Source code for pycbc.inference.sampler.dummy

""" Dummy class when no actual sampling is needed, but we may want to do
some reconstruction supported by the likelihood model.

import numpy

from import PosteriorFile
from pycbc.inference import models
from pycbc.pool import choose_pool

from .base import (BaseSampler, setup_output)

[docs]def call_reconstruct(iteration): """ Accessor to update the global model and call its reconstruction routine. """ models._global_instance.update() return models._global_instance.reconstruct(seed=iteration)
[docs]class DummySampler(BaseSampler): """Dummy sampler for not doing sampling Parameters ---------- model : Model An instance of a model from ``pycbc.inference.models``. """ name = 'dummy' def __init__(self, model, *args, nprocesses=1, use_mpi=False, num_samples=1000, **kwargs): super().__init__(model, *args) models._global_instance = model self.num_samples = int(num_samples) self.pool = choose_pool(mpi=use_mpi, processes=nprocesses) self._samples = {}
[docs] @classmethod def from_config(cls, cp, model, output_file=None, nprocesses=1, use_mpi=False): """This should initialize the sampler given a config file. """ kwargs = {k: cp.get('sampler', k) for k in cp.options('sampler')} obj = cls(model, nprocesses=nprocesses, use_mpi=use_mpi, **kwargs) setup_output(obj, output_file, check_nsamples=False, validate=False) return obj
@property def samples(self): """A dict mapping variable_params to arrays of samples currently in memory. The dictionary may also contain sampling_params. The sample arrays may have any shape, and may or may not be thinned. """ return self._samples @property def model_stats(self): pass
[docs] def run(self): samples =, range(self.num_samples)) self._samples = {k: numpy.array([x[k] for x in samples]) for k in samples[0]}
[docs] def finalize(self): with, "a") as fp: fp.write_samples(samples=self._samples)
checkpoint = resume_from_checkpoint = run @property def io(self): """A class that inherits from ``BaseInferenceFile`` to handle IO with an hdf file. This should be a class, not an instance of class, so that the sampler can initialize it when needed. """ return PosteriorFile