def test_from_msm():
assignments, _ = _metastable_system()
msm = MarkovStateModel()
msm.fit(assignments)
pcca = PCCA.from_msm(msm, 2)
msm = MarkovStateModel()
msm.fit(assignments)
pccaplus = PCCAPlus.from_msm(msm, 2)
def test_from_msm():
assignments, _ = _metastable_system()
msm = MarkovStateModel()
msm.fit(assignments)
pcca = PCCA.from_msm(msm, 2)
msm = MarkovStateModel()
msm.fit(assignments)
pccaplus = PCCAPlus.from_msm(msm, 2)
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)))
for this_assign in geo_assign:
this_list = []
for this_item in this_assign:
try:
this_list.append(partial_raw_to_micro_mapping[this_item])
except KeyError:
pass
partial_micro_assign.append(this_list)
unique_assign = []
for this_assign in partial_micro_assign:
unique_assign.extend(np.unique(this_assign))
unique_assign = np.unique(np.array(unique_assign))
N_MACRO = 10
pcca = PCCA.from_msm(micro_msm,N_MACRO)
micro_to_macro_mapping = {}
for ii in range(len(pcca.microstate_mapping_)):
micro_to_macro_mapping[ii] = pcca.microstate_mapping_[ii]
# for n macrostates, any frame that does not belong to any microstate and thus macrostate is labeled to be in the nth macrostate
for ii in range(len(raw_clusters)):
if ii in micro_to_macro_mapping.keys():
pass
else:
micro_to_macro_mapping[ii] = N_MACRO
# assignments to macro states
macro_assign = []
for this_assign in geo_assign:
for this_assign in geo_assign:
this_list = []
for this_item in this_assign:
try:
this_list.append(partial_raw_to_micro_mapping[this_item])
except KeyError:
pass
partial_micro_assign.append(this_list)
unique_assign = []
for this_assign in partial_micro_assign:
unique_assign.extend(np.unique(this_assign))
unique_assign = np.unique(np.array(unique_assign))
N_MACRO = 5
pcca = PCCA.from_msm(micro_msm, N_MACRO)
micro_to_macro_mapping = {}
for ii in range(len(pcca.microstate_mapping_)):
micro_to_macro_mapping[ii] = pcca.microstate_mapping_[ii]
# for n macrostates, any frame that does not belong to any microstate and thus macrostate is labeled to be in the nth macrostate
for ii in range(len(raw_clusters)):
if ii in micro_to_macro_mapping.keys():
pass
else:
micro_to_macro_mapping[ii] = N_MACRO
# assignments to macro states
macro_assign = []
for this_assign in geo_assign:
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)
