# ===========================================================================
# ===========================================================================
# Just keep the atoms in atom indices, remove other atoms
atom_indices = np.loadtxt('atom_indices', dtype=np.int32).tolist()
print "atom_indices:", atom_indices
trajs_sub_atoms = trajs.atom_slice(atom_indices, inplace=False) #just keep the the atoms in atom indices
print "Trajs:", trajs
print "Sub_atoms_trajs:", trajs_sub_atoms
# ===========================================================================
# do Clustering using KCenters method
#cluster = KCenters(n_clusters=n_clusters, metric="euclidean", random_state=0)
cluster = KCenters(n_clusters=n_clusters, metric="rmsd", random_state=0)
print cluster
#cluster.fit(phi_psi)
cluster.fit(trajs_sub_atoms)
labels = cluster.labels_
print labels
n_microstates = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_microstates)
cluster_centers_ = cluster.cluster_centers_
# plot micro states
clustering_name = "kcenters_n_" + str(n_microstates)
np.savetxt("assignments_"+clustering_name+".txt", labels, fmt="%d")
np.savetxt("cluster_centers_"+clustering_name+".txt", cluster_centers_, fmt="%d")
trajs[cluster_centers_].save("cluster_centers.pdb")
trajs_sub_atoms[cluster_centers_].save("cluster_centers_sub_atoms.pdb")
#plot_cluster(labels=labels, phi_angles=phi_angles, psi_angles=psi_angles, name=clustering_name)
psi=[6, 8, 14, 16],
phi=[4, 6, 8, 14])
#phi_angles, psi_angles = trajreader.get_phipsi(trajs, psi=[5, 7, 13, 15], phi=[3, 5, 7, 13])
#phi_psi = np.column_stack((phi_angles, psi_angles))
np.savetxt("./phi_angles.txt", phi_angles, fmt="%f")
np.savetxt("./psi_angles.txt", psi_angles, fmt="%f")
#phi_angles, psi_angles = trajreader.get_phipsi(trajs, psi=[6, 8, 14, 16], phi=[4, 6, 8, 14])
#phi_psi=np.column_stack((phi_angles, psi_angles))
# ===========================================================================
# do Clustering using KCenters method
#cluster = KCenters(n_clusters=n_clusters, metric="euclidean", random_state=0)
cluster = KCenters(n_clusters=n_clusters, metric="rmsd", random_state=0)
print(cluster)
#cluster.fit(phi_psi)
cluster.fit(trajs)
labels = cluster.labels_
print(labels)
n_microstates = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_microstates)
cluster_centers_ = cluster.cluster_centers_
# plot micro states
clustering_name = "kcenters_n_" + str(n_microstates)
splited_assignments = split_assignments(labels, traj_len)
#np.savetxt("assignments_"+clustering_name+".txt", labels, fmt="%d")
np.savetxt("assignments_" + clustering_name + ".txt",
splited_assignments,
fmt="%d")
np.savetxt("cluster_centers_" + clustering_name + ".txt",
#n_size = X.shape[0]
#dimension = X.shape[1]
'''
# ===========================================================================
n_clusters = 10
print(
'---------------------------------------------------------------------------------'
)
from msmbuilder.cluster import KCenters
cluster = KCenters(n_clusters=n_clusters, metric="euclidean", random_state=0)
print(cluster)
#cluster.fit(phi_psi)
cluster.fit(X)
labels = cluster.labels_
print(labels)
labels = np.concatenate(labels)
n_microstates = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_microstates)
#cluster_centers_ = cluster.cluster_centers_
# plot micro states
clustering_name = "kcenters_n_" + str(n_microstates)
#splited_assignments =split_assignments(labels, traj_len)
#np.savetxt("assignments_"+clustering_name+".txt", labels, fmt="%d")
np.savetxt("assignments_" + clustering_name + ".txt", labels, fmt="%d")
#np.savetxt("cluster_centers_"+clustering_name+".txt", cluster_centers_, fmt="%d")
plot_cluster(labels=labels,
# ===========================================================================
# Just keep the atoms in atom indices, remove other atoms
atom_indices = np.loadtxt('atom_indices', dtype=np.int32).tolist()
print "atom_indices:", atom_indices
trajs_sub_atoms = trajs.atom_slice(
atom_indices, inplace=False) #just keep the the atoms in atom indices
print "Trajs:", trajs
print "Sub_atoms_trajs:", trajs_sub_atoms
# ===========================================================================
# do Clustering using KCenters method
#cluster = KCenters(n_clusters=n_clusters, metric="euclidean", random_state=0)
cluster = KCenters(n_clusters=n_clusters, metric="rmsd", random_state=0)
print cluster
#cluster.fit(phi_psi)
cluster.fit(trajs_sub_atoms)
labels = cluster.labels_
print labels
n_microstates = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_microstates)
cluster_centers_ = cluster.cluster_centers_
# plot micro states
clustering_name = "kcenters_n_" + str(n_microstates)
np.savetxt("assignments_" + clustering_name + ".txt", labels, fmt="%d")
np.savetxt("cluster_centers_" + clustering_name + ".txt",
cluster_centers_,
fmt="%d")
trajs[cluster_centers_].save("cluster_centers.pdb")
trajs_sub_atoms[cluster_centers_].save("cluster_centers_sub_atoms.pdb")