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 make_ifu_cube(data_shape, source=None, background=None,
x_center=None, y_center=None,
radius=None, inner_bkg=None, outer_bkg=None):
"""Create "science" data for testing.
Returns
-------
input_model : `~jwst.datamodels.ifucube.IFUCubeModel`
"""
data = np.zeros(data_shape, dtype=np.float32)
r_2 = radius**2
if inner_bkg is not None and outer_bkg is not None:
create_background = True
bkg = background
inner_2 = inner_bkg**2
outer_2 = outer_bkg**2
elif inner_bkg is not None or outer_bkg is not None:
raise RuntimeError("Specify both inner_bkg and outer_bkg or neither.")
else:
create_background = False
bkg = 0.
for j in range(data_shape[-2]):
for i in range(data_shape[-1]):
dist_2 = (float(i) - x_center)**2 + (float(j) - y_center)**2
if dist_2 <= r_2:
data[:, j, i] = source + bkg
if create_background:
if dist_2 > inner_2 and dist_2 <= outer_2:
data[:, j, i] = bkg
dq = np.zeros(data_shape, dtype=np.uint32)
input_model = datamodels.IFUCubeModel(data=data, dq=dq)
# Populate the BUNIT keyword so that in ifu.py the net will be moved
# to the flux column.
input_model.meta.bunit_data = 'mJy/arcsec^2'
def mock_wcs(x, y, z):
"""Fake wcs method."""
wavelength = np.linspace(0.5975, 5.2975, 941, endpoint=True,
retstep=False, dtype=np.float64)
if hasattr(z, 'dtype'):
iz = np.around(z).astype(np.int64)
else:
iz = round(z)
wl = wavelength[iz]
ra = wl.copy() # dummy values
dec = wl.copy() # dummy values
return (ra, dec, wl)
input_model.meta.wcs = mock_wcs
return input_model
def file_case(request, tmp_path):
"""Generate files with different model states"""
case, allow = request.param
if case == 'good_model':
# Make a model that will always succeed
model = datamodels.Level1bModel((10, 10, 10, 10))
path = tmp_path / 'level1bmodel.fits'
model.save(path)
elif case == 'bad_model':
# Make a model that will fail if not allowed
model = datamodels.IFUCubeModel((10, 10, 10))
path = tmp_path / 'image.fits'
model.save(path)
elif case == 'fits_nomodel':
# Create just a plain anything FITS
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
path = tmp_path / 'empty.fits'
hdul.writeto(path)
else:
assert False, f'Cannot produce a file for {case}'
return path, allow