def computePowerSimulations(s=0, n=10,N=1000,save=True,load=False,NumericallyStable=False,takelog=True):
"""
Args:
s: selection strength
n: computes nth power
N: number of diploid individuals
Returns:
T^10
"""
fname='{}transition/simulation/S{:02.0f}.df'.format(utl.outpath, s * 100,)
if load:
if os.path.exists(fname): return pd.read_pickle(fname)
if NumericallyStable:
T = Markov.computeTransition(s, N, takeLog=True) #OLD NUMERICALLY STABLE
T=T.apply(lambda x: x-x.max(),axis=1).astype(np.float128).apply(np.exp).apply(lambda x: x/x.sum(),axis=1)
Tn=Markov.computePower(T,n)
else:
T=Markov.computeTransition(s=s,N=N)
Tn=Markov.computePower(T,n)
if takelog: Tn=utl.numbaLog(Tn)
# if save : Tn.to_pickle(fname)
print 'computed power for s:',s
return Tn
