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 sampleQSO(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')
othersamples = pickle.load(othersavefile)
othersavefile.close()
else:
othersamples = {}
savefilename = args[0]
if os.path.exists(savefilename):
savefile = open(savefilename, 'rb')
samples = pickle.load(savefile)
type = pickle.load(savefile)
band = pickle.load(savefile)
mean = pickle.load(savefile)
savefile.close()
else:
samples = {}
type = options.type
mean = options.mean
band = options.band
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/'
if options.resampled:
raise NotImplementedError("resampled not implemented yet")
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)
#Register time-out handler
signal.signal(signal.SIGALRM, handler)
savecount = 0
count = len(samples)
for qso in qsos:
if options.resampled:
key = qso[0]
else:
key = os.path.basename(qso)
if samples.has_key(key) or othersamples.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)
if v.nepochs(band) < 20:
print "This object does not have enough epochs ..."
continue
#Set best-fit
v.LCparams = params[key]
v.LC = LCmodel(trainSet=v._build_trainset(band), type=type, mean=mean)
v.LCtype = type
v.LCmean = mean
v.fitband = band
#Now sample
signal.alarm(options.timeout)
try:
v.sampleGP(nsamples=options.nsamples,
metropolis=options.metropolis,
markovpy=options.markovpy,
burnin=int(nu.floor(0.2 * options.nsamples)))
except Exception, exc:
if str(exc) == "Sampling timed out":
print exc
continue
else:
raise
signal.alarm(0)
samples[key] = v.get_sampleGP()
if _DEBUG:
_print_diagnostics(samples[key])
#print samples[key][options.nsamples]
savecount += 1
if savecount == options.saveevery:
print "Saving ..."
save_pickles(samples, type, band, mean, savefilename)
savecount = 0
count += 1
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 compareMagFluxFits(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
params = []
for filename in args:
if os.path.exists(filename):
savefile = open(filename, 'rb')
params.append(pickle.load(savefile))
savefile.close()
else:
print filename + " does not exist ..."
print "Returning ..."
return
if options.plottype == 'AA':
ys = []
xs = []
for key in params[1].keys():
try:
ys.append(params[0][key]['logA'] / 2.)
xs.append(params[1][key]['logA'] / 2.)
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
ys = xs - ys - nu.log(nu.log(10.) / 2.5)
xrange = [-9.21 / 2., 0.]
yrange = [-.25, .25]
xlabel = r'$\log A^{\mathrm{flux}}_' + options.band + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^{\mathrm{flux}}_' + options.band + r'-\log A^{\mathrm{mag}}_' + options.band + r'- \log\left(\frac{\log 10}{2.5}\right)$'
elif options.plottype == 'gg':
ys = []
xs = []
for key in params[1].keys():
try:
ys.append(params[0][key]['gamma'])
xs.append(params[1][key]['gamma'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
print len(xs)
ys = xs - ys
xrange = [0., 1.2]
yrange = [-.25, .25]
xlabel = r'$\gamma^{\mathrm{flux}}_' + options.band + r'\ \mathrm{(power-law\ exponent)}$'
ylabel = r'$\gamma^{\mathrm{flux}}_' + options.band + r'- \gamma^{\mathrm{mag}}_' + options.band + '$'
elif options.plottype == 'loglike2':
ys = []
xs = []
nepochs = []
cnt = 0
for key in params[1].keys():
#cnt+= 1
#print cnt
#if cnt > 10: break
#Get the number of epochs
v = VarQso(os.path.join('../data/s82qsos/', key))
try:
ys.append(params[0][key]['loglike'] -
v.nepochs(options.band) * nu.log(nu.log(10.) / 2.5))
nepochs.append(v.nepochs(options.band))
xs.append(params[1][key]['loglike'])
except KeyError:
continue
xs = -nu.array(xs).reshape(len(xs))
ys = -nu.array(ys).reshape(len(xs))
nepochs = nu.array(nepochs).reshape(len(nepochs))
ys /= nepochs
xs /= nepochs
ys = xs - ys
xrange = [-3., 0.]
yrange = [-.1, .1]
xlabel = r'$\log \mathcal{L}^{\mathrm{flux}}_{' + options.band + r',\mathrm{red}}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log \mathcal{L}^{\mathrm{flux}}_{' + options.band + r',\mathrm{red}}- \log \mathcal{L}^{\mathrm{mag}}_{' + options.band + ',\mathrm{red}}' + r'- \log\left(\frac{\log 10}{2.5}\right)$'
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,
ys,
'k,',
onedhists=True,
yrange=yrange,
xrange=xrange,
bins=31,
xlabel=xlabel,
ylabel=ylabel)
bovy_plot.bovy_plot(nu.array(xrange), [0., 0.], '0.5', overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
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
