1 files changed, 181 insertions, 0 deletions
diff --git a/ipynbs/mcmc_scan.py b/ipynbs/mcmc_scan.py
new file mode 100644
index 0000000..275c869
--- /dev/null
+++ b/ipynbs/mcmc_scan.py
@@ -0,0 +1,181 @@
+#! /usr/bin/env python
+"""
+Sample points from a gaussian likelihood
+"""
+
+from __future__ import absolute_import, division
+
+import sys
+
+import argparse
+import multiprocessing
+from fractions import gcd
+
+import numpy as np
+from scipy.stats import multivariate_normal
+
+import emcee
+import tqdm
+
+MEASURED_FR = [1, 1, 1]
+SIGMA = 0.001
+
+
+def solve_ratio(fr):
+    denominator = reduce(gcd, fr)
+    return [int(x/denominator) for x in fr]
+
+
+def angles_to_fr(angles):
+    sphi4, c2psi = angles
+
+    psi = (0.5)*np.arccos(c2psi)
+
+    sphi2 = np.sqrt(sphi4)
+    cphi2 = 1. - sphi2
+    spsi2 = np.sin(psi)**2
+    cspi2 = 1. - spsi2
+
+    x = sphi2*cspi2
+    y = sphi2*spsi2
+    z = cphi2
+    return x, y, z
+
+
+def triangle_llh(theta):
+    """-Log likelihood function for a given theta."""
+    fr = angles_to_fr(theta)
+    fr_bf = MEASURED_FR
+    cov_fr = np.identity(3) * SIGMA
+    return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr))
+
+
+def lnprior(theta):
+    """Priors on theta."""
+    sphi4, c2psi = theta
+
+    # Flavour ratio bounds
+    if 0. <= sphi4 <= 1.0 and -1.0 <= c2psi <= 1.0:
+        pass
+    else: return -np.inf
+
+    return 0.
+
+
+def lnprob(theta):
+    """Prob function for mcmc."""
+    lp = lnprior(theta)
+    if not np.isfinite(lp):
+        return -np.inf
+    return lp + triangle_llh(theta)
+
+
+def parse_args():
+    """Parse command line arguments"""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--measured-ratio', type=int, nargs=3, default=[1, 1, 1],
+        help='Set the central value for the measured flavour ratio at IceCube'
+    )
+    parser.add_argument(
+        '--sigma-ratio', type=float, default=0.01,
+        help='Set the 1 sigma for the measured flavour ratio'
+    )
+    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(
+        '--seed', type=int, default=99,
+        help='Set the random seed value'
+    )
+    parser.add_argument(
+        '--outfile', type=str, default='./untitled',
+        help='Path to output chains'
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    np.random.seed(args.seed)
+
+    global MEASURED_FR
+    global SIGMA
+
+    MEASURED_FR = np.array(args.measured_ratio) / float(np.sum(args.measured_ratio))
+    SIGMA = args.sigma_ratio
+
+    print 'MEASURED_FR = {0}'.format(MEASURED_FR)
+    print 'SIGMA = {0}'.format(SIGMA)
+
+    ndim = 2
+    nwalkers = args.nwalkers
+    ntemps = 1
+    burnin = args.burnin
+    betas = np.array([1e0, 1e-1, 1e-2, 1e-3, 1e-4])
+    p0_base = [0.5, 0.5]
+    p0_std = [.2, 0.2]
+
+    print 'p0_base', p0_base
+    print 'p0_std', p0_std
+    p0 = np.random.normal(p0_base, p0_std, size=[ntemps, nwalkers, ndim])
+    print map(lnprior, p0[0])
+
+    # threads = multiprocessing.cpu_count()
+    threads = 1
+    sampler = emcee.PTSampler(
+        ntemps, nwalkers, ndim, triangle_llh, lnprior, 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"
+
+    nsteps = args.nsteps
+
+    print "Running"
+    for _ in tqdm.tqdm(sampler.sample(pos, iterations=nsteps), total=nsteps):
+        pass
+    print "Finished"
+
+    mr = solve_ratio(MEASURED_FR)
+    outfile = args.outfile+'_{0}_{1}_{2}_{3:.1E}'.format(
+            mr[0], mr[1], mr[2], SIGMA
+    )
+
+    samples = sampler.chain[0, :, :, :].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 FOR FILE ' + outfile
+    print 'outfile = ', outfile
+
+    fr_samples = np.array(map(angles_to_fr, samples))
+    np.save(outfile+'.npy', fr_samples)
+
+    print "DONE!"
+
+
+main.__doc__ = __doc__
+
+
+if __name__ == '__main__':
+    main()
+