Commit c04a7e97 authored by Gregory Ashton's avatar Gregory Ashton

Merge branch 'add-zero-likelihood-mode' into 'master'

Add a zero-likelihood mode

See merge request lscsoft/bilby!542
parents 16ff390a bfe8dffc
Pipeline #68402 passed with stages
in 15 minutes and 18 seconds
......@@ -85,6 +85,7 @@ scheduled-python-3.7:
# Run tests which are only done on schedule
- pytest test/example_test.py
- pytest test/gw_example_test.py
- pytest test/sample_from_the_prior_test.py
pages:
stage: deploy
......
......@@ -62,6 +62,31 @@ class Likelihood(object):
raise ValueError("The meta_data must be an instance of dict")
class ZeroLikelihood(Likelihood):
""" A special test-only class which already returns zero likelihood
Parameters
----------
likelihood: bilby.core.likelihood.Likelihood
A likelihood object to mimic
"""
def __init__(self, likelihood):
Likelihood.__init__(self, dict.fromkeys(likelihood.parameters))
self.parameters = likelihood.parameters
self._parent = likelihood
def log_likelihood(self):
return 0
def noise_log_likelihood(self):
return 0
def __getattr__(self, name):
return getattr(self._parent, name)
class Analytical1DLikelihood(Likelihood):
"""
A general class for 1D analytical functions. The model
......
......@@ -133,6 +133,11 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
meta_data = dict()
meta_data['likelihood'] = likelihood.meta_data
if command_line_args.bilby_zero_likelihood_mode:
from bilby.core.likelihood import ZeroLikelihood
likelihood = ZeroLikelihood(likelihood)
if isinstance(sampler, Sampler):
pass
elif isinstance(sampler, str):
......
......@@ -539,6 +539,9 @@ def set_up_command_line_arguments():
parser.add_argument("--bilby-test-mode", action="store_true",
help=("Used for testing only: don't run full PE, but"
" just check nothing breaks"))
parser.add_argument("--bilby-zero-likelihood-mode", action="store_true",
help=("Used for testing only: don't run full PE, but"
" just check nothing breaks"))
args, unknown_args = parser.parse_known_args()
if args.quiet:
args.log_level = logging.WARNING
......
......@@ -8,6 +8,7 @@ addopts =
--ignore test/other_test.py
--ignore test/gw_example_test.py
--ignore test/example_test.py
--ignore test/sample_from_the_prior_test.py
[metadata]
license_file = LICENSE.md
from __future__ import absolute_import
import shutil
import os
import logging
import unittest
import numpy as np
import bilby
from scipy.stats import ks_2samp, kstest
class Test(unittest.TestCase):
outdir = 'outdir_for_tests'
@classmethod
def setUpClass(self):
if os.path.isdir(self.outdir):
try:
shutil.rmtree(self.outdir)
except OSError:
logging.warning(
"{} not removed prior to tests".format(self.outdir))
@classmethod
def tearDownClass(self):
if os.path.isdir(self.outdir):
try:
shutil.rmtree(self.outdir)
except OSError:
logging.warning(
"{} not removed prior to tests".format(self.outdir))
def test_fifteen_dimensional_cbc(self):
duration = 4.
sampling_frequency = 2048.
label = 'full_15_parameters'
np.random.seed(88170235)
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
reference_frequency=50., minimum_frequency=20.)
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
waveform_arguments=waveform_arguments)
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
start_time=0)
priors = bilby.gw.prior.BBHPriorDict()
priors.pop('mass_1')
priors.pop('mass_2')
priors['chirp_mass'] = bilby.prior.Uniform(
name='chirp_mass', latex_label='$M$', minimum=10.0, maximum=100.0,
unit='$M_{\\odot}$')
priors['mass_ratio'] = bilby.prior.Uniform(
name='mass_ratio', latex_label='$q$', minimum=0.5, maximum=1.0)
priors['geocent_time'] = bilby.core.prior.Uniform(
minimum=-0.1, maximum=0.1)
likelihood = bilby.gw.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator,
priors=priors, distance_marginalization=False,
phase_marginalization=False, time_marginalization=False)
likelihood = bilby.core.likelihood.ZeroLikelihood(likelihood)
result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty',
npoints=1000, walks=100, outdir=self.outdir, label=label)
pvalues = [ks_2samp(result.priors[key].sample(10000),
result.posterior[key].values).pvalue
for key in priors.keys()]
print("P values per parameter")
for key, p in zip(priors.keys(), pvalues):
print(key, p)
self.assertGreater(kstest(pvalues, "uniform").pvalue, 0.01)
if __name__ == '__main__':
unittest.main()
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