Source code for pycbc.distributions.uniform_log

# Copyright (C) 2017  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 classes for evaluating distributions whose logarithm
are uniform.
"""
import logging
import numpy

from pycbc.distributions import uniform

logger = logging.getLogger('pycbc.distributions.uniform_log')


[docs]class UniformLog10(uniform.Uniform): """ A uniform distribution on the log base 10 of the given parameters. The parameters are independent of each other. Instances of this class can be called like a function. By default, logpdf will be called. Parameters ---------- \**params : The keyword arguments should provide the names of parameters and their corresponding bounds, as either tuples or a `boundaries.Bounds` instance. """ name = "uniform_log10" def __init__(self, **params): super(UniformLog10, self).__init__(**params) self._norm = numpy.prod([numpy.log10(bnd[1]) - numpy.log10(bnd[0]) for bnd in self._bounds.values()]) self._lognorm = numpy.log(self._norm) def _cdfinv_param(self, param, value): """Return the cdfinv for a single given parameter """ lower_bound = numpy.log10(self._bounds[param][0]) upper_bound = numpy.log10(self._bounds[param][1]) return 10. ** ((upper_bound - lower_bound) * value + lower_bound) def _pdf(self, **kwargs): """Returns the pdf at the given values. The keyword arguments must contain all of parameters in self's params. Unrecognized arguments are ignored. """ if kwargs in self: vals = numpy.array([numpy.log(10) * self._norm * kwargs[param] for param in kwargs.keys()]) return 1.0 / numpy.prod(vals) else: return 0. def _logpdf(self, **kwargs): """Returns the log of the pdf at the given values. The keyword arguments must contain all of parameters in self's params. Unrecognized arguments are ignored. """ if kwargs in self: return numpy.log(self._pdf(**kwargs)) else: return -numpy.inf
__all__ = ["UniformLog10"]