def main(args):
# do some initialization
ascii_states = ['AB', 'Ab', 'aB', 'ab']
k = len(ascii_states)
N = args.N
M = np.array(list(gen_states(N, k)))
T = get_inverse_dict(M)
lmcs = omnomnomial.get_lmcs(M)
lps_neutral = np.log(M / float(N))
# write a file for each of the 2x2 option combos
for rate_adjustment in (RAW_RATE, ADJUSTED_RATE):
for mut_style in (DETERMINISTIC_MUT, STOCHASTIC_MUT):
# define the mutation rate between adjacent states
if rate_adjustment == RAW_RATE:
mu = args.mu
elif rate_adjustment == ADJUSTED_RATE:
mu = get_adjusted_rate(args.mu)
else:
raise Exception
# construct population genetic transition matrix P
if mut_style == DETERMINISTIC_MUT:
Q = get_micro_mut_rate_matrix(ascii_states)
mmu = scipy.linalg.expm(mu*Q)
lps = np.log(np.dot(M, mmu) / N)
P = np.exp(omnomnomial.get_log_transition_matrix(M, lmcs, lps))
elif mut_style == STOCHASTIC_MUT:
Q_mut = get_macro_mut_rate_matrix(M, T)
P_mut = scipy.linalg.expm(mu*Q_mut)
P_dft = np.exp(omnomnomial.get_log_transition_matrix(
M, lmcs, lps_neutral))
P = np.dot(P_mut, P_dft)
else:
raise Exception
# compute the equilibrium distribution
v = get_stationary_distribution(P)
# write the equilibrium distribution to the file
file_description = '_'.join((rate_adjustment, mut_style))
filename = file_description + '.txt'
with open(filename, 'w') as fout:
print >> fout, '\t'.join(ascii_states + ['probability'])
for i, (AB, Ab, aB, ab) in enumerate(M):
row = (AB, Ab, aB, ab, v[i])
print >> fout, '\t'.join(str(x) for x in row)
def main(args):
# initialize some variables
N = args.N
mu = args.mu
ascii_states = ['AB', 'Ab', 'aB', 'ab']
k = len(ascii_states)
# construct the microstate mutational transition matrix
#mmu = get_jeff_mut(ascii_states, mu)
Q = get_mut_rate_matrix(ascii_states)
mmu = scipy.linalg.expm(mu*Q)
# show the transition matrix
print 'transition matrix:'
print mmu
print
# construct the population genetic transition matrix
M = np.array(list(gen_states(N, k)))
lmcs = omnomnomial.get_lmcs(M)
lps = np.log(np.dot(M, mmu) / N)
P = np.exp(omnomnomial.get_log_transition_matrix(M, lmcs, lps))
# compute the equilibrium distribution
v = get_stationary_distribution(P)
# print the equilibrium distribution
print '\t'.join(ascii_states + ['probability'])
for i, (AB, Ab, aB, ab) in enumerate(M):
print '\t'.join(str(x) for x in (AB, Ab, aB, ab, v[i]))