import logging
from barry.models import CorrBeutler2017
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]:
dataset1 = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=recon)
data1 = dataset1.get_data()
# Run on mean
model = CorrBeutler2017(name=f"Beutler2017, recon={recon}")
model.set_data(data1)
p, minv = model.optimize(maxiter=2000, niter=200)
sigma_nl = p["sigma_nl"]
print(f"recon={recon}, Sigma_nl found to be {sigma_nl:0.3f} with prob {minv:0.3f}")
model.set_default("sigma_nl", sigma_nl)
model.set_fix_params(["om", "sigma_nl"])
# Recon 5.0
# Prerecon 8.0
import logging
from barry.models import CorrBeutler2017
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 = CorrBeutler2017()
model_smooth = CorrBeutler2017(smooth=True)
dataset1 = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=recon)
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
# Yes, no issue recovering SDSS mean to alpha=0.98 (postrecon) and 1.007 (prerecon)
from barry.samplers import DynestySampler
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, remove_output=False)
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
r_s = c.get_data()[0]
p = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
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,
min_dist=20,
max_dist=250)
# 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])
fit_poles=[0, 2, 4],
min_dist=50.0,
max_dist=170.0,
mocktype=mocktype,
redshift_bin=z + 1,
realisation=None,
num_mocks=1000,
)
# Set up the model we'll use. Fix Omega_m and beta. 5 polynomials (default)
# for each of the fitted multipoles. Use full analytic marginalisation for speed
# Apply the Hartlap correction to the covariance matrix.
model = CorrBeutler2017(
recon=dataset.recon,
isotropic=dataset.isotropic,
marg="full",
fix_params=["om", "beta"],
poly_poles=dataset.fit_poles,
correction=Correction.HARTLAP,
)
# Create a unique name for the fit and add it to the list
name = dataset.name + " mock mean"
fitter.add_model_and_dataset(model, dataset, name=name)
allnames.append(name)
# Now add the individual realisations to the list
for i in range(len(dataset.mock_data)):
dataset.set_realisation(i)
name = dataset.name + f" realisation {i}"
fitter.add_model_and_dataset(model, dataset, name=name)
allnames.append(name)
model.set_data(data.get_data())
model.kvals = pklin[:, 0]
model.pksmooth = smooth(model.kvals, pklin[:, 1])
model.pkratio = pklin[:, 1] / model.pksmooth - 1.0
# Power spectrum
ls = "-"
names = [f"Xinyi-std Pk", f"Pedro-std Pk", f"Baojiu-std Pk", f"Xinyi-Hada Pk", f"Hee-Jong-std Pk", f"Yu-Yu-std Pk", f"Javier-std Pk"]
for i in range(7):
data.set_realisation(i)
fitter.add_model_and_dataset(model, data, name=names[i], color=cs[i], realisation=i, ls=ls)
# Correlation Function
data = CorrelationFunction_DESIMockChallenge(recon=True, isotropic=False, fit_poles=[0, 2])
model = CorrBeutler2017(recon=True, isotropic=False, fix_params=["om"], poly_poles=[0, 2], correction=Correction.NONE, marg="full")
ls = "--"
names = [f"Xinyi-std Xi", f"Pedro-std Xi", f"Baojiu-std Xi", f"Xinyi-Hada Xi", f"Hee-Jong-std Xi", f"Yu-Yu-std Xi", f"Javier-std Xi"]
for i in range(7):
data.set_realisation(i)
fitter.add_model_and_dataset(model, data, name=names[i], color=cs[i], realisation=i, ls=ls)
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
# Everything below is nasty plotting code ###########################################################
if fitter.should_plot():
import logging
import pandas as pd
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=200)
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)
data = CorrelationFunction_DESIMockChallenge_Post(
isotropic=False,
recon=recon,
realisation="data",
fit_poles=fit_poles,
min_dist=smin,
max_dist=157.5,
num_mocks=998,
smoothtype=smoothtype,
covtype="nonfix",
tracer="elg",
)
model = CorrBeutler2017(
recon=data.recon,
isotropic=data.isotropic,
marg="full",
fix_params=["om", "beta"],
poly_poles=fit_poles,
correction=Correction.NONE,
)
smoothnames = [" 5", " 10", " 15"]
hexname = " Hexa " if 4 in fit_poles else " No-Hexa "
name = names[i] + recon + smoothnames[
smoothtype - 1] + hexname + str(
r"$s_{min}=%3.1lf$" % smin)
allnames.append(name)
fitter.add_model_and_dataset(model, data, name=name)
counter += 1
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.fit(file)