"""Drop-in replacement for pymultinest.solve.
Example::
from ultranest.solvecompat import pymultinest_solve_compat as solve
# is a drop-in replacement for
from pymultinest.solve import solve
"""
import numpy as np
import string
from .integrator import ReactiveNestedSampler
from .stepsampler import SliceSampler, generate_mixture_random_direction
[docs]
def pymultinest_solve_compat(
LogLikelihood, Prior, n_dims, paramnames=None,
outputfiles_basename=None, resume=False,
n_live_points=400, evidence_tolerance=0.5,
seed=-1, max_iter=0, wrapped_params=None, verbose=True,
speed="safe",
**kwargs
):
"""Run nested sampling analysis.
Disadvantages compared to using ReactiveNestedSampler directly:
cannot resume easily, cannot plot interactively.
Limited results.
It is recommended that you directly use::
sampler = ReactiveNestedSampler(paramnames, LogLikelihood, transform=Prior)
sampler.run()
following the UltraNest documentation and manuals,
as this gives you more control on resuming and sampler options.
"""
if paramnames is None:
paramnames = list(string.ascii_lowercase)[:n_dims]
if seed >= 0:
np.random.seed(seed)
assert len(paramnames) == n_dims
min_ess = kwargs.pop('min_ess', 0)
frac_remain = kwargs.pop('frac_remain', 0.01)
Lepsilon = kwargs.pop('Lepsilon', 0.001)
outputkwargs = {}
if not verbose:
outputkwargs = dict(viz_callback=False, show_status=False)
sampler = ReactiveNestedSampler(
paramnames, LogLikelihood, transform=Prior,
log_dir=outputfiles_basename, resume='resume' if resume else 'overwrite',
wrapped_params=wrapped_params, draw_multiple=False, vectorized=False,
**outputkwargs)
if speed == "safe":
pass
elif speed == "auto":
sampler.run(
dlogz=evidence_tolerance,
max_iters=max_iter if max_iter > 0 else None,
min_num_live_points=n_live_points,
min_ess=min_ess, frac_remain=frac_remain,
Lepsilon=Lepsilon, max_ncalls=40000)
sampler.stepsampler = SliceSampler(
nsteps=1000,
generate_direction=generate_mixture_random_direction,
adaptive_nsteps='move-distance',
region_filter=kwargs.get('region_filter', True)
)
else:
sampler.stepsampler = SliceSampler(
generate_direction=generate_mixture_random_direction,
nsteps=speed,
adaptive_nsteps=False,
region_filter=False)
sampler.run(dlogz=evidence_tolerance,
max_iters=max_iter if max_iter > 0 else None,
min_num_live_points=n_live_points,
min_ess=min_ess, frac_remain=frac_remain,
Lepsilon=Lepsilon)
if verbose:
sampler.print_results()
results = sampler.results
sampler.plot()
return dict(logZ=results['logz'],
logZerr=results['logzerr'],
samples=results['samples'],
weighted_samples=results['weighted_samples'])