if __name__ == "__main__":
import sys
sys.path.append("..")
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)
plt.show()
from chainconsumer import ChainConsumer
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)
c.configure(shade=True, bins=25, legend_artists=True, cmap="plasma", sigmas=[0, 1, 2])
extents = None # {"$\\alpha$": (0.88, 1.18), "$A$": (0, 10), "$b$": (1.5, 1.8), r"$\gamma_{rec}$": (1, 8)}
params = ["$\\alpha$", "$A$", "$b$"]
truth = {"$\\Omega_m$": 0.31, "$\\alpha$": 1.0}
c.analysis.get_latex_table(filename=pfn + "_params.txt")
c.plotter.plot_summary(filename=[pfn + "_summary.png", pfn + "_summary.pdf"], errorbar=True, truth=truth, extents=extents, parameters=params)
from barry.models.test import TestModel
from barry.datasets.test import TestDataset
from barry.samplers import EnsembleSampler
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
model = TestModel()
data = TestDataset()
sampler = EnsembleSampler(num_walkers=10,
num_steps=1000,
num_burn=300,
temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(model, data)
fitter.set_sampler(sampler)
fitter.set_num_walkers(2)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
res, = fitter.load()
posterior, weight, chain, evidence, model, data, extra = res
c = ChainConsumer()
c.add_chain(chain, weights=weight, parameters=model.get_labels())
c.plotter.plot(filename=pfn + "_contour.png")
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:
if 4 in d.fit_poles:
fitter.add_model_and_dataset(model4, d, name=d.name)
else:
fitter.add_model_and_dataset(model, d, name=d.name)
fitter.set_sampler(sampler)
fitter.set_num_walkers(5)
fitter.set_num_concurrent(100)
fitter.fit(file)
# Everything below is nasty plotting code ###########################################################
if fitter.should_plot():
import logging
logging.info("Creating plots")
from chainconsumer import ChainConsumer
counter = 0
c = ChainConsumer()
for i, (posterior, weight, chain, evidence, model, data,
extra) in enumerate(fitter.load()):