# coding: utf-8

# In this example, we cluster our alanine dipeptide trajectory using the [RMSD distance metric](http://en.wikipedia.org/wiki/Root-mean-square_deviation_of_atomic_positions) and [Ward's method](http://en.wikipedia.org/wiki/Ward's_method).

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from __future__ import print_function
get_ipython().magic(u'matplotlib inline')
import mdtraj as md
import numpy as np
import matplotlib.pyplot as plt
import scipy.cluster.hierarchyfrom scipy.spatial.distance import squareform


# Let's load up our trajectory. This is the trajectory that we generated in the "Running a simulation in OpenMM and analyzing the results with mdtraj" example. The first step is to build the rmsd cache, which precalculates some values for the RMSD computation.

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traj = md.load('ala2.h5')


# Lets compute all pairwise rmsds between conformations.

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distances = np.empty((traj.n_frames, traj.n_frames))
for i in range(traj.n_frames):
    distances[i] = md.rmsd(traj, traj, i)
print('Max pairwise rmsd: %f nm' % np.max(distances))


# `scipy.cluster` implements the ward linkage algorithm (among others)

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linkage = scipy.cluster.hierarchy.ward(squareform(distances))


# Lets plot the resulting dendrogram.

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plt.title('RMSD Ward hierarchical clustering')
scipy.cluster.hierarchy.dendrogram(linkage, no_labels=True, count_sort='descendent')
None


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