Use proseco acq candidate mask for ACA plot (bad_acq_stars)

[taldcroft]

Description

This fixes an issue where stars from AGASC 1.8 are nearly all plotted in the orange-ish color denoting a star that is not selectable as an acq star. This is currently manifested in starcheck review star catalog plots, making it somewhat difficult to evaluate the star field and catalog.

Requires

• Factor out guide and acq candidates mask proseco#399

Interface impacts

• The filtering mask in proseco requires two new AGASC columns compared to the previous chandra_aca.plot code: MAG_ACA and COLOR1.
• The filtering in proseco is somewhat different from the previous code, so there will be changes in the color of plotted stars. In general the changes are for the better and accurately reflect what proseco is using in star selection.

Testing

Unit tests

• Mac

Independent check of unit tests by Jean

• Linux

ska3-jeanconn-fido> export PYTHONPATH=/home/jeanconn/git/proseco
ska3-jeanconn-fido> git rev-parse HEAD
ab418934559da69f8a3afa55433be4d33fa596a2
ska3-jeanconn-fido> pytest
============================================== test session starts ==============================================
platform linux -- Python 3.11.8, pytest-7.4.4, pluggy-1.4.0
rootdir: /proj/sot/ska/jeanproj/git
configfile: pytest.ini
plugins: anyio-4.3.0, timeout-2.2.0
collected 205 items                                                                                             

chandra_aca/tests/test_aca_image.py .................                                                     [  8%]
chandra_aca/tests/test_all.py ........................                                                    [ 20%]
chandra_aca/tests/test_attitude.py .............................................................          [ 49%]
chandra_aca/tests/test_dark_model.py ....                                                                 [ 51%]
chandra_aca/tests/test_drift.py ..........................                                                [ 64%]
chandra_aca/tests/test_maude_decom.py ..................                                                  [ 73%]
chandra_aca/tests/test_planets.py ...............                                                         [ 80%]
chandra_aca/tests/test_psf.py ...                                                                         [ 81%]
chandra_aca/tests/test_residuals.py .....                                                                 [ 84%]
chandra_aca/tests/test_star_probs.py ................................                                     [100%]

======================================== 205 passed in 87.64s (0:01:27)

Functional tests

(ska3) ➜  chandra_aca git:(use-proseco-acq-candidate-mask) env PYTHONPATH=/Users/aldcroft/git/proseco ipython             
Python 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:49:36) [Clang 16.0.6 ]

In [2]: from chandra_aca.plot import plot_stars
In [3]: plot_stars([284.097466, -37.880561, 311.969530])
Out[3]: <Figure size 1064x1064 with 1 Axes>

With this PR

With current release 4.45.1

Starcheck

On kady in flight ska3 environment.
https://icxc.cfa.harvard.edu/aspect/test_review_outputs/chandra_aca/pr170/starcheck.html

export PYTHONPATH=/home/aldcroft/git/chandra_aca:/home/aldcroft/git/proseco
cd ~/git/starcheck  # @ master
./sandbox_starcheck -dir /data/mpcrit1/mplogs/2024/JUL2524/oflsa
# mv files to icxc www area

[jeanconn]

I think this should also get a functional test via starcheck.

[jeanconn]

For kicks, I also ran sparkles here https://icxc.cfa.harvard.edu/aspect/test_review_outputs/chandra_aca/pr170/AUG0524A_sparkles/ which looks fine.