{{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)
meta = load_meta()
## Try to limit RAM usage
def guestimate_stride():
total_data = meta['nframes'].sum()
want = kmed.n_clusters * 10
stride = max(1, total_data // want)
print("Since we have", total_data, "frames, we're going to stride by",
stride, "during fitting, because this is probably adequate for",
kmed.n_clusters, "clusters")
return stride
## Fit
kmed.fit([traj for _, traj in itertrajs(meta, stride=guestimate_stride())])
print(kmed.summarize())
## Save
save_generic(kmed, 'clusterer.pickl')
## Save centroids
def frame(traj_i, frame_i):
# Note: kmedoids does 0-based, contiguous integers so we use .iloc
row = meta.iloc[traj_i]
return md.load_frame(row['traj_fn'], frame_i, top=row['top_fn'])
centroids = md.join((frame(ti, fi) for ti, fi in kmed.cluster_ids_),
check_topology=False)
{{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)
"""Check for abnormally high rmsd values to a reference structure
{{header}}
Meta
----
depends:
- meta.pandas.pickl
- trajs
- top.pdb
"""
import mdtraj as md
from msmbuilder.io import load_meta, itertrajs, save_trajs
## Load reference structure
ref = md.load("top.pdb")
meta = load_meta()
## Do calculation and save
rmsds = {k: md.rmsd(traj, ref) for k, traj in itertrajs(meta)}
save_trajs(rmsds, 'rmsds', meta)
meta = load_meta()
## Try to limit RAM usage
def guestimate_stride():
total_data = meta['nframes'].sum()
want = kmed.n_clusters * 10
stride = max(1, total_data // want)
print("Since we have", total_data, "frames, we're going to stride by",
stride, "during fitting, because this is probably adequate for",
kmed.n_clusters, "clusters")
return stride
## Fit
kmed.fit([traj for _, traj in itertrajs(meta, stride=guestimate_stride())])
print(kmed.summarize())
## Save
save_generic(kmed, 'clusterer.pickl')
## Save centroids
def frame(traj_i, frame_i):
# Note: kmedoids does 0-based, contiguous integers so we use .iloc
row = meta.iloc[traj_i]
return md.load_frame(row['traj_fn'], frame_i, top=row['top_fn'])
centroids = md.join((frame(ti, fi) for ti, fi in kmed.cluster_ids_),
check_topology=False)
