FRBe (Fast Radio Bursts Estimator) evaluate the FRB populations and event counts over binned fluence and Dispersion Measure. The project is under development.
numpy scipy emcee matplotlib
$ git clone https://github.com/himmng/FRBe.git $ cd FRBe $ python setup.py install --user
make two different directories for observational, simulation data to put the data there (if doesn't exist)
$ mkdir obs_data sim_data
Using the FRB class in python:
from frb import FRB # using for specific telescope; use e.g. chime, utmost, askap, parkes chime = FRB(name = 'chime', path = 'path_to_init') # provide name of telescope, path ot init (telescope parameters). # see docstring of Config class for help. # To do the prediction muc = chime.mu(alpha, ebar, gamma, *args) # OR use parameters = alpha, ebar, gamma muc = chime.mu(*parameters, *args) # *args; positional arguments are the simulation values which loaded at once # args = [z, r, theta, dmtot, wwa, cdf]
Using in MCMC (coming soon)
from mcmc import MCMC # create instances nwalkers = 6 ndim = 3 mcmc_filename = 'run.h5' # for specific case of (No-Sc, Sc-I, Sc-II)(cer, sfr,) and (DM50, DMrand) # must be a .h5 file mcmc = MCMC(nwalkers, ndim, filename) # load your dataset... # Uses for joint estimation joint = mcmc.joint_run(method = 'use_loss') # It will use loss function to find out the maximum likelihood region #OR joint = mcmc.joint_run(method = 'use_loglike') # It will use log likelihood function itself # Uses for specific telescope cases # using specific telescopes; ; use e.g. chime, utmost, askap, parkes as name run = mcmc.run(method = 'use_loss', name='chime') # OR run = mcmc.run(method = 'use_loglike', name='chime')