def test_sres_ext():
file = remote_data_file(
filename=MaNGADataCube.build_file_name(7815, 3702, log=True))
hdu = fits.open(file)
assert MaNGADataCube.spectral_resolution_extension(hdu) == 'LSFPRE', \
'Bad spectral resolution extension selection'
assert MaNGADataCube.spectral_resolution_extension(hdu, ext='SPECRES') == 'SPECRES', \
'Bad spectral resolution extension selection'
assert MaNGADataCube.spectral_resolution_extension(hdu, ext='junk') is None, \
'Should return None for a bad extension name.'
def test_read():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
assert cube.file_name == MaNGADataCube.build_file_name(
cube.plate, cube.ifudesign, log=cube.log), 'Name mismatch'
assert cube.log, 'Should read the log-binned version by default.'
assert cube.wcs is not None, 'WCS should be defined.'
assert cube.shape[:
2] == cube.spatial_shape, 'Spatial shape should be first two axes.'
assert cube.nspec == numpy.prod(
cube.spatial_shape), 'Definition of number of spectra changed.'
assert cube.sres is not None, 'Spectral resolution data was not constructed.'
assert cube.sres_ext == 'LSFPRE', 'Should default to LSFPRE extension.'
assert abs(cube.pixelscale -
cube._get_pixelscale()) < 1e-6, 'Bad match in pixel scale.'
# NOTE: This is worse than it should be because of how the WCS in MaNGA is defined.
assert numpy.all(numpy.absolute(cube.wave - cube._get_wavelength_vector(cube.nwave)) < 2e-4), \
'Bad calculation of wavelength vector.'
assert cube.covar is None, 'Covariance should not have been read'
def test_read_drp():
drpfile = os.path.join(remote_data_file(),
MaNGADataCube.build_file_name(7815, 3702))
assert os.path.isfile(drpfile), 'Did not find file'
with fits.open(drpfile) as hdu:
covar = Covariance.from_fits(hdu,
ivar_ext=None,
covar_ext='GCORREL',
impose_triu=True,
correlation=True)
var = numpy.ma.power(
hdu['IVAR'].data[hdu['GCORREL'].header['BBINDEX']].T.ravel(),
-1).filled(0.0)
covar = covar.apply_new_variance(var)
covar.revert_correlation()
assert numpy.array_equal(var, numpy.diag(
covar.toarray())), 'New variance not applied'
