def GetBackgroundFuncs(samples):
ixs = samples.randomSingleSamples_indices()[::40]
DMs = np.zeros((len(ixs), len(redshifts)))
Hs = np.zeros(DMs.shape)
rsDV = np.zeros(DMs.shape)
camb.set_z_outputs(redshifts)
for i, ix in enumerate(ixs):
print(i, ix)
dic = samples.getParamSampleDict(ix)
pars = get_camb_params(dic)
results = camb.get_background(pars)
bao = results.get_background_outputs()
rsDV[i, :] = 1 / bao[:, 0]
DMs[i, :] = bao[:, 2] * (1 + reds)
Hs[i, :] = bao[:, 1]
Hmeans = np.zeros(len(redshifts))
Herrs = np.zeros(len(redshifts))
DMmeans = np.zeros(len(redshifts))
DMerrs = np.zeros(len(redshifts))
for i, z in enumerate(redshifts):
Hmeans[i] = np.mean(Hs[:, i]) / (1 + z)
Herrs[i] = np.std(Hs[:, i]) / (1 + z)
DMmeans[i] = np.mean(DMs[:, i])
DMerrs[i] = np.std(DMs[:, i])
Hinterp = UnivariateSpline([0] + redshifts, [samples.mean('H0')] + list(Hmeans), s=0)
DMinterp = UnivariateSpline([0] + redshifts, [0] + list(DMmeans), s=0)
Herrinterp = UnivariateSpline([0] + redshifts, [samples.std('H0')] + list(Herrs), s=0)
DMerrinterp = UnivariateSpline([0] + redshifts, [0] + list(DMerrs), s=0)
return Hinterp, Herrinterp, DMinterp, DMerrinterp, rsDV
def getBAOCamb(zrange, params, AccuracyBoost=False):
'''
Get BAO's fk=rs/D_V from CAMB. Return 1D array of fk for all redshifts z
'''
pars = camb.CAMBparams()
if AccuracyBoost:
pars.set_accuracy(AccuracyBoost=2.0,
lSampleBoost=2.0,
lAccuracyBoost=2.0)
#pars.set_accuracy(AccuracyBoost=3.0, lSampleBoost=3.0, lAccuracyBoost=3.0)
pars.set_cosmology(H0=params['H0'],
ombh2=params['ombh2'],
omch2=params['omch2'],
tau=params['tau'],
mnu=params['mnu'],
nnu=params['nnu'],
omk=params['omk'])
pars.set_dark_energy(w=params['w'])
pars.InitPower.set_params(As=params['As'], ns=params['ns'], r=params['r'])
#pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=1, max_eta_k=2*params['lmax'])
camb.set_z_outputs(zrange)
results = camb.get_results(pars)
fk = results.get_background_outputs() #results.get_BAO(zrange,pars)[:,0]
#print fk
fk = np.nan_to_num(fk[:, 0])
return fk
def GetBackgroundFuncs(samples):
ixs = samples.randomSingleSamples_indices()[::40]
DMs = np.zeros((len(ixs), len(redshifts)))
Hs = np.zeros(DMs.shape)
rsDV = np.zeros(DMs.shape)
camb.set_z_outputs(redshifts)
for i, ix in enumerate(ixs):
print(i, ix)
dic = samples.getParamSampleDict(ix)
pars = get_camb_params(dic)
results = camb.get_background(pars)
bao = results.get_background_outputs()
rsDV[i, :] = 1 / bao[:, 0]
DMs[i, :] = bao[:, 2] * (1 + reds)
Hs[i, :] = bao[:, 1]
Hmeans = np.zeros(len(redshifts))
Herrs = np.zeros(len(redshifts))
DMmeans = np.zeros(len(redshifts))
DMerrs = np.zeros(len(redshifts))
for i, z in enumerate(redshifts):
Hmeans[i] = np.mean(Hs[:, i]) / (1 + z)
Herrs[i] = np.std(Hs[:, i]) / (1 + z)
DMmeans[i] = np.mean(DMs[:, i])
DMerrs[i] = np.std(DMs[:, i])
Hinterp = UnivariateSpline([0] + redshifts,
[samples.mean('H0')] + list(Hmeans),
s=0)
DMinterp = UnivariateSpline([0] + redshifts, [0] + list(DMmeans), s=0)
Herrinterp = UnivariateSpline([0] + redshifts,
[samples.std('H0')] + list(Herrs),
s=0)
DMerrinterp = UnivariateSpline([0] + redshifts, [0] + list(DMerrs), s=0)
return Hinterp, Herrinterp, DMinterp, DMerrinterp, rsDV
def getBAOCamb(zrange,params,AccuracyBoost=False):
'''
Get BAO's fk=rs/D_V from CAMB. Return 1D array of fk for all redshifts z
'''
pars = camb.CAMBparams()
if AccuracyBoost:
pars.set_accuracy(AccuracyBoost=2.0, lSampleBoost=2.0, lAccuracyBoost=2.0)
#pars.set_accuracy(AccuracyBoost=3.0, lSampleBoost=3.0, lAccuracyBoost=3.0)
pars.set_cosmology(H0=params['H0'], ombh2=params['ombh2'], omch2=params['omch2'], tau=params['tau'],mnu=params['mnu'],nnu=params['nnu'],omk=params['omk'])
pars.set_dark_energy(w=params['w'])
pars.InitPower.set_params(As=params['As'],ns=params['ns'],r=params['r'])
#pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=1, max_eta_k=2*params['lmax'])
camb.set_z_outputs(zrange)
results = camb.get_results(pars)
fk = results.get_background_outputs() #results.get_BAO(zrange,pars)[:,0]
#print fk
fk = np.nan_to_num(fk[:,0])
return fk
p = samples.getParams()
reds = np.asarray(redshifts)
ommh2 = samples.mean('omegamh2')
# Need to get f sigma(8) of z using CAMB. This takes at least some minutes, so cache.
cachename = os.path.join(tempfile.gettempdir(), 'planck2018' + str(hash((samples.jobItem.chainRoot, os.path.getmtime(
samples.jobItem.chainRoot + '_1.txt')) + tuple(redshifts)))
+ '.fsig_evolve')
if os.path.isfile(cachename):
with open(cachename, 'rb') as inp:
ixs, f8s, Hs, DMs, FAPs = pickle.load(inp)
else:
camb.set_z_outputs(redshifts)
ixs = samples.randomSingleSamples_indices()[::4]
DMs = np.zeros((len(ixs), len(redshifts)))
Hs = np.zeros(DMs.shape)
FAPs = np.zeros(DMs.shape)
f8s = np.zeros(DMs.shape)
for i, ix in enumerate(ixs):
print(i, ix)
dic = samples.getParamSampleDict(ix)
pars = get_camb_params(dic)
pars.set_matter_power(redshifts, kmax=2)
results = camb.get_results(pars)
bao = results.get_background_outputs()
DMs[i, :] = bao[:, 2] * (1 + reds)
Hs[i, :] = bao[:, 1]
FAPs[i, :] = bao[:, 3]
p = samples.getParams()
reds = np.asarray(redshifts)
ommh2 = samples.mean('omegamh2')
# Need to get f sigma(8) of z using CAMB. This takes at least some minutes, so cache.
cachename = os.path.join(tempfile.gettempdir(), 'planck2018' + str(hash((samples.jobItem.chainRoot, os.path.getmtime(
samples.jobItem.chainRoot + '_1.txt')) + tuple(redshifts)))
+ '.fsig_evolve')
if os.path.isfile(cachename):
with open(cachename, 'rb') as inp:
ixs, f8s, Hs, DMs, FAPs = pickle.load(inp)
else:
camb.set_z_outputs(redshifts)
ixs = samples.randomSingleSamples_indices()[::4]
DMs = np.zeros((len(ixs), len(redshifts)))
Hs = np.zeros(DMs.shape)
FAPs = np.zeros(DMs.shape)
f8s = np.zeros(DMs.shape)
for i, ix in enumerate(ixs):
print(i, ix)
dic = samples.getParamSampleDict(ix)
pars = get_camb_params(dic)
pars.set_matter_power(redshifts, kmax=2)
results = camb.get_results(pars)
bao = results.get_background_outputs()
DMs[i, :] = bao[:, 2] * (1 + reds)
Hs[i, :] = bao[:, 1]
FAPs[i, :] = bao[:, 3]