def test_eigtransform_2():
model = MarkovStateModel(n_timescales=2)
traj = [4, 3, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 0, 1, 1, 2, 2, 0, 0]
model.fit([traj])
transformed_0 = model.eigtransform([traj], mode='clip')
# clip off the first two states (not ergodic)
assert transformed_0[0].shape == (len(traj) - 2, model.n_timescales)
transformed_1 = model.eigtransform([traj], mode='fill')
assert transformed_1[0].shape == (len(traj), model.n_timescales)
assert np.all(np.isnan(transformed_1[0][:2, :]))
assert not np.any(np.isnan(transformed_1[0][2:]))
def test_12():
# test eigtransform
model = MarkovStateModel(n_timescales=1)
model.fit([[4, 3, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 0, 1, 1, 2, 2, 0, 0]])
assert model.mapping_ == {0: 0, 1: 1, 2: 2}
assert len(model.eigenvalues_) == 2
t = model.eigtransform([[0, 1]], right=True)
assert t[0][0] == model.right_eigenvectors_[0, 1]
assert t[0][1] == model.right_eigenvectors_[1, 1]
s = model.eigtransform([[0, 1]], right=False)
assert s[0][0] == model.left_eigenvectors_[0, 1]
assert s[0][1] == model.left_eigenvectors_[1, 1]
def test_12():
# test eigtransform
model = MarkovStateModel(n_timescales=1)
model.fit([[4, 3, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 0, 1, 1, 2, 2, 0, 0]])
assert model.mapping_ == {0: 0, 1: 1, 2: 2}
assert len(model.eigenvalues_) == 2
t = model.eigtransform([[0, 1]], right=True)
assert t[0][0] == model.right_eigenvectors_[0, 1]
assert t[0][1] == model.right_eigenvectors_[1, 1]
s = model.eigtransform([[0, 1]], right=False)
assert s[0][0] == model.left_eigenvectors_[0, 1]
assert s[0][1] == model.left_eigenvectors_[1, 1]
def test_eigtransform_2():
model = MarkovStateModel(n_timescales=2)
traj = [4, 3, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 0, 1, 1, 2, 2, 0, 0]
model.fit([traj])
transformed_0 = model.eigtransform([traj], mode='clip')
# clip off the first two states (not ergodic)
assert transformed_0[0].shape == (len(traj) - 2, model.n_timescales)
transformed_1 = model.eigtransform([traj], mode='fill')
assert transformed_1[0].shape == (len(traj), model.n_timescales)
assert np.all(np.isnan(transformed_1[0][:2, :]))
assert not np.any(np.isnan(transformed_1[0][2:]))
# In[160]:
####Evaluate the thermodynamics and kinetics from the microstate MSM
#the first dynamic eigenvector is associated to the slowest transitions in the dataset
#we can understand the physical meaning of the first eigenmode through sampling the conformations
micro_msm_lagtime = 4
msm = MarkovStateModel(
lag_time=micro_msm_lagtime,
reversible_type='transpose',
n_timescales=3,
ergodic_cutoff='off'
) #lag time should be chosen such that the model becomes Markovian
#n_timescale specify the number of dynamic mode (the 1st one is the 2nd eigenvector of TPM) that outputs
msm.fit(microstate_sequences.labels_)
msm_eigen_trajs = msm.eigtransform(microstate_sequences.labels_)
sampling_along_msm_eigenmode(
resultdir, msm_eigen_trajs, microstate_sequences.labels_, trajectory_dir,
traj_list_array, pdb_name,
1) #1:the slowest transition,play with the eigenmodes
print(
"the timescale associated with the slowest collective motion in the system is: %d ps"
% (msm.timescales_[0]))
print(
"using vmd to open msm-1-dynamic-mode.xtc, to intepret the slowest dynamic mode of the system"
)
# In[161]:
##Draw the potential of mean force, newly added
pi_0 = msm.populations_[np.concatenate(
