def test_kappa_angle_featurizer_2():
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
feat = KappaAngleFeaturizer(offset=2)
df = pd.DataFrame(feat.describe_features(trajectories[0]))
assert not sorted(df.resids[0]) == [0,1,2]
assert sorted(df.resids[0]) == [0,2,4]
def test_kappa_angle_featurizer_1():
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
feat = KappaAngleFeaturizer(offset=1)
df = pd.DataFrame(feat.describe_features(trajectories[0]))
assert sorted(df.resids[0]) == [0,1,2]
cas = [i.index for i in top.atoms if i.name=='CA']
assert sorted(df.atominds[0]) == cas[:3]
def test_angle_featurizer():
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
feat = KappaAngleFeaturizer(offset=2)
feat_1 = feat.transform([trajectories[0]])
df = pd.DataFrame(feat.describe_features(trajectories[0]))
atom_inds = np.vstack(df.atominds)
feat = AngleFeaturizer(angle_indices=atom_inds)
feat_2 = feat.transform([trajectories[0]])
assert np.all(feat_1[0] == feat_2[0])
def build_dataset():
trajs = MetEnkephalin().get().trajectories
pairs = []
for i in range(trajs[0].n_atoms):
for j in range(i):
pairs.append((i, j))
np.random.seed(0)
np.random.shuffle(pairs)
n_pairs = 200
return AtomPairsFeaturizer(pairs[:n_pairs]).transform(
[traj[::10] for traj in trajs])
def load_met():
from msmbuilder.example_datasets import MetEnkephalin
print(type(MetEnkephalin))
trajs = MetEnkephalin().get().trajectories
from msmbuilder.featurizer import AtomPairsFeaturizer
pairs = []
for i in range(75):
for j in range(i):
pairs.append((j,i))
X = AtomPairsFeaturizer(pairs).fit_transform(trajs)
from msmbuilder.featurizer import DihedralFeaturizer
Y = DihedralFeaturizer().fit_transform(trajs)
return X, Y
def test_common_contacts_featurizer_1():
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
met_seq = top.to_fasta(0)
#fake sequence has an insertion
fake_met_eq ='YGGFMF'
alignment={}
#do "alignment "
alignment["actual"] = met_seq+"-"
alignment["fake"] = fake_met_eq
feat = CommonContactFeaturizer(alignment=alignment, contacts='all',
same_residue=True)
rnd_traj = np.random.randint(len(trajectories))
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
features = feat.transform([trajectories[rnd_traj]])
def test_common_contacts_featurizer_1():
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
met_seq = top.to_fasta(0)
# fake sequence has an insertion
fake_met_eq = 'YGGFMF'
alignment = {}
# do "alignment "
alignment["actual"] = met_seq + "-"
alignment["fake"] = fake_met_eq
max_len = max([len(alignment[i]) for i in alignment.keys()])
contacts = [i for i in itertools.combinations(np.arange(max_len), 2)]
feat = CommonContactFeaturizer(alignment=alignment, contacts=contacts,
same_residue=True)
rnd_traj = np.random.randint(len(trajectories))
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
features = feat.transform([trajectories[rnd_traj]])
def test_common_contacts_featurizer_2():
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
met_seq = top.to_fasta(0)
#fake sequence
fake_met_eq ='FGGFM'
alignment={}
#do "alignment "
alignment["actual"] = met_seq
alignment["fake"] = fake_met_eq
feat = CommonContactFeaturizer(alignment=alignment, contacts='all',
same_residue=True)
rnd_traj = np.random.randint(len(trajectories))
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
assert(np.all([j!=0 for i in df.resids for j in i]))
def test_common_contacts_featurizer_2():
#test randomly mutates one of the residues to make sure that residues contacts are not
#included
trajectories = MetEnkephalin().get_cached().trajectories
top = trajectories[0].topology
met_seq = top.to_fasta(0)
#randomly "mutate one of the residues to alanine
rnd_loc = np.random.randint(len(met_seq))
fake_met_eq=met_seq[:rnd_loc]+"A"+met_seq[rnd_loc+1:]
alignment={}
#do "alignment "
alignment["actual"] = met_seq
alignment["fake"] = fake_met_eq
feat = CommonContactFeaturizer(alignment=alignment, contacts='all',
same_residue=True)
rnd_traj = np.random.randint(len(trajectories))
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
assert(np.all([j!=rnd_loc for i in df.resids for j in i]))