def create_integration_model(input_model, integ_info):
"""Creates an CubeModel from the computed arrays from ramp_fit.
Parameters
----------
input_model: RampModel
Input RampModel for which the output CubeModel is created.
integ_info: tuple
The ramp fitting arrays needed for the CubeModel for each integration.
Returns
-------
int_model: CubeModel
The output CubeModel to be returned from the ramp fit step.
"""
data, dq, var_poisson, var_rnoise, int_times, err = integ_info
int_model = datamodels.CubeModel(
data=np.zeros(data.shape, dtype=np.float32),
dq=np.zeros(data.shape, dtype=np.uint32),
var_poisson=np.zeros(data.shape, dtype=np.float32),
var_rnoise=np.zeros(data.shape, dtype=np.float32),
err=np.zeros(data.shape, dtype=np.float32))
int_model.int_times = None
int_model.update(input_model) # ... and add all keys from input
int_model.data = data
int_model.dq = dq
int_model.var_poisson = var_poisson
int_model.var_rnoise = var_rnoise
int_model.err = err
int_model.int_times = int_times
return int_model
def test_tso_phot_4():
global shape, xcenter, ycenter
value = 17.
background = 0.8
radius = 5.
radius_inner = 8.
radius_outer = 11.
data = mk_data_array(shape, value, background, xcenter, ycenter, radius)
datamodel = datamodels.CubeModel(data)
set_meta(datamodel, sub64p=False)
int_times = include_int_times(datamodel)
# Modify the column of integration numbers so that they extend outside
# the range of integration numbers in the science data. This shouldn't
# happen, i.e. this is to test an edge case.
int_start = datamodel.meta.exposure.integration_start
datamodel.int_times['integration_number'] += (2 * int_start)
catalog = tso_aperture_photometry(datamodel, xcenter, ycenter, radius + 1.,
radius_inner, radius_outer)
int_times = np.array([
58704.62853, 58704.628655, 58704.62878, 58704.62890, 58704.6290,
58704.6291511, 58704.629275
])
assert np.allclose(catalog['MJD'], int_times, rtol=1.e-8)
def test_tso_phot_2():
# pupil = 'WLP8' and subarray = 'SUB64P'
shape = (7, 64, 64)
xcenter = 31.
ycenter = 31.
value = 17.
background = 0.8
radius = 50.
radius_inner = None
radius_outer = None
data = mk_data_array(shape, value, background, xcenter, ycenter, radius)
datamodel = datamodels.CubeModel(data)
set_meta(datamodel, sub64p=True)
catalog = tso_aperture_photometry(datamodel, xcenter, ycenter, radius,
radius_inner, radius_outer)
assert catalog.meta['instrument'] == datamodel.meta.instrument.name
assert catalog.meta['filter'] == datamodel.meta.instrument.filter
assert catalog.meta['subarray'] == datamodel.meta.subarray.name
assert catalog.meta['pupil'] == datamodel.meta.instrument.pupil
assert math.isclose(catalog.meta['xcenter'], xcenter, abs_tol=0.01)
assert math.isclose(catalog.meta['ycenter'], ycenter, abs_tol=0.01)
assert np.allclose(catalog['aperture_sum'],
(value + background) * shape[-1] * shape[-2],
rtol=1.e-6)
assert np.allclose(catalog['aperture_sum_err'], 0., atol=1.e-7)
def test_wcs_bbox_from_shape_3d():
model = datamodels.CubeModel((3, 32, 2048))
bb = wcs_bbox_from_shape(model.data.shape)
assert bb == ((-0.5, 2047.5), (-0.5, 31.5))
model = datamodels.IFUCubeModel((750, 45, 50))
bb = wcs_bbox_from_shape(model.data.shape)
assert bb == ((-0.5, 49.5), (-0.5, 44.5))
def test_multi_integration_input(resample_class):
cube = datamodels.CubeModel((5, 100, 100))
cube.meta.instrument.name = 'MIRI'
cube.meta.observation.date = '2018-09-07'
cube.meta.observation.time = '10:32:20.181'
# Resample can't handle cubes, so it should fail
with pytest.raises(RuntimeError):
resample_class().call(cube)
def test_ami_analyze_calints_fail():
"""Make sure ami_analyze fails if input is CubeModel (_calints)"""
model = datamodels.CubeModel((25, 19, 19))
model.meta.instrument.name = "NIRISS"
model.meta.instrument.filter = "F277W"
model.meta.observation.date = "2019-01-01"
model.meta.observation.time = "00:00:00"
with pytest.raises(RuntimeError):
AmiAnalyzeStep.call(model)
def test_align_models():
""" Test of align_models() in imageregistration.py """
temp = np.arange((15), dtype=np.float32)
temp = temp.reshape((3, 5))
targ = np.zeros((3, 3, 5))
targ[:] = temp
targ[0, 1, 1] += 0.3
targ[0, 2, 1] += 0.7
targ[0, 0, 3] -= 0.5
targ[0, 1, 2] -= 1.3
targ[1, 0, 1] += 0.7
targ[1, 2, 1] += 0.2
targ[1, 2, 3] += 0.8
targ[1, 2, 2] -= 1.8
targ[2, 1, 1] += 0.9
targ[2, 2, 2] -= 0.5
targ[2, 1, 0] += 0.8
targ[2, 1, 2] += 0.8
ref = targ.copy()
ref[1, 2, 3] -= 5.
ref[2, 0, 3] -= 1.6
mask = ref[0, :, :]
mask = ref[0, :, :] * 0 + 1
mask[1, 1] = 0
mask[1, 2] = 0
targ_mod = datamodels.CubeModel(data=targ)
mask_mod = datamodels.ImageModel(data=mask)
ref_mod = datamodels.CubeModel(data=ref)
am_results = imageregistration.align_models(ref_mod, targ_mod, mask_mod)
results_sub = am_results.data[:3, :2, 2, :3]
truth_results_sub = np.array([[[10., 11.7, 12.],
[10.036278, 11.138131, 10.180669]],
[[10.053974, 11.1953335, 11.993213],
[10.36224, 10.805556, 10.274276]],
[[9.988604, 11.33026, 11.968155],
[10.024722, 10.971058, 10.108071]]])
npt.assert_allclose(results_sub, truth_results_sub, atol=1E-6)
def test_klip():
""" Test of klip() in klip.py """
target_model_data = np.array(
[[[0.84456396, 0.75297576, 0.79752606, 1.2884853],
[0.98718196, 0.83042985, 0.76917756, 0.9760479],
[1.4539411, 1.1395805, 0.9635729, 0.92798233]],
[[0.84456396, 0.75297576, 0.79752606, 1.2884853],
[0.98718196, 0.83042985, 0.76917756, 0.9760479],
[1.4539411, 1.1395805, 0.9635729, 0.92798233]]],
dtype=np.float32)
target_model = datamodels.CubeModel(data=target_model_data)
refs_model_data = np.array(
[[[[0.8174741, 0.74938107, 0.73527235, 1.3193785],
[1.0032778, 0.8247719, 0.78944355, 0.99227476],
[1.4609907, 1.1605016, 0.9564753, 0.9186427]],
[[0.86789674, 0.7998908, 0.8557136, 1.2926395],
[0.97756547, 0.7788742, 0.75892323, 0.9819151],
[1.495664, 1.1455023, 1.002115, 0.92159164]],
[[0.84426856, 0.7719569, 0.8088021, 1.2781427],
[0.98734635, 0.8125992, 0.77424014, 0.9934157],
[1.419994, 1.1546139, 0.961317, 0.95088667]]],
[[[0.8174741, 0.74938107, 0.73527235, 1.3193785],
[1.0032778, 0.8247719, 0.78944355, 0.99227476],
[1.4609907, 1.1605016, 0.9564753, 0.9186427]],
[[0.86789674, 0.7998908, 0.8557136, 1.2926395],
[0.97756547, 0.7788742, 0.75892323, 0.9819151],
[1.495664, 1.1455023, 1.002115, 0.92159164]],
[[0.84426856, 0.7719569, 0.8088021, 1.2781427],
[0.98734635, 0.8125992, 0.77424014, 0.9934157],
[1.419994, 1.1546139, 0.961317, 0.95088667]]]],
dtype=np.float32)
refs_model = datamodels.QuadModel(data=refs_model_data)
# Call the KLIP routine
truncate = 50
psf_sub, psf_fit = klip.klip(target_model, refs_model, truncate)
truth_psf_sub_data = np.array(
[[[0.00677787, -0.01639403, 0.00753349, -0.00190761],
[-0.00016282, 0.02299659, -0.00647708, -0.00992276],
[0.00798155, -0.00867007, -0.00287747, 0.00112234]],
[[0.00677787, -0.01639403, 0.00753349, -0.00190761],
[-0.00016282, 0.02299659, -0.00647708, -0.00992276],
[0.00798155, -0.00867007, -0.00287747, 0.00112234]]])
# psf_fit is cuurrently not used in the code, co not compared here
npt.assert_allclose(psf_sub.data, truth_psf_sub_data, atol=1E-6)
def test_tso_phot_5():
global shape, xcenter, ycenter
value = 17.
background = 0.8
radius = 5.
radius_inner = 8.
radius_outer = 11.
data = mk_data_array(shape, value, background, xcenter, ycenter, radius)
datamodel = datamodels.CubeModel(data)
set_meta(datamodel, sub64p=False)
datamodel.meta.bunit_data = 'MJy' # unexpected data unit
with pytest.raises(ValueError):
tso_aperture_photometry(datamodel, xcenter, ycenter, radius + 1.,
radius_inner, radius_outer)
def _generate_data():
"""Generate data of each type of input for master background step"""
image = datamodels.ImageModel((10, 10))
image.meta.instrument.name = 'MIRI'
image.meta.instrument.detector = 'MIRIMAGE'
image.meta.exposure.type = 'MIR_LRS-FIXEDSLIT'
image.meta.observation.date = '2018-01-01'
image.meta.observation.time = '00:00:00'
image.meta.subarray.xstart = 1
image.meta.subarray.ystart = 1
image.meta.wcsinfo.v2_ref = 0
image.meta.wcsinfo.v3_ref = 0
image.meta.wcsinfo.roll_ref = 0
image.meta.wcsinfo.ra_ref = 0
image.meta.wcsinfo.dec_ref = 0
image = AssignWcsStep.call(image)
# hdulist = create_nirspec_fs_file(grating="G140M", filter="F100LP")
# hdulist[1].data = np.zeros((2048, 2048))
# fs = datamodels.ImageModel(hdulist)
# fs = AssignWcsStep.call(fs)
# multislit = Extract2dStep.call(fs)
# hdulist = create_nirspec_ifu_file("OPAQUE", "G140M")
# hdulist[1].data = np.zeros((2048, 2048))
# ifu_image = datamodels.IFUImageModel(hdulist)
# ifu_image = AssignWcsStep.call(ifu_image)
container = datamodels.ModelContainer([image])
cube = datamodels.CubeModel((2, 10, 10))
# Return the data and a status dependent on whether the step can process it
return [
(image, 'COMPLETE'),
# (multislit, 'COMPLETE'),
# (ifu_image, 'COMPLETE'),
(container, 'COMPLETE'),
(cube, 'SKIPPED'),
]
def test_tso_phot_3():
global shape, xcenter, ycenter
value = 17.
background = 0.8
radius = 5.
radius_inner = 8.
radius_outer = 11.
data = mk_data_array(shape, value, background, xcenter, ycenter, radius)
datamodel = datamodels.CubeModel(data)
set_meta(datamodel, sub64p=False)
int_times = include_int_times(datamodel)
catalog = tso_aperture_photometry(datamodel, xcenter, ycenter, radius + 1.,
radius_inner, radius_outer)
offset = datamodel.meta.exposure.integration_start - 1
slc = slice(offset, offset + shape[0])
assert np.allclose(catalog['MJD'], int_times[slc], atol=1.e-8)
def test_tso_phot_1():
global shape, xcenter, ycenter
# pupil = 'CLEAR' and subarray = 'FULL'
value = 17.
background = 0.8
radius = 5.
radius_inner = 8.
radius_outer = 11.
data = mk_data_array(shape, value, background, xcenter, ycenter, radius)
datamodel = datamodels.CubeModel(data)
set_meta(datamodel, sub64p=False)
# Use a larger radius than was used for creating the data.
catalog = tso_aperture_photometry(datamodel, xcenter, ycenter,
radius + 1., radius_inner,
radius_outer)
assert catalog.meta['instrument'] == datamodel.meta.instrument.name
assert catalog.meta['filter'] == datamodel.meta.instrument.filter
assert catalog.meta['subarray'] == datamodel.meta.subarray.name
assert catalog.meta['detector'] == datamodel.meta.instrument.detector
assert catalog.meta['channel'] == datamodel.meta.instrument.channel
assert catalog.meta['target_name'] == datamodel.meta.target.catalog_name
assert math.isclose(catalog.meta['xcenter'], xcenter, abs_tol=0.01)
assert math.isclose(catalog.meta['ycenter'], ycenter, abs_tol=0.01)
assert np.allclose(catalog['aperture_sum'], 1263.4778, rtol=1.e-7)
assert np.allclose(catalog['aperture_sum_err'], 0., atol=1.e-7)
assert np.allclose(catalog['net_aperture_sum'], 1173., rtol=1.e-7)
assert np.allclose(catalog['annulus_sum'], 143.256627, rtol=1.e-7)
assert np.allclose(catalog['annulus_sum_err'], 0., atol=1.e-7)
assert np.allclose(catalog['annulus_mean'], background, rtol=1.e-7)
assert np.allclose(catalog['annulus_mean'], 0.8, rtol=1.e-6)
assert np.allclose(catalog['annulus_mean_err'], 0., rtol=1.e-7)
assert np.allclose(catalog['net_aperture_sum'], 1173., rtol=1.e-7)
assert np.allclose(catalog['net_aperture_sum_err'], 0., atol=1.e-7)
def test_transform_bbox_from_shape_3d():
model = datamodels.CubeModel((3, 32, 2048))
bb = transform_bbox_from_shape(model.data.shape)
assert bb == ((-0.5, 31.5), (-0.5, 2047.5))
