lims = [
[0.04, 0.44],
[0.019, 0.027],
]
out1 = bring_together_samples(dir_name1, chain1, burnins)
out2 = bring_together_samples(dir_name2, chain2, burnins)
#print out1.shape
samples1 = MCSamples(samples=out1, names=names, labels=labels)
samples2 = MCSamples(samples=out2, names=names, labels=labels)
#print out1[-1:,:]
print(samples2.getTable(limit=1).tableTex())
plt.figure()
g = plots.getSubplotPlotter()
g.triangle_plot([samples1, samples2],
params=['massbias', 'yslope', 'scat', 's8', 'om'],
filled=True)
plt.savefig(outdir + "v10_and_v11_testv2.png")
print(samples2.getTable(limit=1).tableTex())
if not args.s8test:
print(samples1.getTable(limit=1).tableTex())
constraints = [(v[1], v[2] - v[1], v[1] - v[0])
for v in zip(*np.percentile(out1, [16, 50, 84], axis=0))]
# print the autocorrelation time
tau = sampler.get_autocorr_time()
print(tau)
# remove the first 100 steps and use only every 15 steps from the chain
flat_samples = sampler.get_chain(discard=100, thin=15, flat=True)
print(flat_samples.shape)
# transform the data into a getdist readable format
samples = MCSamples(samples=flat_samples,
names=["1", "2", "3"],
labels=["m", "b", "log(f)"])
# print the 95% limits for all parameters in LaTex with getdist
print("Print 95% limits using getdist")
print(samples.getTable().tableTex())
# print the 95% limits for all parameters in LaTex with numpy
print("Print 95% limits using numpy")
for i in range(ndim):
mcmc = np.percentile(flat_samples[:, i], [2.5, 50, 97.5])
q = np.diff(mcmc)
txt = "\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}"
txt = txt.format(mcmc[1], q[0], q[1], labels[i])
print(txt)
# plot and show results with getdist
g = plots.get_subplot_plotter()
g.triangle_plot(samples,
filled=True,
markers={
