From 29796e45ebcc271e305c517ee8d8e7d7540f73db Mon Sep 17 00:00:00 2001
From: Moritz Huebner <email@moritz-huebner.de>
Date: Wed, 6 Jun 2018 16:32:44 +1000
Subject: [PATCH] Fixed some of the issues that came up during restructuring
---
examples/injection_examples/basic_tutorial.py | 2 +-
.../basic_tutorial_dist_time_phase_marg.py | 2 +-
.../basic_tutorial_time_phase_marg.py | 2 +-
.../change_sampled_parameters.py | 2 +-
.../create_your_own_source_model.py | 2 +-
...reate_your_own_time_domain_source_model.py | 2 +-
.../how_to_specify_the_prior.py | 2 +-
.../marginalized_likelihood.py | 2 +-
examples/open_data_examples/GW150914.py | 4 ---
.../open_data_examples/GW150914_minimal.py | 4 +--
.../other_examples/hyper_parameter_example.py | 2 +-
.../other_examples/sine_gaussian_example.py | 2 +-
.../supernova_example/supernova_example.py | 11 ------
examples/tutorials/compare_samplers.ipynb | 18 +++++-----
examples/tutorials/making_priors.ipynb | 34 +++++++++----------
.../tutorials/visualising_the_results.ipynb | 16 ++++-----
test/waveform_generator_tests.py | 9 +++--
tupak/core/utils.py | 2 +-
18 files changed, 53 insertions(+), 65 deletions(-)
diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index b5f1fe721..cd6b81ef2 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -59,7 +59,7 @@ priors['geocent_time'] = tupak.core.prior.Uniform(injection_parameters['geocent_
'geocent_time')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator, time_marginalization=False, phase_marginalization=False, distance_marginalization=False, prior=priors)
+likelihood = tupak.gw.likelihood.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 c755d881e..30a76a5cf 100644
--- a/examples/injection_examples/basic_tutorial_dist_time_phase_marg.py
+++ b/examples/injection_examples/basic_tutorial_dist_time_phase_marg.py
@@ -55,7 +55,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'd
priors['luminosity_distance'] = tupak.core.prior.create_default_prior(name='luminosity_distance')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator, time_marginalization=True, phase_marginalization=True, distance_marginalization=True, prior=priors)
+likelihood = tupak.gw.likelihood.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 738ecbbff..cf773a2ea 100644
--- a/examples/injection_examples/basic_tutorial_time_phase_marg.py
+++ b/examples/injection_examples/basic_tutorial_time_phase_marg.py
@@ -55,7 +55,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'd
priors['luminosity_distance'] = tupak.core.prior.create_default_prior(name='luminosity_distance')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator, time_marginalization=True, phase_marginalization=True, distance_marginalization=False, prior=priors)
+likelihood = tupak.gw.likelihood.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 f0f653d39..b3f831084 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -43,7 +43,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi'
priors['luminosity_distance'] = tupak.core.prior.create_default_prior(name='luminosity_distance')
# Initialise GravitationalWaveTransient
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
# Run sampler
result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
diff --git a/examples/injection_examples/create_your_own_source_model.py b/examples/injection_examples/create_your_own_source_model.py
index 2bdd90af5..15f2adfb0 100644
--- a/examples/injection_examples/create_your_own_source_model.py
+++ b/examples/injection_examples/create_your_own_source_model.py
@@ -45,7 +45,7 @@ prior = injection_parameters.copy()
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.core.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
result = tupak.core.sampler.run_sampler(
likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
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 1660d99bd..588aa5315 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
@@ -64,7 +64,7 @@ prior['phase'] = tupak.core.prior.Uniform(-np.pi / 2, np.pi / 2, r'$\phi$')
# define likelihood
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(IFOs, waveform)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform)
# launch sampler
result = tupak.core.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index 25dfe3fa8..19088d11d 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/examples/injection_examples/how_to_specify_the_prior.py
@@ -58,7 +58,7 @@ priors['a_2'] = tupak.core.prior.Interped(name='a_2', xx=a_2, yy=p_a_2, minimum=
# Enjoy.
# Initialise GravitationalWaveTransient
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
# Run sampler
result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index 07df8e929..9a14d33d9 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -38,7 +38,7 @@ 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.core.likelihood.GravitationalWaveTransient(
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(
interferometers=IFOs, waveform_generator=waveform_generator, prior=priors,
distance_marginalization=True, phase_marginalization=True)
diff --git a/examples/open_data_examples/GW150914.py b/examples/open_data_examples/GW150914.py
index de2e0f9fe..9d274eac4 100644
--- a/examples/open_data_examples/GW150914.py
+++ b/examples/open_data_examples/GW150914.py
@@ -54,11 +54,7 @@ waveform_generator = tupak.WaveformGenerator(time_duration=interferometers[0].du
# In this step, we define the likelihood. Here we use the standard likelihood
# function, passing it the data and the waveform generator.
-<<<<<<< HEAD
-likelihood = tupak.core.likelihood.GravitationalWaveTransient(interferometers, waveform_generator, time_marginalization=False, distance_marginalization=True, prior=prior)
-=======
likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
->>>>>>> e8b704afba65886f49cb448402a95ae90c468a24
# 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 3c48a1eab..b63df0d74 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.core.utils.get_event_time("GW150914")
+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.core.likelihood.get_binary_black_hole_likelihood(interferometers)
+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/hyper_parameter_example.py b/examples/other_examples/hyper_parameter_example.py
index ce9aadc09..b35735703 100644
--- a/examples/other_examples/hyper_parameter_example.py
+++ b/examples/other_examples/hyper_parameter_example.py
@@ -67,7 +67,7 @@ for i in range(Nevents):
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.core.likelihood.HyperparameterLikelihood(
+hp_likelihood = tupak.gw.likelihood.HyperparameterLikelihood(
samples, hyper_prior, run_prior)
hp_priors = dict(
diff --git a/examples/other_examples/sine_gaussian_example.py b/examples/other_examples/sine_gaussian_example.py
index 620090d64..b1702e773 100644
--- a/examples/other_examples/sine_gaussian_example.py
+++ b/examples/other_examples/sine_gaussian_example.py
@@ -55,7 +55,7 @@ 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.core.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.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index dd7761a53..8ca9899a4 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -94,14 +94,3 @@ 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 69939d13a..970863a96 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 d47e96fbc..9c09d7c42 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 ae3947f26..6d34f374d 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/test/waveform_generator_tests.py b/test/waveform_generator_tests.py
index d535aebb8..094a1e7a8 100644
--- a/test/waveform_generator_tests.py
+++ b/test/waveform_generator_tests.py
@@ -48,7 +48,8 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
self.assertIsInstance(self.waveform_generator.time_array, np.ndarray)
def test_source_model_parameters(self):
- self.assertListEqual(list(self.waveform_generator.parameters.keys()), list(self.simulation_parameters.keys()))
+ self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
+ sorted(list(self.simulation_parameters.keys())))
class TestParameterSetter(unittest.TestCase):
@@ -74,7 +75,8 @@ class TestParameterSetter(unittest.TestCase):
def test_parameter_setter_none_handling(self):
self.waveform_generator.parameters = None
- self.assertSequenceEqual(self.waveform_generator.parameters.keys(), self.simulation_parameters.keys())
+ self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
+ sorted(list(self.simulation_parameters.keys())))
class TestSourceModelSetter(unittest.TestCase):
@@ -94,7 +96,8 @@ class TestSourceModelSetter(unittest.TestCase):
del self.simulation_parameters
def test_parameters_are_set_correctly(self):
- self.assertListEqual(list(self.waveform_generator.parameters.keys()), list(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/core/utils.py b/tupak/core/utils.py
index 6a5a4e0f9..0fa1ecc57 100644
--- a/tupak/core/utils.py
+++ b/tupak/core/utils.py
@@ -219,7 +219,7 @@ def setup_logger(outdir=None, label=None, log_level=None):
file_handler.setLevel(LEVEL)
logger.addHandler(file_handler)
- version_file = os.path.join(os.path.dirname(__file__), '.version')
+ 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))
--
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