def read_fits_wcs_linear1d(wcs_info, dispersion_unit=None, spectral_axis=0):
"""Read very a very simple 1D WCS mainly comprising of CRVAL, CRPIX, ...
from a FITS WCS Information container
Parameters
----------
fits_wcs_information : ~specutils.io.read_fits.FITSWCSSpectrum
object compiling WCS information to be used in these readers
.. TODO: Examine this further.
"""
# for the 1D reader setting the spectral_axis to anything else than 0 seems
# to be strange;
# actually, it's perfectly reasonable IF you want to extract
# information from, say, a data cube and you're only interested in the
# spectral dimension. This tool can easily be used for that purpose.
dispersion_unit = dispersion_unit
dispersion_delta = None
if wcs_info.transform_matrix is not None:
dispersion_delta = wcs_info.transform_matrix[spectral_axis,
spectral_axis]
if wcs_info.affine_transform_dict['cdelt'][spectral_axis] is not None:
#checking that both cd1_1 and cdelt1 are either the same or
# one of them non-existent
if dispersion_delta is not None:
np.testing.assert_almost_equal(
dispersion_delta,
wcs_info.affine_transform_dict['cdelt'][spectral_axis])
dispersion_delta = wcs_info.affine_transform_dict['cdelt'][
spectral_axis]
if dispersion_delta is None:
raise FITSWCSSpectrum1DError
if wcs_info.affine_transform_dict['crval'][spectral_axis] is None:
raise FITSWCSSpectrum1DError
else:
dispersion_start = wcs_info.affine_transform_dict['crval'][
spectral_axis]
pixel_offset = wcs_info.affine_transform_dict['crpix'][spectral_axis] or 1
pixel_offset -= 1
dispersion_unit = wcs_info.units[spectral_axis] or dispersion_unit
if None in [dispersion_start, dispersion_delta, pixel_offset]:
raise FITSWCSSpectrum1DError
dispersion_start += -pixel_offset * dispersion_delta
return specwcs.Spectrum1DPolynomialWCS(degree=1,
unit=dispersion_unit,
c0=dispersion_start,
c1=dispersion_delta)
def multispec_wcs_reader(wcs_info, dispersion_unit=None):
"""Extracting multispec information out of WAT header keywords and
building WCS with it
Parameters
----------
dispersion_unit : astropy.unit.Unit, optional
specify a unit for the dispersion if none exists or overwrite,
default=None
"""
assert wcs_info.global_wcs_attributes['system'] == 'multispec'
assert wcs_info.wcs_attributes[1]['wtype'] == 'multispec'
if dispersion_unit is None:
dispersion_unit = wcs_info.wcs_attributes[0]['units']
multispec_dict = _parse_multispec_dict(wcs_info.wcs_attributes[1])
wcs_dict = OrderedDict()
for spec_key in multispec_dict:
single_spec_dict = multispec_dict[spec_key]
if single_spec_dict['dispersion_type'] == 1:
#log-linear dispersion
log = True
else:
log = False
if single_spec_dict['dispersion_type'] in [0, 1]:
#linear or log-linear dispersion
dispersion_wcs = specwcs.Spectrum1DPolynomialWCS(
degree=1,
c0=single_spec_dict["dispersion0"],
c1=single_spec_dict["average_dispersion_delta"])
else:
# single_spec_dict['dispersion_type'] == 2
dispersion_wcs = specwcs.WeightedCombinationWCS()
for function_dict in single_spec_dict["functions"]:
if function_dict['type'] == 'legendre':
##### @embray can you figure out if that's the only way to
## instantiate a polynomial (with c0=xx, c1=xx, ...)?
coefficients = dict([
('c{:d}'.format(i), function_dict['coefficients'][i])
for i in range(function_dict['order'])
])
wcs = specwcs.Spectrum1DIRAFLegendreWCS(
function_dict['order'], function_dict['pmin'],
function_dict['pmax'], **coefficients)
elif function_dict['type'] == 'chebyshev':
coefficients = dict([
('c{:d}'.format(i), function_dict['coefficients'][i])
for i in range(function_dict['order'])
])
wcs = specwcs.Spectrum1DIRAFChebyshevWCS(
function_dict['order'], function_dict['pmin'],
function_dict['pmax'], **coefficients)
elif function_dict['type'] in ['linearspline', 'cubicspline']:
if function_dict['type'] == 'linearspline':
degree = 1
else:
degree = 3
n_pieces = function_dict['npieces']
pmin = function_dict['pmin']
pmax = function_dict['pmax']
y = [
function_dict['coefficients'][i]
for i in range(n_pieces + degree)
]
wcs = specwcs.Spectrum1DIRAFBSplineWCS(
degree, n_pieces, y, pmin, pmax)
else:
raise NotImplementedError
dispersion_wcs.add_WCS(
wcs,
weight=function_dict["weight"],
zero_point_offset=function_dict["zero_point_offset"])
composite_wcs = specwcs.MultispecIRAFCompositeWCS(
dispersion_wcs,
single_spec_dict["no_valid_pixels"],
z=single_spec_dict["doppler_factor"],
log=log,
aperture=single_spec_dict["aperture"],
beam=single_spec_dict["beam"],
aperture_low=single_spec_dict["aperture_low"],
aperture_high=single_spec_dict["aperture_high"],
unit=dispersion_unit)
wcs_dict[spec_key] = composite_wcs
return wcs_dict
# containers relevant for plotting
plt_tup = ()
spectra = []
fits_files = [
'fits/AUG_TELL_K_HIP93691NS.fits', 'fits/AUG_TELL_K_HIP79881NS.fits',
'fits/AUG_OBJ_K.fits', 'fits/AUG_OBJ_K_clean_HIP79881.fits',
'fits/AUG_OBJ_K_clean_HIP93691.fits'
]
figure = plt.figure(1, figsize=(14, 8))
# put all Spectrum1D objects created from fits files into container
for raw_file in fits_files:
fits_file = fits.open(raw_file)[0]
fits_wcs = specwcs.Spectrum1DPolynomialWCS(degree=1,
unit='angstrom',
c0=fits_file.header['CRVAL1'],
c1=fits_file.header['CD1_1'])
fits_spec = Spectrum1D(flux=fits_file.data, wcs=fits_wcs)
spectra.append((fits_spec, raw_file))
# get the value of the minimum mean (used for scaling)
min_mean = find_min_mean([x[0] for x in spectra])
i = 0
# plot each spectrum in the "spectra" container
for spectrum in spectra:
fits_spec = spectrum[0]
fits_name = spectrum[1]
# before plotting, calculate spectrum scale relative to spectrum with smallest avg flux
