diff --git a/examples/injection_examples/eccentric_GW150914_inspiral.py b/examples/injection_examples/eccentric_GW150914_inspiral.py
deleted file mode 100644
index b003c6b20999151030fc078ad57651e8e6bb0b29..0000000000000000000000000000000000000000
--- a/examples/injection_examples/eccentric_GW150914_inspiral.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/bin/python
-"""
-Tutorial to demonstrate running parameter estimation on a reduced parameter space for an injected eccentric binary
-black hole signal with masses & distnace similar to GW150914.
-
-This uses the same binary parameters that were used to make Figures 1, 2 & 5 in Lower et al. (2018) -> arXiv:1806.05350.
-
-For a more comprehensive look at what goes on in each step, refer to the "basic_tutorial.py" example.
-"""
-from __future__ import division, print_function
-
-import numpy as np
-
-import tupak
-
-import matplotlib.pyplot as plt
-
-duration = 64.
-sampling_frequency = 2048.
-
-outdir = 'outdir'
-label = 'eccentric_GW140914'
-tupak.core.utils.setup_logger(outdir=outdir, label=label)
-
-# Set up a random seed for result reproducibility.
-np.random.seed(150914)
-
-injection_parameters = dict(mass_1=35., mass_2=30., eccentricity=0.1, luminosity_distance=440.,
- iota=0.4, psi=0.1, phase=1.2, geocent_time=1180002601.0, ra=45, dec=5.73)
-
-waveform_arguments = dict(waveform_approximant='EccentricFD', reference_frequency=10., minimum_frequency=10.)
-
-# Create the waveform_generator using the LAL eccentric black hole no spins source function
-waveform_generator = tupak.gw.WaveformGenerator(
- duration=duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=tupak.gw.source.lal_eccentric_binary_black_hole_no_spins,
- parameters=injection_parameters, waveform_arguments=waveform_arguments)
-
-hf_signal = waveform_generator.frequency_domain_strain()
-
-# Setting up three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)) at their design sensitivities.
-# The maximum frequency is set just prior to the point at which the waveform model terminates. This is to avoid any biases
-# introduced from using a sharply terminating waveform model.
-minimum_frequency = 10.0
-maximum_frequency = 133.0
-
-def get_interferometer(name, injection_polarizations, injection_parameters, duration, sampling_frequency,
- minimum_frequency, maximum_frequency, outdir):
- """
- Sets up the interferometers & injects the signal into them
- """
- start_time = injection_parameters['geocent_time'] + 2 - duration
-
- ifo = tupak.gw.detector.get_empty_interferometer(name)
- if name == 'V1':
- ifo.power_spectral_density.set_from_power_spectral_density_file('AdV_psd.txt')
- else:
- ifo.power_spectral_density.set_from_power_spectral_density_file('aLIGO_ZERO_DET_high_P_psd.txt')
-
- ifo.set_strain_data_from_power_spectral_density(sampling_frequency=sampling_frequency,
- duration=duration, start_time=start_time)
-
- injection_polarizations = ifo.inject_signal(parameters=injection_parameters,
- injection_polarizations=injection_polarizations,
- waveform_generator=waveform_generator)
-
- signal = ifo.get_detector_response(injection_polarizations, injection_parameters)
-
- ifo.minimum_frequency = minimum_frequency
- ifo.maximum_frequency = maximum_frequency
-
- ifo.plot_data(signal=signal, outdir=outdir, label=label)
- ifo.save_data(outdir, label=label)
-
- return ifo
-
-# IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(name, injection_polarizations=hf_signal,
-# injection_parameters=injection_parameters, duration=duration,
-# sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
-
-name = ['H1', 'L1', 'V1']
-IFOs = []
-
-for i in range(0,3):
- IFOs.append(get_interferometer(name[i], injection_polarizations=hf_signal, injection_parameters=injection_parameters,
- duration=duration, sampling_frequency=sampling_frequency,
- minimum_frequency=minimum_frequency, maximum_frequency=maximum_frequency,
- outdir=outdir))
-
-# Now we set up the priors on each of the binary parameters.
-priors = dict()
-priors["mass_1"] = tupak.core.prior.Uniform(name='mass_1', minimum=5, maximum=60)
-priors["mass_2"] = tupak.core.prior.Uniform(name='mass_2', minimum=5, maximum=60)
-priors["eccentricity"] = tupak.core.prior.PowerLaw(name='eccentricity', latex_label='$e$', alpha=-1, minimum=1e-4, maximum=0.4)
-priors["luminosity_distance"] = tupak.gw.prior.UniformComovingVolume(name='luminosity_distance', minimum=1e2, maximum=2e3)
-priors["dec"] = tupak.core.prior.Cosine(name='dec')
-priors["ra"] = tupak.core.prior.Uniform(name='ra', minimum=0, maximum=2 * np.pi)
-priors["iota"] = tupak.core.prior.Sine(name='iota')
-priors["psi"] = tupak.core.prior.Uniform(name='psi', minimum=0, maximum=2 * np.pi)
-priors["phase"] = tupak.core.prior.Uniform(name='phase', minimum=0, maximum=2 * np.pi)
-priors["geocent_time"] = tupak.core.prior.Uniform(1180002600.9, 1180002601.1, name='geocent_time')
-
-# Initialising the likelihood function.
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
- time_marginalization=False, phase_marginalization=False,
- distance_marginalization=False, prior=priors)
-
-# Now we run sampler (PyMultiNest in our case).
-result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='pymultinest', npoints=1000,
- injection_parameters=injection_parameters, outdir=outdir, label=label)
-
-# And finally we make some plots of the output posteriors.
-result.plot_corner()
diff --git a/examples/injection_examples/eccentric_inspiral.py b/examples/injection_examples/eccentric_inspiral.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbe71b0a55fe3bbc38f0b2c6d623f06e8c521438
--- /dev/null
+++ b/examples/injection_examples/eccentric_inspiral.py
@@ -0,0 +1,86 @@
+#!/bin/python
+"""
+Tutorial to demonstrate running parameter estimation on a reduced parameter space
+for an injected eccentric binary black hole signal with masses & distnace similar
+to GW150914.
+
+This uses the same binary parameters that were used to make Figures 1, 2 & 5 in
+Lower et al. (2018) -> arXiv:1806.05350.
+
+For a more comprehensive look at what goes on in each step, refer to the
+"basic_tutorial.py" example.
+"""
+from __future__ import division, print_function
+
+import numpy as np
+
+import tupak
+
+import matplotlib.pyplot as plt
+
+duration = 64.
+sampling_frequency = 256.
+
+outdir = 'outdir'
+label = 'eccentric_GW140914'
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
+
+# Set up a random seed for result reproducibility.
+np.random.seed(150914)
+
+injection_parameters = dict(mass_1=35., mass_2=30., eccentricity=0.1,
+ luminosity_distance=440., iota=0.4, psi=0.1, phase=1.2,
+ geocent_time=1180002601.0, ra=45, dec=5.73)
+
+waveform_arguments = dict(waveform_approximant='EccentricFD', reference_frequency=10., minimum_frequency=10.)
+
+# Create the waveform_generator using the LAL eccentric black hole no spins source function
+waveform_generator = tupak.gw.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=tupak.gw.source.lal_eccentric_binary_black_hole_no_spins,
+ parameters=injection_parameters, waveform_arguments=waveform_arguments)
+
+hf_signal = waveform_generator.frequency_domain_strain()
+
+# Setting up three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1), and
+# Virgo (V1)) at their design sensitivities. The maximum frequency is set just
+# prior to the point at which the waveform model terminates. This is to avoid any
+# biases introduced from using a sharply terminating waveform model.
+minimum_frequency = 10.
+maximum_frequency = 128.
+
+IFOs = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
+for IFO in IFOs:
+ IFO.minimum_frequency = minimum_frequency
+ IFO.maximum_frequency = maximum_frequency
+
+IFOs.set_strain_data_from_power_spectral_densities(sampling_frequency, duration)
+IFOs.inject_signal(waveform_generator=waveform_generator, parameters=injection_parameters)
+
+# Now we set up the priors on each of the binary parameters.
+priors = dict()
+priors["mass_1"] = tupak.core.prior.Uniform(name='mass_1', minimum=5, maximum=60)
+priors["mass_2"] = tupak.core.prior.Uniform(name='mass_2', minimum=5, maximum=60)
+priors["eccentricity"] = tupak.core.prior.PowerLaw(name='eccentricity', latex_label='$e$', alpha=-1, minimum=1e-4, maximum=0.4)
+priors["luminosity_distance"] = tupak.gw.prior.UniformComovingVolume(name='luminosity_distance', minimum=1e2, maximum=2e3)
+priors["dec"] = tupak.core.prior.Cosine(name='dec')
+priors["ra"] = tupak.core.prior.Uniform(name='ra', minimum=0, maximum=2 * np.pi)
+priors["iota"] = tupak.core.prior.Sine(name='iota')
+priors["psi"] = tupak.core.prior.Uniform(name='psi', minimum=0, maximum=2 * np.pi)
+priors["phase"] = tupak.core.prior.Uniform(name='phase', minimum=0, maximum=2 * np.pi)
+priors["geocent_time"] = tupak.core.prior.Uniform(1180002600.9, 1180002601.1, name='geocent_time')
+
+# Initialising the likelihood function.
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs,
+ waveform_generator=waveform_generator, time_marginalization=False,
+ phase_marginalization=False, distance_marginalization=False,
+ prior=priors)
+
+# Now we run sampler (PyMultiNest in our case).
+result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='pymultinest',
+ npoints=1000, injection_parameters=injection_parameters,
+ outdir=outdir, label=label)
+
+# And finally we make some plots of the output posteriors.
+result.plot_corner()
+