def test_motifs_two_motifs():
# Fix seed, because in some case motifs can be off by an index resulting in test
# fails, which is caused since one of the motifs is not repeated perfectly in T.
np.random.seed(1234)
# The time series is random noise with two motifs for m=10:
# * (almost) identical step functions at indices 10, 110 and 210
# * identical linear slopes at indices 70 and 170
T = np.random.normal(size=300)
m = 20
T[10:30] = 1
T[12:28] = 2
T[110:130] = 3
T[112:128] = 6
T[120] = 6.6
T[210:230] = 1
T[212:228] = 2
T[220] = 1.9
# naive.distance(naive.z_norm(T[10:30]), naive.z_norm(T[110:130])) = 0.47
# naive.distance(naive.z_norm(T[10:30]), naive.z_norm(T[210:230])) = 0.24
# naive.distance(naive.z_norm(T[110:130]), naive.z_norm(T[210:230])) = 0.72
# Hence T[10:30] is the motif representative for this motif
T[70:90] = np.arange(m) * 0.1
T[170:190] = np.arange(m) * 0.1
# naive.distance(naive.z_norm(T[70:90]), naive.z_norm(T[170:190])) = 0.0
max_motifs = 2
mp = naive.stump(T, m)
# left_indices = [[70, 170, -1], [10, 210, 110]]
left_profile_values = [
[0.0, 0.0, np.nan],
[
0.0,
naive.distance(core.z_norm(T[10:30]), core.z_norm(T[210:230])),
naive.distance(core.z_norm(T[10:30]), core.z_norm(T[110:130])),
],
]
right_distance_values, right_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
max_distance=0.5,
cutoff=np.inf,
)
# We ignore indices because of sorting ambiguities for equal distances.
# As long as the distances are correct, the indices will be too.
npt.assert_almost_equal(left_profile_values,
right_distance_values,
decimal=6)
# Reset seed
np.random.seed(None)
def test_motifs_max_matches():
# This test covers the following:
# A time series contains motif A at four locations and motif B at two.
# If `max_motifs=2` the result should contain only the top two matches of motif A
# and the top two matches of motif B as two separate motifs.
T = np.array([
0.0, # motif A
1.0,
0.0,
2.3,
-1.0, # motif B
-1.0,
-2.0,
0.0, # motif A
1.0,
0.0,
-2.0,
-1.0, # motif B
-1.03,
-2.0,
-0.5,
2.0, # motif A
3.0,
2.04,
2.3,
2.0, # motif A
3.0,
2.02,
])
m = 3
max_motifs = 3
left_indices = [[0, 7], [4, 11]]
left_profile_values = [
[0.0, 0.0],
[
0.0,
naive.distance(
core.z_norm(T[left_indices[1][0]:left_indices[1][0] + m]),
core.z_norm(T[left_indices[1][1]:left_indices[1][1] + m]),
),
],
]
mp = naive.stump(T, m)
right_distance_values, right_indices = motifs(
T,
mp[:, 0],
max_motifs=max_motifs,
max_distance=0.1,
cutoff=np.inf,
max_matches=2,
)
# We ignore indices because of sorting ambiguities for equal distances.
# As long as the distances are correct, the indices will be too.
npt.assert_almost_equal(left_profile_values,
right_distance_values,
decimal=4)
def test_motifs(T, m):
if T.ndim > 1:
T = T.copy()
T = T[0]
mp = stumpy.aamp(T, m)
ref = stumpy.aamp_motifs(T, mp[:, 0])
comp = stumpy.motifs(T, mp[:, 0], normalize=False)
npt.assert_almost_equal(ref, comp)
def test_motifs_one_motif():
# The top motif for m=3 is a [0 1 0] at indices 0, 5 and 9
T = np.array(
[0.0, 1.0, 0.0, -1.0, -1.0, 0.0, 1.0, 0.0, -0.5, 2.0, 3.0, 2.0])
m = 3
max_motifs = 1
left_indices = [[0, 5, 9]]
left_profile_values = [[0.0, 0.0, 0.0]]
mp = naive.stump(T, m)
right_distance_values, right_indices = motifs(
T,
mp[:, 0],
max_distance=lambda D: 0.001, # Also test lambda functionality
max_motifs=max_motifs,
cutoff=np.inf,
)
npt.assert_array_equal(left_indices, right_indices)
npt.assert_almost_equal(left_profile_values,
right_distance_values,
decimal=4)