def test_AlphaFeaturizer_describe_features():
feat = AlphaAngleFeaturizer()
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
feature_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
if feat.sincos:
func = getattr(np, '%s' % df.iloc[f_index].otherinfo)
feature_value = func(feature_value)
assert (features[0][:, f_index] == feature_value.flatten()).all()
def test_AlphaFeaturizer_describe_features():
feat = AlphaAngleFeaturizer()
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
feature_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
if feat.sincos:
func = getattr(np, '%s' % df.iloc[f_index].otherinfo)
feature_value = func(feature_value)
assert (features[0][:, f_index] == feature_value.flatten()).all()
def _featurizer(self, **kwargs):
return AlphaAngleFeaturizer(sincos=False)