diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 085cc2af9cfeaf85f7db5111e97de84ac7289a9d..2219b72ceea3da845f0815b94bab714810f9d4d5 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,15 +23,15 @@ exitcode-jessie:
- pip install coverage-badge
- python setup.py install
- coverage erase
- - coverage run --include=tupak/* -a test/detector_tests.py
- - coverage run --include=tupak/* -a test/prior_tests.py
- - coverage run --include=tupak/* -a test/sampler_tests.py
- - coverage run --include=tupak/* -a test/waveform_generator_tests.py
- - coverage html
- - coverage-badge -o coverage_badge.svg -f
+ - coverage run --source=tupak/ -a test/detector_tests.py
+ - coverage run --source=tupak/ -a test/prior_tests.py
+ - coverage run --source=tupak/ -a test/sampler_tests.py
+ - coverage run --source=tupak/ -a test/waveform_generator_tests.py
- coverage run --omit=* -a test/example_tests.py
- coverage run --omit=* -a test/noise_realisation_tests.py
- coverage run --omit=* -a test/tests.py
+ - coverage html
+ - coverage-badge -o coverage_badge.svg -f
# Make the documentation
- pip install -r docs/requirements.txt
diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index f5a6589c74576a82ba3d0a5f3d5e09c0ceffa3cb..85cb46977fe00072ed3e95d5998f153d0039fd9f 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -6,17 +6,20 @@ This example estimates the masses using a uniform prior in both component masses
comoving volume prior on luminosity distance between luminosity distances of 100Mpc and 5Gpc, the cosmology is WMAP7.
"""
from __future__ import division, print_function
-import tupak
+
import numpy as np
+import tupak
+
# 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)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(88170235)
@@ -31,13 +34,13 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# 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(
+IFOs = [tupak.gw.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']]
@@ -53,13 +56,15 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'd
# 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')
-priors['geocent_time'] = tupak.prior.Uniform(injection_parameters['geocent_time'] - 1,
- injection_parameters['geocent_time'] + 1,
+priors['luminosity_distance'] = tupak.gw.prior.create_default_prior(name='luminosity_distance')
+priors['geocent_time'] = tupak.core.prior.Uniform(injection_parameters['geocent_time'] - 1,
+ injection_parameters['geocent_time'] + 1,
'geocent_time')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,time_marginalization=False, phase_marginalization=False, distance_marginalization=False, prior=priors)
+likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
+ time_marginalization=False, phase_marginalization=False,
+ distance_marginalization=False, prior=priors)
# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
diff --git a/examples/injection_examples/basic_tutorial_dist_time_phase_marg.py b/examples/injection_examples/basic_tutorial_dist_time_phase_marg.py
index ad10a6a205f1e2f059f169e84780882b61381961..bad5ae9a542ea4e45d970fb4fcc5e817f91d261c 100644
--- a/examples/injection_examples/basic_tutorial_dist_time_phase_marg.py
+++ b/examples/injection_examples/basic_tutorial_dist_time_phase_marg.py
@@ -10,13 +10,15 @@ 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
+import tupak.gw.prior
+
time_duration = 4.
sampling_frequency = 2048.
# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'basic_tutorial_dist_time_phase_marg'
-tupak.utils.setup_logger(outdir=outdir, label=label)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(88170235)
@@ -31,13 +33,13 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# 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(
+IFOs = [tupak.gw.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']]
@@ -52,10 +54,12 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'd
# 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')
+priors['luminosity_distance'] = tupak.gw.prior.create_default_prior(name='luminosity_distance')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,time_marginalization=True, phase_marginalization=True, distance_marginalization=True, prior=priors)
+likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
+ time_marginalization=True, phase_marginalization=True,
+ distance_marginalization=True, prior=priors)
# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
diff --git a/examples/injection_examples/basic_tutorial_time_phase_marg.py b/examples/injection_examples/basic_tutorial_time_phase_marg.py
index 7ffa751709d421452b0782fb75d0037d3e7c2c3a..81c2670a927813d1f41312ba50fa168dd90572cb 100644
--- a/examples/injection_examples/basic_tutorial_time_phase_marg.py
+++ b/examples/injection_examples/basic_tutorial_time_phase_marg.py
@@ -10,13 +10,15 @@ 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
+import tupak.gw.prior
+
time_duration = 4.
sampling_frequency = 2048.
# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'basic_tutorial_time_phase_marg'
-tupak.utils.setup_logger(outdir=outdir, label=label)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(88170235)
@@ -31,13 +33,13 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# 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(
+IFOs = [tupak.gw.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']]
@@ -52,10 +54,12 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'd
# 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')
+priors['luminosity_distance'] = tupak.gw.prior.create_default_prior(name='luminosity_distance')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,time_marginalization=True, phase_marginalization=True, distance_marginalization=False, prior=priors)
+likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
+ time_marginalization=True, phase_marginalization=True,
+ distance_marginalization=False, prior=priors)
# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
diff --git a/examples/injection_examples/change_sampled_parameters.py b/examples/injection_examples/change_sampled_parameters.py
index 6d7734b51b60d73f1782762904a02ab7550ba039..65f818d30be3087b7b905b9520f3bd9b95aca4b4 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -9,7 +9,8 @@ from __future__ import division, print_function
import tupak
import numpy as np
-tupak.utils.setup_logger(log_level="info")
+
+tupak.core.utils.setup_logger(log_level="info")
time_duration = 4.
sampling_frequency = 2048.
@@ -22,16 +23,16 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
# Create the waveform_generator using a LAL BinaryBlackHole source function
-waveform_generator = tupak.waveform_generator.WaveformGenerator(
+waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
sampling_frequency=sampling_frequency, time_duration=time_duration,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
- parameter_conversion=tupak.conversion.convert_to_lal_binary_black_hole_parameters,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
+ parameter_conversion=tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
non_standard_sampling_parameter_keys=['chirp_mass', 'mass_ratio', 'cos_iota'],
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+IFOs = [tupak.gw.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']]
@@ -40,14 +41,14 @@ 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', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
-priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
+priors['luminosity_distance'] = tupak.gw.prior.create_default_prior(name='luminosity_distance')
# Initialise GravitationalWaveTransient
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
# Run sampler
-result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, label='DifferentParameters',
- outdir=outdir, conversion_function=tupak.conversion.generate_all_bbh_parameters)
+result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
+ injection_parameters=injection_parameters, label='DifferentParameters',
+ outdir=outdir, conversion_function=tupak.gw.conversion.generate_all_bbh_parameters)
result.plot_corner()
print(result)
diff --git a/examples/injection_examples/create_your_own_source_model.py b/examples/injection_examples/create_your_own_source_model.py
index 51d548526404169d69c671121c864206de4dbdf9..15f2adfb09a754ebd42952943236f00b63e43200 100644
--- a/examples/injection_examples/create_your_own_source_model.py
+++ b/examples/injection_examples/create_your_own_source_model.py
@@ -7,7 +7,7 @@ import tupak
import numpy as np
# First set up logging and some output directories and labels
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
outdir = 'outdir'
label = 'create_your_own_source_model'
sampling_frequency = 4096
@@ -26,14 +26,14 @@ def sine_gaussian(f, A, f0, tau, phi0, geocent_time, ra, dec, psi):
# We now define some parameters that we will inject and then a waveform generator
injection_parameters = dict(A=1e-23, f0=100, tau=1, phi0=0, geocent_time=0,
ra=0, dec=0, psi=0)
-waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=sine_gaussian,
- parameters=injection_parameters)
+waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(time_duration=time_duration,
+ sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=sine_gaussian,
+ parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+IFOs = [tupak.gw.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)
@@ -42,12 +42,12 @@ IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
# Here we define the priors for the search. We use the injection parameters
# except for the amplitude, f0, and geocent_time
prior = injection_parameters.copy()
-prior['A'] = tupak.prior.PowerLaw(alpha=-1, minimum=1e-25, maximum=1e-21, name='A')
-prior['f0'] = tupak.prior.Uniform(90, 110, 'f')
+prior['A'] = tupak.core.prior.PowerLaw(alpha=-1, minimum=1e-25, maximum=1e-21, name='A')
+prior['f0'] = tupak.core.prior.Uniform(90, 110, 'f')
-likelihood = tupak.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
-result = tupak.sampler.run_sampler(
+result = tupak.core.sampler.run_sampler(
likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
resume=False, sample='unif', injection_parameters=injection_parameters)
result.plot_corner()
diff --git a/examples/injection_examples/create_your_own_time_domain_source_model.py b/examples/injection_examples/create_your_own_time_domain_source_model.py
index af51c5812b5cfc7f49f57f1fb5c3e689c128e1c4..588aa5315b6109bacdb6372a6f969e0d2196ef87 100644
--- a/examples/injection_examples/create_your_own_time_domain_source_model.py
+++ b/examples/injection_examples/create_your_own_time_domain_source_model.py
@@ -8,7 +8,7 @@ and then recovered.
import tupak
import numpy as np
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
# define the time-domain model
@@ -33,9 +33,9 @@ outdir='outdir'
label='time_domain_source_model'
# call the waveform_generator to create our waveform model.
-waveform = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration, sampling_frequency=sampling_frequency,
- time_domain_source_model=time_domain_damped_sinusoid,
- parameters=injection_parameters)
+waveform = tupak.gw.waveform_generator.WaveformGenerator(time_duration=time_duration, sampling_frequency=sampling_frequency,
+ time_domain_source_model=time_domain_damped_sinusoid,
+ parameters=injection_parameters)
hf_signal = waveform.frequency_domain_strain()
#note we could plot the time domain signal with the following code
@@ -48,7 +48,7 @@ hf_signal = waveform.frequency_domain_strain()
# inject the signal into three interferometers
-IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+IFOs = [tupak.gw.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)
@@ -57,19 +57,19 @@ IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
# create the priors
prior = injection_parameters.copy()
-prior['amplitude'] = tupak.prior.Uniform(1e-23, 1e-21, r'$h_0$')
-prior['damping_time'] = tupak.prior.Uniform(0, 1, r'damping time')
-prior['frequency'] = tupak.prior.Uniform(0, 200, r'frequency')
-prior['phase'] = tupak.prior.Uniform(-np.pi/2, np.pi/2, r'$\phi$')
+prior['amplitude'] = tupak.core.prior.Uniform(1e-23, 1e-21, r'$h_0$')
+prior['damping_time'] = tupak.core.prior.Uniform(0, 1, r'damping time')
+prior['frequency'] = tupak.core.prior.Uniform(0, 200, r'frequency')
+prior['phase'] = tupak.core.prior.Uniform(-np.pi / 2, np.pi / 2, r'$\phi$')
# define likelihood
-likelihood = tupak.likelihood.GravitationalWaveTransient(IFOs, waveform)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform)
# launch sampler
-result = tupak.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+result = tupak.core.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
+ injection_parameters=injection_parameters,
+ outdir=outdir, label=label)
result.plot_corner()
print(result)
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index 7a3a0743e4826a4714507f0bca99cf4eb77ad07d..2c14b1e5d84c16560d623d7bce9d5083009b2236 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/examples/injection_examples/how_to_specify_the_prior.py
@@ -6,7 +6,9 @@ from __future__ import division, print_function
import tupak
import numpy as np
-tupak.utils.setup_logger()
+import tupak.gw.prior
+
+tupak.core.utils.setup_logger()
time_duration = 4.
sampling_frequency = 2048.
@@ -19,14 +21,14 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
# Create the waveform_generator using a LAL BinaryBlackHole source function
-waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
- parameters=injection_parameters)
+waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
+ sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
+ parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+IFOs = [tupak.gw.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']]
@@ -36,32 +38,32 @@ priors = dict()
for key in ['tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'iota', 'ra', 'dec', 'geocent_time', 'psi']:
priors[key] = injection_parameters[key]
# We can assign a default prior distribution, note this only works for certain parameters.
-priors['mass_1'] = tupak.prior.create_default_prior(name='mass_1')
+priors['mass_1'] = tupak.gw.prior.create_default_prior(name='mass_1')
# We can make uniform distributions.
-priors['mass_2'] = tupak.prior.Uniform(name='mass_2', minimum=20, maximum=40)
+priors['mass_2'] = tupak.core.prior.Uniform(name='mass_2', minimum=20, maximum=40)
# We can load a prior distribution from a file, e.g., a uniform in comoving volume distribution.
# If no path is given it will look in it's directory of known distributions.
# Note: that this file is used for the default prior distribution for distance.
# Also note: this special case is coded in as tupak.prior.UniformComovingVolume.
-priors['luminosity_distance'] = tupak.prior.FromFile('comoving.txt', name='luminosity_distance',
- minimum=1e3, maximum=5e3)
+priors['luminosity_distance'] = tupak.core.prior.FromFile('comoving.txt', name='luminosity_distance',
+ minimum=1e3, maximum=5e3)
# We can make a power-law distribution, p(x) ~ x^{alpha}
# Note: alpha=0 is a uniform distribution, alpha=-1 is uniform-in-log
-priors['a_1'] = tupak.prior.PowerLaw(name='a_1', alpha=-1, minimum=1e-2, maximum=1)
+priors['a_1'] = tupak.core.prior.PowerLaw(name='a_1', alpha=-1, minimum=1e-2, maximum=1)
# We can define a prior from an array as follows.
# Note: this doesn't have to be properly normalised.
a_2 = np.linspace(0, 1, 1001)
p_a_2 = a_2 ** 4
-priors['a_2'] = tupak.prior.Interped(name='a_2', xx=a_2, yy=p_a_2, minimum=0, maximum=0.5)
+priors['a_2'] = tupak.core.prior.Interped(name='a_2', xx=a_2, yy=p_a_2, minimum=0, maximum=0.5)
# Additionally, we have Gaussian, TruncatedGaussian, Sine and Cosine.
# Finally, if you don't specify any necessary parameters it will be filled in from the default when the sampler starts.
# Enjoy.
# Initialise GravitationalWaveTransient
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
# Run sampler
-result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label='specify_prior')
+result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
+ injection_parameters=injection_parameters, outdir=outdir, label='specify_prior')
result.plot_corner()
print(result)
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index 2cd028c550526dbb455d2da63ed146e89f616325..c3c4f29ef2d0b0706542974fb8f90208b9865a9a 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -7,7 +7,7 @@ from __future__ import division, print_function
import tupak
import numpy as np
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
time_duration = 4.
sampling_frequency = 2048.
@@ -20,13 +20,13 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
# Create the waveform_generator using a LAL BinaryBlackHole source function
-waveform_generator = tupak.waveform_generator.WaveformGenerator(
+waveform_generator = tupak.WaveformGenerator(
time_duration=time_duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole, parameters=injection_parameters)
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole, parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+IFOs = [tupak.gw.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']]
@@ -38,12 +38,12 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'iota
# Initialise GravitationalWaveTransient
# Note that we now need to pass the: priors and flags for each thing that's being marginalised.
-likelihood = tupak.likelihood.GravitationalWaveTransient(
+likelihood = tupak.GravitationalWaveTransient(
interferometers=IFOs, waveform_generator=waveform_generator, prior=priors,
distance_marginalization=True, phase_marginalization=True)
# Run sampler
-result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label='BasicTutorial')
+result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
+ injection_parameters=injection_parameters, outdir=outdir, label='BasicTutorial')
result.plot_corner()
print(result)
diff --git a/examples/open_data_examples/GW150914.py b/examples/open_data_examples/GW150914.py
index 695ba2db4529706fbb49bb5a853b1137d4936283..9d274eac4885b61a636f84f8ec5310cd8873fa0c 100644
--- a/examples/open_data_examples/GW150914.py
+++ b/examples/open_data_examples/GW150914.py
@@ -7,15 +7,16 @@ This example estimates all 15 parameters of the binary black hole system using
commonly used prior distributions. This will take a few hours to run.
"""
from __future__ import division, print_function
-import tupak
# Define some convienence labels and the trigger time of the event
+import tupak.gw.likelihood
+
outdir = 'outdir'
label = 'GW150914'
-time_of_event = tupak.utils.get_event_time(label)
+time_of_event = tupak.core.utils.get_event_time(label)
# This sets up logging output to understand what tupak is doing
-tupak.utils.setup_logger(outdir=outdir, label=label)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
# Here we import the detector data. This step downloads data from the
# LIGO/Virgo open data archives. The data is saved to an `Interferometer`
@@ -24,7 +25,7 @@ tupak.utils.setup_logger(outdir=outdir, label=label)
# makes sense, for each detector a plot is created in the `outdir` called
# H1_frequency_domain_data.png and LI_frequency_domain_data.png. The two
# objects are then placed into a list.
-interferometers = tupak.detector.get_event_data(label)
+interferometers = tupak.gw.detector.get_event_data(label)
# We now define the prior. You'll notice we only do this for the two masses,
# the merger time, and the distance; in each case choosing a prior which
@@ -33,12 +34,12 @@ interferometers = tupak.detector.get_event_data(label)
# using the syntax below, or choose a fixed value by just providing a float
# value as the prior.
prior = dict()
-prior['mass_1'] = tupak.prior.Uniform(30, 50, 'mass_1')
-prior['mass_2'] = tupak.prior.Uniform(20, 40, 'mass_2')
-prior['geocent_time'] = tupak.prior.Uniform(
+prior['mass_1'] = tupak.core.prior.Uniform(30, 50, 'mass_1')
+prior['mass_2'] = tupak.core.prior.Uniform(20, 40, 'mass_2')
+prior['geocent_time'] = tupak.core.prior.Uniform(
time_of_event - 0.1, time_of_event + 0.1, name='geocent_time')
#prior['geocent_time'] = time_of_event
-prior['luminosity_distance'] = tupak.prior.PowerLaw(
+prior['luminosity_distance'] = tupak.core.prior.PowerLaw(
alpha=2, minimum=100, maximum=1000)
# In this step we define a `waveform_generator`. This is out object which
@@ -47,13 +48,13 @@ prior['luminosity_distance'] = tupak.prior.PowerLaw(
# the waveform approximant and reference frequency.
waveform_generator = tupak.WaveformGenerator(time_duration=interferometers[0].duration,
sampling_frequency=interferometers[0].sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters={'waveform_approximant': 'IMRPhenomPv2',
'reference_frequency': 50})
# In this step, we define the likelihood. Here we use the standard likelihood
# function, passing it the data and the waveform generator.
-likelihood = tupak.GravitationalWaveTransient(interferometers, waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
# Finally, we run the sampler. This function takes the likelihood and prio
# along with some options for how to do the sampling and how to save the data
diff --git a/examples/open_data_examples/GW150914_minimal.py b/examples/open_data_examples/GW150914_minimal.py
index 7df645cbbd093c3856920a67cbbbbe05e632dd05..b63df0d742a9a322c69285c1790add37df2a72e1 100644
--- a/examples/open_data_examples/GW150914_minimal.py
+++ b/examples/open_data_examples/GW150914_minimal.py
@@ -6,9 +6,9 @@ stimation on GW150914 using open data.
"""
import tupak
-t0 = tupak.utils.get_event_time("GW150914")
-prior = dict(geocent_time=tupak.prior.Uniform(t0-0.1, t0+0.1, name='geocent_time'))
-interferometers = tupak.detector.get_event_data("GW150914")
-likelihood = tupak.likelihood.get_binary_black_hole_likelihood(interferometers)
+t0 = tupak.gw.utils.get_event_time("GW150914")
+prior = dict(geocent_time=tupak.core.prior.Uniform(t0 - 0.1, t0 + 0.1, name='geocent_time'))
+interferometers = tupak.gw.detector.get_event_data("GW150914")
+likelihood = tupak.gw.likelihood.get_binary_black_hole_likelihood(interferometers)
result = tupak.run_sampler(likelihood, prior, label='GW150914')
result.plot_corner()
diff --git a/examples/other_examples/gaussian_example.py b/examples/other_examples/gaussian_example.py
index b65e3bc0ad46763c7808229d890f4eb5549e9185..e6f505ed53c10478dd27414dfd545a6005e5b522 100644
--- a/examples/other_examples/gaussian_example.py
+++ b/examples/other_examples/gaussian_example.py
@@ -8,7 +8,7 @@ import tupak
import numpy as np
# A few simple setup steps
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
label = 'gaussian_example'
outdir = 'outdir'
@@ -45,8 +45,8 @@ class GaussianLikelihood(tupak.Likelihood):
likelihood = GaussianLikelihood(data)
-priors = dict(mu=tupak.prior.Uniform(0, 5, 'mu'),
- sigma=tupak.prior.Uniform(0, 10, 'sigma'))
+priors = dict(mu=tupak.core.prior.Uniform(0, 5, 'mu'),
+ sigma=tupak.core.prior.Uniform(0, 10, 'sigma'))
# And run sampler
result = tupak.run_sampler(
diff --git a/examples/other_examples/hyper_parameter_example.py b/examples/other_examples/hyper_parameter_example.py
index 936ef26833c2005ba4f614cbc154b0c93c09784e..b357357035d23a1f0a04450e2a3035fff4b1ae4c 100644
--- a/examples/other_examples/hyper_parameter_example.py
+++ b/examples/other_examples/hyper_parameter_example.py
@@ -6,11 +6,11 @@ from __future__ import division
import tupak
import numpy as np
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
outdir = 'outdir'
-class GaussianLikelihood(tupak.likelihood.Likelihood):
+class GaussianLikelihood(tupak.core.likelihood.Likelihood):
def __init__(self, x, y, waveform_generator):
self.x = x
self.y = y
@@ -47,15 +47,15 @@ for i in range(Nevents):
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
- waveform_generator = tupak.waveform_generator.WaveformGenerator(
+ waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
time_duration=time_duration, sampling_frequency=sampling_frequency,
time_domain_source_model=model)
likelihood = GaussianLikelihood(time, data, waveform_generator)
- priors = dict(m=tupak.prior.Uniform(-10, 10, 'm'))
+ priors = dict(m=tupak.core.prior.Uniform(-10, 10, 'm'))
- result = tupak.sampler.run_sampler(
+ result = tupak.core.sampler.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
injection_parameters=injection_parameters, outdir=outdir,
verbose=False, label='individual_{}'.format(i), use_ratio=False,
@@ -64,18 +64,18 @@ for i in range(Nevents):
samples.append(result.samples)
# Now run the hyperparameter inference
-run_prior = tupak.prior.Uniform(minimum=-10, maximum=10, name='mu_m')
-hyper_prior = tupak.prior.Gaussian(mu=0, sigma=1, name='hyper')
+run_prior = tupak.core.prior.Uniform(minimum=-10, maximum=10, name='mu_m')
+hyper_prior = tupak.core.prior.Gaussian(mu=0, sigma=1, name='hyper')
-hp_likelihood = tupak.likelihood.HyperparameterLikelihood(
+hp_likelihood = tupak.gw.likelihood.HyperparameterLikelihood(
samples, hyper_prior, run_prior)
hp_priors = dict(
- mu=tupak.prior.Uniform(-10, 10, 'mu', '$\mu_m$'),
- sigma=tupak.prior.Uniform(0, 10, 'sigma', '$\sigma_m$'))
+ mu=tupak.core.prior.Uniform(-10, 10, 'mu', '$\mu_m$'),
+ sigma=tupak.core.prior.Uniform(0, 10, 'sigma', '$\sigma_m$'))
# And run sampler
-result = tupak.sampler.run_sampler(
+result = tupak.core.sampler.run_sampler(
likelihood=hp_likelihood, priors=hp_priors, sampler='dynesty',
npoints=1000, outdir=outdir, label='hyperparameter', use_ratio=False,
sample='unif', verbose=True)
diff --git a/examples/other_examples/linear_regression.py b/examples/other_examples/linear_regression.py
index 23b22c44084e12ff6e8f53a8e814e78a866e1973..0deb0571bcf6b5020a9500e9061f346bdbdaa68f 100644
--- a/examples/other_examples/linear_regression.py
+++ b/examples/other_examples/linear_regression.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
import inspect
# A few simple setup steps
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
label = 'linear_regression'
outdir = 'outdir'
@@ -96,8 +96,8 @@ likelihood = GaussianLikelihood(time, data, sigma, model)
# From hereon, the syntax is exactly equivalent to other tupak examples
# We make a prior
priors = {}
-priors['m'] = tupak.prior.Uniform(0, 5, 'm')
-priors['c'] = tupak.prior.Uniform(-2, 2, 'c')
+priors['m'] = tupak.core.prior.Uniform(0, 5, 'm')
+priors['c'] = tupak.core.prior.Uniform(-2, 2, 'c')
# And run sampler
result = tupak.run_sampler(
diff --git a/examples/other_examples/linear_regression_unknown_noise.py b/examples/other_examples/linear_regression_unknown_noise.py
index 1d9da4bf6babe0a5ebc7aa10ba7145e39cdf579b..3389de13e2a55e807d3bf4f57e2851b69efcb6cc 100644
--- a/examples/other_examples/linear_regression_unknown_noise.py
+++ b/examples/other_examples/linear_regression_unknown_noise.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
import inspect
# A few simple setup steps
-tupak.utils.setup_logger()
+tupak.core.utils.setup_logger()
label = 'linear_regression_unknown_noise'
outdir = 'outdir'
@@ -104,9 +104,9 @@ likelihood = GaussianLikelihood(time, data, model)
# From hereon, the syntax is exactly equivalent to other tupak examples
# We make a prior
priors = {}
-priors['m'] = tupak.prior.Uniform(0, 5, 'm')
-priors['c'] = tupak.prior.Uniform(-2, 2, 'c')
-priors['sigma'] = tupak.prior.Uniform(0, 10, 'sigma')
+priors['m'] = tupak.core.prior.Uniform(0, 5, 'm')
+priors['c'] = tupak.core.prior.Uniform(-2, 2, 'c')
+priors['sigma'] = tupak.core.prior.Uniform(0, 10, 'sigma')
# And run sampler
result = tupak.run_sampler(
diff --git a/examples/other_examples/sine_gaussian_example.py b/examples/other_examples/sine_gaussian_example.py
index 66d04866394ffad615158e4cea17168dfeea742b..b1702e773b73b72b0fd1e5c5c743e75fdc728706 100644
--- a/examples/other_examples/sine_gaussian_example.py
+++ b/examples/other_examples/sine_gaussian_example.py
@@ -14,7 +14,7 @@ sampling_frequency = 2048.
# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'sine_gaussian'
-tupak.utils.setup_logger(outdir=outdir, label=label)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(170801)
@@ -25,15 +25,15 @@ injection_parameters = dict(hrss = 1e-22, Q = 5.0, frequency = 200.0, ra = 1.375
geocent_time = 1126259642.413, psi= 2.659)
# Create the waveform_generator using a sine Gaussian source function
-waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.sinegaussian,
- parameters=injection_parameters)
+waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(time_duration=time_duration,
+ sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=tupak.gw.source.sinegaussian,
+ parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# 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(
+IFOs = [tupak.gw.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']]
@@ -50,16 +50,16 @@ for key in ['psi', 'ra', 'dec', 'geocent_time']:
# that will be included in the sampler. If we do nothing, then the default priors get used.
#priors['Q'] = tupak.prior.create_default_prior(name='Q')
#priors['frequency'] = tupak.prior.create_default_prior(name='frequency')
-priors['Q'] = tupak.prior.Uniform(2, 50, 'Q')
-priors['frequency'] = tupak.prior.Uniform(30, 1000, 'frequency')
-priors['hrss'] = tupak.prior.Uniform(1e-23, 1e-21, 'hrss')
+priors['Q'] = tupak.core.prior.Uniform(2, 50, 'Q')
+priors['frequency'] = tupak.core.prior.Uniform(30, 1000, 'frequency')
+priors['hrss'] = tupak.core.prior.Uniform(1e-23, 1e-21, 'hrss')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
# 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)
+result = tupak.core.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
result.plot_corner()
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index 51787a6216a8486a00579f9311b6ca22fcb2d954..1a194a965bc3426eccf95ae6d253b332f16dde37 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -8,17 +8,19 @@ factor. (See https://arxiv.org/pdf/1202.3256.pdf)
"""
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
+import tupak.gw.likelihood
+import tupak.gw.prior
+
time_duration = 3.
sampling_frequency = 4096.
# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'supernova'
-tupak.utils.setup_logger(outdir=outdir, label=label)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(170801)
@@ -33,16 +35,16 @@ injection_parameters = dict(file_path='MuellerL15_example_inj.txt',
psi=2.659)
# Create the waveform_generator using a supernova source function
-waveform_generator = tupak.waveform_generator.WaveformGenerator(
+waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
time_duration=time_duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.supernova,
+ frequency_domain_source_model=tupak.gw.source.supernova,
parameters=injection_parameters)
hf_signal = waveform_generator.frequency_domain_strain()
# 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(
+IFOs = [tupak.gw.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)
@@ -57,32 +59,32 @@ imagPCs = np.loadtxt('SupernovaImagPCs.txt')
simulation_parameters = dict(
realPCs=realPCs, imagPCs=imagPCs)
-search_waveform_generator = tupak.waveform_generator.WaveformGenerator(
+search_waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
time_duration=time_duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.supernova_pca_model,
+ frequency_domain_source_model=tupak.gw.source.supernova_pca_model,
parameters=simulation_parameters)
# Set up prior
priors = dict()
for key in ['psi', 'geocent_time']:
priors[key] = injection_parameters[key]
-priors['luminosity_distance'] = tupak.prior.Uniform(
+priors['luminosity_distance'] = tupak.core.prior.Uniform(
2, 20, 'luminosity_distance')
-priors['pc_coeff1'] = tupak.prior.Uniform(-1, 1, 'pc_coeff1')
-priors['pc_coeff2'] = tupak.prior.Uniform(-1, 1, 'pc_coeff2')
-priors['pc_coeff3'] = tupak.prior.Uniform(-1, 1, 'pc_coeff3')
-priors['pc_coeff4'] = tupak.prior.Uniform(-1, 1, 'pc_coeff4')
-priors['pc_coeff5'] = tupak.prior.Uniform(-1, 1, 'pc_coeff5')
-priors['ra'] = tupak.prior.create_default_prior(name='ra')
-priors['dec'] = tupak.prior.create_default_prior(name='dec')
-priors['geocent_time'] = tupak.prior.Uniform(
+priors['pc_coeff1'] = tupak.core.prior.Uniform(-1, 1, 'pc_coeff1')
+priors['pc_coeff2'] = tupak.core.prior.Uniform(-1, 1, 'pc_coeff2')
+priors['pc_coeff3'] = tupak.core.prior.Uniform(-1, 1, 'pc_coeff3')
+priors['pc_coeff4'] = tupak.core.prior.Uniform(-1, 1, 'pc_coeff4')
+priors['pc_coeff5'] = tupak.core.prior.Uniform(-1, 1, 'pc_coeff5')
+priors['ra'] = tupak.gw.prior.create_default_prior(name='ra')
+priors['dec'] = tupak.gw.prior.create_default_prior(name='dec')
+priors['geocent_time'] = tupak.core.prior.Uniform(
injection_parameters['geocent_time'] - 1,
injection_parameters['geocent_time'] + 1,
'geocent_time')
# Initialise the likelihood by passing in the interferometer data (IFOs) and
# the waveoform generator
-likelihood = tupak.GravitationalWaveTransient(
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(
interferometers=IFOs, waveform_generator=search_waveform_generator)
# Run sampler.
@@ -93,14 +95,3 @@ result = tupak.run_sampler(
# make some plots of the outputs
result.plot_corner()
print(result)
-
-
-
-
-
-
-
-
-
-
-
diff --git a/examples/tutorials/compare_samplers.ipynb b/examples/tutorials/compare_samplers.ipynb
index 69939d13ad32cbf7381dde19fd6dedfc6a677a46..970863a96be01496ee1d34ca74160b5a4c4f0996 100644
--- a/examples/tutorials/compare_samplers.ipynb
+++ b/examples/tutorials/compare_samplers.ipynb
@@ -36,22 +36,22 @@
" psi=2.659)\n",
"\n",
"\n",
- "waveform_generator = tupak.waveform_generator.WaveformGenerator(\n",
+ "waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(\n",
" sampling_frequency=sampling_frequency,\n",
" time_duration=time_duration,\n",
- " frequency_domain_source_model=tupak.source.lal_binary_black_hole,\n",
+ " frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,\n",
" parameters=injection_parameters)\n",
"\n",
"hf_signal = waveform_generator.frequency_domain_strain()\n",
"\n",
- "H1 = tupak.detector.get_empty_interferometer('H1')\n",
+ "H1 = tupak.gw.detector.get_empty_interferometer('H1')\n",
"H1.set_data(sampling_frequency=sampling_frequency, duration=time_duration,\n",
" from_power_spectral_density=True)\n",
"H1.inject_signal(waveform_polarizations=hf_signal, parameters=injection_parameters)\n",
"\n",
"IFOs = [H1]\n",
"\n",
- "likelihood = tupak.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)"
+ "likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)"
]
},
{
@@ -69,7 +69,7 @@
"metadata": {},
"outputs": [],
"source": [
- "priors = tupak.prior.fill_priors(injection_parameters, likelihood)\n",
+ "priors = tupak.core.prior.fill_priors(injection_parameters, likelihood)\n",
"priors['ra'] = tupak.prior.Uniform(0, 2*np.pi, 'ra')\n",
"priors['dec'] = tupak.prior.Uniform(-np.pi/2, np.pi/2, 'dec')"
]
@@ -90,7 +90,7 @@
"outputs": [],
"source": [
"%%time \n",
- "result = tupak.sampler.run_sampler(\n",
+ "result = tupak.core.sampler.run_sampler(\n",
" likelihood, priors=priors, sampler='pymultinest', label='pymultinest',\n",
" npoints=200, verbose=False, resume=False)\n",
"fig = result.plot_corner(save=False)\n",
@@ -112,7 +112,7 @@
"outputs": [],
"source": [
"%%time \n",
- "result = tupak.sampler.run_sampler(\n",
+ "result = tupak.gw.sampler.run_sampler(\n",
" likelihood, priors=priors, sampler='dynesty', label='dynesty',\n",
" bound='multi', sample='rwalk', npoints=200, walks=1, verbose=False,\n",
" update_interval=100)\n",
@@ -137,7 +137,7 @@
"outputs": [],
"source": [
"%%time \n",
- "result = tupak.sampler.run_sampler(\n",
+ "result = tupak.gw.sampler.run_sampler(\n",
" likelihood, priors=priors, sampler='dynesty', label='dynesty_dynamic',\n",
" bound='multi', nlive=250, sample='unif', verbose=True,\n",
" update_interval=100, dynamic=True)\n",
@@ -160,7 +160,7 @@
"outputs": [],
"source": [
"%%time \n",
- "result = tupak.sampler.run_sampler(\n",
+ "result = tupak.gw.sampler.run_sampler(\n",
" likelihood, priors=priors, sampler='ptemcee', label='ptemcee',\n",
" nwalkers=100, nsteps=200, nburn=100, ntemps=2,\n",
" tqdm='tqdm_notebook')\n",
diff --git a/examples/tutorials/making_priors.ipynb b/examples/tutorials/making_priors.ipynb
index d47e96fbce67ce0468c5469733b6879515e39dee..9c09d7c4202d9ae22b76fe5be96679b3ee448997 100644
--- a/examples/tutorials/making_priors.ipynb
+++ b/examples/tutorials/making_priors.ipynb
@@ -67,8 +67,8 @@
},
"metadata": {
"image/png": {
- "height": 567,
- "width": 1728
+ "height": 567.0,
+ "width": 1728.0
}
},
"output_type": "display_data"
@@ -78,24 +78,24 @@
"fig = figure(figsize=(24, 8))\n",
"\n",
"priors = [\n",
- " tupak.prior.Uniform(minimum=5, maximum=50),\n",
- " tupak.prior.LogUniform(minimum=5, maximum=50),\n",
- " tupak.prior.PowerLaw(name='name', alpha=2, minimum=100, maximum=1000),\n",
- " tupak.prior.UniformComovingVolume(name='name', minimum=100, maximum=1000, latex_label='label'),\n",
- " tupak.prior.FromFile(name='name', file_name='comoving.txt', minimum=100, maximum=30000, latex_label='label'),\n",
- " tupak.prior.Gaussian(name='name', mu=0, sigma=1, latex_label='label'),\n",
- " tupak.prior.TruncatedGaussian(name='name', mu=1, sigma=0.4, minimum=-1, maximum=1,\n",
+ " tupak.core.prior.Uniform(minimum=5, maximum=50),\n",
+ " tupak.core.prior.LogUniform(minimum=5, maximum=50),\n",
+ " tupak.core.prior.PowerLaw(name='name', alpha=2, minimum=100, maximum=1000),\n",
+ " tupak.core.prior.UniformComovingVolume(name='name', minimum=100, maximum=1000, latex_label='label'),\n",
+ " tupak.core.prior.FromFile(name='name', file_name='comoving.txt', minimum=100, maximum=30000, latex_label='label'),\n",
+ " tupak.core.prior.Gaussian(name='name', mu=0, sigma=1, latex_label='label'),\n",
+ " tupak.core.prior.TruncatedGaussian(name='name', mu=1, sigma=0.4, minimum=-1, maximum=1,\n",
" latex_label='label'),\n",
- " tupak.prior.Cosine(name='name', latex_label='label'),\n",
- " tupak.prior.Sine(name='name', latex_label='label'),\n",
- " tupak.prior.Interped(name='name', xx = np.linspace(0, 10, 1000), yy=np.linspace(0, 10, 1000)**4,\n",
+ " tupak.core.prior.Cosine(name='name', latex_label='label'),\n",
+ " tupak.core.prior.Sine(name='name', latex_label='label'),\n",
+ " tupak.core.prior.Interped(name='name', xx = np.linspace(0, 10, 1000), yy=np.linspace(0, 10, 1000)**4,\n",
" minimum=3, maximum=5, latex_label='label')\n",
"]\n",
"\n",
"for ii, prior in enumerate(priors):\n",
" fig.add_subplot(2, 5, 1 + ii)\n",
" hist(prior.sample(100000), bins=100, histtype='step', normed=1)\n",
- " if not isinstance(prior, tupak.prior.Gaussian):\n",
+ " if not isinstance(prior, tupak.core.prior.Gaussian):\n",
" plot(np.linspace(prior.minimum, prior.maximum, 1000),\n",
" prior.prob(np.linspace(prior.minimum, prior.maximum, 1000)))\n",
" else:\n",
@@ -158,8 +158,8 @@
},
"metadata": {
"image/png": {
- "height": 337,
- "width": 743
+ "height": 337.0,
+ "width": 743.0
}
},
"output_type": "display_data"
@@ -223,8 +223,8 @@
},
"metadata": {
"image/png": {
- "height": 337,
- "width": 716
+ "height": 337.0,
+ "width": 716.0
}
},
"output_type": "display_data"
diff --git a/examples/tutorials/visualising_the_results.ipynb b/examples/tutorials/visualising_the_results.ipynb
index ae3947f26c5a81152bb8fe0e8068fdc53778d979..6d34f374d85897bdd1ac99d72e6bc34861762cce 100644
--- a/examples/tutorials/visualising_the_results.ipynb
+++ b/examples/tutorials/visualising_the_results.ipynb
@@ -65,24 +65,24 @@
" luminosity_distance=200., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,\n",
" waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)\n",
"\n",
- "waveform_generator = tupak.waveform_generator.WaveformGenerator(\n",
+ "waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(\n",
" sampling_frequency=sampling_frequency, time_duration=time_duration,\n",
- " frequency_domain_source_model=tupak.source.lal_binary_black_hole,\n",
+ " frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,\n",
" parameters=injection_parameters)\n",
"hf_signal = waveform_generator.frequency_domain_strain()\n",
"\n",
- "IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(\n",
+ "IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(\n",
" 'H1', injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,\n",
" sampling_frequency=sampling_frequency, outdir=outdir)]\n",
"\n",
"priors = injection_parameters.copy()\n",
- "priors['mass_1'] = tupak.prior.Uniform(20, 50, 'mass_1')\n",
- "priors['mass_2'] = tupak.prior.Uniform(20, 50, 'mass_2')\n",
- "priors['luminosity_distance'] = tupak.prior.Uniform(100, 300, 'luminosity_distance')\n",
+ "priors['mass_1'] = tupak.core.prior.Uniform(20, 50, 'mass_1')\n",
+ "priors['mass_2'] = tupak.core.prior.Uniform(20, 50, 'mass_2')\n",
+ "priors['luminosity_distance'] = tupak.core.prior.Uniform(100, 300, 'luminosity_distance')\n",
"\n",
- "likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)\n",
+ "likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)\n",
"\n",
- "result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=100,\n",
+ "result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=100,\n",
" injection_parameters=injection_parameters, outdir=outdir, label=label,\n",
" walks=5)\n",
"\n",
diff --git a/requirements.txt b/requirements.txt
index 356634c1c863b2c80a7d6f84efbf66d16a6056c7..fe8a629b9a049938e04c73d9c49034b19bffc522 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,4 +14,3 @@ nestle
deepdish
ptemcee
mock
-lalsuite
diff --git a/setup.py b/setup.py
index a82b6c257bd2142d5f1c481b0c740fd8312ba64d..2be1bb3d80b180df8ba156d5f272944864d8b0ed 100644
--- a/setup.py
+++ b/setup.py
@@ -24,8 +24,9 @@ with open('tupak/' + version_file, 'w+') as f:
setup(name='tupak',
version='0.1',
- packages=['tupak'],
+ packages=['tupak', 'tupak.core', 'tupak.gw'],
package_dir={'tupak': 'tupak'},
- package_data={'tupak': ['noise_curves/*.txt', 'prior_files/*.txt',
- version_file]}
+ package_data={'tupak.core': ['prior_files/*.txt'],
+ 'tupak.gw': ['noise_curves/*.txt'],
+ 'tupak': [version_file]}
)
diff --git a/test/detector_tests.py b/test/detector_tests.py
index 9c2c459c966f17f9344010a1282aea7d6c1aa831..d254cc6f85a0fd58e76d0498b734f671fafee201 100644
--- a/test/detector_tests.py
+++ b/test/detector_tests.py
@@ -1,10 +1,10 @@
-from context import tupak
-from tupak import detector
+from __future__ import absolute_import
+
+import tupak
import unittest
import mock
from mock import MagicMock
import numpy as np
-import logging
class TestDetector(unittest.TestCase):
@@ -23,12 +23,12 @@ class TestDetector(unittest.TestCase):
self.xarm_tilt = 0.
self.yarm_tilt = 0.
# noinspection PyTypeChecker
- self.ifo = detector.Interferometer(name=self.name, power_spectral_density=self.power_spectral_density,
- minimum_frequency=self.minimum_frequency,
- maximum_frequency=self.maximum_frequency, length=self.length,
- latitude=self.latitude, longitude=self.longitude, elevation=self.elevation,
- xarm_azimuth=self.xarm_azimuth, yarm_azimuth=self.yarm_azimuth,
- xarm_tilt=self.xarm_tilt, yarm_tilt=self.yarm_tilt)
+ self.ifo = tupak.gw.detector.Interferometer(name=self.name, power_spectral_density=self.power_spectral_density,
+ minimum_frequency=self.minimum_frequency,
+ maximum_frequency=self.maximum_frequency, length=self.length,
+ latitude=self.latitude, longitude=self.longitude, elevation=self.elevation,
+ xarm_azimuth=self.xarm_azimuth, yarm_azimuth=self.yarm_azimuth,
+ xarm_tilt=self.xarm_tilt, yarm_tilt=self.yarm_tilt)
def tearDown(self):
del self.name
@@ -102,24 +102,24 @@ class TestDetector(unittest.TestCase):
def test_vertex_without_update(self):
_ = self.ifo.vertex
- with mock.patch('tupak.utils.get_vertex_position_geocentric') as m:
+ with mock.patch('tupak.gw.utils.get_vertex_position_geocentric') as m:
m.return_value = np.array([1])
self.assertFalse(np.array_equal(self.ifo.vertex, np.array([1])))
def test_vertex_with_latitude_update(self):
- with mock.patch('tupak.utils.get_vertex_position_geocentric') as m:
+ with mock.patch('tupak.gw.utils.get_vertex_position_geocentric') as m:
m.return_value = np.array([1])
self.ifo.latitude = 5
self.assertEqual(self.ifo.vertex, np.array([1]))
def test_vertex_with_longitude_update(self):
- with mock.patch('tupak.utils.get_vertex_position_geocentric') as m:
+ with mock.patch('tupak.gw.utils.get_vertex_position_geocentric') as m:
m.return_value = np.array([1])
self.ifo.longitude = 5
self.assertEqual(self.ifo.vertex, np.array([1]))
def test_vertex_with_elevation_update(self):
- with mock.patch('tupak.utils.get_vertex_position_geocentric') as m:
+ with mock.patch('tupak.gw.utils.get_vertex_position_geocentric') as m:
m.return_value = np.array([1])
self.ifo.elevation = 5
self.assertEqual(self.ifo.vertex, np.array([1]))
@@ -213,14 +213,14 @@ class TestDetector(unittest.TestCase):
self.assertTrue(np.array_equal(self.ifo.power_spectral_density_array, np.array([1, 4])))
def test_antenna_response_default(self):
- with mock.patch('tupak.utils.get_polarization_tensor') as m:
+ with mock.patch('tupak.gw.utils.get_polarization_tensor') as m:
with mock.patch('numpy.einsum') as n:
m.return_value = 0
n.return_value = 1
self.assertEqual(self.ifo.antenna_response(234, 52, 54, 76, 'plus'), 1)
def test_antenna_response_einsum(self):
- with mock.patch('tupak.utils.get_polarization_tensor') as m:
+ with mock.patch('tupak.gw.utils.get_polarization_tensor') as m:
m.return_value = np.ones((3, 3))
self.assertAlmostEqual(self.ifo.antenna_response(234, 52, 54, 76, 'plus'), self.ifo.detector_tensor.sum())
@@ -273,8 +273,8 @@ class TestDetector(unittest.TestCase):
def test_inject_signal_sets_data_with_existing_data_array(self):
self.ifo.get_detector_response = MagicMock(return_value=np.array([1]))
self.ifo.frequency_array = np.array([0, 1])
- with mock.patch('tupak.utils.optimal_snr_squared') as m:
- with mock.patch('tupak.utils.matched_filter_snr_squared') as n:
+ with mock.patch('tupak.gw.utils.optimal_snr_squared') as m:
+ with mock.patch('tupak.gw.utils.matched_filter_snr_squared') as n:
m.return_value = 0
n.return_value = 0
self.ifo.data = np.array([1])
@@ -284,8 +284,8 @@ class TestDetector(unittest.TestCase):
def test_inject_signal_sets_data_without_data_array(self):
self.ifo.get_detector_response = MagicMock(return_value=np.array([1]))
self.ifo.frequency_array = np.array([0, 1])
- with mock.patch('tupak.utils.optimal_snr_squared') as m:
- with mock.patch('tupak.utils.matched_filter_snr_squared') as n:
+ with mock.patch('tupak.gw.utils.optimal_snr_squared') as m:
+ with mock.patch('tupak.gw.utils.matched_filter_snr_squared') as n:
m.return_value = 0
n.return_value = 0
self.ifo.data = 1
@@ -322,7 +322,7 @@ class TestDetector(unittest.TestCase):
self.ifo.set_data(sampling_frequency=1, duration=1)
def test_set_data_sets_data_from_frequency_domain_strain(self):
- with mock.patch('tupak.utils.create_frequency_series') as m:
+ with mock.patch('tupak.core.utils.create_frequency_series') as m:
m.return_value = np.array([1])
self.ifo.minimum_frequency = 0
self.ifo.maximum_frequency = 3
@@ -331,7 +331,7 @@ class TestDetector(unittest.TestCase):
self.assertTrue(np.array_equal(self.ifo.data, np.array([1])))
def test_set_data_sets_frequencies_from_frequency_domain_strain(self):
- with mock.patch('tupak.utils.create_frequency_series') as m:
+ with mock.patch('tupak.core.utils.create_frequency_series') as m:
m.return_value = np.array([1])
self.ifo.minimum_frequency = 0
self.ifo.maximum_frequency = 3
@@ -340,7 +340,7 @@ class TestDetector(unittest.TestCase):
self.assertTrue(np.array_equal(self.ifo.frequency_array, np.array([1])))
def test_set_data_sets_frequencies_from_spectral_density(self):
- with mock.patch('tupak.utils.create_frequency_series') as m:
+ with mock.patch('tupak.core.utils.create_frequency_series') as m:
m.return_value = np.array([1])
self.ifo.minimum_frequency = 0
self.ifo.maximum_frequency = 3
@@ -349,7 +349,7 @@ class TestDetector(unittest.TestCase):
self.assertTrue(np.array_equal(self.ifo.frequency_array, np.array([2])))
def test_set_data_sets_epoch(self):
- with mock.patch('tupak.utils.create_frequency_series') as m:
+ with mock.patch('tupak.core.utils.create_frequency_series') as m:
m.return_value = np.array([1])
self.ifo.minimum_frequency = 0
self.ifo.maximum_frequency = 3
@@ -358,7 +358,7 @@ class TestDetector(unittest.TestCase):
self.assertEqual(self.ifo.epoch, 4)
def test_set_data_sets_sampling_frequency(self):
- with mock.patch('tupak.utils.create_frequency_series') as m:
+ with mock.patch('tupak.core.utils.create_frequency_series') as m:
m.return_value = np.array([1])
self.ifo.minimum_frequency = 0
self.ifo.maximum_frequency = 3
@@ -367,7 +367,7 @@ class TestDetector(unittest.TestCase):
self.assertEqual(self.ifo.sampling_frequency, 1)
def test_set_data_sets_duration(self):
- with mock.patch('tupak.utils.create_frequency_series') as m:
+ with mock.patch('tupak.core.utils.create_frequency_series') as m:
m.return_value = np.array([1])
self.ifo.minimum_frequency = 0
self.ifo.maximum_frequency = 3
@@ -376,12 +376,12 @@ class TestDetector(unittest.TestCase):
self.assertEqual(self.ifo.duration, 1)
def test_time_delay_from_geocenter(self):
- with mock.patch('tupak.utils.time_delay_geocentric') as m:
+ with mock.patch('tupak.gw.utils.time_delay_geocentric') as m:
m.return_value = 1
self.assertEqual(self.ifo.time_delay_from_geocenter(1, 2, 3), 1)
def test_vertex_position_geocentric(self):
- with mock.patch('tupak.utils.get_vertex_position_geocentric') as m:
+ with mock.patch('tupak.gw.utils.get_vertex_position_geocentric') as m:
m.return_value = 1
self.assertEqual(self.ifo.vertex_position_geocentric(), 1)
diff --git a/test/example_tests.py b/test/example_tests.py
index 8c4762de63ebc0b47dcf9d49aa5feaea4d1af350..99b22f89aaffa55f3832d37b8a0bd56e69849a8d 100644
--- a/test/example_tests.py
+++ b/test/example_tests.py
@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+import matplotlib
+matplotlib.use('Agg')
+
import unittest
import os
import shutil
@@ -5,13 +9,13 @@ import logging
# Required to run the tests
from past.builtins import execfile
-from context import tupak
+import tupak.core.utils
# Imported to ensure the examples run
import numpy as np
import inspect
-tupak.utils.command_line_args.test = True
+tupak.core.utils.command_line_args.test = True
class Test(unittest.TestCase):
diff --git a/test/make_standard_data.py b/test/make_standard_data.py
index d4407acb5c7fa5713e4c6fedde9189b7545574b5..193e44f272d0bd82dab4e936b452b6dbe5a424d0 100644
--- a/test/make_standard_data.py
+++ b/test/make_standard_data.py
@@ -5,7 +5,7 @@ import os
import numpy as np
import tupak
-from tupak.waveform_generator import WaveformGenerator
+from tupak.gw.waveform_generator import WaveformGenerator
np.random.seed(10)
@@ -33,18 +33,18 @@ simulation_parameters = dict(
)
waveform_generator = WaveformGenerator(time_duration=time_duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=simulation_parameters)
signal = waveform_generator.frequency_domain_strain()
-IFO = tupak.detector.get_interferometer_with_fake_noise_and_injection(name='H1', injection_polarizations=signal,
- injection_parameters=simulation_parameters,
- time_duration=time_duration, plot=False,
- sampling_frequency=sampling_frequency)
+IFO = tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(name='H1', injection_polarizations=signal,
+ injection_parameters=simulation_parameters,
+ time_duration=time_duration, plot=False,
+ sampling_frequency=sampling_frequency)
hf_signal_and_noise = IFO.data
-frequencies = tupak.utils.create_frequency_series(
+frequencies = tupak.core.utils.create_frequency_series(
sampling_frequency=sampling_frequency, duration=time_duration)
if __name__ == '__main__':
diff --git a/test/noise_realisation_tests.py b/test/noise_realisation_tests.py
index 2fe373bb1868b9c49088db875abe9cd736c37b70..8f96cb30c69383527ae77cdee4c765ed5d95d190 100644
--- a/test/noise_realisation_tests.py
+++ b/test/noise_realisation_tests.py
@@ -1,6 +1,7 @@
+from __future__ import absolute_import
import numpy as np
import unittest
-from context import tupak
+import tupak
class TestNoiseRealisation(unittest.TestCase):
@@ -10,7 +11,7 @@ class TestNoiseRealisation(unittest.TestCase):
factor = np.sqrt(2./time_duration)
n_avg = 1000
psd_avg = 0
- interferometer = tupak.detector.get_empty_interferometer('H1')
+ interferometer = tupak.gw.detector.get_empty_interferometer('H1')
for x in range(0, n_avg):
interferometer.set_data(sampling_frequency, time_duration, from_power_spectral_density=True)
psd_avg += abs(interferometer.data)**2
@@ -25,20 +26,20 @@ class TestNoiseRealisation(unittest.TestCase):
def test_noise_normalisation(self):
time_duration = 1.
sampling_frequency = 4096.
- time_array = tupak.utils.create_time_series(sampling_frequency=sampling_frequency, duration=time_duration)
+ time_array = tupak.core.utils.create_time_series(sampling_frequency=sampling_frequency, duration=time_duration)
# generate some toy-model signal for matched filtering SNR testing
n_avg = 1000
snr = np.zeros(n_avg)
mu = np.exp(-(time_array-time_duration/2.)**2 / (2.*0.1**2)) * np.sin(2*np.pi*100*time_array)
- muf, frequency_array = tupak.utils.nfft(mu, sampling_frequency)
+ muf, frequency_array = tupak.core.utils.nfft(mu, sampling_frequency)
for x in range(0, n_avg):
- interferometer = tupak.detector.get_empty_interferometer('H1')
+ interferometer = tupak.gw.detector.get_empty_interferometer('H1')
interferometer.set_data(sampling_frequency, time_duration, from_power_spectral_density=True)
hf_tmp = interferometer.data
psd = interferometer.power_spectral_density
- snr[x] = tupak.utils.inner_product(hf_tmp, muf, frequency_array, psd) \
- / np.sqrt(tupak.utils.inner_product(muf, muf, frequency_array, psd))
+ snr[x] = tupak.gw.utils.inner_product(hf_tmp, muf, frequency_array, psd) \
+ / np.sqrt(tupak.gw.utils.inner_product(muf, muf, frequency_array, psd))
self.assertTrue(np.isclose(np.std(snr), 1.00, atol=1e-1))
diff --git a/test/prior_tests.py b/test/prior_tests.py
index bca3df485996461a62fbc205325810f9e1e08fcb..c7d39c8c0da6c83b9ea37989625093a50935a463 100644
--- a/test/prior_tests.py
+++ b/test/prior_tests.py
@@ -1,4 +1,5 @@
-from context import tupak
+from __future__ import absolute_import
+import tupak
import unittest
from mock import Mock
import numpy as np
@@ -7,7 +8,7 @@ import numpy as np
class TestPriorInstantiationWithoutOptionalPriors(unittest.TestCase):
def setUp(self):
- self.prior = tupak.prior.Prior()
+ self.prior = tupak.core.prior.Prior()
def tearDown(self):
del self.prior
@@ -22,14 +23,26 @@ class TestPriorInstantiationWithoutOptionalPriors(unittest.TestCase):
self.assertFalse(self.prior.is_fixed)
def test_class_instance(self):
- self.assertIsInstance(self.prior, tupak.prior.Prior)
+ self.assertIsInstance(self.prior, tupak.core.prior.Prior)
+
+ def test_magic_call_is_the_same_as_sampling(self):
+ self.prior.sample = Mock(return_value=0.5)
+ self.assertEqual(self.prior.sample(), self.prior())
+
+ def test_base_rescale_method(self):
+ self.assertIsNone(self.prior.rescale(1))
+
+ def test_base_repr(self):
+ self.prior = tupak.core.prior.Prior(name='test_name', latex_label='test_label', minimum=0, maximum=1)
+ expected_string = "Prior(name='test_name', latex_label='test_label', minimum=0, maximum=1)"
+ self.assertEqual(expected_string, self.prior.__repr__())
class TestPriorName(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.prior = tupak.prior.Prior(self.test_name)
+ self.prior = tupak.core.prior.Prior(self.test_name)
def tearDown(self):
del self.prior
@@ -43,7 +56,7 @@ class TestPriorName(unittest.TestCase):
class TestPriorLatexLabel(unittest.TestCase):
def setUp(self):
self.test_name = 'test_name'
- self.prior = tupak.prior.Prior(self.test_name)
+ self.prior = tupak.core.prior.Prior(self.test_name)
def tearDown(self):
del self.test_name
@@ -72,15 +85,15 @@ class TestPriorIsFixed(unittest.TestCase):
pass
def test_is_fixed_parent_class(self):
- self.prior = tupak.prior.Prior()
+ self.prior = tupak.core.prior.Prior()
self.assertFalse(self.prior.is_fixed)
def test_is_fixed_delta_function_class(self):
- self.prior = tupak.prior.DeltaFunction(peak=0)
+ self.prior = tupak.core.prior.DeltaFunction(peak=0)
self.assertTrue(self.prior.is_fixed)
def test_is_fixed_uniform_class(self):
- self.prior = tupak.prior.Uniform(minimum=0, maximum=10)
+ self.prior = tupak.core.prior.Uniform(minimum=0, maximum=10)
self.assertFalse(self.prior.is_fixed)
@@ -89,18 +102,18 @@ class TestPriorClasses(unittest.TestCase):
def setUp(self):
self.priors = [
- tupak.prior.DeltaFunction(name='test', peak=1),
- tupak.prior.Gaussian(name='test', mu=0, sigma=1),
- tupak.prior.PowerLaw(name='test', alpha=0, minimum=0, maximum=1),
- tupak.prior.PowerLaw(name='test', alpha=2, minimum=1, maximum=1e2),
- tupak.prior.Uniform(name='test', minimum=0, maximum=1),
- tupak.prior.LogUniform(name='test', minimum=5e0, maximum=1e2),
- tupak.prior.UniformComovingVolume(name='test', minimum=2e2, maximum=5e3),
- tupak.prior.Sine(name='test'),
- tupak.prior.Cosine(name='test'),
- tupak.prior.Interped(name='test', xx=np.linspace(0, 10, 1000), yy=np.linspace(0, 10, 1000)**4,
- minimum=3, maximum=5),
- tupak.prior.TruncatedGaussian(name='test', mu=1, sigma=0.4, minimum=-1, maximum=1)
+ tupak.core.prior.DeltaFunction(name='test', peak=1),
+ tupak.core.prior.Gaussian(name='test', mu=0, sigma=1),
+ tupak.core.prior.PowerLaw(name='test', alpha=0, minimum=0, maximum=1),
+ tupak.core.prior.PowerLaw(name='test', alpha=2, minimum=1, maximum=1e2),
+ tupak.core.prior.Uniform(name='test', minimum=0, maximum=1),
+ tupak.core.prior.LogUniform(name='test', minimum=5e0, maximum=1e2),
+ tupak.core.prior.UniformComovingVolume(name='test', minimum=2e2, maximum=5e3),
+ tupak.core.prior.Sine(name='test'),
+ tupak.core.prior.Cosine(name='test'),
+ tupak.core.prior.Interped(name='test', xx=np.linspace(0, 10, 1000), yy=np.linspace(0, 10, 1000) ** 4,
+ minimum=3, maximum=5),
+ tupak.core.prior.TruncatedGaussian(name='test', mu=1, sigma=0.4, minimum=-1, maximum=1)
]
def test_minimum_rescaling(self):
@@ -142,7 +155,7 @@ class TestPriorClasses(unittest.TestCase):
"""Test that the prior probability is non-negative in domain of validity and zero outside."""
for prior in self.priors:
# skip delta function prior in this case
- if isinstance(prior, tupak.prior.DeltaFunction):
+ if isinstance(prior, tupak.core.prior.DeltaFunction):
continue
if prior.maximum != np.inf:
outside_domain = np.linspace(prior.maximum + 1, prior.maximum + 1e4, 1000)
@@ -152,7 +165,7 @@ class TestPriorClasses(unittest.TestCase):
"""Test that the prior probability is non-negative in domain of validity and zero outside."""
for prior in self.priors:
# skip delta function prior in this case
- if isinstance(prior, tupak.prior.DeltaFunction):
+ if isinstance(prior, tupak.core.prior.DeltaFunction):
continue
if prior.minimum != -np.inf:
outside_domain = np.linspace(prior.minimum - 1e4, prior.minimum - 1, 1000)
@@ -162,7 +175,7 @@ class TestPriorClasses(unittest.TestCase):
"""Test that the prior probability is non-negative in domain of validity and zero outside."""
for prior in self.priors:
# skip delta function prior in this case
- if isinstance(prior, tupak.prior.DeltaFunction):
+ if isinstance(prior, tupak.core.prior.DeltaFunction):
continue
if prior.minimum == -np.inf:
prior.minimum = -1e5
@@ -175,7 +188,7 @@ class TestPriorClasses(unittest.TestCase):
"""Test that the prior probability is non-negative in domain of validity and zero outside."""
for prior in self.priors:
# skip delta function prior in this case
- if isinstance(prior, tupak.prior.DeltaFunction):
+ if isinstance(prior, tupak.core.prior.DeltaFunction):
continue
surround_domain = np.linspace(prior.minimum - 1, prior.maximum + 1, 1000)
prior.prob(surround_domain)
@@ -183,9 +196,9 @@ class TestPriorClasses(unittest.TestCase):
def test_normalized(self):
"""Test that each of the priors are normalised, this needs care for delta function and Gaussian priors"""
for prior in self.priors:
- if isinstance(prior, tupak.prior.DeltaFunction):
+ if isinstance(prior, tupak.core.prior.DeltaFunction):
continue
- elif isinstance(prior, tupak.prior.Gaussian):
+ elif isinstance(prior, tupak.core.prior.Gaussian):
domain = np.linspace(-1e2, 1e2, 1000)
else:
domain = np.linspace(prior.minimum, prior.maximum, 1000)
@@ -198,24 +211,24 @@ class TestFillPrior(unittest.TestCase):
self.likelihood = Mock()
self.likelihood.parameters = dict(a=0, b=0, c=0, d=0, asdf=0, ra=1)
self.likelihood.non_standard_sampling_parameter_keys = dict(t=8)
- self.priors = dict(a=1, b=1.1, c='string', d=tupak.prior.Uniform(0, 1))
- self.priors = tupak.prior.fill_priors(self.priors, self.likelihood)
+ self.priors = dict(a=1, b=1.1, c='string', d=tupak.core.prior.Uniform(0, 1))
+ self.priors = tupak.core.prior.fill_priors(self.priors, self.likelihood)
def tearDown(self):
del self.likelihood
del self.priors
def test_prior_instances_are_not_changed_by_parsing(self):
- self.assertIsInstance(self.priors['d'], tupak.prior.Uniform)
+ self.assertIsInstance(self.priors['d'], tupak.core.prior.Uniform)
def test_parsing_ints_to_delta_priors_class(self):
- self.assertIsInstance(self.priors['a'], tupak.prior.DeltaFunction)
+ self.assertIsInstance(self.priors['a'], tupak.core.prior.DeltaFunction)
def test_parsing_ints_to_delta_priors_with_right_value(self):
self.assertEqual(self.priors['a'].peak, 1)
def test_parsing_floats_to_delta_priors_class(self):
- self.assertIsInstance(self.priors['b'], tupak.prior.DeltaFunction)
+ self.assertIsInstance(self.priors['b'], tupak.core.prior.DeltaFunction)
def test_parsing_floats_to_delta_priors_with_right_value(self):
self.assertAlmostEqual(self.priors['b'].peak, 1.1, 1e-8)
@@ -225,7 +238,7 @@ class TestFillPrior(unittest.TestCase):
print(self.priors['asdf'])
def test_with_available_default_priors_a_default_prior_is_set(self):
- self.assertIsInstance(self.priors['ra'], tupak.prior.Uniform)
+ self.assertIsInstance(self.priors['ra'], tupak.core.prior.Uniform)
if __name__ == '__main__':
diff --git a/test/sampler_tests.py b/test/sampler_tests.py
index b380df85992e4c33c05b2d2702a04687f63f504d..a24e472d652dbc82b31014053e82a53e5349b014 100644
--- a/test/sampler_tests.py
+++ b/test/sampler_tests.py
@@ -1,6 +1,7 @@
-from context import tupak
-from tupak import prior
-from tupak.result import Result
+from __future__ import absolute_import
+import tupak
+from tupak.core import prior
+from tupak.core.result import Result
import unittest
from mock import MagicMock
import numpy as np
@@ -12,7 +13,7 @@ import copy
class TestSampler(unittest.TestCase):
def setUp(self):
- likelihood = tupak.likelihood.Likelihood()
+ likelihood = tupak.core.likelihood.Likelihood()
likelihood.parameters = dict(a=1, b=2, c=3)
delta_prior = prior.DeltaFunction(peak=0)
delta_prior.rescale = MagicMock(return_value=prior.DeltaFunction(peak=1))
@@ -29,11 +30,11 @@ class TestSampler(unittest.TestCase):
test_directory = 'test_directory'
if os.path.isdir(test_directory):
os.rmdir(test_directory)
- self.sampler = tupak.sampler.Sampler(likelihood=likelihood,
- priors=priors,
- external_sampler='nestle',
- outdir=test_directory,
- use_ratio=False)
+ self.sampler = tupak.core.sampler.Sampler(likelihood=likelihood,
+ priors=priors,
+ external_sampler='nestle',
+ outdir=test_directory,
+ use_ratio=False)
def tearDown(self):
os.rmdir(self.sampler.outdir)
@@ -68,8 +69,8 @@ class TestSampler(unittest.TestCase):
self.sampler.external_sampler = 'unexpected_sampler'
def test_setting_custom_sampler(self):
- other_sampler = tupak.sampler.Sampler(self.sampler.likelihood,
- self.sampler.priors)
+ other_sampler = tupak.core.sampler.Sampler(self.sampler.likelihood,
+ self.sampler.priors)
self.sampler.external_sampler = other_sampler
self.assertEqual(self.sampler.external_sampler, other_sampler)
diff --git a/test/standard_data.txt b/test/standard_data.txt
index abaab16e47d4b8b78f1952daafcc26af8e6efbbc..7bc38c983a0d134a392896d7880ee73b6fb1a357 100644
--- a/test/standard_data.txt
+++ b/test/standard_data.txt
@@ -1,2502 +1,2502 @@
# frequency hf_real hf_imag
0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
-2.500000000000000000e-01 2.712164704499983446e-20 -8.632846508010759845e-21
-5.000000000000000000e-01 -5.859783771937879104e-20 -1.208855347352751813e-20
-7.500000000000000000e-01 -3.178952922960888016e-22 4.445310922756554263e-20
-1.000000000000000000e+00 2.355955589830128022e-20 -2.813522510571723493e-20
-1.250000000000000000e+00 -2.730390116170350940e-20 9.280414339464840024e-20
-1.500000000000000000e+00 1.006755653562511153e-20 -4.419976568129027883e-20
-1.750000000000000000e+00 4.115897303108330092e-21 2.112765252168851979e-21
-2.000000000000000000e+00 1.627206716214960216e-22 -2.055456486745229570e-20
-2.250000000000000000e+00 -6.620417284972205530e-21 3.193631759085623336e-20
-2.500000000000000000e+00 1.641930478250154448e-20 -7.417676043695194317e-20
-2.750000000000000000e+00 4.561626470555889080e-20 1.651272385360627989e-20
-3.000000000000000000e+00 -3.659295383736936845e-20 1.555295886054513411e-21
-3.250000000000000000e+00 3.898966362323945648e-20 1.189242531546789503e-20
-3.500000000000000000e+00 8.669101029159549862e-21 -5.830811834269203185e-21
-3.750000000000000000e+00 1.687854061667690166e-20 -3.862430892461262588e-20
-4.000000000000000000e+00 -4.309720420032583526e-20 2.344429335749293334e-20
-4.250000000000000000e+00 5.124063269341232469e-21 1.534678338703042296e-20
-4.500000000000000000e+00 5.629004909995830096e-20 2.412527345253236103e-20
-4.750000000000000000e+00 -4.094358710091549087e-20 -1.266852735905987429e-20
-5.000000000000000000e+00 -7.499066839484846846e-20 5.658934250460733550e-20
-5.250000000000000000e+00 -6.610445682003900510e-20 6.603808628804247375e-20
-5.500000000000000000e+00 1.008873647278321625e-20 -3.106870526243811777e-20
-5.750000000000000000e+00 9.043218726204733357e-20 7.139941297647547924e-20
-6.000000000000000000e+00 4.260765191393523254e-20 3.064697755390980978e-20
-6.250000000000000000e+00 6.342178497154278359e-20 3.386211582843387811e-20
-6.500000000000000000e+00 3.759498227853935748e-21 3.019274935156022785e-20
-6.750000000000000000e+00 5.300863812138494611e-20 -4.992794671944581946e-20
-7.000000000000000000e+00 -1.028506702222263807e-20 3.944573096767358533e-21
-7.250000000000000000e+00 2.325121814501505576e-20 3.383388805519979153e-20
-7.500000000000000000e+00 -1.013602062938300552e-20 -1.286739679489225775e-20
-7.750000000000000000e+00 -2.082846795788444472e-20 3.538853331665840101e-20
-8.000000000000000000e+00 5.031977293296024171e-21 2.161467693164155859e-20
-8.250000000000000000e+00 -1.805415248367332283e-20 1.672477547965369346e-20
-8.500000000000000000e+00 4.961413146095949980e-20 4.036387170353681605e-20
-8.750000000000000000e+00 7.394431428515550788e-21 4.461522672971337800e-20
-9.000000000000000000e+00 6.951936760875046112e-22 1.005831327971198444e-21
-9.250000000000000000e+00 -3.490890467256912093e-22 5.157331357101254857e-22
-9.500000000000000000e+00 4.660938766499154217e-22 -3.078507752819964701e-22
-9.750000000000000000e+00 -1.648182151763208975e-22 1.239305361927998421e-22
-1.000000000000000000e+01 1.086065953741803739e-22 -2.513111609839415997e-24
-1.025000000000000000e+01 -4.638186794156298950e-23 -4.496665648935288191e-23
-1.050000000000000000e+01 -1.054855230038820423e-22 -3.567888032427948610e-23
-1.075000000000000000e+01 -4.829272938102087255e-23 2.477063276695814593e-23
-1.100000000000000000e+01 -7.133597231831673365e-23 3.215468854855187099e-23
-1.125000000000000000e+01 -1.719241124490278525e-23 6.188778416424666315e-23
-1.150000000000000000e+01 -2.525173563234492259e-23 2.894155824701237036e-23
-1.175000000000000000e+01 2.157585297555417289e-23 5.880126120067713665e-23
-1.200000000000000000e+01 3.647232262772666639e-23 7.980844331341396293e-23
-1.225000000000000000e+01 -8.925448137390323972e-24 -6.975989958353410904e-23
-1.250000000000000000e+01 -1.477207745046542851e-24 8.376448770418565093e-24
-1.275000000000000000e+01 1.557808352260779713e-23 6.925412952373954596e-25
-1.300000000000000000e+01 -2.757024382832700185e-23 1.850889854380924757e-23
-1.325000000000000000e+01 3.435908771985941978e-23 -1.445282392441980241e-23
-1.350000000000000000e+01 3.891094985410053729e-23 6.970904570357116466e-23
-1.375000000000000000e+01 8.973787468237994024e-24 9.878433566354131233e-23
-1.400000000000000000e+01 1.008930188485294070e-22 9.963840741475518650e-24
-1.425000000000000000e+01 3.700546867783979889e-23 -9.259881699554407047e-24
-1.450000000000000000e+01 -4.341685063508311444e-24 6.257909700035005212e-23
-1.475000000000000000e+01 -1.344629706981722302e-23 -6.947411688912186987e-23
-1.500000000000000000e+01 -8.285691591889364660e-24 -4.065665708720029498e-23
-1.525000000000000000e+01 -1.722999256161710456e-23 3.632522105409022793e-23
-1.550000000000000000e+01 3.734243041658685749e-23 7.826244330206571362e-23
-1.575000000000000000e+01 -2.226014185789581633e-23 -6.190961505704038227e-23
-1.600000000000000000e+01 -2.497621262054429784e-24 3.324326299193254246e-23
-1.625000000000000000e+01 -1.574177567532927727e-23 -2.531448690194617396e-24
-1.650000000000000000e+01 3.008052858430937977e-23 1.639529555625254087e-23
-1.675000000000000000e+01 -3.946196046314530670e-23 5.329125447446961148e-23
-1.700000000000000000e+01 -9.763482306081403310e-24 2.768637950810214537e-23
-1.725000000000000000e+01 -3.181200597396391413e-24 1.573933631904763115e-23
-1.750000000000000000e+01 8.079692972955533547e-24 -4.743982321890858164e-23
-1.775000000000000000e+01 1.131231031437459007e-23 -1.257126889215147412e-23
-1.800000000000000000e+01 -5.141131101100242947e-24 4.229827641443348349e-23
-1.825000000000000000e+01 2.288718283214886607e-23 -5.590657714947022186e-23
-1.850000000000000000e+01 7.078017353934066550e-24 2.106041754686983816e-23
-1.875000000000000000e+01 5.395502093771122398e-23 2.228611560373683290e-23
-1.900000000000000000e+01 -3.208298434871175690e-23 1.025782555321270717e-23
-1.925000000000000000e+01 1.284821480545355912e-23 -1.446427698080464441e-23
-1.950000000000000000e+01 -2.106972477668963616e-23 -2.109789482839201592e-23
-1.975000000000000000e+01 -1.567303604474262492e-23 1.038672156754920147e-23
-2.000000000000000000e+01 2.307254551169242249e-22 7.112373197404001699e-23
-2.025000000000000000e+01 1.271881752377123572e-22 -1.898721599347438438e-22
-2.050000000000000000e+01 -1.867644440433626933e-22 -1.064182893383484942e-22
-2.075000000000000000e+01 -1.416839199867456794e-22 1.448610309021176160e-22
-2.100000000000000000e+01 1.170321301649588327e-22 1.630324566440026571e-22
-2.125000000000000000e+01 1.160913167675419334e-22 -1.044768721229422918e-22
-2.150000000000000000e+01 -1.024544995765774786e-22 -1.322237144827430510e-22
-2.175000000000000000e+01 -1.438329269132518883e-22 9.150762212815487207e-23
-2.200000000000000000e+01 1.078643095797412449e-22 2.029317508458693425e-22
-2.225000000000000000e+01 1.047561254661552070e-22 -1.258368708084358669e-22
-2.250000000000000000e+01 -1.052126762641949879e-22 -1.306132409342473732e-22
-2.275000000000000000e+01 -1.027174841928332129e-22 1.465671669623533371e-22
-2.300000000000000000e+01 1.446867499335273778e-22 7.298626372410399436e-23
-2.325000000000000000e+01 4.779411368917948683e-23 -1.563268356324752608e-22
-2.350000000000000000e+01 -1.517601189526300791e-22 -3.520567877638452850e-23
-2.375000000000000000e+01 2.004211269720131054e-23 1.464184304407333633e-22
-2.400000000000000000e+01 1.468690107124371145e-22 -2.582699124913034830e-23
-2.425000000000000000e+01 -7.971441026882343204e-23 -1.175304566564124156e-22
-2.450000000000000000e+01 -9.589124003158858001e-23 1.049304584713179196e-22
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diff --git a/test/tests.py b/test/tests.py
index 54c55501641924a2dcfa8be92b68132a8cd2f32e..0feeced3f27344e5adc1c765f49b5c1a878110d3 100644
--- a/test/tests.py
+++ b/test/tests.py
@@ -1,11 +1,13 @@
+from __future__ import absolute_import
+
+import os
import unittest
+
import numpy as np
-import os
-import shutil
-from context import tupak
-import logging
from past.builtins import execfile
+import tupak
+
class Test(unittest.TestCase):
outdir = './outdir'
@@ -46,7 +48,7 @@ class Test(unittest.TestCase):
self.assertAlmostEqual(all(self.msd['hf_signal_and_noise'] - hf_signal_and_noise_saved), 0.00000000, 5)
def test_recover_luminosity_distance(self):
- likelihood = tupak.likelihood.GravitationalWaveTransient(
+ likelihood = tupak.gw.likelihood.GravitationalWaveTransient(
[self.msd['IFO']], self.msd['waveform_generator'])
priors = {}
@@ -54,11 +56,10 @@ class Test(unittest.TestCase):
priors[key] = self.msd['simulation_parameters'][key]
dL = self.msd['simulation_parameters']['luminosity_distance']
- priors['luminosity_distance'] = tupak.prior.Uniform(
+ priors['luminosity_distance'] = tupak.core.prior.Uniform(
name='luminosity_distance', minimum=dL - 10, maximum=dL + 10)
-
- result = tupak.sampler.run_sampler(
+ result = tupak.core.sampler.run_sampler(
likelihood, priors, sampler='nestle', verbose=False, npoints=100)
self.assertAlmostEqual(np.mean(result.samples), dL,
delta=3*np.std(result.samples))
diff --git a/test/waveform_generator_tests.py b/test/waveform_generator_tests.py
index 34cc7925d4ba3aa788781810cff60df99247798b..80edc81d00b62231d516db97e11a83531a1ca9f7 100644
--- a/test/waveform_generator_tests.py
+++ b/test/waveform_generator_tests.py
@@ -1,5 +1,6 @@
+from __future__ import absolute_import
import unittest
-from context import tupak
+import tupak
import numpy as np
@@ -18,13 +19,14 @@ def gaussian_frequency_domain_strain_2(frequency_array, a, m, s, ra, dec, geocen
class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestCase):
def setUp(self):
- self.waveform_generator = tupak.waveform_generator.WaveformGenerator(1, 4096,
- frequency_domain_source_model=gaussian_frequency_domain_strain)
+ self.waveform_generator = \
+ tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
+ frequency_domain_source_model=gaussian_frequency_domain_strain)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
- ra=1.375,
- dec=-1.2108,
- geocent_time=1126259642.413,
- psi=2.659)
+ ra=1.375,
+ dec=-1.2108,
+ geocent_time=1126259642.413,
+ psi=2.659)
def tearDown(self):
del self.waveform_generator
@@ -46,20 +48,21 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
self.assertIsInstance(self.waveform_generator.time_array, np.ndarray)
def test_source_model_parameters(self):
- params = self.simulation_parameters.keys()
- self.assertItemsEqual(self.waveform_generator.parameters, params)
+ self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
+ sorted(list(self.simulation_parameters.keys())))
-class TestParameterSetter(unittest.TestCase):
+class TestSetters(unittest.TestCase):
def setUp(self):
- self.waveform_generator = tupak.waveform_generator.WaveformGenerator(1, 4096,
- frequency_domain_source_model=gaussian_frequency_domain_strain)
+ self.waveform_generator = \
+ tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
+ frequency_domain_source_model=gaussian_frequency_domain_strain)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
- ra=1.375,
- dec=-1.2108,
- geocent_time=1126259642.413,
- psi=2.659)
+ ra=1.375,
+ dec=-1.2108,
+ geocent_time=1126259642.413,
+ psi=2.659)
def tearDown(self):
del self.waveform_generator
@@ -72,27 +75,34 @@ class TestParameterSetter(unittest.TestCase):
def test_parameter_setter_none_handling(self):
self.waveform_generator.parameters = None
- self.assertItemsEqual(self.waveform_generator.parameters.keys(), self.simulation_parameters.keys())
+ self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
+ sorted(list(self.simulation_parameters.keys())))
+
+ def test_frequency_array_setter(self):
+ new_frequency_array = np.arange(1, 100)
+ self.waveform_generator.frequency_array = new_frequency_array
+ self.assertTrue(np.array_equal(new_frequency_array, self.waveform_generator.frequency_array))
class TestSourceModelSetter(unittest.TestCase):
def setUp(self):
- self.waveform_generator = tupak.waveform_generator.WaveformGenerator(1, 4096,
- frequency_domain_source_model=gaussian_frequency_domain_strain)
+ self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
+ frequency_domain_source_model=gaussian_frequency_domain_strain)
self.waveform_generator.frequency_domain_source_model = gaussian_frequency_domain_strain_2
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
- ra=1.375,
- dec=-1.2108,
- geocent_time=1126259642.413,
- psi=2.659)
+ ra=1.375,
+ dec=-1.2108,
+ geocent_time=1126259642.413,
+ psi=2.659)
def tearDown(self):
del self.waveform_generator
del self.simulation_parameters
def test_parameters_are_set_correctly(self):
- self.assertItemsEqual(self.waveform_generator.parameters, self.simulation_parameters.keys())
+ self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
+ sorted(list(self.simulation_parameters.keys())))
if __name__ == '__main__':
diff --git a/tupak/__init__.py b/tupak/__init__.py
index b2b4d20d1a2ede870e94b5bb9223385770a24c32..c88098759f893b0ea4c3d6bc61f155e7a03b6807 100644
--- a/tupak/__init__.py
+++ b/tupak/__init__.py
@@ -6,7 +6,7 @@ Tupak is The User friendly Parameter estimAtion Kode
The aim of tupak is to provide user friendly interface to perform parameter
estimation. It is primarily designed and built for inference of compact
-binary coalesence events in interferometric data, but it can also be used for
+binary coalescence events in interferometric data, but it can also be used for
more general problems.
For installation instructions see https://git.ligo.org/Monash/tupak
@@ -17,17 +17,11 @@ For installation instructions see https://git.ligo.org/Monash/tupak
from __future__ import print_function, division, absolute_import
# import local files, utils should be imported first
-from . import utils
-from . import detector
-from . import prior
-from . import source
-from . import likelihood
-from . import waveform_generator
-from . import result
-from . import sampler
-from . import conversion
+from tupak.gw import detector, conversion, source, waveform_generator
+from tupak.core import likelihood, prior, result, sampler, utils
# import a few often-used functions and classes to simplify scripts
-from .likelihood import Likelihood, GravitationalWaveTransient
-from .waveform_generator import WaveformGenerator
-from .sampler import run_sampler
+from tupak.core.likelihood import Likelihood
+from tupak.gw.likelihood import GravitationalWaveTransient
+from tupak.gw.waveform_generator import WaveformGenerator
+from tupak.core.sampler import run_sampler
diff --git a/tupak/core/__init__.py b/tupak/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1329ed8e86ff0e7b23fed6c45aab0f9d4c92c78d
--- /dev/null
+++ b/tupak/core/__init__.py
@@ -0,0 +1,6 @@
+from __future__ import absolute_import
+import tupak.core.likelihood
+import tupak.core.prior
+import tupak.core.result
+import tupak.core.sampler
+import tupak.core.utils
diff --git a/tupak/core/likelihood.py b/tupak/core/likelihood.py
new file mode 100644
index 0000000000000000000000000000000000000000..02e442240d3a135757abd2b693cc9376ca5de538
--- /dev/null
+++ b/tupak/core/likelihood.py
@@ -0,0 +1,26 @@
+from __future__ import division, print_function
+
+import numpy as np
+
+try:
+ from scipy.special import logsumexp
+except ImportError:
+ from scipy.misc import logsumexp
+
+
+class Likelihood(object):
+ """ Empty likelihood class to be subclassed by other likelihoods """
+
+ def __init__(self, parameters=None):
+ self.parameters = parameters
+
+ def log_likelihood(self):
+ return np.nan
+
+ def noise_log_likelihood(self):
+ return np.nan
+
+ def log_likelihood_ratio(self):
+ return self.log_likelihood() - self.noise_log_likelihood()
+
+
diff --git a/tupak/prior.py b/tupak/core/prior.py
similarity index 82%
rename from tupak/prior.py
rename to tupak/core/prior.py
index 9da566ea6ef8dcceab5b2906a1996812673ab126..cb87079704a9b29dd32fb3fa23b9d9a5714c20d7 100644
--- a/tupak/prior.py
+++ b/tupak/core/prior.py
@@ -8,6 +8,8 @@ from scipy.special import erf, erfinv
import logging
import os
+from tupak.gw.prior import create_default_prior, test_redundancy
+
class Prior(object):
"""
@@ -443,54 +445,6 @@ class UniformComovingVolume(FromFile):
return FromFile.__repr__(self)
-def create_default_prior(name):
- """
- Make a default prior for a parameter with a known name.
-
- This is currently set up for binary black holes.
-
- Parameters
- ----------
- name: str
- Parameter name
-
- Return
- ------
- prior: Prior
- Default prior distribution for that parameter, if unknown None is returned.
- """
- default_priors = {
- 'mass_1': Uniform(name=name, minimum=20, maximum=100),
- 'mass_2': Uniform(name=name, minimum=20, maximum=100),
- 'chirp_mass': Uniform(name=name, minimum=25, maximum=100),
- 'total_mass': Uniform(name=name, minimum=10, maximum=200),
- 'mass_ratio': Uniform(name=name, minimum=0.125, maximum=1),
- 'symmetric_mass_ratio': Uniform(name=name, minimum=8 / 81, maximum=0.25),
- 'a_1': Uniform(name=name, minimum=0, maximum=0.8),
- 'a_2': Uniform(name=name, minimum=0, maximum=0.8),
- 'tilt_1': Sine(name=name),
- 'tilt_2': Sine(name=name),
- 'cos_tilt_1': Uniform(name=name, minimum=-1, maximum=1),
- 'cos_tilt_2': Uniform(name=name, minimum=-1, maximum=1),
- 'phi_12': Uniform(name=name, minimum=0, maximum=2 * np.pi),
- 'phi_jl': Uniform(name=name, minimum=0, maximum=2 * np.pi),
- 'luminosity_distance': UniformComovingVolume(name=name, minimum=1e2, maximum=5e3),
- 'dec': Cosine(name=name),
- 'ra': Uniform(name=name, minimum=0, maximum=2 * np.pi),
- 'iota': Sine(name=name),
- 'cos_iota': Uniform(name=name, minimum=-1, maximum=1),
- 'psi': Uniform(name=name, minimum=0, maximum=2 * np.pi),
- 'phase': Uniform(name=name, minimum=0, maximum=2 * np.pi)
- }
- if name in default_priors.keys():
- prior = default_priors[name]
- else:
- logging.info(
- "No default prior found for variable {}.".format(name))
- prior = None
- return prior
-
-
def fill_priors(prior, likelihood):
"""
Fill dictionary of priors based on required parameters of likelihood
@@ -550,55 +504,6 @@ def fill_priors(prior, likelihood):
return prior
-def test_redundancy(key, prior):
- """
- Test whether adding the key would add be redundant.
-
- Parameters
- ----------
- key: str
- The string to test.
- prior: dict
- Current prior dictionary.
-
- Return
- ------
- redundant: bool
- Whether the key is redundant
- """
- redundant = False
- mass_parameters = {'mass_1', 'mass_2', 'chirp_mass', 'total_mass', 'mass_ratio', 'symmetric_mass_ratio'}
- spin_magnitude_parameters = {'a_1', 'a_2'}
- spin_tilt_1_parameters = {'tilt_1', 'cos_tilt_1'}
- spin_tilt_2_parameters = {'tilt_2', 'cos_tilt_2'}
- spin_azimuth_parameters = {'phi_1', 'phi_2', 'phi_12', 'phi_jl'}
- inclination_parameters = {'iota', 'cos_iota'}
- distance_parameters = {'luminosity_distance', 'comoving_distance', 'redshift'}
-
- for parameter_set in [mass_parameters, spin_magnitude_parameters, spin_azimuth_parameters]:
- if key in parameter_set:
- if len(parameter_set.intersection(prior.keys())) > 2:
- redundant = True
- logging.warning('{} in prior. This may lead to unexpected behaviour.'.format(
- parameter_set.intersection(prior.keys())))
- break
- elif len(parameter_set.intersection(prior.keys())) == 2:
- redundant = True
- break
- for parameter_set in [inclination_parameters, distance_parameters, spin_tilt_1_parameters, spin_tilt_2_parameters]:
- if key in parameter_set:
- if len(parameter_set.intersection(prior.keys())) > 1:
- redundant = True
- logging.warning('{} in prior. This may lead to unexpected behaviour.'.format(
- parameter_set.intersection(prior.keys())))
- break
- elif len(parameter_set.intersection(prior.keys())) == 1:
- redundant = True
- break
-
- return redundant
-
-
def write_priors_to_file(priors, outdir, label):
"""
Write the prior distribution to file.
diff --git a/tupak/prior_files/comoving.txt b/tupak/core/prior_files/comoving.txt
similarity index 100%
rename from tupak/prior_files/comoving.txt
rename to tupak/core/prior_files/comoving.txt
diff --git a/tupak/result.py b/tupak/core/result.py
similarity index 100%
rename from tupak/result.py
rename to tupak/core/result.py
diff --git a/tupak/sampler.py b/tupak/core/sampler.py
similarity index 98%
rename from tupak/sampler.py
rename to tupak/core/sampler.py
index 0d18cfce6d623ce5b33c7b4deb5c36ff37aaa51f..2ea3ad08cd7dcc5f77b472bd5442d781f1872bbc 100644
--- a/tupak/sampler.py
+++ b/tupak/core/sampler.py
@@ -8,10 +8,9 @@ import numpy as np
import matplotlib.pyplot as plt
import time
-from .result import Result, read_in_result
-from .prior import Prior, fill_priors
-from . import utils
-from . import prior
+from tupak.core.result import Result, read_in_result
+from tupak.core.prior import Prior, fill_priors
+from tupak.core import utils
import tupak
@@ -520,7 +519,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
Parameters
----------
- likelihood: `tupak.likelihood.GravitationalWaveTransient`
+ likelihood: `tupak.GravitationalWaveTransient`
A `GravitationalWaveTransient` instance
priors: dict
A dictionary of the priors for each parameter - missing parameters will
@@ -558,7 +557,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
if priors is None:
priors = dict()
priors = fill_priors(priors, likelihood)
- tupak.prior.write_priors_to_file(priors, outdir, label)
+ tupak.core.prior.write_priors_to_file(priors, outdir, label)
if implemented_samplers.__contains__(sampler.title()):
sampler_class = globals()[sampler.title()]
diff --git a/tupak/core/utils.py b/tupak/core/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fa1ecc57c2c6a21c319f9909e6f6aaefee4c7b3
--- /dev/null
+++ b/tupak/core/utils.py
@@ -0,0 +1,310 @@
+from __future__ import division
+import logging
+import os
+import numpy as np
+from math import fmod
+import argparse
+
+# Constants
+
+speed_of_light = 299792458.0 # speed of light in m/s
+parsec = 3.085677581 * 1e16
+solar_mass = 1.98855 * 1e30
+
+
+def get_sampling_frequency(time_series):
+ """
+ Calculate sampling frequency from a time series
+ """
+ tol = 1e-10
+ if np.ptp(np.diff(time_series)) > tol:
+ raise ValueError("Your time series was not evenly sampled")
+ else:
+ return 1. / (time_series[1] - time_series[0])
+
+
+def create_time_series(sampling_frequency, duration, starting_time=0.):
+ return np.arange(starting_time, duration, 1./sampling_frequency)
+
+
+def ra_dec_to_theta_phi(ra, dec, gmst):
+ """
+ Convert from RA and DEC to polar coordinates on celestial sphere
+ Input:
+ ra - right ascension in radians
+ dec - declination in radians
+ gmst - Greenwich mean sidereal time of arrival of the signal in radians
+ Output:
+ theta - zenith angle in radians
+ phi - azimuthal angle in radians
+ """
+ phi = ra - gmst
+ theta = np.pi / 2 - dec
+ return theta, phi
+
+
+def gps_time_to_gmst(gps_time):
+ """
+ Convert gps time to Greenwich mean sidereal time in radians
+
+ This method assumes a constant rotation rate of earth since 00:00:00, 1 Jan. 2000
+ A correction has been applied to give the exact correct value for 00:00:00, 1 Jan. 2018
+ Error accumulates at a rate of ~0.0001 radians/decade.
+
+ Input:
+ time - gps time
+ Output:
+ gmst - Greenwich mean sidereal time in radians
+ """
+ omega_earth = 2 * np.pi * (1 / 365.2425 + 1) / 86400.
+ gps_2000 = 630720013.
+ gmst_2000 = (6 + 39. / 60 + 51.251406103947375 / 3600) * np.pi / 12
+ correction_2018 = -0.00017782487379358614
+ sidereal_time = omega_earth * (gps_time - gps_2000) + gmst_2000 + correction_2018
+ gmst = fmod(sidereal_time, 2 * np.pi)
+ return gmst
+
+
+def create_frequency_series(sampling_frequency, duration):
+ """
+ Create a frequency series with the correct length and spacing.
+
+ :param sampling_frequency: sampling frequency
+ :param duration: duration of data
+ :return: frequencies, frequency series
+ """
+ number_of_samples = duration * sampling_frequency
+ number_of_samples = int(np.round(number_of_samples))
+
+ # prepare for FFT
+ number_of_frequencies = (number_of_samples-1)//2
+ delta_freq = 1./duration
+
+ frequencies = delta_freq * np.linspace(1, number_of_frequencies, number_of_frequencies)
+
+ if len(frequencies) % 2 == 1:
+ frequencies = np.concatenate(([0], frequencies, [sampling_frequency / 2.]))
+ else:
+ # no Nyquist frequency when N=odd
+ frequencies = np.concatenate(([0], frequencies))
+
+ return frequencies
+
+
+def create_white_noise(sampling_frequency, duration):
+ """
+ Create white_noise which is then coloured by a given PSD
+
+
+ :param sampling_frequency: sampling frequency
+ :param duration: duration of data
+ """
+
+ number_of_samples = duration * sampling_frequency
+ number_of_samples = int(np.round(number_of_samples))
+
+ delta_freq = 1./duration
+
+ frequencies = create_frequency_series(sampling_frequency, duration)
+
+ norm1 = 0.5*(1./delta_freq)**0.5
+ re1 = np.random.normal(0, norm1, len(frequencies))
+ im1 = np.random.normal(0, norm1, len(frequencies))
+ htilde1 = re1 + 1j*im1
+
+ # convolve data with instrument transfer function
+ otilde1 = htilde1 * 1.
+ # set DC and Nyquist = 0
+ otilde1[0] = 0
+ # no Nyquist frequency when N=odd
+ if np.mod(number_of_samples, 2) == 0:
+ otilde1[-1] = 0
+
+ # normalise for positive frequencies and units of strain/rHz
+ white_noise = otilde1
+ # python: transpose for use with infft
+ white_noise = np.transpose(white_noise)
+ frequencies = np.transpose(frequencies)
+
+ return white_noise, frequencies
+
+
+def nfft(ht, Fs):
+ """
+ performs an FFT while keeping track of the frequency bins
+ assumes input time series is real (positive frequencies only)
+
+ ht = time series
+ Fs = sampling frequency
+
+ returns
+ hf = single-sided FFT of ft normalised to units of strain / sqrt(Hz)
+ f = frequencies associated with hf
+ """
+ # add one zero padding if time series does not have even number of sampling times
+ if np.mod(len(ht), 2) == 1:
+ ht = np.append(ht, 0)
+ LL = len(ht)
+ # frequency range
+ ff = Fs / 2 * np.linspace(0, 1, int(LL/2+1))
+
+ # calculate FFT
+ # rfft computes the fft for real inputs
+ hf = np.fft.rfft(ht)
+
+ # normalise to units of strain / sqrt(Hz)
+ hf = hf / Fs
+
+ return hf, ff
+
+
+def infft(hf, Fs):
+ """
+ inverse FFT for use in conjunction with nfft
+ eric.thrane@ligo.org
+ input:
+ hf = single-side FFT calculated by fft_eht
+ Fs = sampling frequency
+ output:
+ h = time series
+ """
+ # use irfft to work with positive frequencies only
+ h = np.fft.irfft(hf)
+ # undo LAL/Lasky normalisation
+ h = h*Fs
+
+ return h
+
+
+def setup_logger(outdir=None, label=None, log_level=None):
+ """ Setup logging output: call at the start of the script to use
+
+ Parameters
+ ----------
+ outdir, label: str
+ If supplied, write the logging output to outdir/label.log
+ log_level = ['debug', 'info', 'warning']
+ Either a string from the list above, or an interger as specified
+ in https://docs.python.org/2/library/logging.html#logging-levels
+ """
+
+ if type(log_level) is str:
+ try:
+ LEVEL = getattr(logging, log_level.upper())
+ except AttributeError:
+ raise ValueError('log_level {} not understood'.format(log_level))
+ elif log_level is None:
+ LEVEL = command_line_args.log_level
+ else:
+ LEVEL = int(log_level)
+
+ logger = logging.getLogger()
+ stream_handler = logging.StreamHandler()
+ stream_handler.setFormatter(logging.Formatter(
+ '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
+ logger.setLevel(LEVEL)
+ stream_handler.setLevel(LEVEL)
+ logger.addHandler(stream_handler)
+
+ if label:
+ if outdir:
+ check_directory_exists_and_if_not_mkdir(outdir)
+ else:
+ outdir = '.'
+ log_file = '{}/{}.log'.format(outdir, label)
+ file_handler = logging.FileHandler(log_file)
+ file_handler.setFormatter(logging.Formatter(
+ '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
+
+ file_handler.setLevel(LEVEL)
+ logger.addHandler(file_handler)
+
+ version_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.version')
+ with open(version_file, 'r') as f:
+ version = f.readline()
+ logging.info('Running tupak version: {}'.format(version))
+
+
+def get_progress_bar(module='tqdm'):
+ if module in ['tqdm']:
+ try:
+ from tqdm import tqdm
+ except ImportError:
+ def tqdm(x, *args, **kwargs):
+ return x
+ return tqdm
+ elif module in ['tqdm_notebook']:
+ try:
+ from tqdm import tqdm_notebook as tqdm
+ except ImportError:
+ def tqdm(x, *args, **kwargs):
+ return x
+ return tqdm
+
+
+def spherical_to_cartesian(radius, theta, phi):
+ """
+ Convert from spherical coordinates to cartesian.
+
+ :param radius: radial coordinate
+ :param theta: axial coordinate
+ :param phi: azimuthal coordinate
+ :return cartesian: cartesian vector
+ """
+ cartesian = [radius * np.sin(theta) * np.cos(phi), radius * np.sin(theta) * np.sin(phi), radius * np.cos(theta)]
+ return cartesian
+
+
+def check_directory_exists_and_if_not_mkdir(directory):
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+ logging.debug('Making directory {}'.format(directory))
+ else:
+ logging.debug('Directory {} exists'.format(directory))
+
+
+def set_up_command_line_arguments():
+ parser = argparse.ArgumentParser(
+ description="Command line interface for tupak scripts")
+ parser.add_argument("-v", "--verbose", action="store_true",
+ help=("Increase output verbosity [logging.DEBUG]." +
+ " Overridden by script level settings"))
+ parser.add_argument("-q", "--quite", action="store_true",
+ help=("Decrease output verbosity [logging.WARNING]." +
+ " Overridden by script level settings"))
+ parser.add_argument("-c", "--clean", action="store_true",
+ help="Force clean data, never use cached data")
+ parser.add_argument("-u", "--use-cached", action="store_true",
+ help="Force cached data and do not check its validity")
+ parser.add_argument("-d", "--detectors", nargs='+',
+ default=['H1', 'L1', 'V1'],
+ help=("List of detectors to use in open data calls, "
+ "e.g. -d H1 L1 for H1 and L1"))
+ parser.add_argument("-t", "--test", action="store_true",
+ help=("Used for testing only: don't run full PE, but"
+ " just check nothing breaks"))
+ args, _ = parser.parse_known_args()
+
+ if args.quite:
+ args.log_level = logging.WARNING
+ elif args.verbose:
+ args.log_level = logging.DEBUG
+ else:
+ args.log_level = logging.INFO
+
+ return args
+
+
+command_line_args = set_up_command_line_arguments()
+
+if 'DISPLAY' in os.environ:
+ pass
+else:
+ logging.info('No $DISPLAY environment variable found, so importing \
+ matplotlib.pyplot with non-interactive "Agg" backend.')
+ import matplotlib
+ matplotlib.use('Agg')
+
+
+
+
diff --git a/tupak/gw/__init__.py b/tupak/gw/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1422674e12f789e26f76ac1ad3b5766a8bde9edf
--- /dev/null
+++ b/tupak/gw/__init__.py
@@ -0,0 +1,7 @@
+from __future__ import absolute_import
+import tupak.gw.conversion
+import tupak.gw.detector
+import tupak.gw.likelihood
+import tupak.gw.source
+import tupak.gw.utils
+import tupak.gw.waveform_generator
diff --git a/tupak/conversion.py b/tupak/gw/conversion.py
similarity index 94%
rename from tupak/conversion.py
rename to tupak/gw/conversion.py
index 236f5b6ffab4c6dc2661c04be952354f700e4f34..5e01429cb2d355dcf880e86fdd9658fc168c6738 100644
--- a/tupak/conversion.py
+++ b/tupak/gw/conversion.py
@@ -111,7 +111,7 @@ def generate_all_bbh_parameters(sample, likelihood=None, priors=None):
----------
sample: dict or pandas.DataFrame
Samples to fill in with extra parameters, this may be either an injection or posterior samples.
- likelihood: tupak.likelihood.GravitationalWaveTransient
+ likelihood: tupak.GravitationalWaveTransient
GravitationalWaveTransient used for sampling, used for waveform and likelihood.interferometers.
priors: dict, optional
Dictionary of prior objects, used to fill in non-sampled parameters.
@@ -132,7 +132,7 @@ def fill_from_fixed_priors(sample, priors):
"""Add parameters with delta function prior to the data frame/dictionary."""
if priors is not None:
for name in priors:
- if isinstance(priors[name], tupak.prior.DeltaFunction):
+ if isinstance(priors[name], tupak.core.prior.DeltaFunction):
sample[name] = priors[name].peak
@@ -166,7 +166,7 @@ def generate_component_spins(sample):
sample['spin_2y'], sample['spin_2z'] = \
lalsim.SimInspiralTransformPrecessingNewInitialConditions(
sample['iota'], sample['phi_jl'], sample['tilt_1'], sample['tilt_2'], sample['phi_12'], sample['a_1'],
- sample['a_2'], sample['mass_1'] * tupak.utils.solar_mass, sample['mass_2'] * tupak.utils.solar_mass,
+ sample['a_2'], sample['mass_1'] * tupak.core.utils.solar_mass, sample['mass_2'] * tupak.core.utils.solar_mass,
sample['reference_frequency'], sample['phase'])
sample['phi_1'] = np.arctan(sample['spin_1y'] / sample['spin_1x'])
@@ -183,7 +183,7 @@ def generate_component_spins(sample):
lalsim.SimInspiralTransformPrecessingNewInitialConditions(
sample['iota'][ii], sample['phi_jl'][ii], sample['tilt_1'][ii], sample['tilt_2'][ii],
sample['phi_12'][ii], sample['a_1'][ii], sample['a_2'][ii],
- sample['mass_1'][ii] * tupak.utils.solar_mass, sample['mass_2'][ii] * tupak.utils.solar_mass,
+ sample['mass_1'][ii] * tupak.core.utils.solar_mass, sample['mass_2'][ii] * tupak.core.utils.solar_mass,
sample['reference_frequency'][ii], sample['phase'][ii])
for name in new_spin_parameters:
@@ -208,9 +208,9 @@ def compute_snrs(sample, likelihood):
signal = interferometer.get_detector_response(signal_polarizations,
likelihood.waveform_generator.parameters)
sample['{}_matched_filter_snr'.format(interferometer.name)] = \
- tupak.utils.matched_filter_snr_squared(signal, interferometer,
- likelihood.waveform_generator.time_duration)**0.5
- sample['{}_optimal_snr'.format(interferometer.name)] = tupak.utils.optimal_snr_squared(
+ tupak.gw.utils.matched_filter_snr_squared(signal, interferometer,
+ likelihood.waveform_generator.time_duration) ** 0.5
+ sample['{}_optimal_snr'.format(interferometer.name)] = tupak.gw.utils.optimal_snr_squared(
signal, interferometer, likelihood.waveform_generator.time_duration) ** 0.5
else:
logging.info('Computing SNRs for every sample, this may take some time.')
@@ -226,9 +226,9 @@ def compute_snrs(sample, likelihood):
for interferometer in all_interferometers:
signal = interferometer.get_detector_response(signal_polarizations,
likelihood.waveform_generator.parameters)
- matched_filter_snrs[interferometer.name].append(tupak.utils.matched_filter_snr_squared(
- signal, interferometer, likelihood.waveform_generator.time_duration)**0.5)
- optimal_snrs[interferometer.name].append(tupak.utils.optimal_snr_squared(
+ matched_filter_snrs[interferometer.name].append(tupak.gw.utils.matched_filter_snr_squared(
+ signal, interferometer, likelihood.waveform_generator.time_duration) ** 0.5)
+ optimal_snrs[interferometer.name].append(tupak.gw.utils.optimal_snr_squared(
signal, interferometer, likelihood.waveform_generator.time_duration) ** 0.5)
for interferometer in likelihood.interferometers:
diff --git a/tupak/detector.py b/tupak/gw/detector.py
similarity index 96%
rename from tupak/detector.py
rename to tupak/gw/detector.py
index eec24499606a82a948c8621d0a3319c7a725d6bf..9fff9711add52a2eb591568af579e2fda06e195a 100644
--- a/tupak/detector.py
+++ b/tupak/gw/detector.py
@@ -9,8 +9,8 @@ from gwpy.signal import filter_design
from scipy import signal
from scipy.interpolate import interp1d
-import tupak
-from . import utils
+import tupak.gw.utils
+from tupak.core import utils
class Interferometer(object):
@@ -164,7 +164,7 @@ class Interferometer(object):
@property
def vertex(self):
if self.__vertex_updated is False:
- self.__vertex = utils.get_vertex_position_geocentric(self.__latitude, self.__longitude, self.elevation)
+ self.__vertex = tupak.gw.utils.get_vertex_position_geocentric(self.__latitude, self.__longitude, self.elevation)
self.__vertex_updated = True
return self.__vertex
@@ -212,7 +212,7 @@ class Interferometer(object):
:param mode: polarisation mode
:return: detector_response(theta, phi, psi, mode): antenna response for the specified mode.
"""
- polarization_tensor = utils.get_polarization_tensor(ra, dec, time, psi, mode)
+ polarization_tensor = tupak.gw.utils.get_polarization_tensor(ra, dec, time, psi, mode)
detector_response = np.einsum('ij,ij->', self.detector_tensor, polarization_tensor)
return detector_response
@@ -271,11 +271,11 @@ class Interferometer(object):
else:
logging.info('Injecting into zero noise.')
self.data = signal_ifo
- opt_snr = np.sqrt(tupak.utils.optimal_snr_squared(signal=signal_ifo, interferometer=self,
- time_duration=1 / (self.frequency_array[1]
+ opt_snr = np.sqrt(tupak.gw.utils.optimal_snr_squared(signal=signal_ifo, interferometer=self,
+ time_duration=1 / (self.frequency_array[1]
- self.frequency_array[0])).real)
- mf_snr = np.sqrt(tupak.utils.matched_filter_snr_squared(signal=signal_ifo, interferometer=self,
- time_duration=1 / (self.frequency_array[1]
+ mf_snr = np.sqrt(tupak.gw.utils.matched_filter_snr_squared(signal=signal_ifo, interferometer=self,
+ time_duration=1 / (self.frequency_array[1]
- self.frequency_array[0])).real)
logging.info("Injection found with optimal SNR = {:.2f} and matched filter SNR = {:.2f} in {}".format(
opt_snr, mf_snr, self.name))
@@ -375,9 +375,9 @@ class Interferometer(object):
frequency_domain_strain = np.zeros_like(frequencies) * (1 + 1j)
elif frame_file is not None:
logging.info('Reading data from frame, {}.'.format(self.name))
- strain = tupak.utils.read_frame_file(
+ strain = tupak.gw.utils.read_frame_file(
frame_file, t1=epoch, t2=epoch+duration, channel=channel_name, resample=sampling_frequency)
- frequency_domain_strain, frequencies = tupak.utils.process_strain_data(strain, **kwargs)
+ frequency_domain_strain, frequencies = tupak.gw.utils.process_strain_data(strain, **kwargs)
if overwrite_psd:
self.power_spectral_density = PowerSpectralDensity(
frame_file=frame_file, channel_name=channel_name, epoch=epoch, **kwargs)
@@ -404,7 +404,7 @@ class Interferometer(object):
Output:
delta_t - time delay from geocenter
"""
- delta_t = utils.time_delay_geocentric(self.vertex, np.array([0, 0, 0]), ra, dec, time)
+ delta_t = tupak.gw.utils.time_delay_geocentric(self.vertex, np.array([0, 0, 0]), ra, dec, time)
return delta_t
def vertex_position_geocentric(self):
@@ -414,7 +414,7 @@ class Interferometer(object):
Based on arXiv:gr-qc/0008066 Eqs. B11-B13 except for the typo in the definition of the local radius.
See Section 2.1 of LIGO-T980044-10 for the correct expression
"""
- vertex_position = utils.get_vertex_position_geocentric(self.__latitude, self.__longitude, self.__elevation)
+ vertex_position = tupak.gw.utils.get_vertex_position_geocentric(self.__latitude, self.__longitude, self.__elevation)
return vertex_position
@property
@@ -478,8 +478,8 @@ class PowerSpectralDensity:
min(self.amplitude_spectral_density)))
logging.warning("You may have intended to provide this as a power spectral density.")
elif frame_file is not None:
- strain = tupak.utils.read_frame_file(frame_file, t1=epoch - psd_duration - psd_offset,
- t2=epoch - psd_duration, channel=channel_name)
+ strain = tupak.gw.utils.read_frame_file(frame_file, t1=epoch - psd_duration - psd_offset,
+ t2=epoch - psd_duration, channel=channel_name)
sampling_frequency = int(strain.sample_rate.value)
# Low pass filter
@@ -663,11 +663,11 @@ def get_interferometer_with_open_data(
utils.check_directory_exists_and_if_not_mkdir(outdir)
- strain = utils.get_open_strain_data(
+ strain = tupak.gw.utils.get_open_strain_data(
name, center_time - T / 2, center_time + T / 2, outdir=outdir, cache=cache,
raw_data_file=raw_data_file, **kwargs)
- strain_psd = utils.get_open_strain_data(
+ strain_psd = tupak.gw.utils.get_open_strain_data(
name, center_time + psd_offset, center_time + psd_offset + psd_duration,
raw_data_file=raw_data_file,
outdir=outdir, cache=cache, **kwargs)
@@ -842,7 +842,7 @@ def get_event_data(
interferometers: list
A list of tupak.detector.Interferometer objects
"""
- event_time = tupak.utils.get_event_time(event)
+ event_time = tupak.gw.utils.get_event_time(event)
interferometers = []
diff --git a/tupak/likelihood.py b/tupak/gw/likelihood.py
similarity index 87%
rename from tupak/likelihood.py
rename to tupak/gw/likelihood.py
index 44a8e9006e9b814658a2f0ef4c97f7567aa919b6..d01334468456639fb5a11c2d36f6430d180501c9 100644
--- a/tupak/likelihood.py
+++ b/tupak/gw/likelihood.py
@@ -1,35 +1,21 @@
-from __future__ import division, print_function
+import logging
import numpy as np
+from scipy.interpolate import interp2d, interp1d
+
+import tupak.gw.prior
try:
from scipy.special import logsumexp
except ImportError:
from scipy.misc import logsumexp
-from scipy.special import i0e
-from scipy.interpolate import interp1d
-from scipy.interpolate import interp2d
-import tupak
-import logging
-
-
-class Likelihood(object):
- """ Empty likelihood class to be subclassed by other likelihoods """
-
- def __init__(self, parameters=None):
- self.parameters = parameters
+from scipy.special._ufuncs import i0e
- def log_likelihood(self):
- return np.nan
-
- def noise_log_likelihood(self):
- return np.nan
-
- def log_likelihood_ratio(self):
- return self.log_likelihood() - self.noise_log_likelihood()
+import tupak
+from tupak.core import likelihood as likelihood
-class GravitationalWaveTransient(Likelihood):
+class GravitationalWaveTransient(likelihood.Likelihood):
""" A gravitational-wave transient likelihood object
This is the usual likelihood object to use for transient gravitational
@@ -64,7 +50,7 @@ class GravitationalWaveTransient(Likelihood):
def __init__(self, interferometers, waveform_generator, time_marginalization=False, distance_marginalization=False,
phase_marginalization=False, prior=None):
- Likelihood.__init__(self, waveform_generator.parameters)
+ likelihood.Likelihood.__init__(self, waveform_generator.parameters)
self.interferometers = interferometers
self.waveform_generator = waveform_generator
self.non_standard_sampling_parameter_keys = self.waveform_generator.non_standard_sampling_parameter_keys
@@ -99,9 +85,9 @@ class GravitationalWaveTransient(Likelihood):
def noise_log_likelihood(self):
log_l = 0
for interferometer in self.interferometers:
- log_l -= tupak.utils.noise_weighted_inner_product(interferometer.data, interferometer.data,
- interferometer.power_spectral_density_array,
- self.waveform_generator.time_duration) / 2
+ log_l -= tupak.gw.utils.noise_weighted_inner_product(interferometer.data, interferometer.data,
+ interferometer.power_spectral_density_array,
+ self.waveform_generator.time_duration) / 2
return log_l.real
def log_likelihood_ratio(self):
@@ -116,10 +102,10 @@ class GravitationalWaveTransient(Likelihood):
for interferometer in self.interferometers:
signal_ifo = interferometer.get_detector_response(waveform_polarizations,
self.waveform_generator.parameters)
- matched_filter_snr_squared += tupak.utils.matched_filter_snr_squared(
+ matched_filter_snr_squared += tupak.gw.utils.matched_filter_snr_squared(
signal_ifo, interferometer, self.waveform_generator.time_duration)
- optimal_snr_squared += tupak.utils.optimal_snr_squared(
+ optimal_snr_squared += tupak.gw.utils.optimal_snr_squared(
signal_ifo, interferometer, self.waveform_generator.time_duration)
if self.time_marginalization:
interferometer.time_marginalization = self.time_marginalization
@@ -153,7 +139,7 @@ class GravitationalWaveTransient(Likelihood):
log_l = logsumexp(dist_marged_log_l_tc_array, axis=0, b=delta_tc)
elif self.phase_marginalization:
- log_l = logsumexp(self.bessel_function_interped(abs(matched_filter_snr_squared_tc_array)), b=delta_tc)\
+ log_l = logsumexp(self.bessel_function_interped(abs(matched_filter_snr_squared_tc_array)), b=delta_tc) \
- optimal_snr_squared / 2. - tc_log_norm
else:
@@ -192,7 +178,7 @@ class GravitationalWaveTransient(Likelihood):
def setup_distance_marginalization(self):
if 'luminosity_distance' not in self.prior.keys():
logging.info('No prior provided for distance, using default prior.')
- self.prior['luminosity_distance'] = tupak.prior.create_default_prior('luminosity_distance')
+ self.prior['luminosity_distance'] = tupak.gw.prior.create_default_prior('luminosity_distance')
self.distance_array = np.linspace(self.prior['luminosity_distance'].minimum,
self.prior['luminosity_distance'].maximum, int(1e4))
self.delta_distance = self.distance_array[1] - self.distance_array[0]
@@ -231,16 +217,16 @@ class GravitationalWaveTransient(Likelihood):
self.dist_margd_loglikelihood_array)
def setup_phase_marginalization(self):
- if 'phase' not in self.prior.keys() or not isinstance(self.prior['phase'], tupak.prior.Prior):
+ if 'phase' not in self.prior.keys() or not isinstance(self.prior['phase'], tupak.core.prior.Prior):
logging.info('No prior provided for phase at coalescence, using default prior.')
- self.prior['phase'] = tupak.prior.create_default_prior('phase')
+ self.prior['phase'] = tupak.gw.prior.create_default_prior('phase')
self.bessel_function_interped = interp1d(np.linspace(0, 1e6, int(1e5)),
np.log([i0e(snr) for snr in np.linspace(0, 1e6, int(1e5))])
+ np.linspace(0, 1e6, int(1e5)),
bounds_error=False, fill_value=-np.inf)
-class BasicGravitationalWaveTransient(Likelihood):
+class BasicGravitationalWaveTransient(likelihood.Likelihood):
""" A basic gravitational wave transient likelihood
The simplest frequency-domain gravitational wave transient likelihood. Does
@@ -257,14 +243,14 @@ class BasicGravitationalWaveTransient(Likelihood):
Returns
-------
- Likelihood: `tupak.likelihood.Likelihood`
+ Likelihood: `tupak.gw.likelihood.BasicGravitationalWaveTransient`
A likelihood object, able to compute the likelihood of the data given
some model parameters
"""
def __init__(self, interferometers, waveform_generator):
- Likelihood.__init__(self, waveform_generator.parameters)
+ likelihood.Likelihood.__init__(self, waveform_generator.parameters)
self.interferometers = interferometers
self.waveform_generator = waveform_generator
@@ -310,18 +296,18 @@ def get_binary_black_hole_likelihood(interferometers):
Returns
-------
- likelihood: tupak.likelihood.GravitationalWaveTransient
+ likelihood: tupak.GravitationalWaveTransient
The likelihood to pass to `run_sampler`
"""
- waveform_generator = tupak.waveform_generator.WaveformGenerator(
+ waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
time_duration=interferometers[0].duration, sampling_frequency=interferometers[0].sampling_frequency,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+ frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters={'waveform_approximant': 'IMRPhenomPv2', 'reference_frequency': 50})
- likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
+ likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
return likelihood
-class HyperparameterLikelihood(Likelihood):
+class HyperparameterLikelihood(likelihood.Likelihood):
""" A likelihood for infering hyperparameter posterior distributions
See Eq. (1) of https://arxiv.org/abs/1801.02699 for a definition.
@@ -343,7 +329,7 @@ class HyperparameterLikelihood(Likelihood):
"""
def __init__(self, samples, hyper_prior, run_prior):
- Likelihood.__init__(self, parameters=hyper_prior.__dict__)
+ likelihood.Likelihood.__init__(self, parameters=hyper_prior.__dict__)
self.samples = samples
self.hyper_prior = hyper_prior
self.run_prior = run_prior
diff --git a/tupak/noise_curves/AdV_asd.txt b/tupak/gw/noise_curves/AdV_asd.txt
similarity index 100%
rename from tupak/noise_curves/AdV_asd.txt
rename to tupak/gw/noise_curves/AdV_asd.txt
diff --git a/tupak/noise_curves/AdV_psd.txt b/tupak/gw/noise_curves/AdV_psd.txt
similarity index 100%
rename from tupak/noise_curves/AdV_psd.txt
rename to tupak/gw/noise_curves/AdV_psd.txt
diff --git a/tupak/noise_curves/CE_asd.txt b/tupak/gw/noise_curves/CE_asd.txt
similarity index 100%
rename from tupak/noise_curves/CE_asd.txt
rename to tupak/gw/noise_curves/CE_asd.txt
diff --git a/tupak/noise_curves/CE_psd.txt b/tupak/gw/noise_curves/CE_psd.txt
similarity index 100%
rename from tupak/noise_curves/CE_psd.txt
rename to tupak/gw/noise_curves/CE_psd.txt
diff --git a/tupak/noise_curves/CE_wb_asd.txt b/tupak/gw/noise_curves/CE_wb_asd.txt
similarity index 100%
rename from tupak/noise_curves/CE_wb_asd.txt
rename to tupak/gw/noise_curves/CE_wb_asd.txt
diff --git a/tupak/noise_curves/CE_wb_psd.txt b/tupak/gw/noise_curves/CE_wb_psd.txt
similarity index 100%
rename from tupak/noise_curves/CE_wb_psd.txt
rename to tupak/gw/noise_curves/CE_wb_psd.txt
diff --git a/tupak/noise_curves/ET_B_asd.txt b/tupak/gw/noise_curves/ET_B_asd.txt
similarity index 100%
rename from tupak/noise_curves/ET_B_asd.txt
rename to tupak/gw/noise_curves/ET_B_asd.txt
diff --git a/tupak/noise_curves/ET_B_psd.txt b/tupak/gw/noise_curves/ET_B_psd.txt
similarity index 100%
rename from tupak/noise_curves/ET_B_psd.txt
rename to tupak/gw/noise_curves/ET_B_psd.txt
diff --git a/tupak/noise_curves/ET_D_asd.txt b/tupak/gw/noise_curves/ET_D_asd.txt
similarity index 100%
rename from tupak/noise_curves/ET_D_asd.txt
rename to tupak/gw/noise_curves/ET_D_asd.txt
diff --git a/tupak/noise_curves/ET_D_psd.txt b/tupak/gw/noise_curves/ET_D_psd.txt
similarity index 100%
rename from tupak/noise_curves/ET_D_psd.txt
rename to tupak/gw/noise_curves/ET_D_psd.txt
diff --git a/tupak/noise_curves/GEO600_S6e_asd.txt b/tupak/gw/noise_curves/GEO600_S6e_asd.txt
similarity index 100%
rename from tupak/noise_curves/GEO600_S6e_asd.txt
rename to tupak/gw/noise_curves/GEO600_S6e_asd.txt
diff --git a/tupak/noise_curves/LIGO_srd_asd.txt b/tupak/gw/noise_curves/LIGO_srd_asd.txt
similarity index 100%
rename from tupak/noise_curves/LIGO_srd_asd.txt
rename to tupak/gw/noise_curves/LIGO_srd_asd.txt
diff --git a/tupak/noise_curves/LIGO_srd_psd.txt b/tupak/gw/noise_curves/LIGO_srd_psd.txt
similarity index 100%
rename from tupak/noise_curves/LIGO_srd_psd.txt
rename to tupak/gw/noise_curves/LIGO_srd_psd.txt
diff --git a/tupak/noise_curves/README.md b/tupak/gw/noise_curves/README.md
similarity index 100%
rename from tupak/noise_curves/README.md
rename to tupak/gw/noise_curves/README.md
diff --git a/tupak/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt b/tupak/gw/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt
rename to tupak/gw/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt
diff --git a/tupak/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt b/tupak/gw/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt
rename to tupak/gw/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt
diff --git a/tupak/noise_curves/aLIGO_early_asd.txt b/tupak/gw/noise_curves/aLIGO_early_asd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_early_asd.txt
rename to tupak/gw/noise_curves/aLIGO_early_asd.txt
diff --git a/tupak/noise_curves/aLIGO_early_high_asd.txt b/tupak/gw/noise_curves/aLIGO_early_high_asd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_early_high_asd.txt
rename to tupak/gw/noise_curves/aLIGO_early_high_asd.txt
diff --git a/tupak/noise_curves/aLIGO_early_high_psd.txt b/tupak/gw/noise_curves/aLIGO_early_high_psd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_early_high_psd.txt
rename to tupak/gw/noise_curves/aLIGO_early_high_psd.txt
diff --git a/tupak/noise_curves/aLIGO_early_psd.txt b/tupak/gw/noise_curves/aLIGO_early_psd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_early_psd.txt
rename to tupak/gw/noise_curves/aLIGO_early_psd.txt
diff --git a/tupak/noise_curves/aLIGO_late_asd.txt b/tupak/gw/noise_curves/aLIGO_late_asd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_late_asd.txt
rename to tupak/gw/noise_curves/aLIGO_late_asd.txt
diff --git a/tupak/noise_curves/aLIGO_late_psd.txt b/tupak/gw/noise_curves/aLIGO_late_psd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_late_psd.txt
rename to tupak/gw/noise_curves/aLIGO_late_psd.txt
diff --git a/tupak/noise_curves/aLIGO_mid_asd.txt b/tupak/gw/noise_curves/aLIGO_mid_asd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_mid_asd.txt
rename to tupak/gw/noise_curves/aLIGO_mid_asd.txt
diff --git a/tupak/noise_curves/aLIGO_mid_psd.txt b/tupak/gw/noise_curves/aLIGO_mid_psd.txt
similarity index 100%
rename from tupak/noise_curves/aLIGO_mid_psd.txt
rename to tupak/gw/noise_curves/aLIGO_mid_psd.txt
diff --git a/tupak/noise_curves/highf_psd.txt b/tupak/gw/noise_curves/highf_psd.txt
similarity index 100%
rename from tupak/noise_curves/highf_psd.txt
rename to tupak/gw/noise_curves/highf_psd.txt
diff --git a/tupak/noise_curves/lisa_asd.txt b/tupak/gw/noise_curves/lisa_asd.txt
similarity index 100%
rename from tupak/noise_curves/lisa_asd.txt
rename to tupak/gw/noise_curves/lisa_asd.txt
diff --git a/tupak/noise_curves/lisa_psd.txt b/tupak/gw/noise_curves/lisa_psd.txt
similarity index 100%
rename from tupak/noise_curves/lisa_psd.txt
rename to tupak/gw/noise_curves/lisa_psd.txt
diff --git a/tupak/gw/prior.py b/tupak/gw/prior.py
new file mode 100644
index 0000000000000000000000000000000000000000..cabe97beaf8eca0b7facb455a9b24050dfc835b4
--- /dev/null
+++ b/tupak/gw/prior.py
@@ -0,0 +1,102 @@
+import logging
+
+import numpy as np
+
+import tupak.core.prior
+
+
+def create_default_prior(name):
+ """
+ Make a default prior for a parameter with a known name.
+
+ This is currently set up for binary black holes.
+
+ Parameters
+ ----------
+ name: str
+ Parameter name
+
+ Return
+ ------
+ prior: Prior
+ Default prior distribution for that parameter, if unknown None is returned.
+ """
+ default_priors = {
+ 'mass_1': tupak.core.prior.Uniform(name=name, minimum=20, maximum=100),
+ 'mass_2': tupak.core.prior.Uniform(name=name, minimum=20, maximum=100),
+ 'chirp_mass': tupak.core.prior.Uniform(name=name, minimum=25, maximum=100),
+ 'total_mass': tupak.core.prior.Uniform(name=name, minimum=10, maximum=200),
+ 'mass_ratio': tupak.core.prior.Uniform(name=name, minimum=0.125, maximum=1),
+ 'symmetric_mass_ratio': tupak.core.prior.Uniform(name=name, minimum=8 / 81, maximum=0.25),
+ 'a_1': tupak.core.prior.Uniform(name=name, minimum=0, maximum=0.8),
+ 'a_2': tupak.core.prior.Uniform(name=name, minimum=0, maximum=0.8),
+ 'tilt_1': tupak.core.prior.Sine(name=name),
+ 'tilt_2': tupak.core.prior.Sine(name=name),
+ 'cos_tilt_1': tupak.core.prior.Uniform(name=name, minimum=-1, maximum=1),
+ 'cos_tilt_2': tupak.core.prior.Uniform(name=name, minimum=-1, maximum=1),
+ 'phi_12': tupak.core.prior.Uniform(name=name, minimum=0, maximum=2 * np.pi),
+ 'phi_jl': tupak.core.prior.Uniform(name=name, minimum=0, maximum=2 * np.pi),
+ 'luminosity_distance': tupak.core.prior.UniformComovingVolume(name=name, minimum=1e2, maximum=5e3),
+ 'dec': tupak.core.prior.Cosine(name=name),
+ 'ra': tupak.core.prior.Uniform(name=name, minimum=0, maximum=2 * np.pi),
+ 'iota': tupak.core.prior.Sine(name=name),
+ 'cos_iota': tupak.core.prior.Uniform(name=name, minimum=-1, maximum=1),
+ 'psi': tupak.core.prior.Uniform(name=name, minimum=0, maximum=2 * np.pi),
+ 'phase': tupak.core.prior.Uniform(name=name, minimum=0, maximum=2 * np.pi)
+ }
+ if name in default_priors.keys():
+ prior = default_priors[name]
+ else:
+ logging.info(
+ "No default prior found for variable {}.".format(name))
+ prior = None
+ return prior
+
+
+def test_redundancy(key, prior):
+ """
+ Test whether adding the key would add be redundant.
+
+ Parameters
+ ----------
+ key: str
+ The string to test.
+ prior: dict
+ Current prior dictionary.
+
+ Return
+ ------
+ redundant: bool
+ Whether the key is redundant
+ """
+ redundant = False
+ mass_parameters = {'mass_1', 'mass_2', 'chirp_mass', 'total_mass', 'mass_ratio', 'symmetric_mass_ratio'}
+ spin_magnitude_parameters = {'a_1', 'a_2'}
+ spin_tilt_1_parameters = {'tilt_1', 'cos_tilt_1'}
+ spin_tilt_2_parameters = {'tilt_2', 'cos_tilt_2'}
+ spin_azimuth_parameters = {'phi_1', 'phi_2', 'phi_12', 'phi_jl'}
+ inclination_parameters = {'iota', 'cos_iota'}
+ distance_parameters = {'luminosity_distance', 'comoving_distance', 'redshift'}
+
+ for parameter_set in [mass_parameters, spin_magnitude_parameters, spin_azimuth_parameters]:
+ if key in parameter_set:
+ if len(parameter_set.intersection(prior.keys())) > 2:
+ redundant = True
+ logging.warning('{} in prior. This may lead to unexpected behaviour.'.format(
+ parameter_set.intersection(prior.keys())))
+ break
+ elif len(parameter_set.intersection(prior.keys())) == 2:
+ redundant = True
+ break
+ for parameter_set in [inclination_parameters, distance_parameters, spin_tilt_1_parameters, spin_tilt_2_parameters]:
+ if key in parameter_set:
+ if len(parameter_set.intersection(prior.keys())) > 1:
+ redundant = True
+ logging.warning('{} in prior. This may lead to unexpected behaviour.'.format(
+ parameter_set.intersection(prior.keys())))
+ break
+ elif len(parameter_set.intersection(prior.keys())) == 1:
+ redundant = True
+ break
+
+ return redundant
\ No newline at end of file
diff --git a/tupak/source.py b/tupak/gw/source.py
similarity index 99%
rename from tupak/source.py
rename to tupak/gw/source.py
index 00ca0a9bf9716c93401e315c9887bdc8ad5be4b4..cc171985b4b9f64ef5ee66f6dd091c44eb1d8afa 100644
--- a/tupak/source.py
+++ b/tupak/gw/source.py
@@ -9,7 +9,7 @@ except ImportError:
logging.warning("You do not have lalsuite installed currently. You will "
" not be able to use some of the prebuilt functions.")
-from . import utils
+from tupak.core import utils
def lal_binary_black_hole(
diff --git a/tupak/utils.py b/tupak/gw/utils.py
similarity index 56%
rename from tupak/utils.py
rename to tupak/gw/utils.py
index 3a32e54e7adfd2dc9b2337bd7194112ed86b4ae5..430a2bd45513cc505496d3063b03a0a3c41308f4 100644
--- a/tupak/utils.py
+++ b/tupak/gw/utils.py
@@ -1,186 +1,17 @@
-from __future__ import division
import logging
import os
+
import numpy as np
-from math import fmod
-from gwpy.timeseries import TimeSeries
from gwpy.signal import filter_design
+from gwpy.timeseries import TimeSeries
from scipy import signal
-import argparse
-
-# Constants
-speed_of_light = 299792458.0 # speed of light in m/s
-parsec = 3.085677581 * 1e16
-solar_mass = 1.98855 * 1e30
-
-
-def get_sampling_frequency(time_series):
- """
- Calculate sampling frequency from a time series
- """
- tol = 1e-10
- if np.ptp(np.diff(time_series)) > tol:
- raise ValueError("Your time series was not evenly sampled")
- else:
- return 1. / (time_series[1] - time_series[0])
-
-
-def create_time_series(sampling_frequency, duration, starting_time=0.):
- return np.arange(starting_time, duration, 1./sampling_frequency)
-
-
-def ra_dec_to_theta_phi(ra, dec, gmst):
- """
- Convert from RA and DEC to polar coordinates on celestial sphere
- Input:
- ra - right ascension in radians
- dec - declination in radians
- gmst - Greenwich mean sidereal time of arrival of the signal in radians
- Output:
- theta - zenith angle in radians
- phi - azimuthal angle in radians
- """
- phi = ra - gmst
- theta = np.pi / 2 - dec
- return theta, phi
-
-
-def gps_time_to_gmst(gps_time):
- """
- Convert gps time to Greenwich mean sidereal time in radians
-
- This method assumes a constant rotation rate of earth since 00:00:00, 1 Jan. 2000
- A correction has been applied to give the exact correct value for 00:00:00, 1 Jan. 2018
- Error accumulates at a rate of ~0.0001 radians/decade.
-
- Input:
- time - gps time
- Output:
- gmst - Greenwich mean sidereal time in radians
- """
- omega_earth = 2 * np.pi * (1 / 365.2425 + 1) / 86400.
- gps_2000 = 630720013.
- gmst_2000 = (6 + 39. / 60 + 51.251406103947375 / 3600) * np.pi / 12
- correction_2018 = -0.00017782487379358614
- sidereal_time = omega_earth * (gps_time - gps_2000) + gmst_2000 + correction_2018
- gmst = fmod(sidereal_time, 2 * np.pi)
- return gmst
-
-
-def create_frequency_series(sampling_frequency, duration):
- """
- Create a frequency series with the correct length and spacing.
-
- :param sampling_frequency: sampling frequency
- :param duration: duration of data
- :return: frequencies, frequency series
- """
- number_of_samples = duration * sampling_frequency
- number_of_samples = int(np.round(number_of_samples))
-
- # prepare for FFT
- number_of_frequencies = (number_of_samples-1)//2
- delta_freq = 1./duration
-
- frequencies = delta_freq * np.linspace(1, number_of_frequencies, number_of_frequencies)
-
- if len(frequencies) % 2 == 1:
- frequencies = np.concatenate(([0], frequencies, [sampling_frequency / 2.]))
- else:
- # no Nyquist frequency when N=odd
- frequencies = np.concatenate(([0], frequencies))
-
- return frequencies
-
-
-def create_white_noise(sampling_frequency, duration):
- """
- Create white_noise which is then coloured by a given PSD
-
-
- :param sampling_frequency: sampling frequency
- :param duration: duration of data
- """
-
- number_of_samples = duration * sampling_frequency
- number_of_samples = int(np.round(number_of_samples))
-
- delta_freq = 1./duration
-
- frequencies = create_frequency_series(sampling_frequency, duration)
-
- norm1 = 0.5*(1./delta_freq)**0.5
- re1 = np.random.normal(0, norm1, len(frequencies))
- im1 = np.random.normal(0, norm1, len(frequencies))
- htilde1 = re1 + 1j*im1
-
- # convolve data with instrument transfer function
- otilde1 = htilde1 * 1.
- # set DC and Nyquist = 0
- otilde1[0] = 0
- # no Nyquist frequency when N=odd
- if np.mod(number_of_samples, 2) == 0:
- otilde1[-1] = 0
-
- # normalise for positive frequencies and units of strain/rHz
- white_noise = otilde1
- # python: transpose for use with infft
- white_noise = np.transpose(white_noise)
- frequencies = np.transpose(frequencies)
- return white_noise, frequencies
-
-
-def nfft(ht, Fs):
- """
- performs an FFT while keeping track of the frequency bins
- assumes input time series is real (positive frequencies only)
-
- ht = time series
- Fs = sampling frequency
-
- returns
- hf = single-sided FFT of ft normalised to units of strain / sqrt(Hz)
- f = frequencies associated with hf
- """
- # add one zero padding if time series does not have even number of sampling times
- if np.mod(len(ht), 2) == 1:
- ht = np.append(ht, 0)
- LL = len(ht)
- # frequency range
- ff = Fs / 2 * np.linspace(0, 1, int(LL/2+1))
-
- # calculate FFT
- # rfft computes the fft for real inputs
- hf = np.fft.rfft(ht)
-
- # normalise to units of strain / sqrt(Hz)
- hf = hf / Fs
-
- return hf, ff
-
-
-def infft(hf, Fs):
- """
- inverse FFT for use in conjunction with nfft
- eric.thrane@ligo.org
- input:
- hf = single-side FFT calculated by fft_eht
- Fs = sampling frequency
- output:
- h = time series
- """
- # use irfft to work with positive frequencies only
- h = np.fft.irfft(hf)
- # undo LAL/Lasky normalisation
- h = h*Fs
-
- return h
+from tupak.core.utils import gps_time_to_gmst, ra_dec_to_theta_phi, speed_of_light, nfft
def asd_from_freq_series(freq_data, df):
"""
- Calculate the ASD from the frequency domain output of gaussian_noise()
+ Calculate the ASD from the frequency domain output of gaussian_noise()
Input:
freq_data - array of complex frequency domain data
df - spacing of freq_data, 1/(segment length) used to generate the gaussian noise
@@ -286,93 +117,6 @@ def get_vertex_position_geocentric(latitude, longitude, elevation):
return np.array([x_comp, y_comp, z_comp])
-def setup_logger(outdir=None, label=None, log_level=None):
- """ Setup logging output: call at the start of the script to use
-
- Parameters
- ----------
- outdir, label: str
- If supplied, write the logging output to outdir/label.log
- log_level = ['debug', 'info', 'warning']
- Either a string from the list above, or an interger as specified
- in https://docs.python.org/2/library/logging.html#logging-levels
- """
-
- if type(log_level) is str:
- try:
- LEVEL = getattr(logging, log_level.upper())
- except AttributeError:
- raise ValueError('log_level {} not understood'.format(log_level))
- elif log_level is None:
- LEVEL = command_line_args.log_level
- else:
- LEVEL = int(log_level)
-
- logger = logging.getLogger()
- stream_handler = logging.StreamHandler()
- stream_handler.setFormatter(logging.Formatter(
- '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
- logger.setLevel(LEVEL)
- stream_handler.setLevel(LEVEL)
- logger.addHandler(stream_handler)
-
- if label:
- if outdir:
- check_directory_exists_and_if_not_mkdir(outdir)
- else:
- outdir = '.'
- log_file = '{}/{}.log'.format(outdir, label)
- file_handler = logging.FileHandler(log_file)
- file_handler.setFormatter(logging.Formatter(
- '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
-
- file_handler.setLevel(LEVEL)
- logger.addHandler(file_handler)
-
- version_file = os.path.join(os.path.dirname(__file__), '.version')
- with open(version_file, 'r') as f:
- version = f.readline()
- logging.info('Running tupak version: {}'.format(version))
-
-
-def get_progress_bar(module='tqdm'):
- if module in ['tqdm']:
- try:
- from tqdm import tqdm
- except ImportError:
- def tqdm(x, *args, **kwargs):
- return x
- return tqdm
- elif module in ['tqdm_notebook']:
- try:
- from tqdm import tqdm_notebook as tqdm
- except ImportError:
- def tqdm(x, *args, **kwargs):
- return x
- return tqdm
-
-
-def spherical_to_cartesian(radius, theta, phi):
- """
- Convert from spherical coordinates to cartesian.
-
- :param radius: radial coordinate
- :param theta: axial coordinate
- :param phi: azimuthal coordinate
- :return cartesian: cartesian vector
- """
- cartesian = [radius * np.sin(theta) * np.cos(phi), radius * np.sin(theta) * np.sin(phi), radius * np.cos(theta)]
- return cartesian
-
-
-def check_directory_exists_and_if_not_mkdir(directory):
- if not os.path.exists(directory):
- os.makedirs(directory)
- logging.debug('Making directory {}'.format(directory))
- else:
- logging.debug('Directory {} exists'.format(directory))
-
-
def inner_product(aa, bb, frequency, PSD):
"""
Calculate the inner product defined in the matched filter statistic
@@ -619,51 +363,4 @@ def process_strain_data(
frequency_domain_strain, frequencies = nfft(strain.value, sampling_frequency)
- return frequency_domain_strain, frequencies
-
-
-def set_up_command_line_arguments():
- parser = argparse.ArgumentParser(
- description="Command line interface for tupak scripts")
- parser.add_argument("-v", "--verbose", action="store_true",
- help=("Increase output verbosity [logging.DEBUG]." +
- " Overridden by script level settings"))
- parser.add_argument("-q", "--quite", action="store_true",
- help=("Decrease output verbosity [logging.WARNING]." +
- " Overridden by script level settings"))
- parser.add_argument("-c", "--clean", action="store_true",
- help="Force clean data, never use cached data")
- parser.add_argument("-u", "--use-cached", action="store_true",
- help="Force cached data and do not check its validity")
- parser.add_argument("-d", "--detectors", nargs='+',
- default=['H1', 'L1', 'V1'],
- help=("List of detectors to use in open data calls, "
- "e.g. -d H1 L1 for H1 and L1"))
- parser.add_argument("-t", "--test", action="store_true",
- help=("Used for testing only: don't run full PE, but"
- " just check nothing breaks"))
- args, _ = parser.parse_known_args()
-
- if args.quite:
- args.log_level = logging.WARNING
- elif args.verbose:
- args.log_level = logging.DEBUG
- else:
- args.log_level = logging.INFO
-
- return args
-
-
-command_line_args = set_up_command_line_arguments()
-
-if 'DISPLAY' in os.environ:
- pass
-else:
- logging.info('No $DISPLAY environment variable found, so importing \
- matplotlib.pyplot with non-interactive "Agg" backend.')
- import matplotlib
- matplotlib.use('Agg')
-
-
-
-
+ return frequency_domain_strain, frequencies
\ No newline at end of file
diff --git a/tupak/waveform_generator.py b/tupak/gw/waveform_generator.py
similarity index 97%
rename from tupak/waveform_generator.py
rename to tupak/gw/waveform_generator.py
index c53a8fd80fba4e6af6a6c2440c02b9c02ed445b3..b0aef610cbd598c3479b2f361e79f9ccb6f08db3 100644
--- a/tupak/waveform_generator.py
+++ b/tupak/gw/waveform_generator.py
@@ -1,6 +1,6 @@
import inspect
-from . import utils
+from tupak.core import utils
import numpy as np
@@ -94,15 +94,15 @@ class WaveformGenerator(object):
@property
def frequency_array(self):
if self.__frequency_array_updated is False:
- self.__frequency_array = utils.create_frequency_series(
+ self.frequency_array = utils.create_frequency_series(
self.sampling_frequency,
self.time_duration)
- self.__frequency_array_updated = True
return self.__frequency_array
@frequency_array.setter
def frequency_array(self, frequency_array):
self.__frequency_array = frequency_array
+ self.__frequency_array_updated = True
@property
def time_array(self):