alpha_par, alpha_perp = model.get_alphas(alpha, epsilon)
df["$\\alpha_\\parallel$"] = alpha_par
df["$\\alpha_\\perp$"] = alpha_perp
c.add_chain(df, weights=weight, **extra)
max_post = posterior.argmax()
chi2 = -2 * posterior[max_post]
dof = data[0]["pk"].shape[0] - 1 - len(df.columns)
ps = chain[max_post, :]
best_fit = {}
for l, p in zip(model.get_labels(), ps):
best_fit[l] = p
mean_par, std_par = weighted_avg_and_std(alpha_par, weight)
mean_per, std_per = weighted_avg_and_std(alpha_perp, weight)
c2 = ChainConsumer()
c2.add_chain(df[["$\\alpha_\\parallel$", "$\\alpha_\\perp$"]], weights=weight)
_, corr = c2.analysis.get_correlations()
corr = corr[1, 0]
output.append(
f"{data[0]['min_k']:5.2f}, {data[0]['max_k']:5.2f}, {mean_par:5.3f}, {mean_per:5.3f}, {std_par:5.3f}, {std_per:5.3f}, {corr:5.3f}, {r_s:6.3f}, {chi2:5.3f}, {dof:4d}, {chi2/dof:5.2f}"
)
np.savetxt(pfn + "_Pk_linear_CAMB.dat", np.c_[model.camb.ks, pks[0][0]], fmt="%g %g", header="k pk")
np.savetxt(pfn + "_Pk_linear_CAMB_smooth.dat", np.c_[model.camb.ks, pks[0][1]], fmt="%g %g", header="k pk_smooth")
np.savetxt(
pfn + "_bestfit_model.dat",
np.c_[bestfit_model[0][0], bestfit_model[0][1][: len(bestfit_model[0][0])], bestfit_model[0][1][len(bestfit_model[0][0]) :]],
},
axis="columns",
inplace=True)
else:
logging.info("Didn't find alphameans.csv, reading chains")
doonce = True
for posterior, weight, chain, evidence, model, data, extra in fitter.load(
):
n = extra["name"].split(",")[0]
if res.get(n) is None:
res[n] = []
i = posterior.argmax()
chi2 = -2 * posterior[i]
m, s = weighted_avg_and_std(chain[:, 0], weight)
if doonce:
doonce = False
import pandas as pd
df = pd.DataFrame(chain[:, 0], columns=["alpha"])
nsamp = int((weight / weight.max()).sum())
r = []
for ii in range(1000):
r.append(
df.sample(weights=weight, replace=True,
n=nsamp).std())
print(f"SE of std is {np.std(r)}")
res[n].append([
# An alternative way to account for missing uncertainty is to simply integrate over the different results. So try this too.
from barry.utils import weighted_avg_and_std
nalpha = 100
alpha_grid = np.linspace(0.8, 1.2, nalpha)
integrate_val = np.empty((3, len(means[0])))
integrate_err = np.empty((3, len(means[0])))
for i in range(3):
for j, (std, val) in enumerate(zip(stds[i], means[i])):
posteriors = 1.0 / (np.sqrt(2.0 * np.pi) * std) * np.exp(-0.5 * (
(val - np.tile(alpha_grid, (len(val), 1)).T)**2 / std**2))
combined_posterior = np.sum(posteriors, axis=1) / len(std)
norm = np.sum(combined_posterior) * (alpha_grid[-1] -
alpha_grid[0]) / nalpha
combined_posterior /= norm
integrate_val[i, j], integrate_err[i, j] = weighted_avg_and_std(
alpha_grid, combined_posterior)
# Now have a look at the distributions for each of the three methods
for i, (index, type) in enumerate(zip([1, 2, 0], ["Pk", "Xi", "All"])):
best_chi = (best_val[index, 0:] - alpha)**2 / best_err[index, 0:]**2
best_combined_chi = (best_combined_val[index, 0:] -
alpha)**2 / best_combined_err[index, 0:]**2
best_add_chi = (best_add_val[index, 0:] -
alpha)**2 / best_add_err[index, 0:]**2
best_add_combined_chi = (best_add_combined_val[index, 0:] - alpha
)**2 / best_add_combined_err[index, 0:]**2
integrate_chi = (integrate_val[index, 0:] -
alpha)**2 / integrate_err[index, 0:]**2
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()
alphas = []
aas = []
for posterior, weight, chain, evidence, model, data, extra in fitter.load():
print(extra["name"])
c.add_chain(chain, weights=weight, parameters=model.get_labels(), **extra)
m, s = weighted_avg_and_std(evidence, weights=weight)
m2, s2 = weighted_avg_and_std(chain[:, -1], weights=weight)
alphas.append(m)
aas.append(m2)
print(np.std(alphas), np.std(aas))
c.configure(shade=True, bins=25, legend_artists=True, cmap="plasma", sigmas=[0, 1, 2])
params = ["$\\alpha$", "$A$", "$b$"]
truth = {"$\\Omega_m$": 0.31, "$\\alpha$": 0.9982}
c.analysis.get_latex_table(filename=pfn + "_params.txt")
c.plotter.plot_summary(filename=[pfn + "_summary.png", pfn + "_summary.pdf"], errorbar=True, truth=truth, parameters=params)
c.plotter.plot(filename=[pfn + "_contour.png", pfn + "_contour.pdf"], truth=truth, parameters=params, figsize="COLUMN")
# FINDINGS
# Well, looks like where you transition from alternating indices to only using the extractor
# has a strong impact on not only where you fit alpha, b, gamma and A, but also on their uncertainties.
# It also impracts the degeneracy direction between alpha and A.