def counts_from_config(Config,bigDataDir,version,expName,gridName,mexp_edges,z_edges,lkneeTOverride=None,alphaTOverride=None):
suffix = ""
if lkneeTOverride is not None:
suffix += "_"+str(lkneeTOverride)
if alphaTOverride is not None:
suffix += "_"+str(alphaTOverride)
mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+suffix+".pkl",'rb'))
#mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+suffix+".pkl",'rb'),encoding='latin1')
experimentName = expName
cosmoDict = dict_from_section(Config,"params")
constDict = dict_from_section(Config,'constants')
clusterDict = dict_from_section(Config,'cluster_params')
clttfile = Config.get("general","clttfile")
cc = ClusterCosmology(cosmoDict,constDict,clTTFixFile = clttfile)
beam = list_from_config(Config,experimentName,'beams')
noise = list_from_config(Config,experimentName,'noises')
freq = list_from_config(Config,experimentName,'freqs')
lmax = int(Config.getfloat(experimentName,'lmax'))
lknee = float(Config.get(experimentName,'lknee').split(',')[0])
alpha = float(Config.get(experimentName,'alpha').split(',')[0])
fsky = Config.getfloat(experimentName,'fsky')
SZProf = SZ_Cluster_Model(cc,clusterDict,rms_noises = noise,fwhms=beam,freqs=freq,lknee=lknee,alpha=alpha)
hmf = Halo_MF(cc,mexp_edges,z_edges)
hmf.sigN = siggrid.copy()
Ns = np.multiply(hmf.N_of_z_SZ(fsky,SZProf),np.diff(z_edges).reshape(1,z_edges.size-1))
return Ns.ravel().sum()
def sel_counts_from_config(Config,bigDataDir,version,expName,gridName,calName,mexp_edges,z_edges,lkneeTOverride=None,alphaTOverride=None,zmin=-np.inf,zmax=np.inf,mmin=-np.inf,mmax=np.inf,recalculate=False,override_params=None):
suffix = ""
if lkneeTOverride is not None:
suffix += "_"+str(lkneeTOverride)
if alphaTOverride is not None:
suffix += "_"+str(alphaTOverride)
mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+suffix+".pkl",'rb'),encoding='latin1')
experimentName = expName
cosmoDict = dict_from_section(Config,"params")
constDict = dict_from_section(Config,'constants')
clusterDict = dict_from_section(Config,'cluster_params')
clttfile = Config.get("general","clttfile")
if override_params is not None:
for key in override_params.keys():
cosmoDict[key] = override_params[key]
# print(cosmoDict)
cc = ClusterCosmology(cosmoDict,constDict,clTTFixFile = clttfile)
beam = list_from_config(Config,experimentName,'beams')
noise = list_from_config(Config,experimentName,'noises')
freq = list_from_config(Config,experimentName,'freqs')
lmax = int(Config.getfloat(experimentName,'lmax'))
lknee = float(Config.get(experimentName,'lknee').split(',')[0])
alpha = float(Config.get(experimentName,'alpha').split(',')[0])
fsky = Config.getfloat(experimentName,'fsky')
SZProf = SZ_Cluster_Model(cc,clusterDict,rms_noises = noise,fwhms=beam,freqs=freq,lknee=lknee,alpha=alpha)
hmf = Halo_MF(cc,mexp_edges,z_edges)
hmf.sigN = siggrid.copy()
saveId = save_id(expName,gridName,calName,version)
# Fiducial number counts
if recalculate:
from . import counts
# get s/n q-bins
qs = list_from_config(Config,'general','qbins')
qspacing = Config.get('general','qbins_spacing')
if qspacing=="log":
qbin_edges = np.logspace(np.log10(qs[0]),np.log10(qs[1]),int(qs[2])+1)
elif qspacing=="linear":
qbin_edges = np.linspace(qs[0],qs[1],int(qs[2])+1)
else:
raise ValueError
calFile = mass_grid_name_owl(bigDataDir,calName)
mexp_edges, z_edges, lndM = pickle.load(open(calFile,"rb"))
dN_dmqz = hmf.N_of_mqz_SZ(lndM,qbin_edges,SZProf)
nmzq = counts.getNmzq(dN_dmqz,mexp_edges,z_edges,qbin_edges)
else:
nmzq = np.load(fid_file(bigDataDir,saveId))
nmzq = nmzq*fsky
zs = (z_edges[1:]+z_edges[:-1])/2.
zsel = np.logical_and(zs>zmin,zs<=zmax)
M_edges = 10**mexp_edges
M = (M_edges[1:]+M_edges[:-1])/2.
Mexp = np.log10(M)
msel = np.logical_and(Mexp>mmin,Mexp<=mmax)
Ns = nmzq.sum(axis=-1)[msel,:][:,zsel]
return Ns #.ravel().sum()
SZProf = SZ_Cluster_Model(cc,
clusterDict,
rms_noises=noise,
fwhms=beam,
freqs=freq,
lknee=lknee,
alpha=alpha)
fsky = 0.4
N1 = hmf.N_of_z() * fsky
#hmf.sigN = np.loadtxt("temp.txt")
try:
hmf.sigN = np.loadtxt("tempSigN.txt")
N2 = hmf.N_of_z_SZ(SZProf) * fsky
except:
N2 = hmf.N_of_z_SZ(SZProf) * fsky
np.savetxt("tempSigN.txt", hmf.sigN)
pl = Plotter()
pl.plot2d(hmf.sigN)
pl.done(outDir + "signRefactor.png")
pl = Plotter(scaleY='log')
pl.add(zs, N1)
pl.add(zs, N2)
Ntot1 = np.trapz(N2, zs)
print(Ntot1)
clttfile = Config.get('general', 'clttfile')
# get s/n q-bins
qs = list_from_config(Config, 'general', 'qbins')
qspacing = Config.get('general', 'qbins_spacing')
if qspacing == "log":
qbins = np.logspace(np.log10(qs[0]), np.log10(qs[1]), int(qs[2]) + 1)
elif qspacing == "linear":
qbins = np.linspace(qs[0], qs[1], int(qs[2]) + 1)
else:
raise ValueError
cc = ClusterCosmology(fparams, constDict, clTTFixFile=clttfile)
HMF = Halo_MF(cc, mexprange, zrange)
HMF.sigN = siggrid.copy()
SZProf = SZ_Cluster_Model(cc,
clusterDict,
rms_noises=noise,
fwhms=beam,
freqs=freq,
lknee=lknee,
alpha=alpha)
h = 0.05
# s80, As = getA(fparams,constDict,zrange,kmax=11.)
# s8zs = As*s80
dNFid_dmzq = HMF.N_of_mqz_SZ(lndM * massMultiplier, qbins, SZProf)
np.save(bigDataDir + "N_mzq_" + saveId + "_fid_sigma8",
getNmzq(dNFid_dmzq, mgrid, zrange, qbins))
calFile = bigDataDir+"lensgrid_"+exp+"_"+gridName+"_"+calName+ "_v" + version+".pkl"
Mexp_edges, z_edges, lndM = pickle.load(open(calFile,"rb"))
mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+".pkl",'rb'))
zs = old_div((z_edges[1:]+z_edges[:-1]),2.)
hmf = Halo_MF(cc,Mexp_edges,z_edges)
SZProf = SZ_Cluster_Model(cc,clusterDict,rms_noises = noise,fwhms=beam,freqs=freq,lknee=lknee,alpha=alpha)
N1 = hmf.N_of_z()*fsky
hmf.sigN = siggrid
N2 = hmf.N_of_z_SZ(SZProf)*fsky
pl = Plotter(scaleY='log')
pl.add(zs,N1)
pl.add(zs,N2)
Ntot0 = np.dot(N1,np.diff(z_edges))
Ntot1 = np.dot(N2,np.diff(z_edges))
print(("All clusters in the Universe ",Ntot0))
print(("All clusters detectable at qmin ",SZProf.qmin," is ",Ntot1))
sn,ntot = hmf.Mass_err(fsky,lndM,SZProf)
outmerr = lndM
