def skewQSO(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
savefilename= args[0]
if os.path.exists(savefilename):
savefile= open(savefilename,'rb')
skews= pickle.load(savefile)
gaussskews= pickle.load(savefile)
fittype= pickle.load(savefile)
band= pickle.load(savefile)
mean= pickle.load(savefile)
taus= pickle.load(savefile)
savefile.close()
else:
skews= {}
gaussskews= {}
fittype= options.type
mean= options.mean
band= options.band
taus= nu.arange(options.dtau,options.taumax,options.dtau)/365.25
if os.path.exists(options.fitsfile):
fitsfile= open(options.fitsfile,'rb')
params= pickle.load(fitsfile)
fitsfile.close()
else:
raise IOError("--fitsfile (or -f) has to be set to the file holding the best-fits")
if options.star:
dir= '../data/star/'
elif options.nuvx:
dir= '../data/nuvx/'
elif options.nuvxall:
dir= '../data/nuvx_all/'
elif options.uvx:
dir= '../data/uvx/'
elif options.rrlyrae:
dir= '../data/rrlyrae/'
else:
dir= '../data/s82qsos/'
qsos= QSOfilenames(dir=dir)
if not options.split is None:
splitarr= nu.arange(len(qsos)) / int(nu.ceil(len(qsos)/float(options.split)))
splitDict= {}
for ii, qso in enumerate(qsos):
key= os.path.basename(qso)
splitDict[key]= splitarr[ii]
print "Running bin %i ..." % (options.rah-1)
savecount= 0
count= len(skews)
if not options.star and not options.rrlyrae:
#Read master file for redshifts
dataqsos= open_qsos()
qsoDict= {}
ii=0
for qso in dataqsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
for qso in qsos:
key= os.path.basename(qso)
if skews.has_key(key):
continue
if not options.split is None:
if splitDict[key] != (options.rah-1):
continue
else:
try:
if int(key[5:7]) != options.rah and options.rah != -1:
continue
except ValueError:
if options.rah == -2 or options.rah == -1:
pass
else:
print "Skipping ValueError "+key
continue
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
sys.stdout.write('\rWorking on %s: %s\r' % (str(count),key))
sys.stdout.flush()
v= VarQso(qso,flux=options.flux)
if v.nepochs(band) < 20:
#print "This object does not have enough epochs ..."
continue
#Set best-fit
if options.flux:
params[key]['logA']+= 2.*nu.log(nu.log(10.)/2.5)
if options.mean == 'const':
params[key]['m']= -nu.log(10.)/2.5*params[key]['m']
v.LCparams= params[key]
v.LC= LCmodel(trainSet=v._build_trainset(band),type=fittype,mean=mean,
init_params=params[key])
v.LCtype= fittype
v.LCmean= mean
v.fitband= band
#Now compute skew and Gaussian samples
try:
thisskew= v.skew(taus,band)
except RuntimeError:
continue
if options.flux:
thisskew*= -1.
thisgaussskews= nu.zeros((options.nsamples,len(taus)))
for ii in range(options.nsamples):
#First re-sample
if options.star or options.rrlyrae:
redshift= 0.
else:
redshift= dataqsos[qsoDict[key]].z
try:
o= v.resample(v.mjd[band],band=band,noconstraints=True,
wedge=options.wedge,
wedgerate=options.wedgerate*365.25/(1.+redshift),
wedgetau=(1.+redshift)) #1yr
except nu.linalg.linalg.LinAlgError:
if params[key]['gamma'] > 1.5 \
or params[key]['logA'] < -10.:
continue #re-sampling fails because of bad gamma/logA
else:
print key, params[key]
o.LCparams= v.LCparams
o.LC= v.LC
o.fitband= v.fitband
o.LCtype= v.LCtype
o.LCmean= v.LCmean
if options.wedge:
o.LCparams['gamma']= 1.
o.LCparams['logA']= o.LCparams['logA']\
+nu.log(0.025**v.LCparams['gamma']/0.025)
o.LCmean= 'zero' #bc we remove the mean when resampling wedge
#Set up LC with correct params
o.LC= LCmodel(trainSet=o._build_trainset(band),
type=o.LCtype,mean=o.LCmean,
init_params=o.LCparams)
thisgaussskews[ii,:]= o.skew(taus,band)
if options.flux:
thisgaussskews[ii,:]*= -1.
skews[key]= thisskew
gaussskews[key]= thisgaussskews
savecount+= 1
if savecount == options.saveevery:
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
sys.stdout.write('\rSaving ...\r')
sys.stdout.flush()
save_pickles(savefilename,skews,gaussskews,fittype,band,mean,taus)
savecount= 0
count+= 1
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
save_pickles(savefilename,skews,gaussskews,fittype,band,mean,taus)
print "All done"
def skew0957(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
savefilename= args[0]
#Read data
if options.vanderriest:
vA= VarQso('../data/0957-A.fits',band=options.band)
vB= VarQso('../data/0957-B.fits',band=options.band)
else:
vA= VarQso('../data/L0957-A_%s.fits' % options.band,band=options.band)
vB= VarQso('../data/L0957-B_%s.fits' % options.band,band=options.band)
if not options.fitsfile is None and os.path.exists(options.fitsfile):
fitsfile= open(options.fitsfile,'rb')
paramsA= pickle.load(fitsfile)
paramsB= pickle.load(fitsfile)
paramsAB= pickle.load(fitsfile)
fitsfile.close()
vA.LCparams= paramsA
vB.LCparams= paramsB
vA.LCtype='powerlawSF'
vB.LCtype='powerlawSF'
vA.LCmean= 'const'
vB.LCmean= 'const'
vA.fitband= options.band
vB.fitband= options.band
else:
#Fit for means
print "Fitting SF for both images ..."
vA.fit(options.band,mean='const')
vB.fit(options.band,mean='const')
#Load into single new VarQso
newm= list(vA.m[options.band]-vA.LCparams['m'])
newerrm= list(vA.err_m[options.band])
newmjd= list(vA.mjd[options.band])
newm.extend(list(vB.m[options.band]+nu.mean(vA.m[options.band])
-nu.mean(vB.m[options.band])-vB.LCparams['m']))
newerrm.extend(list(vB.err_m[options.band]))
newmjd.extend(list(vB.mjd[options.band]-417./365.25))#shift lagged B
v= VarQso(newmjd,newm,newerrm,band=options.band,medianize=False)
if not options.fitsfile is None and os.path.exists(options.fitsfile):
v.LCparams= paramsAB
v.LCtype='powerlawSF'
v.LCmean= 'zero'
v.fitband= options.band
v.LC= LCmodel(trainSet=v._build_trainset(options.band),
type=v.LCtype,mean=v.LCmean,
init_params=paramsAB)
else:
v.fit(options.band)
if not options.fitsfile is None and not os.path.exists(options.fitsfile):
save_pickles(options.fitsfile,vA.LCparams,vB.LCparams,v.LCparams)
taus= nu.arange(1.,201.,1.)/365.25
thisskew= v.skew(taus,options.band,duration=0.7)
thisgaussskews= nu.zeros((options.nsamples,len(taus)))
print "Calculating simulated skews ..."
for ii in range(options.nsamples):
#First re-sample
redshift= 1.41
o= v.resample(v.mjd[options.band],band=options.band,noconstraints=True,
wedge=options.wedge,
wedgerate=options.wedgerate*365.25/(1.+redshift),
wedgetau=(1.+redshift)) #1yr
o.LCparams= v.LCparams
o.LC= v.LC
o.fitband= v.fitband
o.LCtype= v.LCtype
o.LCmean= v.LCmean
if options.wedge:
o.LCparams['gamma']= 1.
o.LCparams['logA']= o.LCparams['logA']\
+nu.log(0.05**v.LCparams['gamma']/0.05)
o.LCmean= 'zero' #bc we remove the mean when resampling wedge
#Set up LC with correct params
o.LC= LCmodel(trainSet=o._build_trainset(options.band),
type=o.LCtype,mean=o.LCmean,
init_params=o.LCparams)
thisgaussskews[ii,:]= o.skew(taus,options.band,duration=0.7)
skews= {}
gaussskews= {}
skews['0957']= thisskew
gaussskews['0957']= thisgaussskews
save_pickles(savefilename,skews,gaussskews,
None,options.band,None,taus)
return None
