Changed `iota` to `theta_jn` and removed argument `waveform_approximant`

from prior dictionary.

examples/gw_examples/data_examples/GW150914.prior

@@ -14,3 +14,4 @@ ra =  Uniform(name='ra', minimum=0, maximum=2 * np.pi, boundary='periodic')
 theta_jn =  Sine(name='theta_jn', boundary='reflective')
 psi =  Uniform(name='psi', minimum=0, maximum=np.pi, boundary='periodic')
 phase =  Uniform(name='phase', minimum=0, maximum=2 * np.pi, boundary='periodic')
+geocent_time = Uniform(name='geocent_time', minimum =1126259460.4, maximum=1126259464.4)

examples/gw_examples/data_examples/GW150914_advanced.py

@@ -12,66 +12,83 @@ List of events in BILBY dict: run -> help(bilby.gw.utils.get_event_time(event))
 """
 from __future__ import division, print_function
 import bilby
+from gwpy.timeseries import TimeSeries
 
 outdir = 'outdir'
 label = 'GW150914'
+logger = bilby.core.utils.logger
 time_of_event = bilby.gw.utils.get_event_time(label)
 
 bilby.core.utils.setup_logger(outdir=outdir, label=label)
 
 # GET DATA FROM INTERFEROMETER
 interferometer_names = ['H1', 'L1']  # include 'V1' for appropriate O2 events
-duration = 4    # length of data segment containing the signal
-roll_off = 0.2  # smoothness of transition from no signal
-# 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'.
-# This determines the time window used to fetch open data.
-psd_duration = 100
+duration = 4  # length of data segment containing the signal
+post_trigger_duration = 2  # time between trigger time and end of segment
+end_time = time_of_event + post_trigger_duration
+start_time = end_time - duration
+
+roll_off = 0.4  # smoothness in a tukey window, default is 0.4s
+# This determines the time window used to fetch open data
+psd_duration = 32 * duration
+psd_start_time = start_time - psd_duration
+psd_end_time = start_time
+
 filter_freq = None  # low pass filter frequency to cut signal content above
 # Nyquist frequency. The condition is 2 * filter_freq >= sampling_frequency
 
-# Keyword args are passed to 'gwpy.timeseries.TimeSeries.fetch_open_data()'
-# sample_rate: most data are stored by LOSC at 4096 Hz,
-# there may be event-related data releases with a 16384 Hz rate.
-kwargs = {"sample_rate": 4096}
-# For O2 events a "tag" is required to download the data.
-# CLN = clean data; C00 or C01 = raw data
-# kwargs = {"tag": 'CLN'}
-# For some events can specify channels: source data stream for LOSC data.
-# kwargs = {"channel": {'H1': 'H1:DCS-CALIB_STRAIN_C02',
-#                      'L1': 'L1:DCS-CALIB_STRAIN_C02',
-#                      'V1': 'V1:FAKE_h_16384Hz_4R'}}
-
-interferometers = bilby.gw.detector.get_event_data(
-    label, interferometer_names=interferometer_names,
-    duration=duration, roll_off=roll_off, psd_offset=psd_offset,
-    psd_duration=psd_duration, cache=True, outdir=outdir,
-    filter_freq=filter_freq, **kwargs)
+
+ifo_list = bilby.gw.detector.InterferometerList([])
+for det in interferometer_names:
+    logger.info("Downloading analysis data for ifo {}".format(det))
+    ifo = bilby.gw.detector.get_empty_interferometer(det)
+    data = TimeSeries.fetch_open_data(det, start_time, end_time)
+    ifo.set_strain_data_from_gwpy_timeseries(data)
+# Additional arguments you might need to pass to TimeSeries.fetch_open_data:
+# - sample_rate = 4096, most data are stored by LOSC at this frequency
+# there may be event-related data releases with a 16384Hz rate.
+# - tag = 'CLN' for clean data; C00/C01 for raw data (different releases)
+# note that for O2 events a "tag" is required to download the data.
+# - channel =  {'H1': 'H1:DCS-CALIB_STRAIN_C02',
+#               'L1': 'L1:DCS-CALIB_STRAIN_C02',
+#               'V1': 'V1:FAKE_h_16384Hz_4R'}}
+# for some events can specify channels: source data stream for LOSC data.
+    logger.info("Downloading psd data for ifo {}".format(det))
+    psd_data = TimeSeries.fetch_open_data(det, psd_start_time, psd_end_time)
+    psd_alpha = 2 * roll_off / duration  # shape parameter of tukey window
+    psd = psd_data.psd(
+        fftlength=duration,
+        overlap=0,
+        window=("tukey", psd_alpha),
+        method="median")
+    ifo.power_spectral_density = bilby.gw.detector.PowerSpectralDensity(
+        frequency_array=psd.frequencies.value, psd_array=psd.value)
+    ifo_list.append(ifo)
+
+logger.info("Saving data plots to {}".format(outdir))
+bilby.core.utils.check_directory_exists_and_if_not_mkdir(outdir)
+ifo_list.plot_data(outdir=outdir, label=label)
 
 # CHOOSE PRIOR FILE
 # DEFAULT PRIOR FILES: GW150914.prior, binary_black_holes.prior,
 # binary_neutron_stars.prior (if bns, use BNSPriorDict)
-# Needs to specify path for any other prior file.
+# Needs to specify path if you want to use any other prior file.
 prior = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior')
 
 # GENERATE WAVEFORM
-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
+sampling_frequency = 4096.  # same at which the data is stored
+conversion = bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters
 
-# reference_frequency: either low freq. limit or most sensitive freq.
 # OVERVIEW OF APPROXIMANTS:
 # https://www.lsc-group.phys.uwm.edu/ligovirgo/cbcnote/Waveforms/Overview
-waveform_arguments = {'waveform_approximant': 'IMRPhenomPv2',
-                      'reference_frequency': 50}
-source_model = bilby.gw.source.lal_binary_black_hole
+waveform_arguments = {
+    'waveform_approximant': 'IMRPhenomPv2',
+    'reference_frequency': 50  # most sensitive frequency
+}
 
 waveform_generator = bilby.gw.WaveformGenerator(
-    duration=duration, sampling_frequency=sampling_frequency,
-    start_time=start_time,
-    frequency_domain_source_model=source_model,
+    parameter_conversion=conversion,
+    frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
     waveform_arguments=waveform_arguments)
 
 # CHOOSE LIKELIHOOD FUNCTION
@@ -80,26 +97,27 @@ waveform_generator = bilby.gw.WaveformGenerator(
 # Phase marginalisation is done analytically using a Bessel function.
 # If prior given, used in the distance and phase marginalization.
 likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
-    interferometers, waveform_generator, time_marginalization=False,
-    distance_marginalization=False, phase_marginalization=False)
+    interferometers=ifo_list, waveform_generator=waveform_generator,
+    priors=prior, time_marginalization=False, distance_marginalization=False,
+    phase_marginalization=False)
 
 # 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 e.g. source-frame masses.
 
 # Implemented Samplers:
 # LIST OF AVAILABLE SAMPLERS: Run -> bilby.sampler.implemented_samplers
-npoints = 500  # number of live points for the MCMC (dynesty)
+npoints = 512  # number of live points for the nested sampler
+n_steps = 100  # min number of steps before proposing a new live point,
+# defaults `ndim * 10`
 sampler = 'dynesty'
 # Different samplers can have different additional kwargs,
 # visit https://lscsoft.docs.ligo.org/bilby/samplers.html for details.
 
 result = bilby.run_sampler(
     likelihood, prior, outdir=outdir, label=label,
-    sampler=sampler, npoints=npoints, use_ratio=False,
-    injection_parameters=None,
+    sampler=sampler, nlive=npoints, use_ratio=False,
+    walks=n_steps, n_check_point=10000, check_point_plot=True,
     conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
-
 result.plot_corner()