def test(self):
reader = source(self.trajfiles, top=self.topfile)
pcat = pca(dim=2)
n_clusters = 2
clustering = UniformTimeClustering(n_clusters=n_clusters)
D = Discretizer(reader, transform=pcat, cluster=clustering)
D.parametrize()
self.assertEqual(len(D.dtrajs), len(self.trajfiles))
for dtraj in clustering.dtrajs:
unique = np.unique(dtraj)
self.assertEqual(unique.shape[0], n_clusters)
def test(self):
reader = feature_reader(self.trajfiles, self.topfile)
# select all possible distances
pairs = np.array(
[x for x in itertools.combinations(range(self.n_residues), 2)])
#reader.featurizer.distances(pairs)
pcat = pca(dim=2)
n_clusters = 2
clustering = UniformTimeClustering(k=n_clusters)
D = Discretizer(reader, transform=pcat, cluster=clustering)
D.parametrize()
self.assertEqual(len(D.dtrajs), len(self.trajfiles))
for dtraj in clustering.dtrajs:
unique = np.unique(dtraj)
self.assertEqual(unique.shape[0], n_clusters)
def test_save_dtrajs(self):
reader = source(self.trajfiles, top=self.topfile)
cluster = cluster_kmeans(k=2)
d = Discretizer(reader, cluster=cluster)
d.parametrize()
d.save_dtrajs(output_dir=self.dest_dir)
dtrajs = os.listdir(self.dest_dir)
def test_save_dtrajs(self):
reader = feature_reader(self.trajfiles, self.topfile)
# select all possible distances
pairs = np.array(
[x for x in itertools.combinations(range(self.n_residues), 2)])
#reader.featurizer.distances(pairs)
cluster = kmeans(k=2)
d = Discretizer(reader, cluster=cluster)
d.parametrize()
d.save_dtrajs(output_dir=self.dest_dir)
dtrajs = os.listdir(self.dest_dir)