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 product with either the corresponding ESA intermediary file or our verified "truth" (or benchmark) files, and calculates a 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 pipeline, the test will be flagged as an error.

• 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 PTT 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 PTT code. Please contact the testing campaign lead to determine how to proceed.

• PTT 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

NOTE: 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 (3.6 is the minimum supported)

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 PTT. You can get to it from the main page of NIRSpec/NIRSpec JWST Pipeline/NIRSpec Calibration Pipeline Testing Tool (PTT), 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 PTT. There are three ways to install the tool:

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

pip install git+https://github.com/spacetelescope/nirspec_pipe_testing_tool@1.1.0

where the numbers at the end represent the latest stable version of NPTT; for 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 PTT and all necessary dependencies to run the tool. However, this will not install the pipeline, PTT will assume you already have installed the pipeline version you need.

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

pip install .

• Option C. For developers and with the PTT source code. After you clone PTT, 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 PTT, and then type the command:

pip install -e ".[pipeline]"

Should I clone or fork the repo? If you are considering to become a PTT code contributor please choose fork the repository, otherwise choose clone.

IF YOU WANT THE SOURCE CODE

Clone or fork the PTT repository. If you are planing to contribute with code to PTT, 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 PTT to "live" in. However, make sure that the configuration files directory is, at least, at the top level of the directory tree where the PTT will live, e.g. the b713cfg_files directory and the nirspec_pipe_testing_tool directory can be at the same level, but the b713cfg_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 PTT in your directory.

Updating PTT

• 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 (see step 3).

• 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 PTT

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/PTT_FS_benchmark_run

There you will find a FS raw file, a PTT_config.cfg file, and a directory called results_491, which contains all the intermediary fits products obtained from running calwebb_detector1, the output text files from running the corresponding script (which include the cal_detector1_outputs_and_times_DETECTOR.txt and the added keywords to the _uncal file), the all the intermediary fits products obtained from running calwebb_spec2, and all the plots created with the PTT. You can use the PTT_config.cfg (changing the paths appropriately) in there to make sure you obtain the same results from the PTT run. Alternatively, you can create your PTT_config.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 SSB 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 -u

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), remove the -u. 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 PTT. 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 PTT configuration file. This is the file that controls all the input that the tool needs. To create PTT_config.cfg, run the following command:

nptt_mk_pttconfig_file output_directory input_file mode_used raw_data_root_file

where output_directory is the path where you want to save all the PTT 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 PTT 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.
• 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 PTT 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 PTT. In a terminal, please make sure that the testing conda environment is active, and that you are in the directory where your PTT_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_PTT name_of_the_html_report PTT_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 stages 1 and 2/3 pipelines. To do this type the following command:

nptt_run_cal_detector1 /path_where_the_uncal_file_lives/uncal_file.fits

This command runs the the calwebb detector 1 pipeline in a single run, and will create the log file caldetector1_pipeline_DETECTOR.log. This file will be used to determine the times that each step took to run.

To run calwebb detector 1 step-by-step, simply add -sbs flag at the end of the previous command. Note that running the pipeline in full for calwebb detector 1 takes about half the time as it does running it step-by-step, due to IO processing time.

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

# set the variables
fits_input_uncal_file = 'blah.fits'
step_by_step = False

# run the module
nptt.utils.run_cal_detector1.run_caldet1(fits_input_uncal_file, step_by_step)

If everything went well, you will see a text file called cal_detector1_outputs_and_times_DETECTOR.txt, which contains the steps ran, the name of the output fits file, and the time each step took to run. This text file, along with the intermediary products will be located in the path you set for the output_directory variable in the configuration file of the PTT.

NOTE ON calwebb_detector1 ERRORS:

• If you were not able to get the file to run though cal detector1 due to an error saying that the pipeline was not able to find a best reference for dark or superbias, it is possible this is due to the filter keyword in the main header set to OPAQUE.
• In this case, you can run the module change_filter_opaque2science by typing:

nptt_change_filter_opaque2science file.fits

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

# set the variables
fits_file = 'blah.fits'
force_filter_change = True

# run the module
is_filter_opaque, new_input_file = nptt.calwebb_spec2_pytests.auxiliary_code.change_filter_opaque2science.change_filter_opaque(fits_file, force_filter_change)

# change_filter2opaque -> boolean, True if the filter was changed
# new_input_file -> string, file with updated filter

If all went well and you have a final_output_caldet1_DETECTOR.fits file, where DETECTOR can be either NRS1 or NRS2. The calwebb detector 1 pipeline is currently being tested through unit tests and regression tests that run automatically when the developers test the entire pipeline. Hence, PTT does not contain any tests for the calwebb 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
input_fits_file = 'blah.fits'
mode = 'FS'
add_ref_pix = True
only_update = True
proposal_title = 'my_title'
target_name = 'my_target'
new_file = 'a_new_name'  # this is only used if only_update=False
subarray = "200a1"   # this will force the script to use this subarray

# create the pipeline-ready count rate file
stsci_pipe_ready_file = nptt.utils.crm2STpipeline.crm2pipe(input_fits_file, mode_used,
                                                           add_ref_pix, only_update, subarray)

# create the dictionary of special arguments
additional_args_dict = {'TITLE': proposal_title, 'TARGNAME': target_name, 'new_file': new_file}

# modify the keyword values to match IPS information
nptt.utils.level2b_hdr_keywd_dict_map2sim.match_IPS_keywords(stsci_pipe_ready_file, input_fits_file,
                                                             additional_args_dict=additional_args_dict)

The conversion from simulations.erm to simulations.crm, can be done with the script called ESAsim_erm2crm.py in the utils directory of PTT. However, this script does not run within PTT 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 = True
targ_file_list = 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)

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. PTT 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. 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 PTT. 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 PTT lives, then type:

cd nirspec_pipe_testing_tool/calwebb_spec2_pytests
pytest --collect-only

STEP 9. Do the first PTT 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_PTT name_of_the_html_report PTT_config.cfg

The spec3 pipeline can also be run within NPTT. It will be automatically run if the variable associations in the PTT_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 = 'PTT_config_NRS2.cfg'
quiet = False   # this flag will show progress on-screen

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

TO RUN A SINGLE PIPELINE STEP:

-> If you are running a single pipeline step (or only the corresponding pytest), PTT 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 PTT. 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 PTT_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 PTT_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 PTT_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 PTT_config.cfg file is set to False. b) Set to False all the pipeline steps in the PTT_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 PTT_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_PTT_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 PTT_config_NRS1.cfg file (and/or NRS2) to be present in each of the directories provided in the data_sets variable in the
multiprocess_PTT_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 such as jwdata0010010_11010_0001_NRS1_uncal.fits. NPTT will expect to find one these files per PTT_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 PTT_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_PTT_with_multiprocess path_to_find_my_file/multiprocess_PTT_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

• 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 PTT. This sanity check is minor but important to verify. If you have the PTT 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)

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 PTT repository. The tests have to be written in python 3.6 or greater.

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