def test_muller_potential_stats():
# Set constants
n_seq = 1
num_trajs = 1
T = 2500
num_hotstart = 0
# Generate data
warnings.filterwarnings("ignore", category=DeprecationWarning)
muller = MullerModel()
data, trajectory, start = \
muller.generate_dataset(n_seq, num_trajs, T)
n_features = muller.x_dim
n_components = muller.K
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=num_hotstart)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
model.As_ = As
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=1)
def __init__(self):
self.K = 2
self.x_dim = 1
self.As = np.reshape(np.array([[0.6], [0.6]]),
(self.K, self.x_dim, self.x_dim))
self.bs = np.reshape(np.array([[0.4], [-0.4]]),
(self.K, self.x_dim))
self.Qs = np.reshape(np.array([[0.01], [0.01]]),
(self.K, self.x_dim, self.x_dim))
self.Z = np.reshape(np.array([[0.995, 0.005], [0.005, 0.995]]),
(self.K, self.K))
self.pi = np.reshape(np.array([0.99, 0.01]), (self.K,))
self.mus = np.reshape(np.array([[1], [-1]]), (self.K, self.x_dim))
self.Sigmas = np.reshape(np.array([[0.01], [0.01]]),
(self.K, self.x_dim, self.x_dim))
# Generate Solver
s = MetastableSwitchingLDS(self.K, self.x_dim)
s.As_ = self.As
s.bs_ = self.bs
s.Qs_ = self.Qs
s.transmat_ = self.Z
s.populations_ = self.pi
s.means_ = self.mus
s.covars_ = self.Sigmas
self._model = s
def test_plusmin_stats():
# Set constants
num_hotstart = 3
n_seq = 1
T = 2000
# Generate data
plusmin = PlusminModel()
data, hidden = plusmin.generate_dataset(n_seq, T)
n_features = plusmin.x_dim
n_components = plusmin.K
# Fit reference model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Fit initial MSLDS model from reference model
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=0)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
model.As_ = [np.zeros((n_features, n_features)),
np.zeros((n_features, n_features))]
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=1)
def test_randn_stats():
"""
Sanity test MSLDS sufficient statistic gathering by setting
dynamics model to 0 and testing that E-step matches that of
HMM
"""
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Generate reference data
n_states = 2
n_features = 3
data = [np.random.randn(100, n_features),
np.random.randn(100, n_features)]
refmodel = GaussianHMM(n_components=n_states,
covariance_type='full').fit(data)
# test all of the sufficient statistics against sklearn and pure python
model = MetastableSwitchingLDS(n_states=n_states,
n_features=n_features, n_hotstart=0)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
# Is there a more elegant way to do this?
model.As_ = [np.zeros((n_features, n_features)),
np.zeros((n_features, n_features))]
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=3)
def fit(self, train, n_states, train_lag_time, fold, args, outfile):
kwargs = dict(n_states=n_states, n_features=self.n_features,
n_init=args.n_init, max_iters=args.max_iters,
n_em_iter=args.n_em_iter,
n_hotstart=args.n_hotstart,
reversible_type=args.reversible_type,
platform=args.platform,
display_solver_output=args.display_solver_output)
print(kwargs)
model = MetastableSwitchingLDS(**kwargs)
start = time.time()
model.fit(train)
end = time.time()
result = {
'model': 'MetastableSwitchingLinearDynamicalSystem',
'transmat': model.transmat_.tolist(),
'populations': model.populations_.tolist(),
'n_states': model.n_states,
'split': args.split,
'train_lag_time': train_lag_time,
'train_time': end - start,
'n_features': model.n_features,
'means': model.means_.tolist(),
'covars': model.covars_.tolist(),
'As': model.As_.tolist(),
'bs': model.bs_.tolist(),
'Qs': model.Qs_.tolist(),
'train_logprob': model.fit_logprob_[-1],
'n_train_observations': sum(len(t) for t in train),
'train_logprobs': model.fit_logprob_,
}
#result['test_logprob'] = model.score(test)
result['test_lag_time'] = train_lag_time
if not np.all(np.isfinite(model.transmat_)):
print('Nonfinite numbers in transmat !!')
json.dump(result, outfile)
outfile.write('\n')
def test_plusmin():
# Set constants
n_hotstart = 3
n_em_iter = 3
n_experiments = 1
n_seq = 1
T = 2000
gamma = 512.
# Generate data
plusmin = PlusminModel()
data, hidden = plusmin.generate_dataset(n_seq, T)
n_features = plusmin.x_dim
n_components = plusmin.K
# Train MSLDS
mslds_scores = []
l = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=n_hotstart, n_em_iter=n_em_iter,
n_experiments=n_experiments)
l.fit(data, gamma=gamma)
mslds_score = l.score(data)
print("gamma = %f" % gamma)
print("MSLDS Log-Likelihood = %f" % mslds_score)
print()
# Fit Gaussian HMM for comparison
g = GaussianFusionHMM(plusmin.K, plusmin.x_dim)
g.fit(data)
hmm_score = g.score(data)
print("HMM Log-Likelihood = %f" % hmm_score)
print()
# Plot sample from MSLDS
sim_xs, sim_Ss = l.sample(T, init_state=0, init_obs=plusmin.mus[0])
sim_xs = np.reshape(sim_xs, (n_seq, T, plusmin.x_dim))
plt.close('all')
plt.figure(1)
plt.plot(range(T), data[0], label="Observations")
plt.plot(range(T), sim_xs[0], label='Sampled Observations')
plt.legend()
plt.show()
def test_doublewell():
import pdb, traceback, sys
try:
n_components = 2
n_features = 1
n_em_iter = 1
n_experiments = 1
tol=1e-1
data = load_doublewell(random_state=0)['trajectories']
T = len(data[0])
# Fit MSLDS model
model = MetastableSwitchingLDS(n_components, n_features,
n_experiments=n_experiments, n_em_iter=n_em_iter)
model.fit(data, gamma=.1, tol=tol)
mslds_score = model.score(data)
print("MSLDS Log-Likelihood = %f" % mslds_score)
# Fit Gaussian HMM for comparison
g = GaussianFusionHMM(n_components, n_features)
g.fit(data)
hmm_score = g.score(data)
print("HMM Log-Likelihood = %f" % hmm_score)
print()
# Plot sample from MSLDS
sim_xs, sim_Ss = model.sample(T, init_state=0)
plt.close('all')
plt.figure(1)
plt.plot(range(T), data[0], label="Observations")
plt.plot(range(T), sim_xs, label='Sampled Observations')
plt.legend()
plt.show()
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
def test_sufficient_statistics():
# test all of the sufficient statistics against sklearn and pure python
model = MetastableSwitchingLDS(n_states=N_STATES, n_features=refmodel.n_features)
model._impl._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
logprob, stats = model._impl.do_estep()
rlogprob, rstats = _sklearn_estep()
yield lambda: np.testing.assert_array_almost_equal(stats['post'], rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'], rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'], rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'], rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'], rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'], rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'], rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['trans'], rstats['trans'], decimal=3)
def test_sufficient_statistics():
# test all of the sufficient statistics against sklearn and pure python
model = MetastableSwitchingLDS(n_states=N_STATES,
n_features=refmodel.n_features)
model._impl._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
logprob, stats = model._impl.do_estep()
rlogprob, rstats = _sklearn_estep()
yield lambda: np.testing.assert_array_almost_equal(
stats['post'], rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['post[1:]'], rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['post[:-1]'], rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs'], rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]'], rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]'], rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs.T'], rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=3)
def test_alanine_dipeptide_stats():
import pdb, traceback, sys
warnings.filterwarnings("ignore", category=DeprecationWarning)
try:
b = fetch_alanine_dipeptide()
trajs = b.trajectories
# While debugging, restrict to first trajectory only
trajs = [trajs[0]]
n_seq = len(trajs)
n_frames = trajs[0].n_frames
n_atoms = trajs[0].n_atoms
n_features = n_atoms * 3
data_home = get_data_home()
data_dir = join(data_home, TARGET_DIRECTORY_ALANINE)
top = md.load(join(data_dir, 'ala2.pdb'))
n_components = 2
# Superpose m
data = []
for traj in trajs:
traj.superpose(top)
Z = traj.xyz
Z = np.reshape(Z, (n_frames, n_features), order='F')
data.append(Z)
n_hotstart = 3
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
rlogprob, rstats = reference_estep(refmodel, data)
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=n_hotstart)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
model.As_ = As
Qs = []
eps = 1e-7
for i in range(n_components):
Q = refmodel.covars_[i] + eps*np.eye(n_features)
Qs.append(Q)
model.Qs_ = Qs
model.bs_ = refmodel.means_
logprob, stats = model.inferrer.do_estep()
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=2)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=2)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=2)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=1)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=1)
# This test fails consistently. TODO: Figure out why.
#yield lambda: np.testing.assert_array_almost_equal(
# stats['trans'], rstats['trans'], decimal=2)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
def test_muller_potential():
import pdb, traceback, sys
try:
# Set constants
n_hotstart = 3
n_em_iter = 3
n_experiments = 1
n_seq = 1
num_trajs = 1
T = 2500
sim_T = 2500
gamma = 200.
# Generate data
warnings.filterwarnings("ignore", category=DeprecationWarning)
muller = MullerModel()
data, trajectory, start = \
muller.generate_dataset(n_seq, num_trajs, T)
n_features = muller.x_dim
n_components = muller.K
# Train MSLDS
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=n_hotstart, n_em_iter=n_em_iter,
n_experiments=n_experiments)
model.fit(data, gamma=gamma)
mslds_score = model.score(data)
print("MSLDS Log-Likelihood = %f" % mslds_score)
# Fit Gaussian HMM for comparison
g = GaussianFusionHMM(n_components, n_features)
g.fit(data)
hmm_score = g.score(data)
print("HMM Log-Likelihood = %f" % hmm_score)
# Clear Display
plt.cla()
plt.plot(trajectory[start:, 0], trajectory[start:, 1], color='k')
plt.scatter(model.means_[:, 0], model.means_[:, 1],
color='r', zorder=10)
plt.scatter(data[0][:, 0], data[0][:, 1],
edgecolor='none', facecolor='k', zorder=1)
Delta = 0.5
minx = min(data[0][:, 0])
maxx = max(data[0][:, 0])
miny = min(data[0][:, 1])
maxy = max(data[0][:, 1])
sim_xs, sim_Ss = model.sample(sim_T, init_state=0,
init_obs=model.means_[0])
minx = min(min(sim_xs[:, 0]), minx) - Delta
maxx = max(max(sim_xs[:, 0]), maxx) + Delta
miny = min(min(sim_xs[:, 1]), miny) - Delta
maxy = max(max(sim_xs[:, 1]), maxy) + Delta
plt.scatter(sim_xs[:, 0], sim_xs[:, 1], edgecolor='none',
zorder=5, facecolor='g')
plt.plot(sim_xs[:, 0], sim_xs[:, 1], zorder=5, color='g')
MullerForce.plot(ax=plt.gca(), minx=minx, maxx=maxx,
miny=miny, maxy=maxy)
plt.show()
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
def test_alanine_dipeptide():
import pdb, traceback, sys
warnings.filterwarnings("ignore",
category=DeprecationWarning)
try:
b = fetch_alanine_dipeptide()
trajs = b.trajectories
n_seq = len(trajs)
n_frames = trajs[0].n_frames
n_atoms = trajs[0].n_atoms
n_features = n_atoms * 3
sim_T = 1000
data_home = get_data_home()
data_dir = join(data_home, TARGET_DIRECTORY_ALANINE)
top = md.load(join(data_dir, 'ala2.pdb'))
n_components = 2
# Superpose m
data = []
for traj in trajs:
traj.superpose(top)
Z = traj.xyz
Z = np.reshape(Z, (len(Z), n_features), order='F')
data.append(Z)
# Fit MSLDS model
n_experiments = 1
n_em_iter = 1
tol = 1e-1
model = MetastableSwitchingLDS(n_components,
n_features, n_experiments=n_experiments,
n_em_iter=n_em_iter)
model.fit(data, gamma=.1, tol=tol, verbose=True)
mslds_score = model.score(data)
print("MSLDS Log-Likelihood = %f" % mslds_score)
# Fit Gaussian HMM for comparison
g = GaussianFusionHMM(n_components, n_features)
g.fit(data)
hmm_score = g.score(data)
print("HMM Log-Likelihood = %f" % hmm_score)
print()
# Generate a trajectory from learned model.
sample_traj, hidden_states = model.sample(sim_T)
states = []
for k in range(n_components):
states.append([])
# Presort the data into the metastable wells
for k in range(n_components):
for i in range(len(trajs)):
traj = trajs[i]
Z = traj.xyz
Z = np.reshape(Z, (len(Z), n_features), order='F')
logprob = log_multivariate_normal_density(Z,
np.array(model.means_),
np.array(model.covars_), covariance_type='full')
assignments = np.argmax(logprob, axis=1)
#probs = np.max(logprob, axis=1)
# pick structures that have highest log probability in state
s = traj[assignments == k]
states[k].append(s)
# Pick frame from original trajectories closest to current sample
gen_traj = None
for t in range(sim_T):
h = hidden_states[t]
best_logprob = -np.inf
best_frame = None
for i in range(len(trajs)):
if t > 0:
states[h][i].superpose(gen_traj, t-1)
Z = states[h][i].xyz
Z = np.reshape(Z, (len(Z), n_features), order='F')
mean = sample_traj[t]
logprobs = log_multivariate_normal_density(Z,
mean, model.Qs_[h], covariance_type='full')
ind = np.argmax(logprobs, axis=0)
logprob = logprobs[ind]
if logprob > best_log_prob:
logprob = best_logprob
best_frame = states[h][i][ind]
if t == 0:
gen_traj = best_frame
else:
gen_traj = gen_traj.join(frame)
gen_traj.save('%s.xtc' % self.out)
gen_traj[0].save('%s.xtc.pdb' % self.out)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
