def __init__(self, fname_params):
self.p = ParamRun(fname_params)
self.cosmo = self.p.get_cosmo()
self.cosmo_vary = COSMO_VARY(self.p)
self.kwargs = self.p.get_cosmo_pars()
self.hmc = get_hmcalc(**self.kwargs)
self._PP = norm.sf(-1) - norm.sf(1)
def th(pars,d):
if not cosmo_vary:
cosmo_fid = cosmo
hmc_fid = hmc
else:
cosmo_fid = COSMO_ARGS(kwargs)
hmc_fid = get_hmcalc(cosmo_fid, **kwargs)
return get_theory(p, d, cosmo_fid, hmc_fid,
hm_correction=hm_correction,
selection=None,**pars)
def th2h(pars):
if not cosmo_vary:
pars_fid = pars
cosmo_fid = cosmo
hmc_fid = hmc
else:
pars_fid = {**p.get_cosmo_pars(), **pars}
cosmo_fid = COSMO_ARGS(pars_fid)
hmc_fid = get_hmcalc(cosmo_fid, **pars_fid)
return get_theory(p, d, cosmo_fid, hmc_fid,
return_separated=False,
hm_correction=None,
include_1h=False, include_2h=True,
**pars_fid)
def bias_one(p0, num):
"""Calculates the halo model bias for a set of parameters."""
if self.cosmo_vary:
cosmo = COSMO_ARGS(pars)
hmc = get_hmcalc(
cosmo, **{
"mass_function": self.p.get_massfunc(),
"halo_bias": self.p.get_halobias()
})
else:
cosmo = self.cosmo
hmc = self.hmc
bb = hm_bias(cosmo, hmc, 1 / (1 + zarr), d.tracers[num][1],
**lik.build_kwargs(p0))
return bb
def th2h(self, pars):
if not cosmo_vary:
cosmo_fid = cosmo
hmc_fid = hmc
else:
cosmo_fid = COSMO_ARGS(kwargs)
hmc_fid = get_hmcalc(cosmo_fid, **kwargs)
return get_theory(p,
self.d,
cosmo_fid,
hmc_fid,
hm_correction=hm_correction,
selection=sel,
include_2h=True,
include_1h=False,
**pars)
def model_xcorr(p, fields, xcorr):
"""Model the angular power spectrum."""
# copy & reset shape
mcorr = copy.deepcopy(xcorr)
for name1 in mcorr:
for name2 in mcorr[name1]:
mcorr[name1][name2].cell = None
# calculate models where applicable & inherit measurements
for name1 in mcorr:
for name2 in mcorr[name1]:
print(name1, name2, end='')
f1, type1 = fields[name1]
f2, type2 = fields[name2]
is_model = np.array([type1 in types, type2 in types])
is_yy = (type1, type2) == ('y', 'y') # don't model yxy
if is_model.all() and not is_yy:
if mcorr[name2][name1].cell is not None:
cl = mcorr[name2][name1].cell
print(' <--- %s %s' % (name2, name1))
else:
# model the cross correlation
prof1, kwargs1 = get_profile(p, name1)
prof2, kwargs2 = get_profile(p, name2)
# best fit from 1909.09102
if ('y' in (type1, type2)) and ('g' not in (type1, type2)):
kwargs1 = kwargs2 = {**kwargs1, **{'mass_bias': 0.75}}
kwargs = kwargs1
cosmo = COSMO_ARGS(kwargs)
l = mcorr[name1][name2].leff
hmc = get_hmcalc(**kwargs)
cl = hm_ang_power_spectrum(cosmo, hmc, l, (prof1, prof2),
**kwargs)
if type1 == type2 == 'g':
nl = np.load(p.get_fname_cls(f1.name, f2.name))['nls']
cl += nl
print('\n', end='')
mcorr[name1][name2].cell = cl
else:
mcorr[name1][name2] = xcorr[name1][name2]
print(' ## not modelled')
return mcorr
def get_1h_covariance(p, fields, xcorr, f11, f12, f21, f22):
'''Computes and saves the 1-halo covariance.'''
fname_cov = p.get_fname_cov(f11.name, f12.name, f21.name, f22.name,
"1h4pt")
fname_cov_T = p.get_fname_cov(f21.name, f22.name, f11.name, f12.name,
"1h4pt")
# print(fname_cov)
if (not os.path.isfile(fname_cov)) and (not os.path.isfile(fname_cov_T)):
fsky = np.mean(f11.mask * f12.mask * f21.mask * f22.mask)
leff = xcorr[f11.name][f11.name].leff
# Set-up profiles
profiles = [[], [], [], []]
flds = [f11, f12, f21, f22]
for i, F in enumerate(flds):
# retrieve map parameters from param file
for m in p.get("maps"):
if m["name"] == F.name:
break
# initialize profile and update it
prof = ProfTracer(m)
if F.is_ndens:
kwargs = m["model"]
cosmo = COSMO_ARGS(kwargs) # TODO: final cosmo is used
prof.update_parameters(cosmo, **kwargs)
else:
prof.update_parameters(COSMO_DEFAULT(), **{"mass_bias": 0.75})
profiles[i] = prof
# Get single model parameter dictionary
models_a = p.get_models()[f11.name]
models_b = p.get_models()[f12.name]
kwargs = models_a
hmc = get_hmcalc(**kwargs)
# Calculate covariace
dcov = hm_ang_1h_covariance(fsky, leff, cosmo, hmc, profiles, **kwargs)
dcov *= Beam(f11.name + f12.name, leff, p.get_nside())[:, None]
dcov *= Beam(f21.name + f22.name, leff, p.get_nside())[None, :]
cov = Covariance(f11.name, f12.name, f21.name, f22.name, dcov)
cov.to_file(p.get_outdir() + "/dcov_1h4pt_" + f11.name + "_" +
f12.name + "_" + f21.name + "_" + f22.name + ".npz")
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
sample='wisc3'
dgg_wdpj=np.load("output_default/cls_"+sample+"_"+sample+".npz")
dgg_wodpj=np.load("output_default_nodpj/cls_"+sample+"_"+sample+".npz")
cov_gg=np.load("output_default/cov_comb_m_"+sample+"_"+sample+
"_"+sample+"_"+sample+".npz")['cov']
ls=dgg_wdpj['ls']
fname_params = "params_wnarrow.yml"
p = ParamRun(fname_params)
kwargs = p.get_cosmo_pars()
cosmo = p.get_cosmo()
hmc = get_hmcalc(cosmo, **kwargs)
cosmo_vary = COSMO_VARY(p)
hm_correction = HalomodCorrection(cosmo)
v=None
for s,vv in enumerate(p.get("data_vectors")):
if vv['name']==sample:
v=vv
dat = DataManager(p, v, cosmo, all_data=False)
gat = DataManager(p, v, cosmo, all_data=True)
def th(pars,d):
if not cosmo_vary:
cosmo_fid = cosmo
hmc_fid = hmc