Source code for pycbc.distributions.uniform

# Copyright (C) 2016  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.
This modules provides classes for evaluating uniform distributions.

import numpy
from pycbc.distributions import bounded

[docs]class Uniform(bounded.BoundedDist): """ A uniform distribution on 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, but this can be changed by setting the class's __call__ method to its pdf method. Parameters ---------- \**params : The keyword arguments should provide the names of parameters and their corresponding bounds, as either tuples or a `boundaries.Bounds` instance. Attributes ---------- name : 'uniform' The name of this distribution. Attributes ---------- params : list of strings The list of parameter names. bounds : dict A dictionary of the parameter names and their bounds. norm : float The normalization of the multi-dimensional pdf. lognorm : float The log of the normalization. Examples -------- Create a 2 dimensional uniform distribution: >>> dist = prior.Uniform(mass1=(10.,50.), mass2=(10.,50.)) Get the log of the pdf at a particular value: >>> dist.logpdf(mass1=25., mass2=10.) -7.3777589082278725 Do the same by calling the distribution: >>> dist(mass1=25., mass2=10.) -7.3777589082278725 Generate some random values: >>> dist.rvs(size=3) array([(36.90885758394699, 51.294212757995254), (39.109058546060346, 13.36220145743631), (34.49594465315212, 47.531953033719454)], dtype=[('mass1', '<f8'), ('mass2', '<f8')]) Initialize a uniform distribution using a boundaries.Bounds instance, with cyclic bounds: >>> dist = distributions.Uniform(phi=Bounds(10, 50, cyclic=True)) Apply boundary conditions to a value: >>> dist.apply_boundary_conditions(phi=60.) {'mass1': array(20.0)} The boundary conditions are applied to the value before evaluating the pdf; note that the following returns a non-zero pdf. If the bounds were not cyclic, the following would return 0: >>> dist.pdf(phi=60.) 0.025 """ name = 'uniform' def __init__(self, **params): super(Uniform, self).__init__(**params) # compute the norm and save # temporarily suppress numpy divide by 0 warning numpy.seterr(divide='ignore') self._lognorm = -sum([numpy.log(abs(bnd[1]-bnd[0])) for bnd in self._bounds.values()]) self._norm = numpy.exp(self._lognorm) numpy.seterr(divide='warn') @property def norm(self): return self._norm @property def lognorm(self): return self._lognorm def _cdfinv_param(self, param, value): """Return the inverse cdf to map the unit interval to parameter bounds. """ lower_bound = self._bounds[param][0] upper_bound = self._bounds[param][1] return (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: return self._norm 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 self._lognorm else: return -numpy.inf
[docs] def rvs(self, size=1, param=None): """Gives a set of random values drawn from this distribution. Parameters ---------- size : {1, int} The number of values to generate; default is 1. param : {None, string} If provided, will just return values for the given parameter. Otherwise, returns random values for each parameter. Returns ------- structured array The random values in a numpy structured array. If a param was specified, the array will only have an element corresponding to the given parameter. Otherwise, the array will have an element for each parameter in self's params. """ if param is not None: dtype = [(param, float)] else: dtype = [(p, float) for p in self.params] arr = numpy.zeros(size, dtype=dtype) for (p,_) in dtype: arr[p] = numpy.random.uniform(self._bounds[p][0], self._bounds[p][1], size=size) return arr
[docs] @classmethod def from_config(cls, cp, section, variable_args): """Returns a distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by ``VARARGS_DELIM``. These must appear in the "tag" part of the section header. Returns ------- Uniform A distribution instance from the pycbc.inference.prior module. """ return super(Uniform, cls).from_config(cp, section, variable_args, bounds_required=True)
__all__ = ['Uniform']