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)