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_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 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)