def contour(chaindir,cosmo,data,compute_param_function) :
C=ChainIterator(chaindir,cosmo,'phy',data)
z=numpy.arange(0,3,0.01)
# find best model
min_chi2 = numpy.min(C.chain.chain[:,1])
model_best_index = numpy.argmin(C.chain.chain[:,1])
print "minimum chi2 = ", min_chi2
T=C.theory(model_best_index)
# compute param for this model
param_best=numpy.zeros(z.shape)
for i in range(z.shape[0]) :
param_best[i]=compute_param_function(T,z[i])
# allocate param for +- 1 sigma
param_1sigma_plus=param_best.copy()
param_1sigma_minus=param_best.copy()
# selection of models within 1sig
model_1sig_indices=numpy.where(C.chain.chain[:,1]<=min_chi2+1)[0]
print "number of models at 1 sig = ",model_1sig_indices.shape[0]
# if too many, look only at those close to 1 sigma (this is for LCDM, there are too many)
chi2_floor=min_chi2
while (model_1sig_indices.shape[0]>3000) :
chi2_floor+=0.01
model_1sig_indices=numpy.where((C.chain.chain[:,1]<=min_chi2+1)&(C.chain.chain[:,1]>=chi2_floor))[0]
print "number of selected models for the 1sig contour plot = ",model_1sig_indices.shape[0]
# loop on models to find max excursion
count=0
for index in model_1sig_indices :
count+=1
T=C.theory(index)
for i in range(z.shape[0]) :
parami=compute_param_function(T,z[i])
param_1sigma_minus[i]=min(param_1sigma_minus[i],parami)
param_1sigma_plus[i]=max(param_1sigma_plus[i],parami)
if count%100==0 :
print "done %d/%d"%(count,model_1sig_indices.shape[0])
# return -1sig,best,1sig
return z,param_1sigma_minus,param_best,param_1sigma_plus
def contour(chaindir, cosmo, data, compute_param_function):
C = ChainIterator(chaindir, cosmo, 'phy', data)
z = numpy.arange(0, 3, 0.01)
# find best model
min_chi2 = numpy.min(C.chain.chain[:, 1])
model_best_index = numpy.argmin(C.chain.chain[:, 1])
print "minimum chi2 = ", min_chi2
T = C.theory(model_best_index)
# compute param for this model
param_best = numpy.zeros(z.shape)
for i in range(z.shape[0]):
param_best[i] = compute_param_function(T, z[i])
# allocate param for +- 1 sigma
param_1sigma_plus = param_best.copy()
param_1sigma_minus = param_best.copy()
# selection of models within 1sig
model_1sig_indices = numpy.where(C.chain.chain[:, 1] <= min_chi2 + 1)[0]
print "number of models at 1 sig = ", model_1sig_indices.shape[0]
# if too many, look only at those close to 1 sigma (this is for LCDM, there are too many)
chi2_floor = min_chi2
while (model_1sig_indices.shape[0] > 3000):
chi2_floor += 0.01
model_1sig_indices = numpy.where((C.chain.chain[:, 1] <= min_chi2 + 1)
& (C.chain.chain[:,
1] >= chi2_floor))[0]
print "number of selected models for the 1sig contour plot = ", model_1sig_indices.shape[
0]
# loop on models to find max excursion
count = 0
for index in model_1sig_indices:
count += 1
T = C.theory(index)
for i in range(z.shape[0]):
parami = compute_param_function(T, z[i])
param_1sigma_minus[i] = min(param_1sigma_minus[i], parami)
param_1sigma_plus[i] = max(param_1sigma_plus[i], parami)
if count % 100 == 0:
print "done %d/%d" % (count, model_1sig_indices.shape[0])
# return -1sig,best,1sig
return z, param_1sigma_minus, param_best, param_1sigma_plus
if 'True' in Plot_1D:
a = 0
for datasets in datasetl:
b = 0
for params in params_1D:
b += 1
pylab.subplot(1, len(params_1D), b)
C = cosmochain(dire + model_1D + '_' + datasets, 'auto')
C = ChainIterator('chains_140411_155701', 'LCDM', 'phy',
'BBAO+CMBP')
grlist = []
wlist = []
for i in range(0, C.N, 1000):
T = C.theory(i)
grlist.append(T.growth(10.0))
wlist.append(C.weight(i))
grlist = array(grlist)
wlist = array(wlist)
mn = (grlist * wlist).sum() / wlist.sum()
er = sqrt((grlist**2 * wlist).sum() / wlist.sum() - mn**2)
print("growth z=10/z=0 = ", mn, "+/-", er)
a += 1
xx, yy = C.GetHisto(params, NormPeak=True, nbins=NBins_1D)
pylab.plot(xx, yy, colour(a), label=cosmodata(datasets))
if 'True' in xrange:
xmin = "xmin_" + str(b)
b=0
for params in params_1D:
b+=1
pylab.subplot(1,len(params_1D),b)
#fname=self.get_filename(dire,model,extra,dataset)
C=cosmochain(dire+ model_1D+'_'+datasets,'auto')
if(True): #Compute functions= ratio of Fig 2 at z=0.57/0
dire2 = 'chains/DR12/'
D=ChainIterator(dire2, 'LCDM','phy', 'CombBAOzb3')
grlist=[]
wlist=[]
for i in range(0,D.N,1000):
T=D.theory(i)
#grlist.append(T.Da_z(0.57)/T.Hinv_z(0)/(1.0*N.log(1.57)))
#grlist.append(T.DaOverrd(0.51))
grlist.append(T.HIOverrd(0.61))
wlist.append(D.weight(i))
grlist=array(grlist)
wlist=array(wlist)
mn=(grlist*wlist).sum()/wlist.sum()
er=sqrt((grlist**2*wlist).sum()/wlist.sum()-mn**2)
#print "H(0.0)/(1.0) \over c*ln(1.57)/D_m(0.57) = ",mn,"+/-",er
print "z = 0.38 = ",mn,"+/-",er
a+=1
xx,yy=C.GetHisto(params,NormPeak=True,nbins=NBins_1D)
pylab.plot(xx, yy, colour(a), label=cosmodata(datasets))
if 'True' in Plot_1D:
a=0
for datasets in datasetl:
b=0
for params in params_1D:
b+=1
pylab.subplot(1,len(params_1D),b)
C=cosmochain(dire+ model_1D+'_'+datasets,'auto')
C=ChainIterator('chains_140411_155701','LCDM','phy','BBAO+CMBP')
grlist=[]
wlist=[]
for i in range(0,C.N,1000):
T=C.theory(i)
grlist.append(T.growth(10.0))
wlist.append(C.weight(i))
grlist=array(grlist)
wlist=array(wlist)
mn=(grlist*wlist).sum()/wlist.sum()
er=sqrt((grlist**2*wlist).sum()/wlist.sum()-mn**2)
print "growth z=10/z=0 = ",mn,"+/-",er
a+=1
xx,yy=C.GetHisto(params,NormPeak=True,nbins=NBins_1D)
pylab.plot(xx, yy, colour(a), label=cosmodata(datasets))