import numpy as np
sys.path.append("..")
from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge_Handshake
from barry.cosmology.camb_generator import getCambGenerator
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():
from barry.config import setup
from barry.fitter import Fitter
from barry.models.test import TestModel
from barry.datasets.test import TestDataset
from barry.samplers import DynestySampler
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
model = TestModel()
data = TestDataset()
sampler = DynestySampler(temp_dir=dir_name, max_iter=None)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(model, data)
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.fit(file)
if fitter.should_plot():
res, = fitter.load()
posterior, weight, chain, evidence, model, data, extra = res
import matplotlib.pyplot as plt
fig, ax = plt.subplots(nrows=2)
ax[0].plot(weight)
ax[1].plot(evidence)
from barry.models.model import Correction
# Compare model fits with different analytic marginalisation options and where we fix the marginalised
# parameters. The three types of marginalisation should give the same posteriors ("partial" is not strictly
# the same, but looks identical for most purposes). The fit with fixed values will give a bad posterior.
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=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)
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018
from barry.samplers import DynestySampler
from barry.fitter import Fitter
from barry.models.model import Correction
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 [False]:
t = "Recon" if r else "Prerecon"
# Changing fitting range and sigma values to match Hee-Jong
d = PowerSpectrum_DESIMockChallenge0_Z01(recon=r,
isotropic=False,
realisation="data",
min_k=0.001,
max_k=0.30)
model = PowerBeutler2017(recon=r,
isotropic=False,
correction=Correction.NONE)
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerBeutler2017
from barry.samplers import DynestySampler
from barry.fitter import Fitter
from barry.models.model import Correction
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 [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})")
sys.path.append("..")
from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge_Handshake
from barry.cosmology.camb_generator import getCambGenerator
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.samplers import DynestySampler
from barry.fitter import Fitter
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
c = getCambGenerator()
r_s = c.get_data()["r_s"]
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])
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),
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.postprocessing import BAOExtractor
from barry.cosmology.camb_generator import getCambGenerator
from barry.samplers import DynestySampler
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",
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerBeutler2017, CorrBeutler2017
from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge
from barry.datasets.dataset_correlation_function import CorrelationFunction_DESIMockChallenge
from barry.fitter import Fitter
import numpy as np
import pandas as pd
from barry.models.model import Correction
from barry.utils import weighted_avg_and_cov, break_vector_and_get_blocks
from barry.cosmology.power_spectrum_smoothing import smooth
from barry.generate import get_cosmologies
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name, remove_output=True)
sampler = DynestySampler(temp_dir=dir_name, nlive=500)
cs = ["#CAF270", "#84D57B", "#4AB482", "#219180", "#1A6E73", "#234B5B", "#232C3B"]
data = PowerSpectrum_DESIMockChallenge(recon=True, isotropic=False, fit_poles=[0, 2], min_k=0.02, max_k=0.45)
# c = data.get_data()[0]["cosmology"]
# generator = CambGenerator(om_resolution=1, h0_resolution=1, h0=c["h0"], ob=c["ob"], ns=c["ns"], redshift=c["z"],
# mnu=c["mnu"])
# generator.load_data(can_generate=True)
model = PowerBeutler2017(recon=True, isotropic=False, fix_params=["om"], poly_poles=[0, 2], correction=Correction.NONE, marg="full")
pklin = np.array(pd.read_csv("../barry/data/desi_mock_challenge_post_recon/Pk_Planck15_Table4.txt", delim_whitespace=True, header=None))
model.set_fix_params(["om"])
# redorder cov matrix, pk goes first, then the extracted part
mask_extracted = postprocess.get_is_extracted(datas[0].ks)
index_sort = np.arange(mask_extracted.size).astype(
np.int) + (1000 * mask_extracted)
a = np.argsort(index_sort)
cov_noda = cov_noda[:, a]
cov_noda = cov_noda[a, :]
cov_noda_diag = cov_noda_diag[:, a]
cov_noda_diag = cov_noda_diag[a, :]
datas[1].set_cov(cov_noda)
datas[2].set_cov(cov_noda_diag)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(model, datas[0], name="Mock-computed")
fitter.add_model_and_dataset(model, datas[1], name="Nishimichi 2018, full")
fitter.add_model_and_dataset(model,
datas[2],
name="Nishimichi 2018, diagonal")
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(
):
from barry.models.model import Correction
# Check to see if including the hexadecapole or higher order multipoles gives tighter constraints on BAO parameters
# when fitting the mock average
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
# dir_name = dir_name + "nlive_1500/"
c = getCambGenerator()
r_s = c.get_data()["r_s"]
p = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name, nlive=500)
# sampler = EnsembleSampler(temp_dir=dir_name, num_steps=5000)
fitter = Fitter(dir_name)
cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
for r in [True]:
t = "Recon" if r else "Prerecon"
ls = "-" # if r else "--"
d_quad = PowerSpectrum_Beutler2019(recon=r,
isotropic=False,
fit_poles=[0, 2],
reduce_cov_factor=np.sqrt(2000.0))
d_odd = PowerSpectrum_Beutler2019(recon=r,
isotropic=False,
fit_poles=[0, 1, 2],
reduce_cov_factor=np.sqrt(2000.0))
d_hexa = PowerSpectrum_Beutler2019(recon=r,
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018
from barry.samplers import DynestySampler
from barry.fitter import Fitter
from barry.models.model import Correction
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 [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}")
from barry.postprocessing import BAOExtractor
from barry.cosmology.camb_generator import getCambGenerator
from barry.samplers import DynestySampler
from barry.fitter import Fitter
# Check the impact of fixing parameters in the Noda model
if __name__ == "__main__":
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",
import sys
sys.path.append("..")
from barry.cosmology.camb_generator import getCambGenerator
from barry.config import setup
from barry.models import PowerDing2018, CorrDing2018
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):
import numpy as np
sys.path.append("..")
from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge_Handshake
from barry.cosmology.camb_generator import getCambGenerator
from barry.config import setup
from barry.models import PowerSpectrumFit, PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.samplers import DynestySampler
from barry.fitter import Fitter
from barry.cosmology.power_spectrum_smoothing import smooth
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
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.utils import weighted_avg_and_cov
from barry.models import PowerBeutler2017
from barry.datasets.dataset_power_spectrum import PowerSpectrum_SDSS_DR12, PowerSpectrum_eBOSS_LRGpCMASS
from barry.fitter import Fitter
import numpy as np
import pandas as pd
from barry.models.model import Correction
# Run an optimisation on each of the post-recon SDSS DR12 mocks. Then compare to the pre-recon mocks
# to compute the cross-correlation between BAO parameters and pre-recon measurements
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name, remove_output=False)
c = getCambGenerator()
# sampler = DynestySampler(temp_dir=dir_name, nlive=500)
sampler = Optimiser(temp_dir=dir_name, tol=1.0e-4)
cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
datasets = [
PowerSpectrum_SDSS_DR12(redshift_bin=1,
galactic_cap="ngc",
recon="iso",
isotropic=False,
fit_poles=[0, 2],
min_k=0.02,
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.samplers import DynestySampler
from barry.fitter import Fitter
# Checks to see if the better fits from the power spectrum are due to differences in the effective fitting range.
# Spoiler: No.
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
from barry.models import PowerNoda2019, PowerSeo2016, PowerBeutler2017, PowerDing2018
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.postprocessing import BAOExtractor
from barry.cosmology.camb_generator import getCambGenerator
from barry.samplers import DynestySampler
from barry.fitter import Fitter
# 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])
from barry.utils import weighted_avg_and_cov
from barry.models import PowerBeutler2017
from barry.datasets.dataset_power_spectrum import PowerSpectrum_SDSS_DR12, PowerSpectrum_eBOSS_LRGpCMASS
from barry.fitter import Fitter
import numpy as np
import pandas as pd
from barry.models.model import Correction
import matplotlib as plt
from matplotlib import cm
# Run an optimisation on each of the post-recon SDSS DR12 mocks. Then compare to the pre-recon mocks
# to compute the cross-correlation between BAO parameters and pre-recon measurements
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name)
c = getCambGenerator()
sampler = DynestySampler(temp_dir=dir_name, nlive=500)
cmap = plt.cm.get_cmap("viridis")
datasets = [
PowerSpectrum_SDSS_DR12(
redshift_bin=1,
realisation="data",
galactic_cap="ngc",
recon="iso",
isotropic=False,
fit_poles=[0, 2],
import sys
sys.path.append("..")
from barry.samplers import DynestySampler
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.fitter import Fitter
import numpy as np
import pandas as pd
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)
sys.path.append("..")
from barry.config import setup
from barry.models import PowerNoda2019
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.postprocessing import BAOExtractor
from barry.cosmology.camb_generator import CambGenerator
from barry.samplers import DynestySampler
from barry.fitter import Fitter
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
c = CambGenerator()
r_s = c.get_data()[0]
fitter = Fitter(dir_name)
ps = [
BAOExtractor(r_s, extra_ks=(0.095, 0.13)),
BAOExtractor(r_s, extra_ks=(0.095, 0.14)),
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)),
]
ax[0].set_xlabel(l)
axes[0, 0].set_ylabel(r"$N_{\mathrm{mocks}}$")
fig.savefig(figname, bbox_inches="tight", transparent=True, dpi=300)
return nstats, means, covs, corr
if __name__ == "__main__":
# Get the relative file paths and names
pfn, dir_name, file = setup(__file__)
# Set up the Fitting class and Dynesty sampler with 250 live points.
fitter = Fitter(dir_name, remove_output=False)
sampler = DynestySampler(temp_dir=dir_name, nlive=250)
mocktypes = ["abacus_cutsky"]
nzbins = [3]
# Loop over the mocktypes
allnames = []
for i, (mocktype, redshift_bins) in enumerate(zip(mocktypes, nzbins)):
# Loop over the available redshift bins for each mock type
for z in range(redshift_bins):
# Create the data. We'll fit mono-, quad- and hexadecapole between k=0.02 and 0.3.
# First load up mock mean and add it to the fitting list.
dataset = PowerSpectrum_DESI_KP4(
from barry.config import setup
from barry.fitter import Fitter
from barry.models.test import TestModel
from barry.datasets.test import TestDataset
from barry.samplers import MetropolisHastings
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
model = TestModel()
data = TestDataset()
sampler = MetropolisHastings(num_steps=1000,
num_burn=300,
temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(model, data)
fitter.set_num_walkers(2)
fitter.set_sampler(sampler)
fitter.fit(file)
if fitter.should_plot(): # As I'm not sure if the cluster has matplotlib
from chainconsumer import ChainConsumer
res, = fitter.load()
posterior, weight, chain, model, data, extra = res
labels = model.get_labels()
c = ChainConsumer()
c.add_chain(chain, weights=weight, parameters=labels)
c.plotter.plot(filename=pfn + "_contour.png")
from barry.utils import plot_bestfit
from barry.samplers import DynestySampler
# Run a quick test using dynesty to fit a mock mean.
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
data = PowerSpectrum_SDSS_DR12(isotropic=True, recon="iso")
model = PowerBeutler2017(isotropic=data.isotropic,
recon=data.recon,
marg="full")
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(model, data)
fitter.set_sampler(sampler)
fitter.set_num_walkers(1)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
posterior, weight, chain, evidence, model, data, extra = fitter.load(
)[0]
print(extra["name"])
chi2, dof, bband, mods, smooths = plot_bestfit(posterior,
chain,
model,
title=extra["name"],
sys.path.append("..")
from barry.samplers import DynestySampler
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.fitter import Fitter
import numpy as np
import pandas as pd
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
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)
import os
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
sys.path.append("..")
from barry.config import setup
from barry.models import CorrBeutler2017, CorrDing2018, CorrSeo2016
from barry.datasets import CorrelationFunction_SDSS_DR12_Z061_NGC
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])
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.utils import weighted_avg_and_std, get_model_comparison_dataframe
from barry.models import PowerDing2018, PowerBeutler2017
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.samplers import DynestySampler
from barry.fitter import Fitter
import numpy as np
import pandas as pd
# Check if B17 and D18 results change if we apply the BAO extractor technique.
# Spoiler: They do not.
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name, remove_output=False)
c = getCambGenerator()
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)
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 = {}
for posterior, weight, chain, model, data, extra in fitter.load():
c.add_chain(chain,
weights=weight,
parameters=model.get_labels(),
import sys
sys.path.append("..")
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor
from barry.config import setup, weighted_avg_and_std
from barry.models import PowerDing2018, PowerBeutler2017
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.samplers import DynestySampler
from barry.fitter import Fitter
import numpy as np
import pandas as pd
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name, 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)
