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 __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 __init__(self,iniFile,test_cat_file,fix_params,mmin=14.3):
self.fix_params = fix_params
Config = SafeConfigParser()
Config.optionxform=str
Config.read(iniFile)
self.fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
self.fparams[key] = float(param)
else:
self.fparams[key] = float(val)
bigDataDir = Config.get('general','bigDataDirectory')
self.clttfile = Config.get('general','clttfile')
self.constDict = dict_from_section(Config,'constants')
logm_min = 14.0
logm_max = 15.702
logm_spacing = 0.01
self.mgrid = np.arange(logm_min,logm_max,logm_spacing)
self.zgrid = np.arange(0.1,2.001,0.05)
self.cc = ClusterCosmology(self.fparams,self.constDict,clTTFixFile=self.clttfile)
self.HMF = Halo_MF(self.cc,self.mgrid,self.zgrid)
self.fsky = old_div(987.5,41252.9612)
self.mmin = mmin
clust_cat = test_cat_file + '.fits'
self.clst_z,self.clst_zerr,self.clst_m,self.clst_merr = read_test_mock_cat(clust_cat,self.mmin)
def __init__(self, inifile, expname, gridname, version):
self.params = None
self.inifile = inifile
self.config = ConfigParser()
self.config.optionxform = str
self.config.read(self.inifile)
self.constdict = dict_from_section(self.config, 'constants')
self.clttfile = self.config.get('general', 'clttfile')
self.expname = expname
self.gridname = gridname
self.version = version
self.saveid = self.expname + "_" + self.gridname + "_v" + self.version
def get_cc(ini):
Config = ConfigParser()
Config.optionxform = str
Config.read(ini)
clttfile = Config.get('general', 'clttfile')
constDict = dict_from_section(Config, 'constants')
fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
fparams[key] = float(param)
else:
fparams[key] = float(val)
cc = ClusterCosmology(fparams, constDict, clTTFixFile=clttfile)
return cc
def __init__(self, iniFile, kmin=1e-4, kmax=5., knum=200):
Config = SafeConfigParser()
Config.optionxform = str
Config.read(iniFile)
self.fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
self.fparams[key] = float(param)
else:
self.fparams[key] = float(val)
bigDataDir = Config.get('general', 'bigDataDirectory')
self.clttfile = Config.get('general', 'clttfile')
self.constDict = dict_from_section(Config, 'constants')
self.clusterDict = dictFromSection(Config, 'cluster_params')
version = Config.get('general', 'version')
beam = listFromConfig(Config, expName, 'beams')
noise = listFromConfig(Config, expName, 'noises')
freq = listFromConfig(Config, expName, 'freqs')
lknee = listFromConfig(Config, expName, 'lknee')[0]
alpha = listFromConfig(Config, expName, 'alpha')[0]
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.HMF = Halo_MF(self.cc, self.mgrid, self.zgrid)
if powerZK is None:
self.kh, self.pk = self._pk_lin(self.HMF.zarr, kmin, kmax, knum)
else:
assert kh is not None
self.kh = kh
self.pk = powerZK
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()
frac = npfact / (1. + npfact)
ans = np.multiply(frac**2, V0)
return ans
INIFILE = "input/pipeline.ini"
expName = 'S4-1.0-CDT'
gridName = 'grid-owl2'
version = '0.6'
Config = ConfigParser()
Config.optionxform = str
Config.read(INIFILE)
clttfile = Config.get('general', 'clttfile')
constDict = dict_from_section(Config, 'constants')
fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
fparams[key] = float(param)
else:
fparams[key] = float(val)
expName = 'S4-1.0-CDT'
gridName = 'grid-owl2'
version = '0.6'
cc = ClusterCosmology(fparams, constDict, clTTFixFile=clttfile)
clst = Clustering(INIFILE, expName, gridName, version, cc)
bigDataDir = Config.get('general', 'bigDataDirectory')
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')
def __init__(self,iniFile,parDict,nemoOutputDir,noiseFile,fix_params,fitsfile,test=False,simtest=False,simpars=False):
self.fix_params = fix_params
self.test = test
self.simtest = simtest
self.simpars = simpars
Config = SafeConfigParser()
Config.optionxform=str
Config.read(iniFile)
self.fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
self.fparams[key] = float(param)
else:
self.fparams[key] = float(val)
bigDataDir = Config.get('general','bigDataDirectory')
self.clttfile = Config.get('general','clttfile')
self.constDict = dict_from_section(Config,'constants')
#version = Config.get('general','version')
#self.mgrid,self.zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+".pkl",'rb'))
logm_min = 13.7
logm_max = 15.72
logm_spacing = 0.02
self.mgrid = np.arange(logm_min,logm_max,logm_spacing)
self.zgrid = np.arange(0.1,2.01,0.1)
#print self.mgrid
#print self.zgrid
self.qmin = 5.6
self.cc = ClusterCosmology(self.fparams,self.constDict,clTTFixFile=self.clttfile)
self.HMF = Halo_MF(self.cc,self.mgrid,self.zgrid)
self.diagnosticsDir=nemoOutputDir+"diagnostics"
self.filteredMapsDir=nemoOutputDir+"filteredMaps"
self.tckQFit=simsTools.fitQ(parDict, self.diagnosticsDir, self.filteredMapsDir)
FilterNoiseMapFile = nemoOutputDir + noiseFile
MaskMapFile = self.diagnosticsDir + '/areaMask.fits'
#if self.simtest or self.simpars:
# print "mock catalog"
#clust_cat = nemoOutputDir + 'mockCatalog_equD56.fits' #'ACTPol_mjh_cluster_cat.fits'
# clust_cat = nemoOutputDir + 'mockCat_D56equ_v22.fits' #'ACTPol_mjh_cluster_cat.fits'
# self.clst_z,self.clst_zerr,self.clst_y0,self.clst_y0err = read_mock_cat(clust_cat,self.qmin)
#else:
# print "real catalog"
# clust_cat = nemoOutputDir + 'E-D56Clusters.fits' #'ACTPol_mjh_cluster_cat.fits'
# self.clst_z,self.clst_zerr,self.clst_y0,self.clst_y0err = read_clust_cat(clust_cat,self.qmin)
clust_cat = nemoOutputDir + fitsfile
if self.simtest or self.simpars:
print("mock catalog")
self.clst_z,self.clst_zerr,self.clst_y0,self.clst_y0err = read_mock_cat(clust_cat,self.qmin)
else:
print("real catalog")
self.clst_z,self.clst_zerr,self.clst_y0,self.clst_y0err = read_clust_cat(clust_cat,self.qmin)
self.rms_noise_map = read_MJH_noisemap(FilterNoiseMapFile,MaskMapFile)
print ('Number of clusters',len(self.clst_zerr))
#self.wcs=astWCS.WCS(FilterNoiseMapFile)
#self.clst_RA,self.clst_DEC,
#self.clst_xmapInd,self.clst_ymapInd = self.Find_nearest_pixel_ind(self.clst_RA,self.clst_DEC)
self.num_noise_bins = 10
self.area_rads = old_div(987.5,41252.9612) # fraction of sky - ACTPol D56-equ specific
self.LgY = np.arange(-6,-3,0.01)
count_temp,bin_edge =np.histogram(np.log10(self.rms_noise_map[self.rms_noise_map>0]),bins=self.num_noise_bins)
self.frac_of_survey = count_temp*1.0 / np.sum(count_temp)
self.thresh_bin = 10**(old_div((bin_edge[:-1] + bin_edge[1:]),2.))
def __init__(self,iniFile,parDict,nemoOutputDir,noiseFile,params,parlist,mass_grid_log=None,z_grid=None,randoms=False):
Config = SafeConfigParser()
Config.optionxform=str
Config.read(iniFile)
if mass_grid_log:
logm_min,logm_max,logm_spacing = mass_grid_log
else:
logm_min = 12.7
logm_max = 15.72
logm_spacing = 0.04
if z_grid:
zmin,zmax,zdel = z_grid
else:
zmin = 0.0
zmax = 2.01
zdel = 0.1
self.fparams = {}
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
self.fparams[key] = float(param)
else:
self.fparams[key] = float(val)
self.param_vals = alter_fparams(self.fparams,parlist,params)
bigDataDir = Config.get('general','bigDataDirectory')
self.clttfile = Config.get('general','clttfile')
self.constDict = dict_from_section(Config,'constants')
if mass_grid_log:
logm_min,logm_max,logm_spacing = mass_grid_log
else:
logm_min = 12.7
logm_max = 15.72
logm_spacing = 0.04
if z_grid:
zmin,zmax,zdel = z_grid
else:
zmin = 0.0
zmax = 2.01
zdel = 0.1
if randoms:
self.rand = 1
else:
self.rand = 0
self.mgrid = np.arange(logm_min,logm_max,logm_spacing)
self.zgrid = np.arange(zmin,zmax,zdel)
self.Medges = 10.**self.mgrid
self.Mcents = (self.Medges[1:]+self.Medges[:-1])/2.
self.Mexpcents = np.log10(self.Mcents)
self.zcents = (self.zgrid[1:]+self.zgrid[:-1])/2.
self.cc = ClusterCosmology(self.param_vals,self.constDict,clTTFixFile=self.clttfile)
self.HMF = Halo_MF(self.cc,self.mgrid,self.zgrid)
self.diagnosticsDir=nemoOutputDir+"diagnostics"
self.filteredMapsDir=nemoOutputDir+"filteredMaps"
self.tckQFit=simsTools.fitQ(parDict, self.diagnosticsDir, self.filteredMapsDir)
FilterNoiseMapFile = nemoOutputDir + noiseFile
MaskMapFile = self.diagnosticsDir + '/areaMask.fits'
self.rms_noise_map = read_MJH_noisemap(FilterNoiseMapFile,MaskMapFile)
self.wcs=astWCS.WCS(FilterNoiseMapFile)
self.fsky = 987.5/41252.9612 # in rads ACTPol D56-equ specific
self.scat_val = 0.2
self.seedval = np.int(np.round(time.time())) #1
pmaxN=5 numps=1000 tmaxN=5 numts=1000 # dell=1 # pmaxN=25 # numps=10000 # tmaxN=25 # numts=10000 cosmoDict = dict_from_section(Config,cosmologyName) #cosmoDict = dict_from_section(Config,'WMAP9') constDict = dict_from_section(Config,'constants') clusterDict = dict_from_section(Config,clusterParams) cc = ClusterCosmology(cosmoDict,constDict,pickling=True,clTTFixFile = "data/cltt_lensed_Feb18.txt") # make an SZ profile example SZProfExample = SZ_Cluster_Model(clusterCosmology=cc,clusterDict=clusterDict,rms_noises = noise,fwhms=beam,freqs=freq,lmax=lmax,lknee=lknee,alpha=alpha,dell=dell,pmaxN=pmaxN,numps=numps,v3mode=2,fsky=0.4) #MM = 10**np.linspace(13.,14.,5) #print SZProfExample.quickVar(MM,zz,tmaxN=tmaxN,numts=numts) #sys.exit()