def calculate_tica_components(self, cluster_method, calculate_strides=False, feats=None):
'''Load in the features, calculate a given number of tICA components (tica_components) given a
lagtime (lag_time), and save tICA coordinates and eigenvector data. It then creates and populates
a list for each desired component, clusters the data, saving normalized populations as populations.dat
and saving each cluster center as a .pdb. tICA plots are created and saved, and implied timescales are
calculated, saved, and plotted.
'''
# tICA parameters
tica_lagtime = 10 # determine from implied timescales
tica_components = 8 # how many tICs to compute
n_clusters = 100 # denotes number of microstates
n_timescales = tica_components # plot all eigenvalues --> timescales
md_time_step = 0.02 # ns
subsampled_time_step = 1. # ns multiplier of timescales and lagtimes in implied timescale plot
stride = int(subsampled_time_step / md_time_step) #time step stride for sub-sampling
equil_time = 1. # ns
equil_steps = 1 #int(equil_time / md_time_step) time steps to be removed from start
lagtimes = np.array([1,2,4,8,16,32,64,128,256,512,1024])
cluster_method = 'kcenters' # 'kcenters/kmeans'
all_ticas = list(itertools.permutations(range(1,tica_components+1), 2)) # all combinations
all_ticas = [[1,2]] # override: just show analysis for first two components
cluster_percentage_cutoff = 5 # clusters with a relative population less than this
# number will not be labeled on plot i.e. 0 : all clusters labeled
verbose = False
print("\nCalculating tICA components...")
# Load in feature files THIS WILL NEED TO BE CHANGED
if feats == None:
if calculate_strides:
self.calculate_stride_distances(stride, equil_steps)
data = np.load('/home/server/git/fah-scripts/DataAnalysisScripts/stride_dist/stride_dist_%d.npy' % self.proj_num)
else:
data = self.data
else:
data = np.load(feats)
features = []
for run in data:
for clone in run:
gen_seq = []
for gen in clone:
if gen is not None and gen[0] is not None:
if calculate_strides or feats is not None:
gen_seq.append(gen)
else:
gen_seq.append(gen[::stride])
if len(gen_seq) > 0:
gen_cat = np.concatenate(gen_seq)
if calculate_strides:
features.append(gen_cat)
else:
features.append(gen_cat[equil_steps:])
features = np.asarray(features)
print(features.shape)
print(features[0].shape)
tica_coordinates = tICA(lag_time=tica_lagtime,
n_components=int(tica_components)).fit_transform(features)
np.save('%s/lag_%d_coord_%d.npy' %(self.tICA_dir, tica_lagtime, tica_components), tica_coordinates)
# Initiate and populate an array for each component
for i in range(tica_components):
exec('tica_' + str(i+1) + ' = []')
for i in tqdm.tqdm(range(len(features))):
for j in range(len(tica_coordinates[i])):
for k in range(tica_components):
exec('tica_' + str(k+1) + '.append(tica_coordinates[i][j][k])')
# Perform clustering based on the cluster_method parameter.
if cluster_method == 'kcenters':
print("Clustering via KCenters...")
clusters = KCenters(n_clusters)
elif cluster_method == 'kmeans':
print("Clustering via KMeans...")
clusters = KMeans(n_clusters)
else:
sys.exit("Invalid cluster_method. Use kmeans or kcenters.")
# Determine cluster assignment for each frame.
sequences = clusters.fit_transform(tica_coordinates)
np.save('%s/lag_%d_clusters_%d_sequences.npy' %(self.tICA_dir, tica_lagtime, n_clusters), sequences)
np.save('%s/lag_%d_clusters_%d_center.npy' %(self.tICA_dir, tica_lagtime, n_clusters),
clusters.cluster_centers_)
# Determine cluster populations, normalize the counts, and save as percentages for
# labeling if a cluster contains more than cluster_percentage_cutoff percent of the data.
# Finally, save normalized counts.
print("\nDetermining cluster populations...")
if not os.path.exists('%s/cluster_centers' % self.tICA_dir):
os.makedirs('%s/cluster_centers' % self.tICA_dir)
counts = np.array([len(np.where(np.concatenate(sequences)==i)[0]) for i in range(n_clusters)])
normalized_counts = counts/float(counts.sum())
percentages = [ i*100 for i in normalized_counts ]
population_labels = [ [i,"%.2f"%percentages[i]] for i in range(len(percentages)) if percentages[i] > cluster_percentage_cutoff ]
np.savetxt('%s/cluster_centers/populations.dat' % self.tICA_dir, normalized_counts)
# Plot all unique combinations of tICA components
print("\nPlotting tICA components with cluster centers...")
all_ticas = list(itertools.permutations(range(1,tica_components+1), 2))
for j in tqdm.tqdm(range(len(all_ticas))): # For each pair
if all_ticas[j][0] < all_ticas[j][1]:
plt.figure(j, figsize=(20,16))
plt.hexbin(eval("tica_"+str(all_ticas[j][0])), eval("tica_"+str(all_ticas[j][1])), bins='log')
x_centers = [clusters.cluster_centers_[i][all_ticas[j][0]-1] for i in range(len(clusters.cluster_centers_))]
y_centers = [clusters.cluster_centers_[i][all_ticas[j][1]-1] for i in range(len(clusters.cluster_centers_))]
high_pop_x_centers = [ x_centers[i] for i in range(len(x_centers)) if percentages[i] > cluster_percentage_cutoff ]
high_pop_y_centers = [ y_centers[i] for i in range(len(y_centers)) if percentages[i] > cluster_percentage_cutoff ]
plt.plot(x_centers, y_centers, color='y', linestyle="", marker="o")
plt.plot(eval("tica_"+str(all_ticas[j][0])+'[0]'), eval("tica_"+str(all_ticas[j][1])+'[0]'), color='k', marker='*',markersize=24)
plt.xlabel('tic'+str(all_ticas[j][0]))
plt.ylabel('tic'+str(all_ticas[j][1]))
plt.title(self.proj_num)
# Add labels for high-population cluster centers
for label, x, y in zip(population_labels, high_pop_x_centers, high_pop_y_centers):
plt.annotate(
label,
xy = (x, y), xytext = (-15, 15),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.savefig('%s/tica_' % (self.tICA_dir) +str(all_ticas[j][0])+'_'+str(all_ticas[j][1])+'.png')
plt.close()
###########################################################################
for filename in os.listdir(self.tICA_dir + '/cluster_centers'):
if filename.endswith('.pdb'):
os.remove(self.tICA_dir + '/cluster_centers/' + filename)
# Write out PDBs for each cluster center
print("Performing cluster analytics and saving center PDBs...\n")
runs, clones, gens = data.shape[0], data.shape[1], data.shape[2]
x, y, z = 0, 0, 0
for i in range(len(features)):
if i % clones == 0 and i != 0:
x += 1
if i % gens == 0:
y = 0
n_snapshots = len(clusters.distances_[i])
# Determine frames that are cluster centers
cluster_indices = np.arange(n_snapshots)[ (clusters.distances_[i] < 1e-6) ]
# Determine number of each cluster, correlates to populations.dat
cluster_labels = sequences[i][cluster_indices]
# Save each cluster center as a pdb
if list(cluster_indices): # load center-containing xtcs to check length
traj_cat = []
print('x: %d, y: %d, z: %d' % (x, y, z))
while True:
try:
traj = base_dir + 'PROJ%s/RUN%s/CLONE%s/results%s/traj_comp.xtc' % (self.proj_num, x, y, z)
traj_cat.append(md.load(traj, top=self.gro_file))
z += 1
except:
break
if len(traj_cat) > 0:
trajectory_file = md.join(traj_cat)
xtc_len = len(trajectory_file)
y += 1
z = 0
for j in range(len(cluster_indices)):
frames = range(xtc_len) # map the strided frame number back to xtc frame number
strided_frames = frames[equil_steps:][::stride]
xtc_frame = frames.index(strided_frames[cluster_indices[j]])
cluster_traj = trajectory_file[xtc_frame]
cluster_traj.save_pdb('%s/cluster_centers/state_%d_%.3f.pdb'%(self.tICA_dir, cluster_labels[j],percentages[cluster_labels[j]]))
if verbose:
print('Successfully saved PDB for cluster: %d, (rel.pop: %.3f)'%(cluster_labels[j],percentages[cluster_labels[j]]))
print('traj_file: %s (%d/%d)'%(trajectory_file,i,len(features)))
print('frame: %d (%d/%d centers from this trajectory)'%(cluster_indices[j],j,len(cluster_indices)))
print('strided: npy_frame/npy_len = %d/%d = %f'%(cluster_indices[j],n_snapshots,cluster_indices[j]/n_snapshots))
print('re-mapped: orig_frame/xtc_len = %d/%d = %f\n'%(xtc_frame,xtc_len,xtc_frame/xtc_len))
def calculate_tica_components():
print("Calculating tICA components...")
in_files = glob.glob("out*npy")
loaded_files = [ np.load(filename) for filename in in_files ]
tica = tICA(lag_time=tica_lagtime,
n_components=int(tica_components)).fit_transform(loaded_files)
np.save('lag_%d_comp_%d.npy' %(tica_lagtime, tica_components), tica)
tica_data = 'data_lag_%d_comp_%d' %(tica_lagtime, tica_components)
joblib.dump(tica, tica_data)
data = np.load('lag_%d_comp_%d.npy' %(tica_lagtime, tica_components))
for i in range(len(glob.glob('out*npy'))): # extract the four tICA components
for j in range(len(data[i])):
tica_1.append(data[i][j][0])
tica_2.append(data[i][j][1])
tica_3.append(data[i][j][2])
tica_4.append(data[i][j][3])
# Clustering via KCenters
if cluster_method == 'kcenters':
print("Clustering via KCenters...")
clusters = KCenters(n_clusters)
elif cluster_method == 'kmeans':
print("Clustering via KMeans...")
clusters = KMeans(n_clusters)
else:
sys.exit("Invalid cluster_method. Use kmeans or kcenters.")
sequences = clusters.fit_transform(tica)
np.save('lag_%d_clusters_%d_sequences.npy' %(tica_lagtime, n_clusters), sequences)
np.save('lag_%d_clusters_%d_center.npy' %(tica_lagtime, n_clusters),
clusters.cluster_centers_)
cluster_data = 'lag_%d_clusters_%d.pkl' %(tica_lagtime, n_clusters)
joblib.dump(sequences, cluster_data)
# Determining cluster populations
print("Determining cluster populations...")
counts = np.array([len(np.where(np.concatenate(sequences)==i)[0]) for i in range(n_clusters)]) # how many frames are in each cluster
normalized_counts = counts/float(counts.sum())
percentages = [ i*100 for i in normalized_counts ]
# Plotting the tICA components
print("Plotting tICA components with cluster centers...")
plt.figure(0) # plotting tica_1, tica_2
plt.hexbin(tica_1, tica_2, bins='log') #, cmap=cmaps.viridis
x_centers = [clusters.cluster_centers_[i][0] for i in range(len(clusters.cluster_centers_))]
y_centers = [clusters.cluster_centers_[i][1] for i in range(len(clusters.cluster_centers_))]
plt.plot(x_centers, y_centers, 'wo')
for label, x, y in zip(["%.4f"%i for i in percentages], x_centers, y_centers): # adds percentage contribution for each cluster
plt.annotate(
label,
xy = (x, y), xytext = (-20, 20),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.savefig('tica_1_2.png')
plt.figure(1) # plotting tica_1, tica_3
plt.hexbin(tica_1, tica_3, bins='log')
x_centers = [clusters.cluster_centers_[i][0] for i in range(len(clusters.cluster_centers_))]
y_centers = [clusters.cluster_centers_[i][2] for i in range(len(clusters.cluster_centers_))]
plt.plot(x_centers, y_centers, 'wo')
for label, x, y in zip([ "%.4f"%i for i in percentages], x_centers, y_centers):
plt.annotate(
label,
xy = (x, y), xytext = (-20, 20),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.savefig('tica_1_3.png')
plt.figure(2) # plotting tica_2, tica_3
plt.hexbin(tica_2, tica_3, bins='log')
x_centers = [clusters.cluster_centers_[j][1] for j in range(len(clusters.cluster_centers_))]
y_centers = [clusters.cluster_centers_[j][2] for j in range(len(clusters.cluster_centers_))]
plt.plot(x_centers, y_centers, 'wo')
for label, x, y in zip(["%.4f"%i for i in percentages], x_centers, y_centers):
plt.annotate(
label,
xy = (x, y), xytext = (-20, 20),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.savefig('tica_2_3.png')
# Determining cluster entropy ( this yields errors for me )
# print("Determining cluster entropy")
# cluster_entropy = (-1.0*normalized_counts*np.log(normalized_counts)).sum()
# np.savetxt('cluster_entropy.dat', cluster_entropy)
# Determining the cluster populations and writing out PDBs for cluster centers
print("Determining cluster populations...")
counts = np.array([len(np.where(np.concatenate(sequences)==i)[0]) for i in range(n_clusters)]) # how many frames are in each cluster
normalized_counts = counts/float(counts.sum())
np.savetxt('populations.dat', normalized_counts)
print("Performing cluster analytics and saving center PDBs...\n")
for i in range(len(glob.glob("traj*xtc"))):
n_snapshots = len(clusters.distances_[i])
cluster_indices = np.arange(n_snapshots)[ (clusters.distances_[i] < 1e-6) ] # frames that have centers
cluster_labels = sequences[i][cluster_indices] # number of cluster
if cluster_indices.size != 0: # print only the trajectories that have cluster centers
for j in range(len(cluster_labels)): # for each cluster center found in this trajectory
print('Cluster center', cluster_labels[j], 'was found in trajectory', str(i) + '.')
print('It is found on frame', cluster_indices[j], 'and has a relative population of',
"%.4f"%percentages[cluster_labels[j]], '%.')
xtcfile = sorted(glob.glob("traj*xtc"))[i]
for j in range(len(cluster_indices)): # actually saving the snapshots
cluster_traj = md.load_frame(xtcfile, cluster_indices[j], top='structure.gro')
cluster_traj.save_pdb('state_%d.pdb' %cluster_labels[j]+1)
# Calculating IPTs
print("\nCalculating Implied Timescales...")
timescales = implied_timescales(sequences, lagtimes, n_timescales=n_timescales,
msm=MarkovStateModel(verbose=False))
implied_timescale_data = 'ipt_lag_%d_clusters_%d.pkl' %(tica_lagtime, n_clusters)
joblib.dump(timescales, implied_timescale_data)
numpy_timescale_data = 'lag_%d_clusters_%d_timescales.npy' %(tica_lagtime, n_clusters)
np.savetxt('lagtimes.txt', lagtimes)
np.save(numpy_timescale_data, timescales)
# Plotting IPTs (lagtimes and timescales)
print("Plotting Implied Timescales...")
for i in range(n_timescales):
plt.figure(42)
plt.plot(lagtimes * time_step, timescales[:, i] * time_step, 'o-')
plt.yscale('log')
plt.xlabel('lagtime (ns)')
plt.ylabel('Implied timescales (ns)')
plt.savefig('lag_%d_clusters_%d_.png' %(tica_lagtime, n_clusters))
def compute_tica_components():
'''Load in the features, calculate a given number of tICA components (tica_components) given a
lagtime (lag_time), and save tICA coordinates and eigenvector data. It then creates and populates
a list for each desired component, clusters the data, saving normalized populations as populations.dat
and saving each cluster center as a .pdb. tICA plots are created and saved, and implied timescales are
calculated, saved, and plotted.
'''
verbose = False
save_pdb = True
color_by = 'cluster'
if verbose:
print("\nCalculating tICA components...")
if not os.path.exists(project_title + '/tica_%d'%n_clusters):
os.mkdir(project_title + '/tica_%d'%n_clusters)
# load in feature files and determine indices of unbiased ensembles
feature_files = []
for i in range(runs):
run_files = sorted(glob.glob(/features/' + "P*R%d_*npy"%i))
feature_files += run_files
if i in unbiased_runs:
unbiased_indices = [len(feature_files) - len(run_files),len(feature_files)]
features = [np.load(x) for x in feature_files]
# perform tICA calculation and extract score / eigenvectors
tica_coordinates = tICA(lag_time=tica_lagtime,
n_components=int(n_components)).fit_transform(features)
tica_components = tICA(lag_time=tica_lagtime,
n_components=int(n_components)).fit(features)
eigenvectors = np.transpose(tica_components.eigenvectors_)
tica_score = tica_components.score(features)
np.save('%s/tica_%d/tica_coords-lag_%d-comp_%d.npy' %(
project_title, n_clusters, tica_lagtime, n_components), tica_coordinates)
np.save('%s/tica_%d/tica_comps-lag_%d-comp_%d.npy' %(
project_title, n_clusters, tica_lagtime, n_components), tica_components)
# Perform clustering based on the cluster_method parameter.
if verbose:
print('Clustering via %s'%cluster_method)
if cluster_method == 'kcenters':
clusters = KCenters(n_clusters)
elif cluster_method == 'kmeans':
clusters = KMeans(n_clusters)
elif cluster_method == 'kmedoids':
clusters = KMedoids(n_clusters)
else:
sys.exit('Invalid cluster_method. Use kcenters/kmeans/kmedoids.')
# Cluster unbiased data and fit biased data to these centers
new_assignments = []
sequences = clusters.fit_transform(tica_coordinates[unbiased_indices[0]:unbiased_indices[1]])
for i in tqdm.tqdm_notebook(range(unbiased_indices[0])):
tica_traj = tica_coordinates[i]
if isinstance(tica_traj, np.ndarray):
if not (tica_traj.dtype == 'float32' or tica_traj.dtype == 'float64'):
tica_traj = tica_traj.astype('float64')
labels, inertia = msmbuilder.libdistance.assign_nearest(
tica_traj, clusters.cluster_centers_, metric='euclidean')
new_assignments.append(labels)
new_assignments += sequences # tack the unbiased assignments back on to the end.
np.save('%s/tica_%d/lag_%d_clusters_%d_assignments.npy' %(
project_title, n_clusters, tica_lagtime, n_clusters), new_assignments)
np.save('%s/tica_%d/lag_%d_clusters_%d_center.npy' %(
project_title, n_clusters, tica_lagtime, n_clusters), clusters.cluster_centers_)
# Determine cluster populations, normalize the counts, and save as percentages for
# labeling if a cluster contains more than cluster_percentage_cutoff percent of the data.
# Finally, save normalized counts.
if verbose:
print("\nDetermining cluster populations...")
if not os.path.exists('%s/tica_%d/%s_clusters'%(project_title,n_clusters,cluster_method)):
os.mkdir('%s/tica_%d/%s_clusters'%(project_title,n_clusters,cluster_method))
if not os.path.exists('%s/tica_%d/plots'%(project_title,n_clusters)):
os.mkdir('%s/tica_%d/plots'%(project_title,n_clusters))
counts = np.array([len(np.where(np.concatenate(sequences)==i)[0]) for i in range(n_clusters)])
normalized_counts = counts/float(counts.sum())
percentages = [ i*100 for i in normalized_counts ]
population_labels = [ [i,"%.2f"%percentages[i]] for i in range(len(percentages)) if percentages[i] > cluster_percentage_cutoff ]
np.savetxt('%s/tica_%d/%s_clusters/populations.dat'
%(project_title,n_clusters,cluster_method), normalized_counts)
# Plot all unique combinations of tICA components
if verbose:
print("\nPlotting tICA components...")
tica_coordinates = np.concatenate(tica_coordinates)
new_assignments = np.concatenate(new_assignments)
cluster_colors = matplotlib.cm.rainbow(np.linspace(0,1,n_clusters))
for j in tqdm.tqdm_notebook(range(len(all_ticas)),leave=False): # For each pair
if all_ticas[j][0] < all_ticas[j][1]:
plt.figure(j, figsize=(20,16))
tICx, tICy = all_ticas[j][0]-1, all_ticas[j][1]-1
plt.hexbin(tica_coordinates[:,tICx],tica_coordinates[:,tICy], bins='log')
for l in tqdm.tqdm(range(len(tica_coordinates))[::stride*2]):
if color_by == 'cluster':
plt.plot(tica_coordinates[l][tICx], tica_coordinates[l][tICy],
color=cluster_colors[new_assignments[l]], linestyle="", marker="o")
x_centers = [clusters.cluster_centers_[i][tICx] for i in range(len(clusters.cluster_centers_))]
y_centers = [clusters.cluster_centers_[i][tICy] for i in range(len(clusters.cluster_centers_))]
high_pop_x_centers = [ x_centers[i] for i in range(len(x_centers)) if percentages[i] > cluster_percentage_cutoff ]
high_pop_y_centers = [ y_centers[i] for i in range(len(y_centers)) if percentages[i] > cluster_percentage_cutoff ]
plt.plot(x_centers, y_centers, color='y', linestyle="", marker="o")
plt.plot(tica_coordinates[:,tICx][0],tica_coordinates[:,tICy][0], color='k', marker='*',markersize=24)
plt.xlabel('tIC'+str(all_ticas[j][0]))
plt.ylabel('tIC'+str(all_ticas[j][1]))
plt.title(project_title)
# Add labels for high-population cluster centers
for label, x, y in zip(population_labels, high_pop_x_centers, high_pop_y_centers):
plt.annotate(
label,
xy = (x, y), xytext = (-15, 15),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.savefig('%s/tica_%d/plots/tica_%d_%d.png'%(project_title,n_clusters,
all_ticas[j][0], all_ticas[j][1]))
plt.close()
# Write out PDBs for each cluster center
if verbose:
print("Performing cluster analytics and saving center PDBs...\n")
if save_pdb:
trajectory_files, feature_files, cluster_features = [],[],[]
for run in range(runs): # get only xtc files that correlate to cluster-center features
trajectory_files += [re.sub('features',
'traj_data/RUN%d'%run,re.sub('npy','xtc',x)
) for x in sorted(glob.glob('%s/features/*R%d_*npy'%(
project_title,run)))]
feature_files += sorted(glob.glob('%s/features/*R%d_*npy'%(project_title,run)))
for i in tqdm.tqdm_notebook(range(len(trajectory_files)),leave=False):
n_snapshots = len(clusters.distances_[i])
# Determine frames that are cluster centers
cluster_indices = np.arange(n_snapshots)[ (clusters.distances_[i] < 1e-6) ]
# Determine number of each cluster, correlates to populations.dat
cluster_labels = sequences[i][cluster_indices]
# Save each cluster center as a pdb
if list(cluster_indices): # load center-containing xtcs to check length
xtc_len = len(md.load(trajectory_files[i],top=structure_file))
# map strided frame number back to xtc frame number
for j in range(len(cluster_indices)):
frames = range(xtc_len)
strided_frames = frames[equil_steps:][::stride]
xtc_frame = frames.index(strided_frames[cluster_indices[j]])
cluster_traj = md.load_frame(trajectory_files[i], xtc_frame,
top=structure_file)
cluster_features.append(np.load(feature_files[i])[cluster_indices[j]])
cluster_traj.save_pdb('%s/tica_%d/%s_clusters/state_%d.pdb'
%(project_title,n_clusters,cluster_method,
cluster_labels[j]))
# save cluster information
with open('%s/tica_%d/cluster.dat'%(project_title,n_clusters),'w') as f:
f.write('\nSuccessfully saved PDB for cluster: %d, (rel.pop: %.3f)'%(
cluster_labels[j],percentages[cluster_labels[j]]))
f.write('traj_file: %s (%d/%d)'%(trajectory_files[i],i,len(features)))
f.write('frame: %d (%d/%d centers from this trajectory)'%(
cluster_indices[j],j,len(cluster_indices)))
f.write('strided: npy_frame/npy_len = %d/%d = %f'%(
cluster_indices[j],n_snapshots,cluster_indices[j]/n_snapshots))
f.write('re-mapped: orig_frame/xtc_len = %d/%d = %f\n'%(
xtc_frame,xtc_len,xtc_frame/xtc_len))
f.close()
# save features corresponding to each cluster center
np.save('%s/tica_%d/cluster_features.npy'%(project_title,n_clusters),cluster_features)
return tica_score
