def test_add_wcs_default(data_file, tmp_path):
"""Handle when no pointing exists and the default is used."""
expected_name = 'add_wcs_default.fits'
try:
stp.add_wcs(data_file,
siaf_path=siaf_path,
tolerance=0,
allow_default=True)
except ValueError:
pass # This is what we want for the test.
except Exception as e:
pytest.skip('Live ENGDB service is not accessible.'
'\nException={}'.format(e))
# Tests
with datamodels.Level1bModel(data_file) as model:
# Save for post-test comparison and update
model.save(tmp_path / expected_name)
with datamodels.open(DATA_PATH / expected_name) as expected:
for meta in METAS_EQUALITY:
assert model[meta] == expected[meta], f'{meta} has changed'
for meta in METAS_ISCLOSE:
assert np.isclose(model[meta],
expected[meta]), f'{meta} has changed'
assert word_precision_check(model.meta.wcsinfo.s_region,
expected.meta.wcsinfo.s_region)
def test_add_wcs_method_full_nosiafdb(eng_db_ngas, data_file, tmp_path):
"""Test using the database"""
# Only run if `pysiaf` is installed.
pytest.importorskip('pysiaf')
expected_name = 'add_wcs_method_full_nosiafdb.fits'
# Calculate
stp.add_wcs(data_file,
method=stp.Methods.OPS_TR_202111,
engdb_url='http://localhost')
# Tests
with datamodels.Level1bModel(data_file) as model:
# Save for post-test comparison and update
model.save(tmp_path / expected_name)
with datamodels.open(DATA_PATH / expected_name) as expected:
for meta in METAS_EQUALITY:
assert model[meta] == expected[meta]
for meta in METAS_ISCLOSE:
assert np.isclose(model[meta], expected[meta])
assert word_precision_check(model.meta.wcsinfo.s_region,
expected.meta.wcsinfo.s_region)
def test_add_wcs_method_full_siafdb(eng_db_ngas, data_file, tmp_path):
"""Test using the database and a specified siaf db"""
expected_name = 'add_wcs_method_full_siafdb.fits'
# Calculate
stp.add_wcs(data_file,
siaf_path=siaf_path,
method=stp.Methods.OPS_TR_202111,
engdb_url='http://localhost')
# Test
with datamodels.Level1bModel(data_file) as model:
# Save for post-test comparison and update
model.save(tmp_path / expected_name)
with datamodels.open(DATA_PATH / expected_name) as expected:
for meta in METAS_EQUALITY:
if isinstance(model[meta], str):
assert model[meta] == expected[meta]
else:
assert np.isclose(model[meta], expected[meta], atol=1e-13)
for meta in METAS_ISCLOSE:
assert np.isclose(model[meta], expected[meta])
assert word_precision_check(model.meta.wcsinfo.s_region,
expected.meta.wcsinfo.s_region)
def test_add_wcs_method_gscmd(eng_db_ngas, data_file, tmp_path):
"""Test using the database and the original, pre-JSOCINT-555 algorithms"""
expected_name = 'add_wcs_method_gscmd.fits'
# Calculate
stp.add_wcs(data_file,
siaf_path=siaf_path,
method=stp.Methods.GSCMD_J3PAGS,
engdb_url='http://localhost')
# Tests
with datamodels.Level1bModel(data_file) as model:
# Save for post-test comparison and update
model.save(tmp_path / expected_name)
with datamodels.open(DATA_PATH / expected_name) as expected:
for meta in METAS_EQUALITY:
if isinstance(model[meta], str):
assert model[meta] == expected[meta], f'{meta} has changed'
else:
assert np.isclose(model[meta], expected[meta],
atol=1e-13), f'{meta} has changed'
for meta in METAS_ISCLOSE:
assert np.isclose(model[meta],
expected[meta]), f'{meta} has changed'
assert word_precision_check(model.meta.wcsinfo.s_region,
expected.meta.wcsinfo.s_region)
def data_file_nosiaf():
model = datamodels.Level1bModel()
model.meta.exposure.start_time = STARTTIME.mjd
model.meta.exposure.end_time = ENDTIME.mjd
model.meta.target.ra = TARG_RA
model.meta.target.dec = TARG_DEC
model.meta.aperture.name = "UNKNOWN"
model.meta.observation.date = '1/1/2017'
with TemporaryDirectory() as path:
file_path = os.path.join(path, 'fits_nosiaf.fits')
model.save(file_path)
model.close()
yield file_path
def test_tsgrism_siaf_values(eng_db_ngas, data_file_nosiaf):
"""
Test that FITS WCS default values were set.
"""
with datamodels.Level1bModel(data_file_nosiaf) as model:
model.meta.exposure.start_time = STARTTIME.mjd
model.meta.exposure.end_time = ENDTIME.mjd
model.meta.aperture.name = "NRCA5_GRISM256_F444W"
model.meta.observation.date = '1/1/2017'
model.meta.exposure.type = "NRC_TSGRISM"
model.meta.visit.tsovisit = True
stp.update_wcs(model, siaf_path=siaf_db)
assert model.meta.wcsinfo.siaf_xref_sci == 887
assert model.meta.wcsinfo.siaf_yref_sci == 35
def test_default_siaf_values(eng_db_ngas, data_file_nosiaf):
"""
Test that FITS WCS default values were set.
"""
with datamodels.Level1bModel(data_file_nosiaf) as model:
model.meta.exposure.start_time = STARTTIME.mjd
model.meta.exposure.end_time = ENDTIME.mjd
model.meta.target.ra = TARG_RA
model.meta.target.dec = TARG_DEC
model.meta.aperture.name = "MIRIM_TAFULL"
model.meta.observation.date = '1/1/2017'
model.meta.exposure.type = "MIR_IMAGE"
stp.update_wcs(model, siaf_path=siaf_db, allow_default=False)
assert model.meta.wcsinfo.crpix1 == 0
assert model.meta.wcsinfo.crpix2 == 0
assert model.meta.wcsinfo.cdelt1 == 1
assert model.meta.wcsinfo.cdelt2 == 1
def test_add_wcs_fsmcorr_v1(data_file):
"""Test with default value using FSM original correction"""
try:
stp.add_wcs(data_file,
fsmcorr_version='v1',
siaf_path=siaf_db,
tolerance=0,
allow_default=True)
except ValueError:
pass # This is what we want for the test.
except Exception as e:
pytest.skip('Live ENGDB service is not accessible.'
'\nException={}'.format(e))
with datamodels.Level1bModel(data_file) as model:
assert model.meta.pointing.ra_v1 == TARG_RA
assert model.meta.pointing.dec_v1 == TARG_DEC
assert model.meta.pointing.pa_v3 == 0.
assert model.meta.wcsinfo.wcsaxes == 2
assert model.meta.wcsinfo.crpix1 == 693.5
assert model.meta.wcsinfo.crpix2 == 512.5
assert model.meta.wcsinfo.crval1 == TARG_RA
assert model.meta.wcsinfo.crval2 == TARG_DEC
assert model.meta.wcsinfo.ctype1 == "RA---TAN"
assert model.meta.wcsinfo.ctype2 == "DEC--TAN"
assert model.meta.wcsinfo.cunit1 == 'deg'
assert model.meta.wcsinfo.cunit2 == 'deg'
assert np.isclose(model.meta.wcsinfo.cdelt1, 3.0555555e-5)
assert np.isclose(model.meta.wcsinfo.cdelt2, 3.0555555e-5)
assert np.isclose(model.meta.wcsinfo.pc1_1, -0.7558009243361943)
assert np.isclose(model.meta.wcsinfo.pc1_2, 0.654801468211972)
assert np.isclose(model.meta.wcsinfo.pc2_1, 0.654801468211972)
assert np.isclose(model.meta.wcsinfo.pc2_2, 0.7558009243361943)
assert model.meta.wcsinfo.v2_ref == 200.0
assert model.meta.wcsinfo.v3_ref == -350.0
assert model.meta.wcsinfo.vparity == -1
assert model.meta.wcsinfo.v3yangle == 42.0
assert model.meta.wcsinfo.ra_ref == TARG_RA
assert model.meta.wcsinfo.dec_ref == TARG_DEC
assert np.isclose(model.meta.wcsinfo.roll_ref, 358.9045979379)
assert word_precision_check(model.meta.wcsinfo.s_region,
('POLYGON ICRS'
' 345.11054995209815 -87.02586884935684'
' 344.6537904121288 -87.00498014679253'
' 345.04569816117015 -86.98138111042982'
' 345.50498899320183 -87.00187988107017'))
def data_file(tmp_path):
model = datamodels.Level1bModel()
model.meta.exposure.start_time = STARTTIME.mjd
model.meta.exposure.end_time = ENDTIME.mjd
model.meta.target.ra = TARG_RA
model.meta.target.dec = TARG_DEC
model.meta.guidestar.gs_ra = TARG_RA + 0.0001
model.meta.guidestar.gs_dec = TARG_DEC + 0.0001
model.meta.aperture.name = "MIRIM_FULL"
model.meta.observation.date = '2017-01-01'
model.meta.exposure.type = "MIR_IMAGE"
model.meta.ephemeris.velocity_x = -25.021
model.meta.ephemeris.velocity_y = -16.507
model.meta.ephemeris.velocity_z = -7.187
file_path = tmp_path / 'file.fits'
model.save(file_path)
model.close()
yield file_path
def test_default_siaf_values(eng_db_ngas, data_file_nosiaf):
"""
Test that FITS WCS default values were set.
"""
with datamodels.Level1bModel(data_file_nosiaf) as model:
model.meta.exposure.start_time = STARTTIME.mjd
model.meta.exposure.end_time = ENDTIME.mjd
model.meta.target.ra = TARG_RA
model.meta.target.dec = TARG_DEC
model.meta.aperture.name = "MIRIM_TAFULL"
model.meta.observation.date = '2017-01-01'
model.meta.exposure.type = "MIR_IMAGE"
stp.update_wcs(model,
siaf_path=siaf_path,
allow_default=False,
engdb_url='http://localhost')
assert model.meta.wcsinfo.crpix1 == 24.5
assert model.meta.wcsinfo.crpix2 == 24.5
assert model.meta.wcsinfo.cdelt1 == 3.067124166666667e-05
assert model.meta.wcsinfo.cdelt2 == 3.090061944444444e-05
def data_file_fromsim(tmp_path):
"""Create data using times that were executed during a simulation using the OTB Simulator"""
model = datamodels.Level1bModel()
model.meta.exposure.start_time = Time('2022-02-02T22:24:58.942').mjd
model.meta.exposure.end_time = Time('2022-02-02T22:26:24.836').mjd
model.meta.target.ra = TARG_RA
model.meta.target.dec = TARG_DEC
model.meta.guidestar.gs_ra = TARG_RA + 0.0001
model.meta.guidestar.gs_dec = TARG_DEC + 0.0001
model.meta.guidestar.gs_pcs_mode = 'COARSE'
model.meta.aperture.name = "MIRIM_FULL"
model.meta.observation.date = '2017-01-01'
model.meta.exposure.type = "MIR_IMAGE"
model.meta.ephemeris.velocity_x_bary = -25.021
model.meta.ephemeris.velocity_y_bary = -16.507
model.meta.ephemeris.velocity_z_bary = -7.187
file_path = tmp_path / 'file_fromsim.fits'
model.save(file_path)
model.close()
yield file_path
def test_add_wcs_with_db(eng_db_ngas, data_file, tmp_path):
"""Test using the database"""
expected_name = 'add_wcs_with_db.fits'
stp.add_wcs(data_file, siaf_path=siaf_path, engdb_url='http://localhost')
# Tests
with datamodels.Level1bModel(data_file) as model:
# Save for post-test comparison and update
model.save(tmp_path / expected_name)
with datamodels.open(DATA_PATH / expected_name) as expected:
for meta in METAS_EQUALITY:
assert model[meta] == expected[meta]
for meta in METAS_ISCLOSE:
assert np.isclose(model[meta], expected[meta])
assert word_precision_check(model.meta.wcsinfo.s_region,
expected.meta.wcsinfo.s_region)
def test_add_wcs_with_db_fsmcorr_v1(eng_db_ngas, data_file):
"""Test using the database with original FSM correction"""
stp.add_wcs(data_file,
fsmcorr_version='v1',
siaf_path=siaf_db,
j2fgs_transpose=False)
with datamodels.Level1bModel(data_file) as model:
assert np.isclose(model.meta.pointing.ra_v1, 348.9278669)
assert np.isclose(model.meta.pointing.dec_v1, -38.749239)
assert np.isclose(model.meta.pointing.pa_v3, 50.1767077)
assert model.meta.wcsinfo.wcsaxes == 2
assert model.meta.wcsinfo.crpix1 == 693.5
assert model.meta.wcsinfo.crpix2 == 512.5
assert np.isclose(model.meta.wcsinfo.crval1, 348.8776709)
assert np.isclose(model.meta.wcsinfo.crval2, -38.854159)
assert model.meta.wcsinfo.ctype1 == "RA---TAN"
assert model.meta.wcsinfo.ctype2 == "DEC--TAN"
assert model.meta.wcsinfo.cunit1 == 'deg'
assert model.meta.wcsinfo.cunit2 == 'deg'
assert np.isclose(model.meta.wcsinfo.cdelt1, 3.0555555e-5)
assert np.isclose(model.meta.wcsinfo.cdelt2, 3.0555555e-5)
assert np.isclose(model.meta.wcsinfo.pc1_1, 0.03853303979862607)
assert np.isclose(model.meta.wcsinfo.pc1_2, 0.9992573266400789)
assert np.isclose(model.meta.wcsinfo.pc2_1, 0.9992573266400789)
assert np.isclose(model.meta.wcsinfo.pc2_2, -0.03853303979862607)
assert model.meta.wcsinfo.v2_ref == 200.0
assert model.meta.wcsinfo.v3_ref == -350.0
assert model.meta.wcsinfo.vparity == -1
assert model.meta.wcsinfo.v3yangle == 42.0
assert np.isclose(model.meta.wcsinfo.ra_ref, 348.8776709)
assert np.isclose(model.meta.wcsinfo.dec_ref, -38.854159)
assert np.isclose(model.meta.wcsinfo.roll_ref, 50.20832726650)
assert word_precision_check(
model.meta.wcsinfo.s_region,
('POLYGON ICRS'
' 348.8563379013152 -38.874810886750495'
' 348.85810582665334 -38.84318773861823'
' 348.8982592685148 -38.84439628911871'
' 348.89688051688233 -38.876020020321164'))
def data_file():
model = datamodels.Level1bModel()
model.meta.target.ra = 0.
model.meta.target.dec = 0.
model.meta.ephemeris.time = 55727.0
model.meta.ephemeris.spatial_x = -34305.4075983316
model.meta.ephemeris.spatial_y = 1049528.04998405
model.meta.ephemeris.spatial_z = 679175.58185602
model.meta.ephemeris.velocity_x = -0.548663244644384
model.meta.ephemeris.velocity_y = -0.103904924724239
model.meta.ephemeris.velocity_z = 0.000982870964178323
# Assign dummy values to the last three columns.
model.int_times = \
[(1, 55728.0, 55728.00032119, 55728.00064237, -1., -1., -1.),
(2, 55728.00064237, 55728.00096356, 55728.00128474, -1., -1., -1.),
(3, 55728.00128474, 55728.00160593, 55728.00192711, -1., -1., -1.),
(4, 55728.00192711, 55728.0022483, 55728.00256948, -1., -1., -1.)]
with TemporaryDirectory() as path:
file_path = os.path.join(path, 'int_times.fits')
model.save(file_path)
yield file_path
def test_add_wcs_with_mast(data_file_fromsim, fgsid, tmp_path):
"""Test using the database"""
expected_name = f'add_wcs_with_mast_fgs{fgsid}.fits'
# See if access to MAST is available.
try:
engdb_mast.EngdbMast(base_url=engdb_mast.MAST_BASE_URL)
except RuntimeError as exception:
pytest.skip(
f'Live MAST Engineering Service not available: {exception}')
# Execute the operation.
try:
stp.add_wcs(data_file_fromsim,
siaf_path=siaf_path,
engdb_url=engdb_mast.MAST_BASE_URL,
fgsid=fgsid)
except ValueError as exception:
pytest.xfail(
f'No telemetry exists. Update test to use existing telemetry. Exception: {exception}'
)
# Tests
with datamodels.Level1bModel(data_file_fromsim) as model:
# Save for post-test comparison and update
model.save(tmp_path / expected_name)
with datamodels.open(DATA_PATH / expected_name) as expected:
for meta in METAS_EQUALITY:
assert model[meta] == expected[meta]
for meta in METAS_ISCLOSE:
assert np.isclose(model[meta], expected[meta])
assert word_precision_check(model.meta.wcsinfo.s_region,
expected.meta.wcsinfo.s_region)
def file_case(request, tmp_path):
"""Generate files with different model states"""
case, allow = request.param
if case == 'good_model':
# Make a model that will always succeed
model = datamodels.Level1bModel((10, 10, 10, 10))
path = tmp_path / 'level1bmodel.fits'
model.save(path)
elif case == 'bad_model':
# Make a model that will fail if not allowed
model = datamodels.IFUCubeModel((10, 10, 10))
path = tmp_path / 'image.fits'
model.save(path)
elif case == 'fits_nomodel':
# Create just a plain anything FITS
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
path = tmp_path / 'empty.fits'
hdul.writeto(path)
else:
assert False, f'Cannot produce a file for {case}'
return path, allow
dark.close() # Zero-out reference pixel values so that refpix step has no effect data4[0, 0, :4, :] = 0.0 data4[0, 0, -4:, :] = 0.0 data4[0, 0, :, :4] = 0.0 data4[0, 0, :, -4:] = 0.0 # Add superbias into science array bias_file = '/grp/crds/cache/references/jwst/jwst_nirspec_superbias_0030.fits' bias = datamodels.SuperBiasModel(bias_file) data4[0, 0] += bias.data bias.close() # Stuff the data and meta data into an output model output = datamodels.Level1bModel(data=data4) output.update(input) # Set important meta data values output.meta.exposure.ngroups = 1 output.meta.exposure.frame_divisor = 1 output.meta.wcsinfo.cdelt1 = input.meta.wcsinfo.cdelt1 / 3600. output.meta.wcsinfo.cdelt2 = input.meta.wcsinfo.cdelt2 / 3600. output.meta.wcsinfo.pc1_1 = -1.0 output.meta.wcsinfo.v2_ref = 378.770400 output.meta.wcsinfo.v3_ref = -428.155200 output.meta.wcsinfo.v3yangle = 138.492300 output.meta.wcsinfo.vparity = -1 output.meta.wcsinfo.ra_ref = output.meta.wcsinfo.crval1 output.meta.wcsinfo.dec_ref = output.meta.wcsinfo.crval2 output.meta.wcsinfo.roll_ref = 0.0
