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]))