Source code for

# Copyright (C) 2018 Collin Capano
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# self.option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

"""Provides I/O support for multinest.

from .base_sampler import BaseSamplerFile

[docs]class MultinestFile(BaseSamplerFile): """Class to handle file IO for the ``multinest`` sampler.""" name = 'multinest_file'
[docs] def write_samples(self, samples, parameters=None): """Writes samples to the given file. Results are written to ``samples_group/{vararg}``, where ``{vararg}`` is the name of a model params. The samples are written as an array of length ``niterations``. Parameters ---------- samples : dict The samples to write. Each array in the dictionary should have length niterations. parameters : list, optional Only write the specified parameters to the file. If None, will write all of the keys in the ``samples`` dict. """ niterations = len(tuple(samples.values())[0]) assert all(len(p) == niterations for p in samples.values()), ( "all samples must have the same shape") group = self.samples_group + '/{name}' if parameters is None: parameters = samples.keys() # loop over number of dimensions for param in parameters: dataset_name = group.format(name=param) try: fp_niterations = len(self[dataset_name]) if niterations != fp_niterations: # resize the dataset self[dataset_name].resize(niterations, axis=0) except KeyError: # dataset doesn't exist yet self.create_dataset(dataset_name, (niterations,), maxshape=(None,), dtype=samples[param].dtype, fletcher32=True) self[dataset_name][:] = samples[param]
[docs] def write_logevidence(self, lnz, dlnz, importance_lnz, importance_dlnz): """Writes the given log evidence and its error. Results are saved to file's 'log_evidence' and 'dlog_evidence' attributes, as well as the importance-weighted versions of these stats if they exist. Parameters ---------- lnz : float The log of the evidence. dlnz : float The error in the estimate of the log evidence. importance_lnz : float, optional The importance-weighted log of the evidence. importance_dlnz : float, optional The error in the importance-weighted estimate of the log evidence. """ self.attrs['log_evidence'] = lnz self.attrs['dlog_evidence'] = dlnz if all([e is not None for e in [importance_lnz, importance_dlnz]]): self.attrs['importance_log_evidence'] = importance_lnz self.attrs['importance_dlog_evidence'] = importance_dlnz
[docs] def read_raw_samples(self, fields, iteration=None): if isinstance(fields, str): fields = [fields] # load group = self.samples_group + '/{name}' arrays = {} for name in fields: if iteration is not None: arr = self[group.format(name=name)][int(iteration)] else: arr = self[group.format(name=name)][:] arrays[name] = arr return arrays
[docs] def write_resume_point(self): """Keeps a list of the number of iterations that were in a file when a run was resumed from a checkpoint.""" try: resume_pts = self.attrs["resume_points"].tolist() except KeyError: resume_pts = [] try: niterations = self.niterations except KeyError: niterations = 0 resume_pts.append(niterations) self.attrs["resume_points"] = resume_pts
@property def niterations(self): """Returns the number of iterations the sampler was run for.""" return self[self.sampler_group].attrs['niterations']
[docs] def write_niterations(self, niterations): """Writes the given number of iterations to the sampler group.""" self[self.sampler_group].attrs['niterations'] = niterations
[docs] def write_sampler_metadata(self, sampler): """Writes the sampler's metadata.""" self.attrs['sampler'] = if self.sampler_group not in self.keys(): # create the sampler group self.create_group(self.sampler_group) self[self.sampler_group].attrs['nlivepoints'] = sampler.nlivepoints # write the model's metadata sampler.model.write_metadata(self)