<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">from msmbuilder import Project
from euclid import utils
from euclid import metrics
from euclid import clustering
import numpy as np
import matplotlib.pyplot as pp
import scipy
import sys
from time import time
print ''

#proj = Project.Project.LoadFromHDF('/Users/rmcgibbo/msmbuilder/tutorial/ProjectInfo.h5')
proj = Project.Project.LoadFromHDF('/Users/rmcgibbo/Desktop/msmb_tutorial/ProjectInfo.h5')
trajs = [proj.LoadTraj(i) for i in range(proj['NumTrajs'])]
#atomindices = np.loadtxt('/Users/rmcgibbo/msmbuilder/tutorial/AtomIndices.dat', int)
atomindices = np.loadtxt('/Users/rmcgibbo/Desktop/msmb_tutorial/AtomIndices.dat', int)
trajs = [trajs[i] for i in range(30)]


atomindices = range(22)
rmsd = metrics.RMSD(atomindices, omp_parallel=True)
dihedral = metrics.Dihedral(metric='cityblock', angles='phi')
contact = metrics.ContinuousContact(metric='cityblock', contacts='all')
contact2 = metrics.BooleanContact(metric='matching', contacts='all')




#contacts = [[1,2], [7,10], [12,13]]
#metric = metrics.ContinuousContact(metric='cityblock', contacts=contacts)
#clustering.KCenters(metric, trajs, k=16).get_assignments()




# use fewer trajs
#trajs = [trajs[i] for i in range(10)]

print 'start heir'
a = clustering.Hierarchical(contact, trajs, method='ward').get_assignments(k=10)
print np.unique(a)
print 'end hier, start kcenters'
a = clustering.KCenters(contact, trajs, k=10).get_assignments()
print np.unique(a)
print 'end kcenters, start clarans'
t = clustering.Clarans(dihedral, trajs, k=10, num_local_minima=3, max_neighbors=5).get_assignments()
del t
print 'end clarans, start clarans2'
clar = clustering.Clarans(contact, trajs, k=10, num_local_minima=3, max_neighbors=5).get_distances()
del clar
print 'end clarans2, start clarans3'
clar = clustering.Clarans(contact2, trajs, k=10, num_local_minima=3, max_neighbors=5)
print 'end clarans3, start subsampled clarans'
ssc = clustering.SubsampledClarans(dihedral, trajs, k=10, num_samples=5, shrink_multiple=5, num_local_minima=5, max_neighbors=5).get_distances()
print 'end subsampled clarans, start hybrid kmedoids'
clustering.HybridKMedoids(dihedral, trajs, k=10, ignore_max_objective=True, norm_exponent=1)
print 'end hybrid kmedoids'
#pp.hist(np.ma.masked_less(np.ravel(ssc), 0.0), label='ssc', alpha=0.5, bins=50)
#pp.hist(1+np.ma.masked_less(np.ravel(clar), 0.0), label='clar', alpha=0.5, bins=50)
#pp.legend()
#pp.show()
#pp.savefig('hist.png')
#"""



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