def fromMarzJSON(json_spectrum):
from astropy.nddata import (
VarianceUncertainty,
StdDevUncertainty,
InverseVariance,
)
wavelength = Quantity(json_spectrum["wavelength"], u.Angstrom)
spectrum = SpectrumList()
uncertainty = None
if "variance" in json_spectrum.keys():
uncertainty = StdDevUncertainty(
Quantity(np.array(json_spectrum["variance"], dtype=np.float)))
for key in json_spectrum.keys():
if key != 'wavelength' and key != 'variance':
flux = Quantity(np.array(json_spectrum[key], dtype=np.float))
if key == 'intensity' and uncertainty:
aspectrum = Spectrum1D(flux=flux,
spectral_axis=wavelength,
uncertainty=uncertainty,
mask=np.isnan(flux),
meta={'purpose': 'reduced'})
else:
aspectrum = Spectrum1D(flux=flux,
spectral_axis=wavelength,
mask=np.isnan(flux),
meta={'purpose': key})
spectrum.append(aspectrum)
return spectrum
def load_aaomega_file(filename, *args, **kwargs):
with read_fileobj_or_hdulist(filename, *args, **kwargs) as fits_file:
fits_header = fits_file[AAOMEGA_SCIENCE_INDEX].header
# fits_file is the hdulist
var_idx = None
rwss_idx = None
for idx, extn in enumerate(fits_file):
if extn.name == "VARIANCE":
var_idx = idx
if extn.name == "RWSS":
rwss_idx = idx
# science data
fits_data = fits_file[AAOMEGA_SCIENCE_INDEX].data
# read in Fibre table data....
ftable = Table(fits_file[AAOMEGA_FIBRE_INDEX].data)
# A SpectrumList to hold all the Spectrum1D objects
sl = SpectrumList()
# the row var contains the pixel data from the science frame
for i, row in enumerate(fits_data):
# Definitely need deepcopy here, otherwise it does *NOT* work!
fib_header = deepcopy(fits_header)
# Adjusting some values from primary header so individual fibre
# spectra have meaningful headers
fib_header["FLDNAME"] = (fits_header["OBJECT"],
"Name of 2dF .fld file")
fib_header["FLDRA"] = (
fits_header["MEANRA"],
"Right Ascension of 2dF field",
)
fib_header["FLDDEC"] = (fits_header["MEANDEC"],
"Declination of 2dF field")
# Now for the fibre specific information from the Fibre Table
# (extension 2)
# Caution: RA and DEC are stored in RADIANS in the FIBRE TABLE!
fib_header["RA"] = (
ftable["RA"][i] * 180.0 / np.pi,
"Right Ascension of fibre from configure .fld file",
)
fib_header["DEC"] = (
ftable["DEC"][i] * 180.0 / np.pi,
"Declination of fibre from configure .fld file",
)
fib_header["OBJECT"] = (
ftable["NAME"][i],
"Name of target observed by fibre",
)
fib_header["OBJCOM"] = (
ftable["COMMENT"][i],
"Comment from configure .fld file for target",
)
fib_header["OBJMAG"] = (
ftable["MAGNITUDE"][i],
"Magnitude of target observed by fibre",
)
fib_header["OBJTYPE"] = (
ftable["TYPE"][i],
"Type of target observed by fibre",
)
fib_header["OBJPIV"] = (
ftable["PIVOT"][i],
"Pivot number used to observe target",
)
fib_header["OBJPID"] = (
ftable["PID"][i],
"Program ID from configure .fld file",
)
fib_header["OBJX"] = (
ftable["X"][i],
"X coord of target observed by fibre (microns)",
)
fib_header["OBJY"] = (
ftable["Y"][i],
"Y coord of target observed by fibre (microns)",
)
fib_header["OBJXERR"] = (
ftable["XERR"][i],
"X coord error of target observed by fibre (microns)",
)
fib_header["OBJYERR"] = (
ftable["YERR"][i],
"Y coord error of target observed by fibre (microns)",
)
fib_header["OBJTHETA"] = (
ftable["THETA"][i],
"Angle of fibre used to observe target",
)
fib_header["OBJRETR"] = (
ftable["RETRACTOR"][i],
"Retractor number used to observe target",
)
# WLEN added around 2005 according to AAOmega obs manual...
# so not always available
if "WLEN" in ftable.colnames:
fib_header["OBJWLEN"] = (
ftable["WLEN"][i],
"Retractor of target observed by fibre",
)
# ftable['TYPE'][i]:
# P == program (science)
# S == sky
# U == unallocated or unused
# F == fiducial (guide) fibre
# N == broken, dead or no fibre
meta = {"header": fib_header}
if ftable["TYPE"][i] == "P":
meta["purpose"] = "reduced"
elif ftable["TYPE"][i] == "S":
meta["purpose"] = "sky"
else:
# Don't include other fibres that are not science or sky
continue
wcs = compute_wcs_from_keys_and_values(fib_header,
**AAOMEGA_2DF_WCS_SETTINGS)
flux = row * AAOMEGA_2DF_FLUX_UNIT
meta["fibre_index"] = i
# Our science spectrum
spectrum = Spectrum1D(wcs=wcs, flux=flux, meta=meta)
# If the VARIANCE spectrum exists, add it as an additional spectrum
# in the meta dict with key 'variance'
if var_idx is not None:
var_data = fits_file[var_idx].data
var_flux = var_data[i] * AAOMEGA_2DF_FLUX_UNIT**2
spectrum.uncertainty = VarianceUncertainty(var_flux)
# If the RWSS spectrum exists, add it as an additional spectrum in
# the meta dict with key 'science_sky'
# This is an optional extension produced by 2dfdr on request: all
# spectra without the average/median sky subtraction
# Useful in case users want to do their own sky subtraction.
if rwss_idx is not None:
rwss_data = fits_file[rwss_idx].data
rwss_flux = rwss_data[i] * AAOMEGA_2DF_FLUX_UNIT
rwss_meta = {"header": fib_header, "purpose": "science_sky"}
spectrum.meta["science_sky"] = Spectrum1D(wcs=wcs,
flux=rwss_flux,
meta=rwss_meta)
# Add our spectrum to the list.
# The additional spectra are accessed using
# spectrum.meta['variance'] and spectrum.meta['science_sky']
sl.append(spectrum)
add_labels(sl)
return sl
ss_spectrum,
"Custom pipeline sky subtracted spectrum",
sky_flux=avg_sky_flux,
c_range=low_region,
h_range=high_region)
plt.savefig(output_dir / (fits_file_name + ".pdf"), dpi=300)
plt.close()
# Diagnostics - plot the spectra from every fibre (to a different file to the other plots - it's big!!)
if plot_rss_spectra:
plotting.plot_rss_spectra(non_ss_spectra, flux_ratios,
fits_file_name, sky_mask, obj_mask,
low_region, high_region, output_dir,
expand_rss_spectra)
fits_group_ss_spectra.append(ss_spectrum)
# Combine the spectra in the group
image_file_name = output_dir / f"ss_{fits_group}.pdf"
grp_spectra = SpectrumCollectionEx.from_spectra(
fits_group_ss_spectra, fits_group)
grp_spectrum = Spectrum1DEx(flux=np.mean(grp_spectra.flux, axis=0),
spectral_axis=grp_spectra.spectral_axis[0])
plotting.plot_spectrum(
grp_spectrum,
filename=image_file_name,
title=
f"Custom pipeline sky subtracted & combined spectrum for observations {fits_group}"
)
# Save to a new fits file
