def set_mean_data(self): raw_path = self._path_to_simu + 'Results/' ### we path = raw_path + 'list_We.npy' listWe = numpy.load(path) nbTot = listWe[0,0,:].size for i in numpy.arange(self._nb_wedges): self._mean_data._xiWe[:,i,1] = numpy.mean(listWe[:,i,:],axis=1) self._mean_data._xiWe[:,i,2] = numpy.sqrt( numpy.diag(numpy.cov( listWe[:,i,:] ))/nbTot) ### mu path = raw_path + 'cov_Mu.npy' self._mean_data._xiMu[:,:,2] = myTools.convert1DTo2D(numpy.mean(numpy.load(raw_path + 'list_Mu.npy'),axis=1),self._nbBin1D,self._nbBinM) self._mean_data._xiMu[:,:,3] = myTools.convert1DTo2D(numpy.sqrt( numpy.diag(numpy.load(path))/nbTot ),self._nbBin1D,self._nbBinM) ### 1D path = raw_path + 'cov_1D.npy' self._mean_data._xi1D[:,1] = numpy.mean(numpy.load(raw_path + 'list_1D.npy'),axis=1) self._mean_data._xi1D[:,2] = numpy.sqrt( numpy.diag(numpy.load(path))/nbTot ) ### 2D path = raw_path + 'cov_2D.npy' self._mean_data._xi2D[:,:,1] = myTools.convert1DTo2D(numpy.mean(numpy.load(raw_path + 'list_2D.npy'),axis=1),self._nbBinX2D, self._nbBinY2D) self._mean_data._xi2D[:,:,2] = myTools.convert1DTo2D(numpy.sqrt( numpy.diag(numpy.load(path))/nbTot ),self._nbBinX2D, self._nbBinY2D) ### Multipol path = raw_path + 'list_Multipol.npy' listMultipol = numpy.load(path) for i in numpy.arange(5): self._mean_data._xiMul[:,i,1] = numpy.mean(listMultipol[:,i,:],axis=1) self._mean_data._xiMul[:,i,2] = numpy.sqrt( numpy.diag(numpy.cov( listMultipol[:,i,:] ))/nbTot) return
def plotCorrelationFunction():
####################################################################
### Simu
###
for i in range(0, nbMocks__):
plt.errorbar(numpy.arange(nbBin__), xi_dat__[:, i], fmt="o")
plt.xlabel(r"$index \, bin$")
plt.ylabel(r"$\xi_{Simu}(s)$")
myTools.deal_with_plot(False, False, True)
plt.show()
### mean:
meanXi2D = numpy.mean(xi_dat__, axis=1)
plt.errorbar(numpy.arange(nbBin__), meanXi2D, fmt="o")
plt.xlabel(r"$index \, bin$")
plt.ylabel(r"$<\xi_{Simu}(s)>$")
myTools.deal_with_plot(False, False, True)
plt.show()
### 2D:
meanXi2D = myTools.convert1DTo2D(meanXi2D, nbBinY2D__, nbBinX2D__)
myTools.plot2D(
meanXi2D,
[minX2D__, maxX2D__, minY2D__, maxY2D__],
"s_{\perp} \, [h^{-1} Mpc]",
"s_{\parallel} \, [h^{-1} Mpc]",
"\\xi_{Simu}^{qf}",
)
####################################################################
### Fit
###
for i in range(0, nbMocks__):
plt.errorbar(numpy.arange(nbBin__), xi_fit__[:, i], fmt="o")
plt.xlabel(r"$index \, bin$")
plt.ylabel(r"$\xi_{Fit}(s)$")
myTools.deal_with_plot(False, False, True)
plt.show()
### mean:
meanXi2D = numpy.mean(xi_fit__, axis=1)
plt.errorbar(numpy.arange(nbBin__), meanXi2D, fmt="o")
plt.xlabel(r"$index \, bin$")
plt.ylabel(r"$<\xi_{Fit}(s)>$")
myTools.deal_with_plot(False, False, True)
plt.show()
### 2D:
meanXi2D = myTools.convert1DTo2D(meanXi2D, nbBinY2D__, nbBinX2D__)
myTools.plot2D(
meanXi2D,
[minX2D__, maxX2D__, minY2D__, maxY2D__],
"s_{\perp} \, [h^{-1} Mpc]",
"s_{\parallel} \, [h^{-1} Mpc]",
"\\xi_{Fit}^{qf}",
)
def plotDataAndFit_2D():
"""
"""
### but the xi from a flat array to a 2D array
xxx = myTools.convert1DTo2D(xxx__, nbBinY2D__, nbBinX2D__)
yyy_dat = myTools.convert1DTo2D(xi_dat__, nbBinY2D__, nbBinX2D__)
yyy_fit = myTools.convert1DTo2D(xi_fit__, nbBinY2D__, nbBinX2D__)
yyy_err = myTools.convert1DTo2D(xi_err__, nbBinY2D__, nbBinX2D__)
yyy_res = myTools.convert1DTo2D(xi_res__, nbBinY2D__, nbBinX2D__)
yyy_dat[(yyy_fit == 0.0)] = float("nan")
yyy_err[(yyy_fit == 0.0)] = float("nan")
yyy_res[(yyy_fit == 0.0)] = float("nan")
yyy_fit[(yyy_fit == 0.0)] = float("nan")
edge = [0.0, 200.0, -200.0, 200.0]
### Plot the arrays
for i in numpy.arange(0, 3):
coef = numpy.power(xxx, 1.0 * i)
a = ""
if i == 1:
a += "|s|."
elif i == 2:
a += "|s|^{2}."
### data
myTools.plot2D(
coef * yyy_dat,
edge,
"s_{\\perp} \\, [h^{-1} Mpc]",
"s_{\parallel} \\, [h^{-1} Mpc]",
a + "\\xi_{Simu}(\\, \\overrightarrow{s} \\,)",
"Simu",
)
### Fit
myTools.plot2D(
coef * yyy_fit,
edge,
"s_{\\perp} \\, [h^{-1} Mpc]",
"s_{\parallel} \\, [h^{-1} Mpc]",
a + "\\xi_{Fit}(\\, \\overrightarrow{s} \\,)",
"Fit",
)
### residuals
myTools.plot2D(
coef * yyy_res,
edge,
"s_{\\perp} \\, [h^{-1} Mpc]",
"s_{\parallel} \\, [h^{-1} Mpc]",
a + "(\\xi_{Simu}-\\xi_{Fit})/\\xi_{error \, Simu}",
"residuals",
)
return
def replaceValueByMean(): ''' Replace the values of the correlation by the one of the mean ''' rawPath = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_NicolasDistortionWithDistortion/xi_delta_QSO_result_' list1D = numpy.load(rawPath+'1D.npy') list2D = numpy.load(rawPath+'2D.npy') listMu = numpy.load(rawPath+'Mu.npy') listWe = numpy.load(rawPath+'We.npy') listMultipol = numpy.load(rawPath+'Multipol.npy') cov1D = numpy.load(rawPath+'cov_1D.npy') cov2D = numpy.load(rawPath+'cov_2D.npy') covMu = numpy.load(rawPath+'cov_Mu.npy') nbReal = list1D[0,:].size xi1D_[:,1] = numpy.mean(list1D,axis=1) xi1D_[:,2] = numpy.sqrt(numpy.diag(cov1D))/numpy.sqrt(nbReal) xi2D_[:,:,1] = myTools.convert1DTo2D( numpy.mean(list2D,axis=1), nbBinX2D__,nbBinY2D__) xi2D_[:,:,2] = myTools.convert1DTo2D( numpy.sqrt(numpy.diag(cov2D))/numpy.sqrt(nbReal), nbBinX2D__,nbBinY2D__) xiMu_[:,:,2] = myTools.convert1DTo2D( numpy.mean(listMu,axis=1), nbBin1D__,nbBinM__) xiMu_[:,:,3] = myTools.convert1DTo2D( numpy.sqrt(numpy.diag(covMu))/numpy.sqrt(nbReal), nbBin1D__,nbBinM__) result_Multipol_[:,:,1] = numpy.mean(listMultipol,axis=2) result_Multipol_[:,:,2] = numpy.var(listMultipol,axis=2)/numpy.sqrt(nbReal) xiWe_[:,:,1] = numpy.mean(listWe,axis=2) xiWe_[:,:,2] = numpy.var(listWe,axis=2)/numpy.sqrt(nbReal) return
xi1D_2 = numpy.dot(data,xi1D)
plt.errorbar(numpy.arange(nbPixel),xi1D,fmt='o',label='Before correction')
plt.errorbar(numpy.arange(nbPixel),xi1D_2,fmt='o',label='After correction')
myTools.deal_with_plot(False,False,True)
plt.show()
if (nbPixel==nbBin1D__):
xi1D_[:,1] = xi1D_2
plotXi(0)
plotXi(1)
plotXi(2)
pathToCamb = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/CAMB_2_4/xi-z2.4.dat'
fitCamb(xi1D_,pathToCamb,0)
else:
xi2D_[:,:,1] = myTools.convert1DTo2D(xi1D_2,50,100)
plotXi2D(0)
plotXi2D(1)
plotXi2D(2)
#path__ = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/Tests_test_PDFMocksJMC_meanLambda_testNoCap/'
#path = path__ + 'xi_delta_QSO_distortionMatrix_1D_LYA_QSO.txt'
#path = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/Tests_test_PDFMocksJMC_meanLambda_testNoCap//xi_delta_QSO_distortionMatrix_1D_LYA_QSO.txt'
#print path
#data = numpy.loadtxt(path)
#numpy.save('/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/Tests_test_PDFMocksJMC_meanLambda_testNoCap//xi_delta_QSO_distortionMatrix_1D_LYA_QSO',data)
def prepareForBAOFIT():
### Constants
param = numpy.asarray( [1.6,-0.336,0.9,0.,0.,3.25,0.962524,3.26,1.966,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.] )
doBootstraps = False
nbRegions = 80
forest = 'LYA'
QSO = 'QSO'
'''
### Create the .ini file (for simulation)
i = box__
j = simul__
#param = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Code/Python/Annalyse_BAOFIT/param.npy')
#param = param[:,0,i*10+j]
rowPath = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Box_00'+str(i)+'/Simu_00'+str(j)+'/Results/'
path = rowPath + '/xi_delta_QSO_'
cov = numpy.load(rowPath + '/subSampling_LYA_QSO_cov_2D.npy')
cor = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/noNoisenoCont/Results/xi_delta_QSO_result_cor_meanFromFit_2D.npy')
inputFile = rowPath + 'xi_delta_QSO_2D_LYA_QSO.txt'
pathToOutFit = rowPath + 'BaoFit_q_f/'
pathToData = rowPath + 'BaoFit_q_f/'
pathToIni = rowPath + 'BaoFit_q_f/'
createIni2(inputFile, pathToOutFit, pathToData,pathToIni,'2D',param)
'''
### For data
rowPath = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_nicolasEstimator_method1/'
path = rowPath + '/xi_delta_QS0_'
cov = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy/subSampling_'+forest+'_'+QSO+'_cov_2D.npy')
cor = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_cor_2D_allSubSamplingFromFit.npy')
inputFile = rowPath + 'xi_delta_QSO_2D_'+forest+'_'+QSO+'.txt'
pathToDistortionMatrix = rowPath + '/xi_delta_QSO_distortionMatrix_2D_'+forest+'_'+QSO+'.txt'
pathToOutFit = rowPath + 'BaoFit_q_f__'+forest+'__'+QSO+'/'
pathToData = rowPath + 'BaoFit_q_f__'+forest+'__'+QSO+'/'
pathToIni = rowPath + 'BaoFit_q_f__'+forest+'__'+QSO+'/'
if (doBootstraps):
pathToOutFit += 'Bootstraps/boot_'+str(wickIdx__).zfill(4)+'/'
pathToData += 'Bootstraps/boot_'+str(wickIdx__).zfill(4)+'/'
pathToIni += 'Bootstraps/boot_'+str(wickIdx__).zfill(4)+'/'
subprocess.call('mkdir ' + pathToIni, shell=True)
#subprocess.call('cp '+rowPath+'/BaoFit_q_f/bao2D.grid ' + pathToIni, shell=True)
### Set the seed
numpy.random.seed(seed=42)
array = numpy.random.choice(numpy.arange(1000000).astype(int), size=10000, replace=False)
numpy.random.seed(seed=array[wickIdx__])
randomList = numpy.random.randint( 0, high=nbRegions, size=nbRegions)
print array[wickIdx__]
subsampling = numpy.load(rowPath+'/subsampling_LYA_QSO_2D.npy')
bootstrap = numpy.zeros( shape=(nbBin2D__,nbRegions) )
xi2D1D = numpy.zeros(nbBin2D__)
for i in randomList:
bootstrap[:,i] = subsampling[:,i]
xi2D1D = numpy.mean( bootstrap, axis=1)
cov = numpy.cov(bootstrap)/nbRegions
xi2D_ = numpy.zeros(shape=(nbBinX2D__,nbBinY2D__,3))
xi2D_[:,:,1] = myTools.convert1DTo2D(xi2D1D,nbBinX2D__,nbBinY2D__)
else:
### Get the correlation (data)
xi1D_, xi2D_, xiMu_, xiWe_ = loadData('',inputFile,2)
### Create the 'bao2D.grid' file
createIni2(inputFile, pathToOutFit, pathToData,pathToIni,'2D',param)
'''
### Correlation
correlation = numpy.zeros( shape=(nbBin2D__,2) )
indexMatrix = numpy.arange(nbBin2D__)
correlation[:,0] = (indexMatrix%nbBinY2D__)*nbBinX2D__ + indexMatrix/nbBinY2D__
correlation[:,1] = xi2D_[:,:,1].flatten()
cutCorrelation = (correlation[:,1]!=0.)
numpy.savetxt( pathToData + '/bao2D.data',zip(correlation[:,0][cutCorrelation],correlation[:,1][cutCorrelation]),fmt='%u %1.20e')
### Covariance matrix
cov = myTools.getCovarianceMatrix(cor,numpy.diag(cov))
covarianceMatrix = numpy.zeros( shape=(nbBin2D__*nbBin2D__,3) )
indexMatrix1 = numpy.arange(nbBin2D__*nbBin2D__).reshape(nbBin2D__,nbBin2D__)/nbBin2D__
indexMatrix2 = numpy.arange(nbBin2D__*nbBin2D__).reshape(nbBin2D__,nbBin2D__)%nbBin2D__
indexMatrix1 = (indexMatrix1%nbBinY2D__)*nbBinX2D__ + indexMatrix1/nbBinY2D__
indexMatrix2 = (indexMatrix2%nbBinY2D__)*nbBinX2D__ + indexMatrix2/nbBinY2D__
covarianceMatrix[:,0] = numpy.triu(indexMatrix1,k=0).flatten()
covarianceMatrix[:,1] = numpy.triu(indexMatrix2,k=0).flatten()
covarianceMatrix[:,2] = numpy.triu(cov,k=0).flatten()
cutCovarMatrix = (covarianceMatrix[:,2]!=0.)
if (covarianceMatrix[:,2][cutCovarMatrix].size != int(nbBin2D__*(nbBin2D__+1.)/2.) ):
print ' xi_delta_QSO.py::prepareForBAOFIT() size of covariance matrix is incorrect'
print ' size covariance matrix', cutCovarMatrix[:,2][cutCovarMatrix].size
print ' size it should have', nbBin2D__*(nbBin2D__+1.)/2.
return
numpy.savetxt( pathToData + '/bao2D.cov',zip(covarianceMatrix[:,0][cutCovarMatrix],covarianceMatrix[:,1][cutCovarMatrix],covarianceMatrix[:,2][cutCovarMatrix]),fmt='%u %u %1.20e')
### Distortion matrix
dmatData = numpy.loadtxt(pathToDistortionMatrix)
distortionMatrix = numpy.zeros( shape=(nbBin2D__*nbBin2D__,3) )
distortionMatrix[:,0] = indexMatrix1.flatten()
distortionMatrix[:,1] = indexMatrix2.flatten()
distortionMatrix[:,2] = dmatData.flatten()
cutDistortionMatrix = (distortionMatrix[:,2]!=0.)
numpy.savetxt( pathToData + '/bao2D.dmat',zip(distortionMatrix[:,0][cutDistortionMatrix], distortionMatrix[:,1][cutDistortionMatrix], distortionMatrix[:,2][cutDistortionMatrix]),fmt='%u %u %1.20e')
'''
### Send the fit
command = '/home/gpfs/manip/mnt0607/bao/hdumasde/Program/deepzot/bin/baofit -i ' + pathToIni + 'bao2D.ini' #--parameter-scan' ### --toymc-samples 10000
print command
subprocess.call(command, shell=True)
if (doBootstraps):
subprocess.call('rm '+ pathToData + '/bao2D.ini', shell=True)
subprocess.call('rm '+ pathToData + '/bao2D.grid', shell=True)
subprocess.call('rm '+ pathToData + '/bao2D.data', shell=True)
subprocess.call('rm '+ pathToData + '/bao2D.cov', shell=True)
return
