def test_complexes(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5") data = loader.load_data("use_complexes") assert len(data) == 3 assert len(data[0]) == 2 assert len(data[0][0]) == 3 # Z, RC, PC assert len(data[0][1]) == 1 # TS
def test_custom_split(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False, splitting="custom") train, val, test = loader.load_data(shuffle=False) assert test is None assert len(val[0][0]) == 7
def test_classification_data(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False) data = loader.load_data(output_type="classifier") assert len(data) == 3 assert len(data[0][0]) == 2 assert len(data[0][1]) == 1 assert len(data[0][1][0].shape) == 1
def test_load_ts_data(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5") data = loader.load_data() assert len(data) == 3 assert len(data[0]) == 2 assert len(data[0][0]) == 3 # Z, R, P assert len(data[0][1]) == 1 # TS
def test_serving_both(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False) data = loader.load_data(output_type="both") assert len(data) == 3 assert len(data[0][0]) == 3 assert len(data[0][1]) == 2 assert data[0][1][0].shape[1:] == (loader.num_points, 3) assert len(data[0][1][1].shape) == 1
def test_siamese_data(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False) data = loader.load_data(output_type="siamese") assert len(data) == 3 assert data[0][0][0].shape[1:] == ( 2, loader.num_points, ) assert data[0][0][1].shape[1:] == (2, loader.num_points, 3) assert len(data[0][1]) == 1
def test_distance_matrix(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False) data = loader.load_data(output_type="both", output_distance_matrix=True) assert len(data) == 3 assert len(data[0][0]) == 3 assert len(data[0][1]) == 2 assert data[0][1][0].shape[1:] == (loader.num_points, loader.num_points) assert len(data[0][1][1].shape) == 1
from tfn.tools.loaders import TSLoader import numpy as np def get_atomic_histogram_data(z): z = np.where(z == 0, np.nan, z) z = np.where(z == 1, np.nan, z) return [np.count_nonzero(z == i) for i in range(36)] loader = TSLoader(path="/home/riley/dev/python/data/ts.hdf5", splitting=None, map_points=False) x, _ = loader.load_data(remove_noise=True, shuffle=False)[0] z, *_ = x print(f"Size data: {np.count_nonzero(np.where(z == 1, 0, z), axis=1)}") print(f"Type data: {get_atomic_histogram_data(z)}")
def test_energy_serving(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", pre_load=False) data = loader.load_data(output_type="energies") assert len(data) == 3 assert len(data[0][0]) == 3 assert len(data[0][1][0].shape) == 1
def test_remove_noise(self): loader = TSLoader(os.environ["DATADIR"] + "/ts.hdf5", splitting=None) data = loader.load_data(remove_noise=True) assert len(data[0][0][0]) == 55