from barry.config import setup
from barry.models import PowerBeutler2017
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.samplers import DynestySampler
from barry.fitter import Fitter
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
r = True
models = [
PowerBeutler2017(recon=r, smooth_type="hinton2017", name="Hinton2017"),
PowerBeutler2017(recon=r, smooth_type="eh1998", name="EH1998")
]
data = PowerSpectrum_SDSS_DR12_Z061_NGC(name="Recon mean",
recon=r,
min_k=0.02,
max_k=0.30)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(models[0], data, name="Hinton2017")
fitter.add_model_and_dataset(models[1], data, name="EH1998")
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
c = ChainConsumer()
pks = {}
from barry.fitter import Fitter
if __name__ == "__main__":
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,
logging.basicConfig(
level=logging.DEBUG,
format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
recon = True
model1 = PowerSeo2016(recon=recon, name=f"Seo2016, recon={recon}")
model_smooth = PowerSeo2016(recon=recon,
name=f"Seo2016, recon={recon}",
smooth=True)
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.datasets import DummyPowerSpectrum_SDSS_DR12_Z061_NGC
dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(name="Recon mean",
recon=recon,
min_k=0.02,
max_k=0.3,
reduce_cov_factor=30,
step_size=5)
dataset2 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(
name="Dummy data, real window fn",
min_k=0.02,
max_k=0.25,
dummy_window=False)
dataset3 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(name="DummyWindowFnToo",
min_k=0.02,
max_k=0.25,
dummy_window=True)
data1 = dataset1.get_data()
data2 = dataset2.get_data()
data3 = dataset3.get_data()
logging.basicConfig(
level=logging.INFO,
format="[%(levelname)7s |%(funcName)18s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.WARNING)
camb = getCambGenerator()
r_s = c.get_data()[0]
extractor = BAOExtractor(r_s, reorder=False)
extractor2 = PureBAOExtractor(r_s)
step_size = 1
mink = 0.02
maxk = 0.3
data_raw = PowerSpectrum_SDSS_DR12_Z061_NGC(step_size=step_size,
fake_diag=False,
min_k=0.0,
max_k=0.32)
data2 = PowerSpectrum_SDSS_DR12_Z061_NGC(postprocess=extractor,
step_size=step_size,
min_k=mink,
max_k=maxk)
ks = data_raw.ks
pk = data_raw.data
pk_cov = data_raw.cov
denoms = extractor2.postprocess(ks, pk, None, return_denominator=True)
is_extracted = extractor.get_is_extracted(ks)
cov_brute = data2.cov
k_range = extractor.get_krange()
cov_noda = calc_cov_noda_mixed(pk_cov, denoms, ks, pk, k_range,
is_extracted)
if __name__ == "__main__":
pfn, dir_name, file = setup("../config/pk_individual.py")
fitter = Fitter(dir_name, save_dims=2, remove_output=False)
c = getCambGenerator()
r_s = c.get_data()[0]
p = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name, nlive=200)
for r in [True, False]:
t = "Recon" if r else "Prerecon"
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):
if __name__ == "__main__":
import seaborn as sb
import matplotlib.pyplot as plt
logging.basicConfig(level=logging.INFO, format="[%(levelname)7s |%(funcName)18s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.WARNING)
camb = getCambGenerator()
r_s = c.get_data()[0]
extractor = PureBAOExtractor(r_s)
mink = 0.02
step_size = 1
data_raw = PowerSpectrum_SDSS_DR12_Z061_NGC(step_size=step_size, min_k=0.0)
data = PowerSpectrum_SDSS_DR12_Z061_NGC(step_size=step_size, min_k=mink)
data2 = PowerSpectrum_SDSS_DR12_Z061_NGC(postprocess=extractor, step_size=step_size, min_k=mink)
# Get all the data to compute the covariance
ks = data_raw.ks
pk = data_raw.data
pk_cov = data_raw.cov
denoms = extractor.postprocess(ks, pk, None, return_denominator=True)
k_range = extractor.get_krange()
cov_noda = calc_cov_noda(pk_cov, denoms, ks, pk, k_range, assume_diag=False)
cov_noda2 = calc_cov_noda(pk_cov, denoms, ks, pk, k_range, assume_diag=True)
cov_brute = data2.cov
mask = np.where(ks < mink)
# We want to verify the behaviour of other models when the BAO extractor technique is applied to them
# We also want to see what the behaviour is if we keep the polynomial terms enabled or disable them.
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
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)
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"])
c.add_chain(chain, weights=weight, parameters=model.get_labels(), **extra)
import logging
from barry.models import PowerBeutler2017
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 PowerSpectrum_SDSS_DR12_Z061_NGC
for recon in [True, False]:
dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=recon)
data1 = dataset1.get_data()
# Run on mean
model = PowerBeutler2017(recon=recon,
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 6.0
# Prerecon 9.3
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
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 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, min_k=0.03, max_k=0.21)
de = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r,
postprocess=p,
min_k=0.03,
max_k=0.21)
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r)
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(PowerBeutler2017(recon=r),
d,
name=f"Beutler 2017 {t}",
linestyle=ls,
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)
model1.plot(p)
# FINDINGS
# 1.992 for Recon, 1.996 for prerecon
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,
min_k=mink,
max_k=maxk,
postprocess=postprocess),
]
# Compute the pseudo-analytic cov from Noda and Nishimichi
from barry.datasets import CorrelationFunction_SDSS_DR12_Z061_NGC, PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.samplers import DynestySampler
from barry.fitter import Fitter
import numpy as np
import pandas as pd
# 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,
if __name__ == "__main__":
logging.basicConfig(
level=logging.DEBUG,
format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
recon = True
model1 = PowerDing2018(recon=recon, name=f"Ding2018, recon={recon}")
model_smooth = PowerDing2018(recon=recon,
name=f"Ding2018, recon={recon}",
smooth=True)
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.datasets import DummyPowerSpectrum_SDSS_DR12_Z061_NGC
dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(name="Recon mean", recon=recon)
dataset2 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(
name="Dummy data, real window fn", dummy_window=False)
dataset3 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(name="DummyWindowFnToo",
dummy_window=True)
data1 = dataset1.get_data()
data2 = dataset2.get_data()
data3 = dataset3.get_data()
# First comparison - the actual recon data
model1.set_data(data1)
p, minv = model1.optimize()
model_smooth.set_data(data1)
p2, minv2 = model_smooth.optimize()
print(p)
print(minv)
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
p = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name, nlive=1000)
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 = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r)
de = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, postprocess=p)
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r)
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(PowerBeutler2017(recon=r),
d,
name=f"Beutler 2017 {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(
model,
from barry.models import PowerBeutler2017
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 PowerSpectrum_SDSS_DR12_Z061_NGC
for recon in [True, False]:
model = PowerBeutler2017(recon=recon, name=f"Beutler2017, recon={recon}")
model_smooth = PowerBeutler2017(recon=recon, name=f"Beutler2017, 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 = PowerSpectrum_SDSS_DR12_Z061_NGC(name=f"SDSS recon={recon}", recon=recon, reduce_cov_factor=31.62)
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=1.0006
