Python myTools.deal_with_plot函数代码示例

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