def debug_plots(std):
print(std)
res = load_stan_from_folder(std, merge=True, cut=False)
chain, posterior, t, p, f, l, w, ow = res
# print(w.mean())
# import matplotlib.pyplot as plt
# plt.hist(np.log(w), 100)
# plt.show()
# exit()
logw = np.log(w)
m = np.mean(logw)
s = np.std(logw)
print(m, s)
logw -= (m + 3 * s)
good = logw < 0
logw *= good
w = np.exp(logw)
c = ChainConsumer()
c.add_chain(chain, weights=w, name="corrected")
c.configure(summary=True)
c.plot(figsize=2.0, filename="output.png", parameters=9)
c = ChainConsumer()
c.add_chain(chain, name="uncorrected")
c.add_chain(chain, weights=w, name="corrected")
# c.add_chain(chain, name="calib")
c.plot(filename="output_comparison.png", parameters=9, figsize=1.3)
c.plot_walks(chains=1, filename="walks.png")
def debug_plots(std):
print(std)
res = load_stan_from_folder(std, merge=True, cut=False)
chain, posterior, t, p, f, l, w, ow = res
print(w.mean(), np.std(w), np.mean(np.log(w)), np.std(np.log(w)))
# import matplotlib.pyplot as plt
# plt.hist(np.log(w), 100)
# plt.show()
# exit()
logw = np.log(w)
m = np.mean(logw)
s = np.std(logw)
print(m, s)
logw -= (m + 3 * s)
good = logw < 0
logw *= good
w = np.exp(logw)
sorti = np.argsort(w)
for key in chain.keys():
chain[key] = chain[key][sorti]
w = w[sorti]
ow = ow[sorti]
posterior = posterior[sorti]
c = ChainConsumer()
truth = [0.3, 0.14, 3.1, -19.365, 0, 0, 0.1, 1.0, 0.1, 0, 0, 0, 0, 0, 0]
c.add_chain(chain, name="uncorrected", posterior=posterior)
c.add_chain(chain, weights=w, name="corrected", posterior=posterior)
c.plot(filename="output.png", parameters=9, truth=truth, figsize=1.3)
# c = ChainConsumer()
# c.add_chain(chain, weights=w, name="corrected")
c.plot_walks(chains="corrected", filename="walks.png", truth=truth)
def plot_single_cosmology(folder, output, i=0, output_walk=None):
print("Plotting cosmology realisation %d" % i)
res = load_stan_from_folder(folder, merge=False)
c = ChainConsumer()
chain, posterior, t, p, f, l, w, ow = res[i]
c.add_chain(chain, weights=w, posterior=posterior, walkers=l, name="%d"%i)
c.plot(filename=output, truth=t, figsize=0.75)
if output_walk is not None:
c.plot_walks(filename=output_walk)
def plot_all(folder, output, output_walk=None):
""" Plot all chains as one """
print("Plotting all as one")
chain, posterior, t, p, f, l, w, ow = load_stan_from_folder(folder, merge=True)
c = ChainConsumer()
c.add_chain(chain, weights=w, posterior=posterior, walkers=l)
c.plot(filename=output, truth=t, figsize=0.75)
if output_walk is not None:
c.plot_walks(filename=output_walk)
def debug_plots(std):
print(std)
do_weight = True
do_walk = True
load_second = True
res = load_stan_from_folder(std, merge=False, num=0, cut=False)
chain, posterior, t, p, f, l, w, ow = res[0]
if do_walk:
# print("owww ", ow.min(), ow.max())
# print("www ", w.min(), w.max())
chain2 = chain.copy()
logw = np.log10(w)
a = np.argsort(logw)
logw = logw[a]
for k in chain:
chain2[k] = chain2[k][a]
chain2["ow"] = np.log10(ow)
chain2["ww"] = logw
c = ChainConsumer()
c.add_chain(chain2, weights=w, name="calib")
c.plot_walks(truth=t, filename="walk_new.png")
c = ChainConsumer()
if do_weight:
c.add_chain(chain, weights=w, name="calib")
else:
c.add_chain(chain, name="calib")
if load_second:
res2 = load_stan_from_folder(std + "_calib_data_no_calib_model_and_bias", num=0, merge=False, cut=False)
chain, posterior, _, p, f, l, w, ow = res2[0]
if do_weight:
c.add_chain(chain, weights=w, name="nocalib")
else:
c.add_chain(chain, name="nocalib")
c.plot(filename="output.png", truth=t, figsize=0.75)
lnprob,
args=(zhel, zcmb, mb, x1, color, thirdvar,
Cmu, blind_values),
threads=4)
# run the sampler
# how many steps (will have nSteps*nwalkers of samples)
sampler.run_mcmc(pos, nSteps)
if options.plot:
burnin = 0 # change
data = np.genfromtxt(
options.chains +
'/my_params_JLA_FlatwCDM_CPL_uncorrected_PV_blind_%s.txt' % date,
delimiter=',')
data = data[burnin:]
c = ChainConsumer()
c.add_chain(data,
parameters=[
r'$\Omega_m$', r'$w_0$', r'$\\alpha$', r'$\beta$',
r'$M_B$', r'$\Delta_M$'
])
params = c.get_summary()
print params.keys()
figw = c.plot_walks()
figw.show()
fig = c.plot()
figw.savefig(options.chains + '/walks.png')
fig.savefig(options.chains + '/marginals.png')
fig.show()
model_good = EfficiencyModelUncorrected(cs, zs, ts, calibration, zeros, ls, ss, t0s, name="Good%d" % i)
model_good.fit(sampler, chain_consumer=c)
model_un = EfficiencyModelUncorrected(cs[mask], zs[mask], ts[mask], calibration,
zeros, ls[mask], ss[mask], t0s[mask], name="Uncorrected%d" % i)
model_un.fit(sampler, chain_consumer=c)
biased_chain = c.chains[-1]
# model_cor.fit(sampler, chain_consumer=c)
filename = dir_name + "/output/weights.txt"
if not os.path.exists(filename):
weights = []
for i, row in enumerate(biased_chain):
weights.append(get_weights(row[0], row[1], row[2], row[3], row[4], row[5], threshold))
print(100.0 * i / biased_chain.shape[0])
weights = np.array(weights)
np.savetxt(filename, weights)
else:
weights = np.loadtxt(filename)
weights = (1 / np.power(weights, mask.sum()))
c.add_chain(biased_chain, name="Importance Sampled", weights=weights)
c.configure_bar(shade=True)
c.configure_general(bins=1.0, colours=colours)
c.configure_contour(sigmas=[0, 0.01, 1, 2], contourf=True, contourf_alpha=0.2)
c.plot(filename=plot_file, truth=theta, figsize=(7, 7), legend=False, parameters=6)
for i in range(len(c.chains)):
c.plot_walks(filename=walk_file % c.names[i], chain=i, truth=theta)
for i in range(n):
mean, std, observed, errors, alpha, actual, uo, oe, am = get_data(seed=i)
theta_good = [mean, std] + actual.tolist()
theta_bias = [mean, std] + am.tolist()
kwargs = {"num_steps": 70000, "num_burn": 20000, "save_interval": 300,
"plot_covariance": True, "unify_latent": True} # , "callback": v.callback
sampler = BatchMetropolisHastings(num_walkers=w, kwargs=kwargs, temp_dir=t % i, num_cores=4)
model_good = EfficiencyModelUncorrected(uo, oe, name="Good%d" % i)
model_good.fit(sampler, chain_consumer=c)
print("Good ", model_good.get_log_posterior(theta_good), c.posteriors[-1][-1])
model_un = EfficiencyModelUncorrected(observed, errors, name="Uncorrected%d" % i)
model_un.fit(sampler, chain_consumer=c)
print("Uncorrected ", model_un.get_log_posterior(theta_bias), c.posteriors[-1][-1])
model_cor = EfficiencyModelCorrected(observed, errors, alpha, name="Corrected%d" % i)
model_cor.fit(sampler, chain_consumer=c)
print("Corrected ", model_cor.get_log_posterior(theta_bias), c.posteriors[-1][-1])
c.configure_bar(shade=True)
c.configure_general(bins=1.0, colours=colours)
c.configure_contour(sigmas=[0, 0.01, 1, 2], shade=True, shade_alpha=0.3)
c.plot(filename=plot_file, truth=theta_bias, figsize=(5, 5), legend=False)
for i in range(len(c.chains)):
c.plot_walks(filename=walk_file % c.names[i], chain=i, truth=[mean, std])
# c.divide_chain(i, w).configure_general(rainbow=True) \
# .plot(figsize=(5, 5), filename=plot_file.replace(".png", "_%s.png" % c.names[i]),
# truth=theta_bias)
