# Copyright (C) 2016 Christopher M. Biwer
# 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
# 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.
"""
This modules provides a list of implemented samplers for parameter estimation.
"""
import logging
# pylint: disable=unused-import
from .base import (initial_dist_from_config, create_new_output_file)
from .multinest import MultinestSampler
from .ultranest import UltranestSampler
from .dummy import DummySampler
from .refine import RefineSampler
from .snowline import SnowlineSampler
# list of available samplers
samplers = {cls.name: cls for cls in (
MultinestSampler,
UltranestSampler,
DummySampler,
RefineSampler,
SnowlineSampler,
)}
try:
from .emcee import EmceeEnsembleSampler
from .emcee_pt import EmceePTSampler
samplers[EmceeEnsembleSampler.name] = EmceeEnsembleSampler
samplers[EmceePTSampler.name] = EmceePTSampler
except ImportError:
pass
try:
from .epsie import EpsieSampler
samplers[EpsieSampler.name] = EpsieSampler
except ImportError:
pass
try:
from .ptemcee import PTEmceeSampler
samplers[PTEmceeSampler.name] = PTEmceeSampler
except ImportError:
pass
try:
from .cpnest import CPNestSampler
samplers[CPNestSampler.name] = CPNestSampler
except ImportError:
pass
try:
from .dynesty import DynestySampler
samplers[DynestySampler.name] = DynestySampler
except ImportError:
pass
try:
from .nessai import NessaiSampler
samplers[NessaiSampler.name] = NessaiSampler
except ImportError:
pass
[docs]def load_from_config(cp, model, **kwargs):
"""Loads a sampler from the given config file.
This looks for a name in the section ``[sampler]`` to determine which
sampler class to load. That sampler's ``from_config`` is then called.
Parameters
----------
cp : WorkflowConfigParser
Config parser to read from.
model : pycbc.inference.model
Which model to pass to the sampler.
\**kwargs :
All other keyword arguments are passed directly to the sampler's
``from_config`` file.
Returns
-------
sampler :
The initialized sampler.
"""
if len(model.variable_params) == 0:
logging.info('No variable params, so assuming Dummy Sampler')
return DummySampler.from_config(cp, model, **kwargs)
name = cp.get('sampler', 'name')
return samplers[name].from_config(cp, model, **kwargs)