def create_reference_files(datamodel):
refs = {}
step = AssignWcsStep()
for reftype in AssignWcsStep.reference_file_types:
refs[reftype] = step.get_reference_file(datamodel, reftype)
return refs
def test_miri_mrs_1A():
#ref = {}
ref = {
'distortion': '',
'regions': '',
'specwcs': '',
'v2v3': '',
'wavelengthrange': ''
}
im = ImageModel()
im.meta.instrument.name = 'MIRI'
im.meta.instrument.detector = 'MIRIFUSHORT'
im.meta.instrument.channel = '12'
im.meta.instrument.band = 'SHORT'
im.meta.exposure.type = 'MIR_MRS'
step = AssignWcsStep()
for reftype in refs:
ref[reftype] = step.get_reference_file(im, reftype)
pipeline = miri.create_pipeline(im, ref)
wcsobj = wcs.WCS(pipeline)
for ch in im.meta.instrument.channel:
ref_data = mrs_ref_data[ch + band_mapping[im.meta.instrument.band]]
#ref_data = mrs_ref_data[im.meta.instrument.channel + band_mapping[im.meta.instrument.band]]
#x = np.trunc(ref_data['x']).astype(np.int)
#y = np.trunc(ref_data['y']).astype(np.int)
for i, s in enumerate(ref_data['s']):
sl = int(ch) * 100 + s
detector_to_alpha_beta = wcsobj.get_transform(
'detector', 'alpha_beta')
alpha, beta, lam = detector_to_alpha_beta.set_input(sl)(
ref_data['x'][i], ref_data['y'][i])
##xan, yan, lam = wcsobj.forward_transform.set_input(sl)(ref_data['x'][i], ref_data['y'][i])
utils.assert_allclose(alpha, ref_data['alpha'][i], atol=10**-5)
utils.assert_allclose(beta, ref_data['beta'][i], atol=10**-5)
utils.assert_allclose(lam, ref_data['lam'][i], atol=10**-5)
detector_to_xan_yan = wcsobj.get_transform('detector', 'Xan_Yan')
xan, yan, lam = wcsobj(ref_data['x'], ref_data['y'])
utils.assert_allclose(xan, ref_data['v2'][i], atol=10**-5)
utils.assert_allclose(yan, ref_data['v3'][i], atol=10**-5)
utils.assert_allclose(lam, ref_data['lam'][i], atol=10**-5)
def test_miri_mrs_1C():
ref = {}
im = ImageModel()
im.meta.instrument.name = 'MIRI'
im.meta.instrument.detector = 'MIRIFUSHORT'
im.meta.instrument.channel = '12'
im.meta.instrument.band = 'LONG'
im.meta.exposure.type = 'MIR_MRS'
step = AssignWcsStep()
for reftype in refs:
ref[reftype] = step.get_reference_file(im, reftype)
pipeline = miri.create_pipeline(im, ref)
wcsobj = wcs.WCS(pipeline)
for ch in im.meta.instrument.channel:
ref_data = mrs_ref_data[ch + band_mapping[im.meta.instrument.band]]
for i, s in enumerate(ref_data['s']):
sl = int(ch) * 100 + s
xan, yan, lam = wcsobj.forward_transform.set_input(sl)(
ref_data['x'][i], ref_data['y'][i])
utils.assert_allclose(xan, ref_data['v2'][i], atol=10**-5)
utils.assert_allclose(yan, ref_data['v3'][i], atol=10**-5)
utils.assert_allclose(lam, ref_data['lam'][i], atol=10**-5)
for i, s in enumerate(ref_data['s']):
sl = int(ch) * 100 + s
xan, yan, lam = wcsobj.forward_transform(ref_data['x'][i],
ref_data['y'][i])
utils.assert_allclose(xan, ref_data['v2'][i], atol=10**-5)
utils.assert_allclose(yan, ref_data['v3'][i], atol=10**-5)
utils.assert_allclose(lam, ref_data['lam'][i], atol=10**-5)
xin, yin = wcsobj.backward_transform(ref_data['v2'][i],
ref_data['v3'][i],
ref_data['lam'][i])
utils.assert_allclose(xin, ref_data['x'][i], atol=10**-5)
utils.assert_allclose(y, ref_data['y'][i], atol=10**-5)
def test_flag():
wcsinfo = {
'dec_ref': -0.00601415671349804,
'ra_ref': -0.02073605215697509,
'roll_ref': -0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1}
instrument = {
'detector': 'NRS1',
'filter': 'F100LP',
'grating': 'G140M',
'name': 'NIRSPEC',
'gwa_tilt': 37.0610,
'gwa_xtilt': 0.0001,
'gwa_ytilt': 0.0001,
'msa_metadata_id':12}
observation = {
'date': '2016-09-05',
'time': '8:59:37'}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'NRSRAPID',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'NRS_MSASPEC',
'zero_frame': False}
im = ImageModel()
im.data = np.random.rand(2048, 2048)
im.error = np.random.rand(2048, 2048)
im.dq = np.zeros((2048, 2048))
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.observation._instance.update(observation)
im.meta.exposure._instance.update(exposure)
metafl = get_file_path('msa_configuration.fits')
im.meta.instrument.msa_metadata_file = metafl
im.meta.dither.position_number = 1
step = AssignWcsStep()
msa_oper = step.get_reference_file(im, 'msaoper')
im = step.call(im)
wcs_ref_files = create_reference_filename_dictionary(im)
failed_slitlets = create_slitlets(im, msa_oper)
result = flag(im, failed_slitlets, wcs_ref_files)
msa_open_dq = 536870912
# Get all dqflags that are nonzero
nonzero = np.nonzero(result.dq)
# Make sure that all nonzero dqs are equal to the msa_open_dq
assert (all(x == msa_open_dq for x in result.dq[nonzero]))