GRB 950830 Nature script

BATSE trigger 3770 with FRED lens fit.

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gravlensnature.py

import sys, os
import argparse

from PyGRB.main.fitpulse import PulseFitter
from PyGRB.backend.makemodels import create_model_from_key


def main_two_pulse_models():
    """ These are the most relevant models to the analysis of GRB 950830. """
    lens_keys = ['FL', 'FsL', 'XL', 'XsL']
    null_keys = ['FF', 'FsFs', 'XX', 'XsXs']
    keys = lens_keys + null_keys

    model_dict = {}
    for key in keys:
        model_dict[key] = create_model_from_key(key)
    return model_dict


def load_3770(sampler = 'dynesty', nSamples = 100, **kwargs):
    test = PulseFitter(3770, times = (-.1, 1),
                datatype = 'tte', nSamples = nSamples, sampler = sampler,
                priors_pulse_start = -.1, priors_pulse_end = 0.5,
                priors_td_lo = 0.2,  priors_td_hi = 0.5, **kwargs)
    return test


def analysis_for_3770(indices):
    """ Use this function to send each job permutation to a different CPU. """
    num_samples = [500, 2000, 4500]
    for samples in num_samples:
        GRB = load_3770(sampler = SAMPLER, nSamples = samples)
        GRB.offsets = [0, 4000, 8000, -3000]

        model_dict = main_two_pulse_models()
        models = [model for key, model in model_dict.items()]

        GRB._split_array_job_to_4_channels( models   = models,
                                            indices  = indices,
                                            channels = channels)

def evidence_for_3770(**kwargs):
    num_samples = [500, 2000, 4500]
    for samples in num_samples:
        GRB = load_3770(sampler = SAMPLER, nSamples = samples, **kwargs)
        GRB.offsets = [0, 4000, 8000, -3000]

        model_dict = main_two_pulse_models()
        models = [model for key, model in model_dict.items()]

        for model in models:
            GRB.get_residuals(channels = [0, 1, 2, 3], model = model)
            lens_bounds = [(0.37, 0.42), (0.60, 1.8)]
            GRB.lens_calc(model = model, lens_bounds = lens_bounds)
        GRB.get_evidence_from_models(model_dict = model_dict)


if __name__ == '__main__':

    parser = argparse.ArgumentParser(   description = 'Core bilby wrapper')
    parser.add_argument('--HPC', action = 'store_true',
                        help = 'Are you running this on a cluster?')
    parser.add_argument('-i', '--indices', type=int, nargs='+',
                        help='an integer for indexing model/channel')
    args = parser.parse_args()
    HPC = args.HPC


    if not HPC:
        # run the later analysis on a local machine
        from matplotlib import rc
        rc('font', **{'family': 'DejaVu Sans',
                    'serif': ['Computer Modern'],'size': 8})
        rc('text', usetex=True)
        rc('text.latex',
        preamble=r'\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsfonts}')
        SAMPLER = 'Nestle'
        # analysis_for_3770(np.arange(32))
        evidence_for_3770(HPC = HPC)


    else:
        # run the nested sampling on a cluster
        # the indices allow each model/channel to be run simultaneously on
        # different machines. See the slurm scripts for examples
        SAMPLER = 'dynesty'
        analysis_for_3770(args.indices)

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