rs_fid = get_r_s([0.273])[0]
daval = (alpha/(1+epsilon)) * da / rs_fid
hrc = hs * rs_fid / (alpha * (1 + epsilon) * (1 + epsilon)) / c
res = np.vstack((omch2, daval, z/hrc)).T
return res
p1 = [r"$\Omega_c h^2$", r"$\alpha$", r"$\epsilon$"]
p2 = [r"$\Omega_c h^2$", r"$D_A(z)/r_s$", r"$cz/H(z)/r_s $"]
if False:
consumer = ChainConsumer()
consumer.configure_contour(sigmas=[0,1.3])
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z0"), parameters=p1, name="$0.2<z<0.6$")
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z1"), parameters=p1, name="$0.4<z<0.8$")
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z2"), parameters=p1, name="$0.6<z<1.0$")
consumer.plot(figsize="column", filename="wigglez_multipole_alphaepsilon.pdf", truth=[0.113, 1.0, 0.0])
print(consumer.get_latex_table())
if True:
c = ChainConsumer()
c.configure_contour(sigmas=[0,1,2])
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z0", 0.44), parameters=p2, name="$0.2<z<0.6$")
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z1", 0.60), parameters=p2, name="$0.4<z<0.8$")
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z2", 0.73), parameters=p2, name="$0.6<z<1.0$")
print(c.get_latex_table())
#c.plot(figsize="column", filename="wigglez_multipole_dah.pdf")
Choose custom sigma levels and display point cloud.
In this example we display more sigma levels, turn on the point cloud, and
disable the parameter summaries on the top of the marginalised distributions.
Note that because of the very highly correlated distribution we have, it is
useful to increase the number of bins the plots are generated with, to capture the
thinness of the correlation.
"""
import numpy as np
from numpy.random import normal, multivariate_normal
from chainconsumer import ChainConsumer
if __name__ == "__main__":
np.random.seed(1)
cov = normal(size=(3, 3))
data = multivariate_normal(normal(size=3),
0.5 * (cov + cov.T),
size=100000)
c = ChainConsumer().add_chain(data, parameters=["$x$", "$y$", "$z$"])
c.configure_bar(summary=False).configure_general(bins=1.4)
c.configure_contour(cloud=True, sigmas=np.linspace(0, 2, 10))
fig = c.plot()
fig.set_size_inches(
2.5 +
fig.get_size_inches()) # Resize fig for doco. You don't need this.
===================
Customise the plot line styles.
In this example we customise the line styles used, and make use of
the ability to pass lists of parameters to the configuration methods.
"""
import numpy as np
from numpy.random import normal, multivariate_normal
from chainconsumer import ChainConsumer
if __name__ == "__main__":
np.random.seed(1)
cov = normal(size=(3, 3))
data = multivariate_normal(normal(size=3),
0.5 * (cov + cov.T),
size=100000)
data2 = data * 1.1 + 0.5
c = ChainConsumer().add_chain(data, parameters=["$x$", "$y$",
"$z$"]).add_chain(data2)
c.configure_general(linestyles=["-", "--"], linewidths=[1.0, 2.0])
c.configure_contour(shade=[True, False], shade_alpha=[0.2, 0.0])
fig = c.plot()
fig.set_size_inches(
2.5 +
fig.get_size_inches()) # Resize fig for doco. You don't need this.
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)
t = os.path.abspath(dir_name + "/output/data_%d")
plot_file = os.path.abspath(dir_name + "/output/surfaces.png")
walk_file = os.path.abspath(dir_name + "/output/walk_%s.png")
c = ChainConsumer()
n = 2
colours = ["#4CAF50", "#D32F2F", "#1E88E5"] * n # , "#FFA000"] * n
for i in range(n):
mean, sigma, cut, observed, mask = get_data(seed=i)
model_good = EfficiencyModelUncorrected(observed, name="Good")
model_un = EfficiencyModelUncorrected(observed[mask])
model_cor = EfficiencyModelCorrected(observed[mask], cut)
sampler = EnsembleSampler(num_steps=25000, num_burn=1000, temp_dir=t % i)
model_good.fit(sampler, chain_consumer=c)
model_un.fit(sampler, chain_consumer=c)
biased_chain = c.chains[-1]
# model_cor.fit(sampler, chain_consumer=c)
mus = biased_chain[:, 0]
sigmas = biased_chain[:, 1]
weights = 1 / get_weights(cut, mus, sigmas, mask.sum())
c.add_chain(biased_chain, name="Importance Sampled", weights=weights)
c.configure_bar(shade=True)
c.configure_general(colours=colours, bins=0.5)
c.configure_contour(contourf=True, contourf_alpha=0.2)
c.plot(filename=plot_file, figsize=(5, 5), truth=[mean, sigma], legend=False)
if __name__ == "__main__":
dir_name = os.path.dirname(os.path.abspath(__file__))
output = dir_name + "/output/complete.png"
output2 = dir_name + "/output/complete2.png"
folders = ["simple", "approx"] # "stan_mc",
use_weight = [False, True]
c = ChainConsumer()
for f, u in zip(folders, use_weight):
loc = dir_name + os.sep + f + "/stan_output"
t = None
try:
chain, posterior, t, p, ff, l, w, ow = load_stan_from_folder(loc, merge=True)
if u:
c.add_chain(chain, posterior=posterior, walkers=l, name=f)
c.add_chain(chain, weights=w, posterior=posterior, walkers=l, name="full")
else:
c.add_chain(chain, posterior=posterior, walkers=l, name=f)
except Exception as e:
print(e)
print("No files found in %s" % loc)
print(p)
c.configure_general(linestyles=['-', '--', '-'], colours=["#1E88E5", "#555555", "#D32F2F"]) #4CAF50
c.configure_bar(shade=[True, True, True])
c.configure_contour(shade=[True, True, True])
pp = ['$\\Omega_m$', '$\\alpha$', '$\\beta$', '$\\langle M_B \\rangle$', '$\\langle x_1 \\rangle$',
'$\\langle c \\rangle$'] #, '$\\sigma_{\\rm m_B}$', '$\\sigma_{x_1}$', '$\\sigma_c$']
c.plot(filename=output, truth=t, parameters=pp)
c.plot(filename=output2, truth=t)
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)
dir_name = os.path.dirname(__file__)
t = os.path.abspath(dir_name + "/output/data_%d")
plot_file = os.path.abspath(dir_name + "/output/surfaces.png")
walk_file = os.path.abspath(dir_name + "/output/walk_%s.png")
c = ChainConsumer()
n = 3
colours = ["#D32F2F", "#1E88E5"] * n
for i in range(n):
mean, sigma, observed, cut = get_data(seed=i)
model_un = EfficiencyModelUncorrected(observed)
model_cor = EfficiencyModelCorrected(observed, cut)
pgm_file = os.path.abspath(dir_name + "/output/pgm.png")
fig = model_cor.get_pgm(pgm_file)
sampler = EnsembleSampler(num_steps=10000, num_burn=1000, temp_dir=t % i, num_walkers=50)
model_un.fit(sampler, chain_consumer=c)
model_cor.fit(sampler, chain_consumer=c)
c.configure_bar(shade=True)
c.configure_general(colours=colours)
c.configure_contour(shade=True, shade_alpha=0.3)
# c.plot_walks(truth=[mean, sigma], filename=walk_file % "no", chain=0)
# c.plot_walks(truth=[mean, sigma], filename=walk_file % "cor", chain=1)
c.plot(filename=plot_file, figsize=(5, 5), truth=[mean, sigma], legend=False)
theta2 = theta + ls.tolist() + t0s.tolist() + ss.tolist()
kwargs = {"num_steps": 6000, "num_burn": 250000, "save_interval": 60, "plot_covariance": True, "covariance_adjust": 10000}
sampler = BatchMetropolisHastings(num_walkers=w, kwargs=kwargs, temp_dir=t % i, num_cores=4)
model_good = EfficiencyModelUncorrected(cs, zs, ts, types, calibration, zeros, ls, ss, t0s, name="Good%d" % i)
model_good.fit(sampler, chain_consumer=c)
print("Good sampler finished")
mtypes = [t for t, m in zip(types, mask) if m]
model_un = EfficiencyModelUncorrected(cs[mask], zs[mask], ts[mask], mtypes, calibration,
zeros, ls[mask], ss[mask], t0s[mask], name="Uncorrected%d" % i)
model_un.fit(sampler, chain_consumer=c)
print("Uncorrected sampler finished")
print("Getting weights")
biased_chain = c.chains[-1]
filename = dir_name + "/output/weights.txt"
if not os.path.exists(filename):
weights = Parallel(n_jobs=4, verbose=100, batch_size=100)(delayed(get_weight_from_row)(row, threshold) for row in biased_chain)
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)
print("Weights finished")
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=0.2)
c.plot(filename=plot_file, truth=theta, figsize=(10, 10), legend=False, parameters=10)
for i in range(len(c.chains)):
c.plot_walks(filename=walk_file % c.names[i], chain=i, truth=theta)
sampler = EnsembleSampler(temp_dir=temp_dir, num_steps=20000)
my_model.fit(sampler, chain_consumer=c)
c.add_chain(np.random.multivariate_normal(res.parameters[1:], res.covariance, size=int(1e7)),
name="Summary Stats", parameters=["$t_0$", "$x_0$", "$x_1$", "$c$"])
if False:
if not os.path.exists(mcmc_chain):
res2, fitted_model2 = sncosmo.mcmc_lc(lcs[0], model, ['t0', 'x0', 'x1', 'c'], nwalkers=20,
nburn=500, nsamples=4000)
mcchain = res2.samples
np.save(mcmc_chain, mcchain)
else:
mcchain = np.load(mcmc_chain)
c.add_chain(mcchain, name="sncosmo mcmc", parameters=["$t_0$", "$x_0$", "$x_1$", "$c$"])
print("Plot surfaces")
c.configure_contour(shade=True, shade_alpha=0.2, sigmas=[0.0, 1.0, 2.0, 3.0])
c.configure_bar(shade=True)
c.plot(filename=surface, figsize=(7, 7))
if False:
fig = sncosmo.plot_lc(lcs[0], model=fitted_model, errors=res.errors)
fig.savefig(temp_dir + os.sep + "lc_simple.png", bbox_inches="tight", dpi=300)
alpha = 0.14
beta = 3.15
c2 = ChainConsumer()
means = []
stds = []
print("Add chains")
for i in range(len(c.chains)):
chain = c.chains[i]
for n in ["deep", "shallow"]:
is_shallow = n == "shallow"
# bias_file = os.path.dirname(__file__) + "/output/cosmology/bias_%s.npy" % n
temp_dir2 = os.path.dirname(__file__) + "/output/cosmology2_%s" % n
if not os.path.exists(temp_dir2):
os.makedirs(temp_dir2)
logging.basicConfig(level=logging.DEBUG)
zs, mu_mcmc, mu_minuit, std_mcmc, std_minuit = get_supernova_data(shallow=is_shallow)
plot_cosmology(zs, mu_mcmc, mu_minuit, std_mcmc, std_minuit, n)
fitter_mcmc = SimpleCosmologyFitter("mcmc", zs, mu_mcmc, std_mcmc)
fitter_minuit = SimpleCosmologyFitter("minuit", zs, mu_minuit, std_minuit)
sampler = EnsembleSampler(temp_dir=temp_dir2, save_interval=60, num_steps=8000, num_burn=1000)
c = fitter_mcmc.fit(sampler=sampler)
cc.add_chain(c.chains[-1], parameters=c.parameters[-1], name="%s MCMC" % n.title())
c = fitter_minuit.fit(sampler=sampler, chain_consumer=c)
cc.add_chain(c.chains[-1], parameters=c.parameters[-1], name="%s Max. Like." % n.title())
c.names = ["MCMC", "Max. Like."]
c.plot(filename="output/comparison_%s.png" % n, parameters=2, figsize=(5.5, 5.5), truth=[0.3, -1.0])
c.plot(filename="output/comparison_%s.pdf" % n, parameters=2, figsize=(5.5, 5.5), truth=[0.3, -1.0])
print(c.get_latex_table())
print(cc.get_latex_table())
cc.configure_general(colours=["#1E88E5", "#1E88E5", "#D32F2F", "#D32F2F"],
linewidths=[1, 2, 1, 2],
linestyles=["-", "--", "-", "--"])
cc.configure_contour(shade=[True, False, True, False])
cc.plot(filename="output/comparison.png", parameters=2, figsize=(5.5, 5.5), truth=[0.3, -1.0])
cc.plot(filename="output/comparison.pdf", parameters=2, figsize=(5.5, 5.5), truth=[0.3, -1.0])