if (args.printtest):
parvals2 = [
3.46419819e-01, 2.34697120e-02, 6.50170056e+01, 1.33398673e-09,
9.36305025e-01, 2.53310030e-01, 1.93661978e-01, 1.74839544e-01
]
#parvals2 = [1.194e-01,2.34697120e-02,6.50170056e+01,1.33398673e-09,9.36305025e-01,2.53310030e-01,1.93661978e-01,1.74839544e-01]
param_vals = lk.alter_fparams(fparams, parlist, parvals)
cluster_props = np.array(
[CL.clst_z, CL.clst_zerr, CL.clst_y0 * 1e-4, CL.clst_y0err * 1e-4])
start = time.time()
int_cc = ClusterCosmology(param_vals,
CL.constDict,
clTTFixFile=CL.clttfile)
print('CC', time.time() - start)
start = time.time()
int_HMF = Halo_MF(int_cc, CL.mgrid, CL.zgrid)
print('HMF', time.time() - start)
dn_dzdm_int = int_HMF.inter_dndmLogm(200.)
zbins = 10
LgYa = np.outer(np.ones(len(int_HMF.M.copy())), CL.LgY)
Y = 10**LgYa
Ma = np.outer(int_HMF.M.copy(), np.ones(len(LgYa[0, :])))
clustind = 1
Config = SafeConfigParser()
Config.optionxform=str
Config.read(iniFile)
constDict = dictFromSection(Config,'constants')
clusterDict = dictFromSection(Config,'cluster_params')
fparams = {} # the
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
fparams[key] = float(param)
else:
fparams[key] = float(val)
clttfile = Config.get('general','clttfile')
cc = ClusterCosmology(fparams,constDict,lmax=8000,pickling=True)#clTTFixFile=clttfile)
fgs = fgNoises(cc.c,ksz_battaglia_test_csv="data/ksz_template_battaglia.csv",tsz_battaglia_template_csv="data/sz_template_battaglia.csv")
experimentName = "SO-v2-6m"
beams = listFromConfig(Config,experimentName,'beams')
noises = listFromConfig(Config,experimentName,'noises')
freqs = listFromConfig(Config,experimentName,'freqs')
lmax = int(Config.getfloat(experimentName,'lmax'))
lknee = listFromConfig(Config,experimentName,'lknee')[0]
alpha = listFromConfig(Config,experimentName,'alpha')[0]
fsky = Config.getfloat(experimentName,'fsky')
SZProfExample = SZ_Cluster_Model(clusterCosmology=cc,clusterDict=clusterDict,rms_noises = noises,fwhms=beams,freqs=freqs,lmax=lmax,lknee=lknee,alpha=alpha,tsz_cib=True)
noise_P_uK_arcmin = 1. #0.001 #1.0 #0.01
lmax = 6500
tellmax = 6000
pellmax = 6000
tellmin = 200
pellmin = 200
kellmax = min(tellmax, pellmax)
kellmin = 200
gradCut = 2000
#pol_list = ['TT','EB','EE','ET','TE']
pol_list = ['TT'] #,'EB']
out_dir = os.environ['WWW'] + "plots/halotest/lensorder5_"
# === COSMOLOGY ===
cc = ClusterCosmology(lmax=lmax, pickling=True)
TCMB = 2.7255e6
theory = cc.theory
ps = cmb.enmap_power_from_orphics_theory(theory, lmax, lensed=False)
patch_width_arcmin = 40.
sim_pixel_scale = 0.1
pol = False
shape_sim, wcs_sim = enmap.get_enmap_patch(patch_width_arcmin,
sim_pixel_scale,
proj="car",
pol=pol)
modr_sim = enmap.modrmap(shape_sim, wcs_sim) * 180. * 60. / np.pi
lxmap_sim, lymap_sim, modlmap_sim, angmap_sim, lx_sim, ly_sim = fmaps.get_ft_attributes_enmap(
shape_sim, wcs_sim)
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()
from orphics.theory.cosmology import Cosmology
cc = Cosmology(lmax=int(kellmax), pickling=True)
theory = cc.theory
bin_edges = np.arange(kellmin, kellmax, dell)
myNls = NlGenerator(lmap, theory, bin_edges, gradCut=gradCut)
from scipy.interpolate import interp1d
from orphics.tools.io import Plotter
ellkk = np.arange(2, 9000, 1)
Clkk = theory.gCl("kk", ellkk)
clfunc = interp1d(ellkk, Clkk, bounds_error=False, fill_value="extrapolate")
kellmax = 8000
cc = ClusterCosmology(cosmoDict, constDict, kellmax, pickling=True)
theory = cc.theory
pl = Plotter(labelX="Beam (arcmin)",
labelY="$\\sigma(M)/M$ for $N=1000$",
ftsize=16)
for miscenter in [False, True]:
for lensName, linestyle in zip(["CMB_all", "CMB_pol"], ["-", "--"]):
for doFg in [False, True]:
if lensName == "CMB_pol" and not (doFg): continue
if lensName == "CMB_all" and not (doFg) and miscenter: continue
sns = []
for beamNow in beamList:
def theory_from_config(Config, theory_section, dimensionless=True):
sec_type = Config.get(theory_section, "cosmo_type")
lmax = Config.getint(theory_section, "lmax")
cc = None
if sec_type == "pycamb_params":
raise NotImplementedError
elif sec_type == "cluster":
from szar.counts import ClusterCosmology
with oio.nostdout():
with warnings.catch_warnings():
warnings.simplefilter("ignore")
logger.disabled = True
cc = ClusterCosmology(lmax=lmax,
pickling=True,
dimensionless=dimensionless)
theory = cc.theory
logger.disabled = False
elif sec_type == "default":
from orphics.theory.cosmology import Cosmology
with oio.nostdout():
with warnings.catch_warnings():
warnings.simplefilter("ignore")
logger.disabled = True
cc = Cosmology(lmax=lmax,
pickling=True,
dimensionless=dimensionless)
theory = cc.theory
logger.disabled = False
elif sec_type == "camb_file":
cc = None
import orphics.tools.cmb as cmb
file_root = Config.get(theory_section, "camb_file_root")
theory = cmb.loadTheorySpectraFromCAMB(file_root,
unlensedEqualsLensed=False,
useTotal=False,
TCMB=2.7255e6,
lpad=lmax,
get_dimensionless=dimensionless)
try:
cforce = Config.getboolean(theory_section, "cluster_force")
except:
cforce = False
if cforce:
from szar.counts import ClusterCosmology
cc = ClusterCosmology(skipCls=True, dimensionless=dimensionless)
cc.theory = theory
elif sec_type == "enlib_file":
import orphics.tools.cmb as cmb
file_root = Config.get(theory_section, "enlib_file_root")
theory = cmb.load_theory_spectra_from_enlib(
file_root, lpad=lmax, get_dimensionless=dimensionless)
cc = None
else:
print(sec_type)
raise ValueError
return theory, cc, lmax
#cal = "CMB_pol_miscentered"
from orphics.io import dict_from_section, list_from_config
constDict = dict_from_section(Config,'constants')
clusterDict = dict_from_section(Config,'cluster_params')
fparams = {} # the
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)
from matplotlib.patches import Rectangle
#expList = ['CMB-Probe-v3-1']
#expList = ['S4-1.0-CDT','S4-1.5-CDT']#,'S4-2.0-0.4','S4-2.5-0.4','S4-3.0-0.4']
#expList = ['S4-1.0-0.4','S4-1.5-0.4','S4-2.0-0.4','S4-2.5-0.4','S4-3.0-0.4']
#expList = ['S4-2.0-0.4']#,'S4-1.5-0.4','S4-1.5-0.3','S4-1.5-0.2','S4-1.5-0.1','S4-1.5-0.05']
#expList = ['S4-1.0-0.4','S4-1.5-0.4','S4-2.0-0.4','S4-2.5-0.4','S4-3.0-0.4']
#expList = ['SO-v3-goal-40','SO-v3-base-40']#,'SO-v3-goal-20','SO-v3-base-20','SO-v3-goal-10','SO-v3-base-10']
#expList = ['CMB-Probe-v4-CBE','CMB-Probe-v4-REQ']
expList = ['SO-v3-goal-40','SO-v3-goal-40']
pad = 0.05
if True:
yNzs[key] = []
vals = stepdict[key][:maxSteps]
vals.sort()
for val in vals:
print((key, val))
uppassparams = fparams.copy()
dnpassparams = fparams.copy()
uppassparams[key] = fparams[key]+old_div(val,2.)
dnpassparams[key] = fparams[key]-old_div(val,2.)
cc = ClusterCosmology(uppassparams,constDict,clTTFixFile=clttfile)
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)
Nup = HMF.N_of_mqz_SZ(lndM*massMultiplier,qbin_edges,SZProf)
cc = ClusterCosmology(dnpassparams,constDict,clTTFixFile=clttfile)
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)
Ndn = HMF.N_of_mqz_SZ(lndM*massMultiplier,qbin_edges,SZProf)
dNdp = old_div((getNmzq(Nup,mexp_edges,z_edges,qbin_edges)-getNmzq(Ndn,mexp_edges,z_edges,qbin_edges)),val)
# dell=1
# pmaxN=25
# numps=10000
# tmaxN=25
# numts=10000
cosmoDict = dictFromSection(Config,cosmologyName)
#cosmoDict = dictFromSection(Config,'WMAP9')
constDict = dictFromSection(Config,'constants')
clusterDict = dictFromSection(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,qmin=6)
version = Config.get('general','version')
bigDataDir = Config.get('general','bigDataDirectory')
calFile = bigDataDir+"lensgrid_"+gridName+"_"+calName+".pkl"
mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+".pkl",'rb'))
Mexp_edges, z_edges, lndM = pickle.load(open(calFile,"rb"))
exp = experimentName
expName = experimentName
beam = listFromConfig(Config,experimentName,'beams')
noise = listFromConfig(Config,experimentName,'noises')
freq = listFromConfig(Config,experimentName,'freqs')
lmax = int(Config.getfloat(experimentName,'lmax'))
lknee = listFromConfig(Config,experimentName,'lknee')[0]
alpha = listFromConfig(Config,experimentName,'alpha')[0]
fsky = Config.getfloat(experimentName,'fsky')
cosmoDict = dictFromSection(Config,cosmologyName)
constDict = dictFromSection(Config,'constants')
clusterDict = dictFromSection(Config,clusterParams)
cc = ClusterCosmology(cosmoDict,constDict,clTTFixFile = "data/cltt_lensed_Feb18.txt")#,skipCls=True)
if "owl" in calName:
calFile = bigDataDir+"lensgrid_"+gridName+"_"+cal+".pkl"
#calFile = bigDataDir+"lensgrid_grid-"+cal+"_"+cal+".pkl"
else:
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)
from szar.counts import ClusterCosmology, SZ_Cluster_Model, Halo_MF, sampleVarianceOverNsquareOverBsquare, haloBias, getTotN from orphics.tools.io import Plotter, dictFromSection, listFromConfig from configparser import SafeConfigParser clusterParams = 'LACluster' # from ini file cosmologyName = 'LACosmology' # from ini file iniFile = "input/params.ini" Config = SafeConfigParser() Config.optionxform = str Config.read(iniFile) lmax = 3000 cosmoDict = dictFromSection(Config, cosmologyName) constDict = dictFromSection(Config, 'constants') clusterDict = dictFromSection(Config, clusterParams) cc = ClusterCosmology(cosmoDict, constDict, skipCls=True) # mrange = np.arange(13.5,15.71,0.3) # zrange = np.arange(0.05,3.0,0.3) mrange = np.arange(13.5, 15.71, 0.05) zrange = np.arange(0.05, 3.0, 0.1) # mrange = np.arange(13.5,15.71,0.02) # zrange = np.arange(0.02,3.0,0.02) fsky = 0.4 hmf = Halo_MF(cc, mrange, zrange) import os
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
frac_of_survey = count_temp * 1.0 / np.sum(count_temp)
thresh_bin = 10**(old_div((bin_edge[:-1] + bin_edge[1:]), 2.))
parlist = [
'omch2', 'ombh2', 'H0', 'As', 'ns', 'tau', 'massbias', 'yslope', 'scat'
]
parvals = [0.1194, 0.022, 67.0, 2.2e-09, 0.96, 0.06, 0.80, 0.08, 0.2]
#print fparams
params = CL.alter_fparams(fparams, parlist, parvals)
#print params
start = time.time()
int_cc = ClusterCosmology(params, CL.constDict,
clTTFixFile=CL.clttfile) # internal HMF call
print(('CC', time.time() - start))
start = time.time()
int_HMF = Halo_MF(int_cc, CL.mgrid, CL.zgrid)
print(('HMF', time.time() - start))
cluster_prop = np.array([CL.clst_z, CL.clst_zerr, CL.clst_y0, CL.clst_y0err])
cluster_prop2 = np.array(
[CL.clst_z, CL.clst_zerr, CL.clst_y0 * 1e-4, CL.clst_y0err * 1e-4])
print(cluster_prop.shape)
dndm_int = int_HMF.inter_dndm(200.)
start = time.time()
print(
np.log(CL.Prob_per_cluster(int_HMF, cluster_prop2[:, 0], dndm_int,
print((upDict[key])) print((dnDict[key])) beam = io.listFromConfig(Config,experimentName,'beams') noise = io.listFromConfig(Config,experimentName,'noises') freq = io.listFromConfig(Config,experimentName,'freqs') lmax = int(Config.getfloat(experimentName,'lmax')) lknee = Config.getfloat(experimentName,'lknee') alpha = Config.getfloat(experimentName,'alpha') fsky = Config.getfloat(experimentName,'fsky') mass_err_file = Config.get(experimentName,'mass_err') mass_err = np.loadtxt(mass_err_file) ccUp = ClusterCosmology(upDict,constDict,clTTFixFile=clttfile) ccDn = ClusterCosmology(dnDict,constDict,clTTFixFile=clttfile) #mbin = np.arange(12.5,15.5,0.05)+0.05 #zbin_temp = np.arange(0.05,2.05,0.05) zbin_temp = np.arange(0.05,2.0,0.05) zbin = np.insert(zbin_temp,0,0.0) qbin = np.arange(np.log(6),np.log(500),0.08) mbin = np.arange(13.5, 15.71, 0.1) start3 = time.time() mgrid,zgrid,siggrid = pickle.load(open(bigDataDir+"szgrid_"+expName+"_"+gridName+ "_v" + version+".pkl",'rb')) #cc = ClusterCosmology(passParams,constDict,clTTFixFile=clttfile)
Config.optionxform=str
Config.read(iniFile)
bigDataDir = Config.get('general','bigDataDirectory')
version = Config.get('general','version')
fparams = {} # the
for (key, val) in Config.items('params'):
if ',' in val:
param, step = val.split(',')
fparams[key] = float(param)
else:
fparams[key] = float(val)
constDict = dict_from_section(Config,'constants')
clusterDict = dict_from_section(Config,'cluster_params')
cc = ClusterCosmology(fparams,constDict,skipCls=True)
fsky = Config.getfloat(expName,'fsky')
saveId = expName + "_" + gridName + "_v" + version
ms = list_from_config(Config,gridName,'mexprange')
mrange = np.arange(ms[0],ms[1]+ms[2],ms[2])
zs = list_from_config(Config,gridName,'zrange')
zrange = np.arange(zs[0],zs[1]+zs[2],zs[2])
hmf = Halo_MF(cc,mrange,zrange)
zcents, hb = haloBias(mrange,zrange,cc.rhoc0om,hmf.kh,hmf.pk)
powers = sampleVarianceOverNsquareOverBsquare(cc,hmf.kh,hmf.pk,zrange,fsky,lmax=lmax)
Config = SafeConfigParser()
Config.optionxform = str
Config.read(iniFile)
beam = listFromConfig(Config, experimentName, 'beams')
noise = listFromConfig(Config, experimentName, 'noises')
freq = listFromConfig(Config, experimentName, 'freqs')
lmax = int(Config.getfloat(experimentName, 'lmax'))
lknee = Config.getfloat(experimentName, 'lknee')
alpha = Config.getfloat(experimentName, 'alpha')
fsky = Config.getfloat(experimentName, 'fsky')
cosmoDict = dictFromSection(Config, cosmologyName)
constDict = dictFromSection(Config, 'constants')
clusterDict = dictFromSection(Config, clusterParams)
cc = ClusterCosmology(cosmoDict, constDict, lmax)
mass_err_file = Config.get(experimentName, 'mass_err')
mass_err = np.loadtxt(mass_err_file)
zbin_temp = np.arange(0.1, 2.01, 0.05)
zbin = np.insert(zbin_temp, 0, 0.0)
HMF = Halo_MF(clusterCosmology=cc)
errs, Ntot = HMF.Mass_err(mass_err, zbin, beam, noise, freq, clusterDict,
lknee, alpha, fileFunc)
print((np.sqrt(errs)))
print(Ntot)
print('sample time',time.time() - start)
sys.exit(0)
CL = lk.clusterLike(iniFile,pardict,nemoOutputDir,noise_file,fix_params,fixvals,fixlist,fitsfile,test=args.test,simtest=simtst,simpars=args.simpars,y0thresh=args.y0test)
if (args.printtest):
parvals2 = [3.46419819e-01,2.34697120e-02,6.50170056e+01,1.33398673e-09,9.36305025e-01,2.53310030e-01,1.93661978e-01,1.74839544e-01]
#parvals2 = [1.194e-01,2.34697120e-02,6.50170056e+01,1.33398673e-09,9.36305025e-01,2.53310030e-01,1.93661978e-01,1.74839544e-01]
param_vals= lk.alter_fparams(fparams,parlist,parvals)
cluster_props = np.array([CL.clst_z,CL.clst_zerr,CL.clst_y0*1e-4,CL.clst_y0err*1e-4])
start = time.time()
int_cc = ClusterCosmology(param_vals,CL.constDict,clTTFixFile=CL.clttfile)
print('CC',time.time() - start)
start = time.time()
int_HMF = Halo_MF(int_cc,CL.mgrid,CL.zgrid)
print('HMF',time.time() - start)
dn_dzdm_int = int_HMF.inter_dndmLogm(200.)
zbins = 10
LgYa = np.outer(np.ones(len(int_HMF.M.copy())),CL.LgY)
Y = 10**LgYa
Ma = np.outer(int_HMF.M.copy(),np.ones(len(LgYa[0,:])))
Marr = np.outer(int_HMF.M.copy(),np.ones([len(int_HMF.zarr)]))
z_arr = np.outer(np.ones([len(int_HMF.M.copy())]),int_HMF.zarr)
ii = 1
