def runOne(args):
path = utl.outpath + 'markov/simulations/'
utl.mkdir(path)
numExp = int(1e5)
nu0, s = args
print nu0, s
for i, batch in enumerate(utl.batch(range(numExp), 10000)):
print;
print i, batch[0], batch[-1]
a = pd.concat(map(lambda x: Simulation.simulateSingleLoci(nu0=nu0, s=s)[[1, 10, 100]], batch), axis=1).T
a.to_pickle(path + 'nu{:E}.s{:E}.{}.df'.format(nu0, s, i))
def computeLikelihoodReal(args):
"""
Args: (it's more convenient for multiprocessing)
args: a list of [R,s,h].
R: is a dataframe for which each row is a position and columns are allele frequencies.
ColumnsLevels= [REP, TIME] , IndexLevels=[CHROM,POS]
s: is selection strength
h: is overdominance
Returns:
a series containing likelihood of timeseries for the specific values of s and h.
"""
CD, E, s, h = args
if s==0:s=int(0)
print 'Computing for {} SNPs for s={} h={}'.format(CD.shape[0], s, h);
sys.stdout.flush()
powers = pd.Series(pd.Series(CD[r].columns).diff().values[1:] for r in range(3))
T = pd.read_pickle(utl.outpath + 'transition/real/S{:E}.H{:E}.df'.format(np.round(s, 2), h))
# likes = computeLikelihoodRealBatch((CD, E, T, powers))
args = map(lambda x: (x, E, T, powers), utl.batch(CD, int(3e5)));
likes = pd.concat(map(computeLikelihoodRealBatch, args))
likes.name = (s, h)
return likes
def computeLikelihoodReal(args):
CD, E, T = args
powers = pd.Series(pd.Series(CD[r].columns).diff().values[1:] for r in range(3))
args = map(lambda x: (x, E, T, powers), utl.batch(CD, int(3e5)));
likes = pd.concat(map(mkv.computeLikelihoodRealBatch, args))
return likes
