def make_chisq_plots(filename, paramfilename, galaxyname, slitmaskname, startstar=0, globular=False):
""" Plot chisq contours for stars whose [Mn/H] abundances have already been measured.
Inputs:
filename -- file with observed spectra
paramfilename -- file with parameters of observed spectra
galaxyname -- galaxy name, options: 'scl'
slitmaskname -- slitmask name, options: 'scl1'
Keywords:
globular -- if 'False', put into output path of galaxy; else, put into globular cluster path
"""
# Input filename
if globular:
file = '/raid/madlr/glob/'+galaxyname+'/'+slitmaskname+'.csv'
else:
file = '/raid/madlr/dsph/'+galaxyname+'/'+slitmaskname+'.csv'
name = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=0, dtype='str')
mn = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=8)
mnerr = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=9)
#dlam = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=8)
#dlamerr = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=9)
# Get number of stars in file with observed spectra
Nstars = open_obs_file(filename)
# Plot chi-sq contours for each star
for i in range(startstar, Nstars):
try:
# Check if parameters are measured
temp, logg, fe, alpha, fe_err = open_obs_file(filename, retrievespec=i, specparams=True)
if np.isclose(1.5,logg) and np.isclose(fe,-1.5) and np.isclose(fe_err, 0.0):
print('Bad parameter measurement! Skipped #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
# Open star
star = chi_sq.obsSpectrum(filename, paramfilename, i, False, galaxyname, slitmaskname, globular, 'new')
# Check if star has already had [Mn/H] measured
if star.specname in name:
# If so, plot chi-sq contours if error is < 1 dex
idx = np.where(name == star.specname)
if mnerr[idx][0] < 1:
params0 = [mn[idx][0], mnerr[idx][0]]
best_mn, error = star.plot_chisq(params0, minimize=False, plots=True, save=True)
except Exception as e:
print(repr(e))
print('Skipped star #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
print('Finished star '+star.specname, '#'+str(i+1)+'/'+str(Nstars)+' stars')
return
def prep_run(
filename,
galaxyname,
slitmaskname,
membercheck=None,
memberlist='/raid/caltech/articles/kirby_gclithium/table_catalog.dat',
velmemberlist=None,
globular=False):
""" Get data in preparation for measuring chi-sq values.
Inputs:
filename -- file with observed spectra
galaxyname -- galaxy name, options: 'scl'
slitmaskname -- slitmask name, options: 'scl1'
Keywords:
membercheck -- do membership check for this object
memberlist -- member list (from Evan's Li-rich giants paper)
velmemberlist -- member list (from radial velocity check)
globular -- if 'False' (default), put into output path of galaxy; else, put into globular cluster path
Outputs:
Nstars -- total number of stars in the data file
RA, Dec -- coordinate arrays for stars in file
membernames -- list of all members (based on Evan's Li-rich giants paper and/or velocity check)
galaxyname, slitmaskname, filename, membercheck -- from input
"""
# Prep for member check
if membercheck is not None:
# Check if stars are in member list from Evan's Li-rich giants paper
table = ascii.read(memberlist)
memberindex = np.where(table.columns[0] == membercheck)
membernames = table.columns[1][memberindex]
# Also check if stars are in member list from velocity cut
if velmemberlist is not None:
oldmembernames = membernames
membernames = []
velmembernames = np.genfromtxt(velmemberlist, dtype='str')
for i in range(len(oldmembernames)):
if oldmembernames[i] in velmembernames:
membernames.append(oldmembernames)
membernames = np.asarray(membernames)
else:
membernames = None
# Get number of stars in file
Nstars = open_obs_file(filename)
# Get coordinates of stars in file
RA, Dec = open_obs_file(filename, coords=True)
return galaxyname, slitmaskname, filename, Nstars, RA, Dec, membercheck, membernames, globular
def mp_worker(i, filename, paramfilename, wvlcorr, galaxyname, slitmaskname,
globular, lines, plots, Nstars, RA, Dec, membercheck,
membernames, correctionslist, corrections):
""" Function to parallelize: chi-sq fitting for a single star """
try:
# Get metallicity of star to use for initial guess
print('Getting initial metallicity')
temp, logg, fe, alpha, fe_err = open_obs_file(filename,
retrievespec=i,
specparams=True)
# Get dlam (FWHM) of star to use for initial guess
specname, obswvl, obsflux, ivar, dlam, zrest = open_obs_file(
filename, retrievespec=i)
# Check for bad parameter measurement
if np.isclose(temp, 4750.) and np.isclose(fe, -1.5) and np.isclose(
alpha, 0.2):
print('Bad parameter measurement! Parameters: ' + str(temp) +
', ' + str(fe) + ', ' + str(alpha) + ' Skipped #' +
str(i + 1) + '/' + str(Nstars) + ' stars')
return None
# Do membership check
if membercheck is not None:
if specname not in membernames:
print('Not in member list! Skipped ' + specname)
return None
# Vary stellar parameters
if correctionslist is not None:
if int(specname) in np.asarray(correctionslist['ID']):
idx = np.where(
np.asarray(correctionslist['ID']) == int(specname))[0]
# Vary the quantity required
if corrections[0] == 'Teff':
colstring = 'Teff' + str(int(np.abs(corrections[2])))
if corrections[1] == 'up':
temp = temp + corrections[
2] # Make the correction go the right direction
fe = fe + float(
correctionslist['FeH_' + colstring][idx])
else:
temp = temp - corrections[2]
fe = fe - float(
correctionslist['FeH_' + colstring][idx])
elif corrections[0] == 'logg':
colstring = 'logg0' + str(int(10 * np.abs(corrections[2])))
if corrections[1] == 'up':
logg = logg + corrections[
2] # Make the correction go the right direction
fe = fe - float(
correctionslist['FeH_' + colstring][idx])
else:
logg = logg - corrections[2]
fe = fe + float(
correctionslist['FeH_' + colstring][idx])
# Now determine the direction to vary alpha
'''
key = corrections[0]+str(corrections[2])
if corrections[3] == 'up':
alpha = alpha + float(correctionslist['MgFe_'+key][idx] + 4*correctionslist['SiFe_'+key][idx] + 2*correctionslist['CaFe_'+key][idx] + 6*correctionslist['TiFe_'+key][idx])/13
else:
alpha = alpha - float(correctionslist['MgFe_'+key][idx] + 4*correctionslist['SiFe_'+key][idx] + 2*correctionslist['CaFe_'+key][idx] + 6*correctionslist['TiFe_'+key][idx])/13
'''
else:
print('No stellar parameter corrections listed!')
# Run optimization code
star = chi_sq.obsSpectrum(filename,
paramfilename,
i,
wvlcorr,
galaxyname,
slitmaskname,
globular,
lines,
RA[i],
Dec[i],
plot=True,
specialparams=[temp, logg, fe, alpha])
best_mn, error, finalchisq = star.plot_chisq(fe,
output=True,
plots=plots)
print('Finished star ' + star.specname,
'#' + str(i + 1) + '/' + str(Nstars) + ' stars')
result = np.array([
star.temp, star.logg, star.fe, star.fe_err, star.alpha, best_mn,
error
],
dtype=object)
for i in range(len(result)):
if np.isscalar(result[i]) == False:
result[i] = result[i][0]
return star.specname, str(RA[i]), str(Dec[i]), str(result[0]), str(
result[1]), str(result[2]), str(result[3]), str(result[4]), str(
result[5]), str(result[6]), str(finalchisq)
except Exception as e:
print(repr(e))
print('Skipped star #' + str(i + 1) + '/' + str(Nstars) + ' stars')
return None
def __init__(self,
obsfilename,
paramfilename,
starnum,
wvlcorr,
galaxyname,
slitmaskname,
globular,
lines,
RA,
Dec,
obsspecial=None,
plot=False,
hires=None,
smooth=None,
specialparams=None):
# Observed star
self.obsfilename = obsfilename # File with observed spectra
self.paramfilename = paramfilename # File with parameters of observed spectra
self.starnum = starnum # Star number
self.galaxyname = galaxyname # Name of galaxy
self.slitmaskname = slitmaskname # Name of slitmask
self.globular = globular # Parameter marking if globular cluster
self.lines = lines # Parameter marking whether or not to use revised or original linelist
# If observed spectrum comes from moogify file (default), open observed file and continuum normalize as usual
if obsspecial is None:
# Output filename
if self.globular:
self.outputname = '/raid/madlr/glob/' + galaxyname + '/' + slitmaskname
else:
self.outputname = '/raid/madlr/dsph/' + galaxyname + '/' + slitmaskname
# Open observed spectrum
self.specname, self.obswvl, self.obsflux, self.ivar, self.dlam, self.zrest = open_obs_file(
self.obsfilename, retrievespec=self.starnum)
# Get measured parameters from observed spectrum
self.temp, self.logg, self.fe, self.alpha, self.fe_err = open_obs_file(
self.paramfilename,
self.starnum,
specparams=True,
objname=self.specname,
inputcoords=[RA, Dec])
if specialparams is not None:
self.temp = specialparams[0]
self.logg = specialparams[1]
self.fe = specialparams[2]
self.alpha = specialparams[3]
if plot:
# Plot observed spectrum
plt.figure()
plt.plot(self.obswvl, self.obsflux, 'k-')
plt.axvspan(4749, 4759, alpha=0.5, color='blue')
plt.axvspan(4778, 4788, alpha=0.5, color='blue')
plt.axvspan(4818, 4828, alpha=0.5, color='blue')
plt.axvspan(5389, 5399, alpha=0.5, color='blue')
plt.axvspan(5532, 5542, alpha=0.5, color='blue')
plt.axvspan(6008, 6018, alpha=0.5, color='blue')
plt.axvspan(6016, 6026, alpha=0.5, color='blue')
plt.axvspan(4335, 4345, alpha=0.5, color='red')
plt.axvspan(4856, 4866, alpha=0.5, color='red')
plt.axvspan(6558, 6568, alpha=0.5, color='red')
plt.savefig(self.outputname + '/' + self.specname + '_obs.png')
plt.close()
# Get synthetic spectrum, split both obs and synth spectra into red and blue parts
synthfluxmask, obsfluxmask, obswvlmask, ivarmask, mask = mask_obs_for_division(
self.obswvl,
self.obsflux,
self.ivar,
temp=self.temp,
logg=self.logg,
fe=self.fe,
alpha=self.alpha,
dlam=self.dlam,
lines=self.lines)
if plot:
# Plot spliced synthetic spectrum
plt.figure()
plt.plot(obswvlmask[0], synthfluxmask[0], 'b-')
plt.plot(obswvlmask[1], synthfluxmask[1], 'r-')
plt.savefig(self.outputname + '/' + self.specname +
'_synth.png')
plt.close()
# Compute continuum-normalized observed spectrum
self.obsflux_norm, self.ivar_norm = divide_spec(
synthfluxmask,
obsfluxmask,
obswvlmask,
ivarmask,
mask,
sigmaclip=True,
specname=self.specname,
outputname=self.outputname)
if plot:
# Plot continuum-normalized observed spectrum
plt.figure()
plt.plot(self.obswvl, self.obsflux_norm, 'k-')
plt.axvspan(4749, 4759, alpha=0.5, color='blue')
plt.axvspan(4778, 4788, alpha=0.5, color='blue')
plt.axvspan(4818, 4828, alpha=0.5, color='blue')
plt.axvspan(5389, 5399, alpha=0.5, color='blue')
plt.axvspan(5532, 5542, alpha=0.5, color='blue')
plt.axvspan(6008, 6018, alpha=0.5, color='blue')
plt.axvspan(6016, 6026, alpha=0.5, color='blue')
plt.axvspan(4335, 4345, alpha=0.5, color='red')
plt.axvspan(4856, 4866, alpha=0.5, color='red')
plt.axvspan(6558, 6568, alpha=0.5, color='red')
plt.ylim((0, 5))
plt.savefig(self.outputname + '/' + self.specname +
'_obsnormalized.png')
plt.close()
np.savetxt(
self.outputname + '/' + self.specname +
'_obsnormalized.txt',
np.asarray((self.obswvl, self.obsflux_norm)).T)
if wvlcorr:
print('Doing wavelength correction...')
try:
# Compute standard deviation
contdivstd = np.zeros(len(self.ivar_norm)) + np.inf
contdivstd[self.ivar_norm > 0] = np.sqrt(
np.reciprocal(self.ivar_norm[self.ivar_norm > 0]))
# Wavelength correction
self.obswvl = fit_wvl(self.obswvl, self.obsflux_norm,
contdivstd, self.dlam, self.temp,
self.logg, self.fe, self.alpha,
self.specname, self.outputname + '/')
print('Done with wavelength correction!')
except Exception as e:
print(repr(e))
print(
'Couldn\'t complete wavelength correction for some reason.'
)
# Crop observed spectrum into regions around Mn lines
self.obsflux_fit, self.obswvl_fit, self.ivar_fit, self.dlam_fit, self.skip = mask_obs_for_abundance(
self.obswvl,
self.obsflux_norm,
self.ivar_norm,
self.dlam,
lines=self.lines)
# Else, check if we need to open a hi-res file to get the spectrum
elif hires is not None:
# Output filename
if self.globular:
self.outputname = '/raid/madlr/glob/' + galaxyname + '/' + 'hires'
else:
self.outputname = '/raid/madlr/dsph/' + galaxyname + '/' + 'hires'
# Open observed spectrum
self.specname = hires
self.obswvl, self.obsflux, self.dlam = open_obs_file(
self.obsfilename, hires=True)
# Get measured parameters from obsspecial keyword
self.temp = obsspecial[0]
self.logg = obsspecial[1]
self.fe = obsspecial[2]
self.alpha = obsspecial[3]
self.fe_err = obsspecial[4]
self.zrest = obsspecial[5]
self.ivar = np.ones(len(self.obsflux))
# Correct for wavelength
self.obswvl = self.obswvl / (1. + self.zrest)
print('Redshift: ', self.zrest)
# Get synthetic spectrum, split both obs and synth spectra into red and blue parts
synthfluxmask, obsfluxmask, obswvlmask, ivarmask, mask = mask_obs_for_division(
self.obswvl,
self.obsflux,
self.ivar,
temp=self.temp,
logg=self.logg,
fe=self.fe,
alpha=self.alpha,
dlam=self.dlam,
lines=self.lines,
hires=True)
# Compute continuum-normalized observed spectrum
self.obsflux_norm, self.ivar_norm = divide_spec(
synthfluxmask,
obsfluxmask,
obswvlmask,
ivarmask,
mask,
specname=self.specname,
outputname=self.outputname,
hires=True)
if smooth is not None:
# Crop med-res wavelength range to match hi-res spectrum
smooth = smooth[np.where((smooth > self.obswvl[0])
& (smooth < self.obswvl[-1]))]
# Interpolate and smooth the synthetic spectrum onto the observed wavelength array
self.dlam = np.ones(len(smooth)) * 0.7086
self.obsflux_norm = smooth_gauss_wrapper(
self.obswvl, self.obsflux_norm, smooth, self.dlam)
self.obswvl = smooth
self.ivar_norm = np.ones(len(self.obswvl)) * 1.e4
if plot:
# Plot continuum-normalized observed spectrum
plt.figure()
plt.plot(self.obswvl, self.obsflux_norm, 'k-')
plt.savefig(self.outputname + '/' + self.specname +
'_obsnormalized.png')
plt.close()
# Crop observed spectrum into regions around Mn lines
self.obsflux_fit, self.obswvl_fit, self.ivar_fit, self.dlam_fit, self.skip = mask_obs_for_abundance(
self.obswvl,
self.obsflux_norm,
self.ivar_norm,
self.dlam,
lines=self.lines,
hires=True)
# Else, take both spectrum and observed parameters from obsspecial keyword
else:
# Output filename
self.outputname = '/raid/madlr/test/' + slitmaskname
self.obsflux_fit = obsspecial[0]
self.obswvl_fit = obsspecial[1]
self.ivar_fit = obsspecial[2]
self.dlam_fit = obsspecial[3]
self.skip = obsspecial[4]
self.temp = obsspecial[5]
self.logg = obsspecial[6]
self.fe = obsspecial[7]
self.alpha = obsspecial[8]
self.fe_err = obsspecial[9]
self.specname = self.slitmaskname
# Splice together Mn line regions of observed spectra
print('Skip: ', self.skip)
self.obsflux_final = np.hstack((self.obsflux_fit[self.skip]))
self.obswvl_final = np.hstack((self.obswvl_fit[self.skip]))
self.ivar_final = np.hstack((self.ivar_fit[self.skip]))
def run_chisq(filename, paramfilename, galaxyname, slitmaskname, startstar=0, globular=False, lines='new', plots=False, wvlcorr=True, membercheck=None, memberlist=None, velmemberlist=None):
""" Measure Mn abundances from a FITS file.
Inputs:
filename -- file with observed spectra
paramfilename -- file with parameters of observed spectra
galaxyname -- galaxy name, options: 'scl'
slitmaskname -- slitmask name, options: 'scl1'
Keywords:
startstar -- if 0 (default), start at beginning of file and write new datafile;
else, start at #startstar and just append to datafile
globular -- if 'False' (default), put into output path of galaxy;
else, put into globular cluster path
lines -- if 'new' (default), use new revised linelist;
else, use original linelist from Judy's code
plots -- if 'False' (default), don't plot final fits/resids while doing the fits;
else, plot them
wvlcorr -- if 'True' (default), do linear wavelength corrections following G. Duggan's code for 900ZD data;
else (for 1200B data), don't do corrections
membercheck -- do membership check for this object
memberlist -- member list (from Evan's Li-rich giants paper)
velmemberlist -- member list (from radial velocity check)
"""
# Output filename
if globular:
outputname = '/raid/madlr/glob/'+galaxyname+'/'+slitmaskname+'.csv'
else:
outputname = '/raid/madlr/dsph/'+galaxyname+'/'+slitmaskname+'.csv'
# Open new file
if startstar<1:
with open(outputname, 'w+') as f:
f.write('Name\tRA\tDec\tTemp\tlog(g)\t[Fe/H]\terror([Fe/H])\t[alpha/Fe]\t[Mn/H]\terror([Mn/H])\tchisq(reduced)\n')
# Prep for member check
if membercheck is not None:
# Check if stars are in member list from Evan's Li-rich giants paper
table = ascii.read(memberlist)
memberindex = np.where(table.columns[0] == membercheck)
membernames = table.columns[1][memberindex]
# Also check if stars are in member list from velocity cut
if velmemberlist is not None:
oldmembernames = membernames
membernames = []
velmembernames = np.genfromtxt(velmemberlist,dtype='str')
for i in range(len(oldmembernames)):
if oldmembernames[i] in velmembernames:
membernames.append(oldmembernames)
membernames = np.asarray(membernames)
# Get number of stars in file
Nstars = open_obs_file(filename)
# Get coordinates of stars in file
RA, Dec = open_obs_file(filename, coords=True)
# Run chi-sq fitting for all stars in file
for i in range(startstar, Nstars):
try:
# Get metallicity of star to use for initial guess
print('Getting initial metallicity')
temp, logg, fe, alpha, fe_err = open_obs_file(filename, retrievespec=i, specparams=True)
# Get dlam (FWHM) of star to use for initial guess
specname, obswvl, obsflux, ivar, dlam, zrest = open_obs_file(filename, retrievespec=i)
# Check for bad parameter measurement
if np.isclose(temp, 4750.) and np.isclose(fe,-1.5) and np.isclose(alpha,0.2):
print('Bad parameter measurement! Skipped #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
# Do membership check
if membercheck is not None:
if specname not in membernames:
print('Not in member list! Skipped '+specname) #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
# Run optimization code
star = chi_sq.obsSpectrum(filename, paramfilename, i, wvlcorr, galaxyname, slitmaskname, globular, lines, RA[i], Dec[i], plot=True)
best_mn, error, finalchisq = star.plot_chisq(fe, output=True, plots=plots)
except Exception as e:
print(repr(e))
print('Skipped star #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
print('Finished star '+star.specname, '#'+str(i+1)+'/'+str(Nstars)+' stars')
#print('test', star.specname, RA[i], Dec[i], star.temp, star.logg, star.fe, star.fe_err, star.alpha, best_mn, error, finalchisq)
with open(outputname, 'a') as f:
f.write(star.specname+'\t'+str(RA[i])+'\t'+str(Dec[i])+'\t'+str(star.temp)+'\t'+str(star.logg)+'\t'+str(star.fe)+'\t'+str(star.fe_err)+'\t'+str(star.alpha)+'\t'+str(best_mn[0])+'\t'+str(error[0])+'\t'+str(finalchisq)+'\n')
return
def plot_fits_postfacto(filename, paramfilename, galaxyname, slitmaskname, startstar=0, globular=False, lines='new', mn_cluster=None):
""" Plot fits, residuals, and ivar for stars whose [Mn/H] abundances have already been measured.
Inputs:
filename -- file with observed spectra
paramfilename -- file with parameters of observed spectra
galaxyname -- galaxy name, options: 'scl'
slitmaskname -- slitmask name, options: 'scl1'
Keywords:
startstar -- if 0 (default), start at beginning of file and write new datafile;
else, start at #startstar and just append to datafile
globular -- if 'False' (default), put into output path of galaxy;
else, put into globular cluster path
lines -- if 'new' (default), use new revised linelist;
else, use original linelist from Judy's code
mn_cluster -- if not None (default), also plot spectrum with [Mn/H] = mean [Mn/H] of cluster
"""
# Input filename
if globular:
file = '/raid/madlr/glob/'+galaxyname+'/'+slitmaskname+'.csv'
else:
file = '/raid/madlr/dsph/'+galaxyname+'/'+slitmaskname+'.csv'
# Output filepath
if globular:
outputname = '/raid/madlr/glob/'+galaxyname+'/'+slitmaskname
else:
outputname = '/raid/madlr/dsph/'+galaxyname+'/'+slitmaskname
name = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=0, dtype='str')
mn = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=8)
mnerr = np.genfromtxt(file, delimiter='\t', skip_header=1, usecols=9)
# Get number of stars in file with observed spectra
Nstars = open_obs_file(filename)
# Open file to store reduced chi-sq values
chisqfile = outputname+'_chisq.txt'
with open(chisqfile, 'w+') as f:
print('made it here')
f.write('Star'+'\t'+'Line'+'\t'+'redChiSq (best[Mn/H])'+'\t'+'redChiSq (best[Mn/H]+0.15)'+'\t'+'redChiSq (best[Mn/H]-0.15)'+'\n')
# Plot spectra for each star
for i in range(startstar, Nstars):
try:
# Check if parameters are measured
temp, logg, fe, alpha, fe_err = open_obs_file(filename, retrievespec=i, specparams=True)
if np.isclose(1.5,logg) and np.isclose(fe,-1.5) and np.isclose(fe_err, 0.0):
print('Bad parameter measurement! Skipped #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
# Open star
star = chi_sq.obsSpectrum(filename, paramfilename, i, False, galaxyname, slitmaskname, globular, lines, plot=True)
# Check if star has already had [Mn/H] measured
if star.specname in name:
# If so, open data file for star
if globular:
datafile = '/raid/madlr/glob/'+galaxyname+'/'+slitmaskname+'/'+str(star.specname)+'_data.csv'
else:
datafile = '/raid/madlr/dsph/'+galaxyname+'/'+slitmaskname+'/'+str(star.specname)+'_data.csv'
# Get observed and synthetic spectra and inverse variance array
obswvl = np.genfromtxt(datafile, delimiter=',', skip_header=2, usecols=0)
obsflux = np.genfromtxt(datafile, delimiter=',', skip_header=2, usecols=1)
synthflux = np.genfromtxt(datafile, delimiter=',', skip_header=2, usecols=2)
#synthfluxup = np.genfromtxt(datafile, delimiter=',', skip_header=2, usecols=3)
#synthfluxdown = np.genfromtxt(datafile, delimiter=',', skip_header=2, usecols=4)
ivar = np.genfromtxt(datafile, delimiter=',', skip_header=2, usecols=5)
idx = np.where(name == star.specname)
synthfluxup = star.synthetic(obswvl, mn[idx] + 0.15, full=True)
synthfluxdown = star.synthetic(obswvl, mn[idx] - 0.15, full=True)
synthflux_nomn = star.synthetic(obswvl, -10.0, full=True)
if mn_cluster is not None:
synthflux_cluster = [mn_cluster, star.synthetic(obswvl, mn_cluster, full=True)]
else:
synthflux_cluster=None
if mnerr[idx][0] < 1:
# Run code to make plots
make_plots(lines, star.specname+'_', obswvl, obsflux, synthflux, outputname, ivar=ivar, resids=True, synthfluxup=synthfluxup, synthfluxdown=synthfluxdown, synthflux_nomn=synthflux_nomn, synthflux_cluster=synthflux_cluster, title=None, savechisq=chisqfile)
# Write all plotting data to a file
hdr = 'Star '+str(star.specname)+'\n'+'obswvl\tobsflux\tsynthflux\tsynthfluxup\tsynthfluxdown\tsynthflux_nomn\n'
np.savetxt(outputname+'/'+str(star.specname)+'_finaldata.csv', np.asarray((obswvl,obsflux,synthflux,synthfluxup,synthfluxdown,synthflux_nomn)).T, header=hdr)
except Exception as e:
print(repr(e))
print('Skipped star #'+str(i+1)+'/'+str(Nstars)+' stars')
continue
print('Finished star '+star.specname, '#'+str(i+1)+'/'+str(Nstars)+' stars')
return