def saveMeanCorMatrix(ni,nj): ''' Save the mean correlation matrix of the 100 correaltion matrix ''' path = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Box_00' pathToSave = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Results/xi_delta_QSO_result_' ### Get the 0_0 simu cor1D = myTools.getCorrelationMatrix(numpy.load(path + '0/Simu_000/Results/subSampling_LYA_QSO_cov_1D.npy')) cor2D = myTools.getCorrelationMatrix(numpy.load(path + '0/Simu_000/Results/subSampling_LYA_QSO_cov_2D.npy')) for i in numpy.arange(ni): for j in numpy.arange(nj): if (i==0 and j==0): continue cor1D += myTools.getCorrelationMatrix(numpy.load(path + str(i)+'/Simu_00'+str(j)+'/Results/subSampling_LYA_QSO_cov_1D.npy')) cor2D += myTools.getCorrelationMatrix(numpy.load(path + str(i)+'/Simu_00'+str(j)+'/Results/subSampling_LYA_QSO_cov_2D.npy')) cor1D /= 100. cor2D /= 100. numpy.save(pathToSave+'cor_mean_1D', cor1D) numpy.save(pathToSave+'cor_mean_2D', cor2D) return
def getResults():
"""
Set the array of results and of data
"""
for i in numpy.arange(ni__):
for j in numpy.arange(nj__):
idx = i * nj__ + j
pathData__ = (
"/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Box_00"
+ str(i)
+ "/Simu_00"
+ str(j)
+ "/Results/BaoFit_q_f/bao"
+ type__
)
path__ = (
"/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Box_00"
+ str(i)
+ "/Simu_00"
+ str(j)
+ "/Results/BaoFit_q_f/bao"
+ type__
)
pathOtherFit__ = (
"/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Box_00"
+ str(i)
+ "/Simu_00"
+ str(j)
+ "/Results/BaoFit_q_f/bao"
+ type__
)
name2 = ".data"
name1 = ".residuals.dat"
name3 = ".save.pars"
name4 = ".fit.chisq"
name5 = ".save.pcov"
### Get data and fit
data = numpy.loadtxt(path__ + name1)
save0 = data[:, 0].astype(int)
save4 = data[:, 4]
save5 = data[:, 5]
save7 = data[:, 7]
save9 = data[:, 9]
xxxAll__[:, idx][save0] = save4
muuAll__[:, idx][save0] = save5
xi_fitAll__[:, idx][save0] = save7
xi_errAll__[:, idx][save0] = save9
### Get only data
data = numpy.loadtxt(pathData__ + name2)
save0 = data[:, 0].astype(int)
save1 = data[:, 1]
xi_datAll__[:, idx][save0] = save1
### Get the parameters of the fit
data = numpy.loadtxt(path__ + name3)
param__[:, 0, idx] = data[:, 1]
param__[:, 1, idx] = data[:, 2]
### Get the chi2 of the fit
data = numpy.loadtxt(path__ + name4)
chi2__[:, idx] = data
if otherFit__:
data = numpy.loadtxt(pathOtherFit__ + name4)
chi2OtherFit__[:, idx] = data
### Get correlation parameters
data = numpy.loadtxt(path__ + name5)
for el in data:
covar__[el[0], el[1], idx] = el[2]
numpy.save("param", param__)
mean = numpy.average(param__[9, 0, :], weights=numpy.power(param__[9, 1, :], -2.0))
err = numpy.power(numpy.sum(numpy.power(param__[9, 1, :], -2.0)), -0.5)
print " alpha_parallel = ", mean, " +/- ", err
mean = numpy.average(param__[10, 0, :], weights=numpy.power(param__[10, 1, :], -2.0))
err = numpy.power(numpy.sum(numpy.power(param__[10, 1, :], -2.0)), -0.5)
print " alpha_perp = ", mean, " +/- ", err
mean = numpy.mean(param__[9, 0, :])
err = numpy.sqrt(numpy.var(param__[9, 0, :]) / (ni__ * nj__))
print " alpha_parallel = ", mean, " +/- ", err
mean = numpy.mean(param__[10, 0, :])
err = numpy.sqrt(numpy.var(param__[10, 0, :]) / (ni__ * nj__))
print " alpha_perp = ", mean, " +/- ", err
### Pass from covariance matrix to correlation matrix
for i in numpy.arange(nbMocks__).astype(int):
covar__[:, :, i] = myTools.getCorrelationMatrix(covar__[:, :, i])
### Get the mean of all correlation-functions
xi_resAll__[:] = (xi_datAll__ - xi_fitAll__) / xi_errAll__
xxx__[:] = numpy.mean(xxxAll__, axis=1)[:]
muu__[:] = numpy.mean(muuAll__, axis=1)[:]
xi_dat__[:] = numpy.mean(xi_datAll__, axis=1)[:]
xi_fit__[:] = numpy.mean(xi_fitAll__, axis=1)[:]
xi_err__[:] = numpy.mean(xi_errAll__, axis=1)[:] / numpy.sqrt(nbMocks__)
xi_res__[:] = numpy.mean((xi_datAll__ - xi_fitAll__) / xi_errAll__, axis=1)[:]
return
nbBinY2D__ = 2*nbBin1D__
nbBin2D__ = nbBinX2D__*nbBinY2D__
### Mu
nbBinM__ = 25;
path1__ = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/Correlation_test2/'
i = 0
j = 0
cov = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Box_00'+str(i)+'/Simu_00'+str(j)+'/Results_RandomPosInCell/subSampling_LYA_QSO_cov_2D.npy')
### If correlation matrix from another matrix
cor = myTools.getCorrelationMatrix(numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_cov_2D_meanSubSampling.npy'))
cov = myTools.getCovarianceMatrix(cor,numpy.diag(cov))
#myTools.plot2D(cov)
#myTools.plot2D(cor)
print cor.flatten()[ cor.flatten()<0.99 ].size
plt.hist(cor.flatten()[ cor.flatten()<0.99 ], bins=10000, histtype='step', label='<corr each simu> ~ 8000 realisation')
cor = myTools.getCorrelationMatrix(numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Box_000/Simu_000/Results_RandomPosInCell/subSampling_LYA_QSO_cov_2D.npy'))
plt.hist(cor.flatten()[ cor.flatten()<0.99 ], bins=10000, histtype='step', label='Simu 0 0 = 80 realisation')
cor = myTools.getCorrelationMatrix(numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_cov_2D.npy'))
plt.hist(cor.flatten()[ cor.flatten()<0.99 ], bins=10000, histtype='step', label='100 realisation')
cor = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_cor_2D_allSubSampling.npy')
print data.size
print data[0].size
print data
data[ data==0. ] = numpy.float('nan')
myTools.plot2D(data)
print ' is diag <0. : ', numpy.diag(data)[ numpy.diag(data)<0. ].size
print ' is diag ==0. : ', numpy.diag(data)[ numpy.diag(data)==0. ].size
cor = myTools.getCorrelationMatrix(data)
print cor
print cor[ cor==1. ].size
print cor[ cor>1. ].size
print cor[ cor==0. ].size
#cor[ cor==0. ] = numpy.float('nan')
#cor[ cor==1. ] = numpy.float('nan')
#myTools.plot2D(cor)
plt.hist( numpy.diag(data), bins=100 )
plt.show()
plt.hist( cor[ numpy.logical_and( cor!=0., cor!=1.) ], bins=100 )
plt.show()
plt.title(r'$1D: \, \delta_{LYA} \, - \, \delta_{LYA} $', fontsize=40)
plt.xlabel(r'$s \, [h^{-1}.Mpc]$', fontsize=40)
plt.ylabel(r'$\xi (s)$', fontsize=40)
myTools.deal_with_plot(False,False,True)
plt.xlim([ numpy.min(xi1D[:,0])-10., numpy.max(xi1D[:,0])+10. ])
plt.xlim([ 4., numpy.max(xi1D[:,0])+10. ])
plt.show()
cov1D = numpy.cov(numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results/xi_1D_delta_delta_LYA_result_cov.npy'))
myTools.plot2D(cov1D)
myTools.plot2D(myTools.getCorrelationMatrix(cov1D))