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)