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(),
**extra)
# params = dict([(p.name, v) for p, v in zip(model.get_active_params(), chain[posterior.argmax(), :])])
# params["om"] = 0.3121
# model.set_data(datas[0].get_data())
# key = f"{model.name}, alpha={params['alpha']:0.4f}"
# pks[key] = model.get_model(datas[0].get_data(), params)
c.configure(shade=True, bins=0.7)
c.plotter.plot(filename=pfn + "_contour.png",
truth={
"$\\Omega_m$": 0.3121,
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
c = ChainConsumer()
c.add_chain(chain, weights=weight, parameters=model.get_labels())
c.plotter.plot(filename=pfn + "_contour.png")
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
logging.info("Creating plots")
res = fitter.load()
if True:
from chainconsumer import ChainConsumer
if True:
# This is a dirty way of faking another column in the dataset so I can keep plots visually consistent.
# Essentially resampling our weighted chain to be uniform weights.
# Note this requires that xi_individual.py has been run first! #######################################
ind_path = "plots/xi_individual/xi_individual_alphameans.csv"
n = 1000000
stds_dict = None
if os.path.exists(ind_path):
df = pd.read_csv(ind_path)
cols = [c for c in df.columns if "std" in c]
stds = df[cols]
stds_dict = {}
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"],
figname=pfn +
"_bestfit.pdf")
c = ChainConsumer()
c.add_chain(chain, weights=weight, parameters=model.get_labels())
c.plotter.plot(filename=pfn + "_contour.pdf")
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}$",
color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
res = fitter.load()
from chainconsumer import ChainConsumer
import copy
c = ChainConsumer()
for posterior, weight, chain, evidence, model, data, extra in res:
chain_conv = copy.deepcopy(chain)
chain_conv[:, 0], chain_conv[:, 2] = model.get_alphas(
chain[:, 0], chain[:, 2])
parameters = model.get_labels()
parameters[0] = r"$\alpha_{par}$"
parameters[2] = r"$\alpha_{perp}$"
c.add_chain(chain_conv,
weights=weight,
parameters=parameters,
fitter.set_num_walkers(5)
fitter.set_num_concurrent(100)
fitter.fit(file)
# Everything below is nasty plotting code ###########################################################
if fitter.should_plot():
import logging
logging.info("Creating plots")
from chainconsumer import ChainConsumer
counter = 0
c = ChainConsumer()
for i, (posterior, weight, chain, evidence, model, data,
extra) in enumerate(fitter.load()):
fitname = "_".join(extra["name"].split()[:5])
extra["name"] = extra["name"].replace("_", " ")
print(fitname)
color = plt.colors.rgb2hex(cmap(float(i / len(datasets))))
model.set_data(data)
r_s = model.camb.get_data()["r_s"]
df = pd.DataFrame(chain, columns=model.get_labels())
alpha = df["$\\alpha$"].to_numpy()
epsilon = df["$\\epsilon$"].to_numpy()
alpha_par, alpha_perp = model.get_alphas(alpha, epsilon)
df["$\\alpha_\\parallel$"] = alpha_par