def create_mutation_transition_matrix(npop, mutation_ab, mutation_ba):
"""
The states are indexed by the number of mutants.
@param npop: total population size
@param mutation_ab: wild-type to mutant transition probability
@param mutation_ba: mutant to wild-type transition probability
@return: a transition matrix
"""
StatsUtil.assert_probability(mutation_ab)
StatsUtil.assert_probability(mutation_ba)
nstates = npop + 1
P = np.zeros((nstates, nstates))
for a in range(nstates):
for n_mut_to_wild in range(a + 1):
ba_observed_n = n_mut_to_wild
ba_max_n = a
ba_p_success = mutation_ba
ba_log_p = StatsUtil.binomial_log_pmf(ba_observed_n, ba_max_n,
ba_p_success)
for n_wild_to_mut in range(npop - a + 1):
ab_observed_n = n_wild_to_mut
ab_max_n = npop - a
ab_p_success = mutation_ab
ab_log_p = StatsUtil.binomial_log_pmf(ab_observed_n, ab_max_n,
ab_p_success)
#
p = math.exp(ba_log_p + ab_log_p)
b = a + n_wild_to_mut - n_mut_to_wild
P[a, b] += p
return P
def create_mutation_transition_matrix(npop, mutation_ab, mutation_ba):
"""
The states are indexed by the number of mutants.
@param npop: total population size
@param mutation_ab: wild-type to mutant transition probability
@param mutation_ba: mutant to wild-type transition probability
@return: a transition matrix
"""
StatsUtil.assert_probability(mutation_ab)
StatsUtil.assert_probability(mutation_ba)
nstates = npop + 1
P = np.zeros((nstates, nstates))
for a in range(nstates):
for n_mut_to_wild in range(a+1):
ba_observed_n = n_mut_to_wild
ba_max_n = a
ba_p_success = mutation_ba
ba_log_p = StatsUtil.binomial_log_pmf(
ba_observed_n, ba_max_n, ba_p_success)
for n_wild_to_mut in range(npop - a + 1):
ab_observed_n = n_wild_to_mut
ab_max_n = npop - a
ab_p_success = mutation_ab
ab_log_p = StatsUtil.binomial_log_pmf(
ab_observed_n, ab_max_n, ab_p_success)
#
p = math.exp(ba_log_p + ab_log_p)
b = a + n_wild_to_mut - n_mut_to_wild
P[a, b] += p
return P
