reogranise into a python package

1 files changed, 0 insertions, 120 deletions

diff --git a/utils/mcmc.py b/utils/mcmc.py
deleted file mode 100644
index 49e5022..0000000
--- a/utils/mcmc.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# 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
-
-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, args, 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"
-    args.burnin = False
-
-    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=60,
-        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'
-    )
-    parser.add_argument(
-        '--plot-angles', type=parse_bool, default='False',
-        help='Plot MCMC triangle in the angles space'
-    )
-    parser.add_argument(
-        '--plot-elements', type=parse_bool, default='False',
-        help='Plot MCMC triangle in the mixing elements space'
-    )
-
-
-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)
-