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