Commit 503da2dc authored by Colm Talbot's avatar Colm Talbot

Merge branch 'master' into change_sampled_parameters

parents 84eaf326 82db951c
......@@ -9,15 +9,21 @@ from __future__ import division, print_function
import tupak
import numpy as np
# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
time_duration = 4.
sampling_frequency = 2048.
# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'basic_tutorial'
tupak.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
# We are going to inject a binary black hole waveform. We first establish a dictionary of parameters that
# includes all of the different waveform parameters, including masses of the two black holes (mass_1, mass_2),
# spins of both black holes (a, tilt, phi), etc.
injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
......@@ -29,24 +35,33 @@ waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=ti
hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
# Set up interferometers. In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
# and Virgo (V1)). These default to their design sensitivity
IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,
sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
# Set up prior
# Set up prior, which is a dictionary
priors = dict()
# These parameters will not be sampled
for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'iota', 'ra', 'dec', 'geocent_time']:
# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
# not sample any parameter that has a delta-function prior.
for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
# The above list does *not* include mass_1, mass_2, iota and luminosity_distance, which means those are the parameters
# that will be included in the sampler. If we do nothing, then the default priors get used.
priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
# Initialise Likelihood
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator)
# Run sampler
# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
injection_parameters=injection_parameters, outdir=outdir, label=label)
# make some plots of the outputs
......@@ -18,13 +18,26 @@ def result_file_name(outdir, label):
return '{}/{}_result.h5'.format(outdir, label)
def read_in_result(outdir, label):
""" Read in a saved .h5 data file """
filename = result_file_name(outdir, label)
def read_in_result(outdir=None, label=None, filename=None):
""" Read in a saved .h5 data file
outdir, label: str
If given, use the default naming convention for saved results file
filename: str
If given, try to load from this filename
result: tupak.result.Result instance
if filename is None:
filename = result_file_name(outdir, label)
if os.path.isfile(filename):
return Result(
return None
raise ValueError("No information given to load file")
class Result(dict):
......@@ -205,7 +205,12 @@ class Sampler(object):
logging.debug("Command line argument clean given, forcing rerun")
self.cached_result = None
self.cached_result = read_in_result(self.outdir, self.label)
self.cached_result = read_in_result(self.outdir, self.label)
except ValueError:
self.cached_result = None
if utils.command_line_args.use_cached:
logging.debug("Command line argument cached given, no cache check performed")
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