def plotDataAndFit_S():
"""
Plot \\xi( |s| )
"""
binSize = 4.0
sss = numpy.arange(0.0, 50.0) * 4.0 + 2.0
yyy_dat = numpy.zeros(shape=(50, 2))
yyy_fit = numpy.zeros(shape=(50, 2))
cut = numpy.logical_and((xxx__ != 0.0), (xxx__ < 200.0))
xxx = xxx__[cut]
xi_dat = xi_dat__[cut]
xi_fit = xi_fit__[cut]
xi_err = 1.0 / (xi_err__[cut] ** 2.0)
for i in range(0, xxx.size):
sIdx = int(xxx[i] / binSize)
yyy_dat[sIdx][0] += xi_dat[i] * xi_err[i]
yyy_dat[sIdx][1] += xi_err[i]
yyy_fit[sIdx][0] += xi_fit[i] * xi_err[i]
yyy_fit[sIdx][1] += xi_err[i]
yyy_dat[:, 0] /= yyy_dat[:, 1]
yyy_dat[:, 1] = numpy.sqrt(1.0 / yyy_dat[:, 1])
yyy_fit[:, 0] /= yyy_fit[:, 1]
yyy_fit[:, 1] = numpy.sqrt(1.0 / yyy_fit[:, 1])
### Plot the results
for i in numpy.arange(0, 3):
a = ""
if i == 1:
a += "|s|."
elif i == 2:
a += "|s|^{2}."
coef = numpy.power(sss, 1.0 * i)
plt.errorbar(
sss,
coef * yyy_dat[:, 0],
yerr=coef * yyy_dat[:, 1],
linestyle="",
marker="o",
color="blue",
label=r"$<Simu>$",
)
plt.errorbar(sss, coef * yyy_fit[:, 0], color="red", label=r"$<Fit>$")
plt.xlabel(r"$|s| \, [h^{-1} Mpc]$")
plt.ylabel(r"$" + a + "\\xi(|s|)$")
myTools.deal_with_plot(False, False, True)
plt.show()
return
def compare_each_simu(): pathSimu = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v_second_generation/Box_000/Simu_000/Data/' rawPathSimu = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v_second_generation/' chunckNb = 10 simulNb = 10 ## Distribution redshift QSO nb_qso = numpy.zeros(chunckNb*simulNb) for i in range(0,chunckNb): for j in range(0,simulNb): catSimu = pyfits.open(rawPathSimu+'Box_00'+str(i)+'/Simu_00'+str(j)+'/Data/QSO_withRSD.fits',memmap=True)[1].data nb_qso[i*10+j] = catSimu.size print numpy.mean(nb_qso) ### plt.errorbar(numpy.arange(chunckNb*simulNb),nb_qso,fmt='o') plt.plot(numpy.arange(chunckNb*simulNb), numpy.ones(chunckNb*simulNb)*numpy.mean(nb_qso),color='red',label='Mean') plt.xlabel(r'$Mock \, index$', fontsize=40) plt.ylabel(r'$\# \, nb \, QSO$', fontsize=40) plt.xlim( [-1,chunckNb*simulNb] ) myTools.deal_with_plot(False,False,True) plt.show() ### plt.hist(nb_qso) plt.xlabel(r'$nb \, QSO$', fontsize=40) plt.ylabel(r'$\#$', fontsize=40) myTools.deal_with_plot(False,False,True) plt.show() return
def plot():
if (forest__=='LYA'):
lines = LYA_lines
names = LYA_lines_names
if (forest__=='CIV'):
lines = CIV_lines
names = CIV_lines_names
if (forest__=='MGII'):
lines = MGII_lines
names = MGII_lines_names
if (forest__=='LYB'):
lines = LYB_lines
names = LYB_lines_names
if (forest__=='SIIV'):
lines = SIIV_lines
names = SIIV_lines_names
if (forest__=='LYA_JMC'):
lines = numpy.array([])
names = numpy.array([])
path = path__ +'xi_1DlRF_delta_delta_'+forest__+'.txt'
print path
data = numpy.loadtxt(path)
xxx = data[:,1]
yyy = data[:,1]
yer = data[:,]
### remove empty pixels
xxx = data[:,2][ (data[:,5]!=0.) ]/data[:,4][ (data[:,5]!=0.) ]
yyy = data[:,0][ (data[:,5]!=0.) ]/data[:,4][ (data[:,5]!=0.) ]
yer = numpy.sqrt( (data[:,1][ (data[:,5]!=0.) ]/data[:,4][ (data[:,5]!=0.) ] -yyy**2.)/data[:,5][ (data[:,5]!=0.) ] )
plt.errorbar(xxx, yyy, yerr=yer, marker='o')
### Show lines in the correlation
xMax = numpy.amax(xxx)
yMin = numpy.amin(yyy)
yMax = numpy.amax(yyy)
nbLines = lines.size
for i in range(0,nbLines):
for j in range(0,i):
#if (names[i][:3]!=forest__ and names[j][:3]!=forest__): continue
line = abs(lines[i]-lines[j])
if (line==0. or line>xMax): continue
xLi = [line,line]
yLi = [yMin,yMax]
name = names[i]+' - '+names[j]
plt.plot(xLi,yLi,color='green')
plt.text(line, yMax, name, rotation='vertical', fontsize=20)
plt.title(r'$1D: \, \delta_{'+forest__+'} \, - \, \delta_{'+forest__+'} $', fontsize=40)
plt.xlabel(r'$\Delta \lambda_{R.F.} \, [\AA]$', fontsize=40)
plt.ylabel(r'$\xi(\Delta \lambda_{R.F.})$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.show()
return
def loadBootMap(): ''' ''' path = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/map.txt' data = numpy.loadtxt(path) re = data[:,0].astype(int) ra = data[:,1] de = data[:,2] for i in range(0,numpy.amax(re)+1): cut = (re==i) plt.plot(ra[cut], de[cut], linestyle="", marker="o") #plt.xlim([0,360.]) #plt.ylim([-90.,90.]) plt.ticklabel_format(style='sci', axis='z', scilimits=(0,0)) plt.grid() plt.xlabel(r'$R.A. (\degree)$') plt.ylabel(r'$Dec. (\degree)$') myTools.deal_with_plot(False,False,True) plt.show() return
def plotWe(rescale):
a = ['0.8 < |\mu|', '0.5 < |\mu| \leq 0.8', '|\mu| \leq 0.5']
for i in range(0,3):
###
cut = (xiWe_[:,i,2]!=0.)
if (xiWe_[:,i,0][cut].size==0):
continue
xxx = xiWe_[:,i,0][cut]
yyy = xiWe_[:,i,1][cut]
yer = xiWe_[:,i,2][cut]
coef = numpy.power(xxx,rescale)
plt.errorbar(xxx, coef*yyy, yerr=coef*yer, fmt='o', label=r'$'+a[i]+'$')
if (rescale==0):
plt.ylabel(r'$\xi^{qf} (|s|)$', fontsize=40)
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=4)
if (rescale==1):
plt.ylabel(r'$|s|.\xi^{qf} (|s|) \, [h^{-1}.Mpc]$', fontsize=40)
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=4)
if (rescale==2):
plt.ylabel(r'$|s|^{2}.\xi^{qf} (|s|) \, [(h^{-1}.Mpc)^{2}]$', fontsize=40)
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=2)
plt.title(r'$\delta_{'+forest1__+'} \, - \, '+qso1__+'$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.show()
def plotXi(rescale):
xxx = xi1D_[:,0]
yyy = xi1D_[:,1]
yer = xi1D_[:,2]
cut = (yer!=0.)
xxx = xxx[ cut ]
yyy = yyy[ cut ]
yer = yer[ cut ]
if (rescale==0):
plt.errorbar(xxx, yyy, yerr=yer, fmt='o')
plt.ylabel(r'$\xi^{qf} (|s|)$', fontsize=40)
if (rescale==1):
plt.errorbar(xxx, xxx*yyy, yerr=xxx*yer, fmt='o')
plt.ylabel(r'$|s|.\xi^{qf} (|s|) \, [h^{-1}.Mpc]$', fontsize=40)
if (rescale==2):
plt.errorbar(xxx, xxx*xxx*yyy, yerr=xxx*xxx*yer, fmt='o')
plt.ylabel(r'$|s|^{2}.\xi^{qf} (|s|) \, [(h^{-1}.Mpc)^{2}]$', fontsize=40)
plt.title(r'$\delta_{'+forest1__+'} \, - \, '+qso1__+'$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.show()
def plotXi():
xxx = xi1D_[:,0]
yyy = xi1D_[:,1]
yer = xi1D_[:,2]
cut = (yer!=0.)
xxx = xxx[ cut ]
yyy = yyy[ cut ]
yer = yer[ cut ]
yMin = numpy.min(yyy)
yMax = numpy.max(yyy)
nbLines = lines.size
for i in range(0,nbLines):
for j in range(0,i):
if ( lines[i]!=lambdaRFLine and lines[j]!=lambdaRFLine ): continue
line = max( lines[i]/lines[j], lines[j]/lines[i])
if (line<min1D__ or line>max1D__): continue
xLi = [line,line]
yLi = [yMin,yMax]
name = names[i] + ' - ' + names[j]
plt.plot(xLi,yLi,color='green',linewidth=2)
plt.text(line, yMax, name, rotation='vertical', fontsize=20)
plt.errorbar(xxx, yyy, yerr=yer, fmt='o')
plt.ylabel(r'$\xi^{ff}$', fontsize=40)
plt.xlabel(r'$\lambda_{Obs.,2}/\lambda_{Obs.,1}$', fontsize=40)
plt.xlim([ min1D__-0.01, max1D__+0.01 ])
myTools.deal_with_plot(False,False,False)
plt.show()
def plot_1d(self,x_power=0):
xxx = self._xi0[:,0]
yyy = self._xi0[:,1]
coef = numpy.power(xxx,x_power)
plt.errorbar(xxx,coef*yyy,fmt='o')
xxx = self._xi2[:,0]
yyy = self._xi2[:,1]
coef = numpy.power(xxx,x_power)
plt.errorbar(xxx,coef*yyy,fmt='o')
xxx = self._xi4[:,0]
yyy = self._xi4[:,1]
coef = numpy.power(xxx,x_power)
plt.errorbar(xxx,coef*yyy,fmt='o')
if (x_power==0):
plt.ylabel(r'$ \xi (|s|)$', fontsize=40)
if (x_power==1):
plt.ylabel(r'$|s|.\xi (|s|) \, [h^{-1}.Mpc]$', fontsize=40)
if (x_power==2):
plt.ylabel(r'$|s|^{2}.\xi (|s|) \, [(h^{-1}.Mpc)^{2}]$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
plt.xlim([ numpy.amin(xxx)-10., numpy.amax(self._xi0[:,0])+10. ])
myTools.deal_with_plot(False,False,False)
plt.show()
return
def fit_CAMB(self,distortion=False,dic=None):
### Get the data
xxx = self._xi[:,0]
yyy = self._xi[:,1]
yer = self._xi[:,2]
xMin = numpy.amin(xxx)
xMax = numpy.amax(xxx)
### Get CAMB
camb = CAMB.CAMB(dic)._xi0
xxx_Camb = copy.deepcopy(xxx)
yyy_Camb = numpy.interp(xxx,camb[:,0],camb[:,1])
if (distortion):
path = self._path_to_txt_file_folder + self._prefix + '_distortionMatrix_'+ self._middlefix + '.txt'
matrix = numpy.loadtxt(path)
xxx_Camb = numpy.append(xxx_Camb,numpy.zeros(2100-xxx.size) )
yyy_Camb = numpy.append(yyy_Camb,numpy.zeros(2100-yyy_Camb.size) )
yyy_Camb = numpy.dot(matrix,yyy_Camb)
yyy_Camb = yyy_Camb[(xxx_Camb!=0.)]
xxx_Camb = xxx_Camb[(xxx_Camb!=0.)]
b = 0.15
#b = 0.05
'''
### Chi^{2}
def chi2(b):
model = yyy_Camb*b
return numpy.sum( numpy.power( (yyy-model)/yer ,2.) )
### Init and perform the fit
m = Minuit(chi2, b=1.,error_b=0.1,print_level=-1, errordef=0.01)
m.migrad()
b = m.values['b']
#b = 0.01
print b
'''
for i in numpy.arange(1):
coef = numpy.power(xxx,i)
plt.errorbar(xxx, coef*yyy, yerr=coef*yer, fmt='o',color='blue')
coef = numpy.power(xxx_Camb,i)
plt.errorbar(xxx_Camb,coef*b*yyy_Camb,color='red')
plt.title(r'$'+self._title+'$', fontsize=40)
plt.xlabel(r'$'+self._xTitle+'$', fontsize=40)
plt.ylabel(r'$'+self._yTitle+'$', fontsize=40)
if (self._correlation=='f_f_r' or self._correlation=='f_f2_r' or self._correlation=='f_f_lRF' or self._correlation=='f_f_lRF'):
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
if (self._correlation=='f_f_lRF_devide' or self._correlation=='f_f2_lRF_devide'):
plt.xlim([ 0.99*xMin, 1.01*xMax ])
myTools.deal_with_plot(False,False,False)
plt.show()
return
def plotXi(rescale):
cut = (xi1D_[:,2] != 0.)
xxx = xi1D_[:,0][cut]
yyy = xi1D_[:,1][cut]
yer = xi1D_[:,2][cut]
#yyy -= yyy[-1]
if (rescale==0):
plt.errorbar(xxx, yyy, yerr=yer, fmt='o')
plt.ylabel(r'$\xi^{ff} (|s|)$', fontsize=40)
if (rescale==1):
plt.errorbar(xxx, xxx*yyy, yerr=xxx*yer, fmt='o')
plt.ylabel(r'$|s|.\xi^{ff} (|s|) \, [h^{-1}.Mpc]$', fontsize=40)
if (rescale==2):
plt.errorbar(xxx, xxx*xxx*yyy, yerr=xxx*xxx*yer, fmt='o')
plt.ylabel(r'$|s|^{2}.\xi^{ff} (|s|) \, [(h^{-1}.Mpc)^{2}]$', fontsize=40)
plt.title(r'$\delta_{'+forest__+'} \, - \, \delta_{'+forest__+'}$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.show()
return
def difTemplate():
'''
Look at the differences between the templates
'''
path = "/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/chain_annalys_delta/"
data1 = numpy.loadtxt(path + 'template_0_0.txt')
templateData1 = interpolate.interp1d(data1[:,0],data1[:,1],bounds_error=False,fill_value=0)
plt.errorbar(data1[:,0], data1[:,1]/templateData1(1150.), fmt='o', label=r'$Simu$',color='red')
data = numpy.loadtxt(path + 'template.txt')
templateData = interpolate.interp1d(data[:,0],data[:,1],bounds_error=False,fill_value=0)
plt.errorbar(data[:,0], data[:,1]/templateData(1150.), fmt='o', label=r'$Data$',color='blue')
data3 = numpy.loadtxt(path + 'template_0_0_MocksColab.txt')
templateData3 = interpolate.interp1d(data3[:,0],data3[:,1],bounds_error=False,fill_value=0)
plt.errorbar(data3[:,0], data3[:,1]/templateData3(1150.), fmt='o', label=r'$Mock \, colab$',color='green')
plt.title(r'$Template$', fontsize=40)
plt.xlabel(r'$\lambda_{R.F.} \, [\AA]$', fontsize=40)
plt.ylabel(r'$f(\lambda_{R.F.}) / f(1150.)$', fontsize=40)
myTools.deal_with_plot(False,False,True)
plt.show()
plt.errorbar(data[:,0], (data1[:,1]/templateData1(1150.)-data[:,1]/templateData(1150.))/(data1[:,1]/templateData1(1150.)) , fmt='o')
plt.xlabel(r'$\lambda_{R.F.} \, [\AA]$', fontsize=40)
plt.ylabel(r'$( (f(\lambda_{R.F.}) / f(1150.))_{Data} - (f(\lambda_{R.F.}) / f(1150.))_{Simu} ) / (f(\lambda_{R.F.}) / f(1150.))_{Data})$', fontsize=40)
myTools.deal_with_plot(False,False,True)
plt.show()
def getQsoCatalogueAllObjects():
'''
'''
path = '/home/gpfs/manip/mnt0607/bao/hdumasde/Data/Catalogue/'
listPAth = [path+'QSO_DR7_DR12_EBOSS_2016_01_08.fits',
path+'DLA_all.fits',
path+'all_Britt.fits',
path+'VIPERS.fits',
path+'QSO_3DHST.fits',
path+'LOWZ_all.fits',
path+'CMASS_all.fits']
name = ['QSO','DLA','Britt','VIPERS','3DHST','LOWZ','CMASS']
## Distribution redshift
for i in numpy.arange(len(listPAth)):
cat = pyfits.open(listPAth[i], memmap=True )[1].data
cat = cat[ (cat['Z']>0.1) ]
cat = cat[ (cat['Z']<7.) ]
if (cat.size==0): continue
plt.hist(cat['Z'], bins=100,histtype='step',label=name[i])
plt.xlabel("Z")
plt.ylabel("#")
myTools.deal_with_plot(False,False,True)
plt.show()
### Merge everyThing
cat = pyfits.open(listPAth[0], memmap=True )[1].data
ra = cat['RA']
de = cat['DEC']
zz = cat['Z']
for i in numpy.arange(1,len(listPAth)):
cat = pyfits.open(listPAth[i], memmap=True )[1].data
ra = numpy.append(ra, cat['RA'])
de = numpy.append(de, cat['DEC'])
zz = numpy.append(zz, cat['Z'])
## Map
plt.ticklabel_format(style='sci', axis='z', scilimits=(0,0))
plt.grid()
plt.plot(ra, de, linestyle="", marker="o")
plt.xlabel("Right Ascension (degree)")
plt.ylabel("Declination (degree)")
plt.show()
## Distribution redshift
plt.ticklabel_format(style='sci', axis='z', scilimits=(0,0))
plt.grid()
plt.hist(zz, bins=200)
plt.xlabel("Z")
plt.ylabel("#")
plt.show()
### Save
col_ra = pyfits.Column(name='RA', format='D', array=ra, unit='deg')
col_de = pyfits.Column(name='DEC', format='D', array=de, unit='deg')
col_zz = pyfits.Column(name='Z', format='D', array=zz)
tbhdu = pyfits.BinTableHDU.from_columns([col_ra, col_de, col_zz])
tbhdu.writeto('/home/gpfs/manip/mnt0607/bao/hdumasde/Data/Catalogue/ALL_EVERY_OBJECTS_2016_01_08.fits', clobber=True)
def plotXi():
xxx = xi1D_[:,0]
yyy = xi1D_[:,1]
yer = xi1D_[:,2]
cut = (yer!=0.)
xxx = xxx[ cut ]
yyy = yyy[ cut ]
yer = yer[ cut ]
yMin = numpy.min(yyy)
yMax = numpy.max(yyy)
nbLines = lines.size
for i in range(0,nbLines):
line = lines[i]/lambdaRFLine
if (line<min1D__ or line>max1D__): continue
#print ' || QSO - ', names[i], ' || ', line, ' || ', lambdaRFLine, ' || ', lines[i], ' || '
xLi = [line,line]
yLi = [yMin,yMax]
name = 'QSO - ' + names[i]
plt.plot(xLi,yLi,color='green',linewidth=2)
plt.text(line, 0.7*yMin, name, rotation='vertical', fontsize=20)
plt.errorbar(xxx, yyy, yerr=yer, fmt='o')
plt.ylabel(r'$\xi^{qf} \, (\theta<'+str(maxTheta_)+' \, rad)$', fontsize=40)
plt.xlabel(r'$\lambda_{Obs., pix}/\lambda_{Obs., QSO}$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.show()
def plot_histo_residuals(self, other=[]):
### Constants
nbBins=100
list_of_fit = numpy.append( [self],other )
### histo
fig = plt.figure()
ax = fig.add_subplot(111)
for el in list_of_fit:
yyy = numpy.array([])
for el in self._listFit:
xi2D = el.get_residuals()
tmp_yyy = (xi2D[:,:,1][ (xi2D[:,:,2]>0.) ]).flatten()
yyy = numpy.append(yyy,tmp_yyy)
ax.hist(yyy, bins=nbBins, histtype='step', linewidth=2,alpha=0.6)
plt.xlabel(r'$\frac{data-fit}{\sigma_{data}}$')
plt.ylabel(r'$\#$')
myTools.deal_with_plot(False,False,False)
mng = plt.get_current_fig_manager()
textstr = '$nb=%u$\n$\mu=%.5e$\n$\sigma=%.5e$'%(yyy.size, numpy.mean(yyy), numpy.std(yyy))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=30, verticalalignment='top', bbox=props)
plt.show()
return
def plotDataAndFit_1D():
"""
"""
if type__ == "1D":
xxx = numpy.arange(0.0, nbBin__) * 4.0 + 2.0
xxx[(xxx__ != 0.0)] = xxx__[(xxx__ != 0.0)]
b = "|s|"
elif type__ == "2D":
xxx = numpy.arange(0.0, nbBin__)
b = "s"
for i in numpy.arange(0, 3):
if i == 0:
a = ""
c = ""
elif i == 1:
a = "|s|."
c = " \, [h^{-1}.Mpc]"
else:
a = "|s|^{2}."
c = " \, [(h^{-1}.Mpc)^{2}]"
coef = numpy.power(xxx__, 1.0 * i)
plt.errorbar(xxx, coef * xi_dat__, yerr=coef * xi_err__, linestyle="", marker="o", color="blue")
plt.errorbar(xxx, coef * xi_fit__, color="red")
plt.xlabel(r"$" + b + " \, [h^{-1}.Mpc] $")
plt.ylabel(r"$" + a + "\\xi(" + b + ") " + c + "$")
myTools.deal_with_plot(False, False, True)
plt.show()
return
def seePowerSpectrum():
'''
'''
### Constants
pathToPk = '/home/gpfs/manip/mnt0607/bao/hdumasde/Code/Python/CreateRandomField/DR9LyaMocks_matterpower.dat'
### Get the power spectra
pk = scipy.loadtxt(pathToPk)
pkInterpolate = interpolate.interp1d(pk[:,0],pk[:,1],bounds_error=False,fill_value=0)
for i in numpy.arange(3):
coef = numpy.power(pk[:,0],i)
yLabel = 'P(k)'
if (i==1):
yLabel = 'k.' + yLabel + '\, [h/Mpc]'
elif (i==2):
yLabel = 'k^{2}.' + yLabel + '\, [(h/Mpc)^{2}]'
plt.plot(pk[:,0],coef*pk[:,1],marker='o')
plt.xlabel(r'$k \, [h/Mpc]$', fontsize=40)
plt.ylabel(r'$'+yLabel+'$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.show()
def plot(xi1D):
if (forest__=='LYA'):
lines = LYA_lines
names = LYA_lines_names
if (forest__=='CIV'):
lines = CIV_lines
names = CIV_lines_names
if (forest__=='MGII'):
lines = MGII_lines
names = MGII_lines_names
if (forest__=='LYB'):
lines = LYB_lines
names = LYB_lines_names
if (forest__=='SIIV'):
lines = SIIV_lines
names = SIIV_lines_names
xxx = xi1D[:,0]
yyy = xi1D[:,1]
yer = xi1D[:,2]
### Show lines in the correlation
xMax = numpy.amax(xxx)
yMin = numpy.amin(yyy)
yMax = numpy.amax(yyy)
nbLines = lines.size
for i in range(0,nbLines):
for j in range(0,i):
#if (names[i][:3]!=forest__ and names[j][:3]!=forest__): continue
line = lines[j]/lines[i]
#isPresent = False
#for k in strongLines_CIV:
# if (abs(line-k)<=0.00005): isPresent = True
#if (not isPresent): continue
line = numpy.abs( find_dist_correlation_lines(meanZ__,lines[j],lines[i]) )
if (line>xMax): continue
#print line
xLi = [line,line]
yLi = [yMin,yMax]
name = names[i]+' - '+names[j]
plt.plot(xLi,yLi,color='green')
plt.text(line, yMax, name, rotation='vertical', fontsize=20)
plt.errorbar(xxx,yyy, yerr=yer, fmt='o')
plt.title(r'$1D: \, \delta_{'+forest__+'} \, - \, \delta_{'+forest__+'} $', fontsize=40)
plt.xlabel(r'$s \, [h^{-1}.Mpc]$', fontsize=40)
plt.ylabel(r'$\xi (s)$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx)-10., numpy.max(xxx)+10. ])
plt.show()
def plotCovarDifferentMethod(): ''' ''' dim = '1D' path = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy/' listPath = [ path+'subSampling_LYA_QSO_cov_'+dim+'.npy', path+'shuffleQSO_LYA_QSO_cov_'+dim+'.npy', path+'randomQSO_LYA_QSO_cov_'+dim+'.npy', path+'shuffleForest_LYA_QSO_cov_'+dim+'.npy', '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_cov_'+dim+'.npy', '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_cov_'+dim+'_meanSubSampling.npy', ] listPath2 = [ path+'subSampling_LYA_QSO_'+dim+'.npy', path+'shuffleQSO_LYA_QSO_'+dim+'.npy', path+'randomQSO_LYA_QSO_'+dim+'.npy', path+'shuffleForest_LYA_QSO_'+dim+'.npy', '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1547/Results_RandomPosInCell/xi_delta_QSO_result_'+dim+'.npy', ] listName = ['Data \, subsampling', 'Data \, shuffle \, QSO', 'Data \, random \, QSO', 'Data \, shuffle \, forest', 'Mocks', '< Mock \, subsampling >', ] real = [ numpy.load(i) for i in listPath2 ] cov = [ numpy.load(i) for i in listPath ] ### Plot the realisation for i in numpy.arange(len(real)): print listName[i] for j in numpy.arange(real[i][0,:].size): plt.errorbar(numpy.arange(real[i][:,j].size), real[i][:,j],fmt='o',color='blue',alpha=0.1) plt.errorbar(numpy.arange(real[i][:,j].size), numpy.mean(real[i],axis=1),fmt='o',color='red',label=r'$Mean$') plt.xlabel(r'$bin \, index$', fontsize=40) plt.ylabel(r'$\xi(|s|)$', fontsize=40) plt.title(r'$'+listName[i]+'$', fontsize=40) myTools.deal_with_plot(False,False,True) plt.xlim([ -1., cov[i][0,:].size+1 ]) plt.show() ### Plot diagonal for i in numpy.arange(len(cov)): plt.errorbar(numpy.arange(cov[i][0,:].size), numpy.diag(cov[i]),fmt='o',label=r'$'+listName[i]+'$') plt.xlabel(r'$bin \, index$', fontsize=40) plt.ylabel(r'$Var(|s|)$', fontsize=40) myTools.deal_with_plot(False,False,True) plt.xlim([ -1., cov[i][0,:].size+1 ]) plt.show() myTools.plotCovar(cov,listName) return
def plot_given_2d(self, x_power=0, xi2D=None, label=None):
if xi2D is None:
print " annalyseBAOFIT::plot_2d:: xi2D==None"
xi2D = self._xi2D
if label is None:
label = self._label
origin = "lower"
extent = [self._minX2D, self._maxX2D, self._minY2D, self._maxY2D]
if self._correlation == "q_f" or self._correlation == "f_f2":
origin = "upper"
extent = [self._minX2D, self._maxX2D, self._maxY2D, self._minY2D]
xxx = numpy.transpose(xi2D[:, :, 0])
yyy = numpy.transpose(xi2D[:, :, 1])
yer = numpy.transpose(xi2D[:, :, 2])
cut = yer == 0
if xxx[cut].size == xxx.size:
return
yyy[cut] = float("nan")
coef = numpy.power(xxx, x_power)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xticks([i for i in numpy.arange(self._minX2D - 50.0, self._maxX2D + 50.0, 50.0)])
ax.set_yticks([i for i in numpy.arange(self._minY2D - 50.0, self._maxY2D + 50.0, 50.0)])
plt.imshow(coef * yyy, origin=origin, extent=extent, interpolation="None")
cbar = plt.colorbar()
if x_power == 0:
cbar.set_label(r"$" + label + "(r_{\parallel},r_{\perp})$", size=40)
if x_power == 1:
cbar.set_label(r"$r \cdot " + label + "(r_{\parallel},r_{\perp}) \, [\\rm{h}^{-1} \, \\rm{Mpc}]$", size=40)
if x_power == 2:
cbar.set_label(
r"$r^{2} \cdot " + label + "(r_{\parallel},r_{\perp}) \, [(\\rm{h}^{-1} \, \\rm{Mpc})^{2}]$", size=40
)
# plt.plot( [0.,200.],[0.,4*200.],color='white',linewidth=2 )
# plt.plot( [0.,200.],[0.,-4*200.],color='white',linewidth=2 )
# plt.plot( [0.,200.],[0.,200.],color='white',linewidth=2 )
# plt.plot( [0.,200.],[0.,-200.],color='white',linewidth=2 )
# plt.title(r'$'+self._title+'$', fontsize=40)
plt.xlabel(r"$r_{\perp} \, [\rm{h}^{-1} \, \rm{Mpc}]$", fontsize=40)
plt.ylabel(r"$r_{\parallel} \, [\rm{h}^{-1} \, \rm{Mpc}]$", fontsize=40)
plt.grid(True)
cbar.formatter.set_powerlimits((0, 0))
cbar.update_ticks()
myTools.deal_with_plot(False, False, False)
plt.show()
return
def haveAlookForest():
cat = pyfits.open('/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMc/new_mock_1280_0_0.fits', memmap=True)[1].data
print cat.size
print numpy.amin(cat['RA']), numpy.amax(cat['RA'])
print numpy.amin(cat['DEC']), numpy.amax(cat['DEC'])
print numpy.amin(cat['Z_VI']), numpy.amax(cat['Z_VI'])
ar_cut = (cat['NORM_FLUX_IVAR']>0.)
ar_lambdaObs = cat['LAMBDA_OBS'][ ar_cut ]
ar_lambdaRF = cat['LAMBDA_RF'][ ar_cut ]
ar_flux = cat['NORM_FLUX'][ ar_cut ]
ar_zi = cat['LAMBDA_OBS'][ ar_cut ]/(lambdaRFLine__) -1.
ar_weight = cat['DELTA_WEIGHT'][ ar_cut ] #numpy.ones(shape=(cat.size,cat['NORM_FLUX'][0].size))[ ar_cut ]
print numpy.amin(ar_zi), numpy.amax(ar_zi)
### deltaLoglambda (mock_JMc) = 0.00023025850929947467
### deltaLoglambda (data) = 0.00023002222870971423
### deltaLoglambda (mock Julian) = 0.00023002222870971423,
### delta vs. lambda_RF
xxx, yyy, eyyy, nyyy = myTools.Get_TProfile(ar_lambdaRF,ar_flux, lambdaRFTemplateBinEdges__, ar_weight)
plt.errorbar(xxx, yyy, yerr=eyyy, marker="o")
plt.xlabel(r'$\lambda_{R.F.} \, [\AA]$', fontsize=40)
plt.ylabel(r'$flux$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.show()
### delta vs. lambda_RF
xxx, yyy, eyyy, nyyy = myTools.Get_TProfile(ar_lambdaObs,ar_flux, lambdaObsBinEdges__, ar_weight)
plt.errorbar(xxx, yyy, yerr=eyyy, marker="o")
plt.xlabel(r'$\lambda_{R.F.} \, [\AA]$', fontsize=40)
plt.ylabel(r'$flux$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.show()
### Distribution redshift
plt.hist(ar_zi, bins=50)
plt.xlabel(r'$z_{pixel}$')
plt.ylabel(r'$\#$')
myTools.deal_with_plot(False,False,False)
plt.show()
### Map
plt.plot(cat['RA'],cat['DEC'], linestyle="", marker="o")
plt.xlabel(r'$X [Mpc]$')
plt.ylabel(r'$Y [Mpc]$')
myTools.deal_with_plot(False,False,False)
plt.show()
### Distribution redshift
plt.hist(cat['Z_VI'], bins=50)
plt.xlabel(r'$z_{qso}$')
plt.ylabel(r'$\#$')
myTools.deal_with_plot(False,False,False)
plt.show()
return
def plot_data_and_fit_1d(self,x_power,path_to_mapping_1D):
### Load the mapping from 2D to 1D
mapping_2D_to_1D = numpy.load( path_to_mapping_1D )
### Fit
xi1D_data = numpy.zeros(shape=(self._nbBin1D,3))
xi1D_fit = numpy.zeros(shape=(self._nbBin1D,3))
for i in numpy.arange(self._nbBinX2D):
for j in numpy.arange(self._nbBinY2D):
if (self._xi2D_fit[i,j,8]<=0.): continue
ivar = 1./( self._xi2D_fit[i,j,8]*self._xi2D_fit[i,j,8] )
for k in numpy.arange(self._nbBin1D):
coef = mapping_2D_to_1D[i,j,k]
if (coef==0.): continue
xi1D_data[k,0] += coef*ivar*self._xi2D[i,j,0]
xi1D_data[k,1] += coef*ivar*self._xi2D[i,j,1]
xi1D_data[k,2] += coef*ivar
xi1D_fit[k,0] += coef*ivar*self._xi2D_fit[i,j,3]
xi1D_fit[k,1] += coef*ivar*self._xi2D_fit[i,j,6]
xi1D_fit[k,2] += coef*ivar
### Data
cut = (xi1D_data[:,2]>0.)
xi1D_data[:,0][cut] /= xi1D_data[:,2][cut]
xxx = xi1D_data[:,0][cut]
xi1D_data[:,1][cut] /= xi1D_data[:,2][cut]
xi1D_data[:,2][cut] = 1./numpy.sqrt(xi1D_data[:,2][cut])
coef = numpy.power(xi1D_data[:,0][cut],x_power)
plt.errorbar(xi1D_data[:,0][cut], coef*xi1D_data[:,1][cut], yerr=coef*xi1D_data[:,2][cut], fmt='o', label=r'$'+self._name+'$', markersize=10,linewidth=2)
### Fit
cut = (xi1D_fit[:,2]>0.)
xi1D_fit[:,0][cut] /= xi1D_fit[:,2][cut]
xi1D_fit[:,1][cut] /= xi1D_fit[:,2][cut]
xi1D_fit[:,2][cut] = 1./numpy.sqrt(xi1D_fit[:,2][cut])
coef = numpy.power(xi1D_fit[:,0][cut],x_power)
plt.errorbar(xi1D_fit[:,0][cut], coef*xi1D_fit[:,1][cut], label=r'$Fit$', color='red',linewidth=2)
if (x_power==0):
plt.ylabel(r'$'+self._label+' (|s|)$', fontsize=40)
if (x_power==1):
plt.ylabel(r'$|s|.'+self._label+' (|s|) \, [h^{-1}.Mpc]$', fontsize=40)
if (x_power==2):
plt.ylabel(r'$|s|^{2}.'+self._label+' (|s|) \, [(h^{-1}.Mpc)^{2}]$', fontsize=40)
plt.title(r'$'+self._title+'$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
plt.xlim([ numpy.amin(xxx)-10., numpy.amax(xxx)+10. ])
myTools.deal_with_plot(False,False,True)
plt.show()
return
def plot_1d(self, with_lines=False, other=[], verbose=False):
list_corr_to_plot = numpy.append( [self],other )
xMin = numpy.amin(self._xi1D[:,0])
xMax = numpy.amax(self._xi1D[:,0])
yMin = numpy.amin(self._xi1D[:,1])
yMax = numpy.amax(self._xi1D[:,1])
for el in list_corr_to_plot:
cut = (el._xi1D[:,2]>0.)
xxx = el._xi1D[:,0][cut]
yyy = el._xi1D[:,1][cut]
yer = el._xi1D[:,2][cut]
plt.errorbar(self._line_RF*xxx, yyy, yerr=yer, fmt='o', label=r'$'+el._name+'$')
plt.errorbar(self._line_RF*xxx, yyy, color='blue')
xMin = min(xMin, numpy.amin(xxx) )
xMax = max(xMax, numpy.amax(xxx) )
yMin = min(yMin, numpy.amin(yyy) )
yMax = max(yMax, numpy.amax(yyy) )
if (with_lines):
if (verbose): print ' || name_1 - name_2 || line || lambda_rf_1 || lamnda_rf_2 || '
yLi = [yMin,yMax]
nbLines1 = self._lines1.size
for i in range(0,nbLines1):
nbLines2 = 1
for j in range(0,nbLines2):
line = self._lines1[i] #/self._line_RF
if (line<self._line_RF*xMin or line>self._line_RF*xMax): continue
xLi = [line,line]
plt.plot(xLi,yLi,color='green')
if (verbose): print ' || ', self._name_line1[i] ,' - QSO || ', line, ' || ', self._lines1[i], ' || ', self._line_RF, ' || '
name = self._name_line1[i]+' - QSO '
plt.text(line, yMax, name, rotation='vertical', fontsize=20)
plt.title(r'$'+self._title+'$', fontsize=40)
plt.xlabel(r'$'+self._xTitle+'$', fontsize=40)
plt.ylabel(r'$'+self._yTitle+'$', fontsize=40)
myTools.deal_with_plot(False,False,True)
plt.show()
return
def plot():
data = numpy.loadtxt("/home/gpfs/manip/mnt0607/bao/hdumasde/Code/Python/All_Spectrum/spectrum.txt")
xxx = data[:, 0]
yyy = data[:, 1]
yer = data[:, 2]
plt.errorbar(xxx, yyy, yerr=yer, marker="o")
myTools.deal_with_plot(False, False, False)
plt.show()
def getChiScan1D():
'''
get the \chi^2 scan
1st line:
2nd line: settings
3rd line: best fit
4-end lines: scan
'''
### idex of the alpha
idxAlpha = 11
### Constants
name = '.scan.dat'
### Create a file in /tmp with the scan minus the two first lines
idx = 0
f = open(path__+name)
tmp_f = open('/tmp/scan.txt','w')
for line in f:
if (idx>=2): tmp_f.write(line)
idx += 1
tmp_f.close()
f.close()
### Data
data = numpy.loadtxt('/tmp/scan.txt')
### Best Fit
alpha_bestFit = data[0][idxAlpha]
chi2_bestFit = data[0][-1]
print data[0][9], data[0][10], param__[idxAlpha,0], param__[idxAlpha,1]
### Scan
alpha = data[1:,idxAlpha]
chi2 = data[1:,-1]-chi2_bestFit
### Chi^2 scan
plt.errorbar(alpha,chi2,fmt='o')
### Best fit
plt.errorbar([alpha_bestFit],[0.],xerr=param__[idxAlpha,1],fmt='o',color='red',label='Best fit')
### 1,2,3,4 sigma lines
plt.plot(alpha,numpy.ones(alpha.size)*1.,color='green')
plt.plot(alpha,numpy.ones(alpha.size)*4.,color='green')
plt.plot(alpha,numpy.ones(alpha.size)*9.,color='green')
plt.plot(alpha,numpy.ones(alpha.size)*16.,color='green')
plt.xlabel(r'$'+paramName__[idxAlpha]+'$', fontsize=40)
plt.ylabel(r'$\Delta \chi^{2} = \chi^{2}-\chi^{2}_{best \, fit}$', fontsize=40)
plt.ylim([ -2., numpy.amax(chi2)+2. ])
myTools.deal_with_plot(False,False,True)
plt.show()
return
def plotAllParameters():
"""
"""
for i in numpy.arange(paramName__.size):
### If the parameter isn't fitted
if param__[i, 1, 0] == 0.0:
continue
### all:
plt.errorbar(numpy.arange(nbMocks__), param__[i, 0, :], yerr=param__[i, 1, :], linestyle="", marker="o")
plt.xlabel(r"$index \, mock$")
plt.ylabel(r"$" + paramName__[i] + "$")
plt.xlim(-1, nbMocks__ + 1)
myTools.deal_with_plot(False, False, True)
plt.show()
### hist:
yyy = param__[i, 0, :]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(yyy, bins=20)
myTools.deal_with_plot(False, False, False)
plt.xlabel(r"$" + paramName__[i] + "$")
plt.ylabel(r"$\#$")
textstr = "$Nb=%d$ \n $\mu=%.5e +/- %.2e$ \n $\sigma=%.5e$" % (
yyy.size,
numpy.mean(yyy),
numpy.std(yyy) / numpy.sqrt(yyy.size),
numpy.std(yyy),
)
props = dict(boxstyle="round", facecolor="wheat", alpha=0.5)
ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=30, verticalalignment="top", bbox=props)
plt.show()
"""
### hist pull:
yyy = (param__[i,0,:]-1.)/param__[i,1,:]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(yyy, bins=20)
myTools.deal_with_plot(False,False,False)
plt.xlabel(r'$('+paramName__[i]+'-1)/err$')
plt.ylabel(r'$\#$')
textstr = '$Nb=%d$ \n $\mu=%.5e +/- %.2e$ \n $\sigma=%.5e$'%(yyy.size,numpy.mean(yyy),numpy.std(yyy)/numpy.sqrt(yyy.size),numpy.std(yyy))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=30, verticalalignment='top', bbox=props)
plt.show()
"""
return
def plotWe(rescale):
a = ["theta < 0.0001", "0.0001 < theta < 0.0002"]
yMin = 0.
yMax = 0.005
nbLines = lines.size
for i in range(0,nbLines):
for j in range(0,i):
#if ( lines[i]!=1550.77845 and lines[j]!=1550.77845 and lines[i]!=1548.2049 and lines[j]!=1548.2049 ): continue
#if ( lines[i]!=1402.77291 and lines[j]!=1402.77291 and lines[i]!=1393.76018 and lines[j]!=1393.76018 ): continue
if ( lines[i]!=1215.67 and lines[j]!=1215.67): continue
line = max( lines[i]/lines[j], lines[j]/lines[i])
if (line<min1D__ or line>max1D__): continue
xLi = [line,line]
yLi = [yMin,yMax]
name = names[i] + ' - ' + names[j]
plt.plot(xLi,yLi,color='green',linewidth=2)
plt.text(line, yMax, name, rotation='vertical', fontsize=20)
for i in range(0,2):
#if (i>=1):
# xiWe_[:,i,1] += 0.05
#if (i>=2):
# xiWe_[:,i,1] += 0.05
###
cut = (xiWe_[:,i,2]!=0.)
if (xiWe_[:,i,0][cut].size==0):
continue
xxx = xiWe_[:,i,0][cut]
yyy = xiWe_[:,i,1][cut]
yer = xiWe_[:,i,2][cut]
coef = numpy.power(xxx,rescale)
plt.errorbar(xxx, coef*yyy, yerr=coef*yer, marker='o', label=r'$'+a[i]+'$')
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=1)
plt.ylabel(r'$\xi^{ff}$', fontsize=40)
plt.xlabel(r'$\lambda_{Obs.,2}/\lambda_{Obs.,1}$', fontsize=40)
plt.xlim([ min1D__-0.01, max1D__+0.01 ])
myTools.deal_with_plot(False,False,False)
plt.show()
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_function(self,function_index): """ Plot the F1 and F2 function """ if (function_index==0): func = self._F1 elif (function_index==1): func = self._F2 ### Plot selection function def func2(x): return numpy.exp(-0.5*numpy.power(x/self._sigma_gauss,2.))/(numpy.sqrt(2.*numpy.pi)*self._sigma_gauss) xxx = numpy.arange(-10.,10.,0.01) yyy = func(xxx) plt.plot(xxx,yyy, markersize=10,linewidth=2,label=r'$Selection$') yyy = func2(xxx) plt.plot(xxx,yyy, markersize=10,linewidth=2,label=r'$Gauss$') plt.xlabel(r'$\delta$') plt.ylabel(r'$Proba$') myTools.deal_with_plot(False,False,True) plt.show() ### Plot results meanF = self.get_mean_function(function_index) def func2(x): return func(x)*numpy.exp(-0.5*numpy.power(x/self._sigma_gauss,2.))/(numpy.sqrt(2.*numpy.pi)*self._sigma_gauss) xxx = numpy.arange(-10.,10.,0.01) yyy = func2(xxx) def func2(x): return numpy.exp(-0.5*numpy.power(x/self._sigma_gauss,2.))/(numpy.sqrt(2.*numpy.pi)*self._sigma_gauss) meanF = scipy.integrate.quad(func2,-numpy.inf,numpy.inf)[0] xxx2 = numpy.arange(-10.,10.,0.01) yyy2 = func2(xxx) plt.plot(xxx2,yyy2, markersize=10,linewidth=2) plt.plot(xxx,yyy, markersize=10,linewidth=2) plt.xlabel(r'$\delta$') plt.ylabel(r'$F(\delta) \cdot Gauss$') myTools.deal_with_plot(False,False,False) plt.show() return
def plot_data_and_fit_we(self, we_index, x_power, path_to_mapping, title=True):
xi1D_data, xi1D_fit = self.get_data_and_fit_we(path_to_mapping)
cut = self._xiWe[:, we_index, 2] > 0.0
if self._xiWe[:, we_index, 0][cut].size == 0:
return
cut = xi1D_data[:, we_index, 2] > 0.0
xxx = xi1D_data[:, we_index, 0][cut]
yyy = xi1D_data[:, we_index, 1][cut]
yer = xi1D_data[:, we_index, 2][cut]
coef = numpy.power(xxx, x_power)
plt.errorbar(
xxx,
coef * yyy,
yerr=coef * yer,
fmt="o",
label=r"$" + self._label_wedge[we_index] + "$",
color="red",
markersize=10,
linewidth=2,
)
cut = xi1D_fit[:, we_index, 2] > 0.0
xxxF = xi1D_fit[:, we_index, 0][cut]
yyyF = xi1D_fit[:, we_index, 1][cut]
yerF = xi1D_fit[:, we_index, 2][cut]
coefF = numpy.power(xxxF, x_power)
plt.errorbar(xxxF, coefF * yyyF, color="blue", linewidth=2)
if x_power == 0:
plt.ylabel(r"$" + self._label + " (r)$", fontsize=40)
if x_power == 1:
plt.ylabel(r"$r \cdot " + self._label + " (r) \, [\\rm{h}^{-1} \, \\rm{Mpc}]$", fontsize=40)
if x_power == 2:
plt.ylabel(r"$r^{2} \cdot " + self._label + " (r) \, [(\\rm{h}^{-1} \, \\rm{Mpc})^{2}]$", fontsize=40)
if title:
plt.title(r"$" + self._title + "$", fontsize=40)
plt.xlabel(r"$r \, [\rm{h}^{-1} \, \rm{Mpc}]$", fontsize=40)
plt.xlim([numpy.amin(xxx) - 10.0, numpy.amax(xxx) + 10.0])
myTools.deal_with_plot(False, False, False)
plt.legend(fontsize=40, numpoints=1, ncol=1, loc=0)
plt.show()
return
def plotWe(rescale):
###
cut = (xiWe_[:,0,2]!=0.)
xxx0 = xiWe_[:,0,0][cut]
yyy0 = xiWe_[:,0,1][cut]
yer0 = xiWe_[:,0,2][cut]
#yyy0 -= yyy0[-1]
###
cut = (xiWe_[:,1,2]!=0.)
xxx1 = xiWe_[:,1,0][cut]
yyy1 = xiWe_[:,1,1][cut]
yer1 = xiWe_[:,1,2][cut]
#yyy1 -= yyy1[-1]
###
cut = (xiWe_[:,2,2]!=0.)
xxx2 = xiWe_[:,2,0][cut]
yyy2 = xiWe_[:,2,1][cut]
yer2 = xiWe_[:,2,2][cut]
#yyy2 -= yyy2[-1]
if (rescale==0):
plt.errorbar(xxx0, yyy0, yerr=yer0, fmt='o', label=r'$0.8 < |\mu|$')
plt.errorbar(xxx1, yyy1, yerr=yer1, fmt='o', label=r'$0.5 < |\mu| \leq 0.8$')
plt.errorbar(xxx2, yyy2, yerr=yer2, fmt='o', label=r'$|\mu| \leq 0.5$')
plt.ylabel(r'$\xi (|s|)$', fontsize=40)
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=4)
if (rescale==1):
plt.errorbar(xxx0, xxx0*yyy0, yerr=xxx0*yer0, fmt='o', label=r'$0.8 < |\mu|$')
plt.errorbar(xxx1, xxx1*yyy1, yerr=xxx1*yer1, fmt='o', label=r'$0.5 < |\mu| \leq 0.8$')
plt.errorbar(xxx2, xxx2*yyy2, yerr=xxx2*yer2, fmt='o', label=r'$|\mu| \leq 0.5$')
plt.ylabel(r'$|s|.\xi (|s|) \, [h^{-1}.Mpc]$', fontsize=40)
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=4)
if (rescale==2):
plt.errorbar(xxx0, xxx0*xxx0*yyy0, yerr=xxx0*xxx0*yer0, fmt='o', label=r'$0.8 < |\mu|$')
plt.errorbar(xxx1, xxx1*xxx1*yyy1, yerr=xxx1*xxx1*yer1, fmt='o', label=r'$0.5 < |\mu| \leq 0.8$')
plt.errorbar(xxx2, xxx2*xxx2*yyy2, yerr=xxx2*xxx2*yer2, fmt='o', label=r'$|\mu| \leq 0.5$')
plt.ylabel(r'$|s|^{2}.\xi (|s|) \, [(h^{-1}.Mpc)^{2}]$', fontsize=40)
plt.legend(fontsize=30, frameon=False, numpoints=1,ncol=2, loc=2)
plt.title(r'$\delta_{'+forest1+'} \, - \, \delta_{'+forest2+'}$', fontsize=40)
plt.xlabel(r'$|s| \, [h^{-1}.Mpc]$', fontsize=40)
myTools.deal_with_plot(False,False,False)
plt.xlim([ numpy.min(xxx0)-10., numpy.max(xxx0)+10. ])
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.show()