detailed tutorial for open data

The tutorial is based on GW150914.py, with the addition of more in depth comments. The code has been adapted to be used for other LOSC events, i.e. all arguments are explicitly specified (e.g. interferometer_names, sample_rate, npoints..), so that they can be easily edited by the user.

examples/open_data_examples/advanced_opendata.py

@@ -19,11 +19,10 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
 interferometer_names = ['H1', 'L1']  # can also include 'V1'
 duration = 4    # length of data segment containing the signal
 roll_off = 0.2  # smoothness of transition from no signal
-# to max signal in a Turkey Window.
+# to max signal in a Tukey Window.
 psd_offset = -1024  # PSD is estimated using data from
 # 'center_time + psd_offset' to 'center_time + psd_offset + psd_duration'
 psd_duration = 100
-coherence_test = False  # coherence between detectors
 filter_freq = None  # low pass filter frequency to cut signal content above
 # Nyquist frequency. The condition is 2 * filter_freq >= sampling_frequency
 
@@ -46,12 +45,12 @@ interferometers = bilby.gw.detector.get_event_data(
 
 # CHOOSE PRIOR FILE
 # DEFAULT PRIOR FILES: GW150914.prior, binary_black_holes.prior,
-# binary_neutron_stars.prior, comoving.txt
+# binary_neutron_stars.prior
 # Needs to specify path for any other prior file.
 prior = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior')
 
 # GENERATE WAVEFORM
-duration = None  # durantion and sampling frequency will be overwritten
+duration = None  # duration and sampling frequency will be overwritten
 # to match the ones in the interferometers.
 sampling_frequency = kwargs["sample_rate"]  # same at which the data is stored
 start_time = 0  # set the starting time of the time array
@@ -66,11 +65,11 @@ source_model = bilby.gw.source.lal_binary_black_hole
 waveform_generator = bilby.gw.WaveformGenerator(
     duration=duration, sampling_frequency=sampling_frequency,
     start_time=start_time,
-    frequency_domain_source_model=source_model,
+    frequency_domain_source_model=source_model, 
     waveform_arguments=waveform_arguments)
 
 # CHOOSE LIKELIHOOD FUNCTION
-# Time marginalisation uses FFT.
+# Time marginalisation uses FFT and can use a prior file.
 # Distance marginalisation uses a look up table calculated at run time.
 # Phase marginalisation is done analytically using a Bessel function.
 # If prior given, used in the distance and phase marginalization.
@@ -82,18 +81,18 @@ likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
 # RUN SAMPLER
 # Can use log_likelihood_ratio, rather than just the log_likelihood.
 # If using simulated data, pass a dictionary of injection parameters.
-# A function can be specified in 'conversion_function' and
-# applied to posterior to generate additional parameters.
+# A function can be specified in 'conversion_function' and applied
+# to posterior to generate additional parameters e.g. source-frame masses.
 
 # Implemented Samplers:
 # LIST OF AVAILABLE SAMPLERS: Run -> bilby.sampler.implemented_samplers
-npoints = 512  # number of live points for the MCMC
+npoints = 500  # number of live points for the MCMC
 sampler = 'dynesty'
 
 result = bilby.run_sampler(
     likelihood, prior, outdir=outdir, label=label,
     sampler=sampler, npoints=npoints, use_ratio=False,
-    injection_parameters=None, conversion_function=None,
-    default_priors_file=None)
+    injection_parameters=None, 
+    conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
 
 result.plot_corner()