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if T_A.ndim != 1: # pragma: no cover
raise ValueError(
f"T_A is {T_A.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
if T_B.ndim != 1: # pragma: no cover
raise ValueError(
f"T_B is {T_B.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
core.check_dtype(T_B)
core.check_nan(T_B)
core.check_window_size(m)
if ignore_trivial is False and core.are_arrays_equal(T_A, T_B): # pragma: no cover
logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
logger.warning("Try setting `ignore_trivial = True`.")
if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False: # pragma: no cover
logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
logger.warning("Try setting `ignore_trivial = False`.")
# Swap T_A and T_B for GPU implementation
# This keeps the API identical to and compatible with `stumpy.stump`
tmp_T = T_A
T_A = T_B
T_B = tmp_T
n = T_B.shape[0]
k = T_A.shape[0] - m + 1
l = n - m + 1
excl_zone = int(np.ceil(m / 4)) # See Definition 3 and Figure 3
exclusion zone for STOMP is m/4 (See Definition 3 and Figure 3).
For self-joins, set `ignore_trivial = True` in order to avoid the
trivial match.
Note that left and right matrix profiles are only available for self-joins.
"""
core.check_dtype(T_A)
core.check_nan(T_A)
if T_B is None:
T_B = T_A
core.check_dtype(T_B)
core.check_nan(T_B)
core.check_window_size(m)
if ignore_trivial is False and core.are_arrays_equal(T_A, T_B): # pragma: no cover
logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
logger.warning("Try setting `ignore_trivial = True`.")
if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False: # pragma: no cover
logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
logger.warning("Try setting `ignore_trivial = False`.")
n = T_B.shape[0]
l = n - m + 1
excl_zone = int(np.ceil(m / 4)) # See Definition 3 and Figure 3
M_T, Σ_T = core.compute_mean_std(T_A, m)
QT = core.sliding_dot_product(T_B[:m], T_A)
QT_first = core.sliding_dot_product(T_A[:m], T_B)
μ_Q, σ_Q = core.compute_mean_std(T_B, m)
if T_A.ndim != 1: # pragma: no cover
raise ValueError(
f"T_A is {T_A.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
if T_B.ndim != 1: # pragma: no cover
raise ValueError(
f"T_B is {T_B.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
core.check_dtype(T_B)
core.check_nan(T_B)
core.check_window_size(m)
if ignore_trivial is False and core.are_arrays_equal(T_A, T_B): # pragma: no cover
logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
logger.warning("Try setting `ignore_trivial = True`.")
if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False: # pragma: no cover
logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
logger.warning("Try setting `ignore_trivial = False`.")
n = T_B.shape[0]
k = T_A.shape[0] - m + 1
l = n - m + 1
excl_zone = int(np.ceil(m / 4)) # See Definition 3 and Figure 3
M_T, Σ_T = core.compute_mean_std(T_A, m)
μ_Q, σ_Q = core.compute_mean_std(T_B, m)
out = np.empty((l, 4), dtype=object)
if T_A.ndim != 1: # pragma: no cover
raise ValueError(
f"T_A is {T_A.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
if T_B.ndim != 1: # pragma: no cover
raise ValueError(
f"T_B is {T_B.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
core.check_dtype(T_B)
core.check_nan(T_B)
core.check_window_size(m)
if ignore_trivial is False and core.are_arrays_equal(T_A, T_B): # pragma: no cover
logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
logger.warning("Try setting `ignore_trivial = True`.")
if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False: # pragma: no cover
logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
logger.warning("Try setting `ignore_trivial = False`.")
n = T_B.shape[0]
k = T_A.shape[0] - m + 1
l = n - m + 1
excl_zone = int(np.ceil(m / 4)) # See Definition 3 and Figure 3
M_T, Σ_T = core.compute_mean_std(T_A, m)
μ_Q, σ_Q = core.compute_mean_std(T_B, m)
out = np.empty((l, 4), dtype=object)
)
if T_B.ndim != 1: # pragma: no cover
raise ValueError(
f"T_B is {T_B.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
core.check_dtype(T_B)
core.check_nan(T_B)
core.check_window_size(m)
if ignore_trivial is False and core.are_arrays_equal(T_A, T_B): # pragma: no cover
logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
logger.warning("Try setting `ignore_trivial = True`.")
if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False: # pragma: no cover
logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
logger.warning("Try setting `ignore_trivial = False`.")
n = T_B.shape[0]
k = T_A.shape[0] - m + 1
l = n - m + 1
excl_zone = int(np.ceil(m / 4)) # See Definition 3 and Figure 3
M_T, Σ_T = core.compute_mean_std(T_A, m)
μ_Q, σ_Q = core.compute_mean_std(T_B, m)
out = np.empty((l, 4), dtype=object)
profile = np.empty((l,), dtype="float64")
indices = np.empty((l, 3), dtype="int64")
start = 0
)
if T_B.ndim != 1: # pragma: no cover
raise ValueError(
f"T_B is {T_B.ndim}-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
core.check_dtype(T_B)
core.check_nan(T_B)
core.check_window_size(m)
if ignore_trivial is False and core.are_arrays_equal(T_A, T_B): # pragma: no cover
logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
logger.warning("Try setting `ignore_trivial = True`.")
if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False: # pragma: no cover
logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
logger.warning("Try setting `ignore_trivial = False`.")
n = T_B.shape[0]
k = T_A.shape[0] - m + 1
l = n - m + 1
excl_zone = int(np.ceil(m / 4)) # See Definition 3 and Figure 3
M_T, Σ_T = core.compute_mean_std(T_A, m)
μ_Q, σ_Q = core.compute_mean_std(T_B, m)
out = np.empty((l, 4), dtype=object)
profile = np.empty((l,), dtype="float64")
indices = np.empty((l, 3), dtype="int64")
hosts = list(dask_client.ncores().keys())