# Source code for stumpy.mstumped

```
# STUMPY
# Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license.
# STUMPY is a trademark of TD Ameritrade IP Company, Inc. All rights reserved.
import logging
import numpy as np
from .mstump import (
_mstump,
_get_first_mstump_profile,
_get_multi_QT,
_preprocess_include,
)
from . import core, config
from .maamped import maamped
logger = logging.getLogger(__name__)
[docs]@core.non_normalized(maamped)
def mstumped(dask_client, T, m, include=None, discords=False, normalize=True):
"""
Compute the multi-dimensional z-normalized matrix profile with a distributed
dask cluster
This is a highly distributed implementation around the Numba JIT-compiled
parallelized `_mstump` function which computes the multi-dimensional matrix
profile according to STOMP. Note that only self-joins are supported.
Parameters
----------
dask_client : client
A Dask Distributed client that is connected to a Dask scheduler and
Dask workers. Setting up a Dask distributed cluster is beyond the
scope of this library. Please refer to the Dask Distributed
documentation.
T : numpy.ndarray
The time series or sequence for which to compute the multi-dimensional
matrix profile. Each row in `T` represents data from a different
dimension while each column in `T` represents data from the same
dimension.
m : int
Window size
include : list, numpy.ndarray, default None
A list of (zero-based) indices corresponding to the dimensions in `T` that
must be included in the constrained multidimensional motif search.
For more information, see Section IV D in:
`DOI: 10.1109/ICDM.2017.66 \
<https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf>`__
discords : bool, default False
When set to `True`, this reverses the distance matrix which results in a
multi-dimensional matrix profile that favors larger matrix profile values
(i.e., discords) rather than smaller values (i.e., motifs). Note that indices
in `include` are still maintained and respected.
normalize : bool, default True
When set to `True`, this z-normalizes subsequences prior to computing distances.
Otherwise, this function gets re-routed to its complementary non-normalized
equivalent set in the `@core.non_normalized` function decorator.
Returns
-------
P : numpy.ndarray
The multi-dimensional matrix profile. Each row of the array corresponds
to each matrix profile for a given dimension (i.e., the first row is
the 1-D matrix profile and the second row is the 2-D matrix profile).
I : numpy.ndarray
The multi-dimensional matrix profile index where each row of the array
corresponds to each matrix profile index for a given dimension.
See Also
--------
stumpy.mstump : Compute the multi-dimensional z-normalized matrix profile
stumpy.subspace : Compute the k-dimensional matrix profile subspace for a given
subsequence index and its nearest neighbor index
stumpy.mdl : Compute the number of bits needed to compress one array with another
using the minimum description length (MDL)
Notes
-----
`DOI: 10.1109/ICDM.2017.66 \
<https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf>`__
See mSTAMP Algorithm
Examples
--------
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
... dask_client = Client()
... stumpy.mstumped(
... np.array([[584., -11., 23., 79., 1001., 0., -19.],
... [ 1., 2., 4., 8., 16., 0., 32.]]),
... m=3)
(array([[0. , 1.43947142, 0. , 2.69407392, 0.11633857],
[0.777905 , 2.36179922, 1.50004632, 2.92246722, 0.777905 ]]),
array([[2, 4, 0, 1, 0],
[4, 4, 0, 1, 0]]))
"""
T_A = T
T_B = T_A
T_A, M_T, Σ_T = core.preprocess(T_A, m)
T_B, μ_Q, σ_Q = core.preprocess(T_B, m)
if T_A.ndim <= 1: # pragma: no cover
err = f"T is {T_A.ndim}-dimensional and must be at least 1-dimensional"
raise ValueError(f"{err}")
core.check_window_size(m, max_size=min(T_A.shape[1], T_B.shape[1]))
if include is not None:
include = _preprocess_include(include)
d, n = T_B.shape
k = n - m + 1
excl_zone = int(
np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM)
) # See Definition 3 and Figure 3
P = np.empty((d, k), dtype=np.float64)
I = np.empty((d, k), dtype=np.int64)
hosts = list(dask_client.ncores().keys())
nworkers = len(hosts)
step = 1 + k // nworkers
for i, start in enumerate(range(0, k, step)):
P[:, start], I[:, start] = _get_first_mstump_profile(
start, T_A, T_B, m, excl_zone, M_T, Σ_T, μ_Q, σ_Q, include, discords
)
# Scatter data to Dask cluster
T_A_future = dask_client.scatter(T_A, broadcast=True, hash=False)
M_T_future = dask_client.scatter(M_T, broadcast=True, hash=False)
Σ_T_future = dask_client.scatter(Σ_T, broadcast=True, hash=False)
μ_Q_future = dask_client.scatter(μ_Q, broadcast=True, hash=False)
σ_Q_future = dask_client.scatter(σ_Q, broadcast=True, hash=False)
QT_futures = []
QT_first_futures = []
for i, start in enumerate(range(0, k, step)):
QT, QT_first = _get_multi_QT(start, T_A, m)
QT_future = dask_client.scatter(QT, workers=[hosts[i]], hash=False)
QT_first_future = dask_client.scatter(QT_first, workers=[hosts[i]], hash=False)
QT_futures.append(QT_future)
QT_first_futures.append(QT_first_future)
futures = []
for i, start in enumerate(range(0, k, step)):
stop = min(k, start + step)
futures.append(
dask_client.submit(
_mstump,
T_A_future,
m,
stop,
excl_zone,
M_T_future,
Σ_T_future,
QT_futures[i],
QT_first_futures[i],
μ_Q_future,
σ_Q_future,
k,
start + 1,
include,
discords,
)
)
results = dask_client.gather(futures)
for i, start in enumerate(range(0, k, step)):
stop = min(k, start + step)
P[:, start + 1 : stop], I[:, start + 1 : stop] = results[i]
return P, I
```