def test_reversible_disconnected(disconnected_states, lag, count_mode):
r"""disconnected states: 2 <- 0 <-> 1 <-> 3 | 7 -> 4 <-> 5 | 6"""
count_model = TransitionCountEstimator(lagtime=lag, count_mode=count_mode) \
.fit(disconnected_states.dtrajs).fetch_model()
msm = MaximumLikelihoodMSM(reversible=True).fit(count_model).fetch_model()
assert_equal(msm.n_connected_msms, len(disconnected_states.connected_sets))
for i, subset in enumerate(disconnected_states.connected_sets):
# can do this because subsets are ordered in decreasing cardinality
assert_equal(msm.state_symbols(i), subset)
non_reversibly_connected_set = [0, 1, 2, 3]
submodel = count_model.submodel(non_reversibly_connected_set)
msm = MaximumLikelihoodMSM(reversible=True).fit(submodel).fetch_model()
assert_equal(msm.n_connected_msms, 2)
assert_equal(msm.state_symbols(0), [0, 1, 3])
assert_equal(msm.state_symbols(1), [2])
fully_disconnected_set = [6, 2]
submodel = count_model.submodel(fully_disconnected_set)
msm = MaximumLikelihoodMSM(reversible=True).fit(submodel).fetch_model()
assert_equal(msm.n_connected_msms, 2)
assert_equal(msm.state_symbols(0), [6])
assert_equal(msm.state_symbols(1), [2])
def test_nonreversible_disconnected():
msm1 = MarkovStateModel([[.7, .3], [.3, .7]])
msm2 = MarkovStateModel([[.9, .05, .05], [.3, .6, .1], [.1, .1, .8]])
traj = np.concatenate([msm1.simulate(1000000), 2 + msm2.simulate(1000000)])
counts = TransitionCountEstimator(lagtime=1, count_mode="sliding").fit(traj)
msm = MaximumLikelihoodMSM(reversible=True).fit(counts).fetch_model()
assert_equal(msm.transition_matrix.shape, (3, 3))
assert_equal(msm.stationary_distribution.shape, (3,))
assert_equal(msm.state_symbols(), [2, 3, 4])
assert_equal(msm.state_symbols(1), [0, 1])
msm.select(1)
assert_equal(msm.transition_matrix.shape, (2, 2))
assert_equal(msm.stationary_distribution.shape, (2,))
assert_equal(msm.state_symbols(), [0, 1])
assert_equal(msm.state_symbols(0), [2, 3, 4])
with assert_raises(IndexError):
msm.select(2)