def test_count_contacts_per_cluster_and_residue(self):
contacts_per_cluster, _ = histogram.parse_contacts(TestDataManaging.small_contacts_file_name)
filtered_labels = ['266:GLY:A', '395:ASP:A', '442:SER:A', '528:SER:A']
ordered_clusters = ["cluster_0",
"cluster_1",
"cluster_2",
"cluster_3",
"cluster_4",
"cluster_5",
"cluster_6",
"cluster_7",
"cluster_8"]
data = count_contacts_per_cluster_and_residue(
contacts_per_cluster,
ordered_clusters,
filtered_labels,
1.,
False)
correct_data = [[ 0., 7., 7., 0.],
[ 10., 0., 2., 0.],
[ 0., 4., 0., 3.],
[ 0., 0., 0., 6.],
[ 0., 6., 0., 4.],
[ 0., 0., 6., 0.],
[ 1., 0., 0., 4.],
[ 11., 0., 0., 1.],
[ 0., 0., 0., 0.]]
numpy.testing.assert_equal(correct_data, data)
def test_load_data(self):
contacts_per_cluster, contacts_per_residue = histogram.parse_contacts(TestDataManaging.small_contacts_file_name)
number_of_items = {
"cluster_4": 16,
"cluster_2": 16,
"cluster_7": 16,
"cluster_5": 6,
"cluster_6": 16,
"cluster_0": 16,
"cluster_1": 16,
"cluster_3": 16,
"cluster_8": 6
}
for cluster_id in contacts_per_cluster:
self.assertEqual(number_of_items[cluster_id], len(contacts_per_cluster[cluster_id]),"%s is suspicious"%cluster_id)
correct_contacts_per_residue = {'181:TYR:A': 4, '395:ASP:A': 17, '421:LYS:A': 7, '268:ASP:A': 5,
'442:SER:A': 15, '461:ARG:A': 1, '337:TYR:A': 6, '333:GLN:A': 8,
'528:SER:A': 18, '340:ASN:A': 13, '266:GLY:A': 22, '527:TRP:A': 3, '329:ARG:A': 5}
self.assertDictEqual(correct_contacts_per_residue, contacts_per_residue)
def test_calc_prot_drug_contacts_per_motif(self):
contacts_per_cluster, _ = histogram.parse_contacts(TestDataManaging.small_contacts_file_name)
motifs = parse_motifs(os.path.join(Test.__path__[0], "data", "motifs.txt"))
ordered_motifs = ['F/I-II', 'A/IV', 'B/V', 'C/VI', 'D', 'E/VII', 'Priming Loop']
prot_drug_contacts_per_motif = calc_contacts_per_cluster_per_motif(contacts_per_cluster,
motifs["WNV"],
ordered_motifs,
1.)
correct_prot_drug_contacts_per_motif = {
'cluster_8': {'F/I-II': 4.0, 'B/V': 2.0},
'cluster_6': {'A/IV': 2.0, 'B/V': 4.0, 'Priming Loop': 4.0},
'cluster_7': {'A/IV': 15.0, 'Priming Loop': 1.0},
'cluster_4': {'C/VI': 6.0, 'Priming Loop': 4.0, 'B/V': 6.0},
'cluster_5': {'E/VII': 6.0},
'cluster_2': {'C/VI': 4.0, 'D': 5.0, 'Priming Loop': 3.0},
'cluster_3': {'Priming Loop': 6.0, 'B/V': 7.0},
'cluster_0': {'C/VI': 7.0, 'E/VII': 7.0, 'Priming Loop': 2.0},
'cluster_1': {'E/VII': 2.0, 'A/IV': 10.0, 'Priming Loop': 1.0, 'D': 2.0}
}
self.assertDictEqual(correct_prot_drug_contacts_per_motif, prot_drug_contacts_per_motif)
correct_contacts_per_motif = {
'F/I-II': 4.,
'A/IV': 27.,
'B/V': 19.,
'C/VI': 17.,
'D': 7.,
'E/VII': 15.,
'Priming Loop': 21.
}
contacts_per_motif = calc_contacts_per_motif(correct_prot_drug_contacts_per_motif, ordered_motifs)
self.assertDictEqual(correct_contacts_per_motif,contacts_per_motif)
def test_filter(self):
_, contacts_per_residue = histogram.parse_contacts(TestDataManaging.small_contacts_file_name)
ordered_labels = ['181:TYR:A', '266:GLY:A', '268:ASP:A', '329:ARG:A', '333:GLN:A', '337:TYR:A', '340:ASN:A', '395:ASP:A', '421:LYS:A', '442:SER:A', '461:ARG:A', '527:TRP:A', '528:SER:A']
filtered_labels = filter_less_contacts_than( 15, ordered_labels, contacts_per_residue)
self.assertItemsEqual(['266:GLY:A', '395:ASP:A', '442:SER:A', '528:SER:A'], filtered_labels)
def test_get_labels(self):
contacts_per_cluster, contacts_per_residue = histogram.parse_contacts(TestDataManaging.small_contacts_file_name)
labels = get_labels(contacts_per_cluster)
self.assertItemsEqual(labels, contacts_per_residue.keys())
ordered_labels = ['181:TYR:A', '266:GLY:A', '268:ASP:A', '329:ARG:A', '333:GLN:A', '337:TYR:A', '340:ASN:A', '395:ASP:A', '421:LYS:A', '442:SER:A', '461:ARG:A', '527:TRP:A', '528:SER:A']
self.assertSequenceEqual(ordered_labels, labels)