# Copyright (C) 2021 Shichao Wu
# 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
#
# =============================================================================
#
"""
This module provides functions for drawing samples from a standalone .ini file
in a Python script, rather than in the command line.
"""
import logging
import numpy as np
from pycbc.types.config import InterpolatingConfigParser
from pycbc import transforms
from pycbc import distributions
logger = logging.getLogger('pycbc.distributions.utils')
[docs]def prior_from_config(cp, prior_section='prior'):
"""Loads a prior distribution from the given config file.
Parameters
----------
cp : pycbc.workflow.WorkflowConfigParser
The config file to read.
sections : list of str, optional
The sections to retrieve the prior from. If ``None`` (the default),
will look in sections starting with 'prior'.
Returns
-------
distributions.JointDistribution
The prior distribution.
"""
# Read variable and static parameters from the config file
variable_params, static_params = distributions.read_params_from_config(
cp, prior_section=prior_section, vargs_section='variable_params',
sargs_section='static_params')
# Read waveform_transforms to apply to priors from the config file
if any(cp.get_subsections('waveform_transforms')):
waveform_transforms = transforms.read_transforms_from_config(
cp, 'waveform_transforms')
else:
waveform_transforms = None
# Read constraints to apply to priors from the config file
constraints = distributions.read_constraints_from_config(
cp, transforms=waveform_transforms, static_args=static_params)
# Get PyCBC distribution instances for each variable parameter in the
# config file
dists = distributions.read_distributions_from_config(cp, prior_section)
# construct class that will return draws from the prior
return distributions.JointDistribution(variable_params, *dists,
**{"constraints": constraints})
[docs]def draw_samples_from_config(path, num=1, seed=150914):
r""" Generate sampling points from a standalone .ini file.
Parameters
----------
path : str
The path to the .ini file.
num : int
The number of samples.
seed: int
The random seed for sampling.
Returns
--------
samples : pycbc.io.record.FieldArray
The parameter values and names of sample(s).
Examples
--------
Draw a sample from the distribution defined in the .ini file:
>>> import numpy as np
>>> from pycbc.distributions.utils import draw_samples_from_config
>>> # A path to the .ini file.
>>> CONFIG_PATH = "./pycbc_bbh_prior.ini"
>>> random_seed = np.random.randint(low=0, high=2**32-1)
>>> sample = draw_samples_from_config(
>>> path=CONFIG_PATH, num=1, seed=random_seed)
>>> # Print all parameters.
>>> print(sample.fieldnames)
>>> print(sample)
>>> # Print a certain parameter, for example 'mass1'.
>>> print(sample[0]['mass1'])
"""
np.random.seed(seed)
# Initialise InterpolatingConfigParser class.
config_parser = InterpolatingConfigParser()
# Read the file
file = open(path, 'r')
config_parser.read_file(file)
file.close()
# Construct class that will draw the samples.
prior_dists = prior_from_config(cp=config_parser)
# Draw samples from prior distribution.
samples = prior_dists.rvs(size=int(num))
# Apply parameter transformation.
if any(config_parser.get_subsections('waveform_transforms')):
waveform_transforms = transforms.read_transforms_from_config(
config_parser, 'waveform_transforms')
samples = transforms.apply_transforms(samples, waveform_transforms)
return samples