def test_post_likelihood():
"""
Swaps likelihood "gaussian" for "target".
It also tests aggregated chi2's by removing and adding a likelihood to an existing
type.
"""
# Generate original chain
orig_interval = OutputOptions.output_inteveral_s
try:
OutputOptions.output_inteveral_s = 0
info_params_local = deepcopy(info_params)
info_params_local["dummy"] = 0
dummy_loglike_add = 0.1
dummy_loglike_remove = 0.01
info = {
"output": None,
"force": True,
"params": info_params_local,
"sampler": info_sampler,
"likelihood": {
"gaussian": {
"external": sampled_pdf,
"type": "A"
},
"dummy": {
"external": lambda dummy: 1,
"type": "BB"
},
"dummy_remove": {
"external": lambda dummy: dummy_loglike_add,
"type": "BB"
}
}
}
info_out, sampler = run(info)
samples_in = mpi.gather(sampler.products()["sample"])
if mpi.is_main_process():
mcsamples_in = MCSamplesFromCobaya(info_out, samples_in)
else:
mcsamples_in = None
info_out.update({
"post": {
"suffix": "foo",
"remove": {
"likelihood": {
"gaussian": None,
"dummy_remove": None
}
},
"add": {
"likelihood": {
"target": {
"external": target_pdf,
"type": "A",
"output_params": ["cprime"]
},
"dummy_add": {
"external": lambda dummy: dummy_loglike_remove,
"type": "BB"
}
}
}
}
})
info_post_out, products_post = post(info_out,
sampler.products()["sample"])
samples = mpi.gather(products_post["sample"])
# Load with GetDist and compare
if mcsamples_in:
target_mean, target_cov = mpi.share(_get_targets(mcsamples_in))
mcsamples = MCSamplesFromCobaya(info_post_out,
samples,
name_tag="sample")
new_mean = mcsamples.mean(["a", "b"])
new_cov = mcsamples.getCovMat().matrix
mpi.share((new_mean, new_cov))
else:
target_mean, target_cov = mpi.share()
new_mean, new_cov = mpi.share()
assert np.allclose(new_mean, target_mean)
assert np.allclose(new_cov, target_cov)
assert allclose(products_post["sample"]["chi2__A"],
products_post["sample"]["chi2__target"])
assert allclose(
products_post["sample"]["chi2__BB"],
products_post["sample"]["chi2__dummy"] +
products_post["sample"]["chi2__dummy_add"])
finally:
OutputOptions.output_inteveral_s = orig_interval
# Export the results to GetDist
from getdist.mcsamples import MCSamplesFromCobaya
gd_sample = MCSamplesFromCobaya(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)
