def test_fluxes_1():
# depends on tpt.committors
msm = MarkovStateModel(lag_time=1)
assignments = np.random.randint(3, size=(10, 1000))
msm.fit(assignments)
tprob = msm.transmat_
pop = msm.populations_
# forward committors
qplus = tpt.committors(0, 2, msm)
ref_fluxes = np.zeros((3, 3))
ref_net_fluxes = np.zeros((3, 3))
for i in range(3):
for j in range(3):
if i != j:
# Eq. 2.24 in Metzner et al. Transition Path Theory.
# Multiscale Model. Simul. 2009, 7, 1192-1219.
ref_fluxes[i, j] = (pop[i] * tprob[i, j] *
(1 - qplus[i]) * qplus[j])
for i in range(3):
for j in range(3):
ref_net_fluxes[i, j] = np.max([0, ref_fluxes[i, j] -
ref_fluxes[j, i]])
fluxes = tpt.fluxes(0, 2, msm)
net_fluxes = tpt.net_fluxes(0, 2, msm)
npt.assert_array_almost_equal(ref_fluxes, fluxes)
npt.assert_array_almost_equal(ref_net_fluxes, net_fluxes)
def test_fluxes_2():
# depends on tpt.committors
bmsm = BayesianMarkovStateModel(lag_time=1)
assignments = np.random.randint(3, size=(10, 1000))
bmsm.fit(assignments)
# forward committors
qplus = tpt.committors(0, 2, bmsm)
ref_fluxes = np.zeros((3, 3))
ref_net_fluxes = np.zeros((3, 3))
for el in zip(bmsm.all_populations_, bmsm.all_transmats_):
pop = el[0]
tprob = el[1]
for i in range(3):
for j in range(3):
if i != j:
# Eq. 2.24 in Metzner et al. Transition Path Theory.
# Multiscale Model. Simul. 2009, 7, 1192-1219.
ref_fluxes[i, j] += (pop[i] * tprob[i, j] *
(1 - qplus[i]) * qplus[j])
ref_fluxes /= 100.
for i in range(3):
for j in range(3):
ref_net_fluxes[i, j] = np.max([0, ref_fluxes[i, j] -
ref_fluxes[j, i]])
fluxes = tpt.fluxes(0, 2, bmsm)
net_fluxes = tpt.net_fluxes(0, 2, bmsm)
npt.assert_array_almost_equal(ref_fluxes, fluxes, decimal=2)
npt.assert_array_almost_equal(ref_net_fluxes, net_fluxes, decimal=2)
def test_fluxes():
# depends on tpt.committors
msm = MarkovStateModel(lag_time=1)
assignments = np.random.randint(3, size=(10, 1000))
msm.fit(assignments)
tprob = msm.transmat_
pop = msm.populations_
# forward committors
qplus = tpt.committors(0, 2, msm)
ref_fluxes = np.zeros((3, 3))
ref_net_fluxes = np.zeros((3, 3))
for i in xrange(3):
for j in xrange(3):
if i != j:
# Eq. 2.24 in Metzner et al. Transition Path Theory.
# Multiscale Model. Simul. 2009, 7, 1192-1219.
ref_fluxes[i, j] = (pop[i] * tprob[i, j] * (1 - qplus[i]) *
qplus[j])
for i in xrange(3):
for j in xrange(3):
ref_net_fluxes[i, j] = np.max(
[0, ref_fluxes[i, j] - ref_fluxes[j, i]])
fluxes = tpt.fluxes(0, 2, msm)
net_fluxes = tpt.net_fluxes(0, 2, msm)
# print(fluxes)
# print(ref_fluxes)
npt.assert_array_almost_equal(ref_fluxes, fluxes)
npt.assert_array_almost_equal(ref_net_fluxes, net_fluxes)
pos13 = dict(zip(range(len(middles2)), middles2[:,0:3:2]))
ax = msme.plot_tpaths(msm, start_ind[0], [pop_ind], pos=pos)
plt.tight_layout()
plt.savefig('tpath.eps')
plt.clf()
ax = msme.plot_tpaths(msm, start_ind[0], [pop_ind], pos=pos13)
plt.tight_layout()
plt.savefig('tpath_13.eps')
plt.clf()
net_flux_matrix = net_fluxes(start_ind[0], [pop_ind], msm)
flux_matrix = fluxes(start_ind[0], [pop_ind], msm)
paths_list, fluxes1 = paths(start_ind[0], [pop_ind], net_flux_matrix)
paths_list_d, fluxes1_d = paths(start_ind[0], [pop_ind], net_flux_matrix, remove_path='bottleneck')
print('num states in path 1', len(paths_list[0]))
test_3 = np.unique(np.concatenate((paths_list[0],paths_list[1], paths_list[2]), 0))
test_5 = np.unique(np.concatenate((paths_list[0],paths_list[1], paths_list[2], paths_list[3], paths_list[4]), 0))
test_3d = np.unique(np.concatenate((paths_list_d[0],paths_list_d[1], paths_list_d[2]), 0))
test_5d = np.unique(np.concatenate((paths_list_d[0],paths_list_d[1], paths_list_d[2], paths_list_d[3], paths_list_d[4]), 0))
tot_flux = fluxes1.sum()
