def fitExampleDRW(datafilename="../data/SDSSJ203817.37+003029.8.fits"):
"""
NAME:
fitExampleDRW
PURPOSE:
Fit an example of a DRW structure function GP covariance function
to an SDSS quasar in g and r
INPUT:
datafilename
OUTPUT:
HISTORY:
2010-02-21 - Written - Bovy (NYU)
"""
nu.random.seed(1)
# Get data
file = fu.table_fields(datafilename)
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7) # Adjust for non-g
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
listx = [(t, "g") for t in mjd_g]
listx.extend([(t, "r") for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
SF = covarFunc(S=1.0, tau=0.2, B=0.1, C=0.005, gammagr=0.5, Gammagr=0.01)
# Train
print "Training ..."
outcovarFunc = trainGP(trainSet, SF, useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
return None
def fitExampleDRW(datafilename='../data/SDSSJ203817.37+003029.8.fits'):
"""
NAME:
fitExampleDRW
PURPOSE:
Fit an example of a DRW structure function GP covariance function
to an SDSS quasar in g and r
INPUT:
datafilename
OUTPUT:
HISTORY:
2010-02-21 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file = fu.table_fields(datafilename)
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7) #Adjust for non-g
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
SF = covarFunc(S=1., tau=.2, B=.1, C=.005, gammagr=.5, Gammagr=.01)
#Train
print "Training ..."
outcovarFunc = trainGP(trainSet, SF, useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
return None
def get_possibleSegueTargets():
"""
NAME:
get_possibleSegueTargets
PURPOSE:
get the possible Segue targets from the CAS
INPUT:
parser - from optParser
OUTPUT:
HISTORY:
2010-12-21 - Written - Bovy (NYU)
2011-07-07 - Adapted for SEGUE - Bovy
"""
caspwdfile= open('caspwd','r')
casusr= caspwdfile.readline().rstrip()
caspwd= caspwdfile.readline().rstrip()
caspwdfile.close()
#Read plate centers file
platefile= os.path.join(os.getenv('DATADIR'),'bovy',
'segue-local','segueplates.fits')
platecenters= pyfits_utils.table_fields(platefile)[::-1]
nplates= len(platecenters.ra)
done= []
for plate in platecenters:
print "Working on plate "+str(plate.plate)
if plate.plate in done:
print str(plate.plate)+" already done!"
continue
done.append(plate.plate)
tmpsavefilename= os.path.join(os.getenv('DATADIR'),'bovy',
'segue-local','segueplates',
'%i.fit' % plate.plate)
if os.path.exists(tmpsavefilename):
print "file "+tmpsavefilename+" exists"
print "Delete file "+tmpsavefilename+" before running this to update the sample from the CAS"
else:
dbname= prepare_sql(plate)
subprocess.call([_PYTHON,_CASJOBS,casusr,caspwd,'querywait',
'@tmp.sql'])
subprocess.call([_PYTHON,_CASJOBS,casusr,caspwd,
'outputdownloaddelete',dbname,tmpsavefilename])
def get_qsodata():
"""
NAME:
get_qsodata
PURPOSE:
get the QSO sample from the CAS
INPUT:
parser - from optParser
OUTPUT:
HISTORY:
2010-12-21 - Written - Bovy (NYU)
"""
caspwdfile = open('caspwd', 'r')
casusr = caspwdfile.readline().rstrip()
caspwd = caspwdfile.readline().rstrip()
caspwdfile.close()
#Read qso file
s82file = '../data/S82qsos.fits'
s82qsos = pyfits_utils.table_fields(s82file)[::-1]
nqso = len(s82qsos.ra)
done = []
for qso in s82qsos:
print "Working on QSO " + qso.oname.strip().replace(' ', '')
if qso.oname.strip().replace(' ', '') in done:
print qso.oname.strip().replace(' ', '') + " already done!"
return
done.append(qso.oname.strip().replace(' ', ''))
tmpsavefilename = '../data/s82qsos/' + qso.oname.strip().replace(
' ', '') + '.fit'
if os.path.exists(tmpsavefilename):
print "file " + tmpsavefilename + " exists"
print "Delete file " + tmpsavefilename + " before running this to update the sample from the CAS"
else:
dbname = prepare_sql(qso)
subprocess.call(
[_PYTHON, _CASJOBS, casusr, caspwd, 'querywait', '@tmp.sql'])
subprocess.call([
_PYTHON, _CASJOBS, casusr, caspwd, 'outputdownloaddelete',
dbname, tmpsavefilename
])
def get_qsodata():
"""
NAME:
get_qsodata
PURPOSE:
get the QSO sample from the CAS
INPUT:
parser - from optParser
OUTPUT:
HISTORY:
2010-12-21 - Written - Bovy (NYU)
"""
caspwdfile= open('caspwd','r')
casusr= caspwdfile.readline().rstrip()
caspwd= caspwdfile.readline().rstrip()
caspwdfile.close()
#Read qso file
s82file= '../data/S82qsos.fits'
s82qsos= pyfits_utils.table_fields(s82file)[::-1]
nqso= len(s82qsos.ra)
done= []
for qso in s82qsos:
print "Working on QSO "+qso.oname.strip().replace(' ', '')
if qso.oname.strip().replace(' ', '') in done:
print qso.oname.strip().replace(' ', '')+" already done!"
return
done.append(qso.oname.strip().replace(' ', ''))
tmpsavefilename= '../data/s82qsos/'+qso.oname.strip().replace(' ', '')+'.fit'
if os.path.exists(tmpsavefilename):
print "file "+tmpsavefilename+" exists"
print "Delete file "+tmpsavefilename+" before running this to update the sample from the CAS"
else:
dbname= prepare_sql(qso)
subprocess.call([_PYTHON,_CASJOBS,casusr,caspwd,'querywait',
'@tmp.sql'])
subprocess.call([_PYTHON,_CASJOBS,casusr,caspwd,
'outputdownloaddelete',dbname,tmpsavefilename])
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 showExamplegr(filename='../data/SDSSJ203817.37+003029.8.fits',
constraints=None,basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
showExamplegr
PURPOSE:
show an example of a power-law structure function GP covariance function
fit to an SDSS quasar for g and r
INPUT:
filename - filename with the data
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
OUTPUT:
writes several plots
HISTORY:
2010-08-11 - Written - Bovy (NYU)
"""
file= fu.table_fields(filename)
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
mjd_r= nu.array(file.mjd_r)/365.25
r= nu.array(file.r)
err_r= nu.array(file.err_r)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
r= r[mask]
r-= nu.mean(r)
err_r= err_r[mask]
mjd_r= mjd_r[mask]
mjd_r-= nu.amin(mjd_r)
meanErr_r= nu.mean(err_r)
meanErr_gr= nu.mean(nu.sqrt(err_r**2.+err_g**2.))
nu.random.seed(4)
nGP=5
nx=201
params_mean= ()
from powerlawSFgr import covarFunc
params= {'logGamma': array([-9.27821954]), 'logGammagr': array([-19.85172122]), 'gamma': array([ 0.45932629]), 'gammagr': array([ 0.29784021])}
cf= covarFunc(**params)
params_covar= (cf)
ndata= len(g)
if constraints is None:
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listy= [m for m in g]
listy.extend([m for m in r])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
constraints= trainSet
else:
constraints= nu.array([])
useconstraints= constraints
txs= nu.linspace(-0.1,6.5,nx)
xs= [(txs[ii],'g') for ii in range(nx)]
xs.extend([(txs[ii],'r') for ii in range(nx)])
GPsamples= eval_gp(xs,mean,covar,(),params_covar,nGP=nGP,constraints=useconstraints,tiny_cholesky=.000001)
thismean= calc_constrained_mean(xs,mean,params_mean,covar,params_covar,useconstraints)
thiscovar= calc_constrained_covar(xs,covar,params_covar,useconstraints)
plot.bovy_print()
pyplot.plot(txs,GPsamples[0,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_g,g,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
pyplot.plot(txs,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_full.ps')
plot.bovy_print()
pyplot.figure()
pyplot.plot(txs,GPsamples[0,nx-1:-1],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_r,r,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,nx-1:-1],'-',color=str(0.25+ii*.5/(nGP-1)))
pyplot.plot(txs,thismean[nx-1:-1],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_r,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$r-\langle r\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_fullr.ps')
plot.bovy_print()
pyplot.figure()
ii= 0
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color='0.25')
if isinstance(constraints,trainingSet):
plot.bovy_plot(mjd_g,g-r,'k.',zorder=5,ms=10,overplot=True)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color=str(0.25+ii*.5/(nGP-1)))
plotthismean= nu.zeros(nx)
for ii in range(nx):
plotthismean[ii]= thismean[ii]-thismean[ii+nx]
pyplot.plot(txs,plotthismean,'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.18,yerr=meanErr_gr,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-r- \langle g - r \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
if isinstance(constraints,trainingSet):
pyplot.ylim(-0.25,.25)
else:
pass #pyplot.ylim(-10.,10.)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_color.ps')
plot.bovy_print()
pyplot.figure()
ii= 2
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.plot(colors,GPsamples[ii,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(g-r,g,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.plot(colors,GPsamples[ii,0:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
colors= nu.array([thismean[jj]-thismean[jj+nx] for jj in range(nx)])
pyplot.plot(colors,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(.13,-0.3,yerr=meanErr_g,xerr=meanErr_gr,color='k')
pyplot.xlabel(r'$g-r-\langle g-r\rangle\ [\mathrm{mag}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-.2,.2)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_ggr.ps')
return
plot.bovy_print()
pyplot.plot(xs,GPsamples[0,:],'-',color='0.25')
if not constraints == []:
plot.bovy_plot(mjd_g,g,'k.',zorder=5,ms=10,overplot=True)
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(xs,GPsamples[ii,:],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints == []:
pyplot.errorbar(6.15,-0.1,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$m-\langle m\rangle\ [\mathrm{mag}]$')
pyplot.xlim(3,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_zoom.ps')
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
pyplot.loglog(sc.arange(1.,len(GPsamples[ii,:])/2)*(xs[1]-xs[0]),
2.*sc.var(GPsamples[ii,:])-2.*sc.correlate(GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),"same")[1:len(GPsamples[ii,:])/2][::-1]/len(GPsamples[ii,:]),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(xs[1]-xs[0]),xs[len(xs)/2]]
pyplot.loglog(xline,nu.exp(-3.02045715)*nu.array(xline)**(0.5868293),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfunc.ps')
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 fitExamplegr(datafilename='../data/SDSSJ203817.37+003029.8.fits'):
"""
NAME:
fitExamplegr
PURPOSE:
Fit an example of a power-law structure function GP covariance function
to an SDSS quasar in g and r
INPUT:
datafilename
OUTPUT:
HISTORY:
2010-02-21 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file= fu.table_fields(datafilename)
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
mjd_r= nu.array(file.mjd_r)/365.25
r= nu.array(file.r)
err_r= nu.array(file.err_r)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
r= r[mask]
r-= nu.mean(r)
err_r= err_r[mask]
mjd_r= mjd_r[mask]
mjd_r-= nu.amin(mjd_r)
meanErr_r= nu.mean(err_r)
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listy= [m for m in g]
listy.extend([m for m in r])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
#SF= covarFunc(Gamma=0.0058,gamma=0.5,Gammagr=0.0058,gammagr=0.5)
SF= covarFunc(logl=-1.37742591,loga=-3.47341754,
loglgr=-2.3777,logagr= -1.)
#Train
print "Training ..."
outcovarFunc= trainGP(trainSet,SF,useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
return None
def fitExample(N=None,datafilename='../data/SDSSJ203817.37+003029.8.fits',
band='g'):
"""
NAME:
fitExample
PURPOSE:
Fit an example of a power-law structure function GP covariance function
to an SDSS quasar
INPUT:
N - use a random subsample of N points
datafilename
band - band to fit
OUTPUT:
HISTORY:
2010-02-21 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file= fu.table_fields(datafilename)
if band == 'u':
mjd_g= nu.array(file.mjd_u)/365.25
g= nu.array(file.u)
err_g= nu.array(file.err_u)
elif band == 'g':
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
elif band == 'r':
mjd_g= nu.array(file.mjd_r)/365.25
g= nu.array(file.r)
err_g= nu.array(file.err_r)
elif band == 'i':
mjd_g= nu.array(file.mjd_i)/365.25
g= nu.array(file.i)
err_g= nu.array(file.err_i)
elif band == 'z':
mjd_g= nu.array(file.mjd_z)/365.25
g= nu.array(file.z)
err_g= nu.array(file.err_z)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
trainSet= trainingSet(listx=mjd_g,listy=g,noise=err_g)
SF= covarFunc(a=.0001,l=.001)
#SF= covarFunc(gamma=.2,A=0.000524)
#SF= covarFunc(a=0.18,l=0.005)
#Train
print "Training ..."
outcovarFunc= trainGP(trainSet,SF,useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
return None
def dualBandExample(filename='../data/SDSSJ203817.37+003029.8.fits',
constraints=None,basefilename='SDSSJ203817.37+003029.8',
covarType='SF'):
"""
NAME:
dualBandExample
PURPOSE:
show an example of a power-law structure function GP covariance function
fit to an SDSS quasar for g and r
INPUT:
filename - filename with the data
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
covarType - 'SF' or 'DRW'
OUTPUT:
writes several plots
HISTORY:
2010-08-11 - Written - Bovy (NYU)
"""
file= fu.table_fields(filename)
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
mjd_r= nu.array(file.mjd_r)/365.25
r= nu.array(file.r)
err_r= nu.array(file.err_r)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
r= r[mask]
r-= nu.mean(r)
err_r= err_r[mask]
mjd_r= mjd_r[mask]
mjd_r-= nu.amin(mjd_r)
meanErr_r= nu.mean(err_r)
meanErr_gr= nu.mean(nu.sqrt(err_r**2.+err_g**2.))
nu.random.seed(4)
nGP=5
nx=201
params_mean= ()
if covarType == 'SF':
from powerlawSFgr import covarFunc
params= {'logGamma': array([-7.33271548]), 'logGammagr': array([-10.5]), 'gamma': array([ 0.4821092]), 'gammagr': array([ 0.5])}
params= {'logGamma': array([-7.79009776]), 'logGammagr': array([-28.0487848]), 'gamma': array([ 0.45918053]), 'gammagr': array([ 0.21333858])}
else:
from OUgr import covarFunc
params= {'logl': array([ 1.94844503]), 'loglgr': array([ 7.36282174]), 'logagr2': array([ 1.0196474]), 'loga2': array([-0.00588868])}
params= {'logl':-1.37742591,'loga':-3.47341754,
'loglgr': -2.3777,'logagr': -4.}
params= {'logl': array([-1.38968195]), 'loglgr': array([-2.46684501]), 'logagr2': array([-6.62320832]), 'loga2': array([-3.52099305])}
paramsSF= params
cf= covarFunc(**params)
params_covar= (cf)
ndata= len(g)
if constraints is None:
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listy= [m for m in g]
listy.extend([m for m in r])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
constraints= trainSet
else:
constraints= nu.array([])
useconstraints= constraints
txs= nu.linspace(-0.1,6.5,nx)
xs= [(txs[ii],'g') for ii in range(nx)]
xs.extend([(txs[ii],'r') for ii in range(nx)])
GPsamples= eval_gp(xs,mean,covar,(),params_covar,nGP=nGP,constraints=useconstraints,tiny_cholesky=.00000001)
thismean= calc_constrained_mean(xs,mean,params_mean,covar,params_covar,useconstraints)
thiscovar= calc_constrained_covar(xs,covar,params_covar,useconstraints)
#Calculate loglike
if isinstance(constraints,trainingSet):
(params,packing)= pack_params(cf)
covarFuncName= inspect.getmodule(cf).__name__
thisCovarClass= __import__(covarFuncName)
loglike= marginalLikelihood(params,constraints,packing,
thisCovarClass)
plot.bovy_print()
pyplot.plot(txs,GPsamples[0,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_g,g,'k.',zorder=5,ms=10)
title= re.split(r'_',basefilename)[0]
if covarType == 'SF':
method= '\mathrm{power-law\ structure\ functions}'
else:
method= '\mathrm{damped\ random\ walk}'
plot.bovy_text(r'$\mathrm{'+title+'\ / \ '+method+r'}$',title=True)
if isinstance(constraints,trainingSet):
plot.bovy_text(r'$\log P({\bf x}|\mathrm{parameters}) = %5.2f$' %(-loglike),
top_left=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(txs,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
pyplot.ylim(-0.6,0.6)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_fullg.ps')
plot.bovy_print()
pyplot.figure()
pyplot.plot(txs,GPsamples[0,nx-1:-1],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_r,r,'k.',zorder=5,ms=10)
#plot.bovy_text(r'$\mathrm{'+title+'\ / \ '+method+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,nx-1:-1],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(txs,thismean[nx-1:-1],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_r,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$r-\langle r\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
pyplot.ylim(-0.6,0.6)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_fullr.ps')
plot.bovy_print()
pyplot.figure()
ii= 0
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
if not isinstance(constraints,trainingSet):
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color='0.25')
if isinstance(constraints,trainingSet):
plot.bovy_plot(mjd_g,g-r,'k.',zorder=5,ms=10,overplot=True)
#plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
if not isinstance(constraints,trainingSet):
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color=str(0.25+ii*.5/(nGP-1)))
plotthismean= nu.zeros(nx)
for ii in range(nx):
plotthismean[ii]= thismean[ii]-thismean[ii+nx]
#pyplot.plot(txs,plotthismean,'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.18,yerr=meanErr_gr,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-r- \langle g - r \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
if isinstance(constraints,trainingSet):
pyplot.ylim(-0.25,.25)
else:
pass #pyplot.ylim(-10.,10.)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_color.ps')
if covarType == 'DRW':
return
#Plot structure functions
#g
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
thisSample= GPsamples[ii,:nx]
pyplot.loglog(sc.arange(1.,len(thisSample)/2)*(txs[1]-txs[0]),
2.*sc.var(thisSample)\
-2.*sc.correlate(thisSample-sc.mean(thisSample),thisSample-sc.mean(thisSample),"same")[1:len(thisSample)/2][::-1]/len(thisSample),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(txs[1]-txs[0]),txs[len(txs)/2]]
pyplot.loglog(xline,(nu.exp(paramsSF['logGamma'])*nu.array(xline))**(paramsSF['gamma']),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function\ in}\ g$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfuncg.ps')
#r
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
thisSample= GPsamples[ii,nx-1:-1]
pyplot.loglog(sc.arange(1.,len(thisSample)/2)*(txs[1]-txs[0]),
2.*sc.var(thisSample)\
-2.*sc.correlate(thisSample-sc.mean(thisSample),thisSample-sc.mean(thisSample),"same")[1:len(thisSample)/2][::-1]/len(thisSample),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(txs[1]-txs[0]),txs[len(txs)/2]]
pyplot.loglog(xline,(nu.exp(paramsSF['logGamma'])*nu.array(xline))**(paramsSF['gamma']),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function\ in}\ r$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfuncr.ps')
#g-r
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
thisSample= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.loglog(sc.arange(1.,len(thisSample)/2)*(txs[1]-txs[0]),
2.*sc.var(thisSample)\
-2.*sc.correlate(thisSample-sc.mean(thisSample),thisSample-sc.mean(thisSample),"same")[1:len(thisSample)/2][::-1]/len(thisSample),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(txs[1]-txs[0]),txs[len(txs)/2]]
pyplot.loglog(xline,(nu.exp(paramsSF['logGammagr'])*nu.array(xline))**(paramsSF['gammagr']),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{color\ structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfuncgr.ps')
def singleBandExample(filename='../data/SDSSJ203817.37+003029.8.fits',
band='g',
constraints=None,
basefilename='SDSSJ203817.37+003029.8',
covarType='SF'):
"""
NAME:
singleBandExample
PURPOSE:
show an example of a power-law structure function or damped-random walk
GP covariance function fit to an SDSS quasar
INPUT:
filename - filename with the data
band - band to consider
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
covarType - 'SF' or 'DRW'
OUTPUT:
writes several plots
HISTORY:
2010-06-20 - Written - Bovy (NYU)
"""
file = fu.table_fields(filename)
if band == 'u':
mjd_g = nu.array(file.mjd_u) / 365.25
g = nu.array(file.u)
err_g = nu.array(file.err_u)
elif band == 'g':
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
elif band == 'r':
mjd_g = nu.array(file.mjd_r) / 365.25
g = nu.array(file.r)
err_g = nu.array(file.err_r)
elif band == 'i':
mjd_g = nu.array(file.mjd_i) / 365.25
g = nu.array(file.i)
err_g = nu.array(file.err_i)
elif band == 'z':
mjd_g = nu.array(file.mjd_z) / 365.25
g = nu.array(file.z)
err_g = nu.array(file.err_z)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7) #Adjust for non-g
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
nu.random.seed(4)
nGP = 5
nx = 201
params_mean = ()
if covarType == 'SF':
from powerlawSF import covarFunc
params = {'gamma': array([0.49500723]), 'logA': array([-3.36044037])}
else:
from OU_ARD import covarFunc
params = {'logl': array([-1.37742591]), 'loga2': array([-3.47341754])}
cf = covarFunc(**params)
params_covar = (cf)
ndata = len(g)
if constraints is None:
listx = mjd_g
listy = g
noise = err_g
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
constraints = trainSet
else:
constraints = None
useconstraints = constraints
xs = nu.linspace(-0.1, 6.5, nx)
GPsamples = eval_gp(xs,
mean,
covar, (),
params_covar,
nGP=nGP,
constraints=useconstraints)
thismean = calc_constrained_mean(xs, mean, params_mean, covar,
params_covar, useconstraints)
thiscovar = calc_constrained_covar(xs, covar, params_covar, useconstraints)
#If unconstrained, subtract the mean
if constraints is None:
for ii in range(nGP):
GPsamples[ii, :] = GPsamples[ii, :] - nu.mean(GPsamples[ii, :])
#Calculate loglike
if not constraints is None:
(params, packing) = pack_params(cf)
covarFuncName = inspect.getmodule(cf).__name__
thisCovarClass = __import__(covarFuncName)
loglike = marginalLikelihood(params, constraints, packing,
thisCovarClass)
plot.bovy_print()
pyplot.plot(xs, GPsamples[0, :], '-', color='0.25')
if not constraints is None:
plot.bovy_plot(mjd_g, g, 'k.', zorder=5, ms=10, overplot=True)
title = re.split(r'_', basefilename)[0]
if covarType == 'SF':
method = '\mathrm{power-law\ structure\ function}'
else:
method = '\mathrm{damped\ random\ walk}'
plot.bovy_text(r'$\mathrm{' + title + '\ / \ ' + method + r'}$',
title=True)
if not constraints is None:
plot.bovy_text(r'$\log P({\bf x}|\mathrm{parameters}) = %5.2f$' %
(-loglike),
top_left=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(nGP):
pyplot.plot(xs,
GPsamples[ii, :],
'-',
color=str(0.25 + ii * .5 / (nGP - 1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints is None:
pyplot.errorbar(6.15, -0.25, yerr=meanErr_g, color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$' + band + r'-\langle ' + band +
r' \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1, 6.5)
if constraints is None:
pyplot.ylim(-.6, .6)
else:
pyplot.ylim(-.6, .6)
plot._add_ticks()
plot.bovy_end_print(basefilename + '_full.ps')
plot.bovy_print()
pyplot.plot(xs, GPsamples[0, :], '-', color='0.25')
if not constraints is None:
plot.bovy_plot(mjd_g, g, 'k.', zorder=5, ms=10, overplot=True)
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1, nGP):
pyplot.plot(xs,
GPsamples[ii, :],
'-',
color=str(0.25 + ii * .5 / (nGP - 1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints == []:
pyplot.errorbar(6.15, -0.25, yerr=meanErr_g, color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$' + band + '-\langle ' + band +
'\rangle\ [\mathrm{mag}]$')
pyplot.xlim(3, 6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename + '_zoom.ps')
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
pyplot.loglog(
sc.arange(1.,
len(GPsamples[ii, :]) / 2) * (xs[1] - xs[0]),
2. * sc.var(GPsamples[ii, :]) -
2. * sc.correlate(GPsamples[ii, :] - sc.mean(GPsamples[ii, :]),
GPsamples[ii, :] - sc.mean(GPsamples[ii, :]),
"same")[1:len(GPsamples[ii, :]) / 2][::-1] /
len(GPsamples[ii, :]),
color=str(0.25 + ii * .5 / (nGP - 1)))
xline = [(xs[1] - xs[0]), xs[len(xs) / 2]]
pyplot.loglog(xline,
nu.exp(-3.36044037) * nu.array(xline)**(0.49500723), 'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename + '_structfunc.ps')
def singleBandExample(filename='../data/SDSSJ203817.37+003029.8.fits',band='g',
constraints=None,basefilename='SDSSJ203817.37+003029.8',
covarType='SF'):
"""
NAME:
singleBandExample
PURPOSE:
show an example of a power-law structure function or damped-random walk
GP covariance function fit to an SDSS quasar
INPUT:
filename - filename with the data
band - band to consider
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
covarType - 'SF' or 'DRW'
OUTPUT:
writes several plots
HISTORY:
2010-06-20 - Written - Bovy (NYU)
"""
file= fu.table_fields(filename)
if band == 'u':
mjd_g= nu.array(file.mjd_u)/365.25
g= nu.array(file.u)
err_g= nu.array(file.err_u)
elif band == 'g':
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
elif band == 'r':
mjd_g= nu.array(file.mjd_r)/365.25
g= nu.array(file.r)
err_g= nu.array(file.err_r)
elif band == 'i':
mjd_g= nu.array(file.mjd_i)/365.25
g= nu.array(file.i)
err_g= nu.array(file.err_i)
elif band == 'z':
mjd_g= nu.array(file.mjd_z)/365.25
g= nu.array(file.z)
err_g= nu.array(file.err_z)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
nu.random.seed(4)
nGP=5
nx=201
params_mean= ()
if covarType == 'SF':
from powerlawSF import covarFunc
params= {'gamma': array([ 0.49500723]), 'logA': array([-3.36044037])}
else:
from OU_ARD import covarFunc
params= {'logl': array([-1.37742591]), 'loga2': array([-3.47341754])}
cf= covarFunc(**params)
params_covar= (cf)
ndata= len(g)
if constraints is None:
listx= mjd_g
listy= g
noise= err_g
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
constraints= trainSet
else:
constraints= None
useconstraints= constraints
xs= nu.linspace(-0.1,6.5,nx)
GPsamples= eval_gp(xs,mean,covar,(),params_covar,nGP=nGP,constraints=useconstraints)
thismean= calc_constrained_mean(xs,mean,params_mean,covar,params_covar,useconstraints)
thiscovar= calc_constrained_covar(xs,covar,params_covar,useconstraints)
#If unconstrained, subtract the mean
if constraints is None:
for ii in range(nGP):
GPsamples[ii,:]= GPsamples[ii,:]-nu.mean(GPsamples[ii,:])
#Calculate loglike
if not constraints is None:
(params,packing)= pack_params(cf)
covarFuncName= inspect.getmodule(cf).__name__
thisCovarClass= __import__(covarFuncName)
loglike= marginalLikelihood(params,constraints,packing,
thisCovarClass)
plot.bovy_print()
pyplot.plot(xs,GPsamples[0,:],'-',color='0.25')
if not constraints is None:
plot.bovy_plot(mjd_g,g,'k.',zorder=5,ms=10,overplot=True)
title= re.split(r'_',basefilename)[0]
if covarType == 'SF':
method= '\mathrm{power-law\ structure\ function}'
else:
method= '\mathrm{damped\ random\ walk}'
plot.bovy_text(r'$\mathrm{'+title+'\ / \ '+method+r'}$',title=True)
if not constraints is None:
plot.bovy_text(r'$\log P({\bf x}|\mathrm{parameters}) = %5.2f$' %(-loglike),
top_left=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(nGP):
pyplot.plot(xs,GPsamples[ii,:],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints is None:
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$'+band+r'-\langle '+band+r' \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
if constraints is None:
pyplot.ylim(-.6,.6)
else:
pyplot.ylim(-.6,.6)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_full.ps')
plot.bovy_print()
pyplot.plot(xs,GPsamples[0,:],'-',color='0.25')
if not constraints is None:
plot.bovy_plot(mjd_g,g,'k.',zorder=5,ms=10,overplot=True)
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(xs,GPsamples[ii,:],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints == []:
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$'+band+'-\langle '+band+'\rangle\ [\mathrm{mag}]$')
pyplot.xlim(3,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_zoom.ps')
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
pyplot.loglog(sc.arange(1.,len(GPsamples[ii,:])/2)*(xs[1]-xs[0]),
2.*sc.var(GPsamples[ii,:])-2.*sc.correlate(GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),"same")[1:len(GPsamples[ii,:])/2][::-1]/len(GPsamples[ii,:]),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(xs[1]-xs[0]),xs[len(xs)/2]]
pyplot.loglog(xline,nu.exp(-3.36044037)*nu.array(xline)**(0.49500723),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfunc.ps')
def showExample(filename='../data/SDSSJ203817.37+003029.8.fits',band='g',
constraints=None,basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
showExample
PURPOSE:
show an example of a power-law structure function GP covariance function
fit to an SDSS quasar
INPUT:
filename - filename with the data
band - band to consider
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
OUTPUT:
writes several plots
HISTORY:
2010-06-20 - Written - Bovy (NYU)
"""
file= fu.table_fields(filename)
if band == 'u':
mjd_g= nu.array(file.mjd_u)/365.25
g= nu.array(file.u)
err_g= nu.array(file.err_u)
elif band == 'g':
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
elif band == 'r':
mjd_g= nu.array(file.mjd_r)/365.25
g= nu.array(file.r)
err_g= nu.array(file.err_r)
elif band == 'i':
mjd_g= nu.array(file.mjd_i)/365.25
g= nu.array(file.i)
err_g= nu.array(file.err_i)
elif band == 'z':
mjd_g= nu.array(file.mjd_z)/365.25
g= nu.array(file.z)
err_g= nu.array(file.err_z)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
nu.random.seed(4)
nGP=5
nx=201
params_mean= ()
from powerlawSFgr import covarFunc
params= {'logA': array([ 1.63714183]), 'gamma': array([ 0.60216581])}
params= {'gamma': array([ 0.49500723]), 'logA': array([-3.36044037])}
cf= covarFunc(**params)
params_covar= (cf)
ndata= len(g)
if constraints is None:
listx= mjd_g
listy= g
noise= err_g
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
constraints= trainSet
else:
constraints= None
useconstraints= constraints
xs= nu.linspace(-0.1,6.5,nx)
GPsamples= eval_gp(xs,mean,covar,(),params_covar,nGP=nGP,constraints=useconstraints)
thismean= calc_constrained_mean(xs,mean,params_mean,covar,params_covar,useconstraints)
thiscovar= calc_constrained_covar(xs,covar,params_covar,useconstraints)
plot.bovy_print()
pyplot.plot(xs,GPsamples[0,:],'-',color='0.25')
if not constraints is None:
plot.bovy_plot(mjd_g,g,'k.',zorder=5,ms=10,overplot=True)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(nGP):
pyplot.plot(xs,GPsamples[ii,:],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints == []:
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$m-\langle m\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
pyplot.ylim(-1.,1.)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_full.ps')
plot.bovy_print()
pyplot.plot(xs,GPsamples[0,:],'-',color='0.25')
if not constraints is None:
plot.bovy_plot(mjd_g,g,'k.',zorder=5,ms=10,overplot=True)
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(xs,GPsamples[ii,:],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints == []:
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$m-\langle m\rangle\ [\mathrm{mag}]$')
pyplot.xlim(3,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_zoom.ps')
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
pyplot.loglog(sc.arange(1.,len(GPsamples[ii,:])/2)*(xs[1]-xs[0]),
2.*sc.var(GPsamples[ii,:])-2.*sc.correlate(GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),"same")[1:len(GPsamples[ii,:])/2][::-1]/len(GPsamples[ii,:]),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(xs[1]-xs[0]),xs[len(xs)/2]]
pyplot.loglog(xline,nu.exp(-3.02045715)*nu.array(xline)**(0.5868293),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfunc.ps')
def dualBandExample(filename='../data/SDSSJ203817.37+003029.8.fits',
constraints=None,
basefilename='SDSSJ203817.37+003029.8',
covarType='SF'):
"""
NAME:
dualBandExample
PURPOSE:
show an example of a power-law structure function GP covariance function
fit to an SDSS quasar for g and r
INPUT:
filename - filename with the data
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
covarType - 'SF' or 'DRW'
OUTPUT:
writes several plots
HISTORY:
2010-08-11 - Written - Bovy (NYU)
"""
file = fu.table_fields(filename)
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7) #Adjust for non-g
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
meanErr_gr = nu.mean(nu.sqrt(err_r**2. + err_g**2.))
nu.random.seed(4)
nGP = 5
nx = 201
params_mean = ()
if covarType == 'SF':
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.33271548]),
'logGammagr': array([-10.5]),
'gamma': array([0.4821092]),
'gammagr': array([0.5])
}
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
else:
from OUgr import covarFunc
params = {
'logl': array([1.94844503]),
'loglgr': array([7.36282174]),
'logagr2': array([1.0196474]),
'loga2': array([-0.00588868])
}
params = {
'logl': -1.37742591,
'loga': -3.47341754,
'loglgr': -2.3777,
'logagr': -4.
}
params = {
'logl': array([-1.38968195]),
'loglgr': array([-2.46684501]),
'logagr2': array([-6.62320832]),
'loga2': array([-3.52099305])
}
paramsSF = params
cf = covarFunc(**params)
params_covar = (cf)
ndata = len(g)
if constraints is None:
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
constraints = trainSet
else:
constraints = nu.array([])
useconstraints = constraints
txs = nu.linspace(-0.1, 6.5, nx)
xs = [(txs[ii], 'g') for ii in range(nx)]
xs.extend([(txs[ii], 'r') for ii in range(nx)])
GPsamples = eval_gp(xs,
mean,
covar, (),
params_covar,
nGP=nGP,
constraints=useconstraints,
tiny_cholesky=.00000001)
thismean = calc_constrained_mean(xs, mean, params_mean, covar,
params_covar, useconstraints)
thiscovar = calc_constrained_covar(xs, covar, params_covar, useconstraints)
#Calculate loglike
if isinstance(constraints, trainingSet):
(params, packing) = pack_params(cf)
covarFuncName = inspect.getmodule(cf).__name__
thisCovarClass = __import__(covarFuncName)
loglike = marginalLikelihood(params, constraints, packing,
thisCovarClass)
plot.bovy_print()
pyplot.plot(txs, GPsamples[0, :nx], '-', color='0.25')
if isinstance(constraints, trainingSet):
pyplot.plot(mjd_g, g, 'k.', zorder=5, ms=10)
title = re.split(r'_', basefilename)[0]
if covarType == 'SF':
method = '\mathrm{power-law\ structure\ functions}'
else:
method = '\mathrm{damped\ random\ walk}'
plot.bovy_text(r'$\mathrm{' + title + '\ / \ ' + method + r'}$',
title=True)
if isinstance(constraints, trainingSet):
plot.bovy_text(r'$\log P({\bf x}|\mathrm{parameters}) = %5.2f$' %
(-loglike),
top_left=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1, nGP):
pyplot.plot(txs,
GPsamples[ii, :nx],
'-',
color=str(0.25 + ii * .5 / (nGP - 1)))
#pyplot.plot(txs,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints, trainingSet):
pyplot.errorbar(6.15, -0.25, yerr=meanErr_g, color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1, 6.5)
pyplot.ylim(-0.6, 0.6)
plot._add_ticks()
plot.bovy_end_print(basefilename + '_fullg.ps')
plot.bovy_print()
pyplot.figure()
pyplot.plot(txs, GPsamples[0, nx - 1:-1], '-', color='0.25')
if isinstance(constraints, trainingSet):
pyplot.plot(mjd_r, r, 'k.', zorder=5, ms=10)
#plot.bovy_text(r'$\mathrm{'+title+'\ / \ '+method+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1, nGP):
pyplot.plot(txs,
GPsamples[ii, nx - 1:-1],
'-',
color=str(0.25 + ii * .5 / (nGP - 1)))
#pyplot.plot(txs,thismean[nx-1:-1],'k-',linewidth=2)
if isinstance(constraints, trainingSet):
pyplot.errorbar(6.15, -0.25, yerr=meanErr_r, color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$r-\langle r\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1, 6.5)
pyplot.ylim(-0.6, 0.6)
plot._add_ticks()
plot.bovy_end_print(basefilename + '_fullr.ps')
plot.bovy_print()
pyplot.figure()
ii = 0
colors = nu.array(
[GPsamples[ii, jj] - GPsamples[ii, jj + nx] for jj in range(nx)])
if not isinstance(constraints, trainingSet):
colors = colors - nu.mean(colors)
pyplot.plot(txs, colors, '-', color='0.25')
if isinstance(constraints, trainingSet):
plot.bovy_plot(mjd_g, g - r, 'k.', zorder=5, ms=10, overplot=True)
#plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1, nGP):
colors = nu.array(
[GPsamples[ii, jj] - GPsamples[ii, jj + nx] for jj in range(nx)])
if not isinstance(constraints, trainingSet):
colors = colors - nu.mean(colors)
pyplot.plot(txs, colors, '-', color=str(0.25 + ii * .5 / (nGP - 1)))
plotthismean = nu.zeros(nx)
for ii in range(nx):
plotthismean[ii] = thismean[ii] - thismean[ii + nx]
#pyplot.plot(txs,plotthismean,'k-',linewidth=2)
if isinstance(constraints, trainingSet):
pyplot.errorbar(6.15, -0.18, yerr=meanErr_gr, color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-r- \langle g - r \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1, 6.5)
if isinstance(constraints, trainingSet):
pyplot.ylim(-0.25, .25)
else:
pass #pyplot.ylim(-10.,10.)
plot._add_ticks()
plot.bovy_end_print(basefilename + '_color.ps')
if covarType == 'DRW':
return
#Plot structure functions
#g
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
thisSample = GPsamples[ii, :nx]
pyplot.loglog(sc.arange(1.,len(thisSample)/2)*(txs[1]-txs[0]),
2.*sc.var(thisSample)\
-2.*sc.correlate(thisSample-sc.mean(thisSample),thisSample-sc.mean(thisSample),"same")[1:len(thisSample)/2][::-1]/len(thisSample),
color=str(0.25+ii*.5/(nGP-1)))
xline = [(txs[1] - txs[0]), txs[len(txs) / 2]]
pyplot.loglog(xline, (nu.exp(paramsSF['logGamma']) *
nu.array(xline))**(paramsSF['gamma']), 'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function\ in}\ g$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename + '_structfuncg.ps')
#r
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
thisSample = GPsamples[ii, nx - 1:-1]
pyplot.loglog(sc.arange(1.,len(thisSample)/2)*(txs[1]-txs[0]),
2.*sc.var(thisSample)\
-2.*sc.correlate(thisSample-sc.mean(thisSample),thisSample-sc.mean(thisSample),"same")[1:len(thisSample)/2][::-1]/len(thisSample),
color=str(0.25+ii*.5/(nGP-1)))
xline = [(txs[1] - txs[0]), txs[len(txs) / 2]]
pyplot.loglog(xline, (nu.exp(paramsSF['logGamma']) *
nu.array(xline))**(paramsSF['gamma']), 'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function\ in}\ r$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename + '_structfuncr.ps')
#g-r
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
thisSample = nu.array(
[GPsamples[ii, jj] - GPsamples[ii, jj + nx] for jj in range(nx)])
pyplot.loglog(sc.arange(1.,len(thisSample)/2)*(txs[1]-txs[0]),
2.*sc.var(thisSample)\
-2.*sc.correlate(thisSample-sc.mean(thisSample),thisSample-sc.mean(thisSample),"same")[1:len(thisSample)/2][::-1]/len(thisSample),
color=str(0.25+ii*.5/(nGP-1)))
xline = [(txs[1] - txs[0]), txs[len(txs) / 2]]
pyplot.loglog(xline, (nu.exp(paramsSF['logGammagr']) *
nu.array(xline))**(paramsSF['gammagr']), 'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{color\ structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename + '_structfuncgr.ps')
def showExampleDRW(filename='../data/SDSSJ203817.37+003029.8.fits',
constraints=None,basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
showExampleDRW
PURPOSE:
show an example of a pDRW structure function GP covariance function
fit to an SDSS quasar for g and r
INPUT:
filename - filename with the data
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
OUTPUT:
writes several plots
HISTORY:
2010-08-11 - Written - Bovy (NYU)
"""
file= fu.table_fields(filename)
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
mjd_r= nu.array(file.mjd_r)/365.25
r= nu.array(file.r)
err_r= nu.array(file.err_r)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
r= r[mask]
r-= nu.mean(r)
err_r= err_r[mask]
mjd_r= mjd_g#mjd_r[mask]
#mjd_r-= nu.amin(mjd_r)
meanErr_r= nu.mean(err_r)
meanErr_gr= nu.mean(nu.sqrt(err_r**2.+err_g**2.))
nu.random.seed(4)
nGP=5
nx=201
params_mean= ()
from drwcross import covarFunc
#params= {'logS': array([1.63714183]), 'logtau': array([0.]), 'logB': array([ 2.32288434])}
#params= {'logtau': array([-1.36399325]), 'logB': array([-519.45950737]), 'logS': array([-3.51531852])}
params= {'logS': array([-1.08795813]), 'logtau': array([ 1.2790154]), 'logB': array([-1.01029709]), 'logC': array([-79.88349478]), 'gammagr': array([ 7.90536799]), 'logGammagr': array([ 2.8001978])}
cf= covarFunc(**params)
params_covar= (cf)
ndata= len(g)
if constraints is None:
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listy= [m for m in g]
listy.extend([m for m in r])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
constraints= trainSet
else:
constraints= nu.array([])
useconstraints= constraints
txs= nu.linspace(-0.1,6.5,nx)
xs= [(txs[ii],'g') for ii in range(nx)]
xs.extend([(txs[ii],'r') for ii in range(nx)])
GPsamples= eval_gp(xs,mean,covar,(),params_covar,nGP=nGP,constraints=useconstraints,tiny_cholesky=.000001)
thismean= calc_constrained_mean(xs,mean,params_mean,covar,params_covar,useconstraints)
thiscovar= calc_constrained_covar(xs,covar,params_covar,useconstraints)
plot.bovy_print()
pyplot.plot(txs,GPsamples[0,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_g,g,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
pyplot.plot(txs,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_full.ps')
plot.bovy_print()
pyplot.figure()
pyplot.plot(txs,GPsamples[0,nx-1:-1],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_r,r,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,nx-1:-1],'-',color=str(0.25+ii*.5/(nGP-1)))
pyplot.plot(txs,thismean[nx-1:-1],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_r,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$r-\langle r\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_fullr.ps')
plot.bovy_print()
pyplot.figure()
ii= 0
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color='0.25')
if isinstance(constraints,trainingSet):
plot.bovy_plot(mjd_g,g-r,'k.',zorder=5,ms=10,overplot=True)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color=str(0.25+ii*.5/(nGP-1)))
plotthismean= nu.zeros(nx)
for ii in range(nx):
plotthismean[ii]= thismean[ii]-thismean[ii+nx]
pyplot.plot(txs,plotthismean,'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.18,yerr=meanErr_gr,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-r- \langle g - r \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
if isinstance(constraints,trainingSet):
pyplot.ylim(-0.25,.25)
else:
pass #pyplot.ylim(-10.,10.)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_color.ps')
plot.bovy_print()
pyplot.figure()
ii= 2
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.plot(colors,GPsamples[ii,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(g-r,g,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.plot(colors,GPsamples[ii,0:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
colors= nu.array([thismean[jj]-thismean[jj+nx] for jj in range(nx)])
pyplot.plot(colors,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(.13,-0.3,yerr=meanErr_g,xerr=meanErr_gr,color='k')
pyplot.xlabel(r'$g-r-\langle g-r\rangle\ [\mathrm{mag}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-.2,.2)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_ggr.ps')
return
def singleBandSampleExample(
datafilename='../data/SDSSJ203817.37+003029.8.fits',
band='g',
nsamples=1000,
basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
singleBandSampleExample
PURPOSE:
Sample an example of a power-law structure function GP covariance
function to an SDSS quasar
INPUT:
datafilename
band - band to fit
nsamples - number of samples to use
basefilename
OUTPUT:
HISTORY:
2010-08-08 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file = fu.table_fields(datafilename)
if band == 'u':
mjd_g = nu.array(file.mjd_u) / 365.25
g = nu.array(file.u)
err_g = nu.array(file.err_u)
elif band == 'g':
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
elif band == 'r':
mjd_g = nu.array(file.mjd_r) / 365.25
g = nu.array(file.r)
err_g = nu.array(file.err_r)
elif band == 'i':
mjd_g = nu.array(file.mjd_i) / 365.25
g = nu.array(file.i)
err_g = nu.array(file.err_i)
elif band == 'z':
mjd_g = nu.array(file.mjd_z) / 365.25
g = nu.array(file.z)
err_g = nu.array(file.err_z)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mjd_i = nu.array(file.mjd_i) / 365.25
i = nu.array(file.i)
err_i = nu.array(file.err_i)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7)
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
i = i[mask]
i -= nu.mean(i)
err_i = err_i[mask]
mjd_i = mjd_i[mask]
mjd_i -= nu.amin(mjd_i)
meanErr_i = nu.mean(err_i)
savefilename = basefilename + '.sav'
if os.path.exists(savefilename):
savefile = open(savefilename, 'rb')
gammas = pickle.load(savefile)
if 'gr' in basefilename:
logGammas = pickle.load(savefile)
gammagrs = pickle.load(savefile)
logGammagrs = pickle.load(savefile)
else:
logAs = pickle.load(savefile)
out = pickle.load(savefile)
savefile.close()
else:
if 'gri' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
SF = covarFunc(**params)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listx.extend([(t, 'i') for t in mjd_i])
listy = [m for m in g]
listy.extend([m for m in r])
listy.extend([m for m in i])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise.extend([m for m in err_i])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
elif 'gr' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
SF = covarFunc(**params)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
else:
from gp.powerlawSF import covarFunc
trainSet = trainingSet(listx=mjd_g, listy=g, noise=err_g)
params = [0., 0.]
SF = covarFunc(gamma=.2, A=0.000524) #Power
#SF= covarFunc(a=.0001,l=.001) #OU
#Train
print "Training ..."
outcovarFunc = trainGP(trainSet, SF, useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
print "Sampling ..."
out = sampleGP(trainSet,
outcovarFunc,
nsamples=nsamples,
step=[0.2 for ii in range(len(params))])
gammas = nu.array([o._dict['gamma'] for o in out]).reshape(len(out))
if 'gr' in basefilename:
gammagrs = nu.array([o._dict['gammagr']
for o in out]).reshape(len(out))
logGammas = nu.array([o._dict['logGamma']
for o in out]).reshape(len(out))
logGammagrs = nu.array([o._dict['logGammagr']
for o in out]).reshape(len(out))
else:
logAs = nu.array([o._dict['logA'] for o in out]).reshape(len(out))
print nu.mean(logAs), nu.sqrt(nu.var(logAs))
print nu.mean(gammas), nu.sqrt(nu.var(gammas))
#Save
savefile = open(savefilename, 'wb')
pickle.dump(gammas, savefile)
if 'gr' in basefilename:
pickle.dump(logGammas, savefile)
pickle.dump(gammagrs, savefile)
pickle.dump(logGammagrs, savefile)
else:
pickle.dump(logAs, savefile)
pickle.dump(out, savefile)
savefile.close()
#if not 'gr' in basefilename:
# logGammas= logAs/gammas
#Plot
bovy_plot.bovy_print()
bovy_plot.scatterplot(logAs / 2.,
gammas,
'k,',
ylabel=r'$\gamma$',
xlabel=r'\log A',
xrange=[-9.21 / 2., 0.],
yrange=[0., 1.25],
bins=50,
onedhists=True)
bovy_plot.bovy_end_print(basefilename + '_sample_2d.png')
def singleBandSampleExample(datafilename='../data/SDSSJ203817.37+003029.8.fits',
band='g',nsamples=1000,
basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
singleBandSampleExample
PURPOSE:
Sample an example of a power-law structure function GP covariance
function to an SDSS quasar
INPUT:
datafilename
band - band to fit
nsamples - number of samples to use
basefilename
OUTPUT:
HISTORY:
2010-08-08 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file= fu.table_fields(datafilename)
if band == 'u':
mjd_g= nu.array(file.mjd_u)/365.25
g= nu.array(file.u)
err_g= nu.array(file.err_u)
elif band == 'g':
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
elif band == 'r':
mjd_g= nu.array(file.mjd_r)/365.25
g= nu.array(file.r)
err_g= nu.array(file.err_r)
elif band == 'i':
mjd_g= nu.array(file.mjd_i)/365.25
g= nu.array(file.i)
err_g= nu.array(file.err_i)
elif band == 'z':
mjd_g= nu.array(file.mjd_z)/365.25
g= nu.array(file.z)
err_g= nu.array(file.err_z)
mjd_r= nu.array(file.mjd_r)/365.25
r= nu.array(file.r)
err_r= nu.array(file.err_r)
mjd_i= nu.array(file.mjd_i)/365.25
i= nu.array(file.i)
err_i= nu.array(file.err_i)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
r= r[mask]
r-= nu.mean(r)
err_r= err_r[mask]
mjd_r= mjd_r[mask]
mjd_r-= nu.amin(mjd_r)
meanErr_r= nu.mean(err_r)
i= i[mask]
i-= nu.mean(i)
err_i= err_i[mask]
mjd_i= mjd_i[mask]
mjd_i-= nu.amin(mjd_i)
meanErr_i= nu.mean(err_i)
savefilename= basefilename+'.sav'
if os.path.exists(savefilename):
savefile= open(savefilename,'rb')
gammas= pickle.load(savefile)
if 'gr' in basefilename:
logGammas= pickle.load(savefile)
gammagrs= pickle.load(savefile)
logGammagrs= pickle.load(savefile)
else:
logAs= pickle.load(savefile)
out= pickle.load(savefile)
savefile.close()
else:
if 'gri' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params= {'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([ 0.45918053]),
'gammagr': array([ 0.21333858])}
SF= covarFunc(**params)
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listx.extend([(t,'i') for t in mjd_i])
listy= [m for m in g]
listy.extend([m for m in r])
listy.extend([m for m in i])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise.extend([m for m in err_i])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
elif 'gr' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params= {'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([ 0.45918053]),
'gammagr': array([ 0.21333858])}
SF= covarFunc(**params)
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listy= [m for m in g]
listy.extend([m for m in r])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
else:
from gp.powerlawSF import covarFunc
trainSet= trainingSet(listx=mjd_g,listy=g,noise=err_g)
params= [0.,0.]
SF= covarFunc(gamma=.2,A=0.000524) #Power
#SF= covarFunc(a=.0001,l=.001) #OU
#Train
print "Training ..."
outcovarFunc= trainGP(trainSet,SF,useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
print "Sampling ..."
out= sampleGP(trainSet,outcovarFunc,nsamples=nsamples,
step=[0.2 for ii in range(len(params))])
gammas= nu.array([o._dict['gamma'] for o in out]).reshape(len(out))
if 'gr' in basefilename:
gammagrs= nu.array([o._dict['gammagr'] for o in out]).reshape(len(out))
logGammas= nu.array([o._dict['logGamma'] for o in out]).reshape(len(out))
logGammagrs= nu.array([o._dict['logGammagr'] for o in out]).reshape(len(out))
else:
logAs= nu.array([o._dict['logA'] for o in out]).reshape(len(out))
print nu.mean(logAs),nu.sqrt(nu.var(logAs))
print nu.mean(gammas),nu.sqrt(nu.var(gammas))
#Save
savefile= open(savefilename,'wb')
pickle.dump(gammas,savefile)
if 'gr' in basefilename:
pickle.dump(logGammas,savefile)
pickle.dump(gammagrs,savefile)
pickle.dump(logGammagrs,savefile)
else:
pickle.dump(logAs,savefile)
pickle.dump(out,savefile)
savefile.close()
#if not 'gr' in basefilename:
# logGammas= logAs/gammas
#Plot
bovy_plot.bovy_print()
bovy_plot.scatterplot(logAs/2.,gammas,'k,',
ylabel=r'$\gamma$',xlabel=r'\log A',
xrange=[-9.21/2.,0.],
yrange=[0.,1.25],
bins=50,onedhists=True)
bovy_plot.bovy_end_print(basefilename+'_sample_2d.png')
