def dumpFits(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if options.outfilename is None:
print "-o filename options needs to be set ..."
print "Returning ..."
return None
#load fits
if os.path.exists(args[0]):
savefile = open(args[0], 'rb')
params = pickle.load(savefile)
type = pickle.load(savefile)
band = pickle.load(savefile)
mean = pickle.load(savefile)
savefile.close()
else:
raise IOError("input file does not exist ...")
#dump fits, first the arrays
cols = []
#Get basic quasar stuff
qsos = open_qsos(options.inputfilename)
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
paramsKeys = params[params.keys()[0]].keys()
qsoKeys = params.keys()
for key in paramsKeys:
thiscol = []
for qso in qsoKeys:
try:
thiscol.append(params[qso][key][0])
except IndexError:
thiscol.append(params[qso][key])
cols.append(
pyfits.Column(name=key, format='D', array=numpy.array(thiscol)))
if not options.star and not options.nuvx and not options.uvx \
and not options.rrlyrae and not options.nuvxall:
#RA, Dec, name, z, key
thiscol = []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].ra)
cols.append(
pyfits.Column(name='ra', format='D', array=numpy.array(thiscol)))
thiscol = []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].dec)
cols.append(
pyfits.Column(name='dec', format='D', array=numpy.array(thiscol)))
thiscol = []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].oname)
cols.append(
pyfits.Column(name='oname',
format='30A',
array=numpy.array(thiscol)))
thiscol = []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].z)
cols.append(
pyfits.Column(name='z', format='D', array=numpy.array(thiscol)))
else:
if options.star:
dir = '../data/star/'
elif options.nuvx:
dir = '../data/nuvx/'
elif options.uvx:
dir = '../data/uvx/'
elif options.nuvxall:
dir = '../data/nuvx_all/'
elif options.rrlyrae:
dir = '../data/rrlyrae/'
#RA, Dec, key
racol = []
deccol = []
if options.rrlyrae: idcol = []
for qso in qsoKeys:
#open relevant file
file = fu.table_fields(os.path.join(dir, qso))
indx = (file.mjd_g == 0.)
if True in indx:
deccol.append(file.dec[indx][0])
racol.append(file.ra[indx][0])
else:
deccol.append(file.dec[0])
racol.append(file.ra[0])
if options.rrlyrae: idcol.append(file.id[0])
cols.append(
pyfits.Column(name='dec', format='D', array=numpy.array(deccol)))
cols.append(
pyfits.Column(name='ra', format='D', array=numpy.array(racol)))
if options.rrlyrae:
cols.append(
pyfits.Column(name='ID', format='K', array=numpy.array(idcol)))
thiscol = []
for qso in qsoKeys:
thiscol.append(qso)
cols.append(
pyfits.Column(name='key', format='30A', array=numpy.array(thiscol)))
columns = pyfits.ColDefs(cols)
tbhdu = pyfits.new_table(columns)
tbhdu.writeto(options.outfilename)
#create header
outfile = pyfits.open(options.outfilename)
hdr = outfile[1].header
hdr.update('type', type, 'type of covariance fit')
hdr.update('band', type, 'band(s) fit')
hdr.update('mean', type, 'type of mean fit')
return None
def dumpFits(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if options.outfilename is None:
print "-o filename options needs to be set ..."
print "Returning ..."
return None
#load fits
if os.path.exists(args[0]):
savefile= open(args[0],'rb')
params= pickle.load(savefile)
type= pickle.load(savefile)
band= pickle.load(savefile)
mean= pickle.load(savefile)
savefile.close()
else:
raise IOError("input file does not exist ...")
#dump fits, first the arrays
cols= []
#Get basic quasar stuff
qsos= open_qsos(options.inputfilename)
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
paramsKeys= params[params.keys()[0]].keys()
qsoKeys= params.keys()
for key in paramsKeys:
thiscol= []
for qso in qsoKeys:
try:
thiscol.append(params[qso][key][0])
except IndexError:
thiscol.append(params[qso][key])
cols.append(pyfits.Column(name=key,format='D',
array=numpy.array(thiscol)))
if not options.star and not options.nuvx and not options.uvx \
and not options.rrlyrae and not options.nuvxall:
#RA, Dec, name, z, key
thiscol= []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].ra)
cols.append(pyfits.Column(name='ra',format='D',
array=numpy.array(thiscol)))
thiscol= []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].dec)
cols.append(pyfits.Column(name='dec',format='D',
array=numpy.array(thiscol)))
thiscol= []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].oname)
cols.append(pyfits.Column(name='oname',format='30A',
array=numpy.array(thiscol)))
thiscol= []
for qso in qsoKeys:
thiscol.append(qsos[qsoDict[qso]].z)
cols.append(pyfits.Column(name='z',format='D',
array=numpy.array(thiscol)))
else:
if options.star:
dir= '../data/star/'
elif options.nuvx:
dir= '../data/nuvx/'
elif options.uvx:
dir= '../data/uvx/'
elif options.nuvxall:
dir= '../data/nuvx_all/'
elif options.rrlyrae:
dir= '../data/rrlyrae/'
#RA, Dec, key
racol= []
deccol= []
if options.rrlyrae: idcol= []
for qso in qsoKeys:
#open relevant file
file= fu.table_fields(os.path.join(dir,qso))
indx= (file.mjd_g == 0.)
if True in indx:
deccol.append(file.dec[indx][0])
racol.append(file.ra[indx][0])
else:
deccol.append(file.dec[0])
racol.append(file.ra[0])
if options.rrlyrae: idcol.append(file.id[0])
cols.append(pyfits.Column(name='dec',format='D',
array=numpy.array(deccol)))
cols.append(pyfits.Column(name='ra',format='D',
array=numpy.array(racol)))
if options.rrlyrae:
cols.append(pyfits.Column(name='ID',format='K',
array=numpy.array(idcol)))
thiscol= []
for qso in qsoKeys:
thiscol.append(qso)
cols.append(pyfits.Column(name='key',format='30A',
array=numpy.array(thiscol)))
columns= pyfits.ColDefs(cols)
tbhdu= pyfits.new_table(columns)
tbhdu.writeto(options.outfilename)
#create header
outfile= pyfits.open(options.outfilename)
hdr= outfile[1].header
hdr.update('type',type,'type of covariance fit')
hdr.update('band',type,'band(s) fit')
hdr.update('mean',type,'type of mean fit')
return None
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"
