def classQSO(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):
print filename+" exists"
print "Remove this file before running ..."
print "Returning ..."
return None
#Load fit params: Quasars
if options.qsomodel == 'test':
if len(options.band) == 1:
qsoparams= [{'gamma':0.3,'logA':-2.}]
else:#Multi-band
qsoparams= [{'gamma':0.2,'logA':-2.,
'gammagr':0.0001,'logAgr':-2.}]
qsoweights= [1.]
elif options.qsomodel == 'zero':
qsoparams= [{}]
qsoweights= [1.]
elif os.path.exists(options.qsomodel):
qsofile= open(options.qsomodel,'rb')
qsoparams= pickle.load(qsofile)
qsoweights= nu.array(pickle.load(qsofile),dtype='float64')
qsofile.close()
else:
print "Input to 'qsomodel' not recognized ..."
print "Returning ..."
return
#Stars
if options.starmodel == 'test':
if len(options.band) == 1:
starparams= [{'gamma':0.0001,'logA':-3.5}]
else:#Multi-band
starparams= [{'gamma':0.1,'logA':-3.,
'gammagr':0.0001,'logAgr':-2.}]
starweights= [1.]
elif options.starmodel == 'zero':
starparams= [{}]
starweights= [1.]
elif os.path.exists(options.starmodel):
starfile= open(options.starmodel,'rb')
starparams= pickle.load(starfile)
starweights= nu.array(pickle.load(starfile),dtype='float64')
starfile.close()
else:
print "Input to 'starmodel' not recognized ..."
print "Returning ..."
return
#RR Lyrae
if options.rrlyraemodel == 'test':
if len(options.band) == 1:
rrlyraeparams= [{'gamma':0.0001,'logA':-2.}]
else:#Multi-band
rrlyraeparams= [{'gamma':0.1,'logA':-2.,
'gammagr':0.0001,'logAgr':-2.}]
rrlyraeweights= [1.]
elif options.rrlyraemodel == 'zero':
rrlyraeparams= [{}]
rrlyraeweights= [1.]
elif os.path.exists(options.rrlyraemodel):
rrlyraefile= open(options.rrlyraemodel,'rb')
rrlyraeparams= pickle.load(rrlyraefile)
rrlyraeweights= nu.array(pickle.load(rrlyraefile),dtype='float64')
rrlyraefile.close()
else:
print "Input to 'rrlyraemodel' not recognized ..."
print "Returning ..."
return
#normalize weights
qsoweights/= nu.sum(qsoweights)
starweights/= nu.sum(starweights)
rrlyraeweights/= nu.sum(rrlyraeweights)
#Load location of the data
if options.resampled:
if os.path.exists(options.sample):
samplefile= open(options.sample,'rb')
objs= pickle.load(samplefile)
samplefile.close()
else:
print "'--resampled' is set, but --sample= filename does not exist ..."
print "Returning ..."
return None
else:
if options.sample == 'nuvx':
dir= '../data/nuvx/'
if options.sample == 'nuvxall':
dir= '../data/nuvx_all/'
if options.sample == 'uvx':
dir= '../data/uvx/'
objs= QSOfilenames(dir=dir)
#Classify each source
out= []
allcount, count= 0, 0
for obj in objs:
allcount+= 1
if options.resampled:
key= obj[0]
else:
key= os.path.basename(obj)
#if key != 'SDSSJ013306.18-004523.8.fit':
# continue
print "Working on "+str(count)+"(%i/%i): " % (allcount,len(objs))+key
if options.resampled:
v= obj[1]
else:
v= VarQso(obj)
if v.nepochs(options.band) < options.minepochs:
print "This object does not have enough epochs ..."
continue
varout= VarClass()
varout.key= key
#quasar likelihoods
qsolike= []
for ii in range(len(qsoparams)):
qsolike.append(v.loglike(band=options.band,type=options.type,
params=qsoparams[ii])
+nu.log(qsoweights[ii]))
qsolike= logsum(qsolike)
varout.qsologlike= qsolike
#star likelihoods
starlike= []
for ii in range(len(starparams)):
starlike.append(v.loglike(band=options.band,type=options.type,
params=starparams[ii])
+nu.log(starweights[ii]))
starlike= logsum(starlike)
varout.starloglike= starlike
#RR Lyrae likelihoods
rrlyraelike= []
for ii in range(len(rrlyraeparams)):
rrlyraelike.append(v.loglike(band=options.band,type=options.type,
params=rrlyraeparams[ii])
+nu.log(rrlyraeweights[ii]))
rrlyraelike= logsum(rrlyraelike)
varout.rrlyraeloglike= rrlyraelike
#print qsolike, starlike
if qsolike > starlike and qsolike > rrlyraelike:
print qsolike, starlike, rrlyraelike
out.append(varout)
count+= 1
#if count > 500: break
#Save
for jj in range(len(out)):
if out[jj].qsologlike > out[jj].starloglike and out[jj].qsologlike > out[jj].rrlyraeloglike:
print out[jj].key
saveClass(out,args[0])
return None
def classQSO(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):
print filename + " exists"
print "Remove this file before running ..."
print "Returning ..."
return None
#Load fit params: Quasars
if options.qsomodel == 'test':
if len(options.band) == 1:
qsoparams = [{'gamma': 0.3, 'logA': -2.}]
else: #Multi-band
qsoparams = [{
'gamma': 0.2,
'logA': -2.,
'gammagr': 0.0001,
'logAgr': -2.
}]
qsoweights = [1.]
elif options.qsomodel == 'zero':
qsoparams = [{}]
qsoweights = [1.]
elif os.path.exists(options.qsomodel):
qsofile = open(options.qsomodel, 'rb')
qsoparams = pickle.load(qsofile)
qsoweights = nu.array(pickle.load(qsofile), dtype='float64')
qsofile.close()
else:
print "Input to 'qsomodel' not recognized ..."
print "Returning ..."
return
#Stars
if options.starmodel == 'test':
if len(options.band) == 1:
starparams = [{'gamma': 0.0001, 'logA': -3.5}]
else: #Multi-band
starparams = [{
'gamma': 0.1,
'logA': -3.,
'gammagr': 0.0001,
'logAgr': -2.
}]
starweights = [1.]
elif options.starmodel == 'zero':
starparams = [{}]
starweights = [1.]
elif os.path.exists(options.starmodel):
starfile = open(options.starmodel, 'rb')
starparams = pickle.load(starfile)
starweights = nu.array(pickle.load(starfile), dtype='float64')
starfile.close()
else:
print "Input to 'starmodel' not recognized ..."
print "Returning ..."
return
#RR Lyrae
if options.rrlyraemodel == 'test':
if len(options.band) == 1:
rrlyraeparams = [{'gamma': 0.0001, 'logA': -2.}]
else: #Multi-band
rrlyraeparams = [{
'gamma': 0.1,
'logA': -2.,
'gammagr': 0.0001,
'logAgr': -2.
}]
rrlyraeweights = [1.]
elif options.rrlyraemodel == 'zero':
rrlyraeparams = [{}]
rrlyraeweights = [1.]
elif os.path.exists(options.rrlyraemodel):
rrlyraefile = open(options.rrlyraemodel, 'rb')
rrlyraeparams = pickle.load(rrlyraefile)
rrlyraeweights = nu.array(pickle.load(rrlyraefile), dtype='float64')
rrlyraefile.close()
else:
print "Input to 'rrlyraemodel' not recognized ..."
print "Returning ..."
return
#normalize weights
qsoweights /= nu.sum(qsoweights)
starweights /= nu.sum(starweights)
rrlyraeweights /= nu.sum(rrlyraeweights)
#Load location of the data
if options.resampled:
if os.path.exists(options.sample):
samplefile = open(options.sample, 'rb')
objs = pickle.load(samplefile)
samplefile.close()
else:
print "'--resampled' is set, but --sample= filename does not exist ..."
print "Returning ..."
return None
else:
if options.sample == 'nuvx':
dir = '../data/nuvx/'
if options.sample == 'nuvxall':
dir = '../data/nuvx_all/'
if options.sample == 'uvx':
dir = '../data/uvx/'
objs = QSOfilenames(dir=dir)
#Classify each source
out = []
allcount, count = 0, 0
for obj in objs:
allcount += 1
if options.resampled:
key = obj[0]
else:
key = os.path.basename(obj)
#if key != 'SDSSJ013306.18-004523.8.fit':
# continue
print "Working on " + str(count) + "(%i/%i): " % (allcount,
len(objs)) + key
if options.resampled:
v = obj[1]
else:
v = VarQso(obj)
if v.nepochs(options.band) < options.minepochs:
print "This object does not have enough epochs ..."
continue
varout = VarClass()
varout.key = key
#quasar likelihoods
qsolike = []
for ii in range(len(qsoparams)):
qsolike.append(
v.loglike(band=options.band,
type=options.type,
params=qsoparams[ii]) + nu.log(qsoweights[ii]))
qsolike = logsum(qsolike)
varout.qsologlike = qsolike
#star likelihoods
starlike = []
for ii in range(len(starparams)):
starlike.append(
v.loglike(band=options.band,
type=options.type,
params=starparams[ii]) + nu.log(starweights[ii]))
starlike = logsum(starlike)
varout.starloglike = starlike
#RR Lyrae likelihoods
rrlyraelike = []
for ii in range(len(rrlyraeparams)):
rrlyraelike.append(
v.loglike(band=options.band,
type=options.type,
params=rrlyraeparams[ii]) +
nu.log(rrlyraeweights[ii]))
rrlyraelike = logsum(rrlyraelike)
varout.rrlyraeloglike = rrlyraelike
#print qsolike, starlike
if qsolike > starlike and qsolike > rrlyraelike:
print qsolike, starlike, rrlyraelike
out.append(varout)
count += 1
#if count > 500: break
#Save
for jj in range(len(out)):
if out[jj].qsologlike > out[jj].starloglike and out[
jj].qsologlike > out[jj].rrlyraeloglike:
print out[jj].key
saveClass(out, args[0])
return None
