removed relative binning example

examples/gw_examples/data_examples/GW190425_relative_binning.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
-fiducial_parameters = {
-    "a_1": 0.018,
-    "a_2": 0.016,
-    "chirp_mass": 1.48658,
-    "dec": 0.438,
-    "geocent_time": 1240215503.039,
-    "lambda_1": 446.941,
-    "lambda_2": 43.386,
-    "luminosity_distance": 206.751,
-    "mass_ratio": 0.8955,
-    "phase": 3.0136566567608765,
-    "phi_12": 4.319,
-    "phi_jl": 5.07,
-    "psi": 0.281,
-    "ra": 4.2,
-    "theta_jn": 0.185,
-    "tilt_1": 0.879,
-    "tilt_2": 0.514,
-}
-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")
-priors["fiducial"] = 0
-
-# 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_relative_binning,
-    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.RelativeBinningGravitationalWaveTransient(
-    ifo_list,
-    waveform_generator,
-    priors=priors,
-    time_marginalization=False,
-    phase_marginalization=True,
-    distance_marginalization=True,
-    fiducial_parameters=fiducial_parameters,
-)
-
-# 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()