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Likelihood.py
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Likelihood.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Oct 6 19:29:07 2018
@author: qijingzhao
"""
import numpy as np
import scipy.special as S
import scipy.constants as sc
import os
import pandas as pd
from .MCMC import MCMC_class
#print( os.getcwd())
#print(os.path.dirname(os.path.abspath(__file__)))
dataDir=os.path.dirname(os.path.abspath(__file__))+'/data/'
class likelihood(object):
def __init__(self,cosModel,param):
self.cosModel = cosModel
self.param = param
self.pn=len(param)
self.infor='The data used are: '
self.data_num=0
self.SN2=lambda x:0
self.JLA2=lambda x:0
self.OHD2=lambda x:0
#==============================================================================================
@property
def _read_SN(self):
'''
Data from 'The Astrophysical Journal, 2012, 746(1):85'
'''
(self._zsn,self._u_obs,self._u_err)=np.loadtxt(dataDir+"sn.txt", unpack='True')
return len(self._zsn)
def chiSN(self,theta):
ld = self.cosModel(theta[0:self.pn]).lum_dis_z
mu_th = 5.0*np.log10(ld(self._zsn))
A=np.sum(((mu_th-self._u_obs)/self._u_err)**2)
B=np.sum((mu_th-self._u_obs)/self._u_err**2)
C=np.sum(1.0/self._u_err**2)
kaf=A-B**2/C
return kaf
@property
def addSN(self):
snn=self._read_SN
self.SN2=self.chiSN
self.infor=self.infor+'SN(%s)+'%snn
self.data_num=self.data_num+snn
#==================================JLA============================================================
def __covRead(self, file_name):
tmp = np.fromfile(dataDir + file_name, sep=" ")
n = int(tmp[0])
cov = tmp[1:]
cov = np.reshape(cov, (n,n))
return cov
@property
def _read_JLA(self):
jla=pd.read_csv(dataDir+'jla_lcparams.txt',sep='\s+',index_col=0)
jn=len(jla)
self._zcmb=jla['zcmb'].values
self._zhel=jla['zhel'].values
self._mb=jla['mb'].values
self._dmb=jla['dmb'].values
self._x1=jla['x1'].values
self._dx1=jla['dx1'].values
self._color=jla['color'].values
self._dcolor=jla['dcolor'].values
self._3rdvar=jla['3rdvar'].values
self._cov_m_s=jla['cov_m_s'].values
self._cov_m_c=jla['cov_m_c'].values
self._cov_s_c=jla['cov_s_c'].values
self._v0 = self.__covRead('jla_v0_covmatrix.dat')
self._va = self.__covRead('jla_va_covmatrix.dat')
self._vb = self.__covRead('jla_vb_covmatrix.dat')
self._v0a = self.__covRead('jla_v0a_covmatrix.dat')
self._v0b = self.__covRead('jla_v0b_covmatrix.dat')
self._vab = self.__covRead('jla_vab_covmatrix.dat')
self._prob=np.zeros(len(self._zcmb))
self._prob[np.where(self._3rdvar>10)]=1.0
self.jlap_n=len(self.param)
return jn
def chiJLA(self,theta):
# JLA nuiance parameters
# ==========================
# p[0]:alpha
# p[1]:beta
# p[2]:M
# p[3]:DeltaM
p=theta[self.jlap_n:self.jlap_n+4]
cov_stat_sys=self._v0+p[0]**2*self._va+p[1]**2*self._vb+2*p[0]*self._v0a-2*p[1]*self._v0b-2*p[0]*p[1]*self._vab
Dstat=self._dmb**2+(p[0]*self._dx1)**2+(p[1]*self._dcolor)**2+2*p[0]*self._cov_m_s-2.0*p[1]*self._cov_m_c-2.0*p[0]*p[1]*self._cov_s_c
covMatrix=np.diag(Dstat)+cov_stat_sys
mu_sn=self._mb+p[0]*self._x1-p[1]*self._color-p[2]-p[3]*self._prob
mu_th=5.0*np.log10( (1.0+self._zhel)* self.cosModel(theta[0:self.pn]).co_dis_z(self._zcmb))+25.0
res=mu_sn-mu_th
residuals = np.dot(res,np.dot(np.linalg.inv(covMatrix),res))
return residuals
@property
def addJLA(self):
jla_param=[['\\alpha_{JLA}',0.135,0.1,0.2],
['\\beta_{JLA}',3.1,2.8,3.4],
['M_B^1',-19.00,-19.2,-18.9],
['\\Delta M',-0.07,-0.2,0]]
jlan=self._read_JLA
self.param=self.param+jla_param
self.JLA2=self.chiJLA
self.infor=self.infor+'JLA(%s)+'%jlan
self.data_num=self.data_num+jlan
#=============================================================================================
#=============================OHD likelihood=====================================
@property
def _read_OHD(self):
''' Data from ' The Astrophysical Journal, Volume 838, Number 2
DOI:10.3847/1538-4357/aa674b
arXiv:1611.00904
An Improved Method to Measure the Cosmic Curvature'
'''
(self._zhz,self._Hz_obs,self._Hz_err)=np.loadtxt(dataDir+"OHD.txt",unpack='True')
return len(self._zhz)
def chiOHD(self,theta):
# h_obs=(self.Hz_obs/H0)
# h_err=(np.sqrt(self.Hz_err**2/H0**2+(self.Hz_obs**2/H0**4)*H0_s**2))
kaf=np.sum((self.cosModel(theta[0:self.pn]).hubz(self._zhz)*self.cosModel(theta[0:self.pn]).h*1e2-self._Hz_obs)**2/self._Hz_err**2)
return kaf
@property
def addOHD(self):
ohdn=self._read_OHD
self.OHD2=self.chiOHD
self.infor=self.infor+'OHD(%s)+'%ohdn
self.data_num=self.data_num+ohdn
#============================================================================================
def chi2(self,theta):
return self.JLA2(theta)+self.SN2(theta)+self.OHD2(theta)
def MCMC(self,chain_name,steps=500):
print ('\n'+'='*60)
print(self.infor[:-1])
MC=MCMC_class(self.param,self.chi2,chain_name,self.data_num)
MC.MCMC(steps)