/
stackspec_script.py
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stackspec_script.py
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import numpy as np
from astropy.io import fits
from astropy.wcs import WCS
from astropy.table import Table, Column
from glob import glob
import time
import multiprocessing as mp
import argparse
from astropy.cosmology import FlatLambdaCDM
#you can also use pre-defined parameters, e.g.:
#from astropy.cosmology import WMAP7
import astropy.units as u
#define the cosmology (if you import WMAP7, you don't need this line)
cosmo = FlatLambdaCDM(H0=70 * u.km / u.s / u.Mpc, Om0=0.3)
def clip(vec,sigclip,cthresh):
'''
'''
old=vec.std()
ii=(np.abs(vec-vec.mean()) <= sigclip*old)
clipvec=vec[ii]
#sigma=[]#dblarr(1)
sigma=clipvec.std()
veci=clipvec
while np.abs(sigma-old)/old > cthresh:
old = sigma
ii=(np.abs(vec-veci.mean()) <= sigclip*veci.std())
veci=vec[ii]
sigma=veci.std()
avg=veci.mean()
dev=veci.std()
return avg
def removebad(spec):
'''
This function can be used to determine the range of good pixel values,
such that the zero-value pixels in the beginning and the end of the
spectra is removed.
Input
=====
spectra: an array of the spec flux values
Output
======
A list of indices that include non-zero spec values (i.e., exludes
zero specs from the beginning and the end of the spectra)
'''
if spec[0] != 0: bindx=0
else:
for i,s in enumerate(spec):
if s != 0: break
bindx=i+10
arr=range(len(spec))
if spec[-1] != 0: eindx=len(spec)-1
else:
for i in reversed(arr):
if spec[i] != 0: break
eindx=i-10
return np.arange(bindx,eindx+1)
def read_spec(file):
sp=fits.open(file)
header = sp[1].header
wcs = WCS(header)
#read flux and flux err
spec=sp[1].data
specerr=sp[2].data
#convert pixel values to wavelength
#make index array
index = np.arange(len(spec)) # or np.arange(header['NAXIS1'])
specwl = wcs.wcs_pix2world(index[:,np.newaxis], 0)
specwl=np.reshape(specwl, len(specwl)) #reshape to have a 1d array
return header,spec,specerr,specwl
###################### MAIN CODE ########################
def main(specpath,tblpath,obj_ind,outfile,*normto):
tbl=fits.getdata(tblpath, ext=1)
#input arrays
masklist=tbl['maskname'][obj_ind]
idlist=tbl['id'][obj_ind]
zarray=tbl['z_mosfire'][obj_ind]
from astropy.cosmology import WMAP9 as cosmo
#compute distance, read the templates, redshift them
ld=cosmo.luminosity_distance(zarray) * 3.0856e24 #cm
ld=ld.value
ha6565_lum=tbl['ha6565_lum'][obj_ind]#tbl['ha6565_preferredflux'][obj_ind]*4*np.pi*ld*1.0e-40*ld
hb4863_lum=tbl['hb4863_lum'][obj_ind]#tbl['hb4863_preferredflux'][obj_ind]*4*np.pi*ld*1.0e-40*ld
#luvcorr=tbl['luv'][obj_ind]
ha6565_abs_flux=tbl['ha6565_abs_flux'][obj_ind]
hb4863_abs_flux=tbl['hb4863_abs_flux'][obj_ind]
ha6565_lum_err=tbl['ha6565_lum_err'][obj_ind]#tbl['ha6565_preferredflux_err'][obj_ind]*4*np.pi*ld*1.0e-40*ld
hb4863_lum_err=tbl['hb4863_lum_err'][obj_ind]#tbl['hb4863_preferredflux_err'][obj_ind]*4*np.pi*ld*1.0e-40*ld
idliststr=[str(e) for e in idlist]
specfile = specpath + masklist.strip()+'.*.'+idliststr+'.ell.1d.fits'
#counting the number of files
speccount=0
for i,f in enumerate(specfile):
filelisttemp=glob(f)
for n in filelisttemp:
speccount += 1
#making a list of file paths
filelist=['' for i in range(speccount)]
zlist=np.zeros(speccount)
halumlist=np.zeros(speccount)
hblumlist=np.zeros(speccount)
#uvlumlist=np.zeros(speccount)
ha6565_abs_fluxlist=np.zeros(speccount)
hb4863_abs_fluxlist=np.zeros(speccount)
specind=0
for i,f in enumerate(specfile):
filelisttemp=glob(f)
for n in filelisttemp:
filelist[specind]=n
zlist[specind]=zarray[i]
halumlist[specind] = ha6565_lum[i]
hblumlist[specind] = hb4863_lum[i]
#uvlumlist[specind] = luvcorr[i]
ha6565_abs_fluxlist[specind] = ha6565_abs_flux[i]
hb4863_abs_fluxlist[specind] = hb4863_abs_flux[i]
specind += 1
import sys
if sys.argv[5] == 'Ha': norm = 1./halumlist ; normbalm = 1./ha6565_lum
if sys.argv[5] == 'Hb': norm = 1./hblumlist ; normbalm = 1./hb4863_lum
if sys.argv[5] == 'UV': norm = 1./uvlumlist ; normbalm = 1./luvcorr
if sys.argv[5] == 'none': norm = np.ones(len(halumlist)) ; normbalm = np.ones(len(ha6565_lum))
if ((sys.argv[5] != 'Ha') & (sys.argv[5] != 'Hb') & (sys.argv[5] != 'UV') & (sys.argv[5] != 'none')):
print('normto keyword should be set to one of these: "Ha","Hb","UV", or "none" ')
#make a grid of wavelength with the desired resolution
wavemin = 3250
wavemax = 10000
delwave = 0.5 #in AA
gridwl = np.arange(wavemin,wavemax,delwave)
nwave = len(gridwl)
specall = np.zeros((nwave,speccount))
specerrall = np.zeros((nwave,speccount))
t0=time.time()
print('# Stacking ',speccount,' spectra of ',len(idlist),' objects')
for i,specfile in enumerate(filelist):
header,spec,specerr,specwl = read_spec(specfile)
#cut the bad beginning and end of the spectra
goodpix=removebad(spec)
spec=spec[goodpix]
specerr=specerr[goodpix]
specwl=specwl[goodpix]
ldist=cosmo.luminosity_distance(zlist[i]).to(u.cm)
lspec =1e-40 *ldist*ldist*4*np.pi*(1+zlist[i]) * spec
lspecerr =1e-40 *ldist*ldist*4*np.pi*(1+zlist[i]) * specerr
lspec = lspec * norm[i]
lspecerr = lspecerr * norm[i]
#calculate rest-frame wavelength
specwl /= 1.+zlist[i]
#interpolate to the new wavelength grid
UNDEF = -999.
gridspec = np.interp(gridwl,specwl,lspec,left=UNDEF,right=UNDEF)
gridspecerr = np.interp(gridwl,specwl,lspecerr,left=UNDEF,right=UNDEF)
#take into account the resampling for the error spectrum
errfac = np.sqrt(header['CDELT1']/(1.+zlist[i])/delwave)
gridspecerr = gridspecerr*errfac
#remove sky lines (remove those with error > 3. * median(err))
mederr = np.median(gridspecerr[gridspecerr > 0.])
keep = (np.abs(gridspecerr) < mederr*3.)
gridspec[keep == False] = UNDEF
#pdb.set_trace()
#assign the spetra and error spectra to arrays:
specall[:,i]=gridspec
specerrall[:,i]=gridspecerr
#declare arrays
wt_avg=np.zeros(nwave)
nwt_avg=np.zeros(nwave)
med=np.zeros(nwave)
clip_avg=np.zeros(nwave)
wt_err=np.zeros(nwave)
disp=np.zeros(nwave)
z=np.zeros(nwave)
num=np.zeros(nwave)
#stacking
for j in range(nwave):
speccol = specall[j,:]
specerrcol = specerrall[j,:]
good=np.where(speccol != UNDEF)
ngood=len(speccol[good])
if ngood == 0: continue
if ngood == 1:
speccol=speccol[good]
specerrcol = specerrcol[good]
zcol=zlist[good]
weight=1/(specerrcol*specerrcol)
wt_avg[j] = np.nansum(speccol*weight)/np.nansum(weight)
nwt_avg[j] = np.nanmean(speccol)
med[j] = speccol
wt_err[j] = np.sqrt(1./np.nansum(weight))
disp[j] = 0.
z[j] = zcol.mean()
num[j] = ngood
if ngood > 1:
speccol=speccol[good]
specerrcol = specerrcol[good]
zcol=zlist[good]
weight=1/(specerrcol*specerrcol)
wt_avg[j] = np.nansum(speccol*weight)/np.nansum(weight)
nwt_avg[j] = np.nanmean(speccol)
med[j] = np.nanmedian(speccol)
clip_avg[j] = clip(speccol,3.,.01)
wt_err[j] = np.sqrt(1./np.nansum(weight))
disp[j] = np.nanstd(speccol)
z[j] = zcol.mean()
num[j] = ngood
#Measure weighted average Balmer absorption
ldistarr=cosmo.luminosity_distance(zarray).to(u.cm)
goodha=np.where((ha6565_abs_flux != -999.) & (ha6565_lum_err != 0.) & (np.isfinite(ha6565_abs_flux))); n1=len(ha6565_lum_err[goodha])
goodhb=np.where((hb4863_abs_flux != -999.) & (np.isfinite(hb4863_abs_flux))); n2=len(hb4863_abs_flux[goodhb])
print('# Number of objs with Balmer absorption of two lines: ',n1,' and ',n2)
lhaabs = 1e-40 *ldistarr*ldistarr*4*np.pi * ha6565_abs_flux * normbalm
lhbabs = 1e-40 *ldistarr*ldistarr*4*np.pi * hb4863_abs_flux * normbalm
habalm = (np.ma.masked_invalid(lhaabs[goodha]/(ha6565_lum_err[goodha]*ha6565_lum_err[goodha])).sum()/
np.ma.masked_invalid(1/(ha6565_lum_err[goodha]*ha6565_lum_err[goodha])).sum() )
hbbalm = (np.ma.masked_invalid(lhbabs[goodhb]/(hb4863_lum_err[goodhb]*hb4863_lum_err[goodhb])).sum()/
np.ma.masked_invalid(1/(hb4863_lum_err[goodhb]*hb4863_lum_err[goodhb])).sum() )
#defining the columns for the output
#wt_avg_col=fits.Column(name='wt_avg',format='D',array=wt_avg)
#nwt_avg_col=fits.Column(name='nwt_avg',format='D',array=nwt_avg)
#med_col=fits.Column(name='med',format='D',array=med)
#wt_err_col=fits.Column(name='wt_err',format='D',array=wt_err)
#disp_col=fits.Column(name='disp',format='D',array=disp)
#z_col=fits.Column(name='z',format='D',array=z)
#num_col=fits.Column(name='num',format='D',array=num)
#cols = fits.ColDefs([wt_avg_col,nwt_avg_col,med_col,wt_err_col,disp_col,z_col,num_col])
#define the output file
#out = fits.BinTableHDU.from_columns(cols)
out=fits.PrimaryHDU()
hdr=out.header
#making the header of the output file
out.header['UNITS'] = '1.d40 erg/s/A'
out.header['CTYPE1'] = 'LINEAR'
out.header['CRPIX1'] = 1.0
out.header['CRVAL1'] = wavemin
out.header['CDELT1'] = delwave
out.header['CD1_1'] = delwave
out.header['haabs'] = habalm.value
out.header['hbabs'] = hbbalm.value
#out.header['uvcor'] = np.median(uvlumlist)
#out.header['uvcorerr'] = uvlumlist.std()/len(uvlumlist)
out.header['COMMENT'] = 'Ext 1: weighted average'
out.header['COMMENT'] = 'Ext 2: error'
out.header['COMMENT'] = 'Ext 3: unweighted average'
out.header['COMMENT'] = 'Ext 4: median'
out.header['COMMENT'] = 'Ext 5: 3sigma-clipped average'
out.header['COMMENT'] = 'Ext 6: standard deviation in each wavelength bin'
out.header['COMMENT'] = 'Ext 7: average redshift of objs contributing to each wavelength bin'
out.header['COMMENT'] = 'Ext 8: number of objs in each wavelength bin'
for i in range(len(idlist)):
hdrspecname = masklist[i]+'_'+str(idlist[i])
out.header['COMMENT'] = hdrspecname
#Writing the output
#out.writeto('test_py.fits', overwrite=True)
outname=outfile
out.writeto(outname, overwrite=True)
hdr['OBJ']='wt_avg'
fits.append(outname,wt_avg,hdr)
hdr['OBJ']='wt_err'
fits.append(outname,wt_err,hdr)
hdr['OBJ']='nwt_avg'
fits.append(outname,nwt_avg,hdr)
hdr['OBJ']='med'
fits.append(outname,med,hdr)
hdr['OBJ']='clip_avg'
fits.append(outname,clip_avg,hdr)
hdr['OBJ']='disp'
fits.append(outname,disp,hdr)
hdr['OBJ']='z'
fits.append(outname,z,hdr)
hdr['OBJ']='num'
fits.append(outname,num,hdr)
print('# Stacking took: {0:3.2f}'.format((time.time()-t0)/60.),' min.')
#import cProfile
#cProfile.run('main()')
import sys
specpath=sys.argv[1]
tblpath=sys.argv[2]
objfile=sys.argv[3]
outfile=sys.argv[4]
#calculate number of lines in the file
with open(objfile) as f:
n=sum(1 for _ in f)
#put the input in arr array
arr=np.zeros(n)
for i,line in enumerate(open(objfile)):
arr[i]=line.strip()
#covert 0,1 arr to a boolean array
obj_ind = arr == 1
print('# Spectra is normalized to ',sys.argv[5])
print('# Stacked spectra file : ',sys.argv[4])
main(specpath,tblpath,obj_ind,outfile)