diff --git a/bilby/core/result.py b/bilby/core/result.py
index 0ac69ab2bea9b30a3892a6aa6b6459b4074f34de..3ca06858412997c83f0f78fde58e79b0598c2839 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -9,7 +9,7 @@ import matplotlib.pyplot as plt
from collections import OrderedDict
from . import utils
-from .utils import logger
+from .utils import logger, infer_parameters_from_function
from .prior import PriorSet, DeltaFunction
@@ -502,16 +502,22 @@ class Result(dict):
from the outdir and label attributes.
"""
+
+ # Determine model_posterior, the subset of the full posterior which
+ # should be passed into the model
+ model_keys = infer_parameters_from_function(model)
+ model_posterior = self.posterior[model_keys]
+
xsmooth = np.linspace(np.min(x), np.max(x), npoints)
fig, ax = plt.subplots()
logger.info('Plotting {} draws'.format(ndraws))
for _ in range(ndraws):
- s = self.posterior.sample().to_dict('records')[0]
+ s = model_posterior.sample().to_dict('records')[0]
ax.plot(xsmooth, model(xsmooth, **s), alpha=0.25, lw=0.1, color='r',
label=draws_label)
if all(~np.isnan(self.posterior.log_likelihood)):
logger.info('Plotting maximum likelihood')
- s = self.posterior.ix[self.posterior.log_likelihood.idxmax()]
+ s = model_posterior.ix[self.posterior.log_likelihood.idxmax()]
ax.plot(xsmooth, model(xsmooth, **s), lw=1, color='k',
label=maxl_label)
diff --git a/examples/injection_examples/australian_detector.py b/examples/injection_examples/australian_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..29ad908bddebab0c38a185cf36aabd4fc360334e
--- /dev/null
+++ b/examples/injection_examples/australian_detector.py
@@ -0,0 +1,104 @@
+#!/bin/python
+"""
+Tutorial to demonstrate a new interferometer
+
+We place a new instrument in Gingin, with an A+ sensitivity in a network of A+
+interferometers at Hanford and Livingston
+"""
+from __future__ import division, print_function
+
+import numpy as np
+
+import bilby, gwinc
+
+# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+duration = 4.
+sampling_frequency = 2048.
+
+# Specify the output directory and the name of the simulation.
+outdir = 'outdir'
+label = 'australian_detector'
+bilby.core.utils.setup_logger(outdir=outdir, label=label)
+
+# Set up a random seed for result reproducibility. This is optional!
+np.random.seed(88170232)
+
+# create a new detector using a PyGwinc sensitivity curve
+frequencies = np.logspace(0, 3, 1000)
+gwinc_detector = gwinc.load_ifo('A+')
+gwinc_detector = gwinc.precompIFO(frequencies, gwinc_detector)
+gwinc_noises = gwinc.noise_calc(frequencies, gwinc_detector)
+
+Aplus_psd = gwinc_noises['Total']
+
+# Set up the detector as a four-kilometer detector in Gingin
+# The location of this detector is not defined in Bilby, so we need to add it
+AusIFO = bilby.gw.detector.Interferometer(
+ power_spectral_density=bilby.gw.detector.PowerSpectralDensity(
+ frequency_array=frequencies, psd_array=Aplus_psd),
+ name='AusIFO', length=4,
+ minimum_frequency=min(frequencies), maximum_frequency=max(frequencies),
+ latitude=-31.34, longitude=115.91,
+ elevation=0., xarm_azimuth=2., yarm_azimuth=125.)
+
+# Set up two other detectors at Hanford and Livingston
+interferometers = bilby.gw.detector.InterferometerList(['H1', 'L1'])
+for interferometer in interferometers:
+ interferometer.power_spectral_density = bilby.gw.detector.PowerSpectralDensity(
+ frequency_array=frequencies, psd_array=Aplus_psd)
+
+# append the Australian detector to the list of other detectors
+interferometers.append(AusIFO)
+
+
+# Inject a gravitational-wave signal into the network
+# as we're using a three-detector network of A+, we inject a GW150914-like
+# signal at 4 Gpc
+injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
+ luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
+ ra=1.375, dec=0.2108)
+
+
+# Fixed arguments passed into the source model
+waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
+ reference_frequency=50.)
+
+# Create the waveform_generator using a LAL BinaryBlackHole source function
+waveform_generator = bilby.gw.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
+ waveform_arguments=waveform_arguments)
+
+start_time = injection_parameters['geocent_time'] + 2 - duration
+
+# inject the signal into the interferometers
+
+for interferometer in interferometers:
+ interferometer.set_strain_data_from_power_spectral_density(
+ sampling_frequency=sampling_frequency, duration=duration)
+ interferometer.inject_signal(
+ parameters=injection_parameters, waveform_generator=waveform_generator)
+
+ ## plot the data for sanity
+ signal = interferometer.get_detector_response(
+ waveform_generator.frequency_domain_strain(), injection_parameters)
+ interferometer.plot_data(signal=signal, outdir=outdir, label=label)
+
+# set up priors
+priors = bilby.gw.prior.BBHPriorSet()
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'geocent_time', 'phase']:
+ priors[key] = injection_parameters[key]
+
+# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
+likelihood = bilby.gw.GravitationalWaveTransient(
+ interferometers=interferometers, waveform_generator=waveform_generator,
+ time_marginalization=False, phase_marginalization=False,
+ distance_marginalization=False, prior=priors)
+
+
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
+
+# make some plots of the outputs
+result.plot_corner()