def sample_tica_dim(dim=0, n_frames=200, meta=None, ttrajs=None):
## Load
if (not meta is None) & (not ttrajs is None):
## Sample
# These are apparently ordered according tica value
inds = sample_dimension(ttrajs,
dimension=dim,
n_frames=n_frames,
scheme='random')
save_generic(inds, "tica-dimension-{}-inds.pickl".format(dim + 1))
## Get tica components
tica_values = np.array(
[ttrajs[traj_i][frame_i][dim] for traj_i, frame_i in inds])
tica_values = (tica_values - tica_values.min()) / (tica_values.max() -
tica_values.min())
tica_values *= 10
## Make trajectory
top = preload_top(meta)
# Use loc because sample_dimension is nice
traj = md.join(
md.load_frame(meta.loc[traj_i]['traj_fn'], index=frame_i, top=top)
for traj_i, frame_i in inds)
## Supperpose
## Save
traj_fn = "tica-dimension-{}.dcd".format(dim + 1)
backup(traj_fn)
traj.save(traj_fn)
else:
raise ValueError('Specify meta data and trajectory objects')
{{header}}
Meta
----
depends:
- meta.pandas.pickl
- trajs
- top.pdb
"""
import mdtraj as md
from msmbuilder.io import load_meta, itertrajs, save_trajs, preload_top
## Load
meta = load_meta()
centroids = md.load("centroids.xtc", top=preload_top(meta))
## Kernel
SIGMA = 0.3 # nm
from msmbuilder.featurizer import RMSDFeaturizer
import numpy as np
featurizer = RMSDFeaturizer(centroids)
lfeats = {}
for i, traj in itertrajs(meta):
lfeat = featurizer.partial_transform(traj)
lfeat = np.exp(-lfeat**2 / (2 * (SIGMA**2)))
lfeats[i] = lfeat
save_trajs(lfeats, 'ftrajs', meta)
please cite msmbuilder in any publications
"""
import mdtraj as md
import os
from msmbuilder.io.sampling import sample_states
from msmbuilder.io import load_trajs, save_generic, preload_top, backup, load_generic
## Load
meta, ttrajs = load_trajs('ttrajs')
kmeans = load_generic("kmeans.pickl")
## Sample
inds = sample_states(ttrajs, kmeans.cluster_centers_, k=10)
save_generic(inds, "cluster-sample-inds.pickl")
## Make trajectories
top = preload_top(meta)
out_folder = "cluster_samples"
backup(out_folder)
os.mkdir(out_folder)
for state_i, state_inds in enumerate(inds):
traj = md.join(
md.load_frame(meta.loc[traj_i]['traj_fn'], index=frame_i, top=top)
for traj_i, frame_i in state_inds)
traj.save("{}/{}.xtc".format(out_folder, state_i))
- trajs
"""
import mdtraj as md
from msmbuilder.io import load_trajs, save_generic, preload_top, backup, load_generic
from msmbuilder.io.sampling import sample_msm
## Load
meta, ttrajs = load_trajs('ttrajs')
msm = load_generic('msm.pickl')
kmeans = load_generic('kmeans.pickl')
## Sample
# Warning: make sure ttrajs and kmeans centers have
# the same number of dimensions
inds = sample_msm(ttrajs, kmeans.cluster_centers_, msm, n_steps=200, stride=1)
save_generic(inds, "msm-traj-inds.pickl")
## Make trajectory
top = preload_top(meta)
traj = md.join(
md.load_frame(meta.loc[traj_i]['traj_fn'], index=frame_i, top=top)
for traj_i, frame_i in inds
)
## Save
traj_fn = "msm-traj.xtc"
backup(traj_fn)
traj.save(traj_fn)
{{header}}
Meta
----
depends:
- meta.pandas.pickl
- trajs
- top.pdb
"""
import mdtraj as md
from msmbuilder.io import load_meta, itertrajs, save_trajs, preload_top
## Load
meta = load_meta()
centroids = md.load("centroids.xtc", top=preload_top(meta))
## Kernel
SIGMA = 0.3 # nm
from msmbuilder.featurizer import RMSDFeaturizer
import numpy as np
featurizer = RMSDFeaturizer(centroids)
lfeats = {}
for i, traj in itertrajs(meta):
lfeat = featurizer.partial_transform(traj)
lfeat = np.exp(-lfeat ** 2 / (2 * (SIGMA ** 2)))
lfeats[i] = lfeat
save_trajs(lfeats, 'ftrajs', meta)