def test_raw_to_radiance_correctness(raw, rad):
expected = rad[c.radiance_data].isel(
x=slice(1, -2), y=slice(1, -2)
).transpose(c.band_index, c.height_coord, c.width_coord)
actual = fpr.raw_to_radiance(raw)[c.radiance_data].isel(
x=slice(1, -2), y=slice(1, -2)
).transpose(c.band_index, c.height_coord, c.width_coord)
xrt.assert_equal(expected, actual)
def test_raw_to_radiance_keep_variables(raw, rad_computed):
variables = [
c.cfa_data,
c.dark_corrected_cfa_data,
c.dark_reference_data,
c.rgb_data,
]
default = rad_computed
keep_all = fpr.raw_to_radiance(raw, keep_variables=variables)
for v in variables:
assert (v not in default.variables)
assert (v in keep_all.variables)
keep_one = fpr.raw_to_radiance(raw, keep_variables=[v])
assert (v in keep_one.variables)
for notv in [var for var in variables if var is not v]:
assert (notv not in keep_one.variables)
def test_raw_to_radiance_format(raw):
rad = fpr.raw_to_radiance(raw)
assert type(rad) is xr.Dataset
# These should exist in radiances computed from CFA data
dims = c.radiance_dims
for d in dims:
assert d in rad.dims
variables = [
c.image_index,
c.peak_coord,
c.wavelength_data,
]
for v in variables:
assert v in rad.variables
def test_ENVI_raw_to_rad_correspondence(raw_ENVI, rad_ENVI):
rad_computed = fpr.raw_to_radiance(raw_ENVI)
for v in [c.band_index, c.wavelength_data, c.fwhm_data]:
assert np.all(rad_computed[v].values == rad_ENVI[v].values)
def rad_computed(raw):
return fpr.raw_to_radiance(raw)