# 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
# 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
#
# =============================================================================
#
"""Base classes for mofdels with data.
"""
import numpy
from abc import (ABCMeta, abstractmethod)
from .base import BaseModel
[docs]class BaseDataModel(BaseModel, metaclass=ABCMeta):
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``.
See ``BaseModel`` for additional attributes and properties.
"""
def __init__(self, variable_params, data, recalibration=None, gates=None,
injection_file=None, no_save_data=False, **kwargs):
self._data = None
self.data = data
self.recalibration = recalibration
self.no_save_data = no_save_data
self.gates = gates
self.injection_file = injection_file
super(BaseDataModel, self).__init__(variable_params, **kwargs)
@property
def data(self):
"""dict: 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,
or the inner product <s|h> and <h|h> if set the flag
`self.return_sh_hh` to be True.
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):
"""list: Returns the detectors used."""
return list(self._data.keys())