def __add__(
self,
other: "DynestySamples"
) -> "DynestySamples":
"""
Samples can be added together, which combines their `sample_list` meaning that inferred parameters are
computed via their joint PDF.
For dynesty samples, the in-built dynesty function `merge_runs` can be used to combine results_internal in their native
format and therefore retain visualization support.
Parameters
----------
other
Another Samples class
Returns
-------
A class that combined the samples of the two Samples objects.
"""
self._check_addition(other=other)
results_internal = dyfunc.merge_runs(res_list=[self.results_internal, other.results_internal])
return DynestySamples(
model=self.model,
sample_list=self.sample_list + other.sample_list,
number_live_points=self._number_live_points,
unconverged_sample_size=self.unconverged_sample_size,
time=self.time,
results_internal=results_internal
)
def test_ravel_unravel():
""" Here I test that ravel/unravel preserves things correctly """
rstate = get_rstate()
g = Gaussian()
dsampler = dynesty.DynamicNestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
bound='single',
sample='unif',
rstate=rstate,
nlive=nlive)
maxiter = 1800
dsampler.run_nested(maxiter=maxiter,
use_stop=False,
nlive_batch=100,
print_progress=printing)
dres = dsampler.results
dres_list = dyfunc.unravel_run(dres)
dres_merge = dyfunc.merge_runs(dres_list)
assert np.abs(dres.logz[-1] - dres_merge.logz[-1]) < 0.01
def test_gaussian():
sig = 5
rstate = get_rstate()
g = Gaussian()
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(print_progress=printing)
# check that jitter/resample work
# for not dynamic sampler
dyfunc.jitter_run(sampler.results, rstate=rstate)
dyfunc.resample_run(sampler.results, rstate=rstate)
# add samples
# check continuation behavior
sampler.run_nested(dlogz=0.1, print_progress=printing)
# get errors
nerr = 3
result_list = []
for i in range(nerr):
sampler.reset()
sampler.run_nested(print_progress=False)
results = sampler.results
result_list.append(results)
pos = results.samples
wts = np.exp(results.logwt - results.logz[-1])
mean, cov = dyfunc.mean_and_cov(pos, wts)
logz = results.logz[-1]
assert (np.abs(logz - g.logz_truth) < sig * results.logzerr[-1])
res_comb = dyfunc.merge_runs(result_list)
assert (np.abs(res_comb.logz[-1] - g.logz_truth) <
sig * results.logzerr[-1])
# check summary
res = sampler.results
res.summary()
def run(self, nlive=1000, cores=None, filename=None, **kwargs):
merge = "no"
if filename is not None and os.path.isfile(filename):
doit = input(f"There seems to be a file named {filename}. "
f"Would you like to run anyway? [y/n] ").lower()
if doit in ["no", "n"]:
with open(filename, "br") as file:
self.results = pickle.load(file)
return
if cores is None or cores > MAX_CORES:
cores = MAX_CORES
try:
with Pool(cores) as pool:
sampler = NestedSampler(
self.loglike,
self.sample,
self.N,
npdim=self.ndim,
nlive=nlive,
pool=pool,
queue_size=cores,
**kwargs,
)
sampler.run_nested()
except KeyboardInterrupt:
pass
if filename is not None and os.path.isfile(filename):
merge = input("Merge new run with previous data? [y/n] ").lower()
if merge in ["no", "n"]:
self.results = sampler.results
else:
with open(filename, "br") as file:
res = pickle.load(file)
self.results = merge_runs([sampler.results, res])
if filename is not None:
with open(filename, "bw") as file:
pickle.dump(self.results, file)
def test_gaussian():
logz_tol = 1
sampler = dynesty.NestedSampler(loglikelihood_gau,
prior_transform_gau,
ntotdim,
nlive=nlive,
ncdim=ndim_gau)
sampler.run_nested(print_progress=printing)
# check that jitter/resample/simulate_run work
# for not dynamic sampler
dyfunc.jitter_run(sampler.results)
dyfunc.resample_run(sampler.results)
dyfunc.simulate_run(sampler.results)
# add samples
# check continuation behavior
sampler.run_nested(dlogz=0.1, print_progress=printing)
# get errors
nerr = 2
result_list = []
for i in range(nerr):
sampler.reset()
sampler.run_nested(print_progress=False)
results = sampler.results
result_list.append(results)
pos = results.samples
wts = np.exp(results.logwt - results.logz[-1])
mean, cov = dyfunc.mean_and_cov(pos, wts)
logz = results.logz[-1]
assert (np.abs(logz - logz_truth_gau) < logz_tol)
res_comb = dyfunc.merge_runs(result_list)
assert (np.abs(res_comb.logz[-1] - logz_truth_gau) < logz_tol)
# check summary
res = sampler.results
res.summary()
def merge_run(res_list):
return dyfunc.merge_runs(res_list)
# "Static" nested sampling. sampler = dynesty.NestedSampler(loglike, ptform, ndim) sampler.run_nested() sresults = sampler.results # "Dynamic" nested sampling. dsampler = dynesty.DynamicNestedSampler(loglike, ptform, ndim, nlive=500) dsampler.run_nested() dresults = dsampler.results from dynesty import utils as dyfunc # Combine results from "Static" and "Dynamic" runs. results = dyfunc.merge_runs([sresults, dresults]) from dynesty import plotting as dyplot # Plot a summary of the run. rfig, raxes = dyplot.runplot(results) # Plot traces and 1-D marginalized posteriors. tfig, taxes = dyplot.traceplot(results) # Plot the 2-D marginalized posteriors. cfig, caxes = dyplot.cornerplot(results) # we can post-process results