def make_datamodel():
"""Make data for white light tests"""
model = datamodels.MultiSpecModel()
model.meta.exposure.group_time = 0.15904
model.meta.exposure.ngroups = 60
model.meta.exposure.start_time = 58627.0
model.meta.exposure.integration_start = 1
model.meta.exposure.integration_end = 2
# Make data arrays
flux = np.random.rand(200) * 1e-9
wavelength = np.arange(11, 13, step=0.01)
error = np.random.rand(200)
surf_bright = np.zeros(200)
sb_error = np.zeros(200)
dq = np.ones(200)
background = np.zeros(200)
berror = np.zeros(200)
npixels = np.zeros(200)
data = [(i, j, k, l, m, n, o, p, q) for i, j, k, l, m, n, o, p, q in zip(
wavelength, flux, error, surf_bright, sb_error, dq, background, berror,
npixels)]
spec_table = np.array(data,
dtype=[('WAVELENGTH', 'f8'), ('FLUX', 'f8'),
('ERROR', 'f8'), ('SURF_BRIGHT', 'f8'),
('SB_ERROR', 'f8'), ('DQ', 'u4'),
('BACKGROUND', 'f8'), ('BERROR', 'f8'),
('NPIXELS', 'f8')])
spectrum = datamodels.SpecModel()
spectrum.spec_table = spec_table
model.spec.append(spectrum)
integrations = [
(1, 58627.53891071, 58627.53896565, 58627.5390206, 0., 0., 0.),
(2, 58627.5390206, 58627.53907555, 58627.5391305, 0., 0., 0.),
(3, 58627.5391305, 58627.53918544, 58627.53924039, 0., 0., 0.)
]
integration_table = np.array(integrations,
dtype=[('integration_number', 'i4'),
('int_start_MJD_UTC', 'f8'),
('int_mid_MJD_UTC', 'f8'),
('int_end_MJD_UTC', 'f8'),
('int_start_BJD_TDB', 'f8'),
('int_mid_BJD_TDB', 'f8'),
('int_end_BJD_TDB', 'f8')])
model.int_times = (integration_table)
return model
def user_bkg_spec_b():
"""Create "user-background" data for testing.
`expand_to_2d` uses `np.interp` for interpolation, and the wavelength
array that is passed to `np.interp` must be increasing. `expand_to_2d`
is supposed to handle the case that the wavelengths are decreasing.
Create data for checking that the results are the same even if the
wavelength array in `m_bkg_spec` is reversed so that the values are
decreasing, and the corresponding flux array is also reversed to retain
the original (wavelength, flux) relation.
Returns
-------
m_bkg_spec : `~jwst.datamodels.MultiSpecModel`
"""
# This data type is used for creating a MultiSpecModel.
spec_dtype = datamodels.SpecModel().spec_table.dtype
m_bkg_spec = datamodels.MultiSpecModel()
wavelength = np.geomspace(1.5,
4.5,
num=25,
endpoint=True,
dtype=np.float64)[::-1]
flux = np.zeros_like(wavelength)
error = np.ones_like(wavelength)
var_dummy = error.copy()
surf_bright = np.linspace(13.,
25.,
num=25,
endpoint=True,
retstep=False,
dtype=np.float64)[::-1]
sb_error = np.ones_like(wavelength)
dq = np.zeros(wavelength.shape, dtype=np.uint32)
background = np.ones_like(wavelength)
berror = np.ones_like(wavelength)
npixels = np.ones_like(wavelength)
otab = np.array(list(
zip(wavelength, flux, error, var_dummy, var_dummy, var_dummy,
surf_bright, sb_error, var_dummy, var_dummy, var_dummy, dq,
background, berror, var_dummy, var_dummy, var_dummy, npixels)),
dtype=spec_dtype)
spec = datamodels.SpecModel(spec_table=otab)
m_bkg_spec.spec.append(spec)
return m_bkg_spec
def user_bkg_spec_a():
"""Create "user-background" data for testing.
Returns
-------
m_bkg_spec : `~jwst.datamodels.MultiSpecModel`
"""
# This data type is used for creating a MultiSpecModel.
spec_dtype = datamodels.SpecModel().spec_table.dtype
# m_bkg_spec doesn't have to be a MultiSpecModel, but that's an option.
m_bkg_spec = datamodels.MultiSpecModel()
wavelength = np.geomspace(1.5,
4.5,
num=25,
endpoint=True,
dtype=np.float64)
flux = np.zeros_like(wavelength)
error = np.ones_like(wavelength)
var_dummy = error.copy()
surf_bright = np.linspace(13.,
25.,
num=25,
endpoint=True,
retstep=False,
dtype=np.float64)
sb_error = np.ones_like(wavelength)
dq = np.zeros(wavelength.shape, dtype=np.uint32)
background = np.ones_like(wavelength)
berror = np.ones_like(wavelength)
# The npixels column should no longer be used. Set it to a large value
# to make it more obvious in case it actually is still used.
npixels = np.zeros_like(wavelength) + 2000.
otab = np.array(list(
zip(wavelength, flux, error, var_dummy, var_dummy, var_dummy,
surf_bright, sb_error, var_dummy, var_dummy, var_dummy, dq,
background, berror, var_dummy, var_dummy, var_dummy, npixels)),
dtype=spec_dtype)
spec = datamodels.SpecModel(spec_table=otab)
m_bkg_spec.spec.append(spec)
return m_bkg_spec
def test_dq():
"""Test that DQ exclusion works"""
spec1 = create_spec_model(flux=1e-9)
spec2 = create_spec_model(flux=1e-9)
ms = datamodels.MultiSpecModel()
ms.meta.exposure.exposure_time = 1
ms.spec.append(spec1)
ms.spec.append(spec2)
# Make one pixel bad by being large.
# Result should be large
BAD_PIX = 5
spec1.spec_table['FLUX'][BAD_PIX] = 1.0
result = Combine1dStep.call(ms)
assert np.isclose(result.spec_table['FLUX'][BAD_PIX], 0.5)
# Now mark that pixel bad.
# Result should not just contain the second spectrum value.
spec1.spec_table['DQ'][BAD_PIX] = datamodels.dqflags.pixel['DO_NOT_USE']
result_dq = Combine1dStep.call(ms)
assert np.isclose(result_dq.spec_table['FLUX'][BAD_PIX],
spec2.spec_table['FLUX'][BAD_PIX])
def user_bkg_spec_a():
"""Create "user-background" data for testing.
Returns
-------
m_bkg_spec : `~jwst.datamodels.MultiSpecModel`
"""
# This data type is used for creating a MultiSpecModel.
spec_dtype = datamodels.SpecModel().spec_table.dtype
# m_bkg_spec doesn't have to be a MultiSpecModel, but that's an option.
m_bkg_spec = datamodels.MultiSpecModel()
wavelength = np.geomspace(1.5,
4.5,
num=25,
endpoint=True,
dtype=np.float64)
flux = np.linspace(13.,
25.,
num=25,
endpoint=True,
retstep=False,
dtype=np.float64)
fl_error = np.ones_like(wavelength)
dq = np.zeros(wavelength.shape, dtype=np.uint32)
net = np.zeros_like(wavelength)
nerror = np.ones_like(wavelength)
background = np.ones_like(wavelength)
berror = np.ones_like(wavelength)
npixels = np.ones_like(wavelength)
otab = np.array(list(
zip(wavelength, flux, fl_error, dq, net, nerror, background, berror,
npixels)),
dtype=spec_dtype)
spec = datamodels.SpecModel(spec_table=otab)
m_bkg_spec.spec.append(spec)
return m_bkg_spec
def make_datamodel():
"""Make data for white light tests"""
model = datamodels.MultiSpecModel()
model.meta.exposure.group_time = 0.15904
model.meta.exposure.ngroups = 60
model.meta.exposure.start_time = 58627.0
model.meta.exposure.integration_start = 1
model.meta.exposure.integration_end = 2
# Make data arrays
flux = np.random.rand(200) * 1e-9
wavelength = np.arange(11, 13, step=0.01)
f_var_poisson = np.random.rand(200)
f_var_rnoise = np.random.rand(200)
f_var_flat = np.random.rand(200)
error = np.sqrt(f_var_poisson + f_var_rnoise + f_var_flat)
surf_bright = np.zeros(200)
sb_error = np.zeros(200)
sb_var_poisson = sb_error.copy()
sb_var_rnoise = sb_error.copy()
sb_var_flat = sb_error.copy()
dq = np.ones(200)
background = np.zeros(200)
berror = np.zeros(200)
b_var_poisson = sb_error.copy()
b_var_rnoise = sb_error.copy()
b_var_flat = sb_error.copy()
npixels = np.zeros(200)
spec_dtype = datamodels.SpecModel(
).spec_table.dtype # This data type is used for creating an output table.
otab = np.array(list(
zip(wavelength, flux, error, f_var_poisson, f_var_rnoise, f_var_flat,
surf_bright, sb_error, sb_var_poisson, sb_var_rnoise, sb_var_flat,
dq, background, berror, b_var_poisson, b_var_rnoise, b_var_flat,
npixels), ),
dtype=spec_dtype)
spec_model = datamodels.SpecModel(spec_table=otab)
model.spec.append(spec_model)
integrations = [
(1, 58627.53891071, 58627.53896565, 58627.5390206, 0., 0., 0.),
(2, 58627.5390206, 58627.53907555, 58627.5391305, 0., 0., 0.),
(3, 58627.5391305, 58627.53918544, 58627.53924039, 0., 0., 0.)
]
integration_table = np.array(integrations,
dtype=[('integration_number', 'i4'),
('int_start_MJD_UTC', 'f8'),
('int_mid_MJD_UTC', 'f8'),
('int_end_MJD_UTC', 'f8'),
('int_start_BJD_TDB', 'f8'),
('int_mid_BJD_TDB', 'f8'),
('int_end_BJD_TDB', 'f8')])
model.int_times = integration_table
return model
