def test_parameterization():
updated_info, products = run(info)
sample = products["sample"]
from getdist.mcsamples import loadCobayaSamples
gdsample = loadCobayaSamples(updated_info, products["sample"])
for i, point in sample:
a = info[_params]["a"]
b = get_external_function(info[_params]["b"])(a, point["bprime"])
c = get_external_function(info[_params]["c"])(a, point["cprime"])
e = get_external_function(e_func)(b)
f = get_external_function(f_func)(b)
g = get_external_function(info[_params]["g"]["derived"])(x_func(
point["c"]))
assert np.allclose(point[["b", "c", "e", "f", "g"]], [b, c, e, f, g])
bcefg_getdist = [
gdsample.samples[i][gdsample.paramNames.list().index(p)]
for p in ["b", "c", "e", "f", "g"]
]
assert np.allclose(bcefg_getdist, [b, c, e, f, g])
def test_parameterization():
updated_info, products = run(info)
sample = products["sample"]
from getdist.mcsamples import loadCobayaSamples
gdsample = loadCobayaSamples(updated_info, products["sample"])
for i, point in sample:
a = info[_params]["a"]
b = get_external_function(info[_params]["b"])(a, point["bprime"])
c = get_external_function(info[_params]["c"])(a, point["cprime"])
e = get_external_function(e_func)(b)
f = get_external_function(f_func)(b)
g = get_external_function(info[_params]["g"]["derived"])(x_func(point["c"]))
h = get_external_function(info[_params]["h"])(info[_params]["i"])
j = get_external_function(info[_params]["j"])(b)
k = get_external_function(info[_params]["k"]["derived"])(f)
assert np.allclose(
point[["b", "c", "e", "f", "g", "h", "j", "k"]], [b, c, e, f, g, h, j, k])
# Test for GetDist too (except fixed ones, ignored by GetDist)
bcefffg_getdist = [gdsample.samples[i][gdsample.paramNames.list().index(p)]
for p in ["b", "c", "e", "f", "g", "j", "k"]]
assert np.allclose(bcefffg_getdist, [b, c, e, f, g, j, k])
def body_of_test(dimension=1,
n_modes=1,
info_sampler={},
tmpdir="",
modules=None):
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
# Info of likelihood and prior
ranges = np.array([[-1, 1] for _ in range(dimension)])
while True:
info = info_random_gaussian_mixture(ranges=ranges,
n_modes=n_modes,
prefix="a_",
O_std_min=O_std_min,
O_std_max=O_std_max,
derived=True)
if n_modes == 1:
break
means = info["likelihood"]["gaussian_mixture"]["means"]
distances = chain(*[[np.linalg.norm(m1 - m2) for m2 in means[i + 1:]]
for i, m1 in enumerate(means)])
if min(distances) >= distance_factor * O_std_max:
break
if rank == 0:
print("Original mean of the gaussian mode:")
print(info["likelihood"]["gaussian_mixture"]["means"])
print("Original covmat of the gaussian mode:")
print(info["likelihood"]["gaussian_mixture"]["covs"])
info[_sampler] = info_sampler
if list(info_sampler.keys())[0] == "mcmc":
if "covmat" in info_sampler["mcmc"]:
info[_sampler]["mcmc"]["covmat_params"] = (list(
info["params"].keys())[:dimension])
info[_debug] = False
info[_debug_file] = None
info[_output_prefix] = getattr(tmpdir, "realpath()", lambda: tmpdir)()
if modules:
info[_path_install] = process_modules_path(modules)
# Delay to one chain to check that MPI communication of the sampler is non-blocking
# if rank == 1:
# info["likelihood"]["gaussian_mixture"]["delay"] = 0.1
updated_info, products = run(info)
# Done! --> Tests
if rank == 0:
if list(info_sampler.keys())[0] == "mcmc":
ignore_rows = 0.5
else:
ignore_rows = 0
results = loadCobayaSamples(updated_info,
products["sample"],
ignore_rows=ignore_rows,
name_tag="sample")
clusters = None
if "clusters" in products:
clusters = [
loadCobayaSamples(updated_info,
products["clusters"][i]["sample"],
name_tag="cluster %d" % (i + 1))
for i in products["clusters"]
]
# Plots!
try:
import getdist.plots as gdplots
from getdist.gaussian_mixtures import MixtureND
mixture = MixtureND(
info[_likelihood]["gaussian_mixture"]["means"],
info[_likelihood]["gaussian_mixture"]["covs"],
names=[p for p in info[_params] if "deriv" not in p],
label="truth")
g = gdplots.getSubplotPlotter()
to_plot = [mixture, results]
if clusters:
to_plot = to_plot + clusters
g.triangle_plot(to_plot, )
g.export("test.png")
except:
print("Plotting failed!")
# 1st test: KL divergence
if n_modes == 1:
cov_sample, mean_sample = results.getCov(), results.getMeans()
KL_final = KL_norm(
m1=info[_likelihood]["gaussian_mixture"]["means"][0],
S1=info[_likelihood]["gaussian_mixture"]["covs"][0],
m2=mean_sample[:dimension],
S2=cov_sample[:dimension, :dimension])
print("Final KL: ", KL_final)
assert KL_final <= KL_tolerance
# 2nd test: clusters
else:
if "clusters" in products:
assert len(products["clusters"].keys()) >= n_modes, (
"Not all clusters detected!")
for c2 in clusters:
cov_c2, mean_c2 = c2.getCov(), c2.getMeans()
KLs = [
KL_norm(m1=info[_likelihood]["gaussian_mixture"]
["means"][i_c1],
S1=info[_likelihood]["gaussian_mixture"]
["covs"][i_c1],
m2=mean_c2[:dimension],
S2=cov_c2[:dimension, :dimension])
for i_c1 in range(n_modes)
]
extra_tol = 4 * n_modes if n_modes > 1 else 1
assert min(KLs) <= KL_tolerance * extra_tol
else:
assert 0, "Could not check sample convergence: multimodal but no clusters"
# 3rd test: Evidence
if "logZ" in products:
logZprior = sum(np.log(ranges[:, 1] - ranges[:, 0]))
assert (products["logZ"] - logZ_nsigmas * products["logZstd"] <
-logZprior <
products["logZ"] + logZ_nsigmas * products["logZstd"])
# Export the results to GetDist
from getdist.mcsamples import loadCobayaSamples
gd_sample = loadCobayaSamples(updated_info, products["sample"])
# Analyze and plot
mean = gd_sample.getMeans()[:2]
covmat = gd_sample.getCovMat().matrix[:2, :2]
print("Mean:")
print(mean)
print("Covariance matrix:")
print(covmat)
# %matplotlib inline # uncomment if running from the Jupyter notebook
import getdist.plots as gdplt
gdplot = gdplt.getSubplotPlotter()
gdplot.triangle_plot(gd_sample, ["a", "b"], filled=True)