def test_apply_exclusion_zone():
T = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=float)
left = np.empty(T.shape)
right = np.empty(T.shape)
exclusion_zone = 2
for i in range(T.shape[0]):
left[:] = T[:]
naive.apply_exclusion_zone(left, i, exclusion_zone)
right[:] = T[:]
core.apply_exclusion_zone(right, i, exclusion_zone)
naive.replace_inf(left)
naive.replace_inf(right)
npt.assert_array_equal(left, right)
def test_apply_exclusion_zone():
T = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=float)
ref = np.empty(T.shape)
comp = np.empty(T.shape)
exclusion_zone = 2
for i in range(T.shape[0]):
ref[:] = T[:]
naive.apply_exclusion_zone(ref, i, exclusion_zone)
comp[:] = T[:]
core.apply_exclusion_zone(comp, i, exclusion_zone)
naive.replace_inf(ref)
naive.replace_inf(comp)
npt.assert_array_equal(ref, comp)
def test_apply_exclusion_zone_bool():
T = np.ones(10, dtype=bool)
ref = np.empty(T.shape, dtype=bool)
comp = np.empty(T.shape, dtype=bool)
exclusion_zone = 2
for i in range(T.shape[0]):
ref[:] = T[:]
naive.apply_exclusion_zone(ref, i, exclusion_zone, False)
comp[:] = T[:]
core.apply_exclusion_zone(comp, i, exclusion_zone, False)
naive.replace_inf(ref)
naive.replace_inf(comp)
npt.assert_array_equal(ref, comp)
def test_apply_exclusion_zone_multidimensional():
T = np.array(
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
dtype=np.float64,
)
ref = np.empty(T.shape, dtype=np.float64)
comp = np.empty(T.shape, dtype=np.float64)
exclusion_zone = 2
for i in range(T.shape[1]):
ref[:, :] = T[:, :]
naive.apply_exclusion_zone(ref, i, exclusion_zone, np.inf)
comp[:, :] = T[:, :]
core.apply_exclusion_zone(comp, i, exclusion_zone, np.inf)
naive.replace_inf(ref)
naive.replace_inf(comp)
npt.assert_array_equal(ref, comp)
def naive_prescraamp(T_A, m, T_B, s, exclusion_zone=None):
distance_matrix = naive.aamp_distance_matrix(T_A, T_B, m)
n_A = T_A.shape[0]
l = n_A - m + 1
P = np.empty(l)
I = np.empty(l, dtype=np.int64)
P[:] = np.inf
I[:] = -1
for i in np.random.permutation(range(0, l, s)):
distance_profile = distance_matrix[i]
if exclusion_zone is not None:
naive.apply_exclusion_zone(distance_profile, i, exclusion_zone,
np.inf)
I[i] = np.argmin(distance_profile)
P[i] = distance_profile[I[i]]
if P[i] == np.inf:
I[i] = -1
else:
j = I[i]
for k in range(1, min(s, l - max(i, j))):
d = distance_matrix[i + k, j + k]
if d < P[i + k]:
P[i + k] = d
I[i + k] = j + k
if d < P[j + k]:
P[j + k] = d
I[j + k] = i + k
for k in range(1, min(s, i + 1, j + 1)):
d = distance_matrix[i - k, j - k]
if d < P[i - k]:
P[i - k] = d
I[i - k] = j - k
if d < P[j - k]:
P[j - k] = d
I[j - k] = i - k
return P, I
