def markov_mfpts(markov_tmatrix, stateA, stateB, lag_time=1):
'''Computes mean first passage times in both directions A->B and B->A
from a markov model. The mfpts computed this way are directly comparable
with the values obtained by a long back and forth simulation between the
tar
'''
auxiliar_matrix = aux.pseudo_nm_tmatrix(markov_tmatrix, stateA, stateB)
# Is going to return a MARKOVIAN mfpt since the auxiliar
# matrix was build from a pure markovian matrix
return non_markov_mfpts(auxiliar_matrix, stateA, stateB, lag_time)
def fluxBA_distribution_on_A(self):
if self.markovian:
t_matrix = pseudo_nm_tmatrix(self.markov_tmatrix, self.stateA,
self.stateB)
else:
t_matrix = self.nm_tmatrix
distrib_on_A = np.zeros(len(self.stateA))
labeled_pops = pops_from_tmatrix(t_matrix)
for i in range(1, 2 * self.n_states + 1, 2):
for j in range(2 * self.n_states):
if j // 2 in self.stateA:
distrib_on_A[self.stateA.index(j // 2)] += \
labeled_pops[i] * t_matrix[i, j]
return distrib_on_A
def fit(self):
'''Fits the the markov plus color model from a list of sequences
'''
# Non-Markovian count matrix
nm_tmatrix = np.zeros((2 * self.n_states, 2 * self.n_states))
# Markovian transition matrix
markov_tmatrix = np.zeros((self.n_states, self.n_states))
start = self._lag_time
step = 1
lag = self._lag_time
hlength = self.hist_length
if not self.sliding_window:
step = lag
# Markov first
for traj in self.trajectories:
for i in range(start, len(traj), step):
markov_tmatrix[traj[i - lag], traj[i]] += 1.0 # counting
markov_tmatrix = markov_tmatrix + markov_tmatrix.T
markov_tmatrix = normalize_markov_matrix(markov_tmatrix)
p_nm_tmatrix = pseudo_nm_tmatrix(markov_tmatrix, self.stateA,
self.stateB)
pops = pops_from_tmatrix(p_nm_tmatrix)
# Pseudo-Markov Flux matrix
fmatrix = p_nm_tmatrix
for i, _ in enumerate(fmatrix):
fmatrix[i] *= pops[i]
for traj in self.trajectories:
for i in range(start, len(traj), step):
# Previous color determination (index i - lag)
prev_color = "U"
for k in range(i - lag, max(i - lag - hlength, 0) - 1, -1):
if traj[k] in self.stateA:
prev_color = "A"
break
elif traj[k] in self.stateB:
prev_color = "B"
break
# Current Color (in index i)
if traj[i] in self.stateA:
color = "A"
elif traj[i] in self.stateB:
color = "B"
else:
color = prev_color
if prev_color == "A" and color == "B":
nm_tmatrix[2 * traj[i - lag], 2 * traj[i] + 1] += 1.0
elif prev_color == "B" and color == "A":
nm_tmatrix[2 * traj[i - lag] + 1, 2 * traj[i]] += 1.0
elif prev_color == "A" and color == "A":
nm_tmatrix[2 * traj[i - lag], 2 * traj[i]] += 1.0
elif prev_color == "B" and color == "B":
nm_tmatrix[2 * traj[i - lag] + 1, 2 * traj[i] + 1] += 1.0
elif prev_color == "U" and color == "B":
temp_sum = fmatrix[2 * traj[i - lag], 2 * traj[i] + 1] +\
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i] + 1]
nm_tmatrix[2 * traj[i - lag], 2 * traj[i] + 1] += \
fmatrix[2 * traj[i - lag], 2 * traj[i] + 1] / temp_sum
nm_tmatrix[2 * traj[i - lag] + 1, 2 * traj[i] + 1] += \
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i] + 1] /\
temp_sum
elif prev_color == "U" and color == "A":
temp_sum = (fmatrix[2 * traj[i - lag], 2 * traj[i]] +
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i]])
nm_tmatrix[2 * traj[i - lag]][2 * traj[i]] += \
fmatrix[2 * traj[i - lag], 2 * traj[i]] / temp_sum
nm_tmatrix[2 * traj[i - lag] + 1][2 * traj[i]] += \
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i]] / temp_sum
elif prev_color == "U" and color == "U":
temp_sum = fmatrix[2 * traj[i - lag], 2 * traj[i] + 1] +\
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i] + 1] +\
fmatrix[2 * traj[i - lag], 2 * traj[i]] +\
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i]]
nm_tmatrix[2 * traj[i - lag], 2 * traj[i] + 1] += \
fmatrix[2 * traj[i - lag], 2 * traj[i] + 1] / temp_sum
nm_tmatrix[2 * traj[i - lag] + 1][2 * traj[i] + 1] += \
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i] + 1] /\
temp_sum
nm_tmatrix[2 * traj[i - lag]][2 * traj[i]] += \
fmatrix[2 * traj[i - lag], 2 * traj[i]] / temp_sum
nm_tmatrix[2 * traj[i - lag] + 1][2 * traj[i]] += \
fmatrix[2 * traj[i - lag] + 1, 2 * traj[i]] / temp_sum
self.nm_cmatrix = nm_tmatrix # not normalized, it is like count matrix
nm_tmatrix = normalize_markov_matrix(nm_tmatrix)
self.nm_tmatrix = nm_tmatrix
self.markov_tmatrix = markov_tmatrix