pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()[0]
postprocess = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
for r in [True, False]:
rt = "Recon" if r else "Prerecon"
data = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r,
postprocess=postprocess)
n = PowerNoda2019(postprocess=postprocess,
recon=r,
nonlinear_type="spt")
n2 = PowerNoda2019(postprocess=postprocess,
recon=r,
nonlinear_type="halofit")
fitter.add_model_and_dataset(n,
data,
name=f"N19 {rt} SPT",
color="r",
shade_alpha=0.7,
linestyle="-" if r else "--",
zorder=10 if r else 2)
fitter.add_model_and_dataset(n2,
data,
name=f"N19 {rt} Halofit",
fitter = Fitter(dir_name)
p = BAOExtractor(r_s)
cs = ["#262232", "#116A71", "#48AB75", "#b7c742"]
for r in [True]:
t = "Recon" if r else "Prerecon"
datae = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, min_k=0.03, max_k=0.30, postprocess=p)
for ls, n in zip(["-", ":"], ["", " (No Poly)"]):
if n:
fix = ["om", "f", "a1", "a2", "a3", "a4", "a5"]
else:
fix = ["om", "f"]
fitter.add_model_and_dataset(PowerBeutler2017(postprocess=p, recon=r, fix_params=fix), datae, name=f"Beutler 2017{n}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(postprocess=p, recon=r, fix_params=fix), datae, name=f"Seo 2016{n}", linestyle=ls, color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(postprocess=p, recon=r, fix_params=fix), datae, name=f"Ding 2018{n}", linestyle=ls, color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(postprocess=p, recon=r), datae, name=f"Noda 2019", color=cs[3])
sampler = DynestySampler(temp_dir=dir_name, nlive=300)
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
c = ChainConsumer()
for posterior, weight, chain, evidence, model, data, extra in fitter.load():
c.add_chain(chain, weights=weight, parameters=model.get_labels(), **extra)
c.configure(shade=True, bins=30, legend_artists=True)
c.analysis.get_latex_table(filename=pfn + "_params.txt")
c.plotter.plot_summary(filename=pfn + "_summary.png", errorbar=True, truth={"$\\Omega_m$": 0.31, "$\\alpha$": 0.9982})
ls = "-" if r else "--"
d = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, realisation=0)
de = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r,
postprocess=p,
realisation=0)
beutler_not_fixed = PowerBeutler2017(recon=r)
beutler = PowerBeutler2017(recon=r)
sigma_nl = 6.0 if r else 9.3
beutler.set_default("sigma_nl", sigma_nl)
beutler.set_fix_params(["om", "sigma_nl"])
seo = PowerSeo2016(recon=r)
ding = PowerDing2018(recon=r)
noda = PowerNoda2019(recon=r, postprocess=p)
for i in range(999):
d.set_realisation(i)
de.set_realisation(i)
fitter.add_model_and_dataset(
beutler_not_fixed,
d,
name=f"Beutler 2017 {t}, mock number {i}",
linestyle=ls,
color="p",
realisation=i)
fitter.add_model_and_dataset(
beutler,
d,
BAOExtractor(r_s, extra_ks=(0.095, 0.15)),
BAOExtractor(r_s, extra_ks=(0.095, 0.16)),
BAOExtractor(r_s, extra_ks=(0.095, 0.17)),
BAOExtractor(r_s, extra_ks=(0.095, 0.18)),
BAOExtractor(r_s, extra_ks=(0.095, 0.19)),
BAOExtractor(r_s, extra_ks=(0.095, 0.20)),
BAOExtractor(r_s, extra_ks=(0.095, 0.21)),
BAOExtractor(r_s, extra_ks=(0.095, 0.22)),
BAOExtractor(r_s, extra_ks=(0.095, 0.23)),
BAOExtractor(r_s, extra_ks=(0.095, 0.24)),
]
recon = True
for p in ps:
n = f"$k = {p.extra_ks[1]:0.2f}\, h / {{\\rm Mpc}}$"
model = PowerNoda2019(postprocess=p, recon=recon)
data = PowerSpectrum_SDSS_DR12_Z061_NGC(min_k=0.02, max_k=0.30, postprocess=p, recon=recon)
fitter.add_model_and_dataset(model, data, name=n)
sampler = DynestySampler(temp_dir=dir_name)
fitter.set_sampler(sampler)
fitter.set_num_walkers(30)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
c = ChainConsumer()
for posterior, weight, chain, model, data, extra in fitter.load():
print(extra["name"])
from barry.fitter import Fitter
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
sampler = DynestySampler(temp_dir=dir_name, nlive=1000)
fitter = Fitter(dir_name)
cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
d = PowerSpectrum_DESIMockChallenge_Handshake(min_k=0.005, max_k=0.3, isotropic=False, realisation="data", fit_poles=[0, 2])
fitter.add_model_and_dataset(PowerBeutler2017(isotropic=False), d, name=f"Beutler 2017 Prerecon", color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(isotropic=False), d, name=f"Seo 2016 Prerecon", color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(isotropic=False), d, name=f"Ding 2018 Prerecon", color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(isotropic=False), d, name=f"Noda 2019 Prerecon", color=cs[3])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
res = fitter.load()
from chainconsumer import ChainConsumer
import copy
c = ChainConsumer()
if __name__ == "__main__":
logging.basicConfig(
level=logging.DEBUG,
format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
postprocess = BAOExtractor(r_s, mink=0.15)
for recon in [True, False]:
model1 = PowerNoda2019(recon=recon,
name=f"Noda2019, recon={recon}",
postprocess=postprocess,
fix_params=["om", "f", "gamma"])
dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=recon,
postprocess=postprocess,
min_k=0.03,
max_k=0.15)
data1 = dataset1.get_data()
print(list(data1[0].keys()))
print(data1[0]["ks_output"])
exit()
# First comparison - the actual recon data
model1.set_data(data1)
p, minv = model1.optimize()
print(recon)
print(p)
print(minv)
model,
d,
name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(recon=r),
d,
name=f"Seo 2016 {t}",
linestyle=ls,
color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(recon=r),
d,
name=f"Ding 2018 {t}",
linestyle=ls,
color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(recon=r, postprocess=p),
de,
name=f"Noda 2019 {t}",
linestyle=ls,
color=cs[3])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
from chainconsumer import ChainConsumer
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.postprocessing import BAOExtractor, PureBAOExtractor
from barry.cosmology.camb_generator import getCambGenerator
from barry.samplers import DynestySampler
from barry.fitter import Fitter
import numpy as np
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
postprocess = BAOExtractor(r_s)
r = True
model = PowerNoda2019(postprocess=postprocess, recon=r, name="")
mink = 0.03
maxk = 0.30
datas = [
PowerSpectrum_SDSS_DR12_Z061_NGC(name="Mock covariance",
recon=r,
min_k=mink,
max_k=maxk,
postprocess=postprocess),
PowerSpectrum_SDSS_DR12_Z061_NGC(name="Nishimichi, full",
recon=r,
min_k=mink,
max_k=maxk,
postprocess=postprocess),
PowerSpectrum_SDSS_DR12_Z061_NGC(name="Nishimichi, diag",
recon=r,
from barry.postprocessing import BAOExtractor
if __name__ == "__main__":
logging.basicConfig(
level=logging.DEBUG,
format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
recon = True
c = getCambGenerator()
r_s = c.get_data()[0]
postprocess = BAOExtractor(r_s)
model1 = PowerNoda2019(recon=recon,
name=f"Noda2019, recon={recon}",
postprocess=postprocess)
from barry.datasets.mock_power import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.datasets.dummy import DummyPowerSpectrum_SDSS_DR12_Z061_NGC
dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=recon,
postprocess=postprocess,
min_k=0.03,
max_k=0.25)
dataset2 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(
name="Dummy data, real window fn",
dummy_window=False,
postprocess=postprocess)
data1 = dataset1.get_data()
data2 = dataset2.get_data()
pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
postprocess = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
for r in [True, False]:
rt = "Recon" if r else "Prerecon"
data = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r,
postprocess=postprocess)
n = PowerNoda2019(postprocess=postprocess,
recon=r,
fix_params=["om", "f", "gamma", "b"])
n.param_dict["b"].default = 1.992 if r else 1.996
fitter.add_model_and_dataset(
n,
data,
name=f"N19 {rt} fixed $f$, $\\gamma_{{rec}}$, $b$",
linestyle="-" if r else "--",
color="o",
shade_alpha=0.7,
zorder=10)
fitter.add_model_and_dataset(
PowerNoda2019(postprocess=postprocess,
recon=r,
fix_params=["om", "f", "gamma"]),
data,
