def fitQSO(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if len(args) == 2:
othersavefilename = args[1]
othersavefile = open(othersavefilename, 'rb')
otherparams = pickle.load(othersavefile)
othersavefile.close()
else:
otherparams = {}
savefilename = args[0]
if os.path.exists(savefilename):
savefile = open(savefilename, 'rb')
params = pickle.load(savefile)
type = pickle.load(savefile)
band = pickle.load(savefile)
mean = pickle.load(savefile)
savefile.close()
if params.has_key('.fit'): params.pop('.fit')
for key in params.keys():
if (params[key].has_key('gamma') and \
(params[key]['gamma'] < 0. or params[key]['gamma'] > 2.)) \
or (params[key].has_key('gammagr') \
and (params[key]['gammagr'] < 0. \
or params[key]['gammagr'] > 2.)):
print "Popping bad gamma ..."
params.pop(key)
else:
params = {}
type = options.type
mean = options.mean
band = options.band
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/'
if options.resampled:
if os.path.exists(options.infile):
samplefile = open(options.infile, 'rb')
qsos = pickle.load(samplefile)
samplefile.close()
else:
print "'--resampled' is set, but -i filename does not exist ..."
print "Returning ..."
return None
else:
qsos = QSOfilenames(dir=dir)
savecount = 0
count = len(params)
for qso in qsos:
if options.resampled:
key = qso[0]
else:
key = os.path.basename(qso)
if params.has_key(key) or otherparams.has_key(key):
continue
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
print "Working on " + str(count) + ": " + key
if options.resampled:
v = qso[1]
else:
v = VarQso(qso, flux=options.fitflux)
if v.nepochs(band) < 20:
print "This object does not have enough epochs ..."
continue
params[key] = v.fit(band=band, type=type, loglike=True, mean=mean)
if _DEBUG:
print params[key]
if params[key]['loglike'] == -numpy.finfo(numpy.dtype(
numpy.float64)).max:
print "Popping bad fit ..."
params.pop(key)
if savecount == options.saveevery:
print "Saving ..."
save_pickles(params, type, band, mean, savefilename)
savecount = 0
savecount += 1
count += 1
save_pickles(params, type, band, mean, savefilename)
print "All done"
def fitQSO(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if len(args) == 2:
othersavefilename= args[1]
othersavefile= open(othersavefilename,'rb')
otherparams= pickle.load(othersavefile)
othersavefile.close()
else:
otherparams= {}
savefilename= args[0]
if os.path.exists(savefilename):
savefile= open(savefilename,'rb')
params= pickle.load(savefile)
type= pickle.load(savefile)
band= pickle.load(savefile)
mean= pickle.load(savefile)
savefile.close()
if params.has_key('.fit'): params.pop('.fit')
for key in params.keys():
if (params[key].has_key('gamma') and \
(params[key]['gamma'] < 0. or params[key]['gamma'] > 2.)) \
or (params[key].has_key('gammagr') \
and (params[key]['gammagr'] < 0. \
or params[key]['gammagr'] > 2.)):
print "Popping bad gamma ..."
params.pop(key)
else:
params= {}
type= options.type
mean= options.mean
band= options.band
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/'
if options.resampled:
if os.path.exists(options.infile):
samplefile= open(options.infile,'rb')
qsos= pickle.load(samplefile)
samplefile.close()
else:
print "'--resampled' is set, but -i filename does not exist ..."
print "Returning ..."
return None
else:
qsos= QSOfilenames(dir=dir)
savecount= 0
count= len(params)
for qso in qsos:
if options.resampled:
key= qso[0]
else:
key= os.path.basename(qso)
if params.has_key(key) or otherparams.has_key(key):
continue
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
print "Working on "+str(count)+": "+key
if options.resampled:
v= qso[1]
else:
v= VarQso(qso,flux=options.fitflux)
if options.lownepochs:
if v.nepochs(band) >= 20:
print "This object has too many epochs ..."
continue
elif v.nepochs(band) < 3:
print "This object does not have enough epochs ..."
continue
elif not options.lownepochs and v.nepochs(band) < 20:
print "This object does not have enough epochs ..."
continue
params[key]= v.fit(band=band,type=type,loglike=True,mean=mean)
if _DEBUG:
print params[key]
if params[key]['loglike'] == -numpy.finfo(numpy.dtype(numpy.float64)).max:
print "Popping bad fit ..."
params.pop(key)
if savecount == options.saveevery:
print "Saving ..."
save_pickles(params,type,band,mean,savefilename)
savecount= 0
savecount+= 1
count+= 1
save_pickles(params,type,band,mean,savefilename)
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
