def test_moments_with_continuum():
# Read the data
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
drpbm = DRPFitsBitMask()
flux = numpy.ma.MaskedArray(hdu['FLUX'].data,
mask=drpbm.flagged(
hdu['MASK'].data,
MaNGADataCube.do_not_fit_flags()))
ferr = numpy.ma.power(hdu['IVAR'].data, -0.5)
flux[ferr.mask] = numpy.ma.masked
ferr[flux.mask] = numpy.ma.masked
nspec = flux.shape[0]
# Instantiate the template libary
velscale_ratio = 4
tpl = TemplateLibrary('MILESHC',
match_resolution=False,
velscale_ratio=velscale_ratio,
spectral_step=1e-4,
log=True,
hardcopy=False)
tpl_sres = numpy.mean(tpl['SPECRES'].data, axis=0)
# Get the pixel mask
pixelmask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPSCMSK'))
# Instantiate the fitting class
ppxf = PPXFFit(StellarContinuumModelBitMask())
# Perform the fit
fit_wave, fit_flux, fit_mask, fit_par \
= ppxf.fit(tpl['WAVE'].data.copy(), tpl['FLUX'].data.copy(), hdu['WAVE'].data, flux, ferr,
hdu['Z'].data, numpy.full(nspec, 100.), iteration_mode='no_global_wrej',
reject_boxcar=100, ensemble=False, velscale_ratio=velscale_ratio,
mask=pixelmask, matched_resolution=False, tpl_sres=tpl_sres,
obj_sres=hdu['SRES'].data, degree=8, moments=2)
# Remask the continuum fit
sc_continuum = StellarContinuumModel.reset_continuum_mask_window(
numpy.ma.MaskedArray(fit_flux, mask=fit_mask > 0))
# Read the database that define the emission lines and passbands
momdb = EmissionMomentsDB.from_key('ELBMILES')
# Measure the moments
elmombm = EmissionLineMomentsBitMask()
elmom = EmissionLineMoments.measure_moments(momdb,
hdu['WAVE'].data,
flux,
continuum=sc_continuum,
redshift=hdu['Z'].data,
bitmask=elmombm)
# Measure the EW based on the moments
include_band = numpy.array([numpy.invert(momdb.dummy)]*nspec) \
& numpy.invert(elmombm.flagged(elmom['MASK'],
flag=['BLUE_EMPTY', 'RED_EMPTY']))
line_center = (1.0 + hdu['Z'].data)[:, None] * momdb['restwave'][None, :]
elmom['BMED'], elmom['RMED'], pos, elmom['EWCONT'], elmom['EW'], elmom['EWERR'] \
= emission_line_equivalent_width(hdu['WAVE'].data, flux, momdb['blueside'],
momdb['redside'], line_center, elmom['FLUX'],
redshift=hdu['Z'].data,
line_flux_err=elmom['FLUXERR'],
include_band=include_band)
# Check the flags
reference = {
'BLUE_INCOMP': 21,
'MAIN_JUMP': 0,
'UNDEFINED_MOM2': 42,
'JUMP_BTWN_SIDEBANDS': 0,
'RED_JUMP': 0,
'DIVBYZERO': 0,
'NO_ABSORPTION_CORRECTION': 0,
'RED_EMPTY': 21,
'UNDEFINED_BANDS': 8,
'DIDNOTUSE': 0,
'UNDEFINED_MOM1': 0,
'FORESTAR': 0,
'NON_POSITIVE_CONTINUUM': 0,
'LOW_SNR': 0,
'MAIN_EMPTY': 21,
'BLUE_JUMP': 0,
'RED_INCOMP': 21,
'MAIN_INCOMP': 21,
'BLUE_EMPTY': 21
}
assert numpy.all([
reference[k] == numpy.sum(elmombm.flagged(elmom['MASK'], flag=k))
for k in elmombm.keys()
]), 'Number of flagged measurements changed'
# Check that the values are finite
assert numpy.all([ numpy.all(numpy.isfinite(elmom[n])) for n in elmom.dtype.names]), \
'Found non-finite values in output'
# Check the band definitions
assert numpy.all(numpy.equal(elmom['REDSHIFT'],
hdu['Z'].data)), 'Redshift changed'
assert numpy.all(numpy.isclose(numpy.mean(momdb['blueside'], axis=1)[None,:],
elmom['BCEN']/(1+hdu['Z'].data[:,None]))
| elmombm.flagged(elmom['MASK'], flag='UNDEFINED_BANDS')), \
'Blue passband center incorrect'
assert numpy.all(numpy.isclose(numpy.mean(momdb['redside'], axis=1)[None,:],
elmom['RCEN']/(1+hdu['Z'].data[:,None]))
| elmombm.flagged(elmom['MASK'], flag='UNDEFINED_BANDS')), \
'Red passband center incorrect'
# Check the values
assert numpy.all(
numpy.absolute(elmom['FLUX'][0] - numpy.array([
0.63, 0.00, 0.22, -1.32, -0.88, -0.68, -0.44, -0.13, -1.14, -0.07,
-0.11, 0.01, 0.38, 0.73, 0.71, 0.44, 0.08, 0.74, 1.30, 2.34, 0.55,
0.44
])) < 0.01), 'Fluxes too different'
assert numpy.all(numpy.absolute(elmom['MOM1'][0] -
numpy.array([ 14682.6, 0.0, 14843.2, 14865.8, 14890.4, 14404.7,
14208.6, 12376.0, 14662.5, 14148.5, 15804.1, 17948.4,
14874.5, 14774.9, 14840.5, 14746.0, 15093.1, 14857.8,
14839.0, 14840.2, 14876.0, 14859.5])) < 0.1), \
'1st moments too different'
assert numpy.all(numpy.absolute(elmom['MOM2'][0] -
numpy.array([322.2, 0.0, 591.4, 436.4, 474.6, 0.0, 0.0, 0.0,
364.6, 0.0, 0.0, 0.0, 289.1, 226.9, 282.6, 283.8,
227.0, 207.7, 207.7, 253.6, 197.0, 212.4])) < 0.1), \
'2nd moments too different'
assert numpy.all(numpy.absolute(elmom['EW'][0] -
numpy.array([ 0.63, 0.00, 0.20, -1.28, -0.76, -0.54, -0.30, -0.09,
-0.61, -0.03, -0.04, 0.00, 0.13, 0.25, 0.24, 0.13,
0.02, 0.22, 0.38, 0.69, 0.17, 0.13])) < 0.01), \
'EW too different'
# Perform the 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=velscale_ratio, mask=sc_pixel_mask,
matched_resolution=False, tpl_sres=sc_tpl_sres, obj_sres=sres, degree=8,
moments=2, plot=False)
# How many templates did it find:
print(sc_tpl.ntpl)
# From what files:
print(sc_tpl.file_list)
# What were the weights assigned to each template
print(cont_par['TPLWGT'][0, :])
# 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
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()
#-------------------------------------------------------------------
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))
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))
def main():
t = time.perf_counter()
arg = parse_args()
if not os.path.isfile(arg.inp):
raise FileNotFoundError('No file: {0}'.format(arg.inp))
directory_path = os.getcwd(
) if arg.output_root is None else os.path.abspath(arg.output_root)
if not os.path.isdir(directory_path):
os.makedirs(directory_path)
data_file = os.path.abspath(arg.inp)
fit_file = os.path.join(directory_path, arg.out)
flag_db = None if arg.spec_flags is None else os.path.abspath(
arg.spec_flags)
# Read the data
spectral_step = 1e-4
wave, flux, ferr, sres, redshift, fit_spectrum = object_data(
data_file, flag_db)
nspec, npix = flux.shape
dispersion = numpy.full(nspec, 100., dtype=numpy.float)
# fit_spectrum[:] = False
# fit_spectrum[0] = True
# fit_spectrum[171] = True
# fit_spectrum[791] = True
# Mask spectra that should not be fit
indx = numpy.any(numpy.logical_not(numpy.ma.getmaskarray(flux)),
axis=1) & fit_spectrum
flux[numpy.logical_not(indx), :] = numpy.ma.masked
print('Read: {0}'.format(arg.inp))
print('Contains {0} spectra'.format(nspec))
print(' each with {0} pixels'.format(npix))
print('Fitting {0} spectra.'.format(numpy.sum(fit_spectrum)))
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Fit the stellar continuum
# Construct the template library
sc_tpl = TemplateLibrary(arg.sc_tpl,
match_resolution=False,
velscale_ratio=arg.sc_vsr,
spectral_step=spectral_step,
log=True,
hardcopy=False)
# Set the spectral resolution
sc_tpl_sres = numpy.mean(sc_tpl['SPECRES'].data, axis=0).ravel()
# Set the pixel mask
sc_pixel_mask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPMPL8'))
# Instantiate the fitting class
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,
redshift, dispersion, iteration_mode='no_global_wrej', reject_boxcar=100,
ensemble=False, velscale_ratio=arg.sc_vsr, mask=sc_pixel_mask,
matched_resolution=False, tpl_sres=sc_tpl_sres, obj_sres=sres,
degree=arg.sc_deg, moments=2) #, plot=True)
if arg.sc_only:
write(fit_file, wave, cont_flux, cont_mask, cont_par)
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))
return
# if numpy.any(cont_par['KIN'][:,1] < 0):
# embed()
# exit()
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Measure the emission-line moments
# # Remask the continuum fit
# sc_continuum = StellarContinuumModel.reset_continuum_mask_window(
# numpy.ma.MaskedArray(cont_flux, mask=cont_mask>0))
# # Read the database that define the emission lines and passbands
# momdb = EmissionMomentsDB.from_key(arg.el_band)
# # Measure the moments
# elmom = EmissionLineMoments.measure_moments(momdb, wave, flux, continuum=sc_continuum,
# redshift=redshift)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Fit the emission-line model
# Set the emission-line continuum templates if different from those
# used for the stellar continuum
if arg.sc_tpl == arg.el_tpl:
# If the keywords are the same, just copy over the previous
# library and the best fitting stellar kinematics
el_tpl = sc_tpl
el_tpl_sres = sc_tpl_sres
stellar_kinematics = cont_par['KIN'].copy()
else:
# If the template sets are different, we need to match the
# spectral resolution to the galaxy data and use the corrected
# velocity dispersions.
_sres = SpectralResolution(wave, sres[0, :], log10=True)
el_tpl = TemplateLibrary(arg.el_tpl,
sres=_sres,
velscale_ratio=arg.el_vsr,
spectral_step=spectral_step,
log=True,
hardcopy=False)
el_tpl_sres = numpy.mean(el_tpl['SPECRES'].data, axis=0).ravel()
stellar_kinematics = cont_par['KIN'].copy()
stellar_kinematics[:, 1] = numpy.ma.sqrt(
numpy.square(cont_par['KIN'][:, 1]) -
numpy.square(cont_par['SIGMACORR_SRES'])).filled(0.0)
# if numpy.any(cont_par['KIN'][:,1] < 0):
# embed()
# exit()
#
# if numpy.any(stellar_kinematics[:,1] < 0):
# embed()
# exit()
# Mask the 5577 sky line
el_pixel_mask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'))
# Read the emission line fitting database
emldb = EmissionLineDB.from_key(arg.el_list)
# Instantiate the fitting class
emlfit = Sasuke(EmissionLineModelBitMask())
# TODO: Improve the initial velocity guess using the first moment...
# Perform the fit
elfit_time = time.perf_counter()
model_wave, model_flux, eml_flux, model_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=redshift, 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=arg.el_vsr,
matched_resolution=False, mdegree=arg.el_deg, ensemble=False)#, plot=True)
print('EML FIT TIME: ', time.perf_counter() - elfit_time)
# Line-fit metrics (should this be done in the fit method?)
eml_eml_par = EmissionLineFit.line_metrics(emldb,
wave,
flux,
ferr,
model_flux,
eml_eml_par,
model_mask=model_mask,
bitmask=emlfit.bitmask)
# Equivalent widths
EmissionLineFit.measure_equivalent_width(wave,
flux,
emldb,
eml_eml_par,
bitmask=emlfit.bitmask,
checkdb=False)
# Measure the emission-line moments
# - Model continuum
continuum = StellarContinuumModel.reset_continuum_mask_window(model_flux -
eml_flux)
# - Updated redshifts
fit_redshift = eml_eml_par['KIN'][:,numpy.where(emldb['name'] == 'Ha')[0][0],0] \
/ astropy.constants.c.to('km/s').value
# - Set the moment database
momdb = EmissionMomentsDB.from_key(arg.el_band)
# - Set the moment bitmask
mombm = EmissionLineMomentsBitMask()
# - Measure the moments
elmom = EmissionLineMoments.measure_moments(momdb,
wave,
flux,
ivar=numpy.ma.power(ferr, -2),
continuum=continuum,
redshift=fit_redshift,
bitmask=mombm)
# - Select the bands that are valid
include_band = numpy.array([numpy.logical_not(momdb.dummy)]*nspec) \
& numpy.logical_not(mombm.flagged(elmom['MASK'],
flag=['BLUE_EMPTY', 'RED_EMPTY']))
# - Set the line center at the center of the primary passband
line_center = (1.0 + fit_redshift)[:, None] * momdb['restwave'][None, :]
elmom['BMED'], elmom['RMED'], pos, elmom['EWCONT'], elmom['EW'], elmom['EWERR'] \
= emission_line_equivalent_width(wave, flux, momdb['blueside'], momdb['redside'],
line_center, elmom['FLUX'], redshift=fit_redshift,
line_flux_err=elmom['FLUXERR'],
include_band=include_band)
# - Flag non-positive measurements
indx = include_band & numpy.logical_not(pos)
elmom['MASK'][indx] = mombm.turn_on(elmom['MASK'][indx],
'NON_POSITIVE_CONTINUUM')
# - Set the binids
elmom['BINID'] = numpy.arange(nspec)
elmom['BINID_INDEX'] = numpy.arange(nspec)
write(fit_file,
wave,
cont_flux,
cont_mask,
cont_par,
model_flux=model_flux,
model_mask=model_mask,
eml_flux=eml_flux,
eml_fit_par=eml_fit_par,
eml_eml_par=eml_eml_par,
elmom=elmom)
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))
def main():
select_file = 'repr/representative_spectra_selection_v2.fits'
spec_file = 'repr/benchmark_spectra_v2.fits'
flag_file = 'repr/representative_spectra_flags_v2.db'
# mod_file = None
# plot_file = 'repr/representative_spectra_v2.pdf'
# mod_file = 'repr/benchmark_spectra_v2_model.fits'
# plot_file = 'repr/representative_spectra_v2_model.pdf'
# mod_file = 'repr/run5/run5_MILESHC_4_8_MASTARSSP_1_14_ELBMPL9_ELPMPL11.fits.gz'
# plot_file = 'repr/representative_spectra_v2_model_run5_ssp_14_sig1.4.pdf'
# mod_file = 'repr/run5/run5_MILESHC_4_8_MASTARSSP_1_14_ELBMPL9_ELPMPL11A.fits.gz'
# plot_file = 'repr/representative_spectra_v2_model_run5_ssp_14_sig3.0.pdf'
# mod_file = 'repr/run5/run5_MILESHC_4_8_MASTARSSP_1_14_ELBMPL9_ELPMPL11B.fits.gz'
# plot_file = 'repr/representative_spectra_v2_model_run5_ssp_14_sigmix.pdf'
mod_file = 'repr/run7/run7_MILESHC_4_8_MASTARSSP_1_14_ELBMPL9_ELPMPL11UPA.fits.gz'
plot_file = 'repr/representative_spectra_v2_model_run7_ssp_14_sigmixup.pdf'
select_hdu = fits.open(select_file)
spec_hdu = fits.open(spec_file)
mod_hdu = None if mod_file is None else fits.open(mod_file)
nspec = spec_hdu['FLUX'].shape[0]
restwave_limits = numpy.array([3575, 10300])
d4000_limits = numpy.array([3700, 4300])
mg_limits = numpy.array([5100, 5285])
halpha_limits = numpy.array([6460, 6676])
ca_limits = numpy.array([8450, 8750])
drp_bm = DRPFitsBitMask()
cube_bm = DAPCubeBitMask()
wave = spec_hdu['WAVE'].data
flux = numpy.ma.MaskedArray(spec_hdu['FLUX'].data,
mask=drp_bm.flagged(
spec_hdu['MASK'].data,
flag=['DONOTUSE', 'FORESTAR']))
if mod_hdu is None:
model = numpy.ma.MaskedArray(spec_hdu['MODEL'].data,
mask=cube_bm.flagged(
spec_hdu['MODEL_MASK'].data,
flag='NOMODEL'))
cont = numpy.ma.MaskedArray(
spec_hdu['MODEL'].data - spec_hdu['EMLINE'].data,
mask=cube_bm.flagged(spec_hdu['MODEL_MASK'].data, flag='NOMODEL'))
stellar = numpy.ma.MaskedArray(spec_hdu['STELLAR'].data,
mask=cube_bm.flagged(
spec_hdu['STELLAR_MASK'].data,
flag='NOMODEL'))
else:
model = numpy.ma.MaskedArray(mod_hdu['MODEL'].data,
mask=mod_hdu['MODEL_MASK'].data > 0)
model = StellarContinuumModel.reset_continuum_mask_window(model)
cont = numpy.ma.MaskedArray(mod_hdu['MODEL'].data -
mod_hdu['EMLINE'].data,
mask=mod_hdu['MODEL_MASK'].data > 0)
cont = StellarContinuumModel.reset_continuum_mask_window(cont)
stellar = numpy.ma.MaskedArray(mod_hdu['STELLAR'].data,
mask=mod_hdu['STELLAR_MASK'].data > 0)
stellar = StellarContinuumModel.reset_continuum_mask_window(stellar)
if not os.path.isfile(flag_file):
include_flags = numpy.ones(nspec, dtype=int)
numpy.savetxt(flag_file,
numpy.array([
select_hdu['PAR'].data['PLT'].astype(int),
select_hdu['PAR'].data['IFU'].astype(int),
select_hdu['PAR'].data['BIN'].astype(int),
numpy.ones(nspec, dtype=int)
]).T,
fmt=['%5d', '%5d', '%5d', '%3d'],
header='{0:>3} {1:>5} {2:>5} {3:>3}'.format(
'PLT', 'IFU', 'BIN', 'SEL'))
rc('font', size=8)
with PdfPages(plot_file) as pdf:
# j = 0
for i in range(nspec):
print('{0}/{1}'.format((i + 1), nspec), end='\r')
lambda_limits = restwave_limits * (1 +
select_hdu['PAR'].data['Z'][i])
indx = numpy.logical_not(numpy.ma.getmaskarray(flux)[i,:]) \
& (wave > lambda_limits[0]) & (wave < lambda_limits[1])
if numpy.sum(indx) == 0:
continue
if numpy.sum(numpy.logical_not(
numpy.ma.getmaskarray(model)[i, :])) == 0:
continue
mod_lim = [
numpy.ma.amin(model[i, indx]),
numpy.ma.amax(model[i, indx])
]
Df = (mod_lim[1] - mod_lim[0]) * 1.5
flux_limits = numpy.mean(mod_lim) + numpy.array([-Df / 2, Df / 2])
if not numpy.all(numpy.isfinite(flux_limits)):
embed()
exit()
fig = pyplot.figure()
# Full spectrum
ax = fig.add_axes([0.06, 0.45, 0.92, 0.5])
ax.minorticks_on()
ax.tick_params(which='major',
length=10,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=5,
direction='in',
top=True,
right=True)
ax.set_xlim(lambda_limits)
ax.set_ylim(flux_limits)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax.step(wave, flux[i, :], zorder=2, color='k', lw=0.5, where='mid')
ax.plot(wave, stellar[i, :], zorder=3, color='C1', lw=1.0)
ax.plot(wave, model[i, :], zorder=4, color='C3', lw=1.0)
ax.plot(wave, cont[i, :], zorder=5, color='C9', lw=1.0)
ax.text(-0.05,
0.5,
r'$F_\lambda$',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax.text(0.5,
-0.1,
r'$\lambda_{\rm obs}\ [{\rm \AA}]$',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.01,
1.03,
'{0}: {1}-{2}-{3}; '.format(
i, select_hdu['PAR'].data['PLT'][i],
select_hdu['PAR'].data['IFU'][i],
select_hdu['PAR'].data['BIN'][i]) +
'z={0:.4f}; '.format(select_hdu['PAR'].data['Z'][i]) +
'S/N={0:.1f}; '.format(select_hdu['PAR'].data['SNR'][i]) +
r'$\sigma_{\ast,{\rm obs}}$=' +
r'{0:.1f}; D4000={1:.1f}; H$\alpha$ EW={2:.1f}'.format(
select_hdu['PAR'].data['SIGMA'][i],
select_hdu['PAR'].data['D4000'][i],
select_hdu['PAR'].data['HAEW'][i]),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
lambda_limits = d4000_limits * (1 + select_hdu['PAR'].data['Z'][i])
indx = numpy.logical_not(numpy.ma.getmaskarray(flux)[i,:]) \
& (wave > lambda_limits[0]) & (wave < lambda_limits[1])
mod_lim = [0,1] if numpy.sum(indx) == 0 \
else [numpy.ma.amin(model[i,indx]), numpy.ma.amax(model[i,indx])]
Df = (mod_lim[1] - mod_lim[0]) * 1.5
flux_limits = numpy.mean(mod_lim) + numpy.array([-Df / 2, Df / 2])
ax = fig.add_axes([0.06, 0.08, 0.19, 0.27])
ax.minorticks_on()
ax.tick_params(which='major',
length=10,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=5,
direction='in',
top=True,
right=True)
ax.set_xlim(lambda_limits)
ax.set_ylim(flux_limits)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax.plot(wave, flux[i, :], zorder=2, color='k', lw=0.5)
ax.plot(wave, stellar[i, :], zorder=3, color='C1', lw=1.0)
ax.plot(wave, model[i, :], zorder=4, color='C3', lw=1.0)
ax.plot(wave, cont[i, :], zorder=5, color='C9', lw=1.0)
ax.text(0.5,
-0.2,
r'$\lambda_{\rm obs}\ [{\rm \AA}]$',
ha='center',
va='center',
transform=ax.transAxes)
lambda_limits = mg_limits * (1 + select_hdu['PAR'].data['Z'][i])
indx = numpy.logical_not(numpy.ma.getmaskarray(flux)[i,:]) \
& (wave > lambda_limits[0]) & (wave < lambda_limits[1])
mod_lim = [0,1] if numpy.sum(indx) == 0 \
else [numpy.ma.amin(model[i,indx]), numpy.ma.amax(model[i,indx])]
Df = (mod_lim[1] - mod_lim[0]) * 1.5
flux_limits = numpy.mean(mod_lim) + numpy.array([-Df / 2, Df / 2])
ax = fig.add_axes([0.30, 0.08, 0.19, 0.27])
ax.minorticks_on()
ax.tick_params(which='major',
length=10,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=5,
direction='in',
top=True,
right=True)
ax.set_xlim(lambda_limits)
ax.set_ylim(flux_limits)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax.plot(wave, flux[i, :], zorder=2, color='k', lw=0.5)
ax.plot(wave, stellar[i, :], zorder=3, color='C1', lw=1.0)
ax.plot(wave, model[i, :], zorder=4, color='C3', lw=1.0)
ax.plot(wave, cont[i, :], zorder=5, color='C9', lw=1.0)
ax.text(0.5,
-0.2,
r'$\lambda_{\rm obs}\ [{\rm \AA}]$',
ha='center',
va='center',
transform=ax.transAxes)
lambda_limits = halpha_limits * (1 +
select_hdu['PAR'].data['Z'][i])
indx = numpy.logical_not(numpy.ma.getmaskarray(flux)[i,:]) \
& (wave > lambda_limits[0]) & (wave < lambda_limits[1])
mod_lim = [0,1] if numpy.sum(indx) == 0 \
else [numpy.ma.amin(model[i,indx]), numpy.ma.amax(model[i,indx])]
Df = (mod_lim[1] - mod_lim[0]) * 1.5
flux_limits = numpy.mean(mod_lim) + numpy.array([-Df / 2, Df / 2])
ax = fig.add_axes([0.54, 0.08, 0.19, 0.27])
ax.minorticks_on()
ax.tick_params(which='major',
length=10,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=5,
direction='in',
top=True,
right=True)
ax.set_xlim(lambda_limits)
ax.set_ylim(flux_limits)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax.plot(wave, flux[i, :], zorder=2, color='k', lw=0.5)
ax.plot(wave, stellar[i, :], zorder=3, color='C1', lw=1.0)
ax.plot(wave, model[i, :], zorder=4, color='C3', lw=1.0)
ax.plot(wave, cont[i, :], zorder=5, color='C9', lw=1.0)
ax.text(0.5,
-0.2,
r'$\lambda_{\rm obs}\ [{\rm \AA}]$',
ha='center',
va='center',
transform=ax.transAxes)
lambda_limits = ca_limits * (1 + select_hdu['PAR'].data['Z'][i])
indx = numpy.logical_not(numpy.ma.getmaskarray(flux)[i,:]) \
& (wave > lambda_limits[0]) & (wave < lambda_limits[1])
mod_lim = [0,1] if numpy.sum(indx) == 0 \
else [numpy.ma.amin(model[i,indx]), numpy.ma.amax(model[i,indx])]
Df = (mod_lim[1] - mod_lim[0]) * 1.5
flux_limits = numpy.mean(mod_lim) + numpy.array([-Df / 2, Df / 2])
ax = fig.add_axes([0.78, 0.08, 0.19, 0.27])
ax.minorticks_on()
ax.tick_params(which='major',
length=10,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=5,
direction='in',
top=True,
right=True)
ax.set_xlim(lambda_limits)
ax.set_ylim(flux_limits)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax.plot(wave, flux[i, :], zorder=2, color='k', lw=0.5)
ax.plot(wave, stellar[i, :], zorder=3, color='C1', lw=1.0)
ax.plot(wave, model[i, :], zorder=4, color='C3', lw=1.0)
ax.plot(wave, cont[i, :], zorder=5, color='C9', lw=1.0)
ax.text(0.5,
-0.2,
r'$\lambda_{\rm obs}\ [{\rm \AA}]$',
ha='center',
va='center',
transform=ax.transAxes)
pdf.savefig(orientation='landscape')
fig.clear()
pyplot.close(fig)
# j += 1
# if j == 10:
# break
print('{0}/{0}'.format(nspec))