Source code for

# Copyright (C) 2018 Alex Nitz
# 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.

# =============================================================================
#                                   Preamble
# =============================================================================
"""Provides simplified standard format just for posterior data

from .base_hdf import BaseInferenceFile

[docs]class PosteriorFile(BaseInferenceFile): """Class to handle file IO for the simplified Posterior file.""" name = 'posterior_file'
[docs] def read_raw_samples(self, fields, **kwargs): return read_raw_samples_from_file(self, fields, **kwargs)
[docs] def write_samples(self, samples, parameters=None): return write_samples_to_file(self, samples, parameters=parameters)
[docs]def read_raw_samples_from_file(fp, fields, **kwargs): samples = fp[fp.samples_group] return {field: samples[field][:] for field in fields}
[docs]def write_samples_to_file(fp, samples, parameters=None, group=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 ----------- fp : self Pass the 'self' from BaseInferenceFile class. 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. """ # check data dimensions; we'll just use the first array in samples arr = list(samples.values())[0] if not arr.ndim == 1: raise ValueError("samples must be 1D arrays") niterations = arr.size assert all(len(p) == niterations for p in samples.values()), ( "all samples must have the same shape") if group is not None: group = group + '/{name}' else: group = fp.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(fp[dataset_name]) if niterations != fp_niterations: # resize the dataset fp[dataset_name].resize(niterations, axis=0) except KeyError: # dataset doesn't exist yet fp.create_dataset(dataset_name, (niterations,), maxshape=(None,), dtype=samples[param].dtype, fletcher32=True) fp[dataset_name][:] = samples[param]