Source code for pycbc.inference.models.base_data

# Copyright (C) 2018  Collin Capano
# 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
# =============================================================================

"""Base classes for models with data.

import numpy
from abc import (ABCMeta, abstractmethod)

from six import add_metaclass

from .base import BaseModel

[docs]@add_metaclass(ABCMeta) class BaseDataModel(BaseModel): r"""Base class for models that require data and a waveform generator. This adds propeties for the log of the likelihood that the data contain noise, ``lognl``, and the log likelihood ratio ``loglr``. Classes that inherit from this class must define ``_loglr`` and ``_lognl`` functions, in addition to the ``_loglikelihood`` requirement inherited from ``BaseModel``. Parameters ---------- variable_params : (tuple of) string(s) A tuple of parameter names that will be varied. data : dict A dictionary of data, in which the keys are the detector names and the values are the data. recalibration : dict of pycbc.calibration.Recalibrate, optional Dictionary of detectors -> recalibration class instances for recalibrating data. gates : dict of tuples, optional Dictionary of detectors -> tuples of specifying gate times. The sort of thing returned by `pycbc.gate.gates_from_cli`. injection_file : str, optional If an injection was added to the data, the name of the injection file used. If provided, the injection parameters will be written to file when ``write_metadata`` is called. \**kwargs : All other keyword arguments are passed to ``BaseModel``. Attributes ---------- data : dict The data that the class was initialized with. detectors : list List of detector names used. lognl : Returns the log likelihood of the noise. loglr : Returns the log of the likelihood ratio. logplr : Returns the log of the prior-weighted likelihood ratio. See ``BaseModel`` for additional attributes and properties. """ def __init__(self, variable_params, data, recalibration=None, gates=None, injection_file=None, **kwargs): self._data = None = data self.recalibration = recalibration self.gates = gates self.injection_file = injection_file super(BaseDataModel, self).__init__(variable_params, **kwargs) @property def data(self): """Dictionary mapping detector names to data.""" return self._data @data.setter def data(self, data): """Store a copy of the data.""" self._data = {det: d.copy() for (det, d) in data.items()} @property def _extra_stats(self): """Adds ``loglr`` and ``lognl`` to the ``default_stats``.""" return ['loglr', 'lognl'] @property def lognl(self): """The log likelihood of the model assuming the data is noise. This will initially try to return the ``current_stats.lognl``. If that raises an ``AttributeError``, will call `_lognl`` to calculate it and store it to ``current_stats``. """ return self._trytoget('lognl', self._lognl) @abstractmethod def _lognl(self): """Low-level function that calculates the lognl.""" pass @property def loglr(self): """The log likelihood ratio at the current parameters. This will initially try to return the ``current_stats.loglr``. If that raises an ``AttributeError``, will call `_loglr`` to calculate it and store it to ``current_stats``. """ return self._trytoget('loglr', self._loglr, apply_transforms=True) @abstractmethod def _loglr(self): """Low-level function that calculates the loglr.""" pass @property def logplr(self): """Returns the log of the prior-weighted likelihood ratio at the current parameter values. The logprior is calculated first. If the logprior returns ``-inf`` (possibly indicating a non-physical point), then ``loglr`` is not called. """ logp = self.logprior if logp == -numpy.inf: return logp else: return logp + self.loglr @property def detectors(self): """Returns the detectors used.""" return list(self._data.keys())
[docs] def write_metadata(self, fp): """Adds data to the metadata that's written. Parameters ---------- fp : instance The inference file to write to. """ super(BaseDataModel, self).write_metadata(fp) fp.write_stilde( # save injection parameters if self.injection_file is not None: fp.write_injections(self.injection_file)