def test_pcca_1():
assignments, ref_macrostate_assignments = _metastable_system()
pipeline = Pipeline([('msm', MarkovStateModel()), ('pcca+', PCCA(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)))
from msmbuilder.msm import MarkovStateModel
assign, ref_macro_assign = tlp._metastable_system()
fig1 = plt.figure()
plt.plot(assign, 'o')
plt.title('microstate assignments')
plt.savefig('/home/shenglan/TryMSMbuilder/output/test_microassign.png')
plt.close(fig1)
print assign.shape
print ref_macro_assign.shape
print assign[0]
test = PCCA(2).fit(assign)
macro_assign = test.fit_transform(assign)[0]
print test.n_states_
print test.mapping_
print macro_assign.shape
print np.sum(macro_assign)
print len(np.unique(macro_assign)) == 2
fig2 = plt.figure()
plt.plot(macro_assign, 'o')
plt.title('macro assignments')
plt.savefig('/home/shenglan/TryMSMbuilder/output/test_macroassign.png')
plt.close(fig2)
