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 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 get_theory_for_params(self, paramdic, camb_pars=None, camb_results=None):
if camb_pars is None:
from cosmomc_to_camb import get_camb_params
camb_pars = get_camb_params(paramdic)
if camb_results is not None:
results, PKdelta, PKWeyl = camb_results
else:
results, PKdelta, PKWeyl = self.get_camb_theory(camb_pars)
wl_photoz_errors = [paramdic['DES_DzS1'], paramdic['DES_DzS2'], paramdic['DES_DzS3'], paramdic['DES_DzS4']]
lens_photoz_errors = [paramdic['DES_DzL1'], paramdic['DES_DzL2'], paramdic['DES_DzL3'], paramdic['DES_DzL4'],
paramdic['DES_DzL5']]
bin_bias = [paramdic['DES_b1'], paramdic['DES_b2'], paramdic['DES_b3'], paramdic['DES_b4'], paramdic['DES_b5']]
shear_calibration_parameters = [paramdic['DES_m1'], paramdic['DES_m2'], paramdic['DES_m3'], paramdic['DES_m4']]
return self.get_theory(camb_pars, results, PKdelta, PKWeyl, bin_bias=bin_bias,
wl_photoz_errors=wl_photoz_errors,
lens_photoz_errors=lens_photoz_errors,
shear_calibration_parameters=shear_calibration_parameters,
intrinsic_alignment_A=paramdic['DES_AIA'],
intrinsic_alignment_alpha=paramdic['DES_alphaIA'], intrinsic_alignment_z0=0.62)
common = []
for name in like.names:
common.append(name in JLA.names or 'SDSS' + name in JLA.names
or 'sn' + name in JLA.names)
common = np.array(common, dtype=np.bool)
print(like.nsn, np.sum(common), like.nsn - np.sum(common))
redshifts = np.logspace(-2, 1, 1000)
samples = g.sampleAnalyser.samplesForRoot(
'base_plikHM_TTTEEE_lowl_lowE_lensing')
ixs = samples.randomSingleSamples_indices()
dists = np.zeros((len(ixs), len(redshifts)))
sndists = np.zeros((len(ixs), like.nsn))
for i, ix in enumerate(ixs):
dic = samples.getParamSampleDict(ix)
camb_pars = get_camb_params(dic)
results = camb.get_background(camb_pars)
dists[i, :] = 5 * np.log10(
(1 + redshifts)**2 * results.angular_diameter_distance(redshifts))
sndists[i, :] = 5 * np.log10(
(1 + like.zcmb)**2 * results.angular_diameter_distance(like.zcmb))
paramdic = g.bestfit('base_plikHM_TTTEEE_lowl_lowE_lensing').getParamDict()
camb_pars = get_camb_params(paramdic)
results = camb.get_background(camb_pars)
invvars = 1.0 / like.pre_vars
wtval = np.sum(invvars)
offset = 5 * np.log10(1e-5)
lumdists = 5 * np.log10(
JLA = SN.SN_likelihood(os.path.join(os.path.dirname(__file__), r'../../data/jla.dataset'), marginalize=False)
common = []
for name in like.names:
common.append(name in JLA.names or 'SDSS' + name in JLA.names or 'sn' + name in JLA.names)
common = np.array(common, dtype=np.bool)
print(like.nsn, np.sum(common), like.nsn - np.sum(common))
redshifts = np.logspace(-2, 1, 1000)
samples = g.sampleAnalyser.samplesForRoot('base_plikHM_TTTEEE_lowl_lowE_lensing')
ixs = samples.randomSingleSamples_indices()
dists = np.zeros((len(ixs), len(redshifts)))
sndists = np.zeros((len(ixs), like.nsn))
for i, ix in enumerate(ixs):
dic = samples.getParamSampleDict(ix)
camb_pars = get_camb_params(dic)
results = camb.get_background(camb_pars)
dists[i, :] = 5 * np.log10((1 + redshifts) ** 2 * results.angular_diameter_distance(redshifts))
sndists[i, :] = 5 * np.log10((1 + like.zcmb) ** 2 * results.angular_diameter_distance(like.zcmb))
paramdic = g.bestfit('base_plikHM_TTTEEE_lowl_lowE_lensing').getParamDict()
camb_pars = get_camb_params(paramdic)
results = camb.get_background(camb_pars)
invvars = 1.0 / like.pre_vars
wtval = np.sum(invvars)
offset = 5 * np.log10(1e-5)
lumdists = 5 * np.log10((1 + like.zcmb) ** 2 * results.angular_diameter_distance(like.zcmb))
redshifts = np.logspace(-2, 1, 1000)
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]
f8s[i, :] = results.get_fsigma8()[::-1]
assert (abs(dic['fsigma8z038'] / f8s[i, redshifts.index(0.38)] - 1) < 0.001)
with open(cachename, 'wb') as output:
pickle.dump([ixs, f8s, Hs, DMs, FAPs], output, pickle.HIGHEST_PROTOCOL)
fsigmeans = np.zeros(len(redshifts))
fsigerrs = np.zeros(len(redshifts))
FAPmeans = np.zeros(len(redshifts))
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]
f8s[i, :] = results.get_fsigma8()[::-1]
assert (abs(dic['fsigma8z038'] / f8s[i, redshifts.index(0.38)] - 1) < 0.001)
with open(cachename, 'wb') as output:
pickle.dump([ixs, f8s, Hs, DMs, FAPs], output, pickle.HIGHEST_PROTOCOL)
fsigmeans = np.zeros(len(redshifts))
fsigerrs = np.zeros(len(redshifts))
FAPmeans = np.zeros(len(redshifts))