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#!/usr/bin/env python

"""

Tutorial to demonstrate running parameter estimation on a reduced parameter

space for an injected signal.

This example estimates the masses using a uniform prior in both component masses

and distance using a uniform in comoving volume prior on luminosity distance

between luminosity distances of 100Mpc and 5Gpc, the cosmology is Planck15.

"""

import numpy as np

import bilby

# 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 = 'fast_tutorial'

bilby.core.utils.setup_logger(outdir=outdir, label=label)

# Set up a random seed for result reproducibility.  This is optional!

np.random.seed(88170235)

# We are going to inject a binary black hole waveform.  We first establish a

# dictionary of parameters that includes all of the different waveform

# parameters, including masses of the two black holes (mass_1, mass_2),

# spins of both black holes (a, tilt, phi), etc.

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=2000., theta_jn=0.4, psi=2.659,

    phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)

# Fixed arguments passed into the source model

waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',

                          reference_frequency=50., minimum_frequency=20.)

# 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,

    parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,

    waveform_arguments=waveform_arguments)

# Set up interferometers.  In this case we'll use two interferometers

# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design

# sensitivity

ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])

ifos.set_strain_data_from_power_spectral_densities(

    sampling_frequency=sampling_frequency, duration=duration,

    start_time=injection_parameters['geocent_time'] - 3)

ifos.inject_signal(waveform_generator=waveform_generator,

                   parameters=injection_parameters)

# Set up a PriorDict, which inherits from dict.

# By default we will sample all terms in the signal models.  However, this will

# take a long time for the calculation, so for this example we will set almost

# all of the priors to be equall to their injected values.  This implies the

# prior is a delta function at the true, injected value.  In reality, the

# sampler implementation is smart enough to not sample any parameter that has

# a delta-function prior.

# The above list does *not* include mass_1, mass_2, theta_jn and luminosity

# distance, which means those are the parameters that will be included in the

# sampler.  If we do nothing, then the default priors get used.

priors = bilby.gw.prior.BBHPriorDict()

priors['geocent_time'] = bilby.core.prior.Uniform(

    minimum=injection_parameters['geocent_time'] - 1,

    maximum=injection_parameters['geocent_time'] + 1,

    name='geocent_time', latex_label='$t_c$', unit='$s$')

for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra',

            'dec', 'geocent_time', 'phase']:

    priors[key] = injection_parameters[key]

# Initialise the likelihood by passing in the interferometer data (ifos) and

# the waveform generator

likelihood = bilby.gw.GravitationalWaveTransient(

    interferometers=ifos, waveform_generator=waveform_generator)

# Run sampler.  In this case we're going to use the `dynesty` sampler

result = bilby.run_sampler(

    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,

    injection_parameters=injection_parameters, outdir=outdir, label=label)

# Make a corner plot.

result.plot_corner()