import bilby
import gwpopulation
import wcosmo
import numpy as np
from bilby_pipe.bilbyargparser import BilbyArgParser
from bilby_pipe.parser import StoreBoolean
from bilby_pipe.utils import noneint, nonestr
[docs]
def create_parser():
from ._version import __version__
parser = BilbyArgParser(
usage=__doc__, ignore_unknown_config_file_keys=False, allow_abbrev=False
)
parser.add("ini", type=str, is_config_file=True, help="Configuration ini file")
parser.add("-v", "--verbose", action="store_true", help="Verbose output")
parser.add(
"--version",
action="version",
version="%(prog)s={version}\nbilby={bilby_version}\ngwpopulation={gwpopulation_version}".format(
version=__version__,
bilby_version=bilby.__version__,
gwpopulation_version=gwpopulation.__version__,
),
)
base_parser = parser.add_argument_group(
title="Generic arguments", description="Generic arguments"
)
base_parser.add_argument(
"--run-dir",
type=str,
default="outdir",
help="Output directory for posterior samples",
)
base_parser.add_argument(
"--log-dir",
type=str,
default="logs",
help="Output directory for writing log files",
)
base_parser.add_argument("--label", type=str, default="label", help="Run label")
base_parser.add_argument("--user", type=str, help="User name", default=None)
base_parser.add_argument(
"--vt-file",
type=nonestr,
default=None,
help="File to load VT data from or a glob string matching multiple files to combine.",
)
base_parser.add_argument(
"--vt-ifar-threshold",
type=float,
default=1,
help="IFAR threshold for resampling injections",
)
base_parser.add_argument(
"--vt-snr-threshold",
type=float,
default=11,
help="IFAR threshold for resampling injections. "
"This is only used for O1/O2 injections",
)
base_parser.add_argument(
"--vt-function",
type=str,
default="injection_resampling_vt",
help="Function to generate selection function from.",
)
base_parser.add_argument(
"--prior-file",
type=str,
help="Prior file containing priors for all considered parameters",
)
base_parser.add_argument(
"--request-gpu",
default=0,
help="Whether to request a GPU for the relevant jobs.",
)
base_parser.add_argument(
"--require-gpus",
default='DeviceName=="GeForce GTX 1050 Ti"',
type=str,
help="The GPU requirements to pass for HTCondor.",
)
base_parser.add_argument(
"--backend",
default="jax",
choices=gwpopulation.backend.SUPPORTED_BACKENDS,
type=str,
help="The backend to use for the analysis, default is jax",
)
base_parser.add_argument(
"--cosmo",
action=StoreBoolean,
default=False,
help="Whether to fit cosmological parameters.",
)
base_parser.add_argument(
"--cosmology",
type=str,
default="Planck15_LAL",
help=(
"Cosmology to use for the analysis, this should be one of "
f"{', '.join(wcosmo.available.keys())}, Planck15_LAL. Some of these are "
"fixed pre-defined cosmologies while others are parameterized "
"cosmologies. If a parameterized cosmology is used the parameters relevant"
" parameters should be included in the prior specification."
),
)
base_parser.add_argument(
"--container",
type=nonestr,
default=None,
help="The path to the singularity image to use",
)
base_parser.add_argument(
"--conda-env", type=nonestr, default=None, help="The conda environment to use"
)
base_parser.add_argument(
"--pool",
type=str,
choices=["osg", "local"],
default="osg",
help="Which HTCondor pool to submit to, if osg, the local pool is also allowed",
)
model_parser = parser.add_argument_group(
title="Analysis models", description="Analysis models"
)
model_parser.add_argument(
"--all-models", type=str, help="All models to use, formatted as a json string"
)
model_parser.add_argument(
"--source-files",
action="append",
help=(
"Files containing source models to use for user-defined models. "
"These files are transferred to the execute node when using the "
"HTCondor file transfer mechanism. If the job is being run "
"locally the file should be in the users PYTHONPATH."
),
)
collection_parser = parser.add_argument_group(
title="Data collection arguments", description="Data collection arguments"
)
collection_parser.add_argument(
"--existing-data-directory",
type=str,
default="/fail",
help="Directory containing existing data",
)
collection_parser.add_argument(
"--parameters",
action="append",
help=(
"Parameters that are fit with the model. "
"These are the parameters that will be extracted from the posterior samples "
"and should follow Bilby naming conventions with the exception that all masses "
"are assumed to be in the source frame. Here is a list of parameters for which "
"prior factors will be properly accounted. "
"mass_1: source frame primary mass, mass_2: source frame secondary mass, "
"mass_1_detector: detector frame primary mass, mass_2_detector: detector frame secondary mass, "
"chirp_mass: source frame chirp mass, chirp_mass_detector: detector frame chirp mass,"
"mass_ratio: mass ratio, redshift: redshift, luminosity_distance: luminosity distance,"
"a_1: primary spin magnitude, a_2: secondary spin magnitude, cos_tilt_1: "
"cosine primary spin tilt, cos_tilt_2: cosine secondary spin tilt, "
"chi_1: aligned primary spin, chi_2: aligned secondary spin."
"Any other parameters will be assumed to have a flat prior."
"These parameters are also used to set the fiducial prior values. "
"No redundancy checks are performed so users should be careful to not "
"include unused parameters as that may have unintended consequences."
),
)
collection_parser.add_argument(
"--ignore", action="append", help="Events to ignore."
)
collection_parser.add_argument(
"--sample-regex", type=str, help="Pattern to match for sample files"
)
collection_parser.add_argument(
"--preferred-labels",
action="append",
help="Run labels to search for in sample files",
)
collection_parser.add_argument(
"--plot",
default=True,
action=StoreBoolean,
help="Whether to generate diagnostic plots",
)
collection_parser.add_argument(
"--n-simulations", type=noneint, help="Number of posteriors to simulate"
)
collection_parser.add_argument(
"--samples-per-posterior",
type=int,
default=int(1e6),
help="Number of samples per posterior. If larger than the number of samples in the shortest posterior dataset, will ignore this input.",
)
collection_parser.add_argument(
"--collection-seed",
type=noneint,
help="Seed for the downsampling of the posteriors for each event. For reproducibility.",
)
collection_parser.add_argument(
"--data-label", default="posteriors", help="Label for data product."
)
collection_parser.add_argument(
"--distance-prior",
default="comoving",
type=str,
help=(
"Distance prior format, e.g., euclidean, comoving. 'euclidean' assumes the distance prior goes "
"like D^2. 'comoving' assumes sources are uniformly distributed in the comoving frame using "
"the Planck15_LAL cosmology. Can be in the format of a dict with the same keys as the sample-regex"
),
)
collection_parser.add_argument(
"--mass-prior",
default="flat-detector",
type=str,
help=(
"Mass prior used during the initial sampling, must match one of the following options. "
"\n 'flat-detector': Flat in detector frame primary and secondary masses. "
"\n 'chirp-mass': Flat in detector frame chirp mass and mass ratio. "
"\n 'flat-detector-components': Flat in detector frame primary and secondary masses. "
"This is the default for LVK samples and the same as the deprecated 'flat-detector' option. "
"\n 'flat-source-components': Flat in source frame primary and secondary masses. "
"\n 'flat-detector-chirp-mass-ratio': Flat in detector frame chirp mass and mass ratio. "
"This is the same as the deprecated 'chirp-mass' option. Can be in the format of a dict with the same keys as the sample-regex"
),
)
collection_parser.add_argument(
"--spin-prior",
default="component",
type=str,
help=(
"Spin prior, the only supported spin prior assumes the spins are isotropically distributed "
"with a flat prior on the magnitude. Can be in the format of a dict with the same keys as the sample-regex."
),
)
analysis_parser = parser.add_argument_group(
title="Arguments describing analysis jobs", description="Analysis arguments"
)
analysis_parser.add_argument(
"--max-redshift",
default=2.3,
type=float,
help="The maximum redshift considered, this should match the injections.",
)
analysis_parser.add_argument(
"--minimum-mass",
default=2,
type=float,
help="The minimum mass considered, this should match the injections "
"and is important for smoothed mass models.",
)
analysis_parser.add_argument(
"--maximum-mass",
default=100,
type=float,
help="The maximum mass considered, this should match the injections "
"and is important for smoothed mass models.",
)
analysis_parser.add_argument(
"--sampler",
default="dynesty",
type=str,
help="The sampler to use, the default is dynesty",
)
analysis_parser.add_argument(
"--sampler-kwargs",
type=str,
default="Default",
help=(
"Dictionary of sampler-kwargs to pass in, e.g., {nlive: 1000} OR "
"pass pre-defined set of sampler-kwargs {Default, FastTest}"
),
)
analysis_parser.add_argument(
"--vt-parameters",
action="append",
help=(
"Which parameters to include in the VT estimate, should be some "
"combination of mass, redshift, spin parameters, see the '--parameters' "
"option for more details."
),
)
analysis_parser.add_argument(
"--enforce-minimum-neffective-per-event",
action=StoreBoolean,
default=True,
help=(
"Require that all Monte Carlo integrals for the single event "
"marignalizaed likleihoods have at least as many effective samples"
" as the number of events."
),
)
analysis_parser.add_argument(
"--maximum-uncertainty",
type=float,
default=np.inf,
help=(
"Require that all log likelihood evaluations have an uncertainty (standard deviation) less than"
" this value. It is not recommended to use with the '--enforce-minimum-neffective-per-event' option."
"See Talbot and Golomb (2023) arxiv:2304.06138"
),
)
injection_parser = parser.add_argument_group(
title="Arguments describing injections", description="Injection arguments"
)
injection_parser.add_argument(
"--injection-file",
default=None,
type=nonestr,
help="JSON file containing population parameters, should be pandas readable.",
)
injection_parser.add_argument(
"--injection-index", type=noneint, help="Index in injection file to use."
)
injection_parser.add_argument(
"--sample-from-prior",
action=StoreBoolean,
help="Simulate posteriors from prior.",
)
post_parser = parser.add_argument_group(
title="Post processing arguments", description="Post arguments"
)
post_parser.add_argument(
"--post-plots",
action=StoreBoolean,
default=True,
help="Whether to make post-processing plots.",
)
post_parser.add_argument(
"--make-summary",
action=StoreBoolean,
default=True,
help="Whether to make a summary page.",
)
post_parser.add_argument(
"--n-post-samples",
default=5000,
type=int,
help="Number of samples to use in the common format script",
)
return parser