def get_emission_lines(self, vac_or_air='air'):
if vac_or_air == 'vac':
vacuum = True
else:
vacuum = False
self.emission_lines, self.line_names, self.line_wave = P.emission_lines(
self.log_lam_template,
self.lam_range_gal,
self.FWHM_gal,
vacuum=vacuum)
def make_paintbox_model(wave,
store,
name="test",
porder=45,
nssps=1,
sigma=100):
""" Prepare a model with paintbox. """
ssp = pb.CvD18(sigma=sigma, store=store, libpath=context.cvd_dir)
twave = ssp.wave
limits = ssp.limits
if nssps > 1:
for i in range(nssps):
p0 = pb.Polynomial(twave, 0, pname="w")
p0.parnames = [f"w_{i+1}"]
s = copy.deepcopy(ssp)
s.parnames = ["{}_{}".format(_, i + 1) for _ in s.parnames]
if i == 0:
pop = p0 * s
else:
pop += (p0 * s)
else:
pop = ssp
vname = "vsyst_{}".format(name)
stars = pb.Resample(wave, pb.LOSVDConv(pop, losvdpars=[vname, "sigma"]))
# Adding a polynomial
poly = pb.Polynomial(wave, porder, zeroth=True, pname=f"p{name}")
# Including emission lines
target_fwhm = lambda w: sigma / const.c.to("km/s").value * w * 2.355
gas_templates, gas_names, line_wave = ppxf_util.emission_lines(
np.log(twave), [wave[0], wave[-1]],
target_fwhm,
tie_balmer=True,
vacuum=True)
gas_templates /= np.max(gas_templates, axis=0) # Normalize line amplitudes
gas_names = [_.replace("_", "") for _ in gas_names]
# for em in gas_templates.T:
# plt.plot(twave, em)
# plt.show()
if len(gas_names) > 0:
emission = pb.NonParametricModel(twave,
gas_templates.T,
names=gas_names)
emkin = pb.Resample(
wave, pb.LOSVDConv(emission, losvdpars=[vname, "sigma_gas"]))
sed = (stars + emkin) * poly
else:
sed = stars * poly
return sed, limits, gas_names
def ppxf_example_population_gas_sdss(tie_balmer, limit_doublets):
ppxf_dir = path.dirname(path.realpath(ppxf_package.__file__))
cube_id = 468
# reading cube_data
cube_file = "data/cubes/cube_" + str(cube_id) + ".fits"
hdu = fits.open(cube_file)
t = hdu[0].data
# using our redshift estimate from lmfit
cube_result_file = ("results/cube_" + str(cube_id) + "/cube_" +
str(cube_id) + "_lmfit.txt")
cube_result_file = open(cube_result_file)
line_count = 0
for crf_line in cube_result_file:
if (line_count == 20):
curr_line = crf_line.split()
z = float(curr_line[1])
line_count += 1
cube_x_data = np.load("results/cube_" + str(int(cube_id)) + "/cube_" +
str(int(cube_id)) + "_cbd_x.npy")
cube_y_data = np.load("results/cube_" + str(int(cube_id)) + "/cube_" +
str(int(cube_id)) + "_cbs_y.npy")
# Only use the wavelength range in common between galaxy and stellar library.
#
mask = (cube_x_data > 3540) & (cube_x_data < 7409)
flux = cube_y_data[mask]
galaxy = flux / np.median(
flux) # Normalize spectrum to avoid numerical issues
wave = cube_x_data[mask]
# The SDSS wavelengths are in vacuum, while the MILES ones are in air.
# For a rigorous treatment, the SDSS vacuum wavelengths should be
# converted into air wavelengths and the spectra should be resampled.
# To avoid resampling, given that the wavelength dependence of the
# correction is very weak, I approximate it with a constant factor.
#
wave *= np.median(util.vac_to_air(wave) / wave)
# The noise level is chosen to give Chi^2/DOF=1 without regularization (REGUL=0).
# A constant noise is not a bad approximation in the fitted wavelength
# range and reduces the noise in the fit.
#
noise = np.full_like(galaxy,
0.01635) # Assume constant noise per pixel here
# The velocity step was already chosen by the SDSS pipeline
# and we convert it below to km/s
#
c = 299792.458 # speed of light in km/s
velscale = c * np.log(wave[1] / wave[0]) # eq.(8) of Cappellari (2017)
FWHM_gal = 2.76 # SDSS has an approximate instrumental resolution FWHM of 2.76A.
#------------------- Setup templates -----------------------
pathname = ppxf_dir + '/miles_models/Mun1.30*.fits'
miles = lib.miles(pathname, velscale, FWHM_gal)
# The stellar templates are reshaped below into a 2-dim array with each
# spectrum as a column, however we save the original array dimensions,
# which are needed to specify the regularization dimensions
#
reg_dim = miles.templates.shape[1:]
stars_templates = miles.templates.reshape(miles.templates.shape[0], -1)
# See the pPXF documentation for the keyword REGUL,
regul_err = 0.013 # Desired regularization error
# Construct a set of Gaussian emission line templates.
# Estimate the wavelength fitted range in the rest frame.
#
lam_range_gal = np.array([np.min(wave), np.max(wave)]) / (1 + z)
gas_templates, gas_names, line_wave = util.emission_lines(
miles.log_lam_temp,
lam_range_gal,
FWHM_gal,
tie_balmer=tie_balmer,
limit_doublets=limit_doublets)
# Combines the stellar and gaseous templates into a single array.
# During the PPXF fit they will be assigned a different kinematic
# COMPONENT value
#
templates = np.column_stack([stars_templates, gas_templates])
#-----------------------------------------------------------
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below as described
# in PPXF_EXAMPLE_KINEMATICS_SAURON and Sec.2.4 of Cappellari (2017)
#
c = 299792.458
dv = c * (miles.log_lam_temp[0] - np.log(wave[0])
) # eq.(8) of Cappellari (2017)
vel = c * np.log(1 + z) # eq.(8) of Cappellari (2017)
start = [vel, 180.] # (km/s), starting guess for [V, sigma]
n_temps = stars_templates.shape[1]
n_forbidden = np.sum(["[" in a
for a in gas_names]) # forbidden lines contain "[*]"
n_balmer = len(gas_names) - n_forbidden
# Assign component=0 to the stellar templates, component=1 to the Balmer
# gas emission lines templates and component=2 to the forbidden lines.
component = [0] * n_temps + [1] * n_balmer + [2] * n_forbidden
gas_component = np.array(
component) > 0 # gas_component=True for gas templates
# Fit (V, sig, h3, h4) moments=4 for the stars
# and (V, sig) moments=2 for the two gas kinematic components
moments = [4, 2, 2]
# Adopt the same starting value for the stars and the two gas components
start = [start, start, start]
# If the Balmer lines are tied one should allow for gas reddeining.
# The gas_reddening can be different from the stellar one, if both are fitted.
gas_reddening = 0 if tie_balmer else None
# Here the actual fit starts.
#
# IMPORTANT: Ideally one would like not to use any polynomial in the fit
# as the continuum shape contains important information on the population.
# Unfortunately this is often not feasible, due to small calibration
# uncertainties in the spectral shape. To avoid affecting the line strength of
# the spectral features, we exclude additive polynomials (DEGREE=-1) and only use
# multiplicative ones (MDEGREE=10). This is only recommended for population, not
# for kinematic extraction, where additive polynomials are always recommended.
#
t = clock()
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
plot=False,
moments=moments,
degree=-1,
mdegree=10,
vsyst=dv,
lam=wave,
clean=False,
regul=1. / regul_err,
reg_dim=reg_dim,
component=component,
gas_component=gas_component,
gas_names=gas_names,
gas_reddening=gas_reddening)
# When the two Delta Chi^2 below are the same, the solution
# is the smoothest consistent with the observed spectrum.
#
print('Desired Delta Chi^2: %.4g' % np.sqrt(2 * galaxy.size))
print('Current Delta Chi^2: %.4g' % ((pp.chi2 - 1) * galaxy.size))
print('Elapsed time in PPXF: %.2f s' % (clock() - t))
weights = pp.weights[
~gas_component] # Exclude weights of the gas templates
weights = weights.reshape(reg_dim) / weights.sum() # Normalized
miles.mean_age_metal(weights)
miles.mass_to_light(weights, band="r")
# Plot fit results for stars and gas.
plt.clf()
plt.subplot(211)
pp.plot()
# Plot stellar population mass fraction distribution
plt.subplot(212)
miles.plot(weights)
plt.tight_layout()
#plt.pause(1)
plt.show()
def emission(dirfile, w1, f1, redshift, plateifu, tie_balmer, limit_doublets):
ppxf_dir = path.dirname(path.realpath(ppxf_package.__file__))
z = redshift
flux = f1
galaxy = flux
wave = w1
wave *= np.median(util.vac_to_air(wave) / wave)
noise = np.full_like(galaxy,
0.01635) # Assume constant noise per pixel here
c = 299792.458 # speed of light in km/s
velscale = c * np.log(wave[1] / wave[0]) # eq.(8) of Cappellari (2017)
# SDSS has an approximate instrumental resolution FWHM of 2.76A.
FWHM_gal = 2.76
# ------------------- Setup templates -----------------------
pathname = ppxf_dir + '/miles_models/Mun1.30*.fits'
# The templates are normalized to mean=1 within the FWHM of the V-band.
# In this way the weights and mean values are light-weighted quantities
miles = lib.miles(pathname, velscale, FWHM_gal)
reg_dim = miles.templates.shape[1:]
regul_err = 0.013 # Desired regularization error
lam_range_gal = np.array([np.min(wave), np.max(wave)]) / (1 + z)
gas_templates, gas_names, line_wave = util.emission_lines(
miles.log_lam_temp,
lam_range_gal,
FWHM_gal,
tie_balmer=tie_balmer,
limit_doublets=limit_doublets)
templates = gas_templates
c = 299792.458
dv = c * (miles.log_lam_temp[0] - np.log(wave[0])
) # eq.(8) of Cappellari (2017)
vel = c * np.log(1 + z) # eq.(8) of Cappellari (2017)
start = [vel, 180.] # (km/s), starting guess for [V, sigma]
# n_temps = stars_templates.shape[1]
n_forbidden = np.sum(["[" in a
for a in gas_names]) # forbidden lines contain "[*]"
n_balmer = len(gas_names) - n_forbidden
component = [0] * n_balmer + [1] * n_forbidden
gas_component = np.array(
component) >= 0 # gas_component=True for gas templates
moments = [2, 2]
start = [start, start]
gas_reddening = 0 if tie_balmer else None
t = clock()
pp = gas.ppxf(dirfile,
templates,
galaxy,
noise,
velscale,
start,
z,
plot=True,
moments=moments,
degree=-1,
mdegree=10,
vsyst=dv,
lam=wave,
clean=False,
component=component,
gas_component=gas_component,
gas_names=gas_names,
gas_reddening=gas_reddening)
pp.plot()
return pp.bestfit, pp.lam
def dump_ppxf_results(ppfit, miles, z, outfile):
"""
Write the stnadard results and the
gas_component measurements to a simple ASCII file
Parameters
----------
ppfit: ppxf
outfile: str
Returns
-------
"""
# Get the lines (air)
emission_lines, line_names, line_wave = util.emission_lines(
np.array([0.1, 0.2]), [1000., 1e5],
0,
limit_doublets=False,
vacuum=True)
# Construct a simple Table
gas_tbl = Table()
# Standard pPXF fit results
meta = {}
meta['EBV'] = ppfit.reddening
star_weights = ppfit.weights[~ppfit.gas_component]
star_weights = star_weights.reshape(ppfit.reg_dim)
age, metals = miles.mean_age_metal(star_weights)
meta['AGE'] = age
meta['METALS'] = metals
# Mass -- Approximate
# Mass -- This is a bit approximate as Dwv is a guess for now
actualflux = ppfit.bestfit * constants.L_sun.cgs / units.angstrom / (
4 * np.pi * (cosmo.luminosity_distance(z).to(units.cm))**2 / (1 + z))
# When fitting, the routine thought our data and model spectra had same units...
Dwv = 1700. # Ang, width of the band pass
scfactor = np.median(ppfit.bestfit *
(units.erg / units.s / units.cm**2 / units.angstrom) /
actualflux) * Dwv
# To get the actual model mass required to fit spectrum, scale by this ratio
massmodels = scfactor * miles.total_mass(star_weights)
meta['LOGMSTAR'] = np.log10(massmodels.value)
gas_tbl.meta = meta
gas = ppfit.gas_component
comp = ppfit.component[gas]
gas_tbl['comp'] = comp
gas_tbl['name'] = ppfit.gas_names
gas_tbl['flux'] = ppfit.gas_flux
gas_tbl['err'] = ppfit.gas_flux_error
# Wavelengths
waves = []
for name in ppfit.gas_names:
idx = np.where(line_names == name)[0][0]
waves.append(line_wave[idx])
gas_tbl['wave'] = waves
vs = [ppfit.sol[icomp][0] for icomp in comp]
sigs = [ppfit.sol[icomp][1] for icomp in comp]
gas_tbl['v'] = vs
gas_tbl['sig'] = sigs
# Write
gas_tbl.write(outfile, format='ascii.ecsv', overwrite=True)
print("Wrote: {:s}".format(outfile))
def ppxf_single(object_id,
z_init,
lambda_spec,
galaxy_lin,
error_lin,
cars_model,
emsub_specfile=None,
plotfile=None,
outfile=None,
outfile_spec=None,
mpoly=None,
apoly=None,
reflines=None):
# Speed of light
c = 299792.458
#h_spec = spec_hdu[0].header
#Ang_air = h_spec['CRVAL3'] + np.arange(0,h_spec['CDELT3']*(h_spec['NAXIS3']),h_spec['CDELT3'])
#s = 10**4/Ang_air
#n = 1 + 0.00008336624212083 + 0.02408926869968 / (130.1065924522 - s**2) + 0.000159740894897 / (38.92568793293 - s**2)
#lambda_spec = Ang_air*n
#wave = h_spec['CRVAL3'] + np.arange(0,h_spec['CDELT3']*(h_spec['NAXIS3']),h_spec['CDELT3'])
#lambda_spec = vactoair(wave)
#lambda_spec = h_spec['CRVAL3'] + np.arange(0,h_spec['CDELT3']*(h_spec['NAXIS3']),h_spec['CDELT3'])
# Crop to finite
use = (np.isfinite(galaxy_lin) & (galaxy_lin > 0.0))
# Making a "use" vector
use_indices = np.arange(galaxy_lin.shape[0])[use]
galaxy_lin = (galaxy_lin[use_indices.min():(use_indices.max() + 1)])[2:-3]
error_lin = (error_lin[use_indices.min():(use_indices.max() + 1)])[2:-3]
lambda_spec = (lambda_spec[use_indices.min():(use_indices.max() +
1)])[2:-3]
lamRange_gal = np.array([np.min(lambda_spec), np.max(lambda_spec)])
# New resolution estimate = 0.9 \AA (sigma), converting from sigma to FWHM
FWHM_gal = 2.355 * (np.max(lambda_spec) -
np.min(lambda_spec)) / len(lambda_spec)
print('FWHM', FWHM_gal)
lamRange_gal = lamRange_gal / (
1 + float(z_init)) # Compute approximate restframe wavelength range
FWHM_gal = FWHM_gal / (1 + float(z_init)) # Adjust resolution in Angstrom
sigma_gal = FWHM_gal / (2.3548 * 4500.0) * c # at ~4500 \AA
# log rebinning for the fits
galaxy, logLam_gal, velscale = util.log_rebin(np.around(lamRange_gal,
decimals=3),
galaxy_lin,
flux=True)
noise, logLam_noise, velscale = util.log_rebin(np.around(lamRange_gal,decimals=3), error_lin, \
velscale=velscale, flux=True)
# correcting for infinite or zero noise
noise[np.logical_or((noise == 0.0), np.isnan(noise))] = 1.0
galaxy[np.logical_or((galaxy < 0.0), np.isnan(galaxy))] = 0.0
if galaxy.shape != noise.shape:
galaxy = galaxy[:-1]
logLam_gal = logLam_gal[:-1]
# Define lamRange_temp and logLam_temp
#lamRange_temp, logLam_temp = setup_spectral_library_conroy(velscale[0], FWHM_gal)
# Construct a set of Gaussian emission line templates.
# Estimate the wavelength fitted range in the rest frame.
#
gas_templates, line_names, line_wave = util.emission_lines(
logLam_gal, lamRange_gal, FWHM_gal)
goodpixels = np.arange(galaxy.shape[0])
wave = np.exp(logLam_gal)
# crop very red end (only affects a very small subsample)
# goodpixels = goodpixels[wave <= 5300]
# exclude lines at the edges (where things can go very very wrong :))
include_lines = np.where((line_wave > (wave.min() + 10.0))
& (line_wave < (wave.max() - 10.0)))
if line_wave[include_lines[0]].shape[0] < line_wave.shape[0]:
line_wave = line_wave[include_lines[0]]
line_names = line_names[include_lines[0]]
gas_templates = gas_templates[:, include_lines[0]]
#reg_dim = stars_templates.shape[1:]
reg_dim = gas_templates.shape[1:]
templates = gas_templates
#np.hstack([gas_templates, gas_templates])
dv = 0 #(logLam_temp[0]-logLam_gal[0])*c # km/s
vel = 0 #np.log(z_init + 1)*c # Initial estimate of the galaxy velocity in km/s
#z = np.exp(vel/c) - 1 # Relation between velocity and redshift in pPXF
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
t = clock()
nNLines = gas_templates.shape[1]
component = [0] * nNLines
start_gas = [vel, 100.]
start = start_gas # adopt the same starting value for both gas (BLs and NLs) and stars
moments = [
2
] # fit (V,sig,h3,h4) for the stars and (V,sig) for the gas' broad and narrow components
fixed = None
## Additive polynomial degree
if apoly is None:
degree = int(
np.ceil((lamRange_gal[1] - lamRange_gal[0]) /
(100.0 * (1. + float(z_init)))))
else:
degree = int(apoly)
if mpoly is None: mdegree = 3
else: mdegree = int(mpoly)
# Trying: sigmas must be kinematically decoupled
# #Trying: velocities must be kinematically decoupled
#A_ineq = [[0,0,2,0,-1,0]]
#b_ineq = [0]
bounds_gas = [[-1000, 1000], [0, 1000]]
bounds = bounds_gas
pp = ppxf.ppxf(templates,
galaxy,
noise,
velscale,
start,
fixed=fixed,
plot=False,
moments=moments,
mdegree=mdegree,
degree=degree,
vsyst=dv,
reg_dim=reg_dim,
goodpixels=goodpixels,
bounds=bounds)
#component=component,
# redshift_to_newtonian:
# return (v-astropy.constants.c.to('km/s').value*redshift)/(1.0+redshift)
# "v" from above is actually the converted velocity which is
# (np.exp(v_out/c) - 1)*c
v_gas = pp.sol[0]
ev_gas = pp.error[0]
conv_vel_gas = (np.exp(pp.sol[0] / c) - 1) * c
vel_gas = (1 + z_init) * (conv_vel_gas - c * z_init)
sigma_gas = pp.sol[1] #first # is template, second # is the moment
esigma_gas = pp.error[1] #*np.sqrt(pp.chi2)
zfit_gas = (z_init + 1) * (1 + pp.sol[0] / c) - 1
zfit_stars = z_init
ezfit_gas = (z_init + 1) * pp.error[0] * np.sqrt(pp.chi2) / c
if plotfile is not None:
#
### All of the rest of this plots and outputs the results of the fit
### Feel free to comment anything out at will
#
maskedgalaxy = np.copy(galaxy)
lowSN = np.where(noise > (0.9 * np.max(noise)))
maskedgalaxy[lowSN] = np.nan
wave = np.exp(logLam_gal) * (1. + z_init) / (1. + zfit_stars)
fig = plt.figure(figsize=(12, 7))
ax1 = fig.add_subplot(211)
# plotting smoothed spectrum
smoothing_fact = 3
ax1.plot(wave,
convolve(maskedgalaxy, Box1DKernel(smoothing_fact)),
color='Gray',
linewidth=0.5)
ax1.plot(wave[goodpixels],
convolve(maskedgalaxy[goodpixels],
Box1DKernel(smoothing_fact)),
'k',
linewidth=1.)
label = "Best fit template from high res Conroy SSPs + emission lines at z={0:.3f}".format(
zfit_stars)
# overplot stellar templates alone
ax1.plot(wave, pp.bestfit, 'r', linewidth=1.0, alpha=0.75, label=label)
ax1.set_ylabel('Flux')
ax1.legend(loc='upper right', fontsize=10)
ax1.set_title(object_id)
xmin, xmax = ax1.get_xlim()
ax2 = fig.add_subplot(413, sharex=ax1, sharey=ax1)
# plotting emission lines if included in the fit
gas = pp.matrix[:, -nNLines:].dot(pp.weights[-nNLines:])
ax2.plot(wave, gas, 'b', linewidth=2,\
label = '$\sigma_{gas}$'+'={0:.0f}$\pm${1:.0f} km/s'.format(sigma_gas, esigma_gas)+', $V_{gas}$'+'={0:.0f}$\pm${1:.0f} km/s'.format(v_gas, ev_gas)) # overplot emission lines alone
cars_model = (cars_model[use_indices.min():(use_indices.max() +
1)])[2:-3]
ax2.plot(np.array(lambda_spec)/(1+z_init), cars_model, color='orange', linewidth=1,\
label = 'CARS Model')
#(lambda_spec[use_indices.min():(use_indices.max()+1)])[2:-3]
stars = pp.bestfit - gas
#if (ymax > 3.0*np.median(stars)): ymax = 3.0*np.median(stars)
#if (ymin < -0.5): ymin = -0.5
ax2.set_ylabel('Best Fits')
ax2.legend(loc='upper left', fontsize=10)
# Plotting the residuals
ax3 = fig.add_subplot(817, sharex=ax1)
ax3.plot(wave[goodpixels],
(convolve(maskedgalaxy, Box1DKernel(smoothing_fact)) -
pp.bestfit)[goodpixels],
'k',
label='Fit Residuals')
#ax3.set_yticks([-0.5,0,0.5])
ax3.set_ylabel('Residuals')
ax4 = fig.add_subplot(818, sharex=ax1)
ax4.plot(wave, noise, 'k', label='Flux Error')
ax4.set_ylabel('Noise')
ax4.set_xlabel('Rest Frame Wavelength [$\AA$]')
#ax4.set_yticks(np.arange(0,0.5,0.1))
'''if reflines is not None:
for i,w,label in zip(range(len(reflines)),reflines['wave'],reflines['label']):
if ((w > xmin) and (w < xmax)):
# ax1.text(w,ymin+(ymax-ymin)*(0.03+0.08*(i % 2)),'$\mathrm{'+label+'}$',fontsize=10,\
# horizontalalignment='center',\
# bbox=dict(boxstyle='round', facecolor='white', alpha=0.5))
print(label.decode("utf-8"))
ax1.text(w,ymin+(ymax-ymin)*(0.03+0.08*(i % 2)),'$\mathrm{'+label.decode("utf-8")+'}$',fontsize=10,\
horizontalalignment='center',\
bbox=dict(boxstyle='round', facecolor='white', alpha=0.5))
ax1.plot([w,w],[ymin,ymax],':k',alpha=0.5)'''
fig.subplots_adjust(hspace=0.05)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
#print('Saving figure to {0}'.format(plotfile))
ax1.set_xlim([6450, 6750])
ax2.set_xlim([6450, 6750])
#ymin, ymax = ax1.get_ylim([])
plt.savefig(plotfile, dpi=150)
plt.close()
return v_gas, vel_gas, sigma_gas, pp.chi2
# print('# id z_stars ez_stars sigma_stars esigma_stars z_gas ez_gas sigma_gas esigma_gas SN_median SN_rf_4000 SN_obs_8030 chi2dof')
print('{0:d} {1:.6f} {2:.6f} {3:.1f} {4:.1f} {5:.6f} {6:.6f} {7:.1f} {8:.1f} {9:.1f} {10:.1f} {11:.1f} {12:.2f}\n'.format(\
object_id,zfit_stars,ezfit_stars, sigma_stars,esigma_stars,\
zfit_gas, ezfit_gas, sigma_gas, esigma_gas, SN_median, SN_rf_4000, SN_obs_8030, pp.chi2))
## This prints the fit parameters to an open file object called outfile
if outfile is not None:
outfile.write('{0:d} {1:d} {2:.6f} {3:.6f} {4:.1f} {5:.1f} {6:.6f} {7:.6f} {8:.1f} {9:.1f} {10:.1f} {11:.1f} {12:.1f} {13:.2f}\n'.format(\
object_id, row2D, zfit_stars, ezfit_stars, sigma_stars,esigma_stars,\
zfit_gas, ezfit_gas, sigma_gas, esigma_gas, SN_median, SN_rf_4000, SN_obs_8030, pp.chi2))
## This outputs the spectrum and best-fit model to an open file object called outfile_spec
if outfile_spec is not None:
outfile_spec.write(
'# l f ef f_stars f_gas f_model_tot used_in_fit add_poly mult_poly\n'
)
for i in np.arange(wave.shape[0]):
isgood = 0
if goodpixels[goodpixels == i].shape[0] == 1: isgood = 1
outfile_spec.write(
'{0:0.4f} {1:0.4f} {2:0.4f} {3:0.4f} {4:0.4f} {5:0.4f} {6} {7:0.4f} {8:0.4f}\n'
.format(wave[i], galaxy[i], noise[i], stars[i], gas[i],
pp.bestfit[i], isgood, add_polynomial[i],
mult_polynomial[i]))
# ## This outputs the best-fit emission-subtracted spectrum to a fits file
# ## but I've commented it out since you are unlikely to need this!
# if emsub_specfile is not None:
# wave = np.exp(logLam_gal)*(1.+z_init)
# if include_gas:
# emsub = galaxy - gas
# else:
# emsub = galaxy
# col1 = fits.Column(name='wavelength', format='E', array=wave)
# col2 = fits.Column(name='flux',format='E',array=galaxy)
# col3 = fits.Column(name='error',format='E',array=noise)
# col4 = fits.Column(name='flux_emsub',format='E',array=emsub)
# cols = fits.ColDefs([col1,col2,col3,col4])
# tbhdu = fits.BinTableHDU.from_columns(cols)
# # delete old file if it exists
# if os.path.isfile(emsub_specfile): os.remove(emsub_specfile)
# tbhdu.writeto(emsub_specfile)
# return zfit_stars, ezfit_stars, sigma_stars, esigma_stars, zfit_gas, ezfit_gas, \
# sigma_gas, esigma_gas, SN_median, SN_rf_4000, SN_obs_8030, pp.chi2
return zfit_stars, sigma_stars, sigma_blr, wave, pp.bestfit