def cluster(data_dir, traj_dir, n_clusters):
reduced_data = verboseload(data_dir)
trajs = np.concatenate(reduced_data)
plt.hexbin(trajs[:, 0], trajs[:, 1], bins='log', mincnt=1)
clusterer = MiniBatchKMedoids(n_clusters=n_clusters)
clusterer.fit_transform(reduced_data)
centers = clusterer.cluster_centers_
for i in range(0, np.shape(centers)[0]):
center = centers[i, :]
plt.scatter(center[0], center[1])
plt.annotate('C%d' % i,
xy=(center[0], center[1]),
xytext=(center[0] + 0.1, center[1] + 0.1),
arrowprops=dict(facecolor='black', shrink=0.05))
location = clusterer.cluster_ids_[i, :]
print(location)
traj = get_trajectory_files(traj_dir)[location[0]]
print(("traj = %s" % traj))
print(("frame = %d" % location[1]))
conformation = md.load_frame(traj, location[1])
conformation.save_pdb(
"/scratch/users/enf/b2ar_analysis/cluster_%d.pdb" % i)
plt.show()
def cluster(data_dir, traj_dir, n_clusters, lag_time): clusterer_dir = "/scratch/users/enf/b2ar_analysis/clusterer_%d_t%d.h5" %(n_clusters, lag_time) if (os.path.exists(clusterer_dir)): print "Already clustered" else: reduced_data = verboseload(data_dir) trajs = np.concatenate(reduced_data) clusterer = MiniBatchKMedoids(n_clusters = n_clusters) clusterer.fit_transform(reduced_data) verbosedump(clusterer, "/scratch/users/enf/b2ar_analysis/clusterer_%d_t%d.h5" %(n_clusters, lag_time))
def cluster(data_dir, traj_dir, n_clusters, lag_time):
clusterer_dir = "/scratch/users/enf/b2ar_analysis/clusterer_%d_t%d.h5" % (
n_clusters, lag_time)
if (os.path.exists(clusterer_dir)):
print("Already clustered")
else:
reduced_data = verboseload(data_dir)
trajs = np.concatenate(reduced_data)
clusterer = MiniBatchKMedoids(n_clusters=n_clusters)
clusterer.fit_transform(reduced_data)
verbosedump(
clusterer, "/scratch/users/enf/b2ar_analysis/clusterer_%d_t%d.h5" %
(n_clusters, lag_time))
def cluster_features(features, clusterer, n_clusters=8):
'''
Input
features : list of arrays, length n_trajs, each of shape (n_samples, n_features)
Output
clst : msmbuilder.cluster object, with attributes
cluster_centers_ : (n_clusters, n_features)
labels_ : list of arrays, each of shape (n_samples, )
'''
if clusterer == 'KMeans':
from msmbuilder.cluster import KMeans
clst = KMeans(n_clusters=n_clusters)
elif clusterer == 'KCenters':
from msmbuilder.cluster import KCenters
clst = KCenters(n_clusters=n_clusters)
elif clusterer == 'KMedoids':
from msmbuilder.cluster import KMedoids
clst = KMedoids(n_clusters=n_clusters)
elif clusterer == 'MiniBatchKMeans':
from msmbuilder.cluster import MiniBatchKMeans
clst = MiniBatchKMeans(n_clusters=n_clusters)
elif clusterer == 'MiniBatchKMedoids':
from msmbuilder.cluster import MiniBatchKMedoids
clst = MiniBatchKMedoids(n_clusters=n_clusters)
clusters = clst.fit_transform(features)
return clst
def cluster(data_dir, traj_dir, n_clusters):
reduced_data = verboseload(data_dir)
trajs = np.concatenate(reduced_data)
plt.hexbin(trajs[:,0], trajs[:,1], bins='log', mincnt=1)
clusterer = MiniBatchKMedoids(n_clusters = n_clusters)
clusterer.fit_transform(reduced_data)
centers = clusterer.cluster_centers_
for i in range(0, np.shape(centers)[0]):
center = centers[i,:]
plt.scatter(center[0],center[1])
plt.annotate('C%d' %i, xy=(center[0],center[1]),xytext=(center[0]+0.1,center[1]+0.1), arrowprops=dict(facecolor='black',shrink=0.05))
location = clusterer.cluster_ids_[i,:]
print location
traj = get_trajectory_files(traj_dir)[location[0]]
print("traj = %s" %traj)
print("frame = %d" %location[1])
conformation = md.load_frame(traj, location[1])
conformation.save_pdb("/scratch/users/enf/b2ar_analysis/cluster_%d.pdb" %i)
plt.show()
"""Cluster based on RMSD between conformations
msmbuilder autogenerated template version 2
created 2017-05-23T16:38:49.093656
please cite msmbuilder in any publications
"""
import mdtraj as md
from msmbuilder.cluster import MiniBatchKMedoids
from msmbuilder.io import load_meta, itertrajs, save_generic, backup
## Set up parameters
kmed = MiniBatchKMedoids(
n_clusters=500,
metric='rmsd',
)
## Load
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
Meta
----
depends:
- meta.pandas.pickl
- trajs
- top.pdb
"""
import mdtraj as md
from msmbuilder.cluster import MiniBatchKMedoids
from msmbuilder.io import load_meta, itertrajs, save_generic, backup
## Set up parameters
kmed = MiniBatchKMedoids(
n_clusters=500,
metric='rmsd',
)
## Load
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
Meta
----
depends:
- meta.pandas.pickl
- trajs
- top.pdb
"""
import mdtraj as md
from msmbuilder.cluster import MiniBatchKMedoids
from msmbuilder.io import load_meta, itertrajs, save_trajs, preload_top
## Set up parameters
kmed = MiniBatchKMedoids(
n_clusters=500,
metric='rmsd',
)
## 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)
