def plot_mass_step(self,sn_name,HOST_pkl):
HOST=cPickle.load(open(HOST_pkl))
mb=self.HF.y_corrected+5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)-5.
data= mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5.
error= N.sqrt(self.HF.y_error_corrected**2+self.Y_err**2)
Masse=N.zeros(len(data))
Masse_minus=N.zeros(len(data))
Masse_plus=N.zeros(len(data))
Filtre_M=N.array([True]*len(data))
for i in range(len(sn_name)):
sn=sn_name[i]
if sn in HOST.keys() :
Masse[i]=HOST[sn]['mchost.mass']
Masse_minus[i]=HOST[sn]['mchost.mass_m.err']
Masse_plus[i]=HOST[sn]['mchost.mass_p.err']
if HOST[sn]['mchost.mass']==0:
Filtre_M[i]=False
else :
Filtre_M[i]=False
MASS_SUP=(Masse[Filtre_M]>10)
Xmass_SUP=N.linspace(10,14,10)
Xmass_low=N.linspace(6,10,10)
Masse_err=[Masse_minus[Filtre_M],Masse_plus[Filtre_M]]
M_SI_SUP=N.average(data[Filtre_M][MASS_SUP],weights=1./error[Filtre_M][MASS_SUP]**2)
wRMS_sup=H.comp_rms(data[Filtre_M][MASS_SUP]-M_SI_SUP, 10, err=False, variance=error[Filtre_M][MASS_SUP]**2)
M_SI_SUP=N.average(data[Filtre_M][MASS_SUP],weights=1./(error[Filtre_M][MASS_SUP]**2+wRMS_sup**2))
M_SI_SUP_err=N.sqrt((1./N.sum(1./(error[Filtre_M][MASS_SUP]**2+wRMS_sup**2))))
M_SI_low=N.average(data[Filtre_M][~MASS_SUP],weights=1./error[Filtre_M][~MASS_SUP]**2)
wRMS_low=H.comp_rms(data[Filtre_M][~MASS_SUP]-M_SI_low, 10, err=False, variance=error[Filtre_M][~MASS_SUP]**2)
M_SI_low=N.average(data[Filtre_M][~MASS_SUP],weights=1./(error[Filtre_M][~MASS_SUP]**2+wRMS_low**2))
M_SI_low_err=N.sqrt((1./N.sum(1./(error[Filtre_M][~MASS_SUP]**2+wRMS_low**2))))
print M_SI_SUP-M_SI_low, ' +/- ', N.sqrt(M_SI_SUP_err**2+M_SI_low_err**2)
pull=abs(M_SI_SUP-M_SI_low)/N.sqrt(M_SI_SUP_err**2+M_SI_low_err**2)
X_SI=N.linspace(min(data)-N.std(data),max(data)+N.std(data),100)
P.figure()
P.text(11,0.4,'$\sigma=%.3f$'%(pull),fontsize=14)
P.scatter(Masse[Filtre_M],data[Filtre_M],c='b')
P.errorbar(Masse[Filtre_M],data[Filtre_M],linestyle='', yerr=error[Filtre_M],xerr=Masse_err,ecolor='grey',alpha=0.9,marker='.',zorder=0)
P.plot(10*N.ones(2),[-0.6,0.6],'k-.',linewidth=2)
P.plot(Xmass_low,N.ones(len(Xmass_low))*M_SI_low,'b')
P.fill_between(Xmass_low,N.ones(len(Xmass_low))*M_SI_low-M_SI_low_err,N.ones(len(Xmass_low))*M_SI_low+M_SI_low_err,color='b',alpha=0.5)
P.plot(Xmass_SUP,N.ones(len(Xmass_SUP))*M_SI_SUP,'b')
P.fill_between(Xmass_SUP,N.ones(len(Xmass_SUP))*M_SI_SUP-M_SI_SUP_err,N.ones(len(Xmass_SUP))*M_SI_SUP+M_SI_SUP_err,color='b',alpha=0.5)
P.ylim(min(data)-N.std(data),max(data)+2*N.std(data))
P.xlabel('$\log(M/M_{\odot})$',fontsize=16)
P.xlim(7,12)
P.ylabel(r'$\mu-\mu_{\Lambda CDM}$',fontsize=15)
P.ylim(-0.6,0.6)
def Make_hubble_diagram(self):
self.HF=Multi.Multilinearfit(self.data,self.Y,yerr=self.Y_err,xerr=None,covx=self.data_cov)
self.HF.Multilinearfit(adddisp=True)
self.HF.comp_stat()
mb=self.Y+5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)-5.
self.residuals=mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5.
def plot_result_before(self,SALT=True):
P.figure(figsize=(9,9))
mb=self.Y+5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)-5.
gs = gridspec.GridSpec(2, 1,height_ratios=[3,1])
#P.subplots_adjust(hspace=0.001)
P.subplots_adjust(left=0.09, bottom=0.07, right=0.99, top=0.95,hspace=0.001)
P.subplot(gs[0])
wwRMS,wwRMS_ERR=H.comp_rms(mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5., 10, err=True, variance=self.Y_err**2)
P.scatter(self.z,mb-self.HF.M0,label='wRMS = %.3f$ \pm$ %.3f'%((wwRMS,wwRMS_ERR)))
P.errorbar(self.z,mb-self.HF.M0 , xerr=None ,
yerr=N.sqrt(self.HF.y_error_corrected**2+self.Y_err**2),linestyle='',alpha=0.5,marker='.',zorder=0)
P.plot(N.linspace(0.001,0.11,10),5.*N.log(d_l(N.linspace(0.001,0.11,10),SNLS=True))/N.log(10.)-5.)
P.xlim(0.01,0.11)
P.ylim(32.2,39)
if SALT:
P.ylabel(r'$\mu=m_B^*-M_0$',fontsize=20)
P.title('Hubble diagram (%i supernovae)'%(len(self.z)))
else:
P.ylabel(r'$\mu=m_B^*-M_0$',fontsize=20)
P.title('SUGAR Hubble diagram (%i supernovae)'%(len(self.z)))
P.semilogx()
P.xticks([2500.,9500.],['toto','pouet'])
P.xlim(0.01,0.11)
P.legend(loc=4)
P.subplot(gs[1])
P.scatter(self.z,mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5.)
P.errorbar(self.z,mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5., xerr=None ,
yerr=N.sqrt(self.HF.y_error_corrected**2+self.Y_err**2),linestyle='',alpha=0.5,marker='.',zorder=0)
P.plot(N.linspace(0.001,0.11,10),N.zeros(10))
P.xlim(0.01,0.11)
P.ylim(-1,1)
P.ylabel(r'$\mu-\mu_{\Lambda CDM}$',fontsize=20)
P.semilogx()
P.xlabel('z',fontsize=20)
def plot_result_after(self,SALT=True):
P.figure(figsize=(9,9))
mb=self.HF.y_corrected+5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)-5.
gs = gridspec.GridSpec(2, 1,height_ratios=[3,1])
P.subplots_adjust(left=0.09, bottom=0.07, right=0.99, top=0.95,hspace=0.001)
P.subplot(gs[0])
#P.scatter(self.z,mb-self.HF.M0,c='b',label='wRMS = %.3f $\pm$ %.3f, $\sigma_{int}$=%.3f'%((self.HF.WRMS,self.HF.WRMS_err,self.HF.disp)))
P.scatter(self.z,mb-self.HF.M0,c='b',label='wRMS = %.3f $\pm$ %.3f'%((self.HF.WRMS,self.HF.WRMS_err)))
P.errorbar(self.z,mb-self.HF.M0 , xerr=None ,
yerr=N.sqrt(self.HF.y_error_corrected**2+self.Y_err**2),linestyle='',alpha=0.5,marker='.',zorder=0)
P.plot(N.linspace(0.001,0.11,10),5.*N.log(d_l(N.linspace(0.001,0.11,10),SNLS=True))/N.log(10.)-5.)
P.xlim(0.01,0.11)
P.ylim(32.2,39)
if SALT:
P.ylabel(r'$\mu=m_B^*-M_0+\alpha x_1 -\beta c$',fontsize=20)
P.title('SALT2 Hubble diagram (%i supernovae)'%(len(self.z)))
else:
P.ylabel(r'$\mu=m_B^*-M_0-\sum_i^{n=3}a_i q_i -b A_V$',fontsize=20)
P.title('SUGAR Hubble diagram (%i supernovae)'%(len(self.z)))
P.semilogx()
P.xticks([2500.,9500.],['toto','pouet'])
P.xlim(0.01,0.11)
P.legend(loc=4)
P.subplot(gs[1])
self.residuals=mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5.
P.scatter(self.z,mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5.,c='b')
P.errorbar(self.z,mb-self.HF.M0-5.*N.log(d_l(self.z,SNLS=True))/N.log(10.)+5., xerr=None ,
yerr=N.sqrt(self.HF.y_error_corrected**2+self.Y_err**2),linestyle='',alpha=0.5,marker='.',zorder=0)
P.plot(N.linspace(0.001,0.11,10),N.zeros(10))
P.xlim(0.01,0.11)
P.ylim(-1,1)
P.ylabel(r'$\mu-\mu_{\Lambda CDM}$',fontsize=20)
P.semilogx()
P.xlabel('z',fontsize=20)
def load_photometry_and_data(self,Filter='B'):
self.Photometry=N.zeros(len(self.sn_name))
self.Photometry_err=N.zeros(len(self.sn_name))
self.data=N.zeros((len(self.sn_name),2))
self.Cov_data=N.zeros((len(self.sn_name),2,2))
self.z=N.zeros(len(self.sn_name))
self.z_err=N.zeros(len(self.sn_name))
self.DayMax=N.zeros(len(self.sn_name))
for i,sn in enumerate(self.sn_name):
self.z[i]=self.dic[sn]['host.zcmb']
self.Photometry[i]=self.dic[sn]['salt2.RestFrameMag_0_'+Filter]-5.*N.log(d_l(self.z[i],SNLS=True))/N.log(10.)+5.
self.Photometry_err[i]=N.sqrt(self.dic[sn]['salt2.RestFrameMag_0_'+Filter+'.err']**2+0.03**2)
self.data[i,0]=self.dic[sn]['salt2.X1']
self.data[i,1]=self.dic[sn]['salt2.Color']
self.Cov_data[i,0,0]=self.dic[sn]['salt2.X1.err']**2
self.Cov_data[i,1,1]=self.dic[sn]['salt2.Color.err']**2
self.Cov_data[i,0,1]=self.dic[sn]['salt2.CovColorX1']
self.Cov_data[i,1,0]=self.dic[sn]['salt2.CovColorX1']
self.z_err[i]=self.dic[sn]['host.zhelio.err']
self.DayMax[i]=self.dic[sn]['salt2.DayMax']