def __init__(self,iniFile,expName,gridName,version,ClusterCosmology):
Config = SafeConfigParser()
Config.optionxform=str
Config.read(iniFile)
self.cc = ClusterCosmology
bigDataDir = Config.get('general','bigDataDirectory')
self.clttfile = Config.get('general','clttfile')
self.constDict = dict_from_section(Config,'constants')
self.clusterDict = dict_from_section(Config,'cluster_params')
#version = Config.get('general','version')
beam = list_from_config(Config,expName,'beams')
noise = list_from_config(Config,expName,'noises')
freq = list_from_config(Config,expName,'freqs')
lknee = list_from_config(Config,expName,'lknee')[0]
alpha = list_from_config(Config,expName,'alpha')[0]
self.fsky = Config.getfloat(expName,'fsky')
self.mgrid,self.zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+".pkl",'rb'))
#self.cc = ClusterCosmology(self.fparams,self.constDict,clTTFixFile=self.clttfile)
self.SZProp = SZ_Cluster_Model(self.cc,self.clusterDict,rms_noises = noise,fwhms=beam,freqs=freq,lknee=lknee,alpha=alpha)
self.HMF = Halo_MF(self.cc,self.mgrid,self.zgrid)
self.HMF.sigN = siggrid.copy()
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 priors_from_config(Config,
expName,
calName,
fishName,
paramList,
tauOverride=None):
fishSection = 'fisher-' + fishName
try:
priorNameList = Config.get(fishSection, 'prior_names').split(',')
priorValueList = list_from_config(Config, fishSection, 'prior_values')
except:
priorNameList = []
priorValueList = []
if tauOverride is not None:
try:
tauind = priorNameList.index('tau')
priorValueList[tauind] = tauOverride
except ValueError:
priorNameList.append("tau")
priorValueList.append(tauOverride)
# if "CMB" in calName:
# assert "sigR" not in paramList
# paramList.append("sigR")
# try:
# priorNameList.append("sigR")
# beam = list_from_config(Config,expName,'beams')
# freq = list_from_config(Config,expName,'freqs')
# freq_to_use = Config.getfloat(calName,'freq')
# ind = np.where(np.isclose(freq,freq_to_use))
# beamFind = np.array(beam)[ind]
# priorValueList.append(old_div(beamFind,2.))
# print("Added sigR prior ", priorValueList[-1])
# except:
# traceback.print_exc()
# print("Couldn't add sigR prior. Is this CMB lensing? Exiting.")
# sys.exit(1)
# if not("b_wl" in paramList):
# print "OWL but b_wl not found in paramList. Adding with a 1% prior."
# paramList.append("b_wl")
#priorNameList.append("b_wl")
#priorValueList.append(0.01)
# if "owl" in calName:
# if not("b_wl" in paramList):
# print "OWL but b_wl not found in paramList. Adding with a 1% prior."
# paramList.append("b_wl")
# priorNameList.append("b_wl")
# priorValueList.append(0.01)
print(paramList, priorNameList, priorValueList)
return paramList, priorNameList, priorValueList
def instantiate_grids(self):
mubins = list_from_config(self.config, 'general', 'mubins')
self.mus = np.linspace(mubins[0], mubins[1], int(mubins[2]) + 1)
cc = ClusterCosmology(paramDict=self.params,
constDict=self.constdict,
clTTFixFile=self.clttfile)
clst = Clustering(self.inifile, self.expname, self.gridname,
self.version, cc)
self.ks = clst.HMF.kh
self.ps_fid = clst.fine_ps_bar(self.mus)
self.veff_fid = clst.V_eff(self.mus)
self.deltaks = np.gradient(self.ks)
self.deltamus = np.gradient(self.mus)
# Get list of parameters
paramList = Config.get("fisher","paramList").split(',')
# Load fiducials and derivatives
fidCls = tryLoad(derivRoot+'_fCls.csv',',')
dCls = {}
for paramName in paramList:
dCls[paramName] = tryLoad(derivRoot+'_dCls_'+paramName+'.csv',',')
# Get CMB noise functions and ell ranges.
if (noise_func_tt is None) or (noise_func_ee is None):
fnTT, fnEE = noiseFromConfig(Config,expName,TCMB=TCMB,beamsOverride=None,noisesOverride=None,lkneeTOverride=None,lkneePOverride=None,alphaTOverride=None,alphaPOverride=None)
tellmin,tellmax = list_from_config(Config,expName,'tellrange')
pellmin,pellmax = list_from_config(Config,expName,'pellrange')
if (noise_func_tt is not None):
fnTT = cosmology.noise_pad_infinity(noise_func_tt,tellmin,tellmax)
if (noise_func_ee is not None):
fnEE = cosmology.noise_pad_infinity(noise_func_ee,pellmin,pellmax)
# Pad CMB lensing noise with infinity outside L ranges
kellmin,kellmax = list_from_config(Config,'lensing','Lrange')
fnKK = cosmology.noise_pad_infinity(interp1d(ls,Nls,fill_value=np.inf,bounds_error=False),kellmin,kellmax)
# Decide on what ell range to calculate the Fisher matrix
ellrange = np.arange(min(tellmin,pellmin,kellmin),max(tellmax,pellmax,kellmax)).astype(int)
# Get fsky
pzcutoff = Config.getfloat('general', 'photoZCutOff')
mgrid, zgrid, siggrid = pickle.load(open(
bigDataDir + "szgrid_" + expName + "_" + gridName + "_v" + version +
".pkl", 'rb'),
encoding='latin1')
assert np.all(mgrid == mexprange)
assert np.all(zrange == zgrid)
saveId = expName + "_" + gridName + "_" + calName + "_v" + version
from orphics.io import dict_from_section, list_from_config
constDict = dict_from_section(Config, 'constants')
clusterDict = dict_from_section(Config, 'cluster_params')
beam = list_from_config(Config, expName, 'beams')
noise = list_from_config(Config, expName, 'noises')
freq = list_from_config(Config, expName, 'freqs')
lknee = list_from_config(Config, expName, 'lknee')[0]
alpha = list_from_config(Config, expName, 'alpha')[0]
fsky = Config.getfloat(expName, 'fsky')
massMultiplier = Config.getfloat('general', 'mass_calib_factor')
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)
def lensNoise(Config,
expName,
lensName,
beamOverride=None,
noiseTOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None,
tellminOverride=None,
pellminOverride=None,
tellmaxOverride=None,
pellmaxOverride=None,
deg=5.,
px=1.0,
gradCut=10000,
bigell=9000,
plot=False,
theoryOverride=None,
lensedEqualsUnlensed=True,
noiseFuncT=None,
noiseFuncP=None):
from orphics.io import list_from_config
beam = list_from_config(Config, expName, 'beams')
noise = list_from_config(Config, expName, 'noises')
freq = list_from_config(Config, expName, 'freqs')
lkneeT, lkneeP = list_from_config(Config, expName, 'lknee')
alphaT, alphaP = list_from_config(Config, expName, 'alpha')
if (noiseFuncT is None) and (noiseFuncP is None):
print 'Not using noise files for generating lensing noise'
else:
print 'Using noise files for generating lensing noise'
tellmin, tellmax = list_from_config(Config, expName, 'tellrange')
if tellminOverride is not None: tellmin = tellminOverride
if tellmaxOverride is not None: tellmax = tellmaxOverride
pellmin, pellmax = list_from_config(Config, expName, 'pellrange')
if pellminOverride is not None: pellmin = pellminOverride
if pellmaxOverride is not None: pellmax = pellmaxOverride
lmax = int(Config.getfloat(expName, 'lmax'))
pols = Config.get(lensName, 'polList').split(',')
freq_to_use = Config.getfloat(lensName, 'freq')
ind = np.where(np.isclose(freq, freq_to_use))
beamFind = np.array(beam)[ind]
noiseFind = np.array(noise)[ind]
assert beamFind.size == 1
assert noiseFind.size == 1
if beamOverride is not None:
beamX = beamY = beamOverride
else:
beamX = beamY = beamFind[0]
if noiseTOverride is not None:
noiseTX = noiseTY = noiseTOverride
else:
noiseTX = noiseTY = noiseFind[0]
if lkneeTOverride is not None: lkneeT = lkneeTOverride
if lkneePOverride is not None: lkneeP = lkneePOverride
if alphaTOverride is not None: alphaT = alphaTOverride
if alphaPOverride is not None: alphaP = alphaPOverride
from orphics.lensing import NlGenerator, getMax
from orphics import maps
#deg = 5.
#px = 1.0
dell = 10
kellmin = 10
shape, wcs = maps.rect_geometry(width_deg=deg, px_res_arcmin=px)
kellmax = max(tellmax, pellmax)
if theoryOverride is None:
from orphics.cosmology import Cosmology
cc = Cosmology(lmax=int(kellmax), pickling=True)
theory = cc.theory
else:
theory = theoryOverride
cc = None
bin_edges = np.arange(kellmin, kellmax, dell)
myNls = NlGenerator(shape,
wcs,
theory,
bin_edges,
gradCut=gradCut,
bigell=bigell,
unlensedEqualsLensed=lensedEqualsUnlensed)
myNls.updateNoise(beamX,
noiseTX,
np.sqrt(2.) * noiseTX,
tellmin,
tellmax,
pellmin,
pellmax,
beamY=beamY,
noiseTY=noiseTY,
noisePY=np.sqrt(2.) * noiseTY,
lkneesX=(lkneeT, lkneeP),
lkneesY=(lkneeT, lkneeP),
alphasX=(alphaT, alphaP),
alphasY=(alphaT, alphaP),
noiseFuncTX=noiseFuncT,
noiseFuncTY=noiseFuncT,
noiseFuncPX=noiseFuncP,
noiseFuncPY=noiseFuncP)
lsmv, Nlmv, ells, dclbb, efficiency = myNls.getNlIterative(pols,
kellmin,
kellmax,
tellmax,
pellmin,
pellmax,
dell=dell,
halo=True,
plot=plot)
return lsmv, Nlmv, ells, dclbb, efficiency, cc
iniFile = "input/pipeline.ini"
Config = SafeConfigParser()
Config.optionxform = str
Config.read(iniFile)
expList = [
"dummy-S3", "S4-3.0-0.4", "S4-2.5-0.4", "S4-2.0-0.4", "S4-1.5-0.4",
"S4-1.0-0.4"
]
beams = {}
noises = {}
for i, expName in enumerate(expList):
beams[expName] = list_from_config(Config, expName, 'beams')
freqs = list_from_config(Config, expName, 'freqs')
if i == 0: freqOrig = list(freqs)
assert [f == forig for f, forig in zip(freqs, freqOrig)]
latex = ""
print("BEAMS")
print("============")
for i, freq in enumerate(freqs):
latex += '{:.0f}'.format(freq)
for exp in expList:
val = beams[exp][i]
".pkl", 'rb'))
#for expName,col in zip(expList,colList):
# mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+".pkl",'rb'))
if (cal == 'owl2'):
massGridName = bigDataDir + "lensgrid_grid-" + cal + "_" + cal + ".pkl"
else:
massGridName = bigDataDir + "lensgrid_" + expName + "_" + gridName + "_" + cal + "_v" + version + ".pkl"
mexp_edges, z_edges, lndM = pickle.load(open(massGridName, "rb"))
zrange = (z_edges[1:] + z_edges[:-1]) / 2.
beam = list_from_config(Config, expName, 'beams')
noise = list_from_config(Config, expName, 'noises')
freq = list_from_config(Config, expName, 'freqs')
lknee = list_from_config(Config, expName, 'lknee')[0]
alpha = list_from_config(Config, expName, 'alpha')[0]
fsky = Config.getfloat(expName, 'fsky')
HMF = Halo_MF(cc, mexp_edges, z_edges)
HMF.sigN = siggrid.copy()
SZProf = SZ_Cluster_Model(cc,
clusterDict,
rms_noises=noise,
fwhms=beam,
freqs=freq,
lknee=lknee,
alpha=alpha)
fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
if key == 'sigR':
rayFid = float(param)
rayStep = float(step)
fparams[key] = float(param)
else:
fparams[key] = float(val)
from orphics.io import dict_from_section, list_from_config
bigDataDir = Config.get('general', 'bigDataDirectory')
ms = list_from_config(Config, gridName, 'mexprange')
Mexp_edges = np.arange(ms[0], ms[1] + ms[2], ms[2])
zs = list_from_config(Config, gridName, 'zrange')
z_edges = np.arange(zs[0], zs[1] + zs[2], zs[2])
M_edges = 10**Mexp_edges
M = old_div((M_edges[1:] + M_edges[:-1]), 2.)
mgrid = np.log10(M)
zgrid = old_div((z_edges[1:] + z_edges[:-1]), 2.)
beam = list_from_config(Config, expName, 'beams')
noise = list_from_config(Config, expName, 'noises')
freq = list_from_config(Config, expName, 'freqs')
fsky = Config.getfloat(expName, 'fsky')
try:
def noiseFromConfig(Config,
expName,
TCMB=2.7255e6,
beamsOverride=None,
noisesOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None,
tellminOverride=None,
pellminOverride=None,
tellmaxOverride=None,
pellmaxOverride=None):
tellmin, tellmax = list_from_config(Config, expName, 'tellrange')
if tellminOverride is not None: tellmin = tellminOverride
if tellmaxOverride is not None: tellmax = tellmaxOverride
pellmin, pellmax = list_from_config(Config, expName, 'pellrange')
if pellminOverride is not None: pellmin = pellminOverride
if pellmaxOverride is not None: pellmax = pellmaxOverride
if beamsOverride is not None:
beams = beamsOverride
else:
beams = list_from_config(Config, expName, 'beams')
if noisesOverride is not None:
noises = noisesOverride
else:
noises = list_from_config(Config, expName, 'noises')
lkneeT, lkneeP = list_from_config(Config, expName, 'lknee')
alphaT, alphaP = list_from_config(Config, expName, 'alpha')
if lkneeTOverride is not None: lkneeT = lkneeTOverride
if lkneePOverride is not None: lkneeP = lkneePOverride
if alphaTOverride is not None: alphaT = alphaTOverride
if alphaPOverride is not None: alphaP = alphaPOverride
invnTTs = 0.
invnEEs = 0.
for beam, noise in zip(beams, noises):
invnTTs += 1. / cosmo.noise_func(np.arange(tellmin, tellmax),
beam,
noise,
lknee=lkneeT,
alpha=alphaT,
TCMB=TCMB,
dimensionless=True)
invnEEs += 1. / cosmo.noise_func(np.arange(pellmin, pellmax),
beam,
noise * np.sqrt(2.),
lknee=lkneeP,
alpha=alphaP,
TCMB=TCMB,
dimensionless=True)
fnTT = interp1d(np.arange(tellmin, tellmax),
1. / invnTTs,
bounds_error=False,
fill_value=np.inf)
fnEE = interp1d(np.arange(pellmin, pellmax),
1. / invnEEs,
bounds_error=False,
fill_value=np.inf)
return fnTT, fnEE
def fisher_from_config(fidCls,
dCls,
paramList,
Config,
expName,
lensName=None,
TCMB=2.7255e6,
beamsOverride=None,
noisesOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None,
tellminOverride=None,
pellminOverride=None,
tellmaxOverride=None,
pellmaxOverride=None):
fnTT, fnEE = noiseFromConfig(Config, expName, TCMB, beamsOverride,
noisesOverride, lkneeTOverride,
lkneePOverride, alphaTOverride,
alphaPOverride, tellminOverride,
pellminOverride, tellmaxOverride,
pellmaxOverride)
tellmin, tellmax = list_from_config(Config, expName, 'tellrange')
pellmin, pellmax = list_from_config(Config, expName, 'pellrange')
if tellminOverride is not None: tellmin = tellminOverride
if tellmaxOverride is not None: tellmax = tellmaxOverride
if pellminOverride is not None: pellmin = pellminOverride
if pellmaxOverride is not None: pellmax = pellmaxOverride
if lensName is not None:
doLens = True
ls, Nls, ellbb, dlbb, efficiency, cc = lensNoise(
Config, expName, lensName, beamsOverride, noisesOverride,
lkneeTOverride, lkneePOverride, alphaTOverride, alphaPOverride)
# Pad CMB lensing noise with infinity outside L ranges
kellmin, kellmax = list_from_config(Config, lensName, 'Lrange')
fnKK = cosmo.noise_pad_infinity(
interp1d(ls, Nls, fill_value=np.inf, bounds_error=False), kellmin,
kellmax)
else:
doLens = False
fnKK = lambda x: np.nan
kellmin = np.inf
kellmax = -np.inf
# Decide on what ell range to calculate the Fisher matrix
ellrange = np.arange(min(tellmin, pellmin, kellmin),
max(tellmax, pellmax, kellmax)).astype(int)
# Get fsky
fsky = Config.getfloat(expName, 'fsky')
# Calculate the Fisher matrix and add to other Fishers
Fisher = calcFisher(paramList,
ellrange,
fidCls,
dCls,
lambda x: fnTT(x) * TCMB**2.,
lambda x: fnEE(x) * TCMB**2.,
fnKK,
fsky,
lensing=doLens,
verbose=True)
return Fisher
#Initiate foreground class
fgs = fgNoises(cc.c,
ksz_battaglia_test_csv="data/ksz_template_battaglia.csv",
tsz_battaglia_template_csv="data/sz_template_battaglia.csv")
#constrained ilc flag 0=False 1=True
cf = 0
constraint_tag = ['', '_constrained']
#choose experiment
#experimentName = "SO-v3-goal-40"
experimentName = "CCATpSOg-v1-40"
#experimentName = "CMB-Probe-v4-CBE"
#experimentName = "CCATp-v1-40"
#experimentName = "CCATp-v1-40-noatm"
beams = list_from_config(Config, experimentName, 'beams')
noises = list_from_config(Config, experimentName, 'noises')
freqs = list_from_config(Config, experimentName, 'freqs')
lmax = int(Config.getfloat(experimentName, 'lmax'))
lknee = list_from_config(Config, experimentName, 'lknee')[0]
alpha = list_from_config(Config, experimentName, 'alpha')[0]
fsky = Config.getfloat(experimentName, 'fsky')
try:
v3mode = Config.getint(experimentName, 'V3mode')
except:
v3mode = -1
try:
noatm = Config.getint(experimentName, 'noatm')
except:
noatm = False
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()
import pickle as pickle zz = 0.5 MM = 5.e14 clusterParams = 'LACluster' # from ini file cosmologyName = 'LACosmology' # from ini file experimentName = "v3test" iniFile = "input/params.ini" #iniFile = "input/pipeline.ini" Config = SafeConfigParser() Config.optionxform=str Config.read(iniFile) 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 = Config.getfloat(experimentName,'lknee') alpha = Config.getfloat(experimentName,'alpha') fsky = Config.getfloat(experimentName,'fsky') # accuracy params dell=10 pmaxN=5 numps=1000 tmaxN=5 numts=1000
def cluster_fisher_from_config(Config,expName,gridName,calName,fishName,
overridePlanck=None,overrideBAO=None,overrideOther=None,pickling=True,s8=False,
tauOverride=None,do_clkk_override=None):
"""
Returns
1. Fisher - the Fisher matrix
2. paramList - final parameter list defining the Fisher contents. Might add extra params (e.g. sigR, bwl)
Accepts
1. Config - a ConfigParser object containing the ini file contents
2. expName - name of experiment section in ini file
3. gridName - name of M,q,z grid definition in ini file
4. calName - name of weak lensing calibration section
5. fishName - looks for a section in ini file named "fisher-"+fishName for Fisher options
6. overridePlanck - Fisher matrix to add to upper left corner of original in place of Planck fisher
matrix specified in ini. Can be zero.
7. overrideBAO - Fisher matrix to add to upper left corner of original in place of BAO fisher
matrix specified in ini. Can be zero.
8. overrideOther - Fisher matrix to add to upper left corner of original in place of "other" fisher
matrix specified in ini. Can be zero.
"""
pickling = False #!!!!!
bigDataDir = Config.get('general','bigDataDirectory')
version = Config.get('general','version')
pzcutoff = Config.getfloat('general','photoZCutOff')
fsky = Config.getfloat(expName,'fsky')
# Fisher params
fishSection = 'fisher-'+fishName
paramList = Config.get(fishSection,'paramList').split(',')
zs = list_from_config(Config,gridName,'zrange')
z_edges = np.arange(zs[0],zs[1]+zs[2],zs[2])
saveId = save_id(expName,gridName,calName,version)
derivRoot = deriv_root(bigDataDir,saveId)
# Fiducial number counts
new_z_edges, N_fid = rebinN(np.load(fid_file(bigDataDir,saveId)),pzcutoff,z_edges)#,mass_bin=None)
N_fid = N_fid*fsky
# get mass and z grids
# ms = list_from_config(Config,gridName,'mexprange')
# mexp_edges = np.arange(ms[0],ms[1]+ms[2],ms[2])
# M_edges = 10**mexp_edges
# Masses = (M_edges[1:]+M_edges[:-1])/2.
# print Masses.shape
# print N_fid.shape
# print N_fid.sum()
# print N_fid[Masses>2.e14,:,:].sum()
# sys.exit()
print("Effective number of clusters: ", N_fid.sum())
paramList, priorNameList, priorValueList = priors_from_config(Config,expName,calName,fishName,paramList,tauOverride)
if s8:
zrange = old_div((z_edges[1:]+z_edges[:-1]),2.)
zlist = ["S8Z"+str(i) for i in range(len(zrange))]
paramList = paramList+zlist
Fisher = getFisher(N_fid,paramList,priorNameList,priorValueList,derivRoot,pzcutoff,z_edges,fsky)
# Number of non-SZ params (params that will be in Planck/BAO)
numCosmo = Config.getint(fishSection,'numCosmo')
#numLeft = len(paramList) - numCosmo
try:
do_cmb_fisher = Config.getboolean(fishSection,"do_cmb_fisher")
except:
do_cmb_fisher = False
try:
do_clkk_fisher = Config.getboolean(fishSection,"do_clkk_fisher")
except:
do_clkk_fisher = False
if do_clkk_override is not None:
do_clkk_fisher = do_clkk_override
if do_clkk_fisher:
assert do_cmb_fisher, "Sorry, currently Clkk fisher requires CMB fisher to be True as well."
lensName = Config.get(fishSection,"clkk_section")
else:
lensName = None
if do_cmb_fisher:
import pyfisher.clFisher as pyfish
# Load fiducials and derivatives
cmbDerivRoot = Config.get("general","cmbDerivRoot")
cmbParamList = paramList[:numCosmo]
cmb_fisher_loaded = False
if pickling:
import time
hashval = hash_func(cmbParamList,expName,lensName,do_clkk_fisher,time.strftime('%Y%m%d'))
pkl_file = "output/pickledFisher_"+hashval+".pkl"
try:
cmb_fisher = pickle.load(open(pkl_file,'rb'))
cmb_fisher_loaded = True
print("Loaded pickled CMB fisher.")
except:
pass
if not(cmb_fisher_loaded):
fidCls = np.loadtxt(cmbDerivRoot+'_fCls.csv',delimiter=',')
dCls = {}
for paramName in cmbParamList:
dCls[paramName] = np.loadtxt(cmbDerivRoot+'_dCls_'+paramName+'.csv',delimiter=',')
print("Calculating CMB fisher matrix...")
print(cmbParamList,expName,lensName)
cmb_fisher = pyfish.fisher_from_config(fidCls,dCls,cmbParamList,Config,expName,lensName)
if pickling:
print("Pickling CMB fisher...")
pickle.dump(cmb_fisher,open(pkl_file,'wb'))
numLeft = len(paramList) - cmb_fisher.shape[0]
print("numLeft , " ,numLeft)
cmb_fisher = pad_fisher(cmb_fisher,numLeft)
else:
cmb_fisher = 0.
print(len(paramList))
print(Fisher.shape)
from orphics import stats
tszFish = stats.FisherMatrix(Fisher+cmb_fisher,param_list=paramList)
stats.write_fisher("tsz_fish.txt",tszFish)
#np.savetxt("tsz.txt",Fisher)
try:
otherFishers = Config.get(fishSection,'otherFishers').split(',')
except:
traceback.print_exc()
print("No other fishers found.")
otherFishers = []
try:
external_param_list = Config.get(fishSection,'external_param_list').split(',')
except:
traceback.print_exc()
external_param_list = "H0,ombh2,omch2,tau,As,ns,mnu,w0,wa".split(',')
print("No external param list found in fisher section. Assuming ", external_param_list)
nex = len(external_param_list)
all_others = np.zeros((nex,nex))
for otherFisherFile in otherFishers:
do_other = True
try:
other_fisher = np.loadtxt(otherFisherFile)
except:
try:
other_fisher = np.loadtxt(otherFisherFile,delimiter=',') #[:numCosmo,:numCosmo]
except:
print("WARNING: Skipped ",otherFisherFile, " either because it was not found or doesn't have enough elements. This means no external fishers have been added!!!")
do_other = False
pass
if do_other:
#numLeft = len(paramList) - other_fisher.shape[0]
#other_fisher = pad_fisher(other_fisher,numLeft)
print("shapes",other_fisher.shape)
all_others += other_fisher
otherFish = stats.FisherMatrix(all_others,param_list=external_param_list)
for p in otherFish.params:
if p not in paramList:
otherFish.delete(p)
print("Deleted ", p , " from external fishers.")
print("External params : ",otherFish.params)
#print(otherFish.sigmas()['mnu']*1000.)
retfish = (tszFish + otherFish).ix[paramList,paramList]
#print(stats.FisherMatrix(retfish.as_matrix(),paramList).marge_var_2param('mnu','w0'))
return retfish.as_matrix(), paramList
# load the mass calibration grid
mexp_edges, z_edges, lndM = pickle.load(open(calFile, "rb"))
mgrid, zgrid, siggrid = pickle.load(
open(
bigDataDir + "szgrid_" + expName + "_" + gridName + "_v" +
version + ".pkl", 'rb'))
assert np.all(mgrid == mexp_edges)
assert np.all(z_edges == zgrid)
saveId = expName + "_" + gridName + "_" + calName + "_v" + version
from orphics.io import dict_from_section, list_from_config
constDict = dict_from_section(Config, 'constants')
clusterDict = dict_from_section(Config, 'cluster_params')
beam = list_from_config(Config, expName, 'beams')
noise = list_from_config(Config, expName, 'noises')
freq = list_from_config(Config, expName, 'freqs')
lknee = list_from_config(Config, expName, 'lknee')[0]
alpha = list_from_config(Config, expName, 'alpha')[0]
fsky = Config.getfloat(expName, 'fsky')
try:
v3mode = Config.getint(expName, 'V3mode')
except:
v3mode = -1
clttfile = Config.get('general', 'clttfile')
# get s/n q-bins
mus = list_from_config(Config, 'general', 'mubins')
mubin_edges = np.linspace(mus[0], mus[1], int(mus[2]) + 1)
#fishSection = "mnu-w0-wa"
fishSection = "lcdm"
noatm = ""
#noatm = "-noatm"
#cal = "CMB_all_miscentered"
cal = "CMB_all"
#cal = "owl2"
derivSet = "v0.3_ysig_0.127"
#gridName = "grid-default"
gridName = "grid-high"
cosmoFisher = Config.get('fisher-'+fishSection,'saveSuffix')
from orphics.io import list_from_config
zs = list_from_config(Config,gridName,'zrange')
z_edges = np.arange(zs[0],zs[1]+zs[2],zs[2])
zrange = old_div((z_edges[1:]+z_edges[:-1]),2.)
zlist = ["S8Z"+str(i) for i in range(len(zrange))]
paramList = Config.get('fisher-'+fishSection,'paramList').split(',')+["S8Z20"]#+zlist
print(paramList)
paramLatexList = Config.get('fisher-'+fishSection,'paramLatexList').split(',')
fparams = {}
for (key, val) in Config.items('params'):
param = val.split(',')[0]
fparams[key] = float(param)
"""RES STUDY"""
#cmbfisher3 = getFisher("S4-3.0-0.4"+noatm,gridName,cal,cosmoFisher,paramList,derivSet)
gridName = sys.argv[2]
calName = sys.argv[3]
fishName = sys.argv[4]
iniFile = "input/pipeline.ini"
Config = SafeConfigParser()
Config.optionxform = str
Config.read(iniFile)
bigDataDir = Config.get('general', 'bigDataDirectory')
version = Config.get('general', 'version')
pzcutoff = Config.getfloat('general', 'photoZCutOff')
saveId = expName + "_" + gridName + "_" + calName + "_v" + version
# 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]))
elif qspacing == "linear":
qbins = np.linspace(qs[0], qs[1], int(qs[2]))
else:
raise ValueError
dq = np.diff(qbins)
fsky = Config.getfloat(expName, 'fsky')
# get mass and z grids
ms = list_from_config(Config, gridName, 'mexprange')
mexp_edges = np.arange(ms[0], ms[1] + ms[2], ms[2])
zs = list_from_config(Config, gridName, 'zrange')
