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 plot_distortion_matrix(self):
path = self._path_to_txt_file_folder + self._prefix + '_distortionMatrix_'+ self._middlefix + '.txt'
matrix = numpy.loadtxt(path)
### Blind the zero terms
matrix[ (matrix==0.) ] = numpy.float('nan')
myTools.plot2D(matrix)
#myTools.plotCovar([matrix],['Distortion matrix'])
return
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 plotDataAndFit_2D():
'''
'''
### but the xi from a flat array to a 2D array
xxx = numpy.zeros( shape=(100,50) )
yyy_dat = numpy.zeros( shape=(100,50) )
yyy_fit = numpy.zeros( shape=(100,50) )
yyy_err = numpy.zeros( shape=(100,50) )
for i in range(0,nbBin__):
xxx[i/50][i%50] = xxx__[i]
yyy_dat[i/50][i%50] = xi_dat__[i]
yyy_fit[i/50][i%50] = xi_fit__[i]
yyy_err[i/50][i%50] = xi_err__[i]
yyy_dat[ (yyy_fit==0.) ] = float('nan')
yyy_err[ (yyy_fit==0.) ] = float('nan')
yyy_fit[ (yyy_fit==0.) ] = float('nan')
edge = [0., 200., -200., 200.]
### Plot the arrays
for i in numpy.arange(0,3):
coef = numpy.power(xxx,1.*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(\\, \\overrightarrow{s} \\,)', 'data')
### Fit
myTools.plot2D(coef*yyy_fit, edge, 's_{\\perp} \\, [h^{-1} Mpc]', 's_{\parallel} \\, [h^{-1} Mpc]', a+'\\xi(\\, \\overrightarrow{s} \\,)', 'fit')
### residuals
myTools.plot2D(coef*(yyy_dat-yyy_fit)/yyy_err, edge, 's_{\\perp} \\, [h^{-1} Mpc]', 's_{\parallel} \\, [h^{-1} Mpc]', a+'\\xi_{data}-\\xi_{fit}', 'residuals')
return
def plot_cat():
path = '/home/gpfs/manip/mnt0607/bao/Spectra/DR14Q_v1_0.fits'
cat = pyfits.open(path, memmap=True)[1].data
print " The size of the catalogue is : " + str(cat.size)
cat = cat[ cat['RA']!=0. ]
#print ' removed ra==0 : size = ', cat.size
#cat = cat[ cat["Z"]>minRedshift__]
#print " We keep Z > " + str(minRedshift__) + " , the size is : " + str(cat.size)
#cat = cat[ cat["Z"]<=maxRedshift__]
#print " We keep Z <= " + str(maxRedshift__) + " , the size is : " + str(cat.size)
#cat = cat[ cat['MJD']>=55000]
#print " We keep MJD>=55000 , the size is : " + str(cat.size)
'''
cut = [ [2.,2.5], [2.5,3.], [3.,3.5], [3.5,4.], [4.,4.5], [4.5,5.],[5.,5.96] ]
for el in cut:
a = cat[ numpy.logical_and( cat['Z']>=el[0],cat['Z']<el[1] ) ]
print el[0], el[1], a.size
'''
print cat.size
gal = cat['GAL_EXT'][:,0]
psf = cat['PSFMAG'][:,0]
### redshift
ar1 = cat['RA']
ar2 = cat['DEC']
ar3 = cat['Z']
nbBinsX = numpy.arange(0,360+5,5)
nbBinsY = numpy.arange(-90,90+5,5)
we = numpy.ones(ar1.size)
mean, err, number = myTools.Get_2DTProfile(ar1, ar2, ar3, nbBinsX, nbBinsY,we)
mean[mean==0.] = numpy.float('nan')
mean = numpy.transpose(mean)
myTools.plot2D(mean)
number[number==0.] = numpy.float('nan')
number = numpy.transpose(number)
myTools.plot2D(number)
### gal extinction
ar1 = cat['RA'][gal>0.]
ar2 = cat['DEC'][gal>0.]
ar3 = gal[gal>0.]
nbBinsX = numpy.arange(0,360+5,5)
nbBinsY = numpy.arange(-90,90+5,5)
we = numpy.ones(ar1.size)
mean, err, number = myTools.Get_2DTProfile(ar1, ar2, ar3, nbBinsX, nbBinsY,we)
mean[mean==0.] = numpy.float('nan')
mean = numpy.transpose(mean)
myTools.plot2D(mean)
number[number==0.] = numpy.float('nan')
number = numpy.transpose(number)
myTools.plot2D(number)
### psf
ar1 = cat['RA'][psf>0.]
ar2 = cat['DEC'][psf>0.]
ar3 = psf[psf>0.]
nbBinsX = numpy.arange(0,360+5,5)
nbBinsY = numpy.arange(-90,90+5,5)
we = numpy.ones(ar1.size)
mean, err, number = myTools.Get_2DTProfile(ar1, ar2, ar3, nbBinsX, nbBinsY,we)
mean[mean==0.] = numpy.float('nan')
mean = numpy.transpose(mean)
myTools.plot2D(mean)
number[number==0.] = numpy.float('nan')
number = numpy.transpose(number)
myTools.plot2D(number)
### Geometry
plt.errorbar(cat['RA'],cat['DEC'],fmt='o')
plt.xlabel('RA')
plt.ylabel('DEC')
plt.show()
### redshift
plt.hist(cat['Z'],bins=100)
plt.xlabel('Z')
plt.show()
### other
plt.hist(gal[gal>0.],bins=100)
plt.show()
### other
plt.hist(psf[psf>0.],bins=100)
plt.show()
### Geometry
plt.errorbar(cat['Z'][gal>0.],gal[gal>0.],fmt='o',alpha=0.3)
plt.show()
### Geometry
plt.errorbar(cat['Z'][psf>0.],psf[psf>0.],fmt='o',alpha=0.3)
plt.show()
return
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.show()
return b1b2
path = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy/xi_delta_QSO_distortionMatrix_2D_LYA_QSO.txt'
print path
data = numpy.loadtxt(path)
print data
print numpy.diag(data)
myTools.plot2D(data)
a = myTools.plotCovar([data],['a'])
#nbPixel = nbBin1D__
nbPixel = nbBin2D__
'''
#### Matrix from Nicolas
data = numpy.loadtxt("/home/gpfs/manip/mnt0607/bao/hdumasde/Code/CrossCorrelation/chain_annalys_delta/Correlation/run/xcf_v5_8_guy_c2_baseline.dmat")
distortionMatrix = numpy.zeros( shape=(nbBin2D__,nbBin2D__) )
save0 = data[:,0].astype(int)
save1 = data[:,1].astype(int)
save2 = data[:,2]
size = save2.size
plt.title(r'$1D: \, \delta_{LYA} \, - \, \delta_{LYA} $', fontsize=40)
plt.xlabel(r'$s \, [h^{-1}.Mpc]$', fontsize=40)
plt.ylabel(r'$\xi (s)$', fontsize=40)
myTools.deal_with_plot(False,False,True)
plt.xlim([ numpy.min(xi1D[:,0])-10., numpy.max(xi1D[:,0])+10. ])
plt.xlim([ 4., numpy.max(xi1D[:,0])+10. ])
plt.show()
cov1D = numpy.cov(numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results/xi_1D_delta_delta_LYA_result_cov.npy'))
myTools.plot2D(cov1D)
myTools.plot2D(myTools.getCorrelationMatrix(cov1D))
plt.title(r'$\delta_{'+forest__+'} \, - \, \delta_{'+forest__+'}$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
myTools.deal_with_plot(False,False,True)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.show()
ni = 10
nj = 10
saveListRealMocks(ni,nj)
'''
cov = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results/xi_A_delta_delta_result_cov_1D.npy')
myTools.plot2D(cov)
myTools.plot2D(myTools.getCorrelationMatrix(cov))
myTools.plotCovar([cov],['a'])
cov = numpy.load('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results/xi_A_delta_delta_result_cov_2D.npy')
myTools.plot2D(cov)
myTools.plot2D(myTools.getCorrelationMatrix(cov))
myTools.plotCovar([cov],['a'],50,50)
'''
#xi1D_, xi2D_, xiMu_, xiWe_ = loadData('/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_primery_correctedBadFit3/xi_A_delta_delta_Mu_LYA.txt','/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_primery_correctedBadFit3/xi_A_delta_delta_2D_LYA.txt')
#xi1D_, xi2D_, xiMu_, xiWe_ = loadData('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Box_000/Simu_000/Results/xi_A_delta_delta_Mu_LYA.txt','/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Box_000/Simu_000/Results/xi_A_delta_delta_2D_LYA.txt')
plt.plot( x[cut]/1215.67-1., numpy.ones(x[cut].size)*0.133177484547 )
plt.show()
###
for i in range(5):
y = corr[:,i]
x = numpy.arange(y.size)
cut = y!=0.
plt.plot( x[cut], y[cut] )
plt.show()
###
#corr[ (numpy.abs(corr)>0.015)] = numpy.float('nan')
corr[ corr==0. ] = numpy.float('nan')
myTools.plot2D(corr)
def getACorrelation(nd,nr,nbRand,rawPath): ''' Given a path to DD, DR, RR, return all the correlations. nd : number of data nr : number of random nbRand : number of realisation of random rawPath : path to ASCII files ''' ### Which estimator LS = True ### Plot or not plot = False print rawPath ### Coef to normalize coefDD = nd*(nd-1.)/2. if (LS): coefDR = nd*nr coefRR = nr*(nr-1.)/2. ### 2D: DD dd_2D = loadData2D(rawPath + 'xi_QSO_QSO_2D_QSO_DD.txt',coefDD) ### Mu: DD dd_1D, dd_Mu, dd_We = loadDataMu(rawPath + 'xi_QSO_QSO_Mu_QSO_DD.txt',coefDD) ### 2D: RR,DR rr_2D = loadData2D(rawPath + 'xi_QSO_QSO_2D_QSO_RR_0.txt',coefRR) if (LS): dr_2D = loadData2D(rawPath + 'xi_QSO_QSO_2D_QSO_DR_0.txt',coefDR) ### Mu: RR,DR rr_1D, rr_Mu, rr_We = loadDataMu(rawPath + 'xi_QSO_QSO_Mu_QSO_RR_0.txt',coefRR) if (LS): dr_1D, dr_Mu, dr_We = loadDataMu(rawPath + 'xi_QSO_QSO_Mu_QSO_DR_0.txt',coefDR) for i in range(1,nbRand): ### 2D: rr_2D += loadData2D(rawPath + 'xi_QSO_QSO_2D_QSO_RR_'+str(i)+'.txt',coefRR) if (LS): dr_2D += loadData2D(rawPath + 'xi_QSO_QSO_2D_QSO_DR_'+str(i)+'.txt',coefDR) ### Mu: tmp_rr_1D, tmp_rr_Mu, tmp_rr_We = loadDataMu(rawPath + 'xi_QSO_QSO_Mu_QSO_RR_'+str(i)+'.txt',coefRR) rr_1D += tmp_rr_1D rr_Mu += tmp_rr_Mu rr_We += tmp_rr_We if (LS): tmp_dr_1D, tmp_dr_Mu, tmp_dr_We = loadDataMu(rawPath + 'xi_QSO_QSO_Mu_QSO_DR_'+str(i)+'.txt',coefDR) dr_1D += tmp_dr_1D dr_Mu += tmp_dr_Mu dr_We += tmp_dr_We ### 2D: rr_2D /= nbRand if (LS): dr_2D /= nbRand ### Mu: rr_1D /= nbRand if (LS): dr_1D /= nbRand rr_Mu /= nbRand if (LS): dr_Mu /= nbRand rr_We /= nbRand if (LS): dr_We /= nbRand if (plot): ### If plot plt.errorbar(rr_1D[:,0],rr_1D[:,1],fmt='o', label='RR') plt.errorbar(dr_1D[:,0],dr_1D[:,1],fmt='o', label='DR') plt.errorbar(dd_1D[:,0],dd_1D[:,1],fmt='o', label='DD') myTools.deal_with_plot(False,False,True) plt.show() ### Plot 1D plt.errorbar(dd_1D[:,0], (dd_1D[:,0]-2.*dr_1D[:,0]+rr_1D[:,0])/dd_1D[:,0] ) myTools.deal_with_plot(False,False,True) plt.show() ### Plot 2D myTools.plot2D( (dd_2D[:,:,0]-2.*dr_2D[:,:,0]+rr_2D[:,:,0])/dd_2D[:,:,0] ) ### Plot 2D myTools.plot2D( (dd_Mu[:,:,0]-2.*dr_Mu[:,:,0]+rr_Mu[:,:,0])/dd_Mu[:,:,0] ) ### Get the Landy-Saley estimator if (LS): ### 1D: result_1D = numpy.array( dd_1D ) result_1D[:,1] = (dd_1D[:,1]-2.*dr_1D[:,1]+rr_1D[:,1])/rr_1D[:,1] result_1D[:,2] = numpy.sqrt(dd_1D[:,1]*coefDD)/(coefDD*rr_1D[:,1]) cut = (dd_1D[:,1]==0.) result_1D[:,1][cut] = 0. result_1D[:,2][cut] = 0. ### 2D: result_2D = numpy.array( dd_2D ) result_2D[:,:,1] = (dd_2D[:,:,1]-2.*dr_2D[:,:,1]+rr_2D[:,:,1])/rr_2D[:,:,1] cut = (dd_2D[:,:,1]==0.) result_2D[:,:,1][cut] = 0. ### Mu: result_Mu = numpy.zeros(shape=(nbBin1D__,nbBinM__,3)) result_Mu[:,:,0] = dd_Mu[:,:,0] result_Mu[:,:,1] = (dd_Mu[:,:,1] -2.*dr_Mu[:,:,1] + rr_Mu[:,:,1])/rr_Mu[:,:,1] result_Mu[:,:,2] = numpy.sqrt(dd_Mu[:,:,1]*coefDD)/(coefDD*rr_Mu[:,:,1]) cut = (dd_Mu[:,:,1]==0.) result_Mu[:,:,2][cut] = 0. result_Mu[:,:,1][cut] = 0. ### xiWe result_We = numpy.zeros(shape=(nbBin1D__,3,3)) result_We[:,:,0] = dd_We[:,:,0] result_We[:,:,1] = (dd_We[:,:,1] -2.*dr_We[:,:,1] + rr_We[:,:,1])/rr_We[:,:,1] result_We[:,:,2] = numpy.sqrt(dd_We[:,:,1]*coefDD)/(coefDD*rr_We[:,:,1]) cut = (dd_We[:,:,1]==0.) result_We[:,:,2][cut] = 0. result_We[:,:,1][cut] = 0. ### Save numpy.save(rawPath+'xi_QSO_QSO_result_1D',result_1D) numpy.save(rawPath+'xi_QSO_QSO_result_2D',result_2D) numpy.save(rawPath+'xi_QSO_QSO_result_Mu',result_Mu) numpy.save(rawPath+'xi_QSO_QSO_result_We',result_We) result_Multipol = plotMultipol(result_Mu) numpy.save(rawPath+'xi_QSO_QSO_result_Multipol',result_Multipol) return result_1D, result_2D, result_Mu, result_We,result_Multipol
