def ALA4_msstates(T_AA): # Define the metastable states from analysis of T_AA_mle membership = pcca(T_AA, 4) # the precise definitions were fine-tuned by hand, I should plot the PCCA states to double check! E1 = np.where(membership[:, 0] > 0.4)[0] E2 = np.where(membership[:, 1] > 0.5)[0] H = np.where(membership[:, 2] > 0.5)[0] I = np.where(membership[:, 3] > 0.5)[0] # make sure there is no overlap #print "checking the overlap of metastable states:" check = [val for val in H if val in I] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in H if val in E1] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in H if val in E2] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in I if val in E1] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in I if val in E2] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in E1 if val in E2] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') return H, I, E2, E1
def ALA4_msstates( T_AA ): # Define the metastable states from analysis of T_AA_mle membership=pcca(T_AA, 4) # the precise definitions were fine-tuned by hand, I should plot the PCCA states to double check! H = np.where(membership[:,0]>0.61)[0] I = np.where(membership[:,1]>0.80)[0] E1 = np.where(membership[:,2]>0.61)[0] E2 = np.where(membership[:,3]>0.80)[0] # make sure there is no overlap #print "checking the overlap of metastable states:" check = [val for val in H if val in I] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in H if val in E1] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in H if val in E2] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in I if val in E1] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in I if val in E2] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in E1 if val in E2] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') return H, I, E2, E1
def main(): args = handleArgs() # read transition matrix T = read_matrix(args.inputT) if not is_transition_matrix(T): raise ValueError( 'given transition matrix is not a valid transition matrix') #if args.inputEigVecs: # eig_vecs = read_matrix(args.inputEigVecs) memberships = pcca(T, args.nclusters) print memberships # write memberships to file crisp try: write_matrix(args.ocrisp, memberships) except Exception: log.exception('error during writing of PCCA memberships.') return 1 # TODO: add fuzzy assignment to clusters if supported by pyemma pcca impl # TODO: add output sets return 0
def ALA3_msstates( T_AA ): # Define the metastable states from analysis of T_AA_mle membership=pcca(T_AA, 3) # the precise definitions were fine-tuned by hand, I should plot the PCCA states to double check! lH = np.where(membership[:,0]>0.60)[0] aH = np.where(membership[:,1]>0.60)[0] B = np.where(membership[:,2]>0.60)[0] # make sure there is no overlap #print "checking the overlap of metastable states:" check = [val for val in lH if val in aH] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in lH if val in B] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') check = [val for val in aH if val in B] if (len(check) != 0 ): raise ValueError('Some metastable states are overlapping!') return B, aH, lH
def ALA3_msstates(T_AA): # Define the metastable states from analysis of T_AA_mle membership = pcca(T_AA, 3) # the precise definitions were fine-tuned by hand, I should plot the PCCA states to double check! lH = np.where(membership[:, 0] > 0.60)[0] aH = np.where(membership[:, 1] > 0.60)[0] B = np.where(membership[:, 2] > 0.60)[0] # make sure there is no overlap #print "checking the overlap of metastable states:" check = [val for val in lH if val in aH] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in lH if val in B] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') check = [val for val in aH if val in B] if (len(check) != 0): raise ValueError('Some metastable states are overlapping!') return B, aH, lH
def pcca_sets(P, n, lcc): r"""Compute partition into Perron clusters. Parameters ---------- P : (M, M) ndarray Transition matrix n : int Number of Perron clusters Returns ------- sets : list List of arrays, sets[i] contains the states in the i-th Perron cluster """ sets=[] pcca_prob=pcca(P, n) pcca_ind=np.argmax(pcca_prob, axis=1) for i in range(n): sets.append(lcc[pcca_ind==i]) return sets
def PCCA(self, m): """Use pyemma pcca """ from pyemma.msm.analysis import pcca self.pcca = pcca(self.T, m) return self.pcca
def PCCA(self, m): """Use pyemma pcca """ from pyemma.msm.analysis import pcca self.pcca = pcca(self.T, m) return self.pcca