Source code for pycbc.inference.sampler.base_cube

# Copyright (C) 2020 Sumit Kumar, 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
# 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
# =============================================================================
Common utilities for samplers that rely on transforming between a unit cube
and the prior space. This is typical of many nested sampling algorithms.
import numpy

from .. import models

[docs]def call_global_loglikelihood(cube): return models._global_instance.log_likelihood(cube)
[docs]def call_global_logprior(cube): return models._global_instance.prior_transform(cube)
[docs]def setup_calls(model, loglikelihood_function=None, copy_prior=False): """ Configure calls for MPI support """ model_call = CubeModel(model, loglikelihood_function, copy_prior=copy_prior) # these are used to help paralleize over multiple cores / MPI models._global_instance = model_call log_likelihood_call = call_global_loglikelihood prior_call = call_global_logprior return log_likelihood_call, prior_call
[docs]class CubeModel(object): """ Class for making PyCBC Inference 'model class' Parameters ---------- model : inference.BaseModel instance A model instance from pycbc. """ def __init__(self, model, loglikelihood_function=None, copy_prior=False): if model.sampling_transforms is not None: raise ValueError("Ultranest or dynesty do not support sampling transforms") self.model = model if loglikelihood_function is None: loglikelihood_function = 'loglikelihood' self.loglikelihood_function = loglikelihood_function self.copy_prior = copy_prior
[docs] def log_likelihood(self, cube): """ returns log likelihood function """ params = dict(zip(self.model.sampling_params, cube)) self.model.update(**params) if self.model.logprior == -numpy.inf: return -numpy.inf return getattr(self.model, self.loglikelihood_function)
[docs] def prior_transform(self, cube): """ prior transform function for ultranest sampler It takes unit cube as input parameter and apply prior transforms """ if self.copy_prior: cube = cube.copy() # we preserve the type of cube to whatever we were given dict_cube = dict(zip(self.model.variable_params, cube)) inv = self.model.prior_distribution.cdfinv(**dict_cube) for i, param in enumerate(self.model.variable_params): cube[i] = inv[param] return cube