`scipy.cluster`

. The idea for the algorithim is that we'll cluster every N-th frame directly, and then, considering the clusters fixed "assign" the remaining frames to the nearest cluster. It's not the most sophisticated algorithm, but it's a good demonstration of how MDTraj can be integrated with other data science tools.

```
In [ ]:
```from __future__ import print_function
import random
from collections import defaultdict
import mdtraj as md
import numpy as np
import scipy.cluster.hierarchy

```
In [ ]:
```stride = 5
subsampled = md.load('ala2.h5', stride=stride)
print(subsampled)

```
In [ ]:
```distances = np.empty((subsampled.n_frames, subsampled.n_frames))
for i in range(subsampled.n_frames):
distances[i] = md.rmsd(subsampled, subsampled, i)

Now that we have the distances, we can use out favorite clustering algorithm. I like ward.

```
In [ ]:
```n_clusters = 3
linkage = scipy.cluster.hierarchy.ward(distances)
labels = scipy.cluster.hierarchy.fcluster(linkage, t=n_clusters, criterion='maxclust')
labels

```
In [ ]:
```mapping = defaultdict(lambda : [])
for i, label in enumerate(labels):
mapping[label].append(i)
mapping

```
In [ ]:
```Now we can iterate through the mapping and select n_leaders random
samples from each list. As we select them, we'll extract the
conformation and append it to a new trajectory called `leaders`, which
will start empty.

```
In [ ]:
```n_leaders_per_cluster = 2
leaders = md.Trajectory(xyz=np.empty((0, subsampled.n_atoms, 3)),
topology=subsampled.topology)
leader_labels = []
for label, indices in mapping.items():
leaders = leaders.join(subsampled[np.random.choice(indices, n_leaders_per_cluster)])
leader_labels.extend([label] * n_leaders_per_cluster)
print(leaders)
print(leader_labels)

Now our `leaders`

trajectory contains a set of representitive conformations
for each cluster. Here comes the second pass of the two-pass clustering.
Let's now consider these clusters as fixed objects and iterate through
*every* frame in our data set, assigning each frame to the cluster
it's closest to. We take the simple approach here of computing the distance
from each frame to each leader and assigning the frame to the cluster whose
leader is closest.

Note that this whole algorithm never requires having the entire dataset in memory at once

```
In [ ]:
```labels = []
for frame in md.iterload('ala2.h5', chunk=1):
labels.append(leader_labels[np.argmin(md.rmsd(leaders, frame, 0))])
labels = np.array(labels)
print(labels)
print(labels.shape)

```
In [ ]:
```