def test_reffile_crds():
"""Make this a copy of test_reffile_crds_full_name (including reffile_overrides)
but use 'crds'
"""
catalogs = None
# Make an instance of the SimInput class
input_xml = os.path.join(__location__, 'test_data/misc/12345/12345_nircam_imaging_prime_niriss_wfss_parallel.xml')
pointing_file = os.path.join(__location__, 'test_data/misc/12345/12345_nircam_imaging_prime_niriss_wfss_parallel.pointing')
temp_output_dir = os.path.join(__location__, "temp")
yam = SimInput(input_xml, pointing_file, verbose=True, catalogs=catalogs,
offline=True, output_dir=temp_output_dir,
simdata_output_dir=temp_output_dir,
datatype='raw', reffile_defaults='crds')
yam.use_linearized_darks = True
yam.create_inputs()
# Check that all reference files are simply the string 'crds'
expected_value = 'crds'
assert np.all(np.array(yam.info['superbias']) == expected_value)
assert np.all(np.array(yam.info['linearity']) == expected_value)
assert np.all(np.array(yam.info['saturation']) == expected_value)
assert np.all(np.array(yam.info['gain']) == expected_value)
assert np.all(np.array(yam.info['astrometric']) == expected_value)
assert np.all(np.array(yam.info['pixelAreaMap']) == expected_value)
assert np.all(np.array(yam.info['badpixmask']) == expected_value)
assert np.all(np.array(yam.info['ipc']) == expected_value)
# Clean up
os.system('rm -r {}'.format(temp_output_dir))
def call_with_assorted_user_inputs(catalogs, crs, dates, backgrounds,
roll_angles):
"""Test various user-inputs to yaml_generator, including dates, backgrounds,
roll angles, cosmic rays, and catalogs
"""
# Make an instance of the SimInput class
input_xml = os.path.join(
__location__,
'test_data/misc/54321/54321_niriss_wfss_prime_nircam_imaging_parallel_mult_targets.xml'
)
pointing_file = os.path.join(
__location__,
'test_data/misc/54321/54321_niriss_wfss_prime_nircam_imaging_parallel_mult_targets.pointing'
)
temp_output_dir = os.path.join(__location__, "temp")
yam = SimInput(input_xml,
pointing_file,
verbose=True,
catalogs=catalogs,
offline=True,
output_dir=temp_output_dir,
simdata_output_dir=temp_output_dir,
datatype='raw',
reffile_defaults='crds',
cosmic_rays=crs,
dates=dates,
background=backgrounds,
roll_angle=roll_angles)
yam.use_linearized_darks = True
yam.create_inputs()
return yam.info
def call_yaml_generator(xml_file):
"""Call the yaml generator. Assume the pointing file is in the same directory
and has the same name as the xml file
"""
temp_output_dir = os.path.join(__location__, "temp")
pointing_file = xml_file.replace('.xml', '.pointing')
yam = SimInput(xml_file,
pointing_file,
verbose=True,
offline=True,
output_dir=temp_output_dir,
simdata_output_dir=temp_output_dir,
datatype='raw',
reffile_defaults='crds')
yam.use_linearized_darks = True
yam.create_inputs()
return yam
def test_reffile_crds_full_name():
"""Test that the correct values for reference files are found
"""
reffile_overrides = {'nircam': {'superbias': {'nrcb5': {'bright1': 'my_reffiles/my_superbias_for_b5.fits',
'shallow4': 'my_reffiles/my_superbias_for_b5.fits'
},
'nrcb4': {'shallow2': 'my_reffiles/my_superbias_for_b4.fits'}
},
'linearity': {'nrcb5': 'my_reffiles/my_linearity_for_b5.fits',
'nrcb4': 'my_reffiles/my_linearity_for_b4.fits'},
'saturation': {'nrcb5': 'my_reffiles/my_saturation_for_b5.fits',
'nrcb4': 'my_reffiles/my_saturation_for_b4.fits'},
'gain': {'nrcb5': 'my_reffiles/my_gain_for_b5.fits',
'nrcb4': 'my_reffiles/my_gain_for_b4.fits'},
'distortion': {'nrcb5': {'f322w2': {'NRC_IMAGE': 'my_reffiles/my_distortion_for_b5.asdf'}},
'nrcb4': {'f150w': {'NRC_IMAGE': 'my_reffiles/my_distortion_for_b4.asdf'}}},
'area': {'nrcb5': {'f322w2': {'clear': {'nrc_image': 'my_reffiles/my_pam_for_b5.fits'}}},
'nrcb4': {'f150w': {'clear': {'nrc_image': 'my_reffiles/my_pam_for_b4.fits'}}}},
'transmission': {'nrcb5': {'f322w2': {'clear': 'my_reffiles/my_transmission_for_b5.fits'},
'f444w': {'clear': 'my_reffiles/my_transmission_for_b5.fits'},
'f335m': {'clear': 'my_reffiles/my_transmission_for_b5.fits'},
'f300m': {'clear': 'my_reffiles/my_transmission_for_b5.fits'}},
'nrcb1': {'f150w': {'clear': 'my_reffiles/my_transmission_for_b1.fits'},
'f070w': {'clear': 'my_reffiles/my_transmission_for_b1.fits'},
'f150w2': {'clear': 'my_reffiles/my_transmission_for_b1.fits'},
'f187n': {'clear': 'my_reffiles/my_transmission_for_b1.fits'}},
'nrcb2': {'f150w': {'clear': 'my_reffiles/my_transmission_for_b2.fits'},
'f070w': {'clear': 'my_reffiles/my_transmission_for_b2.fits'},
'f150w2': {'clear': 'my_reffiles/my_transmission_for_b2.fits'},
'f187n': {'clear': 'my_reffiles/my_transmission_for_b2.fits'}},
'nrcb3': {'f150w': {'clear': 'my_reffiles/my_transmission_for_b3.fits'},
'f070w': {'clear': 'my_reffiles/my_transmission_for_b3.fits'},
'f150w2': {'clear': 'my_reffiles/my_transmission_for_b3.fits'},
'f187n': {'clear': 'my_reffiles/my_transmission_for_b3.fits'}},
'nrcb4': {'f150w': {'clear': 'my_reffiles/my_transmission_for_b4.fits'},
'f070w': {'clear': 'my_reffiles/my_transmission_for_b4.fits'},
'f150w2': {'clear': 'my_reffiles/my_transmission_for_b4.fits'},
'f187n': {'clear': 'my_reffiles/my_transmission_for_b4.fits'}},
},
'badpixmask': {'nrcb5': 'my_reffiles/my_bpm_for_b5.fits',
'nrcb4': 'my_reffiles/my_bpm_for_b4.fits'},
'pixelflat': {'nrcb5': {'f322w2': {'clear': 'my_reffiles/my_flatfield_for_b5.fits'}}}
},
'niriss': {'superbias': {'nisrapid': 'my_niriss_supebias.fits'},
'linearity': 'my_niriss_linearity,fits',
'saturation': 'my_niriss_saturation.fits',
'gain': 'my_niriss_gain.fits',
'distortion': {'F115W': {'nis_image': 'my_niriss_disotrtion.asdf'}},
'area': {'clear': {'f115w': {'nis_image': 'my_niriss_area.fits'}}},
'transmission': {'clear': {'f115w': 'my_niriss_transmission.fits'},
'gr150c': {'f115w': 'my_niriss_gr_transmission.fits'}
},
'badpixmask': 'my_niriss_badpixmask.fits',
'pixelflat': {'clear': {'f115w': 'my_niriss_flatfield.fits'}}
}
}
catalogs = None
# Make an instance of the SimInput class
input_xml = os.path.join(__location__, 'test_data/misc/12345/12345_nircam_imaging_prime_niriss_wfss_parallel.xml')
pointing_file = os.path.join(__location__, 'test_data/misc/12345/12345_nircam_imaging_prime_niriss_wfss_parallel.pointing')
temp_output_dir = os.path.join(__location__, "temp")
yam = SimInput(input_xml, pointing_file, verbose=True, catalogs=catalogs,
offline=True, output_dir=temp_output_dir,
simdata_output_dir=temp_output_dir,
datatype='raw', reffile_defaults='crds_full_name',
reffile_overrides=reffile_overrides)
yam.use_linearized_darks = True
yam.create_inputs()
# Make into numpy arrays for easier matching
sw_filternames = np.array(yam.info['ShortFilter'])
lw_filternames = np.array(yam.info['LongFilter'])
sw_pupilnames = np.array(yam.info['ShortPupil'])
lw_pupilnames = np.array(yam.info['LongPupil'])
filternames = np.array(yam.info['FilterWheel'])
pupilnames = np.array(yam.info['PupilWheel'])
detectors = np.array(yam.info['detector'])
instruments = np.array(yam.info['Instrument'])
read_patterns = np.array(yam.info['ReadoutPattern'])
match_nrc_sw_superbias = np.where((read_patterns == 'SHALLOW2') & (detectors == 'B4'))[0]
match_nrc_lw_superbias = np.where((read_patterns == 'BRIGHT1') & (detectors == 'B5'))[0]
match_nrc_sw_common = np.where(detectors == 'B4')[0]
match_nrc_lw_common = np.where(detectors == 'B5')[0]
match_nrc_sw_distortion_area = np.where((sw_filternames == 'F150W') & (detectors == 'B4') & (sw_pupilnames == 'CLEAR'))[0]
match_nrc_lw_distortion_area = np.where((lw_filternames == 'F322W2') & (detectors == 'B5') & (lw_pupilnames == 'CLEAR'))[0]
match_nrc_lw_flat = np.where((lw_filternames == 'F322W2') & (detectors == 'B5') & (lw_pupilnames == 'CLEAR'))[0]
match_nis_superbias = np.where(read_patterns == 'NISRAPID')[0]
match_nis_common = np.where(instruments == 'NIRISS')[0]
match_nis_distortion_area = np.where((pupilnames == 'F115W') & (instruments == 'NIRISS') & (filternames == 'CLEAR'))[0]
match_nis_flat = np.where((pupilnames == 'F115W') & (instruments == 'NIRISS') & (filternames == 'CLEAR'))[0]
# Check that reference files that are covered by reffile_overrides
# are equal to the override values.
for index in match_nrc_sw_superbias:
assert yam.info['superbias'][index] == 'my_reffiles/my_superbias_for_b4.fits'
for index in match_nrc_lw_superbias:
assert yam.info['superbias'][index] == 'my_reffiles/my_superbias_for_b5.fits'
for index in match_nrc_sw_common:
assert yam.info['linearity'][index] == 'my_reffiles/my_linearity_for_b4.fits'
assert yam.info['saturation'][index] == 'my_reffiles/my_saturation_for_b4.fits'
assert yam.info['gain'][index] == 'my_reffiles/my_gain_for_b4.fits'
assert yam.info['badpixmask'][index] == 'my_reffiles/my_bpm_for_b4.fits'
for index in match_nrc_lw_common:
assert yam.info['linearity'][index] == 'my_reffiles/my_linearity_for_b5.fits'
assert yam.info['saturation'][index] == 'my_reffiles/my_saturation_for_b5.fits'
assert yam.info['gain'][index] == 'my_reffiles/my_gain_for_b5.fits'
assert yam.info['badpixmask'][index] == 'my_reffiles/my_bpm_for_b5.fits'
for index in match_nrc_sw_distortion_area:
assert yam.info['astrometric'][index] == 'my_reffiles/my_distortion_for_b4.asdf'
assert yam.info['pixelAreaMap'][index] == 'my_reffiles/my_pam_for_b4.fits'
assert yam.info['transmission'][index] == 'my_reffiles/my_transmission_for_b4.fits'
for index in match_nrc_lw_distortion_area:
assert yam.info['astrometric'][index] == 'my_reffiles/my_distortion_for_b5.asdf'
assert yam.info['pixelAreaMap'][index] == 'my_reffiles/my_pam_for_b5.fits'
assert yam.info['transmission'][index] == 'my_reffiles/my_transmission_for_b5.fits'
for index in match_nrc_lw_flat:
assert yam.info['pixelflat'][index] == 'my_reffiles/my_flatfield_for_b5.fits'
for index in match_nis_superbias:
assert yam.info['superbias'][index] == 'my_niriss_supebias.fits'
for index in match_nis_common:
assert yam.info['linearity'][index] == 'my_niriss_linearity,fits'
assert yam.info['saturation'][index] == 'my_niriss_saturation.fits'
assert yam.info['gain'][index] == 'my_niriss_gain.fits'
assert yam.info['badpixmask'][index] == 'my_niriss_badpixmask.fits'
for info in match_nis_distortion_area:
assert yam.info['astrometric'][index] == 'my_niriss_disotrtion.asdf'
assert yam.info['pixelAreaMap'][index] == 'my_niriss_area.fits'
assert yam.info['transmission'][index] == 'my_niriss_transmission.fits'
for info in match_nis_flat:
assert yam.info['pixelflat'][index] == 'my_niriss_flatfield.fits'
# Check that reference files covered by reffile_overrides contain
# the CRDS_PATH, which here has been set to the temp directory
match_nrc_sw_defaults = np.where((instruments == 'NIRCAM') & (sw_filternames == 'F070W') & (detectors != 'B5'))[0]
match_nrc_lw_defaults = np.where((instruments == 'NIRCAM') & (lw_filternames == 'F335M') & (detectors == 'B5'))[0]
for index in list(match_nrc_sw_defaults) + list(match_nrc_lw_defaults):
assert temp_output_dir in yam.info['superbias'][index]
assert temp_output_dir in yam.info['astrometric'][index]
assert temp_output_dir in yam.info['pixelAreaMap'][index]
assert temp_output_dir in yam.info['ipc'][index]
# Clean up
os.system('rm -r {}'.format(temp_output_dir))
def test_get_psf_path():
"""Test that the get_psf_path method of the yaml_generator.SimInput class
is working as expected.
"""
# Make an instance of the SimInput class
input_xml = os.path.join(__location__, 'test_data', 'NIRCam', '1144-OTE-10.xml')
pointing_file = os.path.join(__location__, 'test_data', 'NIRCam', '1144-OTE-10.pointing')
yam = SimInput(input_xml, pointing_file, offline=True)
# Fake the activity IDs and instrument
n_activities = 101
act_ids = [np.base_repr(i, 36).zfill(2) for i in range(n_activities)]
yam.info = {}
yam.info['act_id'] = act_ids
yam.info['Instrument'] = ['NIRCam'] * n_activities
# Put the entry_number entry of yam.apt_xml_dict into yam.info.
# Typically this is done when running create_inputs() in the
# yaml_generator. This step is skipped here to save time and
# avoid having to deal with lots of output files.
yam.info['entry_number'] = yam.apt_xml_dict['entry_number']
# Test for a default path
paths_out = yam.get_psf_path()
assert len(paths_out) == n_activities,\
'Default PSF path not properly provided.'
assert 'nircam/gridded_psf_library' in paths_out[0],\
'Default PSF path not properly provided.'
np.testing.assert_array_equal(paths_out, paths_out[0],
err_msg='Default PSF path not properly defined.')
# Test for a single path
yam.psf_paths = __location__
paths_out = yam.get_psf_path()
assert paths_out == [__location__] * n_activities,\
'Single PSF path not properly assigned.'
# Test for a list of paths of incorrect length
with pytest.raises(ValueError) as e:
yam.psf_paths = [__location__] * 99
yam.get_psf_path()
assert 'Please provide the psf_paths in the form of a list of strings ' \
'with a length equal to the number of activities in the APT ' \
'program (103), not equal to 99.' in e, \
'Failed to reject psf_path of incorrect length.'
# Test for a list of paths of correct length
list_paths = [__location__] * 9 + [os.path.dirname(__location__)] * 9
yam.psf_paths = list_paths
paths_out = yam.get_psf_path()
assert paths_out == list_paths,\
'List of PSF paths not properly assigned.'
# Test for a completely invalid path
with pytest.raises(TypeError) as e:
yam.psf_paths = 3.054
yam.get_psf_path()
assert 'Please provide the psf_paths in the form of a list or ' \
'string, not float' in e, \
'Failed to reject psf_path of incorrect type.'
