Merge branch...

Merge branch '591-get_event_data-still-used-in-some-examples-despite-being-removed-as-a-deprecated-function' into 'master'

Resolve "`get_event_data` still used in some examples despite being removed as a deprecated function"

Closes #591

See merge request !1050

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
     hooks:
       - id: black
         language_version: python3
-        files: ^bilby/bilby_mcmc/
+        files: ^(bilby/bilby_mcmc/|examples/gw_examples/data_examples)
 -   repo: https://github.com/codespell-project/codespell
     rev: v2.1.0
     hooks:
@@ -20,4 +20,4 @@ repos:
     hooks:
     -   id: isort # sort imports alphabetically and separates import into sections
         args: [-w=88, -m=3, -tc, -sp=setup.cfg ]
-        files: ^bilby/bilby_mcmc/
+        files: ^(bilby/bilby_mcmc/|examples/gw_examples/data_examples)

examples/gw_examples/data_examples/GW150914.prior

-mass_ratio = Uniform(name='mass_ratio', minimum=0.125, maximum=1, boundary='reflective')
-chirp_mass = Uniform(name='chirp_mass', minimum=25, maximum=35, unit='$M_{\odot}$', boundary='reflective')
+chirp_mass = bilby.gw.prior.UniformInComponentsChirpMass(name='chirp_mass', minimum=25, maximum=35, unit='$M_{\odot}$')
+mass_ratio = bilby.gw.prior.UniformInComponentsMassRatio(name='mass_ratio', minimum=0.125, maximum=1)
 mass_1 = Constraint(name='mass_1', minimum=10, maximum=80)
 mass_2 = Constraint(name='mass_2', minimum=10, maximum=80)
-a_1 = Uniform(name='a_1', minimum=0, maximum=0.99, boundary='reflective')
-a_2 = Uniform(name='a_2', minimum=0, maximum=0.99, boundary='reflective')
-tilt_1 = Sine(name='tilt_1', boundary='reflective')
-tilt_2 = Sine(name='tilt_2', boundary='reflective')
+a_1 = Uniform(name='a_1', minimum=0, maximum=0.99)
+a_2 = Uniform(name='a_2', minimum=0, maximum=0.99)
+tilt_1 = Sine(name='tilt_1')
+tilt_2 = Sine(name='tilt_2')
 phi_12 = Uniform(name='phi_12', minimum=0, maximum=2 * np.pi, boundary='periodic')
 phi_jl = Uniform(name='phi_jl', minimum=0, maximum=2 * np.pi, boundary='periodic')
 luminosity_distance = PowerLaw(alpha=2, name='luminosity_distance', minimum=50, maximum=2000, unit='Mpc', latex_label='$d_L$')
-dec =  Cosine(name='dec', boundary='reflective')
+dec =  Cosine(name='dec')
 ra =  Uniform(name='ra', minimum=0, maximum=2 * np.pi, boundary='periodic')
-theta_jn =  Sine(name='theta_jn', boundary='reflective')
+theta_jn =  Sine(name='theta_jn')
 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.py

@@ -13,12 +13,16 @@ import bilby
 from gwpy.timeseries import TimeSeries
 
 logger = bilby.core.utils.logger
-outdir = 'outdir'
-label = 'GW150914'
-
-# Data set up
-trigger_time = 1126259462
+outdir = "outdir"
+label = "GW150914"
 
+# Note you can get trigger times using the gwosc package, e.g.:
+# > from gwosc import datasets
+# > datasets.event_gps("GW150914")
+trigger_time = 1126259462.4
+detectors = ["H1", "L1"]
+maximum_frequency = 512
+minimum_frequency = 20
 roll_off = 0.4  # Roll off duration of tukey window in seconds, default is 0.4s
 duration = 4  # Analysis segment duration
 post_trigger_duration = 2  # Time between trigger time and end of segment
@@ -31,7 +35,7 @@ psd_end_time = start_time
 
 # We now use gwpy to obtain analysis and psd data and create the ifo_list
 ifo_list = bilby.gw.detector.InterferometerList([])
-for det in ["H1", "L1"]:
+for det in detectors:
     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)
@@ -41,13 +45,13 @@ for det in ["H1", "L1"]:
     psd_data = TimeSeries.fetch_open_data(det, psd_start_time, psd_end_time)
     psd_alpha = 2 * roll_off / duration
     psd = psd_data.psd(
-        fftlength=duration,
-        overlap=0,
-        window=("tukey", psd_alpha),
-        method="median"
+        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)
+        frequency_array=psd.frequencies.value, psd_array=psd.value
+    )
+    ifo.maximum_frequency = maximum_frequency
+    ifo.minimum_frequency = minimum_frequency
     ifo_list.append(ifo)
 
 logger.info("Saving data plots to {}".format(outdir))
@@ -59,7 +63,12 @@ ifo_list.plot_data(outdir=outdir, label=label)
 # The prior is printed to the terminal at run-time.
 # You can overwrite this using the syntax below in the file,
 # or choose a fixed value by just providing a float value as the prior.
-priors = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior')
+priors = bilby.gw.prior.BBHPriorDict(filename="GW150914.prior")
+
+# Add the geocent time prior
+priors["geocent_time"] = bilby.core.prior.Uniform(
+    trigger_time - 0.1, trigger_time + 0.1, name="geocent_time"
+)
 
 # In this step we define a `waveform_generator`. This is the object which
 # creates the frequency-domain strain. In this instance, we are using the
@@ -69,19 +78,36 @@ priors = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior')
 waveform_generator = bilby.gw.WaveformGenerator(
     frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
     parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
-    waveform_arguments={'waveform_approximant': 'IMRPhenomPv2',
-                        'reference_frequency': 50})
+    waveform_arguments={
+        "waveform_approximant": "IMRPhenomPv2",
+        "reference_frequency": 50,
+    },
+)
 
 # In this step, we define the likelihood. Here we use the standard likelihood
 # function, passing it the data and the waveform generator.
+# Note, phase_marginalization is formally invalid with a precessing waveform such as IMRPhenomPv2
 likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
-    ifo_list, waveform_generator, priors=priors, time_marginalization=True,
-    phase_marginalization=True, distance_marginalization=True)
+    ifo_list,
+    waveform_generator,
+    priors=priors,
+    time_marginalization=True,
+    phase_marginalization=False,
+    distance_marginalization=True,
+)
 
 # Finally, we run the sampler. This function takes the likelihood and prior
 # along with some options for how to do the sampling and how to save the data
 result = bilby.run_sampler(
-    likelihood, priors, sampler='dynesty', outdir=outdir, label=label,
-    nlive=1000, walks=100, n_check_point=10000, check_point_plot=True,
-    conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
+    likelihood,
+    priors,
+    sampler="dynesty",
+    outdir=outdir,
+    label=label,
+    nlive=1000,
+    check_point_delta_t=600,
+    check_point_plot=True,
+    npool=1,
+    conversion_function=bilby.gw.conversion.generate_all_bbh_parameters,
+)
 result.plot_corner()

examples/gw_examples/data_examples/GW150914_advanced.py

-#!/usr/bin/env python
-"""
-This tutorial includes advanced specifications for analysing known events.
-Here GW150914 is used as an example.
-
-LIST OF AVAILABLE EVENTS:
->> from gwosc import datasets
->> for event in datasets.find_datasets(type="event"):
-...     print(event, datasets.event_gps(event))
-
-List of events in BILBY dict: run -> help(bilby.gw.utils.get_event_time(event))
-"""
-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
-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
-
-
-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 if you want to use any other prior file.
-prior = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior')
-
-# GENERATE WAVEFORM
-sampling_frequency = 4096.  # same at which the data is stored
-conversion = bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters
-
-# OVERVIEW OF APPROXIMANTS:
-# https://www.lsc-group.phys.uwm.edu/ligovirgo/cbcnote/Waveforms/Overview
-waveform_arguments = {
-    'waveform_approximant': 'IMRPhenomPv2',
-    'reference_frequency': 50  # most sensitive frequency
-}
-
-waveform_generator = bilby.gw.WaveformGenerator(
-    parameter_conversion=conversion,
-    frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
-    waveform_arguments=waveform_arguments)
-
-# CHOOSE LIKELIHOOD FUNCTION
-# 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.
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
-    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.
-# 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 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, 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()

examples/gw_examples/data_examples/GW150914_minimal.py

-#!/usr/bin/env python
-"""
-Tutorial to demonstrate the minimum number of steps required to run parameter
-stimation on GW150914 using open data.
-
-"""
-import bilby
-
-prior = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior')
-interferometers = bilby.gw.detector.get_event_data("GW150914")
-likelihood = bilby.gw.likelihood.get_binary_black_hole_likelihood(interferometers)
-result = bilby.run_sampler(likelihood, prior, label='GW150914')
-result.plot_corner()

examples/gw_examples/data_examples/GW170817.py

-#!/usr/bin/env python
-"""
-This tutorial includes advanced specifications
-for analysing binary neutron star event data.
-Here GW170817 is used as an example.
-"""
-import bilby
-
-outdir = 'outdir'
-label = 'GW170817'
-time_of_event = bilby.gw.utils.get_event_time(label)
-bilby.core.utils.setup_logger(outdir=outdir, label=label)
-# GET DATA FROM INTERFEROMETER
-# include 'V1' for appropriate O2 events
-interferometer_names = ['H1', 'L1', 'V1']
-duration = 32
-roll_off = 0.2  # how smooth is the transition from no signal
-# to max signal in a Tukey Window.
-psd_offset = -512  # 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 = 1024
-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
-
-
-# All keyword arguments are passed to
-# `gwpy.timeseries.TimeSeries.fetch_open_data()'
-kwargs = {}
-# Data are stored by LOSC at 4096 Hz, however
-# 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; C02 = raw data
-kwargs['tag'] = 'C02'
-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,
-    filter_freq=filter_freq,
-    **kwargs)
-# CHOOSE PRIOR FILE
-prior = bilby.gw.prior.BNSPriorDict(filename='GW170817.prior')
-deltaT = 0.1
-prior['geocent_time'] = bilby.core.prior.Uniform(
-    minimum=time_of_event - deltaT / 2,
-    maximum=time_of_event + deltaT / 2,
-    name='geocent_time',
-    latex_label='$t_c$',
-    unit='$s$')
-# GENERATE WAVEFORM
-# OVERVIEW OF APPROXIMANTS:
-# https://www.lsc-group.phys.uwm.edu/ligovirgo/cbcnote/Waveforms/Overview
-duration = None  # duration and sampling frequency will be overwritten
-# to match the ones in interferometers.
-sampling_frequency = kwargs['sample_rate']
-start_time = 0  # set the starting time of the time array
-waveform_arguments = {
-    'waveform_approximant': 'IMRPhenomPv2_NRTidal', 'reference_frequency': 20}
-
-source_model = bilby.gw.source.lal_binary_neutron_star
-convert_bns = bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters
-waveform_generator = bilby.gw.WaveformGenerator(
-    duration=duration,
-    sampling_frequency=sampling_frequency,
-    start_time=start_time,
-    frequency_domain_source_model=source_model,
-    parameter_conversion=convert_bns,
-    waveform_arguments=waveform_arguments,)
-
-# CHOOSE LIKELIHOOD FUNCTION
-# Time marginalisation uses FFT.
-# Distance marginalisation uses a look up table calculated at run time.
-# Phase marginalisation is done analytically using a Bessel function.
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
-    interferometers,
-    waveform_generator,
-    time_marginalization=False,
-    distance_marginalization=False,
-    phase_marginalization=False,)
-
-# RUN SAMPLER
-# Implemented Samplers:
-# LIST OF AVAILABLE SAMPLERS: Run -> bilby.sampler.implemented_samplers
-# conversion function = bilby.gw.conversion.generate_all_bns_parameters
-npoints = 512
-sampler = 'dynesty'
-result = bilby.run_sampler(
-    likelihood,
-    prior,
-    outdir=outdir,
-    label=label,
-    sampler=sampler,
-    npoints=npoints,
-    use_ratio=False,
-    conversion_function=bilby.gw.conversion.generate_all_bns_parameters)
-
-result.plot_corner()

examples/gw_examples/data_examples/GW190425.prior

+chirp_mass = bilby.gw.prior.UniformInComponentsChirpMass(name='chirp_mass', minimum=1.485, maximum=1.49, unit='$M_{\odot}$')
+mass_ratio = bilby.gw.prior.UniformInComponentsMassRatio(name='mass_ratio', minimum=0.125, maximum=1)
+mass_1 = Constraint(name='mass_1', minimum=1, maximum=2.5)
+mass_2 = Constraint(name='mass_2', minimum=1, maximum=2.5)
+a_1 = Uniform(name='a_1', minimum=0, maximum=0.05)
+a_2 = Uniform(name='a_2', minimum=0, maximum=0.05)
+tilt_1 = Sine(name='tilt_1')
+tilt_2 = Sine(name='tilt_2')
+phi_12 = Uniform(name='phi_12', minimum=0, maximum=2 * np.pi, boundary='periodic')
+phi_jl = Uniform(name='phi_jl', minimum=0, maximum=2 * np.pi, boundary='periodic')
+luminosity_distance = PowerLaw(alpha=2, name='luminosity_distance', minimum=10, maximum=1000, unit='Mpc', latex_label='$d_L$')
+dec =  Cosine(name='dec')
+ra =  Uniform(name='ra', minimum=0, maximum=2 * np.pi, boundary='periodic')
+theta_jn =  Sine(name='theta_jn')
+psi =  Uniform(name='psi', minimum=0, maximum=np.pi, boundary='periodic')
+phase =  Uniform(name='phase', minimum=0, maximum=2 * np.pi, boundary='periodic')
+lambda_1 = Uniform(name='lambda_1', minimum=0, maximum=5000)
+lambda_2 = Uniform(name='lambda_2', minimum=0, maximum=5000)

examples/gw_examples/data_examples/GW190425.py

+#!/usr/bin/env python
+"""
+Tutorial to demonstrate running parameter estimation on GW190425
+
+This example estimates all 17 parameters of the binary neutron star system using
+commonly used prior distributions. This will take several hours to run. The
+data is obtained using gwpy, see [1] for information on how to access data on
+the LIGO Data Grid instead.
+
+[1] https://gwpy.github.io/docs/stable/timeseries/remote-access.html
+"""
+import bilby
+from gwpy.timeseries import TimeSeries
+
+logger = bilby.core.utils.logger
+outdir = "outdir"
+label = "GW190425"
+
+# Note you can get trigger times using the gwosc package, e.g.:
+# > from gwosc import datasets
+# > datasets.event_gps("GW190425")
+trigger_time = 1240215503.0
+detectors = ["L1", "V1"]
+maximum_frequency = 512
+minimum_frequency = 20
+roll_off = 0.4  # Roll off duration of tukey window in seconds, default is 0.4s
+duration = 128  # Analysis segment duration
+post_trigger_duration = 2  # Time between trigger time and end of segment
+end_time = trigger_time + post_trigger_duration
+start_time = end_time - duration
+
+psd_duration = 1024
+psd_start_time = start_time - psd_duration
+psd_end_time = start_time
+
+# We now use gwpy to obtain analysis and psd data and create the ifo_list
+ifo_list = bilby.gw.detector.InterferometerList([])
+for det in detectors:
+    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.strain_data.set_from_gwpy_timeseries(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
+    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.maximum_frequency = maximum_frequency
+    ifo.minimum_frequency = minimum_frequency
+    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)
+
+# We now define the prior.
+# We have defined our prior distribution in a local file, GW190425.prior
+# The prior is printed to the terminal at run-time.
+# You can overwrite this using the syntax below in the file,
+# or choose a fixed value by just providing a float value as the prior.
+priors = bilby.gw.prior.BBHPriorDict(filename="GW190425.prior")
+
+# Add the geocent time prior
+priors["geocent_time"] = bilby.core.prior.Uniform(
+    trigger_time - 0.1, trigger_time + 0.1, name="geocent_time"
+)
+
+# In this step we define a `waveform_generator`. This is the object which
+# creates the frequency-domain strain. In this instance, we are using the
+# `lal_binary_black_hole model` source model. We also pass other parameters:
+# the waveform approximant and reference frequency and a parameter conversion
+# which allows us to sample in chirp mass and ratio rather than component mass
+waveform_generator = bilby.gw.WaveformGenerator(
+    frequency_domain_source_model=bilby.gw.source.lal_binary_neutron_star,
+    parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters,
+    waveform_arguments={
+        "waveform_approximant": "IMRPhenomPv2_NRTidalv2",
+        "reference_frequency": 20,
+    },
+)
+
+# In this step, we define the likelihood. Here we use the standard likelihood
+# function, passing it the data and the waveform generator.
+# Note, phase_marginalization is formally invalid with a precessing waveform such as IMRPhenomPv2
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+    ifo_list,
+    waveform_generator,
+    priors=priors,
+    time_marginalization=True,
+    phase_marginalization=False,
+    distance_marginalization=True,
+)
+
+# Finally, we run the sampler. This function takes the likelihood and prior
+# along with some options for how to do the sampling and how to save the data
+result = bilby.run_sampler(
+    likelihood,
+    priors,
+    sampler="dynesty",
+    outdir=outdir,
+    label=label,
+    nlive=1000,
+    check_point_delta_t=600,
+    check_point_plot=True,
+    npool=1,
+    conversion_function=bilby.gw.conversion.generate_all_bbh_parameters,
+)
+result.plot_corner()

examples/gw_examples/data_examples/get_LOSC_event_data.py

-#!/usr/bin/env python
-""" Helper script to facilitate downloading data from LOSC
-
-Usage: To download the GW150914 data from https://losc.ligo.org/events/
-
-$ python get_LOSC_event_data -e GW150914
-
-"""
-
-import numpy as np
-import os
-import argparse
-
-parser = argparse.ArgumentParser(description='Script to download LOSC data.')
-parser.add_argument('-e', '--event', metavar='event', type=str)
-parser.add_argument('-o', '--outdir', metavar='outdir',
-                    default='tutorial_data')
-
-args = parser.parse_args()
-
-url_dictionary = dict(
-    GW150914="https://losc.ligo.org/s/events/GW150914/{}-{}1_LOSC_4_V2-1126259446-32.txt.gz",
-    LVT151012="https://losc.ligo.org/s/events/LVT151012/{}-{}1_LOSC_4_V2-1128678884-32.txt.gz",
-    GW151226="https://losc.ligo.org/s/events/GW151226/{}-{}1_LOSC_4_V2-1135136334-32.txt.gz",
-    GW170104="https://losc.ligo.org/s/events/GW170104/{}-{}1_LOSC_4_V1-1167559920-32.txt.gz",
-    GW170608="https://losc.ligo.org/s/events/GW170608/{}-{}1_LOSC_CLN_4_V1-1180922478-32.txt.gz",
-    GW170814="https://dcc.ligo.org/public/0146/P1700341/001/{}-{}1_LOSC_CLN_4_V1-1186741845-32.txt.gz",
-    GW170817="https://dcc.ligo.org/public/0146/P1700349/001/{}-{}1_LOSC_CLN_4_V1-1187007040-2048.txt.gz")
-
-outdir = 'tutorial_data'
-
-data = []
-for det, in ['H', 'L']:
-    url = url_dictionary[args.event].format(det, det)
-    filename = os.path.basename(url)
-    if os.path.isfile(filename.rstrip('.gz')) is False:
-        print("Downloading data from {}".format(url))
-        os.system("wget {} ".format(url))
-        os.system("gunzip {}".format(filename))
-        filename = filename.rstrip('.gz')
-    data.append(np.loadtxt(filename))
-    with open(filename, 'r') as f:
-        header = f.readlines()[:3]
-        event = header[0].split(' ')[5]
-        detector = header[0].split(' ')[7]
-        sampling_frequency = header[1].split(' ')[4]
-        starttime = header[2].split(' ')[3]
-        duration = header[2].split(' ')[5]
-        print('Loaded data for event={}, detector={}, sampling_frequency={}'
-              ', starttime={}, duration={}'.format(
-                  event, detector, sampling_frequency, starttime, duration))
-    os.remove(filename)
-
-time = np.arange(0, int(duration), 1 / int(sampling_frequency)) + int(starttime)
-arr = [time] + data
-
-outfile = '{}/{}_strain_data.npy'.format(args.outdir, args.event)
-np.save(outfile, arr)
-print("Saved data to {}".format(outfile))

examples/gw_examples/data_examples/read_data_from_channel_name.py

@@ -8,28 +8,28 @@ This tutorial must be run on a LIGO cluster.
 
 import bilby
 
-label = 'GW170608'
-outdir = label + '_out'
+label = "GW170608"
+outdir = label + "_out"
 # List of detectors involved in the event. GW170608 is pre-August 2017 so only
 # H1 and L1 were involved
-detectors = ['H1', 'L1']
+detectors = ["H1", "L1"]
 # Names of the channels and query types to use, and the event GraceDB ID - ref.
 # https://ldas-jobs.ligo.caltech.edu/~eve.chase/monitor/online_pe/C02_clean/
 # C02_HL_Pv2/lalinferencenest/IMRPhenomPv2pseudoFourPN/config.ini
-channel_names = ['H1:DCH-CLEAN_STRAIN_C02', 'L1:DCH-CLEAN_STRAIN_C02']
-gracedb = 'G288686'
+channel_names = ["H1:DCH-CLEAN_STRAIN_C02", "L1:DCH-CLEAN_STRAIN_C02"]
+gracedb = "G288686"
 # Duration of data around the event to use
 duration = 16
 # Duration of PSD data
 psd_duration = 1024
 # Minimum frequency and reference frequency
 minimum_frequency = 10  # Hz
-sampling_frequency = 2048.
+sampling_frequency = 2048.0
 
 # Get trigger time
 candidate = bilby.gw.utils.gracedb_to_json(gracedb, outdir=outdir)
-trigger_time = candidate['gpstime']
-gps_start_time = trigger_time + 2. - duration
+trigger_time = candidate["gpstime"]
+gps_start_time = trigger_time + 2.0 - duration
 
 # Load the PSD data starting after the segment you want to analyze
 psd_start_time = gps_start_time + duration
@@ -39,9 +39,14 @@ interferometers = bilby.gw.detector.InterferometerList([])
 
 for channel_name in channel_names:
     interferometer = bilby.gw.detector.load_data_by_channel_name(
-        start_time=gps_start_time, segment_duration=duration,
-        psd_duration=psd_duration, psd_start_time=psd_start_time,
-        channel_name=channel_name, sampling_frequency=sampling_frequency, outdir=outdir)
+        start_time=gps_start_time,
+        segment_duration=duration,
+        psd_duration=psd_duration,
+        psd_start_time=psd_start_time,
+        channel_name=channel_name,
+        sampling_frequency=sampling_frequency,
+        outdir=outdir,
+    )
     interferometer.minimum_frequency = minimum_frequency
     interferometers.append(interferometer)
 

examples/gw_examples/data_examples/read_gracedb_data.py

@@ -8,17 +8,17 @@ This tutorial must be run on a LIGO cluster.
 
 import bilby
 
-label = 'GW170608'
-outdir = label + '_out'
+label = "GW170608"
+outdir = label + "_out"
 # List of detectors involved in the event. GW170608 is pre-August 2017 so only
 # H1 and L1 were involved
-detectors = ['H1', 'L1']
+detectors = ["H1", "L1"]
 # Names of the channels and query types to use, and the event GraceDB ID - ref.
 # https://ldas-jobs.ligo.caltech.edu/~eve.chase/monitor/online_pe/C02_clean/
 # C02_HL_Pv2/lalinferencenest/IMRPhenomPv2pseudoFourPN/config.ini
-channel_names = ['H1:DCH-CLEAN_STRAIN_C02', 'L1:DCH-CLEAN_STRAIN_C02']
-query_types = ['H1_CLEANED_HOFT_C02', 'L1_CLEANED_HOFT_C02']
-gracedb = 'G288686'
+channel_names = ["H1:DCH-CLEAN_STRAIN_C02", "L1:DCH-CLEAN_STRAIN_C02"]
+query_types = ["H1_CLEANED_HOFT_C02", "L1_CLEANED_HOFT_C02"]
+gracedb = "G288686"
 # Calibration number to inform bilby's guess of query type if those provided are
 # not recognised
 calibration = 2
@@ -33,14 +33,16 @@ minimum_frequency = 10  # Hz
 
 # Get frame caches
 candidate, frame_caches = bilby.gw.utils.get_gracedb(
-    gracedb, outdir, duration, calibration, detectors, query_types)
+    gracedb, outdir, duration, calibration, detectors, query_types
+)
 
 # Set up interferometer objects from the cache files
 interferometers = bilby.gw.detector.InterferometerList([])
 
 for cache_file, channel_name in zip(frame_caches, channel_names):
     interferometer = bilby.gw.detector.load_data_from_cache_file(
-        cache_file, trigger_time, duration, psd_duration, channel_name)
+        cache_file, trigger_time, duration, psd_duration, channel_name
+    )
     interferometer.minimum_frequency = minimum_frequency
     interferometers.append(interferometer)
 

setup.cfg

@@ -21,3 +21,6 @@ gw =
 	astropy
 	gwpy
 	lalsuite
+
+[isort]
+known_third_party = astropy,attr,bilby,deepdish,gwin,gwinc,gwpy,lal,lalsimulation,matplotlib,mock,nflows,numpy,packaging,pandas,past,pycbc,pymc3,pytest,scipy,setuptools,skimage,torch