def Forecast_Cellrsx(self, ellBinEdges, fsky, option='None'):
ellMids = old_div((ellBinEdges[1:] + ellBinEdges[:-1]), 2)
cls_rsx = self.fgs.rs_cross(self.evalells,self.freq[0]) / self.cc.c['TCMBmuK']**2. \
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_rsx = old_div(cls_rsx, (self.fgs.rs_nu(
self.freq[0]))) # Normalized to get Cell^rsrs fiducial
LF = LensForecast()
if (option == 'None'):
LF.loadGenericCls("rr", self.evalells, cls_rsx, self.evalells,
self.N_ll_rsx)
elif (option == 'NoILC'):
LF.loadGenericCls("rr", self.evalells, cls_rsx, self.evalells,
self.N_ll_noILC)
else:
return "Wrong option"
sn, errs = LF.sn(ellBinEdges, fsky, "rr") # not squared
cls_out = np.interp(ellMids, self.evalells, cls_rsx)
return ellMids, cls_out, errs, sn
def Forecast_CellrsxEE(self,ellBinEdges,fsky,option='None'):
ellMids = old_div((ellBinEdges[1:] + ellBinEdges[:-1]), 2)
cls_rsxEE = self.fgs.rs_crossEE(self.evalells,self.fgs.nu_rs)/self.cc.c['TCMBmuK']**2./((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_rs = self.fgs.rs_auto(self.evalells,self.fgs.nu_rs,self.fgs.nu_rs) / self.cc.c['TCMBmuK']**2.\
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_cmbEE = self.cc.cleefunc(self.evalells)
LF = LensForecast()
if (option=='None'):
LF.loadGenericCls("rr",self.evalells,cls_rs,self.evalells,self.N_ll_rsxEE_c_cmb)#self.N_ll_rsxEE)
LF.loadGenericCls("xx",self.evalells,cls_rsxEE,self.evalells,self.N_ll_rsx*0.0)
LF.loadGenericCls("ee",self.evalells,cls_cmbEE,self.evalells,self.N_ll_rsxEE_c_rs)#self.N_ll_cmb)
Nellrs = self.N_ll_rsxEE_c_cmb
NellcmbEE = self.N_ll_rsxEE_c_rs
elif (option=='NoILC'):
LF.loadGenericCls("rr",self.evalells,cls_rs,self.evalells,self.N_ll_rsxEE_NoFG)
LF.loadGenericCls("xx",self.evalells,cls_rsxEE,self.evalells,self.N_ll_rsx*0.0)
LF.loadGenericCls("ee",self.evalells,cls_cmbEE,self.evalells,self.N_ll_cmbee_NoFG)
Nellrs = self.N_ll_rsxEE_NoFG
NellcmbEE = self.N_ll_cmbee_NoFG
else:
return "Wrong option"
sn2=(2.*self.evalells+1.)*np.nan_to_num((cls_rsxEE**2)/((cls_rs+Nellrs)*(cls_cmbEE+NellcmbEE)+(cls_rsxEE)**2))#**2)#)/(cls_cmbEE+NellcmbEE))
snsq=fsky/2.*sum(sn2)
sn=np.sqrt(snsq)
cls_out = np.interp(ellMids,self.evalells,cls_rsxEE)
ellMids = (ellBinEdges[1:] + ellBinEdges[:-1]) / 2
ellWidths = np.diff(ellBinEdges)
covs = []
for ell_left,ell_right in zip(ellBinEdges[:-1],ellBinEdges[1:]):
ClSum = LF._bin_cls("rr",ell_left,ell_right)*LF._bin_cls("ee",ell_left,ell_right)+(LF._bin_cls("xx",ell_left,ell_right))**2
ellMid = (ell_right+ell_left)/2.
ellWidth = ell_right-ell_left
var = ClSum/(2.*ellMid+1.)/ellWidth/fsky
covs.append(var)
#print(LF._bin_cls("xx",ell_left,ell_right))
errs=np.sqrt(np.array(covs))
return ellMids,cls_out,errs,sn
def Forecast_Cellcmb(self,ellBinEdges,fsky,constraint='None'):
ellMids = old_div((ellBinEdges[1:] + ellBinEdges[:-1]), 2)
cls_cmb = self.cc.clttfunc(self.evalells)
LF = LensForecast()
if (constraint=='None'):
LF.loadGenericCls("tt",self.evalells,cls_cmb,self.evalells,self.N_ll_cmb)
elif (constraint=='tsz'):
LF.loadGenericCls("tt",self.evalells,cls_cmb,self.evalells,self.N_ll_cmb_c_tsz)
else:
return "Wrong option"
sn,errs = LF.sn(ellBinEdges,fsky,"tt") # not squared
cls_out = np.interp(ellMids,self.evalells,cls_cmb)
return ellMids,cls_out,errs,sn
def Forecast_Cellyy(self, ellBinEdges, fsky, constraint='None'):
ellMids = old_div((ellBinEdges[1:] + ellBinEdges[:-1]), 2)
cls_tsz = self.fgs.tSZ(self.evalells,self.freq[0],self.freq[0]) / self.cc.c['TCMBmuK']**2. \
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_yy = old_div(cls_tsz,
(f_nu(self.cc.c,
self.freq[0]))**2) # Normalized to get Cell^yy
LF = LensForecast()
if (constraint == 'None'):
LF.loadGenericCls("yy", self.evalells, cls_yy, self.evalells,
self.N_ll_tsz)
elif (constraint == 'cmb'):
LF.loadGenericCls("yy", self.evalells, cls_yy, self.evalells,
self.N_ll_tsz_c_cmb)
elif (constraint == 'cib'):
LF.loadGenericCls("yy", self.evalells, cls_yy, self.evalells,
self.N_ll_tsz_c_cib)
else:
return "Wrong option"
sn, errs = LF.sn(ellBinEdges, fsky, "yy") # not squared
cls_out = np.interp(ellMids, self.evalells, cls_yy)
return ellMids, cls_out, errs, sn
def Forecast_CellrsxEE(self, ellBinEdges, fsky, option='None'):
ellMids = old_div((ellBinEdges[1:] + ellBinEdges[:-1]), 2)
cls_rsxEE = self.fgs.rs_crossEE(
self.evalells, self.fgs.nu_rs) / self.cc.c['TCMBmuK']**2. / (
(self.evalells + 1.) * self.evalells) * 2. * np.pi
LF = LensForecast()
if (option == 'None'):
LF.loadGenericCls("ee", self.evalells, cls_rsxEE, self.evalells,
self.N_ll_rsxEE)
elif (option == 'NoILC'):
LF.loadGenericCls("ee", self.evalells, cls_rsxEE, self.evalells,
self.N_ll_rsx_NoFG)
elif (option == 'Onlyfg'):
LF.loadGenericCls("ee", self.evalells, cls_rsxEE, self.evalells,
self.N_ll_rsxEE - self.N_ll_rsx_NoFG)
else:
return "Wrong option"
sn, errs = LF.sn(ellBinEdges, fsky, "ee") # not squared
cls_out = np.interp(ellMids, self.evalells, cls_rsxEE)
return ellMids, cls_out, errs, sn
def Forecast_Cellrsx(self,ellBinEdges,fsky,option='None'):
ellMids = (ellBinEdges[1:] + ellBinEdges[:-1])/ 2.
cls_rsx = self.fgs.rs_cross(self.evalells,self.fgs.nu_rs) / self.cc.c['TCMBmuK']**2.\
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_rs = self.fgs.rs_auto(self.evalells,self.fgs.nu_rs,self.fgs.nu_rs) / self.cc.c['TCMBmuK']**2.\
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_cmb = self.cc.clttfunc(self.evalells)
LF = LensForecast()
if (option=='None'):
LF.loadGenericCls("rr",self.evalells,cls_rs,self.evalells,self.N_ll_rs_c_cmb)#self.N_ll_rsx)
LF.loadGenericCls("xx",self.evalells,cls_rsx,self.evalells,self.N_ll_rsx*0.0)
LF.loadGenericCls("tt",self.evalells,cls_cmb,self.evalells,self.N_ll_cmb_c_rs)#self.N_ll_cmb)
Nellrs = self.N_ll_rs_c_cmb #self.N_ll_rsx
Nellcmb = self.N_ll_cmb_c_rs #self.N_ll_cmb
elif (option=='NoILC'):
LF.loadGenericCls("rr",self.evalells,cls_rs,self.evalells,self.N_ll_rsx_NoFG)
LF.loadGenericCls("xx",self.evalells,cls_rsx,self.evalells,self.N_ll_rsx*0.0)
LF.loadGenericCls("tt",self.evalells,cls_cmb,self.evalells,self.N_ll_cmb_NoFG)
Nellrs = self.N_ll_rsx_NoFG
Nellcmb = self.N_ll_cmb_NoFG
else:
return "Wrong option"
sn2=(2.*self.evalells+1.)*np.nan_to_num((cls_rsx**2)/((cls_rs+Nellrs)*(cls_cmb+Nellcmb)+(cls_rsx)**2))
snsq=fsky/2.*sum(sn2)
sn=np.sqrt(snsq)
cls_out = np.interp(ellMids,self.evalells,cls_rsx)
#errs = cls_out * 0.0 + 1.
ellMids = (ellBinEdges[1:] + ellBinEdges[:-1]) / 2
ellWidths = np.diff(ellBinEdges)
covs = []
for ell_left,ell_right in zip(ellBinEdges[:-1],ellBinEdges[1:]):
ClSum = LF._bin_cls("rr",ell_left,ell_right)*LF._bin_cls("tt",ell_left,ell_right)+(LF._bin_cls("xx",ell_left,ell_right))**2
ellMid = (ell_right+ell_left)/2.
ellWidth = ell_right-ell_left
var = ClSum/(2.*ellMid+1.)/ellWidth/fsky
covs.append(var)
errs=np.sqrt(np.array(covs))
'''
ellMids = (ellBinEdges[1:] + ellBinEdges[:-1])/ 2.
ellWidth = (ellBinEdges[1:] - ellBinEdges[:-1])
#cls_rsx = self.fgs.rs_cross(self.evalells,self.freq[0]) / self.cc.c['TCMBmuK']**2.\
# / ((self.evalells+1.)*self.evalells) * 2.* np.pi
#print (cls_rsx, self.freq[0],self.fgs.nu_rs)
cls_rsx = self.fgs.rs_cross(self.evalells,self.fgs.nu_rs) / self.cc.c['TCMBmuK']**2.\
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_rs = self.fgs.rs_auto(self.evalells,self.fgs.nu_rs,self.fgs.nu_rs) / self.cc.c['TCMBmuK']**2.\
/ ((self.evalells+1.)*self.evalells) * 2.* np.pi
cls_cmb = self.cc.clttfunc(self.evalells)
print (cls_rsx,cls_rs, cls_cmb)
LF = LensForecast()
if (option=='None'):
LF.loadGenericCls("rr",self.evalells,cls_rsx,self.evalells,self.N_ll_rsx)
#LF.loadGenericCls("tt",self.evalells,cls_cmb,self.evalells,self.N_ll_cmb)
Nellrsx = self.N_ll_rs_c_cmb #self.N_ll_rsx
Nellcmb = self.N_ll_cmb_c_rs #self.N_ll_cmb
elif (option=='NoILC'):
LF.loadGenericCls("rr",self.evalells,cls_rsx,self.evalells,self.N_ll_rsx_NoFG)
#LF.loadGenericCls("tt",self.evalells,cls_cmb,self.evalells,self.N_ll_rsx_NoFG)
Nellrsx = self.N_ll_rsx_NoFG
Nellcmb = self.N_ll_cmb_NoFG
else:
return "Wrong option"
print (Nellrsx,Nellcmb)
#vars, _, _ = LF.KnoxCov("rr","tt",ellBinEdges,fsky)
#sn,errs = LF.sn(ellBinEdges,fsky,"rr") # not squared
ind = np.isfinite(Nellrsx)
sn = np.sqrt ( fsky / 2 * np.sum( (2.*self.evalells[ind] + 1) * (cls_rsx[ind]**2) / ((cls_rs[ind] + Nellrsx[ind])* (cls_cmb[ind] + Nellcmb[ind])) ) )
errs = 1.#np.sqrt(vars)
cls_out = np.interp(ellMids,self.evalells,cls_rsx)
#errs = cls_out * 0.0 + 1.
return ellMids,cls_out,errs,sn
'''
return ellMids,cls_out,errs,sn