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, _ = distributions.read_params_from_config(
cp, prior_section=prior_section, vargs_section='variable_params',
sargs_section='static_params')
# Read constraints to apply to priors from the config file
constraints = distributions.read_constraints_from_config(cp)
# 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})
def _init_args_from_config(cls, cp):
"""Helper function for loading parameters."""
section = "model"
prior_section = "prior"
vparams_section = 'variable_params'
sparams_section = 'static_params'
constraint_section = 'constraint'
# check that the name exists and matches
name = cp.get(section, 'name')
if name != cls.name:
raise ValueError("section's {} name does not match mine {}".format(
name, cls.name))
# get model parameters
variable_params, static_params = distributions.read_params_from_config(
cp,
prior_section=prior_section,
vargs_section=vparams_section,
sargs_section=sparams_section)
# get prior
prior = cls.prior_from_config(cp, variable_params, prior_section,
constraint_section)
args = {
'variable_params': variable_params,
'static_params': static_params,
'prior': prior
}
# get any other keyword arguments provided
args.update(cls.extra_args_from_config(cp, section,
skip_args=['name']))
return args
def __init__(self,
config_file,
random_seed):
# Fix the seed for the random number generator
np.random.seed(random_seed)
# Read in the configuration file using a WorkflowConfigParser.
# Note that the argument `configFiles` has to be a list here,
# so we need to wrap the `config_file` argument accordingly...
config_file = WorkflowConfigParser(configFiles=[config_file])
# Extract variable arguments and constraints
# We don't need the static_args here, hence they do not get amended.
self.var_args, _ = read_params_from_config(config_file)
self.constraints = read_constraints_from_config(config_file)
# Extract distributions
dist = read_distributions_from_config(config_file)
# Extract transformations
self.trans = read_transforms_from_config(config_file)
# Set up a joint distribution to sample from
self.pval = JointDistribution(self.var_args,
*dist,
**{'constraints': self.constraints})
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, _ = distributions.read_params_from_config(
cp,
prior_section=prior_section,
vargs_section='variable_params',
sargs_section='static_params')
# Read constraints to apply to priors from the config file
constraints = distributions.read_constraints_from_config(cp)
# 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})
def setUp(self):
# set random seed
numpy.random.seed(1024)
# path to example configuration file for testing
config_path = "/".join([os.path.dirname(os.path.realpath(__file__)),
"../examples/distributions/example.ini"])
# get a set of simulated command line options for
# configuration file reading
class Arguments(object):
config_overrides = []
config_delete = []
config_files = [config_path]
self.opts = Arguments()
# read configuration files
self.cp = WorkflowConfigParser.from_cli(self.opts)
self.variable_args, self.static_args = \
distributions.read_params_from_config(self.cp)
self.constraints = distributions.read_constraints_from_config(self.cp)
# read distributions
self.dists = distributions.read_distributions_from_config(self.cp)
# check that all distriubtions will be tested
for dname in distributions.distribs:
dclass = distributions.distribs[dname]
if (not numpy.any([isinstance(dist, dclass)
for dist in self.dists])
and dname not in EXCLUDE_DIST_NAMES):
raise ValueError("There is no test for {}".format(dname))
def __init__(
self,
config_files: Union[List[Union[str, os.PathLike]], Union[str,
os.PathLike]],
seed: Optional[int] = None,
):
"""Class to generate gravitational waveform parameters using PyCBC workflow and distribution packages.
"""
if seed is not None:
raise NotImplementedError(
"Reproducible random seed not yet implemented.")
self.config_files = config_files if isinstance(
config_files, list) else [config_files]
self.config_parser = WorkflowConfigParser(
configFiles=self.config_files)
self.parameters, self.static_args = read_params_from_config(
self.config_parser)
self.constraints = read_constraints_from_config(self.config_parser)
self.transforms = read_transforms_from_config(self.config_parser)
self.distribution = JointDistribution(
self.parameters,
*read_distributions_from_config(self.config_parser),
**{'constraints': self.constraints})
# ensure statistics match output of self.parameters
self.statistics = compute_parameter_statistics({
parameter: self.distribution.bounds[parameter]
for parameter in self.parameters
})
def _init_args_from_config(cls, cp):
"""Helper function for loading parameters.
This retrieves the prior, variable parameters, static parameterss,
constraints, sampling transforms, and waveform transforms
(if provided).
Parameters
----------
cp : ConfigParser
Config parser to read.
Returns
-------
dict :
Dictionary of the arguments. Has keys ``variable_params``,
``static_params``, ``prior``, and ``sampling_transforms``. If
waveform transforms are in the config file, will also have
``waveform_transforms``.
"""
section = "model"
prior_section = "prior"
vparams_section = 'variable_params'
sparams_section = 'static_params'
constraint_section = 'constraint'
# check that the name exists and matches
name = cp.get(section, 'name')
if name != cls.name:
raise ValueError("section's {} name does not match mine {}".format(
name, cls.name))
# get model parameters
variable_params, static_params = distributions.read_params_from_config(
cp,
prior_section=prior_section,
vargs_section=vparams_section,
sargs_section=sparams_section)
# get prior
prior = cls.prior_from_config(cp, variable_params, prior_section,
constraint_section)
args = {
'variable_params': variable_params,
'static_params': static_params,
'prior': prior
}
# try to load sampling transforms
try:
sampling_transforms = SamplingTransforms.from_config(
cp, variable_params)
except NoSectionError:
sampling_transforms = None
args['sampling_transforms'] = sampling_transforms
# get any waveform transforms
if any(cp.get_subsections('waveform_transforms')):
logging.info("Loading waveform transforms")
args['waveform_transforms'] = \
transforms.read_transforms_from_config(
cp, 'waveform_transforms')
return args
def _init_args_from_config(cls, cp):
"""Helper function for loading parameters.
This retrieves the prior, variable parameters, static parameterss,
constraints, sampling transforms, and waveform transforms
(if provided).
Parameters
----------
cp : ConfigParser
Config parser to read.
Returns
-------
dict :
Dictionary of the arguments. Has keys ``variable_params``,
``static_params``, ``prior``, and ``sampling_transforms``. If
waveform transforms are in the config file, will also have
``waveform_transforms``.
"""
section = "model"
prior_section = "prior"
vparams_section = 'variable_params'
sparams_section = 'static_params'
constraint_section = 'constraint'
# check that the name exists and matches
name = cp.get(section, 'name')
if name != cls.name:
raise ValueError("section's {} name does not match mine {}".format(
name, cls.name))
# get model parameters
variable_params, static_params = distributions.read_params_from_config(
cp, prior_section=prior_section, vargs_section=vparams_section,
sargs_section=sparams_section)
# get prior
prior = cls.prior_from_config(cp, variable_params, prior_section,
constraint_section)
args = {'variable_params': variable_params,
'static_params': static_params,
'prior': prior}
# try to load sampling transforms
try:
sampling_transforms = SamplingTransforms.from_config(
cp, variable_params)
except ValueError:
sampling_transforms = None
args['sampling_transforms'] = sampling_transforms
# get any waveform transforms
if any(cp.get_subsections('waveform_transforms')):
logging.info("Loading waveform transforms")
args['waveform_transforms'] = \
transforms.read_transforms_from_config(
cp, 'waveform_transforms')
return args
def _init_args_from_config(cls, cp):
"""Helper function for loading parameters.
This retrieves the prior, variable parameters, static parameterss,
constraints, and sampling transforms.
Parameters
----------
cp : ConfigParser
Config parser to read.
Returns
-------
dict :
Dictionary of the arguments. Has keys ``variable_params``,
``static_params``, ``prior``, and ``sampling_transforms``.
"""
section = "model"
prior_section = "prior"
vparams_section = 'variable_params'
sparams_section = 'static_params'
constraint_section = 'constraint'
# check that the name exists and matches
name = cp.get(section, 'name')
if name != cls.name:
raise ValueError("section's {} name does not match mine {}".format(
name, cls.name))
# get model parameters
variable_params, static_params = distributions.read_params_from_config(
cp,
prior_section=prior_section,
vargs_section=vparams_section,
sargs_section=sparams_section)
# get prior
prior = cls.prior_from_config(cp, variable_params, prior_section,
constraint_section)
args = {
'variable_params': variable_params,
'static_params': static_params,
'prior': prior
}
# try to load sampling transforms
try:
sampling_transforms = SamplingTransforms.from_config(
cp, variable_params)
except ValueError:
sampling_transforms = None
args['sampling_transforms'] = sampling_transforms
# get any other keyword arguments provided
args.update(cls.extra_args_from_config(cp, section,
skip_args=['name']))
return args
def read_ini_config(file_path):
"""
Read in a `*.ini` config file, which is used mostly to specify the
waveform simulation (for example, the waveform model, the parameter
space for the binary black holes, etc.) and return its contents.
Args:
file_path (str): Path to the `*.ini` config file to be read in.
Returns:
A tuple `(variable_arguments, static_arguments)` where
* `variable_arguments` should simply be a list of all the
parameters which get randomly sampled from the specified
distributions, usually using an instance of
:class:`utils.waveforms.WaveformParameterGenerator`.
* `static_arguments` should be a dictionary containing the keys
and values of the parameters that are the same for each
example that is generated (i.e., the non-physical parameters
such as the waveform model and the sampling rate).
"""
# Make sure the config file actually exists
if not os.path.exists(file_path):
raise IOError('Specified configuration file does not exist: '
'{}'.format(file_path))
# Set up a parser for the PyCBC config file
workflow_config_parser = WorkflowConfigParser(configFiles=[file_path])
# Read the variable_arguments and static_arguments using the parser
variable_arguments, static_arguments = \
read_params_from_config(workflow_config_parser)
# Typecast and amend the static arguments
static_arguments = typecast_static_args(static_arguments)
static_arguments = amend_static_args(static_arguments)
return variable_arguments, static_arguments
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()
# Get the vairable arguments from the .ini file.
variable_args, _ = distributions.read_params_from_config(
config_parser, prior_section='prior', vargs_section='variable_params')
constraints = distributions.read_constraints_from_config(config_parser)
if any(config_parser.get_subsections('waveform_transforms')):
waveform_transforms = transforms.read_transforms_from_config(
config_parser, 'waveform_transforms')
else:
waveform_transforms = None
# Get prior distribution for each variable parameter.
dists = distributions.read_distributions_from_config(config_parser)
# Construct class that will draw the samples.
randomsampler = distributions.JointDistribution(
variable_args, *dists, **{"constraints": constraints})
# Draw samples from prior distribution.
samples = randomsampler.rvs(size=int(num))
# Apply parameter transformation.
if waveform_transforms is not None:
samples = transforms.apply_transforms(samples, waveform_transforms)
else:
pass
return samples
