def resampleObjs(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
if options.fitsfile is None:
print "-f or --fitsfile must be set to the file containing the fits ..."
print "Returning ..."
return None
#Load location of the data
if options.sample == 'nuvx':
dir = '../data/nuvx/'
elif options.sample == 'qso':
dir = '../data/s82qsos/'
objs = QSOfilenames(dir=dir)
#Load the fits
if os.path.exists(options.fitsfile):
fitsfile = open(options.fitsfile, 'rb')
params = pickle.load(fitsfile)
type = pickle.load(fitsfile)
band = pickle.load(fitsfile)
fitsfile.close()
else:
print options.fitsfile + " does not exist ..."
print "Returning ..."
return None
#Load sampling
if options.sampling == 'PS1':
sampling = panstarrs_sampling(3, startmjd=2. * 365.25)
elif options.sampling == 'SDSS-PS1':
sampling = sdss_sampling(startmjd=-2. * 365.25)
sampling.extend(panstarrs_sampling(1, startmjd=2. * 365.25))
elif options.sampling == 'SDSS':
pass
else:
print "Input to --sampling not understood ..."
print "Returning ..."
return None
#Re-sample each source
out = []
savecount, count = 0, 0
for obj in objs:
key = os.path.basename(obj)
print "Working on " + str(count) + " (" + str(savecount) + "): " + key
savecount += 1
v = VarQso(obj)
#Find fit
try:
thisfit = params[key]
except KeyError:
print "Fit not found, skipping this object ..."
nepochs = v.nepochs(band)
if nepochs < 20:
print "Because #epochs < 20 ..."
continue
#Set LC model
if options.nocolorvar:
thisfit['gammagr'] = 0.
if 'logAgr' in thisfit.keys():
thisfit['logAgr'] = -7.
else:
thisfit['logAri'] = -7.
if options.sampling == 'SDSS':
sampling = v.mjd['g']
sampling = [(s, 'g') for s in sampling]
v.setLCmodel(thisfit, band, type)
indx = v.mjd_overlap(band=band)
refband = 'r'
try:
o = v.resample([(mjd, refband)
for mjd in v.mjd[refband][indx[refband]]],
band=refband,
errors=False)
except nu.linalg.LinAlgError:
print thisfit
continue
xs = []
ys = []
errs = []
for b in band:
#Add errors to the underlying
xs.extend([(mjd, b) for mjd in v.mjd[b][indx[b]]])
for ii in range(len(v.mjd[b][indx[b]])):
ys.append(o.m[refband][ii] +
nu.random.randn() * v.err_m[b][indx[b]][ii])
errs.append(v.err_m[b][indx[b]][ii])
out.append([key, VarQso(xs, ys, errs, band=band, medianize=False)])
else:
v.setLCmodel(thisfit, band, type)
#Resample
out.append([key, v.resample(sampling, band=band)])
count += 1
#Save
outfile = open(args[0], 'wb')
pickle.dump(out, outfile)
pickle.dump(band, outfile)
outfile.close()
return None
def resampleObjs(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
if options.fitsfile is None:
print "-f or --fitsfile must be set to the file containing the fits ..."
print "Returning ..."
return None
#Load location of the data
if options.sample == 'nuvx':
dir= '../data/nuvx/'
elif options.sample == 'qso':
dir= '../data/s82qsos/'
objs= QSOfilenames(dir=dir)
#Load the fits
if os.path.exists(options.fitsfile):
fitsfile= open(options.fitsfile,'rb')
params= pickle.load(fitsfile)
type= pickle.load(fitsfile)
band= pickle.load(fitsfile)
fitsfile.close()
else:
print options.fitsfile+" does not exist ..."
print "Returning ..."
return None
#Load sampling
if options.sampling == 'PS1':
sampling= panstarrs_sampling(3,startmjd=2.*365.25)
elif options.sampling == 'SDSS-PS1':
sampling= sdss_sampling(startmjd=-2.*365.25)
sampling.extend(panstarrs_sampling(1,startmjd=2.*365.25))
elif options.sampling == 'SDSS': pass
else:
print "Input to --sampling not understood ..."
print "Returning ..."
return None
#Re-sample each source
out= []
savecount, count= 0, 0
for obj in objs:
key= os.path.basename(obj)
print "Working on "+str(count)+" ("+str(savecount)+"): "+key
savecount+= 1
v= VarQso(obj)
#Find fit
try:
thisfit= params[key]
except KeyError:
print "Fit not found, skipping this object ..."
nepochs= v.nepochs(band)
if nepochs < 20:
print "Because #epochs < 20 ..."
continue
#Set LC model
if options.nocolorvar:
thisfit['gammagr']= 0.
if 'logAgr' in thisfit.keys():
thisfit['logAgr']= -7.
else:
thisfit['logAri']= -7.
if options.sampling == 'SDSS':
sampling= v.mjd['g']
sampling= [(s,'g') for s in sampling]
v.setLCmodel(thisfit,band,type)
indx= v.mjd_overlap(band=band)
refband= 'r'
try:
o= v.resample([(mjd,refband) for mjd in v.mjd[refband][indx[refband]]],
band=refband,errors=False)
except nu.linalg.LinAlgError:
print thisfit
continue
xs= []
ys= []
errs= []
for b in band:
#Add errors to the underlying
xs.extend([(mjd,b) for mjd in v.mjd[b][indx[b]]])
for ii in range(len(v.mjd[b][indx[b]])):
ys.append(o.m[refband][ii]+
nu.random.randn()*v.err_m[b][indx[b]][ii])
errs.append(v.err_m[b][indx[b]][ii])
out.append([key,VarQso(xs,ys,errs,band=band,medianize=False)])
else:
v.setLCmodel(thisfit,band,type)
#Resample
out.append([key,v.resample(sampling,band=band)])
count+= 1
#Save
outfile= open(args[0],'wb')
pickle.dump(out,outfile)
pickle.dump(band,outfile)
outfile.close()
return None
