def Prob_per_cluster(self,int_HMF,cluster_props,dn_dzdm_int):
c_z, c_zerr, c_m, c_merr = cluster_props
if (c_zerr > 0):
z_arr = np.arange(-3.*c_zerr,(3.+0.1)*c_zerr,c_zerr) + c_z
dn_dzdm = dn_dzdm_int(z_arr,np.log10(c_m))
N_per = np.trapz(dn_dzdm*gaussian(z_arr,c_z,c_zerr),dx=np.diff(z_arr))
ans = N_per
else:
dn_dzdm = dn_dzdm_int(c_z,np.log10(c_m))
ans = dn_dzdm
#print dn_dzdm
#kmin=1e-4
#kmax=5.
#knum=200
#int_kh, int_pk = int_HMF._pk(c_z,kmin,kmax,knum)
#delts = int_HMF.zarr*0.0 + 200.
#print c_m
#dn_dlnm = dn_dlogM(np.array([c_m,c_m*1.01]),c_z,int_HMF.cc.rhoc0om,200,int_kh,int_pk,'comoving')
#print dn_dlogM(np.array([c_m,c_m*1.01]),int_HMF.zarr,int_HMF.cc.rhoc0om,delts,int_HMF.kh,int_HMF.pk,'comoving')
#print dn_dlnm
#print int_HMF.dn_dM(np.outer(np.array([1e14*1.01,1e14*1.05]),np.ones(len(int_HMF.zarr))),200.)
#print int_HMF.M200[0:2,0]
#m1 = int_HMF.M200[0,0]
#m2 = int_HMF.M200[1,0]
#print m1,m2,c_m
#print int_HMF.dn_dM(np.outer(np.array([m1*1.01,c_m*1.01]),np.ones(len(int_HMF.zarr))),200.)
#print int_HMF.dn_dM(int_HMF.M200[0:2,:],200.)
#print int_HMF.dn_dM(np.outer(np.array([c_m*1.01,1e14*1.01]),np.ones(len(int_HMF.zarr))),200.)
#blahs = dn_dlnm/np.array([c_m,c_m])
#print blahs
return ans
def lnprior(self,theta,parlist,priorval,priorlist):
param_vals = alter_fparams(self.fparams,parlist,theta)
prioravg = priorval[0,:]
priorwth = priorval[1,:]
lnp = 0.
for k,prioravg in enumerate(prioravg):
lnp += np.log(gaussian(param_vals[priorlist[k]],prioravg,priorwth[k],noNorm=True))
try:
if ((param_vals['scat'] < 0) or (param_vals['tau'] < 0)) :
lnp += -np.inf
except:
pass
if (self.simpars):
pars = ['omch2','ombh2','H0','As','ns']
mins = [0.001,0.005,40.,7.389056098930651e-10,0.8]
maxs = [0.99,0.1,100.,5.459815003314424e-09,1.2]
else:
pars = ['omch2','ombh2','H0','As','ns','massbias','yslope','scat']
mins = [0.001,0.005,40.,7.389056098930651e-10,0.8,0.2,-0.6,0.001 ]
maxs = [0.99,0.1,100.,5.459815003314424e-09,1.2,1.4,0.6,0.8 ]
for k,par in enumerate(pars):
if param_vals[par]<mins[k] or param_vals[par]>maxs[k]: lnp += -np.inf
return lnp
def Prob_per_cluster(self, int_HMF, cluster_props, dn_dzdm_int,
param_vals):
c_z, c_zerr, c_y, c_yerr = cluster_props
if (c_zerr > 0):
z_arr = np.arange(-3. * c_zerr, (3. + 0.1) * c_zerr, c_zerr) + c_z
Pfunc_ind, M200 = self.Pfunc_per_zarr(int_HMF.M.copy(), z_arr, c_y,
c_yerr, int_HMF, param_vals)
dn_dzdm = dn_dzdm_int(z_arr, np.log10(int_HMF.M.copy()))
N_z_ind = np.trapz(dn_dzdm * Pfunc_ind,
dx=np.diff(M200, axis=0),
axis=0)
N_per = np.trapz(N_z_ind * gaussian(z_arr, c_z, c_zerr),
dx=np.diff(z_arr))
ans = N_per
else:
Pfunc_ind = self.Pfunc_per(int_HMF.M.copy(), c_z, c_y, c_yerr,
param_vals)
#print "PFunc",Pfunc_ind
M200 = int_HMF.cc.Mass_con_del_2_del_mean200(
int_HMF.M.copy(), 500, c_z)
dn_dzdm = dn_dzdm_int(c_z, np.log10(int_HMF.M.copy()))[:, 0]
#print "dndm", dn_dzdm,dn_dzdm_int(c_z,np.log10(int_HMF.M.copy()))
#print "M200", M200
N_z_ind = np.trapz(dn_dzdm * Pfunc_ind,
dx=np.diff(M200, axis=0),
axis=0)
ans = N_z_ind
return ans
def Y_prob (self,Y_c,LgY,YNoise):
Y = 10**(LgY)
ans = gaussian(Y,Y_c,YNoise)
return ans
