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 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 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
