def fitSigz(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
params= pickle.load(savefile)
samples= pickle.load(savefile)
savefile.close()
if _DEBUG:
print "Printing mean and std dev of samples ..."
for ii in range(len(params)):
xs= numpy.array([s[ii] for s in samples])
print numpy.mean(xs), numpy.std(xs)
else:
#First read the data
if _VERBOSE:
print "Reading and parsing data ..."
XYZ,vxvyvz,cov_vxvyvz,rawdata= readData(metal=options.metal,
sample=options.sample)
vxvyvz= vxvyvz.astype(numpy.float64)
cov_vxvyvz= cov_vxvyvz.astype(numpy.float64)
R= ((8.-XYZ[:,0])**2.+XYZ[:,1]**2.)**(0.5)
XYZ[:,2]+= _ZSUN
d= numpy.fabs((XYZ[:,2]-numpy.mean(numpy.fabs(XYZ[:,2]))))
#Optimize likelihood
if _VERBOSE:
print "Optimizing the likelihood ..."
if options.model.lower() == 'hwr':
if options.metal == 'rich':
params= numpy.array([0.02,numpy.log(25.),0.,0.,numpy.log(6.)])
elif options.metal == 'poor':
params= numpy.array([0.02,numpy.log(40.),0.,0.,numpy.log(15.)])
else:
params= numpy.array([0.02,numpy.log(30.),0.,0.,numpy.log(15.)])
like_func= _HWRLikeMinus
pdf_func= _HWRLike
#Slice sampling keywords
step= [0.01,0.05,0.3,0.3,0.3]
create_method=['full','step_out','step_out',
'step_out','step_out']
isDomainFinite=[[True,True],[False,False],
[False,False],[False,False],
[False,False]]
domain=[[0.,1.],[0.,0.],[0.,0.],[0.,0.],
[0.,4.6051701859880918]]
elif options.model.lower() == 'isotherm':
if options.metal == 'rich':
params= numpy.array([0.02,numpy.log(25.),numpy.log(6.)])
elif options.metal == 'poor':
params= numpy.array([0.02,numpy.log(40.),numpy.log(15.)])
else:
params= numpy.array([0.02,numpy.log(30.),numpy.log(15.)])
like_func= _IsothermLikeMinus
pdf_func= _IsothermLike
#Slice sampling keywords
step= [0.01,0.05,0.3]
create_method=['full','step_out','step_out']
isDomainFinite=[[True,True],[False,False],
[False,True]]
domain=[[0.,1.],[0.,0.],[0.,4.6051701859880918]]
params= optimize.fmin_powell(like_func,params,
args=(XYZ,vxvyvz,cov_vxvyvz,R,d))
if _VERBOSE:
print "Optimal likelihood:", params
#Now sample
if _VERBOSE:
print "Sampling the likelihood ..."
samples= bovy_mcmc.slice(params,
step,
pdf_func,
(XYZ,vxvyvz,cov_vxvyvz,R,d),
create_method=create_method,
isDomainFinite=isDomainFinite,
domain=domain,
nsamples=options.nsamples)
if _DEBUG:
print "Printing mean and std dev of samples ..."
for ii in range(len(params)):
xs= numpy.array([s[ii] for s in samples])
print numpy.mean(xs), numpy.std(xs)
if _VERBOSE:
print "Saving ..."
savefile= open(args[0],'wb')
pickle.dump(params,savefile)
pickle.dump(samples,savefile)
savefile.close()
if options.noplots: return None
#Plot
if options.plotfunc:
#First plot the best fit
zs= numpy.linspace(0.3,1.2,1001)
ds= zs-1.
func= zfunc
maxys= math.exp(params[1])+params[2]*ds+params[3]*ds**2.
if options.xmin is None or options.xmax is None:
xrange= [numpy.amin(zs)-0.2,numpy.amax(zs)+0.1]
else:
xrange= [options.xmin,options.xmax]
if options.ymin is None or options.ymax is None:
yrange= [numpy.amin(ys)-1.,numpy.amax(ys)+1.]
else:
yrange= [options.ymin,options.ymax]
#Now plot the mean and std-dev from the posterior
zmean= numpy.zeros(len(zs))
nsigs= 3
zsigs= numpy.zeros((len(zs),2*nsigs))
fs= numpy.zeros((len(zs),len(samples)))
ds= zs-1.
for ii in range(len(samples)):
thisparams= samples[ii]
fs[:,ii]= math.exp(thisparams[1])+thisparams[2]*ds+thisparams[3]*ds**2.
#Record mean and std-devs
zmean[:]= numpy.mean(fs,axis=1)
bovy_plot.bovy_print()
bovy_plot.bovy_plot(zs,zmean,'k-',xrange=xrange,yrange=yrange,
xlabel=options.xlabel,
ylabel=options.ylabel)
for ii in range(nsigs):
for jj in range(len(zs)):
thisf= sorted(fs[jj,:])
thiscut= 0.5*special.erfc((ii+1.)/math.sqrt(2.))
zsigs[jj,2*ii]= thisf[int(math.floor(thiscut*len(samples)))]
thiscut= 1.-thiscut
zsigs[jj,2*ii+1]= thisf[int(math.floor(thiscut*len(samples)))]
colord, cc= (1.-0.75)/nsigs, 1
nsigma= nsigs
pyplot.fill_between(zs,zsigs[:,0],zsigs[:,1],color='0.75')
while nsigma > 1:
pyplot.fill_between(zs,zsigs[:,cc+1],zsigs[:,cc-1],
color='%f' % (.75+colord*cc))
pyplot.fill_between(zs,zsigs[:,cc],zsigs[:,cc+2],
color='%f' % (.75+colord*cc))
cc+= 1.
nsigma-= 1
bovy_plot.bovy_plot(zs,zmean,'k-',overplot=True)
#bovy_plot.bovy_plot(zs,maxys,'w--',overplot=True)
bovy_plot.bovy_end_print(options.plotfile)
else:
xs= numpy.array([s[options.d1] for s in samples])
ys= numpy.array([s[options.d2] for s in samples])
if options.expd1: xs= numpy.exp(xs)
if options.expd2: ys= numpy.exp(ys)
if options.xmin is None or options.xmax is None:
xrange= [numpy.amin(xs),numpy.amax(xs)]
else:
xrange= [options.xmin,options.xmax]
if options.ymin is None or options.ymax is None:
yrange= [numpy.amin(ys),numpy.amax(ys)]
else:
yrange= [options.ymin,options.ymax]
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,ys,'k,',onedhists=True,xrange=xrange,
yrange=yrange,xlabel=options.xlabel,
ylabel=options.ylabel)
maxx, maxy= params[options.d1], params[options.d2]
if options.expd1: maxx= math.exp(maxx)
if options.expd2: maxy= math.exp(maxy)
bovy_plot.bovy_plot([maxx],[maxy],'wx',
overplot=True,ms=10.,mew=2.)
bovy_plot.bovy_end_print(options.plotfile)
def sampleGP(trainingSet,covar,mean=None,nsamples=100,
step=None,fix=None,metropolis=False,markovpy=False):
"""
NAME:
sampleGP
PURPOSE:
sample a GP
INPUT:
trainingSet - a trainingSet instance
covar - an instance of your covariance function of
choice, with initialized parameters
mean= an instance of your mean function of choice, with initialized
parameters
nsamples - number of samples desired
step= step-size for slice creation or metropolis sampling
(number or list)
fix= None or list of parameters to hold fixed
metropolis= if True, use Metropolis sampling
markovpy= if True, use markovpy sampling
OUTPUT:
list of outcovarFunc
HISTORY:
2010-08-08 - Written - Bovy (NYU)
"""
#Put in dummy mean if mean is None
if mean is None:
from flexgp.zeroMean import meanFunc
mean= meanFunc()
noMean= True
out= [covar]
else:
noMean= False
out= [(covar,mean)]
#Pack the covariance parameters
(params,packing)= pack_params(covar,mean,fix)
if step is None:
step= [0.1 for ii in range(len(params))]
#Grab the covariance class
covarFuncName= inspect.getmodule(covar).__name__
thisCovarClass= __import__(covarFuncName)
meanFuncName= inspect.getmodule(mean).__name__
thisMeanClass= __import__(meanFuncName)
#Set up isDomainFinite, domain, and create_method, even when metropolis
isDomainFinite, domain, create_method= [], [], []
covarIsDomainFinite= covar.isDomainFinite()
covarDomain= covar.paramsDomain()
covarCreate= covar.create_method()
meanIsDomainFinite= mean.isDomainFinite()
meanDomain= mean.paramsDomain()
meanCreate= mean.create_method()
for ii in range(len(packing.GPhyperPackingList)):
p= packing.GPhyperPackingList[ii]
try:
for jj in range(packing.GPhyperPackingDim[ii]):
isDomainFinite.append(covarIsDomainFinite[p])
domain.append(covarDomain[p])
create_method.append(covarCreate[p])
except KeyError:
for jj in range(len(packing.GPhyperPackingDim[ii])):
isDomainFinite.append(meanIsDomainFinite[p])
domain.append(meanDomain[p])
create_method.append(meanCreate[p])
if len(packing.GPhyperPackingList) == 1: #one-d
isDomainFinite= isDomainFinite[0]
domain= domain[0]
create_method= create_method[0]
if isinstance(step,(list,numpy.ndarray)): step= step[0]
if not metropolis and not markovpy:
#slice sample the marginal likelihood
samples= bovy_mcmc.slice(params,step,
_lnpdf,(trainingSet,packing,thisCovarClass,
thisMeanClass),
isDomainFinite=isDomainFinite,
domain=domain,
nsamples=nsamples,
create_method=create_method)
elif metropolis:
samples, faccept= bovy_mcmc.metropolis(params,step,
_lnpdf,(trainingSet,packing,
thisCovarClass,
thisMeanClass),
symmetric=True,
nsamples=nsamples)
if numpy.any((faccept < 0.15)) or numpy.any((faccept > 0.6)):
print "WARNING: Metropolis acceptance ratio was < 0.15 or > 0.6 for a direction"
print "Full acceptance ratio list:"
print faccept
elif markovpy:
samples= bovy_mcmc.markovpy(params,step,
_lnpdf,(trainingSet,packing,thisCovarClass,
thisMeanClass),
isDomainFinite=isDomainFinite,
domain=domain,
nsamples=nsamples)
print nsamples, len(samples)
if noMean:
for ii in range(nsamples):
hyperParamsDict= unpack_params(samples[ii],packing)
out.append(packing.covarFunc(**hyperParamsDict))
return out
else:
for ii in range(nsamples):
hyperParamsDict= unpack_params(samples[ii],packing)
out.append((packing.covarFunc(**hyperParamsDict),
packing.meanFunc(**hyperParamsDict)))
return out