reogranise into a python package

1 files changed, 120 insertions, 0 deletions

diff --git a/golemflavor/mcmc.py b/golemflavor/mcmc.py
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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
+
+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)
+