# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# 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 write_sampler_metadata(self, sampler):
sampler.model.write_metadata(self)
[docs] def write_resume_point(self):
pass
write_run_start_time = write_run_end_time = write_resume_point
[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]