def __init__(
self,
name="Corr Beutler 2017",
fix_params=("om",),
smooth_type="hinton2017",
recon=None,
smooth=False,
correction=None,
isotropic=True,
poly_poles=(0, 2),
marg=None,
):
self.recon_smoothing_scale = None
if isotropic:
poly_poles = [0]
if marg is not None:
fix_params = list(fix_params)
for pole in poly_poles:
fix_params.extend([f"b{{{pole}}}"])
fix_params.extend([f"a{{{pole}}}_1", f"a{{{pole}}}_2", f"a{{{pole}}}_3"])
super().__init__(
name=name,
fix_params=fix_params,
smooth_type=smooth_type,
smooth=smooth,
correction=correction,
isotropic=isotropic,
poly_poles=poly_poles,
marg=marg,
)
self.parent = PowerBeutler2017(
fix_params=fix_params,
smooth_type=smooth_type,
recon=recon,
smooth=smooth,
correction=correction,
isotropic=isotropic,
marg=marg,
)
if self.marg:
for pole in self.poly_poles:
self.set_default(f"b{{{pole}}}", 1.0)
self.set_default(f"a{{{pole}}}_1", 0.0)
self.set_default(f"a{{{pole}}}_2", 0.0)
self.set_default(f"a{{{pole}}}_3", 0.0)
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)
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)
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
min_k=0.0,
max_k=0.30,
num_mocks=999),
PowerSpectrum_eBOSS_LRGpCMASS(galactic_cap="sgc",
recon=None,
isotropic=False,
fit_poles=[0, 2, 4],
min_k=0.0,
max_k=0.30,
num_mocks=999),
]
# Standard Beutler Model
model = PowerBeutler2017(recon="iso",
isotropic=False,
fix_params=["om"],
poly_poles=[0, 2],
correction=Correction.HARTLAP,
marg="full")
model4 = PowerBeutler2017(recon="iso",
isotropic=False,
fix_params=["om"],
poly_poles=[0, 2, 4],
correction=Correction.HARTLAP,
marg="full")
for d in datasets:
for i in range(999):
d.set_realisation(i)
if 4 in d.fit_poles:
fitter.add_model_and_dataset(model4,
d,
sampler = DynestySampler(temp_dir=dir_name, nlive=500)
# sampler = EnsembleSampler(temp_dir=dir_name, num_steps=5000)
fitter = Fitter(dir_name)
cs = ["#262232", "#294D5F", "#197D7A", "#48AC7C"]
for r in [False]:
t = "Recon" if r else "Prerecon"
ls = "-" # if r else "--"
d = PowerSpectrum_Beutler2019(recon=r,
isotropic=True,
reduce_cov_factor=np.sqrt(2000.0))
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r,
isotropic=d.isotropic,
fix_params=["om"],
correction=Correction.HARTLAP)
model_marg_full = PowerBeutler2017(recon=r,
isotropic=d.isotropic,
fix_params=["om"],
correction=Correction.HARTLAP,
marg="full")
model_marg_partial = PowerBeutler2017(recon=r,
isotropic=d.isotropic,
fix_params=["om"],
correction=Correction.HARTLAP,
marg="partial")
model_fixed = PowerBeutler2017(
recon=r,
isotropic=d.isotropic,
fix_params=[
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 [False]:
t = "Recon" if r else "Prerecon"
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r, isotropic=False, correction=Correction.NONE)
model.set_default("sigma_nl_par", 10.9)
model.set_default("sigma_nl_perp", 5.98)
model.set_fix_params(["om", "sigma_nl_par", "sigma_nl_perp"])
for mink, maxk in [(0.02, 0.3), (0.02, 0.4), (0.02, 0.5), (0.03, 0.3), (0.03, 0.5), (0.03, 0.4), (0.04, 0.3), (0.04, 0.4), (0.04, 0.5)]:
d = PowerSpectrum_DESIMockChallenge0_Z01(recon=r, isotropic=False, realisation="data", min_k=mink, max_k=maxk)
fitter.add_model_and_dataset(model, d, name=f"B17 Fixed {t}, k = ({mink:0.2f}, {maxk:0.2f})")
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
import logging
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG, format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge0_Z01
from barry.models import PowerBeutler2017
from barry.models.model import Correction
model = PowerBeutler2017(recon=False, isotropic=False, correction=Correction.NONE, fix_params=["om"])
dataset = PowerSpectrum_DESIMockChallenge0_Z01(recon=False, isotropic=False, realisation="data")
data = dataset.get_data()
for i in range(20):
model.sanity_check(dataset, figname="desi_mock0_optimised_bestfit.png", niter=100)
# print(likelihood)
#
# model.plot_default(dataset)
def __init__(self, name="Corr Beutler 2017", smooth_type="hinton2017", fix_params=("om"), smooth=False, correction=None):
super().__init__(name, smooth_type, fix_params, smooth, correction=correction)
self.parent = PowerBeutler2017(fix_params=fix_params, smooth_type=smooth_type, recon=True, smooth=smooth, correction=correction)
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_Z051
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_Z051(name=f"SDSS recon={recon}", 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=0.998
names = ["PreRecon", "PostRecon Julian RecIso", "PostRecon Julian RecSym", "PostRecon Martin RecIso", "PostRecon Martin RecSym"]
# colors = ["#CAF270", "#CAF270", "#4AB482", "#1A6E73", "#232C3B"]
colors = ["#CAF270", "#66C57F", "#219180", "#205C68", "#232C3B"]
types = ["julian_reciso", "julian_reciso", "julian_recsym", "martin_reciso", "martin_recsym"]
recons = [False, True, True, True, True]
recon_types = ["None", "iso", "ani", "iso", "ani"]
realisations = ["data", "data", "data", "data", "data"]
for i, (name, type, recon, recon_type, realisation, color) in enumerate(zip(names, types, recons, recon_types, realisations, colors)):
print(i, name, type, recon, recon_type, realisation)
data = PowerSpectrum_DESILightcone_Mocks_Recon(
isotropic=False, recon=recon, realisation=realisation, fit_poles=[0, 2], min_k=0.02, max_k=0.30, type=type
)
model = PowerBeutler2017(
recon=recon_type, isotropic=False, fix_params=["om"], poly_poles=[0, 2], correction=Correction.NONE, marg="full"
)
fitter.add_model_and_dataset(model, data, name=name, color=color)
fitter.set_sampler(sampler)
fitter.set_num_walkers(5)
fitter.fit(file)
# Everything below is nasty plotting code ###########################################################
if fitter.should_plot():
import logging
logging.info("Creating plots")
from chainconsumer import ChainConsumer
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 [False]:
t = "Recon" if r else "Prerecon"
d = PowerSpectrum_DESIMockChallenge0_Z01(recon=r, isotropic=False, realisation="data")
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r, isotropic=False, correction=Correction.NONE)
model.set_default("sigma_nl_par", 10.9)
model.set_default("sigma_nl_perp", 5.98)
model.set_fix_params(["om", "sigma_nl_par", "sigma_nl_perp"])
fitter.add_model_and_dataset(PowerBeutler2017(recon=r, isotropic=False, correction=Correction.NONE, fix_params=["om"]), d, name=f"Beutler 2017 {t}")
fitter.add_model_and_dataset(model, d, name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}")
# fitter.add_model_and_dataset(PowerSeo2016(recon=r, isotropic=False), d, name=f"Seo 2016 {t}", linestyle=ls, color=cs[1])
# fitter.add_model_and_dataset(PowerDing2018(recon=r, isotropic=False), d, name=f"Ding 2018 {t}", linestyle=ls, color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
isotropic=False,
fit_poles=[0, 1, 2],
reduce_cov_factor=np.sqrt(2000.0))
d_hexa = PowerSpectrum_Beutler2019(recon=r,
isotropic=False,
fit_poles=[0, 2, 4],
reduce_cov_factor=np.sqrt(2000.0))
d_all = PowerSpectrum_Beutler2019(recon=r,
isotropic=False,
fit_poles=[0, 1, 2, 3, 4],
reduce_cov_factor=np.sqrt(2000.0))
# Fix sigma_nl for one of the Beutler models
model_quad = PowerBeutler2017(recon=r,
isotropic=False,
fix_params=["om"],
poly_poles=[0, 2],
correction=Correction.HARTLAP,
marg="full")
model_odd = PowerBeutler2017(recon=r,
isotropic=False,
fix_params=["om"],
poly_poles=[0, 1, 2],
correction=Correction.HARTLAP,
marg="full")
model_hexa = PowerBeutler2017(recon=r,
isotropic=False,
fix_params=["om"],
poly_poles=[0, 2, 4],
correction=Correction.HARTLAP,
marg="full")
model_all = PowerBeutler2017(recon=r,
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),
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"Cullan's Pk",
color=cs[0])
# Re-do the desi fits, but now using the linear pk provided for the UNIT sim
pklin = np.array(
pd.read_csv(
"../barry/data/desi_mock_challenge_post_recon/Pk_Planck15_Table4.txt",
delim_whitespace=True,
header=None))
model = PowerBeutler2017(isotropic=False)
model.set_fix_params(["om"])
model.set_data(d.get_data())
model.kvals = pklin[:, 0]
model.pksmooth = smooth(model.kvals, pklin[:, 1])
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 [False]:
t = "Recon" if r else "Prerecon"
ls = "-" # if r else "--"
d = PowerSpectrum_DESIMockChallenge0_Z01(recon=r, isotropic=False)
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r, isotropic=False)
sigma_nl = 6.0 if r else 9.3
model.set_default("sigma_nl_par", 14.1)
model.set_default("sigma_nl_perp", 2.64)
model.set_fix_params(["om", "f", "sigma_nl_par", "sigma_nl_perp"])
fitter.add_model_and_dataset(PowerBeutler2017(recon=r,
isotropic=False),
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}",
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])
d2 = PowerSpectrum_DESIMockChallenge_Handshake(min_k=0.005,
max_k=0.3,
isotropic=False,
realisation="data",
fit_poles=[0, 2, 4])
fitter.add_model_and_dataset(PowerBeutler2017(isotropic=False),
d,
name=r"$P_{0}$+P_{2}$",
color=cs[0])
fitter.add_model_and_dataset(
PowerBeutler2017(isotropic=False),
d2,
name=r"$P_{0}$+P_{2}+P_{4},\,No\,a_{4}\,poly$",
color=cs[1])
fitter.add_model_and_dataset(PowerBeutler2017(isotropic=False,
poly_poles=[0, 2, 4]),
d2,
name=r"$P_{0}$+P_{2}+P_{4}$",
color=cs[2])
fitter.set_sampler(sampler)
isotropic=False,
recon=recon,
realisation="data",
fit_poles=fit_poles,
min_k=0.0075,
max_k=kmax,
num_mocks=998,
smoothtype=smoothtype,
covtype="analytic",
tracer=tracer,
)
model = PowerBeutler2017(
recon=data.recon,
isotropic=data.isotropic,
marg="full",
fix_params=["om", "beta"],
poly_poles=fit_poles,
correction=Correction.NONE,
n_poly=n_poly,
)
smoothnames = [" 5", " 10", " 15", " 20"]
hexname = " Hexa " if 4 in fit_poles else " No-Hexa "
polyname = "3-Poly " if n_poly == 3 else "5-Poly "
name = (names[i] + recon + smoothnames[smoothtype - 1] +
hexname + polyname + tracer + " " +
str(r"$k_{max}=%3.2lf$" % kmax))
print(name)
fitter.add_model_and_dataset(model, data, name=name)
allnames.append(name)
counter += 1
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 [False]:
t = "Recon" if r else "Prerecon"
ls = "-" # if r else "--"
d = PowerSpectrum_Beutler2019_Z061_SGC(recon=r, isotropic=False)
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r, isotropic=False)
sigma_nl = 6.0 if r else 9.3
model.set_default("sigma_nl_par", 14.1)
model.set_default("sigma_nl_perp", 2.64)
model.set_fix_params(["om", "f", "sigma_nl_par", "sigma_nl_perp"])
fitter.add_model_and_dataset(PowerBeutler2017(recon=r, isotropic=False), 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(PowerSeo2016(recon=r, isotropic=False), d, name=f"Seo 2016 {t}", linestyle=ls, color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(recon=r, isotropic=False), d, name=f"Ding 2018 {t}", linestyle=ls, color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
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):
d.set_realisation(i)
de.set_realisation(i)
fitter.add_model_and_dataset(
beutler_not_fixed,
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,
color=cs[0])
fitter.add_model_and_dataset(
model,
d,
name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}",
linestyle=ls,
color=cs[0])
min_k=0.02,
max_k=0.30,
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 = PowerBeutler2017(
recon=dataset.recon,
isotropic=dataset.isotropic,
marg="full",
fix_params=["om", "beta"],
poly_poles=dataset.fit_poles,
correction=Correction.HARTLAP,
n_poly=5,
)
# 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)
r_s = c.get_data()["r_s"]
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)
ding = PowerDing2018(recon=r)
beutler = PowerBeutler2017(recon=r)
sigma_nl = 6.0
beutler.set_default("sigma_nl", sigma_nl)
beutler.set_fix_params(["om", "sigma_nl"])
beutler_extracted = PowerBeutler2017(recon=r, postprocess=p)
beutler_extracted.set_default("sigma_nl", sigma_nl)
beutler_extracted.set_fix_params(["om", "sigma_nl"])
ding_extracted = PowerDing2018(recon=r, postprocess=p)
for i in range(999):
d.set_realisation(i)
de.set_realisation(i)
fitter.add_model_and_dataset(ding,
d,
name=f"D18",
import sys
sys.path.append("..")
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 barry.config import setup
from barry.models import PowerSpectrumFit, PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
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)
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