monte_carlo_sims.py

#!/usr/bin/env python
import io
import pickle
import sys
import os
import json
import random
import time
import pandas as pd

from functools import partial
from urllib import parse, request
from tqdm import tqdm

import numpy as np
import scipy.stats as spstat
from collections import namedtuple
from astropy.time import Time
from astropy.coordinates import Distance, SkyCoord
import astropy.coordinates as coord
import astropy.table as at
import astropy.units as u, astropy.constants as c
import argparse
import matplotlib.pyplot as plt
from astropy.visualization import hist
import schwimmbad
from scipy.linalg import cholesky
import scipy.integrate as scinteg
from sklearn.preprocessing import MinMaxScaler

from sed_to_lc import SEDDerviedLC
from dns_mass_distribution import Galaudage21, Farrow19
from dns_mass_distribution import MIN_MASS, MAX_MASS, M_TOV, EOS_interpolator
from interpolate_bulla_sed import uniq_cos_theta, uniq_phi
from rates_models import LVK_UG

import inspiral_range
import ligo.em_bright
import ligo.em_bright.computeDiskMass
from ligo.em_bright.computeDiskMass import computeCompactness, computeDiskMass
import lalsimulation as lalsim
from gwemlightcurves.EjectaFits import DiUj2017, CoDi2019
from kilopop.kilonovae import bns_kilonovae_population_distribution as s22p
from kilopop.kilonovae import bns_kilonova as saeev

#np.random.seed(seed=42)
disable_tqdm = True

detector_asd_links_O2 = dict(
    ligo='https://dcc.ligo.org/public/0156/G1801952/001/2017-08-06_DCH_C02_L1_O2_Sensitivity_strain_asd.txt',
    virgo='https://dcc.ligo.org/public/0157/P1800374/001/Hrec_hoft_V1O2Repro2A_16384Hz.txt',
    kagra='https://dcc.ligo.org/public/0180/T2200043/003/kagra_10Mpc.txt' # This is ignored due to 0 duty cycle
)

detector_asd_links_O3 = dict(
    ligo='https://dcc.ligo.org/public/0169/P2000251/001/O3-L1-C01_CLEAN_SUB60HZ-1240573680.0_sensitivity_strain_asd.txt',
    virgo='https://dcc.ligo.org/public/0169/P2000251/001/O3-V1_sensitivity_strain_asd.txt',
    kagra='https://dcc.ligo.org/public/0180/T2200043/003/kagra_10Mpc.txt' # This is ignored due to 0 duty cycle
)

# asd from https://emfollow.docs.ligo.org/userguide/capabilities.html
detector_asd_links_O4 = dict(
    ligo='https://dcc.ligo.org/public/0180/T2200043/003/aligo_O4high.txt',
    virgo='https://dcc.ligo.org/public/0180/T2200043/003/avirgo_O4high_NEW.txt',
    kagra='https://dcc.ligo.org/public/0180/T2200043/003/kagra_10Mpc.txt'
)

# asd from https://emfollow.docs.ligo.org/userguide/capabilities.html
detector_asd_links_O5 = dict(
    ligo='https://dcc.ligo.org/public/0180/T2200043/003/AplusDesign.txt',
    virgo='https://dcc.ligo.org/public/0180/T2200043/003/avirgo_O5low_NEW.txt',
    kagra='https://dcc.ligo.org/public/0180/T2200043/003/kagra_128Mpc.txt'
)

def compute_dyn_ej(m1, c1, m2, c2):

    a = -0.0719
    b = 0.2116
    d = -2.42
    n = -2.905

    mej_dyn = np.power(
        10.0,
        (
            ((a * (1.0 - 2.0 * c1) * m1) / (c1))
            + b * m2 * np.power((m1 / m2), n)
            + (d / 2.0)
        )
        + (
            ((a * (1.0 - 2.0 * c2) * m2) / (c2))
            + b * m1 * np.power((m2 / m1), n)
            + (d / 2.0)
        ),
    )

    # Imposing a maximum value for the dynamical ejecta
    max_mej_dyn = 0.09
    mej_dyn[mej_dyn > max_mej_dyn] = max_mej_dyn

    return mej_dyn


def compute_wind_ej(m1, m2, zetas):

    a = -31.335
    b = -0.9760
    c = 1.0474
    d = 0.05957

    # make sure these okay
    M_radius_1_dot_6 = EOS_interpolator(1.6)

    M_thresh = (2.38 - (3.606 * (M_TOV / M_radius_1_dot_6))) * M_TOV

    remnant_disk_mass = np.power(10.0, 
                                 a * (1.0 + b * np.tanh(
                                 (c - ((m1 + m2) /
                                  M_thresh)) / d)))

    remnant_disk_mass[remnant_disk_mass < 1.0e-3] = 1.0e-3
    mej_wind = zetas * remnant_disk_mass
    return mej_wind

def compute_compactness(m):

    G = 6.6743 * 10**-11 # m3 kg-1 s-2
    c = 3 * 10**8       # m s^-1
    M_sun = 1.9891 * 10**30 # kg

    R = EOS_interpolator(m) * 1000 # from km to m

    compactness = (G * m * M_sun) / (c**2 * R)

    return compactness

def get_ejecta_mass(m1, m2):

    # Different EOS
    c_ns_1 = compute_compactness(m1)
    c_ns_2 = compute_compactness(m2)

    n_events = len(m1)
    zetas = np.random.uniform(low=0.1, high=0.4, size=n_events)

    # treat as BNS
    m_dyn = compute_dyn_ej(m1, c_ns_1, m2, c_ns_2)
    m_wind = compute_wind_ej(m1, m2, zetas)

    # Check for prompt collapse to a BH
    M_radius_1_dot_6 = EOS_interpolator(1.6)
    M_thresh = (2.38 - (3.606 * (M_TOV / M_radius_1_dot_6))) * M_TOV

    m_total = m1 + m2
    m_dyn = np.where(m_total < M_thresh, m_dyn, 0)
    m_wind = np.where(m_total < M_thresh, m_wind, 0)

    return m_dyn, m_wind


def get_range(detector, ligo_run):
    if ligo_run == 'O4':
        psd_url = detector_asd_links_O4[detector]
    elif ligo_run == 'O5':
        psd_url = detector_asd_links_O5[detector]
    elif ligo_run == 'O2':
        psd_url = detector_asd_links_O2[detector]
    elif ligo_run == 'O3':
        psd_url = detector_asd_links_O3[detector]
    print(psd_url)
    try:
        # if downloaded locally
        asd_fp = open(os.path.basename(parse.urlparse(psd_url).path), "rb")
    except FileNotFoundError:
        print(f"Downloading PSD for {detector}")
        asd_fp = io.BytesIO(request.urlopen(psd_url).read())
    freq, asd = np.loadtxt(asd_fp, unpack=True)
    psd = asd**2
    # https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/group___l_a_l_sim_inspiral__h.html#ggab955e4603c588fe19b39e47870a7b69cac65622993fd7f475a0ad423f35992906
    return partial(inspiral_range.range, freq, psd, approximant="TaylorF2")


def get_correlated_series(n_events, upper_chol):
    """
    Get some correlated uniformly distributed random series between 0 and 1
    """
    rnd = np.random.uniform(0., 1., size=(n_events, 4))
    series = rnd @ upper_chol
    return series


def get_sim_dutycycles(n_events, upper_chol, h_duty, l_duty, v_duty, k_duty):
    """
    Get some correlated duty cycle series
    """
    series = get_correlated_series(n_events, upper_chol)
    scaler = MinMaxScaler()
    scaler.fit(series)
    series = scaler.transform(series)
    series = series.T
    duty_cycles = np.zeros(series.shape)

    h_series = series[0,:]
    l_series = series[1,:]
    v_series = series[2,:]
    k_series = series[3,:]

    h_on = duty_cycles[0,:]
    l_on = duty_cycles[1,:]
    v_on = duty_cycles[2,:]
    k_on = duty_cycles[3,:]

    h_on[h_series <= h_duty] = 1
    l_on[l_series <= l_duty] = 1
    v_on[v_series <= v_duty] = 1
    k_on[k_series <= k_duty] = 1

    h_on = h_on.astype(bool)
    l_on = l_on.astype(bool)
    v_on = v_on.astype(bool)
    k_on = k_on.astype(bool)

    return h_on, l_on, v_on, k_on


class MinZeroAction(argparse.Action):
    def __call__(self, parser, namespace, values, option_string=None):
        if values <= 0 :
            parser.error("Minimum value for {0} is 0".format(option_string))
        setattr(namespace, self.dest, values)


def get_options(argv=None):
    '''
    Get commandline options
    '''
    parser = argparse.ArgumentParser()
    parser.add_argument('--trials_dir', default='trial-exg-100', help='Directory to store simulation results')
    parser.add_argument('--ligo_run', choices=['O2','O3','O4','O5'], default='O4', help='Pick LIGO observing run')
    parser.add_argument('--mass_distrib', choices=['Galaudage21','Farrow19','flat'], default='Galaudage21', help='Pick BNS mass distribution')
    parser.add_argument('--rate_model', choices=['LVK_UG_O4'], default='LVK_UG_O4', help='Pick BNS merger rate model')
    parser.add_argument('--ntry', default=500, type=int, action=MinZeroAction, help='Set the number of MC samples')
    parser.add_argument('--detection_passband', default='desr', help='Pick detection passband. Should be from https://sncosmo.readthedocs.io/en/stable/bandpass-list.html')
    parser.add_argument('--detection_threshold', default=23, type=float, help='Pick detection threshold in detection passband.')
    parser.add_argument('--sun_loss', default=0.5, help='The fraction not observed due to sun', type=float)
    parser.add_argument('--bns_ligo_range', default= 150, help = 'Set the bns detection range for the two ligo detectors')
    parser.add_argument('--bns_virgo_range', default= 70, help = 'Set the bns detection range for the virgo detectors')
    parser.add_argument('--bns_kagra_range', default= 5, help = 'Set the bns detection range for the kagra detectors')

    # TODO: Add argument for overlap between survey and ligo run
    # duty factor motivation: https://dcc.ligo.org/public/0167/G2000497/002/G2000497_OpenLVEM_02Apr2020_kk_v2.pdf
    args = parser.parse_args(args=argv)
    return args


def main(argv=None):

    args = get_options(argv=argv)

    # LIGO run 
    ligo_observing_run = args.ligo_run 

    if ligo_observing_run == 'O4':
        print("Configuring parameters for O4...")
        # setup time-ranges
        Range = namedtuple('Range', ['start', 'end'])
        ligo_run_start = Time('2023-05-24T00:00:00.0')
        ligo_run_end   = Time('2024-11-24T00:00:00.0')
        survey_cyc_start = Time('2023-05-24T00:00:00.0')
        survey_cyc_end = Time('2024-11-24T00:00:00.0')
        eng_time       = 2.*u.week

        # setup duty cycles
        h_duty = 0.7
        l_duty = 0.7
        v_duty = 0.47
        k_duty = 0.27

        box_size = 510

    elif ligo_observing_run == 'O3':
        
        print("Configuring parameters for O3...")
        # setup time-ranges
        Range = namedtuple('Range', ['start', 'end'])
        ligo_run_start = Time('2019-04-1T00:00:00.0')
        ligo_run_end   = Time('2020-03-27T00:00:00.0')
        survey_cyc_start = Time('2019-04-1T00:00:00.0')
        survey_cyc_end = Time('2020-03-27T00:00:00.0')
        eng_time       = 2.*u.week

        # setup duty cycles
        h_duty = 0.7
        l_duty = 0.7
        v_duty = 0.47
        k_duty = 0.0

        box_size = 510

    elif ligo_observing_run == 'O2':

        print("Configuring parameters for O2...")
        # setup time-ranges
        Range = namedtuple('Range', ['start', 'end'])
        ligo_run_start = Time('2016-11-30T00:00:00.0')
        ligo_run_end   = Time('2017-08-25T00:00:00.0')
        survey_cyc_start = Time('2016-11-30T00:00:00.0')
        survey_cyc_end = Time('2017-08-25T00:00:00.0')
        eng_time       = 2.*u.week

        # setup duty cycles
        h_duty = 0.7
        l_duty = 0.7
        v_duty = 0.47
        k_duty = 0.0

        box_size = 510


    elif ligo_observing_run == 'O5':
        print("Configuring parameters for O5...")
        Range = namedtuple('Range', ['start', 'end'])
        ligo_run_start = Time('2026-10-1T00:00:00.0')
        ligo_run_end   = Time('2029-06-1T00:00:00.0')
        survey_cyc_start = Time('2026-10-1T00:00:00.0')
        survey_cyc_end = Time('2029-06-1T00:00:00.0')
        eng_time       = 2.*u.week

        # setup duty cycles
        h_duty = 0.7
        l_duty = 0.7
        v_duty = 0.7
        k_duty = 0.7

        box_size = 910


    ligo_run  = Range(start=ligo_run_start, end=ligo_run_end)
    survey_cycle = Range(start=survey_cyc_start,  end=survey_cyc_end)
    latest_start = max(ligo_run_start, survey_cyc_start)
    earliest_end = min(ligo_run_end, survey_cyc_end)
    td = (earliest_end - latest_start) + eng_time
    fractional_duration = (td/(1.*u.year)).decompose().value


    volume = box_size**3

    # the two ligo detectors ahve strongly correlated duty cycles
    # they are both not very correlated with Virgo
    lvc_cor_matrix = np.array([[1., 0.56, 0.56, 0.56],
                               [0.56, 1., 0.58, 0.58],
                               [0.56, 0.58, 1., 0.56],
                               [0.56, 0.58, 0.56, 1.]])
    upper_chol = cholesky(lvc_cor_matrix)

    # create the mass distribution of the merging neutron star
    mass_distrib = args.mass_distrib

    # setup event rates
    n_try = args.ntry
    rate_model = args.rate_model

    # Create the dir
    trials_dir = args.trials_dir
    os.mkdir(f"{trials_dir}")

    # Save args file
    with open(f'{trials_dir}/mc_args.txt', 'w') as f:
        json.dump(args.__dict__, f, indent=2)

    # Photometric detections
    detection_band = args.detection_passband
    detection_threshold = args.detection_threshold

    # define ranges
    ligo_range = get_range('ligo', ligo_observing_run)
    virgo_range = get_range('virgo', ligo_observing_run)
    kagra_range = get_range('kagra', ligo_observing_run)



    def dotry(n):
        
        trial_df = pd.DataFrame()

        # Sample from rate models
        if rate_model == "LVK_UG_O4":
            rate = LVK_UG(1)[0][0]


        n_events = np.around(rate*volume*fractional_duration*(10**-9)).astype('int')
        n_events = max(n_events,1)
        cos_thetas = np.random.uniform(0, 1, size=n_events)
        phis = np.random.uniform(15, 75, size=n_events)

        thetas = np.rad2deg(np.arccos(cos_thetas))
        omegas = np.minimum(thetas, 180 - thetas)
        
        em_bool = np.array([], dtype=bool)
        discovery_mags = np.array([])
        discovery_phases = np.array([])
        peak_mags = np.array([])
        discovery_windows = np.array([])
    
        ra_arr = np.array([])
        dec_arr = np.array([])
        d_Mpc = np.array([])

        trial_number = np.array(([n] * n_events))
        n1_bool = np.array(([False] * n_events))
        n2_bool = np.array(([False] * n_events))
        n3_bool = np.array(([False] * n_events))
        n4_bool = np.array(([False] * n_events))

        gw1_bool = np.array(([False] * n_events))
        gw2_bool = np.array(([False] * n_events))
        gw3_bool = np.array(([False] * n_events))
        gw4_bool = np.array(([False] * n_events))

        scaling_factors = np.array([])

        print(f"### Starting trial = {n}; Num events = {n_events}", flush=True)
        if mass_distrib == 'Galaudage21':

            mass1, mass2 = Galaudage21(n_events)
            mej_dyn_arr, mej_wind_arr = get_ejecta_mass(mass1, mass2)

        elif mass_distrib == 'Farrow19':

            mass1, mass2 = Farrow19(n_events)
            mej_dyn_arr, mej_wind_arr = get_ejecta_mass(mass1, mass2)


        elif mass_distrib == 'flat':

            stars = s22p(population_size=n_events)

            mass1 = np.array([stars.compute_lightcurve_properties_per_kilonova(i)['mass1'] for i in range(n_events)])
            mass2 = np.array([stars.compute_lightcurve_properties_per_kilonova(i)['mass2'] for i in range(n_events)])

            # For some reason this is really slow. Not using it anyway but idk how to fix it since its not our code
            mej_dyn_arr = np.array([stars.compute_lightcurve_properties_per_kilonova(i)['dynamical_ejecta_mass'] for i in range(n_events)])
            mej_wind_arr = np.array([stars.compute_lightcurve_properties_per_kilonova(i)['secular_ejecta_mass'] for i in range(n_events)])

        bns_range_ligo = np.array([])
        bns_range_virgo = np.array([])
        bns_range_kagra = np.array([])

        for m1, m2, o in tqdm(zip(mass1, mass2, omegas), total=n_events, disable=disable_tqdm):

            bns_range_ligo = np.append(bns_range_ligo, [ligo_range(m1=m1, m2=m2, inclination = o)])
            bns_range_virgo = np.append(bns_range_virgo,[virgo_range(m1=m1, m2=m2, inclination = o)])
            bns_range_kagra = np.append(bns_range_kagra, [kagra_range(m1=m1, m2=m2, inclination = o)])

        bns_range_ligo = bns_range_ligo*u.Mpc
        bns_range_virgo = bns_range_virgo*u.Mpc
        bns_range_kagra = bns_range_kagra*u.Mpc

        # bns_range_ligo = args.bns_ligo_range * u.Mpc
        # bns_range_virgo = args.bns_virgo_range * u.Mpc
        # bns_range_kagra = args.bns_kagra_range * u.Mpc

        tot_mass = mass1 + mass2
        tot_ejecta_masses = mej_dyn_arr + mej_wind_arr

        av = np.random.exponential(0.334, n_events)*0.334

        # simulate coordinates. Additional term ensures minimum distance of 0.05 Mpc
        x = np.random.uniform(-box_size/2., box_size/2., n_events)*u.megaparsec 
        y = np.random.uniform(-box_size/2., box_size/2., n_events)*u.megaparsec 
        z = np.random.uniform(-box_size/2., box_size/2., n_events)*u.megaparsec 
        dist = (x**2. + y**2. + z**2.)**0.5 + (0.05 * u.megaparsec)


        h_on, l_on, v_on, k_on = get_sim_dutycycles(n_events, upper_chol,
                                                    h_duty, l_duty, v_duty, k_duty)
        n_detectors_on = np.array(
            [sum(_) for _ in np.vstack((h_on, l_on, v_on, k_on)).T]
        )
        # which detectors observed
        dist_ligo_bool  = dist <= bns_range_ligo
        dist_virgo_bool = dist <= bns_range_virgo
        dist_kagra_bool = dist <= bns_range_kagra

        h_on_and_observed = h_on * dist_ligo_bool
        l_on_and_observed = l_on * dist_ligo_bool
        v_on_and_observed = v_on * dist_virgo_bool
        k_on_and_observed = k_on * dist_kagra_bool

        n_detectors_on_and_obs = np.sum(np.vstack(
            (h_on_and_observed, l_on_and_observed, v_on_and_observed,
             k_on_and_observed)).T,
            axis=1
        )

        one_det_obs = n_detectors_on_and_obs == 1 
        two_det_obs = n_detectors_on_and_obs == 2
        three_det_obs = n_detectors_on_and_obs == 3
        four_det_obs = n_detectors_on_and_obs == 4

        # decide whether there is a kilonova based on remnant matter
        has_ejecta_bool = tot_ejecta_masses > 0

        # arrays to store peaks mags in different pbs
        peak_u = []
        peak_g = []
        peak_r = []
        peak_i = []
        peak_z = []
        peak_y = []
            
        for i, (cos_theta, phi, mej_dyn, mej_wind, d) in tqdm(enumerate(zip(cos_thetas, phis, mej_dyn_arr, mej_wind_arr, dist)), total=n_events, disable=disable_tqdm):

            #print(f"Sample parameters: mass1 = {mass1[i]}, mass2 = {mass2[i]}, cos_theta = {cos_theta}, phi = {phi}, ejecta_mass_dyn = {mej_dyn}, ejecta_mass_wind = {mej_wind}, dist = {d}")

            r, dec, ra = coord.cartesian_to_spherical(x[i], y[i], z[i])

            ra_arr = np.append(ra_arr, [ra.value])
            dec_arr = np.append(dec_arr, [dec.value])
            d_Mpc = np.append(d_Mpc, [d.value])

            coordinates = coord.SkyCoord(ra=ra, dec=dec)

            p = np.arange(0.3, 20.1, 0.2) 

            obj = SEDDerviedLC(mej_dyn = mej_dyn, mej_wind = mej_wind, phi = phi, cos_theta = cos_theta, dist=d, coord=coordinates, av =av[i])
            lcs = obj.getAppMagsInPassbands([detection_band], lc_phases=p)

            # Add peaks to data
            peaks = obj.getPeakAppMagsInPassbands(['lsstu','lsstg','lsstr','lssti','lsstz','lssty'],lc_phases=p)
            peak_u.append(peaks['lsstu'])
            peak_g.append(peaks['lsstg'])
            peak_r.append(peaks['lsstr'])
            peak_i.append(peaks['lssti'])
            peak_z.append(peaks['lsstz'])
            peak_y.append(peaks['lssty'])

            scaling_factors = np.append(scaling_factors, [obj.scaling_factor])

            min_mag = min(lcs[detection_band])

            # Minimum magnitude is the peak value
            peak_mags = np.append(peak_mags, [min_mag])
            
            idx = lcs[detection_band] < detection_threshold
            
            if min_mag < detection_threshold:
                em_bool = np.append(em_bool, [True])
                discovery_mag = (lcs[detection_band][idx])[0]
                discovery_phase = (p[idx])[0]
                discovery_mags = np.append(discovery_mags, [discovery_mag])
                discovery_phases = np.append(discovery_phases, [discovery_phase])
                discovery_window = (p[idx])[-1] - (p[idx])[0] + 0.2
                discovery_windows = np.append(discovery_windows, [discovery_window])
            else:
                em_bool = np.append(em_bool, [False])
                discovery_mags = np.append(discovery_mags, [np.nan])
                discovery_phases = np.append(discovery_phases, [np.nan])
                discovery_windows = np.append(discovery_windows, [np.nan])


        # whether this event was not affected by then sun
        detected_events = np.where(em_bool)
        sun_bool = np.random.random(len(detected_events[0])) >= args.sun_loss
        em_bool[detected_events] = sun_bool

        n1_gw_only = np.where(one_det_obs)[0]
        n1_gw = len(n1_gw_only)
        gw1_bool[n1_gw_only] = True
        n1_good = np.where(one_det_obs & em_bool & has_ejecta_bool)[0]
        n1 = len(n1_good)
        n1_bool[n1_good] = True
        # sanity check
        assert n1_gw >= n1, "GW events ({}) less than EM follow events ({})".format(n1_gw, n1)

        n2_gw_only = np.where(two_det_obs)[0]
        n2_gw = len(n2_gw_only)
        gw2_bool[n2_gw_only] = True
        n2_good = np.where(two_det_obs & em_bool & has_ejecta_bool)[0]
        n2 = len(n2_good)
        n2_bool[n2_good] = True
        # sanity check
        assert n2_gw >= n2, "GW events ({}) less than EM follow events ({})".format(n2_gw, n2)

        n3_gw_only = np.where(three_det_obs)[0]
        n3_gw = len(n3_gw_only)
        gw3_bool[n3_gw_only] = True
        n3_good = np.where(three_det_obs & em_bool & has_ejecta_bool)[0]
        n3 = len(n3_good)
        n3_bool[n3_good] = True
        # sanity check
        assert n3_gw >= n3, "GW events ({}) less than EM follow events ({})".format(n3_gw, n3)

        n4_gw_only = np.where(four_det_obs)[0]
        n4_gw = len(n4_gw_only)
        gw4_bool[n4_gw_only] = True
        n4_good = np.where(four_det_obs & em_bool & has_ejecta_bool)[0]
        n4 = len(n4_good)
        n4_bool[n4_good] = True
        # sanity check
        assert n4_gw >= n4, "GW events ({}) less than EM follow events ({})".format(n4_gw, n4)

        # Events which gw detection on >=2 instrument
        gw_recovered = (n2_gw + n3_gw + n4_gw) / n_events

        # Events which have em detection
        n_em = len(np.where(em_bool & has_ejecta_bool)[0])
        em_recovered = n_em / n_events

        # Events which gw detection on one instrument but also have detectable em counterparts - could've been caught if we had better duty cycles
        single_gw_detection = n1 / n_events

        # print("Number of events at each step")
        # print(f"gw_recovered: {gw_recovered} em_recovered: {em_recovered} single gw detection: {single_gw_detection}")
        # print(f"Events that could be caught if LVK duty cycles were more correlated {n1}")


        # Create a data frame with all the information
        trial_df['trial_number'] = trial_number
        trial_df['m1'] = mass1
        trial_df['m2'] = mass2
        trial_df['total_mass'] = tot_mass
        trial_df['mej_dyn'] = mej_dyn_arr
        trial_df['mej_wind'] = mej_wind_arr
        trial_df['cos_theta'] = cos_thetas
        trial_df['phi'] = phis
        trial_df['a_v'] = av
        trial_df['dist'] = d_Mpc
        trial_df['ra'] = ra_arr
        trial_df['dec'] = dec_arr
        trial_df['n_detectors_on_and_obs'] = n_detectors_on_and_obs
        trial_df['em_bool'] = em_bool
        trial_df['peak_mag'] = peak_mags
        trial_df['discovery_mag'] = discovery_mags
        trial_df['discovery_phase'] = discovery_phases
        trial_df['scaling_factor'] = scaling_factors
        trial_df['one_detector_event'] = n1_bool
        trial_df['two_detector_event'] = n2_bool
        trial_df['three_detector_event'] = n3_bool
        trial_df['four_detector_event'] = n4_bool
        trial_df['gw1'] = gw1_bool
        trial_df['gw2'] = gw2_bool
        trial_df['gw3'] = gw3_bool
        trial_df['gw4'] = gw4_bool
        trial_df['peak_u'] = peak_u
        trial_df['peak_g'] = peak_g
        trial_df['peak_r'] = peak_r
        trial_df['peak_i'] = peak_i
        trial_df['peak_z'] = peak_z
        trial_df['peak_y'] = peak_y


        print(f"Finished Trial = {n}; Num events = {n_events}\nNumber of:\n1 detector events: {n1}\n2 detector events: {n2}\n3 detector events: {n3}\n4 detector events: {n4}", flush=True)
        print(f"GW Detections:\n1 detector events: {n1_gw}\n2 detector events: {n2_gw}\n3 detector events: {n3_gw}\n4 detector events: {n4_gw}", flush=True)

        return dist[n1_good].value.tolist(), tot_mass[n1_good].tolist(),\
            dist[n2_good].value.tolist(), tot_mass[n2_good].tolist(),\
            dist[n3_good].value.tolist(), tot_mass[n3_good].tolist(),\
            dist[n4_good].value.tolist(), tot_mass[n4_good].tolist(),\
            discovery_mags[n1_good].tolist(), discovery_mags[n2_good].tolist(), \
            discovery_mags[n3_good].tolist(), discovery_mags[n4_good].tolist(),\
            peak_mags[n1_good].tolist(), peak_mags[n2_good].tolist(),\
            peak_mags[n3_good].tolist(), peak_mags[n4_good].tolist(),\
            discovery_phases[n1_good].tolist(), discovery_phases[n2_good].tolist(),\
            discovery_phases[n3_good].tolist(), discovery_phases[n4_good].tolist(),\
            discovery_windows[n1_good].tolist(), discovery_windows[n2_good].tolist(),\
            discovery_windows[n3_good].tolist(),discovery_windows[n4_good].tolist(),\
            n1, n2, n3, n4, \
            gw_recovered, em_recovered, single_gw_detection, \
            trial_df

    with schwimmbad.JoblibPool(5) as pool:
        values = list(pool.map(dotry, range(n_try)))

    with open(f'{trials_dir}/raw_mc_data.pkl', 'wb') as f:
        pickle.dump(values, f)
    print("Finished computation...")
    data_dump = dict()
    n_detect1 = []
    n_detect2 = []
    n_detect3 = []
    n_detect4 = []
    dist_detect1 = []
    mass_detect1 = []
    dist_detect2 = []
    mass_detect2 = []
    dist_detect3 = []
    mass_detect3 = []
    dist_detect4 = []
    mass_detect4 = []
    mag_detect1 = []
    mag_detect2 = []
    mag_detect3 = []
    mag_detect4 = []
    mag_peak1 = []
    mag_peak2 = []
    mag_peak3 = []
    mag_peak4 = []
    discovery_phase1 = []
    discovery_phase2 = []
    discovery_phase3 = []
    discovery_phase4 = []
    discovery_window1 = []
    discovery_window2 = []
    discovery_window3 = []
    discovery_window4 = []
    gw_recovered_arr = []
    em_recovered_arr = []
    single_gw_detection_arr = []

    df_list = []

    for idx, (d1, m1, d2, m2, d3, m3, d4, m4, h1, h2, h3, h4, p1, p2, p3, p4, phase1, phase2, phase3, phase4, window1, window2, window3, window4, n1, n2, n3, n4, gw_recovered, em_recovered, single_gw_detection, df) in enumerate(values):

        df_list.append(df)
        gw_recovered_arr.append(gw_recovered)
        em_recovered_arr.append(em_recovered)
        single_gw_detection_arr.append(single_gw_detection)

        if n1 >= 0:
            n_detect1.append(n1)
            if n1>0:
                dist_detect1 += d1
                mass_detect1 += m1
                mag_detect1 += h1
                mag_peak1 += p1
                discovery_phase1 += phase1
                discovery_window1 += window1
        if n2 >= 0:
            n_detect2.append(n2)
            if n2>0:
                dist_detect2 += d2
                mass_detect2 += m2
                mag_detect2  += h2
                mag_peak2 += p2
                discovery_phase2 += phase2
                discovery_window2 += window2
        if n3>=0:
            n_detect3.append(n3)
            if n3 > 0:
                dist_detect3 += d3
                mass_detect3 += m3
                mag_detect3  += h3
                mag_peak3 += p3
                discovery_phase3 += phase3
                discovery_window3 += window3
        if n4>=0:
            n_detect4.append(n4)
            if n4 > 0:
                dist_detect4 += d4
                mass_detect4 += m4
                mag_detect4  += h4
                mag_peak4 += p4
                discovery_phase4 += phase4
                discovery_window4 += window4
        data_dump[f"{idx}"] = {"d1": d1, "m1": m1, 
                               "d2": d2, "m2": m2, 
                               "d3": d3, "m3": m3,
                               "d4": d4, "m4": m4,
                               "h1": h1, "h2": h2, "h3": h3, "h4": h4,
                               "n1": n1, "n2": n2, "n3": n3, "n4": n4,
                               "p1": p1, "p2": p2, "p3": p3, "p4": p4,
                               "phase1": phase1, "phase2": phase2, "phase3": phase3, "phase4": phase4,
                               "window1": window1, "window2": window2, 'window3': window3, 'window4':window4}
        
    with open(f"{trials_dir}/data_dump.pickle", "wb") as f:
        pickle.dump(data_dump, f)
    
    with open(f'{trials_dir}/plotting_data.pickle', 'wb') as f:
        res = dict(n_detect1=n_detect1, n_detect2=n_detect2, n_detect3=n_detect3, n_detect4=n_detect4,
                    dist_detect1=dist_detect1, dist_detect2=dist_detect2, dist_detect3=dist_detect3, dist_detect4=dist_detect4,
                    mass_detect1=mass_detect1, mass_detect2=mass_detect2, mass_detect3=mass_detect3, mass_detect4=mass_detect4,
                    mag_detect1=mag_detect1, mag_detect2=mag_detect2, mag_detect3=mag_detect3, mag_detect4=mag_detect4,
                    mag_peak1 =mag_peak1, mag_peak2 =mag_peak2, mag_peak3=mag_peak3, mag_peak4=mag_peak4,
                    discovery_phase1=discovery_phase1, discovery_phase2=discovery_phase2, discovery_phase3=discovery_phase3, discovery_phase4=discovery_phase4, 
                    discovery_window1=discovery_window1, discovery_window2=discovery_window2, discovery_window3=discovery_window3, discovery_window4 = discovery_window4,
                    gw_recovered=gw_recovered_arr, em_recovered=em_recovered_arr, single_gw_detection=single_gw_detection_arr)
        pickle.dump(res, f)

    df_master = pd.concat(df_list, ignore_index=True)
    df_master.to_csv(f"{trials_dir}/trials_df.csv")


if __name__=='__main__':
    argv = sys.argv[1:]
    sys.exit(main(argv=argv))