Ice features for kinetic models¶

IceFeaturizer walks a trajectory and returns one feature vector per frame and one integer state per molecule per frame. The arrays are plain NumPy.

Trajectory is an alias of Frame. Ring-adjacent completion is the featurizer default.

from pydseams import Trajectory
from pydseams.features import IceFeaturizer

traj = Trajectory("nucleation.lammpstrj", frame=1, atom_type=2, cutoff=3.5)
feat = IceFeaturizer(traj, ring_adjacent=True)
X, S = feat.transform()
print(feat.feature_names)

X follows feat.feature_names: n_ice, n_max, n_clusters, n_ic, n_ih, n_mixed, cubicity, the chill_plus counts on the cutoff graph, the largest chill_plus bulk cluster, and the six-ring count. X[:, 1] is n_max. S lists STATE_WATER, STATE_IC, STATE_IH, or STATE_MIXED per molecule.

Pass ion_types to append n_ion_ice, n_ion_front, n_ion_liquid and the mean shell ice fraction.

deeptime¶

from pydseams.features import discretize_nmax, to_deeptime

dtrajs = discretize_nmax(X[:, 1], edges=[10, 50, 150, 400])
msm = to_deeptime(X, lagtime=5)

to_deeptime fits a time-lagged independent component analysis on the full vector and returns the fitted model. Call model.transform(X) for the projection.

PyEMMA¶

from pydseams.features import to_pyemma_featurizer

featurizer = to_pyemma_featurizer(feat, topology="nucleation.pdb")

The per-frame vector registers as a custom feature. PyEMMA is unmaintained; deeptime succeeds it.

Book: features how-to.