def test_extinction_correction():
# Load
extinct = flux_calib.load_extinction_data(121.6428, 37.3413889)
# Correction
wave = np.arange(3000., 10000.) * units.AA
AM = 1.5
flux_corr = flux_calib.extinction_correction(wave, AM, extinct)
# Test
np.testing.assert_allclose(flux_corr[0], 4.47095192)
def apply_flux_calib(self,
wave_sens,
sensfunc,
exptime,
telluric=None,
extinct_correct=False,
airmass=None,
longitude=None,
latitude=None):
"""
Apply a sensitivity function to our spectrum
FLAM, FLAM_SIG, and FLAM_IVAR are generated
Args:
sens_dict (dict):
Sens Function dict
exptime (float):
telluric_correct:
extinct_correct:
airmass (float, optional):
longitude (float, optional):
longitude in degree for observatory
latitude:
latitude in degree for observatory
Used for extinction correction
"""
# Loop on extraction modes
for attr in ['BOX', 'OPT']:
if attr + '_WAVE' not in self._data.keys():
continue
msgs.info("Fluxing {:s} extraction for:".format(attr) +
msgs.newline() + "{}".format(self))
wave = self[attr + '_WAVE']
# Interpolate the sensitivity function onto the wavelength grid of the data
# TODO Telluric corrections via this method are deprecated
# Did the user request a telluric correction?
if telluric is not None:
# This assumes there is a separate telluric key in this dict.
msgs.info('Applying telluric correction')
sensfunc = sensfunc * (telluric > 1e-10) / (telluric +
(telluric < 1e-10))
sensfunc_obs = np.zeros_like(wave)
wave_mask = wave > 1.0 # filter out masked regions or bad wavelengths
try:
sensfunc_obs[wave_mask] = interpolate.interp1d(
wave_sens, sensfunc, bounds_error=True)(wave[wave_mask])
except ValueError:
msgs.error(
"Your data extends beyond the bounds of your sensfunc. " +
msgs.newline() +
"Adjust the par['sensfunc']['extrap_blu'] and/or par['sensfunc']['extrap_red'] to extrapolate "
"further and recreate your sensfunc.")
if extinct_correct:
if longitude is None or latitude is None:
msgs.error(
'You must specify longitude and latitude if we are extinction correcting'
)
# Apply Extinction if optical bands
msgs.info("Applying extinction correction")
msgs.warn(
"Extinction correction applyed only if the spectra covers <10000Ang."
)
extinct = flux_calib.load_extinction_data(longitude, latitude)
ext_corr = flux_calib.extinction_correction(
wave * units.AA, airmass, extinct)
senstot = sensfunc_obs * ext_corr
else:
senstot = sensfunc_obs.copy()
flam = self[attr + '_COUNTS'] * senstot / exptime
flam_sig = (senstot / exptime) / (np.sqrt(
self[attr + '_COUNTS_IVAR']))
flam_var = self[attr + '_COUNTS_IVAR'] / (senstot / exptime)**2
# Mask bad pixels
msgs.info(" Masking bad pixels")
msk = np.zeros_like(senstot).astype(bool)
msk[senstot <= 0.] = True
msk[self[attr + '_COUNTS_IVAR'] <= 0.] = True
flam[msk] = 0.
flam_sig[msk] = 0.
flam_var[msk] = 0.
# TODO JFH We need to update the mask here. I think we need a mask for the counts and a mask for the flam,
# since they can in principle be different. We are masking bad sensfunc locations.
# Finish
self[attr + '_FLAM'] = flam
self[attr + '_FLAM_SIG'] = flam_sig
self[attr + '_FLAM_IVAR'] = flam_var
def coadd_cube(files, parset, overwrite=False):
""" Main routine to coadd spec2D files into a 3D datacube
Args:
files (list):
List of all spec2D files
parset (:class:`pypeit.par.core.PypeItPar`):
An instance of the parameter set.
overwrite (bool):
Overwrite the output file, if it exists?
"""
# Get the detector number
det = 1 if parset is None else parset['rdx']['detnum']
# Load the spectrograph
spec2DObj = spec2dobj.Spec2DObj.from_file(files[0], det)
specname = spec2DObj.head0['PYP_SPEC']
spec = load_spectrograph(specname)
# Grab the parset, if not provided
if parset is None: parset = spec.default_pypeit_par()
cubepar = parset['reduce']['cube']
# Check the output file
outfile = cubepar['output_filename'] if ".fits" in cubepar[
'output_filename'] else cubepar['output_filename'] + ".fits"
out_whitelight = outfile.replace(".fits", "_whitelight.fits")
if os.path.exists(outfile) and not overwrite:
msgs.error("Output filename already exists:" + msgs.newline() +
outfile)
elif os.path.exists(
out_whitelight) and cubepar['save_whitelight'] and not overwrite:
msgs.error("Output filename already exists:" + msgs.newline() +
out_whitelight)
# Check the reference cube and image exist, if requested
ref_scale = None # This will be used to correct relative scaling among the various input frames
if cubepar['standard_cube'] is not None:
if not os.path.exists(cubepar['standard_cube']):
msgs.error("Standard cube does not exist:" + msgs.newline() +
cubepar['reference_cube'])
cube = fits.open(cubepar['standard_cube'])
ref_scale = cube['REFSCALE'].data
if cubepar['reference_image'] is not None:
if not os.path.exists(cubepar['reference_image']):
msgs.error("Reference cube does not exist:" + msgs.newline() +
cubepar['reference_image'])
if cubepar['flux_calibrate']:
msgs.error("Flux calibration is not currently implemented" +
msgs.newline() + "Please set 'flux_calibrate = False'")
# prep
numfiles = len(files)
combine = cubepar['combine']
all_ra, all_dec, all_wave = np.array([]), np.array([]), np.array([])
all_sci, all_ivar, all_idx, all_wghts = np.array([]), np.array(
[]), np.array([]), np.array([])
all_wcs = []
dspat = None if cubepar['spatial_delta'] is None else cubepar[
'spatial_delta'] / 3600.0 # binning size on the sky (/3600 to convert to degrees)
dwv = cubepar[
'wave_delta'] # binning size in wavelength direction (in Angstroms)
wave_ref = None
whitelight_img = None # This is the whitelight image based on all input spec2d frames
weights = np.ones(numfiles) # Weights to use when combining cubes
for ff, fil in enumerate(files):
# Load it up
spec2DObj = spec2dobj.Spec2DObj.from_file(fil, det)
detector = spec2DObj.detector
# Setup for PypeIt imports
msgs.reset(verbosity=2)
if ref_scale is None:
ref_scale = spec2DObj.scaleimg.copy()
# Extract the information
sciimg = (spec2DObj.sciimg - spec2DObj.skymodel) * (
ref_scale / spec2DObj.scaleimg
) # Subtract sky and apply relative sky
ivar = spec2DObj.ivarraw / (ref_scale / spec2DObj.scaleimg)**2
waveimg = spec2DObj.waveimg
bpmmask = spec2DObj.bpmmask
# Grab the slit edges
slits = spec2DObj.slits
wave0 = waveimg[waveimg != 0.0].min()
diff = waveimg[1:, :] - waveimg[:-1, :]
dwv = float(np.median(diff[diff != 0.0]))
msgs.info(
"Using wavelength solution: wave0={0:.3f}, dispersion={1:.3f} Angstrom/pixel"
.format(wave0, dwv))
msgs.info("Constructing slit image")
slitid_img_init = slits.slit_img(pad=0,
initial=True,
flexure=spec2DObj.sci_spat_flexure)
onslit_gpm = (slitid_img_init > 0) & (bpmmask == 0)
# Grab the WCS of this frame
wcs = spec.get_wcs(spec2DObj.head0, slits, detector.platescale, wave0,
dwv)
all_wcs.append(copy.deepcopy(wcs))
# Find the largest spatial scale of all images being combined
# TODO :: probably need to put this in the DetectorContainer
pxscl = detector.platescale * parse.parse_binning(
detector.binning)[1] / 3600.0 # This should be degrees/pixel
slscl = spec.get_meta_value([spec2DObj.head0], 'slitwid')
if dspat is None:
dspat = max(pxscl, slscl)
elif max(pxscl, slscl) > dspat:
dspat = max(pxscl, slscl)
# Generate an RA/DEC image
msgs.info("Generating RA/DEC image")
raimg, decimg, minmax = slits.get_radec_image(
wcs, initial=True, flexure=spec2DObj.sci_spat_flexure)
# Perform the DAR correction
if wave_ref is None:
wave_ref = 0.5 * (np.min(waveimg[onslit_gpm]) +
np.max(waveimg[onslit_gpm]))
# Get DAR parameters
raval = spec.get_meta_value([spec2DObj.head0], 'ra')
decval = spec.get_meta_value([spec2DObj.head0], 'dec')
obstime = spec.get_meta_value([spec2DObj.head0], 'obstime')
pressure = spec.get_meta_value([spec2DObj.head0], 'pressure')
temperature = spec.get_meta_value([spec2DObj.head0], 'temperature')
rel_humidity = spec.get_meta_value([spec2DObj.head0], 'humidity')
coord = SkyCoord(raval, decval, unit=(units.deg, units.deg))
location = spec.location # TODO :: spec.location should probably end up in the TelescopePar (spec.telescope.location)
ra_corr, dec_corr = dc_utils.dar_correction(waveimg[onslit_gpm],
coord,
obstime,
location,
pressure,
temperature,
rel_humidity,
wave_ref=wave_ref)
raimg[onslit_gpm] += ra_corr
decimg[onslit_gpm] += dec_corr
# Get copies of arrays to be saved
wave_ext = waveimg[onslit_gpm].copy()
flux_ext = sciimg[onslit_gpm].copy()
ivar_ext = ivar[onslit_gpm].copy()
# Perform extinction correction
msgs.info("Applying extinction correction")
longitude = spec.telescope['longitude']
latitude = spec.telescope['latitude']
airmass = spec2DObj.head0[spec.meta['airmass']['card']]
extinct = load_extinction_data(longitude, latitude)
# extinction_correction requires the wavelength is sorted
wvsrt = np.argsort(wave_ext)
ext_corr = extinction_correction(wave_ext[wvsrt] * units.AA, airmass,
extinct)
# Correct for extinction
flux_sav = flux_ext[wvsrt] * ext_corr
ivar_sav = ivar_ext[wvsrt] / ext_corr**2
# sort back to the original ordering
resrt = np.argsort(wvsrt)
# Calculate the weights relative to the zeroth cube
if ff != 0:
weights[ff] = np.median(flux_sav[resrt] *
np.sqrt(ivar_sav[resrt]))**2
# Store the information
numpix = raimg[onslit_gpm].size
all_ra = np.append(all_ra, raimg[onslit_gpm].copy())
all_dec = np.append(all_dec, decimg[onslit_gpm].copy())
all_wave = np.append(all_wave, wave_ext.copy())
all_sci = np.append(all_sci, flux_sav[resrt].copy())
all_ivar = np.append(all_ivar, ivar_sav[resrt].copy())
all_idx = np.append(all_idx, ff * np.ones(numpix))
all_wghts = np.append(all_wghts, weights[ff] * np.ones(numpix))
# Grab cos(dec) for convenience
cosdec = np.cos(np.mean(all_dec) * np.pi / 180.0)
# Register spatial offsets between all frames if several frames are being combined
if combine:
# Check if a reference whitelight image should be used to register the offsets
if cubepar["reference_image"] is None:
# Generate white light images
whitelight_imgs, _, _ = dc_utils.make_whitelight(
all_ra, all_dec, all_wave, all_sci, all_wghts, all_idx, dspat)
# ref_idx will be the index of the cube with the highest S/N
ref_idx = np.argmax(weights)
reference_image = whitelight_imgs[:, :, ref_idx].copy()
msgs.info(
"Calculating spatial translation of each cube relative to cube #{0:d})"
.format(ref_idx + 1))
else:
ref_idx = -1 # Don't use an index
# Load reference information
reference_image, whitelight_imgs, wlwcs = \
dc_utils.make_whitelight_fromref(all_ra, all_dec, all_wave, all_sci, all_wghts, all_idx, dspat,
cubepar['reference_image'])
msgs.info(
"Calculating the spatial translation of each cube relative to user-defined 'reference_image'"
)
# Calculate the image offsets - check the reference is a zero shift
ra_shift_ref, dec_shift_ref = calculate_image_offset(
reference_image.copy(), reference_image.copy())
for ff in range(numfiles):
# Don't correlate the reference image with itself
if ff == ref_idx:
continue
# Calculate the shift
ra_shift, dec_shift = calculate_image_offset(
whitelight_imgs[:, :, ff], reference_image.copy())
# Convert to reference
ra_shift -= ra_shift_ref
dec_shift -= dec_shift_ref
# Convert pixel shift to degress shift
ra_shift *= dspat / cosdec
dec_shift *= dspat
msgs.info(
"Spatial shift of cube #{0:d}: RA, DEC (arcsec) = {1:+0.3f}, {2:+0.3f}"
.format(ff + 1, ra_shift * 3600.0, dec_shift * 3600.0))
# Apply the shift
all_ra[all_idx == ff] += ra_shift
all_dec[all_idx == ff] += dec_shift
# Generate a white light image of *all* data
msgs.info("Generating global white light image")
if cubepar["reference_image"] is None:
whitelight_img, _, wlwcs = dc_utils.make_whitelight(
all_ra, all_dec, all_wave, all_sci, all_wghts,
np.zeros(all_ra.size), dspat)
else:
_, whitelight_img, wlwcs = \
dc_utils.make_whitelight_fromref(all_ra, all_dec, all_wave, all_sci, all_wghts, np.zeros(all_ra.size),
dspat, cubepar['reference_image'])
# Calculate the relative spectral weights of all pixels
all_wghts = dc_utils.compute_weights(
all_ra,
all_dec,
all_wave,
all_sci,
all_ivar,
all_idx,
whitelight_img[:, :, 0],
dspat,
dwv,
relative_weights=cubepar['relative_weights'])
# Check if a whitelight image should be saved
if cubepar['save_whitelight']:
# Check if the white light image still needs to be generated - if so, generate it now
if whitelight_img is None:
msgs.info("Generating global white light image")
if cubepar["reference_image"] is None:
whitelight_img, _, wlwcs = dc_utils.make_whitelight(
all_ra, all_dec, all_wave, all_sci, all_wghts,
np.zeros(all_ra.size), dspat)
else:
_, whitelight_img, wlwcs = \
dc_utils.make_whitelight_fromref(all_ra, all_dec, all_wave, all_sci, all_wghts,
np.zeros(all_ra.size),
dspat, cubepar['reference_image'])
# Prepare and save the fits file
msgs.info("Saving white light image as: {0:s}".format(out_whitelight))
img_hdu = fits.PrimaryHDU(whitelight_img.T, header=wlwcs.to_header())
img_hdu.writeto(out_whitelight, overwrite=overwrite)
# Setup the cube ranges
ra_min = cubepar['ra_min'] if cubepar['ra_min'] is not None else np.min(
all_ra)
ra_max = cubepar['ra_max'] if cubepar['ra_max'] is not None else np.max(
all_ra)
dec_min = cubepar['dec_min'] if cubepar['dec_min'] is not None else np.min(
all_dec)
dec_max = cubepar['dec_max'] if cubepar['dec_max'] is not None else np.max(
all_dec)
wav_min = cubepar['wave_min'] if cubepar[
'wave_min'] is not None else np.min(all_wave)
wav_max = cubepar['wave_max'] if cubepar[
'wave_max'] is not None else np.max(all_wave)
if cubepar['wave_delta'] is not None: dwv = cubepar['wave_delta']
# Generate a master WCS to register all frames
coord_min = [ra_min, dec_min, wav_min]
coord_dlt = [dspat, dspat, dwv]
masterwcs = dc_utils.generate_masterWCS(coord_min,
coord_dlt,
name=specname)
msgs.info(msgs.newline() + "-" * 40 + msgs.newline() +
"Parameters of the WCS:" + msgs.newline() +
"RA min, max = {0:f}, {1:f}".format(ra_min, ra_max) +
msgs.newline() +
"DEC min, max = {0:f}, {1:f}".format(dec_min, dec_max) +
msgs.newline() +
"WAVE min, max = {0:f}, {1:f}".format(wav_min, wav_max) +
msgs.newline() + "Spaxel size = {0:f}''".format(3600.0 * dspat) +
msgs.newline() + "Wavelength step = {0:f} A".format(dwv) +
msgs.newline() + "-" * 40)
# Generate the output binning
if combine:
numra = int((ra_max - ra_min) * cosdec / dspat)
numdec = int((dec_max - dec_min) / dspat)
numwav = int((wav_max - wav_min) / dwv)
xbins = np.arange(1 + numra) - 0.5
ybins = np.arange(1 + numdec) - 0.5
spec_bins = np.arange(1 + numwav) - 0.5
else:
slitlength = int(
np.round(
np.median(slits.get_slitlengths(initial=True, median=True))))
numwav = int((np.max(waveimg) - wave0) / dwv)
xbins, ybins, spec_bins = spec.get_datacube_bins(
slitlength, minmax, numwav)
# Make the cube
msgs.info("Generating pixel coordinates")
if combine:
pix_coord = masterwcs.wcs_world2pix(all_ra, all_dec,
all_wave * 1.0E-10, 0)
hdr = masterwcs.to_header()
else:
pix_coord = wcs.wcs_world2pix(
np.vstack((all_ra, all_dec, all_wave * 1.0E-10)).T, 0)
hdr = wcs.to_header()
# Find the NGP coordinates for all input pixels
msgs.info("Generating data cube")
bins = (xbins, ybins, spec_bins)
datacube, edges = np.histogramdd(pix_coord,
bins=bins,
weights=all_sci * all_wghts)
norm, edges = np.histogramdd(pix_coord, bins=bins, weights=all_wghts)
norm_cube = (norm > 0) / (norm + (norm == 0))
datacube *= norm_cube
# Create the variance cube, including weights
msgs.info("Generating variance cube")
all_var = (all_ivar > 0) / (all_ivar + (all_ivar == 0))
var_cube, edges = np.histogramdd(pix_coord,
bins=bins,
weights=all_var * all_wghts**2)
var_cube *= norm_cube**2
# Save the datacube
debug = False
if debug:
datacube_resid, edges = np.histogramdd(pix_coord,
bins=(xbins, ybins, spec_bins),
weights=all_sci *
np.sqrt(all_ivar))
norm, edges = np.histogramdd(pix_coord, bins=(xbins, ybins, spec_bins))
norm_cube = (norm > 0) / (norm + (norm == 0))
outfile = "datacube_resid.fits"
msgs.info("Saving datacube as: {0:s}".format(outfile))
hdu = fits.PrimaryHDU((datacube_resid * norm_cube).T,
header=masterwcs.to_header())
hdu.writeto(outfile, overwrite=overwrite)
msgs.info("Saving datacube as: {0:s}".format(outfile))
final_cube = dc_utils.DataCube(datacube.T,
var_cube.T,
specname,
refscale=ref_scale,
fluxed=cubepar['flux_calibrate'])
final_cube.to_file(outfile, hdr=hdr, overwrite=overwrite)
def apply_flux_calib(self,
sens_dict,
exptime,
telluric_correct=False,
extinct_correct=False,
airmass=None,
longitude=None,
latitude=None):
"""
Apply a sensitivity function to our spectrum
FLAM, FLAM_SIG, and FLAM_IVAR are generated
Args:
sens_dict (dict):
Sens Function dict
exptime (float):
telluric_correct:
extinct_correct:
airmass (float, optional):
longitude (float, optional):
longitude in degree for observatory
latitude:
latitude in degree for observatory
Used for extinction correction
"""
# Loop on extraction modes
for attr in ['BOX', 'OPT']:
if attr + '_WAVE' not in self._data.keys():
continue
msgs.info("Fluxing {:s} extraction for:".format(attr) +
msgs.newline() + "{}".format(self))
#
#try:
# wave = np.copy(np.array(extract['WAVE_GRID']))
#except KeyError:
wave = self[attr + '_WAVE']
wave_sens = sens_dict['wave']
sensfunc = sens_dict['sensfunc'].copy()
# Did the user request a telluric correction from the same file?
if telluric_correct and 'telluric' in sens_dict.keys():
# This assumes there is a separate telluric key in this dict.
telluric = sens_dict['telluric']
msgs.info('Applying telluric correction')
sensfunc = sensfunc * (telluric > 1e-10) / (telluric +
(telluric < 1e-10))
sensfunc_obs = interpolate.interp1d(wave_sens,
sensfunc,
bounds_error=False,
fill_value='extrapolate')(wave)
if extinct_correct:
if longitude is None or latitude is None:
msgs.error(
'You must specify longitude and latitude if we are extinction correcting'
)
# Apply Extinction if optical bands
msgs.info("Applying extinction correction")
msgs.warn(
"Extinction correction applyed only if the spectra covers <10000Ang."
)
extinct = flux_calib.load_extinction_data(longitude, latitude)
ext_corr = flux_calib.extinction_correction(
wave * units.AA, airmass, extinct)
senstot = sensfunc_obs * ext_corr
else:
senstot = sensfunc_obs.copy()
flam = self[attr + '_COUNTS'] * senstot / exptime
flam_sig = (senstot / exptime) / (np.sqrt(
self[attr + '_COUNTS_IVAR']))
flam_var = self[attr + '_COUNTS_IVAR'] / (senstot / exptime)**2
# Mask bad pixels
msgs.info(" Masking bad pixels")
msk = np.zeros_like(senstot).astype(bool)
msk[senstot <= 0.] = True
msk[self[attr + '_COUNTS_IVAR'] <= 0.] = True
flam[msk] = 0.
flam_sig[msk] = 0.
flam_var[msk] = 0.
# Finish
self[attr + '_FLAM'] = flam
self[attr + '_FLAM_SIG'] = flam_sig
self[attr + '_FLAM_IVAR'] = flam_var
def coadd_cube(files, det=1, overwrite=False):
""" Main routine to coadd spec2D files
Args:
files (list):
List of all spec2D files
det (int):
detector
overwrite (bool):
Overwrite the output file, if it exists?
"""
outfile = "datacube.fits"
if os.path.exists(outfile) and not overwrite:
msgs.error("Output filename already exists:" + msgs.newline() +
outfile)
# prep
numfiles = len(files)
combine = True if numfiles > 1 else False
all_ra, all_dec, all_wave = np.array([]), np.array([]), np.array([])
all_sci, all_ivar, all_idx, all_wghts = np.array([]), np.array(
[]), np.array([]), np.array([])
all_wcs = []
dspat = None # binning size on the sky (in arcsec)
ref_scale = None # This will be used to correct relative scaling among the various input frames
wave_ref = None
weights = np.ones(numfiles) # Weights to use when combining cubes
for ff, fil in enumerate(files):
# Load it up
spec2DObj = spec2dobj.Spec2DObj.from_file(fil, det)
# Load the spectrograph
specname = spec2DObj.head0['SPECTROG']
spec = load_spectrograph(specname)
detector = spec2DObj.detector
# Setup for PypeIt imports
msgs.reset(verbosity=2)
if ref_scale is None:
ref_scale = spec2DObj.scaleimg.copy()
# Extract the information
sciimg = (spec2DObj.sciimg - spec2DObj.skymodel) * (
ref_scale / spec2DObj.scaleimg
) # Subtract sky and apply relative sky
ivar = spec2DObj.ivarraw * (ref_scale / spec2DObj.scaleimg)**2
waveimg = spec2DObj.waveimg
bpmmask = spec2DObj.bpmmask
# Grab the slit edges
slits = spec2DObj.slits
wave0 = waveimg[waveimg != 0.0].min()
diff = waveimg[1:, :] - waveimg[:-1, :]
dwv = float(np.median(diff[diff != 0.0]))
msgs.info(
"Using wavelength solution: wave0={0:.3f}, dispersion={1:.3f} Angstrom/pixel"
.format(wave0, dwv))
msgs.info("Constructing slit image")
slitid_img_init = slits.slit_img(pad=0,
initial=True,
flexure=spec2DObj.sci_spat_flexure)
onslit_gpm = (slitid_img_init > 0) & (bpmmask == 0)
# Grab the WCS of this frame
wcs = spec.get_wcs(spec2DObj.head0, slits, detector.platescale, wave0,
dwv)
all_wcs.append(copy.deepcopy(wcs))
# Find the largest spatial scale of all images being combined
# TODO :: probably need to put this in the DetectorContainer
pxscl = detector.platescale * parse.parse_binning(
detector.binning)[1] / 3600.0 # This should be degrees/pixel
slscl = spec.get_meta_value([spec2DObj.head0], 'slitwid')
if dspat is None:
dspat = max(pxscl, slscl)
elif max(pxscl, slscl) > dspat:
dspat = max(pxscl, slscl)
# Generate an RA/DEC image
msgs.info("Generating RA/DEC image")
raimg, decimg, minmax = slits.get_radec_image(
wcs, initial=True, flexure=spec2DObj.sci_spat_flexure)
# Perform the DAR correction
if wave_ref is None:
wave_ref = 0.5 * (np.min(waveimg[onslit_gpm]) +
np.max(waveimg[onslit_gpm]))
# Get DAR parameters
raval = spec.get_meta_value([spec2DObj.head0], 'ra')
decval = spec.get_meta_value([spec2DObj.head0], 'dec')
obstime = spec.get_meta_value([spec2DObj.head0], 'obstime')
pressure = spec.get_meta_value([spec2DObj.head0], 'pressure')
temperature = spec.get_meta_value([spec2DObj.head0], 'temperature')
rel_humidity = spec.get_meta_value([spec2DObj.head0], 'humidity')
coord = SkyCoord(raval, decval, unit=(units.deg, units.deg))
location = spec.location # TODO :: spec.location should probably end up in the TelescopePar (spec.telescope.location)
ra_corr, dec_corr = dc_utils.dar_correction(waveimg[onslit_gpm],
coord,
obstime,
location,
pressure,
temperature,
rel_humidity,
wave_ref=wave_ref)
raimg[onslit_gpm] += ra_corr
decimg[onslit_gpm] += dec_corr
# Get copies of arrays to be saved
wave_ext = waveimg[onslit_gpm].copy()
flux_ext = sciimg[onslit_gpm].copy()
ivar_ext = ivar[onslit_gpm].copy()
# Perform extinction correction
msgs.info("Applying extinction correction")
longitude = spec.telescope['longitude']
latitude = spec.telescope['latitude']
airmass = spec2DObj.head0[spec.meta['airmass']['card']]
extinct = load_extinction_data(longitude, latitude)
# extinction_correction requires the wavelength is sorted
wvsrt = np.argsort(wave_ext)
ext_corr = extinction_correction(wave_ext[wvsrt] * units.AA, airmass,
extinct)
# Correct for extinction
flux_sav = flux_ext[wvsrt] * ext_corr
ivar_sav = ivar_ext[wvsrt] / ext_corr**2
# sort back to the original ordering
resrt = np.argsort(wvsrt)
# Calculate the weights relative to the zeroth cube
if ff != 0:
weights[ff] = np.median(flux_sav[resrt] *
np.sqrt(ivar_sav[resrt]))**2
# Store the information
numpix = raimg[onslit_gpm].size
all_ra = np.append(all_ra, raimg[onslit_gpm].copy())
all_dec = np.append(all_dec, decimg[onslit_gpm].copy())
all_wave = np.append(all_wave, wave_ext.copy())
all_sci = np.append(all_sci, flux_sav[resrt].copy())
all_ivar = np.append(all_ivar, ivar_sav[resrt].copy())
all_idx = np.append(all_idx, ff * np.ones(numpix))
all_wghts = np.append(all_wghts, weights[ff] * np.ones(numpix))
# Grab cos(dec) for convenience
cosdec = np.cos(np.mean(all_dec) * np.pi / 180.0)
# Register spatial offsets between all frames if several frames are being combined
if combine:
# Generate white light images
whitelight_imgs, _ = dc_utils.make_whitelight(all_ra,
all_dec,
all_wave,
all_sci,
all_wghts,
all_idx,
dspat,
numfiles=numfiles)
# ref_idx will be the index of the cube with the highest S/N
ref_idx = np.argmax(weights)
msgs.info(
"Calculating the relative spatial translation of each cube (reference cube = {0:d})"
.format(ref_idx + 1))
# Calculate the image offsets - check the reference is a zero shift
ra_shift_ref, dec_shift_ref = calculate_image_offset(
whitelight_imgs[:, :, ref_idx], whitelight_imgs[:, :, ref_idx])
for ff in range(numfiles):
# Don't correlate the reference image with itself
if ff == ref_idx:
continue
# Calculate the shift
ra_shift, dec_shift = calculate_image_offset(
whitelight_imgs[:, :, ff], whitelight_imgs[:, :, ref_idx])
# Convert to reference
ra_shift -= ra_shift_ref
dec_shift -= dec_shift_ref
# Convert pixel shift to degress shift
ra_shift *= dspat / cosdec
dec_shift *= dspat
msgs.info(
"Image shift of cube {0:d}: RA, DEC (arcsec) = {1:+0.3f}, {2:+0.3f}"
.format(ff + 1, ra_shift * 3600.0, dec_shift * 3600.0))
# Apply the shift
all_ra[all_idx == ff] += ra_shift
all_dec[all_idx == ff] += dec_shift
# Calculate the relative spectral weights of all pixels
all_wghts = dc_utils.compute_weights(all_ra,
all_dec,
all_wave,
all_sci,
all_ivar,
all_wghts,
all_idx,
dspat,
dwv,
numfiles=numfiles)
# Generate a master WCS to register all frames
coord_min = [np.min(all_ra), np.min(all_dec), np.min(all_wave)]
coord_dlt = [dspat, dspat, dwv]
masterwcs = dc_utils.generate_masterWCS(coord_min, coord_dlt)
# Generate the output binning
if combine:
numra = int((np.max(all_ra) - np.min(all_ra)) * cosdec / dspat)
numdec = int((np.max(all_dec) - np.min(all_dec)) / dspat)
numwav = int((np.max(all_wave) - np.min(all_wave)) / dwv)
xbins = np.arange(1 + numra) - 0.5
ybins = np.arange(1 + numdec) - 0.5
spec_bins = np.arange(1 + numwav) - 0.5
else:
slitlength = int(
np.round(
np.median(slits.get_slitlengths(initial=True, median=True))))
numwav = int((np.max(waveimg) - wave0) / dwv)
xbins, ybins, spec_bins = spec.get_datacube_bins(
slitlength, minmax, numwav)
# Make the cube
msgs.info("Generating pixel coordinates")
if combine:
pix_coord = masterwcs.wcs_world2pix(all_ra, all_dec,
all_wave * 1.0E-10, 0)
hdr = masterwcs.to_header()
else:
pix_coord = wcs.wcs_world2pix(
np.vstack((all_ra, all_dec, all_wave * 1.0E-10)).T, 0)
hdr = wcs.to_header()
# Find the NGP coordinates for all input pixels
msgs.info("Generating data cube")
bins = (xbins, ybins, spec_bins)
datacube, edges = np.histogramdd(pix_coord,
bins=bins,
weights=all_sci * all_wghts)
norm, edges = np.histogramdd(pix_coord, bins=bins, weights=all_wghts)
norm_cube = (norm > 0) / (norm + (norm == 0))
datacube *= norm_cube
# Create the variance cube, including weights
msgs.info("Generating variance cube")
all_var = (all_ivar > 0) / (all_ivar + (all_ivar == 0))
var_cube, edges = np.histogramdd(pix_coord,
bins=bins,
weights=all_var * all_wghts**2)
var_cube *= norm_cube**2
# Save the datacube
debug = False
if debug:
datacube_resid, edges = np.histogramdd(pix_coord,
bins=(xbins, ybins, spec_bins),
weights=all_sci *
np.sqrt(all_ivar))
norm, edges = np.histogramdd(pix_coord, bins=(xbins, ybins, spec_bins))
norm_cube = (norm > 0) / (norm + (norm == 0))
outfile = "datacube_resid.fits"
msgs.info("Saving datacube as: {0:s}".format(outfile))
hdu = fits.PrimaryHDU((datacube_resid * norm_cube).T,
header=masterwcs.to_header())
hdu.writeto(outfile, overwrite=overwrite)
msgs.info("Saving datacube as: {0:s}".format(outfile))
primary_hdu = fits.PrimaryHDU(header=spec2DObj.head0)
sci_hdu = fits.ImageHDU(datacube.T, name="scicube", header=hdr)
var_hdu = fits.ImageHDU(var_cube.T, name="varcube", header=hdr)
hdulist = fits.HDUList([primary_hdu, sci_hdu, var_hdu])
hdulist.writeto(outfile, overwrite=overwrite)