cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
for r in [True, False]:
t = "Recon" if r else "Prerecon"
ls = "-" if r else "--"
d = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=r)
# Fix sigma_nl for one of the Beutler models
model = CorrBeutler2017()
sigma_nl = 6.0 if r else 9.3
model.set_default("sigma_nl", sigma_nl)
model.set_fix_params(["om", "sigma_nl"])
fitter.add_model_and_dataset(CorrBeutler2017(), d, name=f"Beutler 2017 {t}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(model, d, name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(CorrSeo2016(recon=r), d, name=f"Seo 2016 {t}", linestyle=ls, color=cs[1])
fitter.add_model_and_dataset(CorrDing2018(recon=r), d, name=f"Ding 2018 {t}", linestyle=ls, color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(30)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
res = fitter.load()
if True:
from chainconsumer import ChainConsumer
if True:
# This is a dirty way of faking another column in the dataset so I can keep plots visually consistent.
import logging
from barry.models import CorrDing2018
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG, format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
from barry.datasets import CorrelationFunction_SDSS_DR12_Z061_NGC
for recon in [True, False]:
model = CorrDing2018(recon=recon)
model_smooth = CorrDing2018(recon=recon, smooth=True)
model.set_default("om", 0.31)
model_smooth.set_default("om", 0.31)
# Assuming the change from 0.675 to 0.68 is something we can ignore, or we can add h0 to the default parameters.
dataset1 = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=recon, reduce_cov_factor=10)
data1 = dataset1.get_data()
# First comparison - the actual recon data
model.set_data(data1)
p, minv = model.optimize()
model_smooth.set_data(data1)
p2, minv2 = model_smooth.optimize()
print(p)
print(minv)
model.plot(p, smooth_params=p2)
# FINDINGS
# Good. gets 0.998
c4 = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
for r in [True, False]:
t = "Recon" if r else "Prerecon"
ls = "-" if r else "--"
d = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=r, realisation=0)
beutler_not_fixed = CorrBeutler2017()
beutler = CorrBeutler2017()
sigma_nl = 5.0 if r else 8.0
beutler.set_default("sigma_nl", sigma_nl)
beutler.set_fix_params(["om", "sigma_nl"])
seo = CorrSeo2016(recon=r)
ding = CorrDing2018(recon=r)
for i in range(999):
d.set_realisation(i)
fitter.add_model_and_dataset(beutler_not_fixed, d, name=f"Beutler 2017 {t}, mock number {i}", linestyle=ls, color=c4[0], realisation=i)
fitter.add_model_and_dataset(beutler, d, name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}, mock number {i}", linestyle=ls, color=c4[0], realisation=i)
fitter.add_model_and_dataset(seo, d, name=f"Seo 2016 {t}, mock number {i}", linestyle=ls, color=c4[1], realisation=i)
fitter.add_model_and_dataset(ding, d, name=f"Ding 2018 {t}, mock number {i}", linestyle=ls, color=c4[2], realisation=i)
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.set_num_concurrent(400)
if not fitter.should_plot():
fitter.fit(file)
d,
name=f"Beutler 2017 {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(
model,
d,
name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(CorrSeo2016(recon=r),
d,
name=f"Seo 2016 {t}",
linestyle=ls,
color=cs[1])
fitter.add_model_and_dataset(CorrDing2018(recon=r),
d,
name=f"Ding 2018 {t}",
linestyle=ls,
color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(30)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
from chainconsumer import ChainConsumer
# Check correlation between pk and xi results using recon Ding
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name, save_dims=2, remove_output=False)
sampler = DynestySampler(temp_dir=dir_name, nlive=400)
for r in [True]:
t = "Recon" if r else "Prerecon"
d_pk = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, realisation=0)
d_xi = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=r, realisation=0)
ding_pk = PowerDing2018(recon=r)
ding_pk_smooth = PowerDing2018(recon=r, smooth=True)
ding_xi = CorrDing2018(recon=r)
ding_xi_smooth = CorrDing2018(recon=r, smooth=True)
for i in range(999):
d_pk.set_realisation(i)
d_xi.set_realisation(i)
fitter.add_model_and_dataset(
ding_pk,
d_pk,
name=f"Ding 2018 $P(k)$, mock number {i}",
linestyle="-",
color="p",
realisation=i)
fitter.add_model_and_dataset(
ding_pk_smooth,