spec2 in the NPTT_config.cfg file is set to False. b) Set to False all the pipeline steps in the NPTT_config.cfg file. The steps are located in the section run_pipe_steps of the file. c) Set to True all pytest you want to run in the NPTT_config.cfg file. These are located in the section run_pytest of the file.

MULTIPROCESSING

We chose multiprocessing instead of multithreading because the multiprocess library uses separate memory space, multiple CPU cores, bypasses Global Interpretor Lock (GIL) limitations in CPython, child processes are killable, and is much easier to use. Threads, in turn, run in the same unique memory heap, so multiple threads can write to the same location in memory. This is why Python uses the GIL, to prevent conflicts between parallel threads of execution on multiple cores.

NPTT can run several data sets at the same time using the Python library multiprocessing. To use this mode you have to create a multiprocess_NPTT_config.cfg file with :

nptt_mk_multiprocessing_cfg

this command will create a .cfg file in the directory you are in. Open this file and modify it as you need.

NPTT expects to find a NPTT_config_NRS1.cfg file (and/or NRS2) to be present in each of the directories provided in the data_sets variable in the multiprocess_NPTT_config.cfg file.

The variable cal_det1 is for the calwebb detector1 pipeline. The variable can be set to one of three possibilities:

a) cal_det1=skip then the code will jump directly to run the spec2 and/or spec3 pipelines

b) cal_det1=all then the code will assume that the prepare_data2run script was run and there will be files with names that contain _uncal.fits. NPTT will expect to find one these files per NPTT_config_NRS1.cfg file in each of the directores given in the data_sets variable.

c) Give the specific names of the _uncal files to use for running the Stage 1 pipeline, e.g. cal_det1 = file1_uncal.fits,file2_uncal.fits,file3_uncal.fits. Note that this list of files is expected to correspond with the total number of NPTT_config_NRS1.cfg files in the directores given in the data_sets variable.

If the variable is cores2use in the multiprocess_PTT_config.cfg file is set to all, then the code will automatically use all available processors. If you wish to know how many processors your computer has type the following in python:

>>> import os
>>> print(os.cpu_count())

Once all of this is set, run the following command and enjoy:

nptt_run_with_multiprocess path_to_find_my_file/multiprocess_NPTT_config.cfg

STEP 10. Report your findings. Contact the testing lead to determine if you should create a report Jira ticket per step. If this is the case, you will need to link the ticket to any corresponding bug or problem found (each bug or issue should have its own Jira bug issue). In the ticket you can link to either the validation notebook or the corresponding web page of this repo, and remember to add the following information to the Jira report ticket:

• Version of the pipeline tested
• Description of the test performed
• Link to code used
• What data set was used
• Result

TO KEEP IN MIND

• A text file containing an intermediary product name map will be created in the pytests directory.
• If any of the central store directory calls do not respond (e.g. when looking at the flats), the pytest will be skipped even if the step is set to True in the config file. The failing message will say that the step was set to False (this is a known bug). To force the tests run, you will have to download the files the tool is calling, and change the corresponding paths in the configuration file.
• The output in the terminal can be a bit overwhelming if there was a failed test or an error, since it shows both, the pipeline messages and the PTT messages. In the html report is much clearer to understand what happened.
• As part of the testing campaign, it is important that you run the pipeline from the command line as well, and that you make sure that the outcome intermediary files are consistent with the ones ran with scripts, i.e. the NPTT. This sanity check is minor but important to verify. If you have the NPTT source code, you will find two very useful text files in the utils/data directory. The two text files are named terminal_commands_calwebb_detector1_steps.txt , terminal_commands_calwebb_spec2_steps.txt, terminal_commands_calwebb_spec3_steps.txt. These files contain all the commands you can use from the terminal to run the calwebb_detector1, calwebb_spec2 steps, and calwebb_spec3, respectively.
• Finally, remember that:

a. Whenever you need to read either the main or science headers of a file, you can always use the nptt_read_hdr module. See step 4.c for instructions on how to use this module.

b. If you need to change/add a keyword value to a specific extension of a file, you can use the nptt_change_keywd module from the terminal as:

nptt_change_keywd blah.fits TARGOOPP F 0

This module can also be called from a script in the following way:

# set the variables
fits_file = 'blah.fits'
keyword = 'TARGOOPP'
value = 'F'
ext_number = 0

# run the module
nptt.utils.change_keywd.chkeywd(fits_file, keyword, value, ext_number)

ADDING TESTING ROUTINES

Talk to the testing lead to determine if the test you have in mind should be a script or a validation Jupyter Notebook (link to the pipeline validation Notebooks at the top of this page).

To add additional testing routines you will need to have forked the NPTT repository. The tests have to be written in python 3.6 or greater.

Enjoy your pipeline testing!

ACKNOWLEDGEMENT

The conversion of this tool into a package could not have been possible without the help of J. Hunkeler.