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# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : March 17, 2018
"""
Useful functions to use an MCMC for the BSM flavour ratio analysis
"""
from __future__ import absolute_import, division
import argparse
from functools import partial
import emcee
import tqdm
import numpy as np
from utils.enums import MCMCSeedType
from utils.misc import enum_parse, make_dir, parse_bool
def mcmc(p0, ln_prob, ndim, nwalkers, burnin, nsteps, threads=1):
"""Run the MCMC."""
sampler = emcee.EnsembleSampler(
nwalkers, ndim, ln_prob, threads=threads
)
print "Running burn-in"
for result in tqdm.tqdm(sampler.sample(p0, iterations=burnin), total=burnin):
pos, prob, state = result
sampler.reset()
print "Finished burn-in"
print "Running"
for _ in tqdm.tqdm(sampler.sample(pos, iterations=nsteps), total=nsteps):
pass
print "Finished"
samples = sampler.chain.reshape((-1, ndim))
print 'acceptance fraction', sampler.acceptance_fraction
print 'sum of acceptance fraction', np.sum(sampler.acceptance_fraction)
print 'np.unique(samples[:,0]).shape', np.unique(samples[:,0]).shape
try:
print 'autocorrelation', sampler.acor
except:
print 'WARNING : NEED TO RUN MORE SAMPLES'
return samples
def mcmc_argparse(parser):
parser.add_argument(
'--run-mcmc', type=parse_bool, default='True',
help='Run the MCMC'
)
parser.add_argument(
'--burnin', type=int, default=100,
help='Amount to burnin'
)
parser.add_argument(
'--nwalkers', type=int, default=100,
help='Number of walkers'
)
parser.add_argument(
'--nsteps', type=int, default=2000,
help='Number of steps to run'
)
parser.add_argument(
'--mcmc-seed-type', default='uniform',
type=partial(enum_parse, c=MCMCSeedType), choices=MCMCSeedType,
help='Type of distrbution to make the initial MCMC seed'
)
def flat_seed(paramset, nwalkers):
"""Get gaussian seed values for the MCMC."""
ndim = len(paramset)
low = np.array(paramset.seeds).T[0]
high = np.array(paramset.seeds).T[1]
p0 = np.random.uniform(
low=low, high=high, size=[nwalkers, ndim]
)
return p0
def gaussian_seed(paramset, nwalkers):
"""Get gaussian seed values for the MCMC."""
ndim = len(paramset)
p0 = np.random.normal(
paramset.values, paramset.stds, size=[nwalkers, ndim]
)
return p0
def save_chains(chains, outfile):
"""Save the chains.
Parameters
----------
chains : numpy ndarray
MCMC chains to save
outfile : str
Output file location of chains
"""
make_dir(outfile)
print 'Saving chains to location {0}'.format(outfile+'.npy')
np.save(outfile+'.npy', chains)
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