def test_ntimescales_1():
# see issue #603
trajs = [np.random.randint(0, 30, size=500) for _ in range(5)]
pccap = PCCAPlus(n_macrostates=11).fit(trajs)
lumped_trajs = pccap.transform(trajs)
assert len(np.unique(lumped_trajs)) == 11
def test_ntimescales_1():
# see issue #603
trajs = [random.randint(0, 100, size=500) for _ in range(15)]
pccap = PCCAPlus(n_macrostates=11).fit(trajs)
lumped_trajs = pccap.transform(trajs)
observed_macros = len(np.unique(lumped_trajs))
assert observed_macros == 11, observed_macros
def test_pcca_plus_1():
assignments, ref_macrostate_assignments = _metastable_system()
pipeline = Pipeline([('msm', MarkovStateModel()), ('pcca+', PCCAPlus(2))])
macro_assignments = pipeline.fit_transform(assignments)[0]
# we need to consider any permutation of the state labels when we
# test for equality. Since it's only a 2-state that's simple using
# the logical_not to flip the assignments.
assert (np.all(macro_assignments == ref_macrostate_assignments) or np.all(
macro_assignments == np.logical_not(ref_macrostate_assignments)))
