def test_j_PCCA(self):
TC = scipy.io.mmread(os.path.join(WorkingDir, "Data", "tProb.mtx"))
A = io.loadh(os.path.join(WorkingDir, "Data", "Assignments.Fixed.h5"),
'arr_0')
PCCA.run_pcca(NumMacroStates, A, TC, os.path.join(WorkingDir, 'Data'))
mm = np.loadtxt(os.path.join(WorkingDir, "Data", "MacroMapping.dat"),
'int')
mm_r = np.loadtxt(
os.path.join(ReferenceDir, "Data", "MacroMapping.dat"), 'int')
ma = io.loadh(os.path.join(WorkingDir, "Data", "MacroAssignments.h5"),
'arr_0')
ma_r = io.loadh(
os.path.join(ReferenceDir, "Data", "MacroAssignments.h5"), 'Data')
num_macro = NumMacroStates
permutation_mapping = np.zeros(num_macro, 'int')
#The order of macrostates might be different between the reference and new lumping.
#We therefore find a permutation to match them.
for i in range(num_macro):
j = np.where(mm == i)[0][0]
permutation_mapping[i] = mm_r[j]
mm_permuted = permutation_mapping[mm]
MSMLib.ApplyMappingToAssignments(ma, permutation_mapping)
npt.assert_array_almost_equal(mm_permuted, mm_r)
npt.assert_array_almost_equal(ma, ma_r)
def interactive_scre(assignments):
Counts = MSMLib.get_count_matrix_from_assignments(assignments, lag_time=1)
CountsAfterTrimming,Mapping = MSMLib.ErgodicTrim(Counts)
MSMLib.ApplyMappingToAssignments(assignments,Mapping)
ReversibleCounts = MSMLib.EstimateReversibleCountMatrix(CountsAfterTrimming)
T = MSMLib.EstimateTransitionMatrix(ReversibleCounts).toarray()
populations = np.array(ReversibleCounts.sum(0)).flatten()
populations /= populations.sum()
K0=SCRE.ConvertTIntoK(T)
M,X=SCRE.get_parameter_mapping(K0)
while len(X) > 0:
lagtime_list = get_lagtimes()
KList = scre_iteration(assignments,K0,lagtime_list,M,X,populations)
matplotlib.pyplot.show()
if len(X) > 1:
i,j,lagtime = get_input()
SCRE.FixEntry(M,X,populations,K0,i,j,KList[lagtime][i,j])
else:
lagtime = get_lagtime()
return KList[lagtime]
