from msmbuilder import Project
import os, sys
import numpy as np
import matplotlib.pyplot as pp
sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)), '../lib'))

import distance_metrics
import clustering
from time import time

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/Desktop/msmb_tutorial/AtomIndices.dat', int)

atomindices = range(22)
rmsd = distance_metrics.RMSDMetric(atomindices, omp_parallel=True)
dihedral = distance_metrics.DihedralMetric(metric='cityblock', angles='phi')
contact = distance_metrics.ContinuousContactMetric(metric='cityblock', contacts='all')

traj = clustering.concatenate_trajectories(trajs)
ptraj = dihedral.prepare_trajectory(traj)
#print ptraj.dtype
#print dihedral.one_to_all(ptraj, ptraj, 1)
#sys.exit(1)
#dihedral.one_to_all(ptraj, ptraj, 1)

#import cProfile
def go(i):
    for k in range(i):
        dihedral.one_to_all(ptraj, ptraj, 1), range(i)
#cProfile.run('go(1000)')
print 'go\n'
go(100000)
#clustering.Clarans(dihedral, trajs, k=10, num_local_minima=5, max_neighbors=5).get_distances()