def test_bhddb():
bhddb = BandheadIndexDB.from_key('BHBASIC')
assert bhddb.size == 3
_bhddb = BandheadIndexDB(bhddb.file)
assert _bhddb.size == 3
def test_avail():
database_list = available_spectral_index_databases()
assert len(database_list) > 0, 'No spectral index databases available'
# For the available databases, make sure that the ancillary
# databases can be loaded.
for database in database_list:
if database['artifacts'] is not None:
artdb = ArtifactDB.from_key(database['artifacts'])
if database['absindex'] is not None:
absdb = AbsorptionIndexDB.from_key(database['absindex'])
if database['bandhead'] is not None:
bhddb = BandheadIndexDB.from_key(database['bandhead'])
def test_model_indices():
# Setup
bm = SpectralIndicesBitMask()
absdb = AbsorptionIndexDB.from_key('EXTINDX')
bhddb = BandheadIndexDB.from_key('BHBASIC')
# Grab the model spectra
tpl = TemplateLibrary('M11MILES', spectral_step=1e-4, log=True, hardcopy=False)
flux = numpy.ma.MaskedArray(tpl['FLUX'].data,
mask=tpl.bitmask.flagged(tpl['MASK'].data,
flag=['NO_DATA', 'WAVE_INVALID',
'FLUX_INVALID', 'SPECRES_NOFLUX']))
# Try to measure only absorption-line indices
indices = SpectralIndices.measure_indices(absdb, None, tpl['WAVE'].data, flux[:2,:],
bitmask=bm)
# Try to measure only bandhead indices
indices = SpectralIndices.measure_indices(None, bhddb, tpl['WAVE'].data, flux[:2,:],
bitmask=bm)
# Measure both
indices = SpectralIndices.measure_indices(absdb, bhddb, tpl['WAVE'].data, flux[:2,:],
bitmask=bm)
# Test the output
indx = numpy.ma.MaskedArray(indices['INDX'], mask=bm.flagged(indices['MASK']))
indx_bf = numpy.ma.MaskedArray(indices['INDX_BF'], mask=bm.flagged(indices['MASK']))
assert indx.shape == (2,46), 'Incorrect output shape'
assert numpy.sum(indx.mask[0,:]) == 12, 'Incorrect number of masked indices'
assert numpy.allclose(indx[1,:].compressed(),
numpy.array([-2.67817275e-02, 1.94113040e-02, 2.92495672e-01,
1.91132126e+00, 1.86182351e+00, 1.13929718e+00,
2.45269081e+00, 2.24190385e+00, 3.55293194e+00,
5.25612762e+00, 2.09506842e-02, 5.47343625e-02,
2.29779119e-01, 1.92039303e+00, 1.95663049e+00,
9.81723064e-01, 7.44058546e-01, 5.46484409e-01,
1.56520293e+00, 8.93716574e-03, 1.82158534e-02,
2.46748795e+00, 1.03916389e+00, 2.27668045e+00,
2.68943564e+00, 6.77715967e+00, 1.31272416e+00,
9.12401084e-03, 3.52657124e-03, 1.92826953e-03,
-1.00946682e-02, 2.01732938e-02, 1.17072128e+00,
1.12525642e+00]),
rtol=0.0, atol=1e-4), 'Index values are different'
assert numpy.std(indx.compressed() - indx_bf.compressed()) < 0.01, \
'Index definitions are too different'
nwalkers = 100 # number of monte carlo chains
nsteps= 100 # number of steps in the monte carlo chain
opstart = [0.7, 9.0, 1.25] # starting place of all the chains
burnin = 500 # number of steps in the burn in phase of the monte carlo chain
ndim = 3
ages = Planck15.age(mpl6agn['nsa_z']).value
define_em_db = SpectralFeatureDBDef(key='USEREM',
file_path='elpsnitch.par')
emlines = EmissionLineDB(u"USEREM", emldb_list=define_em_db)
define_abs_db = SpectralFeatureDBDef(key='USERABS',
file_path='extindxsnitch.par')
abs_db = AbsorptionIndexDB(u"USERABS", indxdb_list=define_abs_db)
band_db = BandheadIndexDB(u"BHBASIC")
indx_names = np.hstack([abs_db.data["name"], band_db.data["name"]])
for n in trange(len(mpl6agn)):
if os.path.isfile('../data/manga-'+str(mpl6agn[n]['plate'])+'-'+str(mpl6agn[n]['ifudsgn'].strip())+'-LOGCUBE.fits.gz'):
# pass
# else:
# r = requests.get(top_level_url+str(mpl6agn[n]['plate'])+'/stack/manga-'+str(mpl6agn[n]['plate'])+'-'+str(mpl6agn[n]['ifudsgn'].strip())+'-LOGCUBE.fits.gz', auth=HTTPBasicAuth(up[0].rstrip('\n'), up[1].rstrip('\n')), stream=True)
# if r.ok:
# with open('../data/manga-'+str(mpl6agn[n]['plate'])+'-'+str(mpl6agn[n]['ifudsgn'].strip())+'-LOGCUBE.fits.gz', 'wb') as file:
# file.write(r.content)
# s = requests.get(top_level_url+str(mpl6agn[n]['plate'])+'/stack/manga-'+str(mpl6agn[n]['plate'])+'-'+str(mpl6agn[n]['ifudsgn'].strip())+'-LOGRSS.fits.gz', auth=HTTPBasicAuth(up[0].rstrip('\n'), up[1].rstrip('\n')), stream=True)
# if s.ok:
# with open('../data/manga-'+str(mpl6agn[n]['plate'])+'-'+str(mpl6agn[n]['ifudsgn'].strip())+'-LOGRSS.fits.gz', 'wb') as sfile:
# sfile.write(s.content)
# p = requests.get(top_level_url_par+str(mpl6agn[n]['plate'])+'/'+str(mpl6agn[n]['ifudsgn'].strip())+'/ref/mangadap-'+str(mpl6agn[n]['plate'])+'-'+str(mpl6agn[n]['ifudsgn'].strip())+'-LOGCUBE-input.par', auth=HTTPBasicAuth(up[0].rstrip('\n'), up[1].rstrip('\n')), stream=True)
def test_basic():
bhddb = BandheadIndexDB.from_key('BHBASIC')
assert len(bhddb) == 3, 'Incorrect number of bandhead indices'
assert 'Dn4000' in bhddb['name'], 'Does not contain Dn4000 in list'
def test_read():
dbs = BandheadIndexDB.available_databases()
for key in dbs.keys():
bhddb = BandheadIndexDB.from_key(key)
def test_bhd_channel_names():
bhddb = BandheadIndexDB.from_key('BHBASIC')
names = bhddb.channel_names()
assert 'D4000' in list(names.keys())
assert names['D4000'] == 0
def measure_spec(fluxes, errors=None, ivar=None, sres=None):
""" This function takes an array of spectra of shape (# of spectra, # of wavelengths) and returns the pre-defined emission line and
absorption features in each spectra in two recorded arrays of shape (# of spectra,). The recorded arrays can be accessed to give the
quantities returned by the MaNGA DAP, e.g. ["EW"], ["EWERR"], ["INDX"].
The user can change the emission and absorption quantities returned by specifying different emission line and absorption feature
databases.
INPUTS
:fluxes:
Fluxes at each manga wavelength, M for any number of spectra, X. Shape (X, M).
OUTPUTS
:em_model_eml_par:
Recorded array containing the emisison line parameter measurements from the defined user database. Shape (X, ).
:indx_measurement:
Recorded array containing the absorption feature parameter measurements from the defined user database. Shape (X, ).
"""
if ivar is None:
ivar = 1/(0.1*fluxes)**2
if errors is None:
errors = 0.1*fluxes
if sres is None:
sres = np.ones_like(fluxes)
tpl = TemplateLibrary("MILESHC",
match_to_drp_resolution=False,
velscale_ratio=1,
spectral_step=1e-4,
log=True,
directory_path=".",
processed_file="mileshc.fits",
clobber=True)
# Instantiate the object that does the fitting
contbm = StellarContinuumModelBitMask()
ppxf = PPXFFit(contbm)
# Define the absorption and bandhead feature databases
define_abs_db = SpectralFeatureDBDef(key='USERABS',
file_path='extindxsnitch.par')
abs_db = AbsorptionIndexDB(u"USERABS", indxdb_list=define_abs_db)
band_db = BandheadIndexDB(u"BHBASIC")
global indx_names
indx_names = np.hstack([abs_db.data["name"], band_db.data["name"]])
# Define the emission line feature database
specm = EmissionLineModelBitMask()
elric = Elric(specm)
global emlines
define_em_db = SpectralFeatureDBDef(key='USEREM',
file_path='elpsnitch.par')
emlines = EmissionLineDB(u"USEREM", emldb_list=define_em_db)
# Check to see if a single spectra has been input. If the single spectra is shape (# of wavelengths,) then reshape
# to give (1, #number of wavelengths)
if fluxes.shape[0] == len(manga_wave):
fluxes = fluxes.reshape(1,-1)
else:
pass
nspec = fluxes.shape[0]
# Provide the guess redshift and guess velocity dispersion
guess_redshift = np.full(nspec, 0.0001, dtype=float)
guess_dispersion = np.full(nspec, 77.0, dtype=float)
# Perform the fits to the continuum, emission lines and absorption features
model_wave, model_flux, model_mask, model_par = ppxf.fit(tpl["WAVE"].data.copy(), tpl["FLUX"].data.copy(), manga_wave, fluxes, errors, guess_redshift, guess_dispersion, iteration_mode="none", velscale_ratio=1, degree=8, mdegree=-1, moments=2, quiet=True)
em_model_wave, em_model_flux, em_model_base, em_model_mask, em_model_fit_par, em_model_eml_par = elric.fit(manga_wave, fluxes, emission_lines=emlines, ivar=ivar, sres=sres, continuum=model_flux, guess_redshift = model_par["KIN"][:,0]/c, guess_dispersion=model_par["KIN"][:,1], base_order=1, quiet=True)
indx_measurements = SpectralIndices.measure_indices(absdb=abs_db, bhddb=band_db, wave=manga_wave, flux=fluxes-em_model_flux, ivar=ivar, mask=None, redshift=model_par["KIN"][:,0]/c, bitmask=None)
# Close all plots generated by the MaNGA DAP pipeline
plt.close("all")
plt.cla()
plt.clf()
return em_model_eml_par, indx_measurements
def main():
t = time.perf_counter()
#-------------------------------------------------------------------
# Read spectra to fit. The following reads a single MaNGA spectrum.
# This is where you should read in your own spectrum to fit.
# Plate-IFU to use
plt = 7815
ifu = 3702
# Spaxel coordinates
x = 25 #30
y = 25 #37
# Where to find the relevant datacube. This example accesses the test data
# that can be downloaded by executing the script here:
# https://github.com/sdss/mangadap/blob/master/download_test_data.py
directory_path = defaults.dap_source_dir() / 'data' / 'remote'
# Read a spectrum
wave, flux, ivar, sres = get_spectra(plt, ifu, x, y, directory_path=directory_path)
# In general, the DAP fitting functions expect data to be in 2D
# arrays with shape (N-spectra,N-wave). So if you only have one
# spectrum, you need to expand the dimensions:
flux = flux.reshape(1,-1)
ivar = ivar.reshape(1,-1)
ferr = numpy.ma.power(ivar, -0.5)
sres = sres.reshape(1,-1)
# The majority (if not all) of the DAP methods expect that your
# spectra are binned logarithmically in wavelength (primarily
# because this is what pPXF expects). You can either have the DAP
# function determine this value (commented line below) or set it
# directly. The value is used to resample the template spectra to
# match the sampling of the spectra to fit (up to some integer; see
# velscale_ratio).
# spectral_step = spectral_coordinate_step(wave, log=True)
spectral_step = 1e-4
# Hereafter, the methods expect a wavelength vector, a flux array
# with the spectra to fit, an ferr array with the 1-sigma errors in
# the flux, and sres with the wavelength-dependent spectral
# resolution, R = lambda / Dlambda
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# The DAP needs a reasonable guess of the redshift of the spectrum
# (within +/- 2000 km/s). In this example, I'm pulling the redshift
# from the DRPall file. There must be one redshift estimate per
# spectrum to fit. Here that means it's a single element array
# This example accesses the test data
# that can be downloaded by executing the script here:
# https://github.com/sdss/mangadap/blob/master/download_test_data.py
drpall_file = directory_path / f'drpall-{drp_test_version}.fits'
z = numpy.array([get_redshift(plt, ifu, drpall_file)])
print('Redshift: {0}'.format(z[0]))
# The DAP also requires an initial guess for the velocity
# dispersion. A guess of 100 km/s is usually robust, but this may
# depend on your spectral resolution.
dispersion = numpy.array([100.])
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# The following sets the keyword for the template spectra to use
# during the fit. You can specify different template sets to use
# during the stellar-continuum (stellar kinematics) fit and the
# emission-line modeling.
# Templates used in the stellar continuum fits
sc_tpl_key = 'MILESHC'
# Templates used in the emission-line modeling
el_tpl_key = 'MASTARSSP'
# You also need to specify the sampling for the template spectra.
# The templates must be sampled with the same pixel step as the
# spectra to be fit, up to an integer factor. The critical thing
# for the sampling is that you do not want to undersample the
# spectral resolution element of the template spectra. Here, I set
# the sampling for the MILES templates to be a factor of 4 smaller
# than the MaNGA spectrum to be fit (which is a bit of overkill
# given the resolution difference). I set the sampling of the
# MaStar templates to be the same as the galaxy data.
# Template pixel scale a factor of 4 smaller than galaxy data
sc_velscale_ratio = 4
# Template sampling is the same as the galaxy data
el_velscale_ratio = 1
# You then need to identify the database that defines the
# emission-line passbands (elmom_key) for the non-parametric
# emission-line moment calculations, and the emission-line
# parameters (elfit_key) for the Gaussian emission-line modeling.
# See
# https://sdss-mangadap.readthedocs.io/en/latest/emissionlines.html.
elmom_key = 'ELBMPL9'
elfit_key = 'ELPMPL11'
# If you want to also calculate the spectral indices, you can
# provide a keyword that indicates the database with the passband
# definitions for both the absorption-line and bandhead/color
# indices to measure. The script allows these to be None, if you
# don't want to calculate the spectral indices. See
# https://sdss-mangadap.readthedocs.io/en/latest/spectralindices.html
absindx_key = 'EXTINDX'
bhdindx_key = 'BHBASIC'
# Now we want to construct a pixel mask that excludes regions with
# known artifacts and emission lines. The 'BADSKY' artifact
# database only masks the 5577, which can have strong left-over
# residuals after sky-subtraction. The list of emission lines (set
# by the ELPMPL8 keyword) can be different from the list of
# emission lines fit below.
sc_pixel_mask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPMPL11'))
# Mask the 5577 sky line
el_pixel_mask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'))
# Finally, you can set whether or not to show a set of plots.
#
# Show the ppxf-generated plots for each fit stage.
fit_plots = False
# Show summary plots
usr_plots = True
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Fit the stellar continuum
# First, we construct the template library. The keyword that
# selects the template library (sc_tpl_key) is defined above. The
# following call reads in the template library and processes the
# data to have the appropriate pixel sampling. Note that *no*
# matching of the spectral resolution to the galaxy spectrum is
# performed.
sc_tpl = TemplateLibrary(sc_tpl_key, match_resolution=False, velscale_ratio=sc_velscale_ratio,
spectral_step=spectral_step, log=True, hardcopy=False)
# This calculation of the mean spectral resolution is a kludge. The
# template library should provide spectra that are *all* at the
# same spectral resolution. Otherwise, one cannot freely combine
# the spectra to fit the Doppler broadening of the galaxy spectrum
# in a robust (constrained) way (without substantially more
# effort). There should be no difference between what's done below
# and simply taking the spectral resolution to be that of the first
# template spectrum (i.e., sc_tpl['SPECRES'].data[0])
sc_tpl_sres = numpy.mean(sc_tpl['SPECRES'].data, axis=0).ravel()
# Instantiate the fitting class, including the mask that it should
# use to flag the data. [[This mask should just be default...]]
ppxf = PPXFFit(StellarContinuumModelBitMask())
# The following call performs the fit to the spectrum. Specifically
# note that the code only fits the first two moments, uses an
# 8th-order additive polynomial, and uses the 'no_global_wrej'
# iteration mode. See
# https://sdss-mangadap.readthedocs.io/en/latest/api/mangadap.proc.ppxffit.html#mangadap.proc.ppxffit.PPXFFit.fit
cont_wave, cont_flux, cont_mask, cont_par \
= ppxf.fit(sc_tpl['WAVE'].data.copy(), sc_tpl['FLUX'].data.copy(), wave, flux, ferr,
z, dispersion, iteration_mode='no_global_wrej', reject_boxcar=100,
ensemble=False, velscale_ratio=sc_velscale_ratio, mask=sc_pixel_mask,
matched_resolution=False, tpl_sres=sc_tpl_sres, obj_sres=sres, degree=8,
moments=2, plot=fit_plots)
# The returned objects from the fit are the wavelength, model, and
# mask vectors and the record array with the best-fitting model
# parameters. The datamodel of the best-fitting model parameters is
# set by:
# https://sdss-mangadap.readthedocs.io/en/latest/api/mangadap.proc.spectralfitting.html#mangadap.proc.spectralfitting.StellarKinematicsFit._per_stellar_kinematics_dtype
# Remask the continuum fit
sc_continuum = StellarContinuumModel.reset_continuum_mask_window(
numpy.ma.MaskedArray(cont_flux, mask=cont_mask>0))
# Show the fit and residual
if usr_plots:
pyplot.plot(wave, flux[0,:], label='Data')
pyplot.plot(wave, sc_continuum[0,:], label='Model')
pyplot.plot(wave, flux[0,:] - sc_continuum[0,:], label='Resid')
pyplot.legend()
pyplot.xlabel('Wavelength')
pyplot.ylabel('Flux')
pyplot.show()
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Get the emission-line moments using the fitted stellar continuum
# Read the database that define the emission lines and passbands
momdb = EmissionMomentsDB.from_key(elmom_key)
# Measure the moments
elmom = EmissionLineMoments.measure_moments(momdb, wave, flux, continuum=sc_continuum,
redshift=z)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Fit the emission-line model
# Set the emission-line continuum templates if different from those
# used for the stellar continuum
if sc_tpl_key == el_tpl_key:
# If the keywords are the same, just copy over the previous
# library ...
el_tpl = sc_tpl
el_tpl_sres = sc_tpl_sres
# ... and the best fitting stellar kinematics
stellar_kinematics = cont_par['KIN']
else:
# If the template sets are different, we need to match the
# spectral resolution to the galaxy data ...
_sres = SpectralResolution(wave, sres[0,:], log10=True)
el_tpl = TemplateLibrary(el_tpl_key, sres=_sres, velscale_ratio=el_velscale_ratio,
spectral_step=spectral_step, log=True, hardcopy=False)
el_tpl_sres = numpy.mean(el_tpl['SPECRES'].data, axis=0).ravel()
# ... and use the corrected velocity dispersions.
stellar_kinematics = cont_par['KIN']
stellar_kinematics[:,1] = numpy.ma.sqrt(numpy.square(cont_par['KIN'][:,1]) -
numpy.square(cont_par['SIGMACORR_EMP']))
# Read the emission line fitting database
emldb = EmissionLineDB.from_key(elfit_key)
# Instantiate the fitting class
emlfit = Sasuke(EmissionLineModelBitMask())
# Perform the fit
efit_t = time.perf_counter()
eml_wave, model_flux, eml_flux, eml_mask, eml_fit_par, eml_eml_par \
= emlfit.fit(emldb, wave, flux, obj_ferr=ferr, obj_mask=el_pixel_mask, obj_sres=sres,
guess_redshift=z, guess_dispersion=dispersion, reject_boxcar=101,
stpl_wave=el_tpl['WAVE'].data, stpl_flux=el_tpl['FLUX'].data,
stpl_sres=el_tpl_sres, stellar_kinematics=stellar_kinematics,
etpl_sinst_mode='offset', etpl_sinst_min=10.,
velscale_ratio=el_velscale_ratio, matched_resolution=False, mdegree=8,
plot=fit_plots)
print('TIME: ', time.perf_counter() - efit_t)
# Line-fit metrics
eml_eml_par = EmissionLineFit.line_metrics(emldb, wave, flux, ferr, model_flux, eml_eml_par,
model_mask=eml_mask, bitmask=emlfit.bitmask)
# Get the stellar continuum that was fit for the emission lines
elcmask = eml_mask.ravel() > 0
goodpix = numpy.arange(elcmask.size)[numpy.invert(elcmask)]
start, end = goodpix[0], goodpix[-1]+1
elcmask[start:end] = False
el_continuum = numpy.ma.MaskedArray(model_flux - eml_flux,
mask=elcmask.reshape(model_flux.shape))
# Plot the result
if usr_plots:
pyplot.plot(wave, flux[0,:], label='Data')
pyplot.plot(wave, model_flux[0,:], label='Model')
pyplot.plot(wave, el_continuum[0,:], label='EL Cont.')
pyplot.plot(wave, sc_continuum[0,:], label='SC Cont.')
pyplot.legend()
pyplot.xlabel('Wavelength')
pyplot.ylabel('Flux')
pyplot.show()
# Remeasure the emission-line moments with the new continuum
new_elmom = EmissionLineMoments.measure_moments(momdb, wave, flux, continuum=el_continuum,
redshift=z)
# Compare the summed flux and Gaussian-fitted flux for all the
# fitted lines
if usr_plots:
pyplot.scatter(emldb['restwave'], (new_elmom['FLUX']-eml_eml_par['FLUX']).ravel(),
c=eml_eml_par['FLUX'].ravel(), cmap='viridis', marker='.', s=60, lw=0,
zorder=4)
pyplot.grid()
pyplot.xlabel('Wavelength')
pyplot.ylabel('Summed-Gaussian Difference')
pyplot.show()
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Measure the spectral indices
if absindx_key is None or bhdindx_key is None:
# Neither are defined, so we're done
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))
return
# Setup the databases that define the indices to measure
absdb = None if absindx_key is None else AbsorptionIndexDB.from_key(absindx_key)
bhddb = None if bhdindx_key is None else BandheadIndexDB.from_key(bhdindx_key)
# Remove the modeled emission lines from the spectra
flux_noeml = flux - eml_flux
redshift = stellar_kinematics[:,0] / astropy.constants.c.to('km/s').value
sp_indices = SpectralIndices.measure_indices(absdb, bhddb, wave, flux_noeml, ivar=ivar,
redshift=redshift)
# Calculate the velocity dispersion corrections
# - Construct versions of the best-fitting model spectra with and without
# the included dispersion
continuum = Sasuke.construct_continuum_models(emldb, el_tpl['WAVE'].data, el_tpl['FLUX'].data,
wave, flux.shape, eml_fit_par)
continuum_dcnvlv = Sasuke.construct_continuum_models(emldb, el_tpl['WAVE'].data,
el_tpl['FLUX'].data, wave, flux.shape,
eml_fit_par, redshift_only=True)
# - Get the dispersion corrections and fill the relevant columns of the
# index table
sp_indices['BCONT_MOD'], sp_indices['BCONT_CORR'], sp_indices['RCONT_MOD'], \
sp_indices['RCONT_CORR'], sp_indices['MCONT_MOD'], sp_indices['MCONT_CORR'], \
sp_indices['AWGT_MOD'], sp_indices['AWGT_CORR'], \
sp_indices['INDX_MOD'], sp_indices['INDX_CORR'], \
sp_indices['INDX_BF_MOD'], sp_indices['INDX_BF_CORR'], \
good_les, good_ang, good_mag, is_abs \
= SpectralIndices.calculate_dispersion_corrections(absdb, bhddb, wave, flux,
continuum, continuum_dcnvlv,
redshift=redshift,
redshift_dcnvlv=redshift)
# Apply the index corrections. This is only done here for the
# Worthey/Trager definition of the indices, as an example
corrected_indices = numpy.zeros(sp_indices['INDX'].shape, dtype=float)
corrected_indices_err = numpy.zeros(sp_indices['INDX'].shape, dtype=float)
# Unitless indices
corrected_indices[good_les], corrected_indices_err[good_les] \
= SpectralIndices.apply_dispersion_corrections(sp_indices['INDX'][good_les],
sp_indices['INDX_CORR'][good_les],
err=sp_indices['INDX_ERR'][good_les])
# Indices in angstroms
corrected_indices[good_ang], corrected_indices_err[good_ang] \
= SpectralIndices.apply_dispersion_corrections(sp_indices['INDX'][good_ang],
sp_indices['INDX_CORR'][good_ang],
err=sp_indices['INDX_ERR'][good_ang],
unit='ang')
# Indices in magnitudes
corrected_indices[good_mag], corrected_indices_err[good_mag] \
= SpectralIndices.apply_dispersion_corrections(sp_indices['INDX'][good_mag],
sp_indices['INDX_CORR'][good_mag],
err=sp_indices['INDX_ERR'][good_mag],
unit='mag')
# Print the results for a few indices
index_names = numpy.append(absdb['name'], bhddb['name'])
print('-'*73)
print(f'{"NAME":<8} {"Raw Index":>12} {"err":>12} {"Index Corr":>12} {"Index":>12} {"err":>12}')
print(f'{"-"*8:<8} {"-"*12:<12} {"-"*12:<12} {"-"*12:<12} {"-"*12:<12} {"-"*12:<12}')
for name in ['Hb', 'HDeltaA', 'Mgb', 'Dn4000']:
i = numpy.where(index_names == name)[0][0]
print(f'{name:<8} {sp_indices["INDX"][0,i]:12.4f} {sp_indices["INDX_ERR"][0,i]:12.4f} '
f'{sp_indices["INDX_CORR"][0,i]:12.4f} {corrected_indices[0,i]:12.4f} '
f'{corrected_indices_err[0,i]:12.4f}')
print('-'*73)
embed()
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))