def test_lagged_iterator_1d(self):
n = 57
chunksize = 10
lag = 1
data = [np.arange(n), np.arange(50), np.arange(30)]
input_lens = [x.shape[0] for x in data]
reader = DataInMemory(data)
reader.chunksize = chunksize
self.assertEqual(reader.n_frames_total(), sum(input_lens))
# store results by traj
chunked_trajs = [[] for _ in range(len(data))]
chunked_lagged_trajs = [[] for _ in range(len(data))]
# iterate over data
for itraj, X, Y in reader.iterator(lag=lag):
chunked_trajs[itraj].append(X)
chunked_lagged_trajs[itraj].append(Y)
trajs = [np.vstack(ichunks) for ichunks in chunked_trajs]
lagged_trajs = [np.vstack(ichunks) for ichunks in chunked_lagged_trajs]
# unlagged data
for traj, input_traj in zip(trajs, data):
np.testing.assert_equal(traj.reshape(input_traj.shape), input_traj)
# lagged data
lagged_0 = [d[lag:] for d in data]
for traj, input_traj in zip(lagged_trajs, lagged_0):
np.testing.assert_equal(traj.reshape(input_traj.shape), input_traj)
def test_lagged_iterator_2d(self):
chunksize = 10
lag = 1
data = [
np.arange(300).reshape((100, 3)),
np.arange(29 * 3).reshape((29, 3)),
np.arange(150).reshape(50, 3)
]
input_lens = [x.shape[0] for x in data]
# print data[0].shape
reader = DataInMemory(data)
reader.chunksize = chunksize
self.assertEqual(reader.n_frames_total(), sum(input_lens))
# store results by traj
chunks = [[] for _ in range(len(data))]
lagged_chunks = [[] for _ in range(len(data))]
# iterate over data
for itraj, X, Y in reader.iterator(lag=lag):
chunks[itraj].append(X)
lagged_chunks[itraj].append(Y)
trajs = [np.vstack(ichunks) for ichunks in chunks]
lagged_trajs = [np.vstack(ichunks) for ichunks in lagged_chunks]
# unlagged data
for traj, input_traj in zip(trajs, data):
# do not consider chunks that have no lagged counterpart
input_shape = input_traj.shape
np.testing.assert_equal(traj.reshape((input_shape[0] - lag, 3)),
input_traj[:len(input_traj) - lag])
# lagged data
lagged_0 = [d[lag:] for d in data]
for traj, input_traj in zip(lagged_trajs, lagged_0):
np.testing.assert_equal(traj.reshape(input_traj.shape), input_traj)
def testChunksizeResultsTica(self):
chunk = 40
lag = 100
np.random.seed(0)
X = np.random.randn(23000, 3)
# un-chunked
d = DataInMemory(X)
tica_obj = api.tica(data=d, lag=lag, dim=1)
cov = tica_obj.cov.copy()
mean = tica_obj.mean.copy()
# ------- run again with new chunksize -------
d = DataInMemory(X)
d.chunksize = chunk
tica_obj = tica(data=d, lag=lag, dim=1)
np.testing.assert_allclose(tica_obj.mean, mean)
np.testing.assert_allclose(tica_obj.cov, cov)
def testChunksizeResultsTica(self):
chunk = 40
lag = 100
np.random.seed(0)
X = np.random.randn(23000, 3)
# un-chunked
d = DataInMemory(X)
tica_obj = api.tica(data=d, lag=lag, dim=1)
cov = tica_obj.cov.copy()
mean = tica_obj.mu.copy()
# ------- run again with new chunksize -------
d = DataInMemory(X)
d.chunksize = chunk
tica_obj = tica(data=d, lag=lag, dim=1)
np.testing.assert_allclose(tica_obj.mu, mean)
np.testing.assert_allclose(tica_obj.cov, cov)
def test_lagged_iterator_2d(self):
n = 57
chunksize = 10
lag = 1
# data = [np.random.random((n, 3)),
# np.zeros((29, 3)),
# np.random.random((n - 50, 3))]
data = [np.arange(300).reshape((100, 3)),
np.arange(29 * 3).reshape((29, 3)),
np.arange(150).reshape(50, 3)]
input_lens = [x.shape[0] for x in data]
# print data[0].shape
reader = DataInMemory(data)
reader.chunksize = chunksize
self.assertEqual(reader.n_frames_total(), sum(input_lens))
# store results by traj
chunks = [[] for _ in xrange(len(data))]
lagged_chunks = [[] for _ in xrange(len(data))]
# iterate over data
for itraj, X, Y in reader.iterator(lag=lag):
chunks[itraj].append(X)
lagged_chunks[itraj].append(Y)
trajs = [np.vstack(ichunks) for ichunks in chunks]
lagged_trajs = [np.vstack(ichunks) for ichunks in lagged_chunks]
# unlagged data
for traj, input_traj in zip(trajs, data):
np.testing.assert_equal(traj.reshape(input_traj.shape), input_traj)
# lagged data
lagged_0 = [d[lag:] for d in data]
for traj, input_traj in zip(lagged_trajs, lagged_0):
np.testing.assert_equal(traj.reshape(input_traj.shape), input_traj)
