STUMPY API#

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Overview

`stumpy.stump`	Compute the z-normalized matrix profile
`stumpy.stumped`	Compute the z-normalized matrix profile with a `dask`/`ray` cluster
`stumpy.gpu_stump`	Compute the z-normalized matrix profile with one or more GPU devices
`stumpy.mass`	Compute the distance profile using the MASS algorithm
`stumpy.scrump`	A class to ompute an approximate z-normalized matrix profile
`stumpy.stumpi`	A class to compute an incremental z-normalized matrix profile for streaming data
`stumpy.mstump`	Compute the multi-dimensional z-normalized matrix profile
`stumpy.mstumped`	Compute the multi-dimensional z-normalized matrix profile with a `dask`/`ray` cluster
`stumpy.subspace`	Compute the `k`-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index
`stumpy.mdl`	Compute the multi-dimensional number of bits needed to compress one multi-dimensional subsequence with another along each of the `k`-dimensions using the minimum description length (MDL)
`stumpy.atsc`	Compute the anchored time series chain (ATSC)
`stumpy.allc`	Compute the all-chain set (ALLC)
`stumpy.fluss`	Compute the Fast Low-cost Unipotent Semantic Segmentation (FLUSS) for static data (i.e., batch processing)
`stumpy.floss`	A class to compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for streaming data
`stumpy.ostinato`	Find the z-normalized consensus motif of multiple time series
`stumpy.ostinatoed`	Find the z-normalized consensus motif of multiple time series with a `dask`/`ray` cluster
`stumpy.gpu_ostinato`	Find the z-normalized consensus motif of multiple time series with one or more GPU devices
`stumpy.mpdist`	Compute the z-normalized matrix profile distance (MPdist) measure between any two time series
`stumpy.mpdisted`	Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with a `dask`/`ray` cluster
`stumpy.gpu_mpdist`	Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices
`stumpy.motifs`	Discover the top motifs for time series `T`
`stumpy.match`	Find all matches of a query `Q` in a time series `T`
`stumpy.mmotifs`	Discover the top motifs for the multi-dimensional time series `T`.
`stumpy.snippets`	Identify the top `k` snippets that best represent the time series, `T`
`stumpy.stimp`	A class to compute the Pan Matrix Profile
`stumpy.stimped`	A class to compute the Pan Matrix Profile with a `dask`/`ray` cluster
`stumpy.gpu_stimp`	A class to compute the Pan Matrix Profile with with one or more GPU devices

stump#

stumpy.stump(T_A, m, T_B=None, ignore_trivial=True, normalize=True, p=2.0, k=1, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)[source]#

Compute the z-normalized matrix profile

This is a convenience wrapper around the Numba JIT-compiled parallelized _stump function which computes the (top-k) matrix profile according to STOMPopt with Pearson correlations.

Parameters:

T_Anumpy.ndarray: The time series or sequence for which to compute the matrix profile.
mint: Window size.
T_Bnumpy.ndarray, default None: The time series or sequence that will be used to annotate T_A. For every subsequence in T_A, its nearest neighbor in T_B will be recorded. Default is None which corresponds to a self-join.
ignore_trivialbool, default True: Set to True if this is a self-join. Otherwise, for AB-join, set this to False.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
kint, default 1: The number of top k smallest distances used to construct the matrix profile. Note that this will increase the total computational time and memory usage when k > 1. If you have access to a GPU device, then you may be able to leverage gpu_stump for better performance and scalability.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

outnumpy.ndarray

When k = 1 (default), the first column consists of the matrix profile, the second column consists of the matrix profile indices, the third column consists of the left matrix profile indices, and the fourth column consists of the right matrix profile indices. However, when k > 1, the output array will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k]) consists of the top-k matrix profile, the next set of k columns (i.e., out[:, k : 2 * k]) consists of the corresponding top-k matrix profile indices, and the last two columns (i.e., out[:, 2 * k] and out[:, 2 * k + 1] or, equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left matrix profile indices and the top-1 right matrix profile indices, respectively.

For convenience, the matrix profile (distances) and matrix profile indices can also be accessed via their corresponding named array attributes, .P_ and .I_,respectively. Similarly, the corresponding left matrix profile indices and right matrix profile indices may also be accessed via the .left_I_ and .right_I_ array attributes. See examples below.

See also

stumpy.stumped: Compute the z-normalized matrix profile with a dask/ ray cluster
stumpy.gpu_stump: Compute the z-normalized matrix profile with one or more GPU devices
stumpy.scrump: Compute an approximate z-normalized matrix profile

Notes

DOI: 10.1007/s10115-017-1138-x

See Section 4.5

The above reference outlines a general approach for traversing the distance matrix in a diagonal fashion rather than in a row-wise fashion.

DOI: 10.1145/3357223.3362721

See Section 3.1 and Section 3.3

The above reference outlines the use of the Pearson correlation via Welford’s centered sum-of-products along each diagonal of the distance matrix in place of the sliding window dot product found in the original STOMP method.

DOI: 10.1109/ICDM.2016.0085

See Table II

Timeseries, T_A, will be annotated with the distance location (or index) of all its subsequences in another times series, T_B.

Return: For every subsequence, Q, in T_A, you will get a distance and index for the closest subsequence in T_B. Thus, the array returned will have length T_A.shape[0] - m + 1. Additionally, the left and right matrix profiles are also returned.

Note: Unlike in the Table II where T_A.shape is expected to be equal to T_B.shape, this implementation is generalized so that the shapes of T_A and T_B can be different. In the case where T_A.shape == T_B.shape, then our algorithm reduces down to the same algorithm found in Table II.

Additionally, unlike STAMP where the exclusion zone is m/2, the default 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.

Examples

>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> mp
mparray([[0.11633857113691416, 4, -1, 4],
         [2.694073918063438, 3, -1, 3],
         [3.0000926340485923, 0, 0, 4],
         [2.694073918063438, 1, 1, -1],
         [0.11633857113691416, 0, 0, -1]], dtype=object)
>>>
>>> mp.P_
mparray([0.11633857, 2.69407392, 3.00009263, 2.69407392, 0.11633857])
>>> mp.I_
mparray([4, 3, 0, 1, 0])

stumped#

stumpy.stumped(client, T_A, m, T_B=None, ignore_trivial=True, normalize=True, p=2.0, k=1, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)[source]#

Compute the z-normalized matrix profile with a dask/ray cluster

This is a highly distributed implementation around the Numba JIT-compiled parallelized _stump function which computes the (top-k) matrix profile according to STOMPopt with Pearson correlations.

Parameters:

clientclient: A dask/ray client. Setting up a cluster is beyond the scope of this library. Please refer to the dask/ray documentation.
T_Anumpy.ndarray: The time series or sequence for which to compute the matrix profile.
mint: Window size.
T_Bnumpy.ndarray, default None: The time series or sequence that will be used to annotate T_A. For every subsequence in T_A, its nearest neighbor in T_B will be recorded. Default is None which corresponds to a self-join.
ignore_trivialbool, default True: Set to True if this is a self-join. Otherwise, for AB-join, set this to False.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
kint, default 1: The number of top k smallest distances used to construct the matrix profile. Note that this will increase the total computational time and memory usage when k > 1. If you have access to a GPU device, then you may be able to leverage gpu_stump for better performance and scalability.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

outnumpy.ndarray

See also

stumpy.stump: Compute the z-normalized matrix profile cluster
stumpy.gpu_stump: Compute the z-normalized matrix profile with one or more GPU devices
stumpy.scrump: Compute an approximate z-normalized matrix profile

Notes

DOI: 10.1007/s10115-017-1138-x

See Section 4.5

The above reference outlines a general approach for traversing the distance matrix in a diagonal fashion rather than in a row-wise fashion.

DOI: 10.1145/3357223.3362721

See Section 3.1 and Section 3.3

DOI: 10.1109/ICDM.2016.0085

See Table II

This is a dask/ray implementation of stump that scales across multiple servers and is a convenience wrapper around the parallelized stump._stump function

Timeseries, T_A, will be annotated with the distance location (or index) of all its subsequences in another times series, T_B.

Additionally, unlike STAMP where the exclusion zone is m/2, the default 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.

Examples

>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
...     with Client() as dask_client:
...         stumpy.stumped(
...             dask_client,
...             np.array([584., -11., 23., 79., 1001., 0., -19.]),
...             m=3)
mparray([[0.11633857113691416, 4, -1, 4],
         [2.694073918063438, 3, -1, 3],
         [3.0000926340485923, 0, 0, 4],
         [2.694073918063438, 1, 1, -1],
         [0.11633857113691416, 0, 0, -1]], dtype=object)
>>>
>>>         mp.P_
mparray([0.11633857, 2.69407392, 3.00009263, 2.69407392, 0.11633857])
>>>         mp.I_
mparray([4, 3, 0, 1, 0])

Alternatively, you can also use ray

>>> import ray
>>> if __name__ == "__main__":
>>>     ray.init()
>>>     stumpy.stumped(
...             ray,
...             np.array([584., -11., 23., 79., 1001., 0., -19.]),
...             m=3)
>>>     ray.shutdown()

gpu_stump#

stumpy.gpu_stump(T_A, m, T_B=None, ignore_trivial=True, device_id=0, normalize=True, p=2.0, k=1, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)#

Compute the z-normalized matrix profile with one or more GPU devices

This is a convenience wrapper around the Numba cuda.jit _gpu_stump function which computes the matrix profile according to GPU-STOMP. The default number of threads-per-block is set to 512 and may be changed by setting the global parameter config.STUMPY_THREADS_PER_BLOCK to an appropriate number based on your GPU hardware.

Parameters:

T_Anumpy.ndarray: The time series or sequence for which to compute the matrix profile.
mint: Window size.
T_Bnumpy.ndarray, default None: The time series or sequence that will be used to annotate T_A. For every subsequence in T_A, its nearest neighbor in T_B will be recorded. Default is None which corresponds to a self-join.
ignore_trivialbool, default True: Set to True if this is a self-join. Otherwise, for AB-join, set this to False.
device_idint or list, default 0: The (GPU) device number to use. The default value is 0. A list of valid device ids (int) may also be provided for parallel GPU-STUMP computation. A list of all valid device ids can be obtained by executing [device.id for device in numba.cuda.list_devices()].
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
kint, default 1: The number of top k smallest distances used to construct the matrix profile. Note that this will increase the total computational time and memory usage when k > 1.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

outnumpy.ndarray

When k = 1 (default), the first column consists of the matrix profile, the second column consists of the matrix profile indices, the third column consists of the left matrix profile indices, and the fourth column consists of the right matrix profile indices. However, when k > 1, the output array will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k]) consists of the top-k matrix profile, the next set of k columns (i.e., out[:, k : 2 * k]) consists of the corresponding top-k matrix profile indices, and the last two columns (i.e., out[:, 2 * k] and out[:, 2 * k + 1] or, equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left matrix profile indices and the top-1 right matrix profile indices, respectively.

See also

stumpy.stump: Compute the z-normalized matrix profile
stumpy.stumped: Compute the z-normalized matrix profile with a dask/ ray cluster
stumpy.scrump: Compute an approximate z-normalized matrix profile

Notes

DOI: 10.1109/ICDM.2016.0085

See Table II, Figure 5, and Figure 6

Timeseries, T_A, will be annotated with the distance location (or index) of all its subsequences in another times series, T_B.

Additionally, unlike STAMP where the exclusion zone is m/2, the default 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.

Examples

>>> import stumpy
>>> import numpy as np
>>> from numba import cuda
>>> if __name__ == "__main__":
...     all_gpu_devices = [device.id for device in cuda.list_devices()]
...     mp = stumpy.gpu_stump(
...         np.array([584., -11., 23., 79., 1001., 0., -19.]),
...         m=3,
...         device_id=all_gpu_devices)
>>>     mp
mparray([[0.11633857113691416, 4, -1, 4],
         [2.694073918063438, 3, -1, 3],
         [3.0000926340485923, 0, 0, 4],
         [2.694073918063438, 1, 1, -1],
         [0.11633857113691416, 0, 0, -1]], dtype=object)
>>>
>>>     mp.P_
mparray([0.11633857, 2.69407392, 3.00009263, 2.69407392, 0.11633857])
>>>     mp.I_
mparray([4, 3, 0, 1, 0])

mass#

stumpy.mass(Q, T, M_T=None, Σ_T=None, normalize=True, p=2.0, T_subseq_isfinite=None, T_subseq_isconstant=None, Q_subseq_isconstant=None, query_idx=None)[source]#

Compute the distance profile using the MASS algorithm

This is a convenience wrapper around the Numba JIT compiled _mass function.

Parameters:

Qnumpy.ndarray: Query array or subsequence.
Tnumpy.ndarray: Time series or sequence.
M_Tnumpy.ndarray, default None: Sliding mean of T.
Σ_Tnumpy.ndarray, default None: Sliding standard deviation of T.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
T_subseq_isfinitenumpy.ndarray, default None: A boolean array that indicates whether a subsequence in T contains a np.nan/np.inf value (False). This parameter is ignored when normalize == True.
T_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
Q_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether the subsequence in Q is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether the subsequence in Q is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
query_idxint, default None: This is the index position along the time series, T, where the query subsequence, Q, is located. query_idx should be set to None if Q is not a subsequence of T. If Q is a subsequence of T, provding this argument is optional. If query_idx is provided, the distance between Q and T[query_idx : query_idx + m] will automatically be set to zero.

Returns:

distance_profilenumpy.ndarray: Distance profile.

See also

stumpy.motifs: Discover the top motifs for time series T
stumpy.match: Find all matches of a query Q in a time series T

Notes

DOI: 10.1109/ICDM.2016.0179

See Table II

Note that Q, T are not directly required to calculate D

Note: Unlike the Matrix Profile I paper, here, M_T, Σ_T can be calculated once for all subsequences of T and passed in so the redundancy is removed

Examples

>>> import stumpy
>>> import numpy as np
>>> stumpy.mass(
...     np.array([-11.1, 23.4, 79.5, 1001.0]),
...     np.array([584., -11., 23., 79., 1001., 0., -19.]))
array([3.18792463e+00, 1.11297393e-03, 3.23874018e+00, 3.34470195e+00])

scrump#

stumpy.scrump(T_A, m, T_B=None, ignore_trivial=True, percentage=0.01, pre_scrump=False, s=None, normalize=True, p=2.0, k=1, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)[source]#

A class to ompute an approximate z-normalized matrix profile

This is a convenience wrapper around the Numba JIT-compiled parallelized _stump function which computes the matrix profile according to SCRIMP.

Parameters:

T_Anumpy.ndarray: The time series or sequence for which to compute the matrix profile.
T_Bnumpy.ndarray: The time series or sequence that will be used to annotate T_A. For every subsequence in T_A, its nearest neighbor in T_B will be recorded.
mint: Window size.
ignore_trivialbool: Set to True if this is a self-join. Otherwise, for AB-join, set this to False.
percentagefloat: Approximate percentage completed. The value is between 0.0 and 1.0.
pre_scrumpbool: A flag for whether or not to perform the PreSCRIMP calculation prior to computing SCRIMP. If set to True, this is equivalent to computing SCRIMP++ and may lead to faster convergence
sint: The size of the PreSCRIMP fixed interval. If pre_scrump = True and s = None, then s will automatically be set to s = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM)), which is the size of the exclusion zone.
normalizebool, default True: When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this class gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized class decorator.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
kint, default 1: The number of top k smallest distances used to construct the matrix profile. Note that this will increase the total computational time and memory usage when k > 1.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Attributes:

P_numpy.ndarray: Get the updated (top-k) matrix profile.
I_numpy.ndarray: Get the updated (top-k) matrix profile indices.
left_I_numpy.ndarray: Get the updated left (top-1) matrix profile indices
right_I_numpy.ndarray: Get the updated right (top-1) matrix profile indices

Methods

update()

Update the matrix profile and the matrix profile indices by computing additional new distances (limited by percentage) that make up the full distance matrix. It updates the (top-k) matrix profile, (top-1) left matrix profile, (top-1) right matrix profile, (top-k) matrix profile indices, (top-1) left matrix profile indices, and (top-1) right matrix profile indices.

See also

stumpy.stump: Compute the z-normalized matrix profile
stumpy.stumped: Compute the z-normalized matrix profile with a dask/ray cluster
stumpy.gpu_stump: Compute the z-normalized matrix profile with one or more GPU devices

Notes

DOI: 10.1109/ICDM.2018.00099

See Algorithm 1 and Algorithm 2

Examples

>>> import stumpy
>>> import numpy as np
>>> approx_mp = stumpy.scrump(
...     np.array([584., -11., 23., 79., 1001., 0., -19.]),
...     m=3)
>>> approx_mp.update()
>>> approx_mp.P_
array([2.982409  , 3.28412702,        inf, 2.982409  , 3.28412702])
>>> approx_mp.I_
array([ 3,  4, -1,  0,  1])

stumpi#

stumpy.stumpi(T, m, egress=True, normalize=True, p=2.0, k=1, mp=None, T_subseq_isconstant_func=None)[source]#

A class to compute an incremental z-normalized matrix profile for streaming data

This is based on the on-line STOMPI and STAMPI algorithms.

Parameters:

Tnumpy.ndarray: The time series or sequence for which the matrix profile and matrix profile indices will be returned.
mint: Window size.
egressbool, default True: If set to True, the oldest data point in the time series is removed and the time series length remains constant rather than forever increasing
normalizebool, default True: When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this class gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized class decorator.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
kint, default 1: The number of top k smallest distances used to construct the matrix profile. Note that this will increase the total computational time and memory usage when k > 1.
mpnumpy.ndarray, default None: A pre-computed matrix profile (and corresponding matrix profile indices). This is a 2D array of shape (len(T) - m + 1, 2 * k + 2), where the first k columns are top-k matrix profile, and the next k columns are their corresponding indices. The last two columns correspond to the top-1 left and top-1 right matrix profile indices. When None (default), this array is computed internally using stumpy.stump.
T_subseq_isconstant_funcfunction, default None: A custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Attributes:

P_numpy.ndarray: Get the (top-k) matrix profile.
I_numpy.ndarray: Get the (top-k) matrix profile indices.
left_P_numpy.ndarray: Get the (top-1) left matrix profile
left_I_numpy.ndarray: Get the (top-1) left matrix profile indices
T_numpy.ndarray: Get the time series

Methods

update(t)

Append a single new data point, t, to the time series, T, and update the matrix profile.

Notes

DOI: 10.1007/s10618-017-0519-9

See Table V

Note that line 11 is missing an important sqrt operation!

Examples

>>> import stumpy
>>> import numpy as np
>>> stream = stumpy.stumpi(
...     np.array([584., -11., 23., 79., 1001., 0.]),
...     m=3)
>>> stream.update(-19.0)
>>> stream.left_P_
array([       inf, 3.00009263, 2.69407392, 3.05656417])
>>> stream.left_I_
array([-1,  0,  1,  2])

mstump#

stumpy.mstump(T, m, include=None, discords=False, normalize=True, p=2.0, T_subseq_isconstant=None)[source]#

Compute the multi-dimensional z-normalized matrix profile

This is a convenience wrapper around the Numba JIT-compiled parallelized _mstump function which computes the multi-dimensional matrix profile and multi-dimensional matrix profile index according to mSTOMP, a variant of mSTAMP. Note that only self-joins are supported.

Parameters:

Tnumpy.ndarray

The time series or sequence for which to compute the multi-dimensional matrix profile. Each row in T represents data from the same dimension while each column in T represents data from a different dimension.

mint

Window size.

includelist, 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

discordsbool, 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.

normalizebool, 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.

pfloat, default 2.0

The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.

T_subseq_isconstantnumpy.ndarray, function, or list, default None

A parameter that is used to show whether a subsequence of a time series in T is constant (True) or not. T_subseq_isconstant can be a 2D boolean numpy.ndarray or a function that can be applied to each time series in T. Alternatively, for maximum flexibility, a list (with length equal to the total number of time series) may also be used. In this case, T_subseq_isconstant[i] corresponds to the i-th time series T[i] and each element in the list can either be a 1D boolean numpy.ndarray, a function, or None.

Returns:

Pnumpy.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).
Inumpy.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.mstumped: Compute the multi-dimensional z-normalized matrix profile with a dask/ray cluster
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

See mSTAMP Algorithm

Examples

>>> stumpy.mstump(
...     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]]))

mstumped#

stumpy.mstumped(client, T, m, include=None, discords=False, p=2.0, normalize=True, T_subseq_isconstant=None)[source]#

Compute the multi-dimensional z-normalized matrix profile with a dask/ray 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:

clientclient

A dask/ray client. Setting up a cluster is beyond the scope of this library. Please refer to the dask/ray documentation.

Tnumpy.ndarray

mint

Window size.

includelist, 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

discordsbool, default False

pfloat, default 2.0

normalizebool, default True

T_subseq_isconstantnumpy.ndarray, function, or list, default None

Returns:

Pnumpy.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).
Inumpy.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

See mSTAMP Algorithm

Examples

>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
...     with Client() as dask_client:
...         stumpy.mstumped(
...             dask_client,
...             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]]))

Alternatively, you can also use ray

>>> import ray
>>> if __name__ == "__main__":
>>>     ray.init()
>>>     stumpy.mstumped(
...         ray,
...         np.array([[584., -11., 23., 79., 1001., 0., -19.],
...                   [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...         m=3)
>>>     ray.shutdown()

subspace#

stumpy.subspace(T, m, subseq_idx, nn_idx, k, include=None, discords=False, discretize_func=None, n_bit=8, normalize=True, p=2.0, T_subseq_isconstant=None)[source]#

Compute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index

Parameters:

Tnumpy.ndarray

The time series or sequence for which the multi-dimensional matrix profile, multi-dimensional matrix profile indices were computed.

mint

Window size.

subseq_idxint

The subsequence index in T.

nn_idxint

The nearest neighbor index in T.

kint

The subset number of dimensions out of D = T.shape[0]-dimensions to return the subspace for. Note that zero-based indexing is used.

includenumpy.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

discordsbool, default False

When set to True, this reverses the distance profile to favor discords rather than motifs. Note that indices in include are still maintained and respected.

discretize_funcfunc, default None

A function for discretizing each input array. When this is None, an appropriate discretization function (based on the normalize parameter) will be applied.

n_bitint, default 8

The number of bits used for discretization. For more information on an appropriate value, see Figure 4 in:

DOI: 10.1109/ICDM.2016.0069

and Figure 2 in:

DOI: 10.1109/ICDM.2011.54

normalizebool, default True

pfloat, default 2.0

T_subseq_isconstantnumpy.ndarray, function, or list, default None

Returns:

Snumpy.ndarray: An array that contains the (singular) k-th-dimensional subspace for the subsequence with index equal to subseq_idx. Note that k + 1 rows will be returned.

See also

stumpy.mstump: Compute the multi-dimensional z-normalized matrix profile
stumpy.mstumped: Compute the multi-dimensional z-normalized matrix profile with a dask/ray cluster
stumpy.mdl: Compute the number of bits needed to compress one array with another using the minimum description length (MDL)

Examples

>>> import stumpy
>>> import numpy as np
>>> mps, indices = stumpy.mstump(
...     np.array([[584., -11., 23., 79., 1001., 0., -19.],
...               [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...     m=3)
>>> motifs_idx = np.argsort(mps, axis=1)[:, :2]
>>> k = 1
>>> stumpy.subspace(
...     np.array([[584., -11., 23., 79., 1001., 0., -19.],
...               [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...     m=3,
...     subseq_idx=motifs_idx[k][0],
...     nn_idx=indices[k][motifs_idx[k][0]],
...     k=k)
array([0, 1])

mdl#

stumpy.mdl(T, m, subseq_idx, nn_idx, include=None, discords=False, discretize_func=None, n_bit=8, normalize=True, p=2.0, T_subseq_isconstant=None)[source]#

Compute the multi-dimensional number of bits needed to compress one multi-dimensional subsequence with another along each of the k-dimensions using the minimum description length (MDL)

Parameters:

Tnumpy.ndarray

The time series or sequence for which the multi-dimensional matrix profile, multi-dimensional matrix profile indices were computed.

mint

Window size.

subseq_idxnumpy.ndarray

The multi-dimensional subsequence indices in T

nn_idxnumpy.ndarray

The multi-dimensional nearest neighbor index in T

includenumpy.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

discordsbool, default False

When set to True, this reverses the distance profile to favor discords rather than motifs. Note that indices in include are still maintained and respected.

discretize_funcfunc, default None

A function for discretizing each input array. When this is None, an appropriate discretization function (based on the normalization parameter) will be applied.

n_bitint, default 8

The number of bits used for discretization and for computing the bit size. For more information on an appropriate value, see Figure 4 in:

DOI: 10.1109/ICDM.2016.0069

and Figure 2 in:

DOI: 10.1109/ICDM.2011.54

normalizebool, default True

pfloat, default 2.0

T_subseq_isconstantnumpy.ndarray, function, or list, default None

Returns:

bit_sizesnumpy.ndarray: The total number of bits computed from MDL for representing each pair of multidimensional subsequences.
Slist: A list of numpy.ndarrays that contain the k-th-dimensional subspaces.

See also

stumpy.mstump: Compute the multi-dimensional z-normalized matrix profile
stumpy.mstumped: Compute the multi-dimensional z-normalized matrix profile with a dask/ray cluster
stumpy.subspace: Compute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index

Examples

>>> import stumpy
>>> import numpy as np
>>> mps, indices = stumpy.mstump(
...     np.array([[584., -11., 23., 79., 1001., 0., -19.],
...               [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...     m=3)
>>> motifs_idx = np.argsort(mps, axis=1)[:, 0]
>>> stumpy.mdl(
...     np.array([[584., -11., 23., 79., 1001., 0., -19.],
...               [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...     m=3,
...     subseq_idx=motifs_idx,
...     nn_idx=indices[np.arange(motifs_idx.shape[0]), motifs_idx])
(array([ 80.      , 111.509775]), [array([1]), array([0, 1])])

atsc#

stumpy.atsc(IL, IR, j)[source]#

Compute the anchored time series chain (ATSC)

Note that since the matrix profile indices, IL and IR, are pre-computed, this function is agnostic to subsequence normalization.

Parameters:

ILnumpy.ndarray: Left matrix profile indices.
IRnumpy.ndarray: Right matrix profile indices.
jint: The index value for which to compute the ATSC.

Returns:

outnumpy.ndarray: Anchored time series chain for index, j

See also

stumpy.allc: Compute the all-chain set (ALLC)

Notes

DOI: 10.1109/ICDM.2017.79

See Table I

This is the implementation for the anchored time series chains (ATSC).

Unlike the original paper, we’ve replaced the while-loop with a more stable for-loop.

Examples

>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.atsc(mp[:, 2], mp[:, 3], 1)
array([1, 3])

>>> # Alternative example using named attributes
>>>
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.atsc(mp.left_I_, mp.right_I_, 1)
array([1, 3])

allc#

stumpy.allc(IL, IR)[source]#

Compute the all-chain set (ALLC)

Note that since the matrix profile indices, IL and IR, are pre-computed, this function is agnostic to subsequence normalization.

Parameters:

ILnumpy.ndarray: Left matrix profile indices.
IRnumpy.ndarray: Right matrix profile indices.

Returns:

Slist(numpy.ndarray): All-chain set.
Cnumpy.ndarray: Anchored time series chain for the longest chain (also known as the unanchored chain). Note that when there are multiple different chains with length equal to len(C), then only one chain from this set is returned. You may iterate over the all-chain set, S, to find all other possible chains with length len(C).

See also

stumpy.atsc: Compute the anchored time series chain (ATSC)

Notes

DOI: 10.1109/ICDM.2017.79

See Table II

Unlike the original paper, we’ve replaced the while-loop with a more stable for-loop.

This is the implementation for the all-chain set (ALLC) and the unanchored chain is simply the longest one among the all-chain set. Both the all-chain set and unanchored chain are returned.

The all-chain set, S, is returned as a list of unique numpy arrays.

Examples

>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.allc(mp[:, 2], mp[:, 3])
([array([1, 3]), array([2]), array([0, 4])], array([0, 4]))

>>> # Alternative example using named attributes
>>>
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.allc(mp.left_I_, mp.right_I_)
([array([1, 3]), array([2]), array([0, 4])], array([0, 4]))

fluss#

stumpy.fluss(I, L, n_regimes, excl_factor=5, custom_iac=None)[source]#

Compute the Fast Low-cost Unipotent Semantic Segmentation (FLUSS) for static data (i.e., batch processing)

Essentially, this is a wrapper to compute the corrected arc curve and regime locations. Note that since the matrix profile indices, I, are pre-computed, this function is agnostic to subsequence normalization.

Parameters:

Inumpy.ndarray: The matrix profile indices for the time series of interest.
Lint: The subsequence length that is set roughly to be one period length. This is likely to be the same value as the window size, m, used to compute the matrix profile and matrix profile index but it can be different since this is only used to manage edge effects and has no bearing on any of the IAC or CAC core calculations.
n_regimesint: The number of regimes to search for. This is one more than the number of regime changes as denoted in the original paper.
excl_factorint, default 5: The multiplying factor for the regime exclusion zone.
custom_iacnumpy.ndarray, default None: A custom idealized arc curve (IAC) that will used for correcting the arc curve.

Returns:

cacnumpy.ndarray: A corrected arc curve (CAC).
regime_locsnumpy.ndarray: The locations of the regimes.

See also

stumpy.floss: Compute the Fast Low-Cost Online Semantic Segmentation (FLOSS) for streaming data

Notes

DOI: 10.1109/ICDM.2017.21

See Section A

This is the implementation for Fast Low-cost Unipotent Semantic Segmentation (FLUSS).

Examples

>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.fluss(mp[:, 0], 3, 2)
(array([1., 1., 1., 1., 1.]), array([0]))

>>> # Alternative example using named attributes
>>>
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.fluss(mp.P_, 3, 2)
(array([1., 1., 1., 1., 1.]), array([0]))

floss#

stumpy.floss(mp, T, m, L, excl_factor=5, n_iter=1000, n_samples=1000, custom_iac=None, normalize=True, p=2.0, T_subseq_isconstant_func=None)[source]#

A class to compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for streaming data

Parameters:

mpnumpy.ndarray: The first column consists of the matrix profile, the second column consists of the matrix profile indices, the third column consists of the left matrix profile indices, and the fourth column consists of the right matrix profile indices.
Tnumpy.ndarray: A 1-D time series data used to generate the matrix profile and matrix profile indices found in mp. Note that the the right matrix profile index is used and the right matrix profile is intelligently recomputed on the fly from T instead of using the bidirectional matrix profile.
mint: The window size for computing sliding window mass. This is identical to the window size used in the matrix profile calculation. For managing edge effects, see the L parameter.
Lint: The subsequence length that is set roughly to be one period length. This is likely to be the same value as the window size, m, used to compute the matrix profile and matrix profile index but it can be different since this is only used to manage edge effects and has no bearing on any of the IAC or CAC core calculations.
excl_factorint, default 5: The multiplying factor for the regime exclusion zone. Note that this is unrelated to the excl_zone used in to compute the matrix profile.
n_iterint, default 1000: Number of iterations to average over when determining the parameters for the IAC beta distribution.
n_samplesint, default 1000: Number of distribution samples to draw during each iteration when computing the IAC.
custom_iacnumpy.ndarray, default None: A custom idealized arc curve (IAC) that will used for correcting the arc curve.
normalizebool, default True: When set to True, this z-normalizes subsequences prior to computing distances
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isconstant_funcfunction, default None: A custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Attributes:

cac_1d_numpy.ndarray: Get the updated 1-dimensional corrected arc curve (CAC_1D)
P_numpy.ndarray: Get the updated matrix profile
I_numpy.ndarray: Get the updated (right) matrix profile indices
T_numpy.ndarray: Get the updated time series, T

Methods

update(t)

Ingress a new data point, t, onto the time series, T, followed by egressing the oldest single data point from T. Then, update the 1-dimensional corrected arc curve (CAC_1D) and the matrix profile.

See also

stumpy.fluss: Compute the Fast Low-cost Unipotent Semantic Segmentation (FLUSS) for static data (i.e., batch processing)

Notes

DOI: 10.1109/ICDM.2017.21

See Section C

This is the implementation for Fast Low-cost Online Semantic Segmentation (FLOSS).

Examples

>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0.]), m=3)
>>> stream = stumpy.floss(
...     mp,
...     np.array([584., -11., 23., 79., 1001., 0.]),
...     m=3,
...     L=3)
>>> stream.update(19.)
>>> stream.cac_1d_
array([1., 1., 1., 1.])

ostinato#

stumpy.ostinato(Ts, m, normalize=True, p=2.0, Ts_subseq_isconstant=None)[source]#

Find the z-normalized consensus motif of multiple time series

This is a wrapper around the vanilla version of the ostinato algorithm which finds the best radius and a helper function that finds the most central conserved motif.

Parameters:

Tslist: A list of time series for which to find the most central consensus motif.
mint: Window size.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
Ts_subseq_isconstantlist, default None: A list of rolling window isconstant for each time series in Ts.

Returns:

central_radiusfloat: Radius of the most central consensus motif.
central_Ts_idxint: The time series index in Ts that contains the most central consensus motif.
central_subseq_idxint: The subsequence index within time series Ts[central_motif_Ts_idx] that contains the most central consensus motif.

See also

stumpy.ostinatoed: Find the z-normalized consensus motif of multiple time series with a dask/ray cluster
stumpy.gpu_ostinato: Find the z-normalized consensus motif of multiple time series with one or more GPU devices

Notes

DOI: 10.1109/ICDM.2019.00140

See Table 2

The ostinato algorithm proposed in the paper finds the best radius in Ts. Intuitively, the radius is the minimum distance of a subsequence to encompass at least one nearest neighbor subsequence from all other time series. The best radius in Ts is the minimum radius amongst all radii. Some data sets might contain multiple subsequences which have the same optimal radius. The greedy Ostinato algorithm only finds one of them, which might not be the most central motif. The most central motif amongst the subsequences with the best radius is the one with the smallest mean distance to nearest neighbors in all other time series. To find this central motif it is necessary to search the subsequences with the best radius via stumpy.ostinato._get_central_motif.

Examples

>>> import stumpy
>>> import numpy as np
>>> stumpy.ostinato(
...     [np.array([584., -11., 23., 79., 1001., 0., 19.]),
...      np.array([600., -10., 23., 17.]),
...      np.array([  1.,   9.,  6.,  0.])],
...     m=3)
(1.2370237678153826, 0, 4)

ostinatoed#

stumpy.ostinatoed(client, Ts, m, normalize=True, p=2.0, Ts_subseq_isconstant=None)[source]#

Find the z-normalized consensus motif of multiple time series with a dask/ray cluster

This is a wrapper around the vanilla version of the ostinato algorithm which finds the best radius and a helper function that finds the most central conserved motif.

Parameters:

clientclient: A dask/ray client. Setting up a dask/ray cluster is beyond the scope of this library. Please refer to the dask/ray Distributed documentation.
Tslist: A list of time series for which to find the most central consensus motif.
mint: Window size.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
Ts_subseq_isconstantlist, default None: A list of rolling window isconstant for each time series in Ts.

Returns:

central_radiusfloat: Radius of the most central consensus motif.
central_Ts_idxint: The time series index in Ts that contains the most central consensus motif.
central_subseq_idxint: The subsequence index within time series Ts[central_motif_Ts_idx] that contains the most central consensus motif.

See also

stumpy.ostinato: Find the z-normalized consensus motif of multiple time series
stumpy.gpu_ostinato: Find the z-normalized consensus motif of multiple time series with one or more GPU devices

Notes

DOI: 10.1109/ICDM.2019.00140

See Table 2

Examples

>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
>>>     with Client() as dask_client:
>>>         stumpy.ostinatoed(
...             dask_client,
...             [np.array([584., -11., 23., 79., 1001., 0., 19.]),
...              np.array([600., -10., 23., 17.]),
...              np.array([  1.,   9.,  6.,  0.])],
...             m=3)
(1.2370237678153826, 0, 4)

Alternatively, you can also use ray

>>> import ray
>>> if __name__ == "__main__":
>>>     ray.init()
>>>     stumpy.ostinatoed(
...         ray,
...         [np.array([584., -11., 23., 79., 1001., 0., 19.]),
...          np.array([600., -10., 23., 17.]),
...          np.array([  1.,   9.,  6.,  0.])],
...         m=3)
>>>     ray.shutdown()

gpu_ostinato#

stumpy.gpu_ostinato(Ts, m, device_id=0, normalize=True, p=2.0, Ts_subseq_isconstant=None)#

Find the z-normalized consensus motif of multiple time series with one or more GPU devices

This is a wrapper around the vanilla version of the ostinato algorithm which finds the best radius and a helper function that finds the most central conserved motif.

Parameters:

Tslist: A list of time series for which to find the most central consensus motif.
mint: Window size.
device_idint or list, default 0: The (GPU) device number to use. The default value is 0. A list of valid device ids (int) may also be provided for parallel GPU-STUMP computation. A list of all valid device ids can be obtained by executing [device.id for device in numba.cuda.list_devices()].
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
Ts_subseq_isconstantlist, default None: A list of rolling window isconstant for each time series in Ts.

Returns:

central_radiusfloat: Radius of the most central consensus motif.
central_Ts_idxint: The time series index in Ts that contains the most central consensus motif.
central_subseq_idxint: The subsequence index within time series Ts[central_motif_Ts_idx] that contains the most central consensus motif.

See also

stumpy.ostinato: Find the z-normalized consensus motif of multiple time series
stumpy.ostinatoed: Find the z-normalized consensus motif of multiple time series with a dask/ray cluster

Notes

DOI: 10.1109/ICDM.2019.00140

See Table 2

Examples

>>> import stumpy
>>> import numpy as np
>>> from numba import cuda
>>> if __name__ == "__main__":
...     all_gpu_devices = [device.id for device in cuda.list_devices()]
...     stumpy.gpu_ostinato(
...         [np.array([584., -11., 23., 79., 1001., 0., 19.]),
...          np.array([600., -10., 23., 17.]),
...          np.array([  1.,   9.,  6.,  0.])],
...         m=3,
...         device_id=all_gpu_devices)
(1.2370237678153826, 0, 4)

mpdist#

stumpy.mpdist(T_A, T_B, m, percentage=0.05, k=None, normalize=True, p=2.0, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)[source]#

Compute the z-normalized matrix profile distance (MPdist) measure between any two time series

The MPdist distance measure considers two time series to be similar if they share many subsequences, regardless of the order of matching subsequences. MPdist concatenates the output of an AB-join and a BA-join and returns the k-th smallest value as the reported distance. Note that MPdist is a measure and not a metric. Therefore, it does not obey the triangular inequality but the method is highly scalable.

Parameters:

T_Anumpy.ndarray: The first time series or sequence for which to compute the matrix profile.
T_Bnumpy.ndarray: The second time series or sequence for which to compute the matrix profile.
mint: Window size.
percentagefloat, default 0.05: The percentage of distances that will be used to report mpdist. The value is between 0.0 and 1.0.
kint: Specify the k-th value in the concatenated matrix profiles to return. When k is not None, then the percentage parameter is ignored.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

MPdistfloat: The matrix profile distance.

See also

mpdisted: Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with a dask/ray cluster
gpu_mpdist: Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices

Notes

DOI: 10.1109/ICDM.2018.00119

See Section III

Examples

>>> import stumpy
>>> import numpy as np
>>> stumpy.mpdist(
...     np.array([-11.1, 23.4, 79.5, 1001.0]),
...     np.array([584., -11., 23., 79., 1001., 0., -19.]),
...     m=3)
0.00019935236191097894

mpdisted#

stumpy.mpdisted(client, T_A, T_B, m, percentage=0.05, k=None, normalize=True, p=2.0, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)[source]#

Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with a dask/ray cluster

Parameters:

clientclient: A dask/ray client. Setting up a dask/ray cluster is beyond the scope of this library. Please refer to the dask/ray documentation.
T_Anumpy.ndarray: The first time series or sequence for which to compute the matrix profile.
T_Bnumpy.ndarray: The second time series or sequence for which to compute the matrix profile.
mint: Window size.
percentagefloat, default 0.05: The percentage of distances that will be used to report mpdist. The value is between 0.0 and 1.0. This parameter is ignored when k is not None.
kint: Specify the k-th value in the concatenated matrix profiles to return. When k is not None, then the percentage parameter is ignored.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

MPdistfloat: The matrix profile distance.

See also

mpdist: Compute the z-normalized matrix profile distance (MPdist) measure between any two time series
gpu_mpdist: Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices

Notes

DOI: 10.1109/ICDM.2018.00119

See Section III

Examples

>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
>>>     with Client() as dask_client:
>>>         stumpy.mpdisted(
...             dask_client,
...             np.array([-11.1, 23.4, 79.5, 1001.0]),
...             np.array([584., -11., 23., 79., 1001., 0., -19.]),
...             m=3)
0.00019935236191097894

Alternatively, you can also use ray

>>> import ray
>>> if __name__ == "__main__":
>>>     ray.init()
>>>     stumpy.mpdisted(
...         ray,
...         np.array([-11.1, 23.4, 79.5, 1001.0]),
...         np.array([584., -11., 23., 79., 1001., 0., -19.]),
...         m=3)
>>>     ray.shutdown()

gpu_mpdist#

stumpy.gpu_mpdist(T_A, T_B, m, percentage=0.05, k=None, device_id=0, normalize=True, p=2.0, T_A_subseq_isconstant=None, T_B_subseq_isconstant=None)#

Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices

The MPdist distance measure considers two time series to be similar if they share many subsequences, regardless of the order of matching subsequences. MPdist concatenates and sorts the output of an AB-join and a BA-join and returns the value of the k-th smallest number as the reported distance. Note that MPdist is a measure and not a metric. Therefore, it does not obey the triangular inequality but the method is highly scalable.

Parameters:

T_Anumpy.ndarray: The first time series or sequence for which to compute the matrix profile.
T_Bnumpy.ndarray: The second time series or sequence for which to compute the matrix profile.
mint: Window size.
percentagefloat, default 0.05: The percentage of distances that will be used to report mpdist. The value is between 0.0 and 1.0. This parameter is ignored when k is not None.
kint, default None: Specify the k-th value in the concatenated matrix profiles to return. When k is not None, then the percentage parameter is ignored.
device_idint or list, default 0: The (GPU) device number to use. The default value is 0. A list of valid device ids (int) may also be provided for parallel GPU-STUMP computation. A list of all valid device ids can be obtained by executing [device.id for device in numba.cuda.list_devices()].
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_A_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_A is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_A is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
T_B_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T_B is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T_B is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

MPdistfloat: The matrix profile distance.

Notes

DOI: 10.1109/ICDM.2018.00119

See Section III

Examples

>>> import stumpy
>>> import numpy as np
>>> from numba import cuda
>>> if __name__ == "__main__":
...     all_gpu_devices = [device.id for device in cuda.list_devices()]
...     stumpy.gpu_mpdist(
...         np.array([-11.1, 23.4, 79.5, 1001.0]),
...         np.array([584., -11., 23., 79., 1001., 0., -19.]),
...         m=3,
...         device_id=all_gpu_devices)
0.00019935236191097894

motifs#

stumpy.motifs(T, P, min_neighbors=1, max_distance=None, cutoff=None, max_matches=10, max_motifs=1, atol=1e-08, normalize=True, p=2.0, T_subseq_isconstant=None)[source]#

Discover the top motifs for time series T

A subsequence, Q, becomes a candidate motif if there are at least min_neighbor number of other subsequence matches in T (outside the exclusion zone) with a distance less or equal to max_distance.

Note that, in the best case scenario, the returned arrays would have shape (max_motifs, max_matches) and contain all finite values. However, in reality, many conditions (see below) need to be satisfied in order for this to be true. Any truncation in the number of rows (i.e., motifs) may be the result of insufficient candidate motifs with matches greater than or equal to min_neighbors or that the matrix profile value for the candidate motif was larger than cutoff. Similarly, any truncation in the number of columns (i.e., matches) may be the result of insufficient matches being found with distances (to their corresponding candidate motif) that are equal to or less than max_distance. Only motifs and matches that satisfy all of these constraints will be returned.

If you must return a shape of (max_motifs, max_matches), then you may consider specifying a smaller min_neighbors, a larger max_distance, and/or a larger cutoff. For example, while it is ill advised, setting min_neighbors=1, max_distance = np.inf, and cutoff = np.inf will ensure that the shape of the output arrays will be (max_motifs, max_matches). However, given the lack of constraints, the quality of each motif and the quality of each match may be drastically different. Setting appropriate conditions will help ensure appropriately constrained results that may be easier to interpret.

Parameters:

Tnumpy.ndarray: The time series or sequence.
Pnumpy.ndarray: The (1-dimensional) matrix profile of T. In the case where the matrix profile was computed with k > 1 (i.e., top-k nearest neighbors), you must summarize the top-k nearest-neighbor distances for each subsequence into a single value (e.g., np.mean, np.min, etc) and then use that derived value as your P.
min_neighborsint, default 1: The minimum number of similar matches a subsequence needs to have in order to be considered a motif. This defaults to 1, which means that a subsequence must have at least one similar match in order to be considered a motif.
max_distancefloat or function, default None: For a candidate motif, Q, and a non-trivial subsequence, S, max_distance is the maximum distance allowed between Q and S so that S is considered a match of Q. If max_distance is a function, then it must be a function that accepts a single parameter, D, in its function signature, which is the distance profile between Q and T. If None, this defaults to np.nanmax([np.nanmean(D) - 2.0 * np.nanstd(D), np.nanmin(D)]).
cutofffloat, default None: The largest matrix profile value (distance) that a candidate motif is allowed to have. If None, this defaults to np.nanmax([np.nanmean(P) - 2.0 * np.nanstd(P), np.nanmin(P)]).
max_matchesint, default 10: The maximum amount of similar matches of a motif representative to be returned. The resulting matches are sorted by distance, so a value of 10 means that the indices of the most similar 10 subsequences is returned. If None, all matches within max_distance of the motif representative will be returned. Note that the first match is always the self-match/trivial-match for each motif.
max_motifsint, default 1: The maximum number of motifs to return. To consider returning all possible valid motifs, try setting max_motifs to the length of your input matrix profile (i.e., max_motifs=len(P))
atolfloat, default 1e-8: The absolute tolerance parameter. This value will be added to max_distance when comparing distances between subsequences.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence in T is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

motif_distancesnumpy.ndarray: The distances corresponding to a set of subsequence matches for each motif. Note that the first column always corresponds to the distance for the self-match/trivial-match for each motif.
motif_indicesnumpy.ndarray: The indices corresponding to a set of subsequences matches for each motif. Note that the first column always corresponds to the index for the self-match/trivial-match for each motif.

See also

stumpy.match: Find all matches of a query Q in a time series T
stumpy.mmotifs: Discover the top motifs for the multi-dimensional time series T
stumpy.stump: Compute the z-normalized matrix profile
stumpy.stumped: Compute the z-normalized matrix profile with a dask/ray cluster
stumpy.gpu_stump: Compute the z-normalized matrix profile with one or more GPU devices
stumpy.scrump: Compute an approximate z-normalized matrix profile

Examples

>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.motifs(
...     np.array([584., -11., 23., 79., 1001., 0., -19.]),
...     mp[:, 0],
...     max_distance=2.0)
(array([[0.        , 0.11633857]]), array([[0, 4]]))

>>> # Alternative example using named attributes
>>>
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.motifs(
...     np.array([584., -11., 23., 79., 1001., 0., -19.]),
...     mp.P_,
...     max_distance=2.0)
(array([[0.        , 0.11633857]]), array([[0, 4]]))

match#

stumpy.match(Q, T, M_T=None, Σ_T=None, max_distance=None, max_matches=None, atol=1e-08, query_idx=None, normalize=True, p=2.0, T_subseq_isfinite=None, T_subseq_isconstant=None, Q_subseq_isconstant=None)[source]#

Find all matches of a query Q in a time series T

The indices of subsequences whose distances to Q are less than or equal to max_distance, sorted by distance (lowest to highest). Around each occurrence, an exclusion zone is applied before searching for the next.

Parameters:

Qnumpy.ndarray: The query sequence. Q does not have to be a subsequence of T.
Tnumpy.ndarray: The time series of interest.
M_Tnumpy.ndarray, default None: Sliding mean of time series, T.
Σ_Tnumpy.ndarray, default None: Sliding standard deviation of time series, T.
max_distancefloat or function, default None: Maximum distance between Q and a subsequence, S, for S to be considered a match. If max_distance is a function, then it must be a function that accepts a single parameter, D, in its function signature, which is the distance profile between Q and T (a 1D numpy array of size n - m + 1). If None, this defaults to np.nanmax([np.nanmean(D) - 2 * np.nanstd(D), np.nanmin(D)]) (i.e. at least the closest match will be returned).
max_matchesint, default None: The maximum amount of similar occurrences to be returned. The resulting occurrences are sorted by distance, so a value of 10 means that the indices of the most similar 10 subsequences is returned. If None, then all occurrences are returned.
atolfloat, default 1e-8: The absolute tolerance parameter. This value will be added to max_distance when comparing distances between subsequences.
query_idxint, default None: This is the index position along the time series, T, where the query subsequence, Q, is located. query_idx should only be used when the matrix profile is a self-join and should be set to None for matrix profiles computed from AB-joins. If query_idx is set to a specific integer value, then this will help ensure that the self-match will be returned first.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isfinitenumpy.ndarray: A boolean array that indicates whether a subsequence in T contains a np.nan/np.inf value (False). This parameter is ignored when normalize=True.
T_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence (of length equal to len(Q)) in T is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.
Q_subseq_isconstantnumpy.ndarray or function, default None: A boolean array (of size 1) that indicates whether Q is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in Q is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

outnumpy.ndarray: The first column consists of distances of subsequences of T whose distances to Q are less than or equal to max_distance, sorted by distance (lowest to highest). The second column consists of the corresponding indices in T.

See also

stumpy.motifs: Discover the top motifs for time series T
stumpy.mmotifs: Discover the top motifs for the multi-dimensional time series T
stumpy.stump: Compute the z-normalized matrix profile
stumpy.stumped: Compute the z-normalized matrix profile with a dask/ray cluster
stumpy.gpu_stump: Compute the z-normalized matrix profile with one or more GPU devices
stumpy.scrump: Compute an approximate z-normalized matrix profile

Examples

>>> import stumpy
>>> import numpy as np
>>> stumpy.match(
...     np.array([-11.1, 23.4, 79.5, 1001.0]),
...     np.array([584., -11., 23., 79., 1001., 0., -19.])
...     )
array([[0.0011129739290248121, 1]], dtype=object)

mmotifs#

stumpy.mmotifs(T, P, I, min_neighbors=1, max_distance=None, cutoffs=None, max_matches=10, max_motifs=1, atol=1e-08, k=None, include=None, normalize=True, p=2.0, T_subseq_isconstant=None)[source]#

Discover the top motifs for the multi-dimensional time series T.

Parameters:

Tnumpy.ndarray: The multi-dimensional time series or sequence.
Pnumpy.ndarray: Multi-dimensional Matrix Profile of T.
Inumpy.ndarray: Multi-dimensional Matrix Profile indices.
min_neighborsint, default 1: The minimum number of similar matches a subsequence needs to have in order to be considered a motif. This defaults to 1, which means that a subsequence must have at least one similar match in order to be considered a motif.
max_distancefloat, default None: Maximal distance that is allowed between a query subsequence (a candidate motif) and all subsequences in T to be considered as a match. If None, this defaults to np.nanmax([np.nanmean(D) - 2 * np.nanstd(D), np.nanmin(D)]) (i.e. at least the closest match will be returned).
cutoffsnumpy.ndarray or float, default None: The largest matrix profile value (distance) for each dimension of the multidimensional matrix profile that a multidimenisonal candidate motif is allowed to have. If cutoffs is a scalar value, then this value will be applied to every dimension.
max_matchesint, default 10: The maximum number of similar matches (nearest neighbors) to return for each motif. The first match is always the self/trivial-match for each motif.
max_motifsint, default 1: The maximum number of motifs to return. To consider returning all possible valid motifs, try setting max_motifs to the length of your input matrix profile (i.e., max_motifs=len(P))
atolfloat, default 1e-8: The absolute tolerance parameter. This value will be added to max_distance when comparing distances between subsequences.
kint, default None: The number of dimensions (k + 1) required for discovering all motifs. This value is available for doing guided search or, together with include, for constrained search. If k is None, then this will be automatically be computed for each motif using MDL (unconstrained search).
includenumpy.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.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isconstantnumpy.ndarray, function, or list, default None: A parameter that is used to show whether a subsequence of a time series in T is constant (True) or not. T_subseq_isconstant can be a 2D boolean numpy.ndarray or a function that can be applied to each time series in T. Alternatively, for maximum flexibility, a list (with length equal to the total number of time series) may also be used. In this case, T_subseq_isconstant[i] corresponds to the i-th time series T[i] and each element in the list can either be a 1D boolean numpy.ndarray, a function, or None.

Returns:

motif_distances: numpy.ndarray: The distances corresponding to a set of subsequence matches for each motif.
motif_indices: numpy.ndarray: The indices corresponding to a set of subsequences matches for each motif.
motif_subspaces: list: A list consisting of arrays that contain the k-dimensional subspace for each motif.
motif_mdls: list: A list consisting of arrays that contain the mdl results for finding the dimension of each motif.

See also

stumpy.motifs: Find the top motifs for time series T
stumpy.match: Find all matches of a query Q in a time series T
stumpy.mstump: Compute the multi-dimensional z-normalized matrix profile
stumpy.mstumped: Compute the multi-dimensional z-normalized matrix profile with a dask/ray cluster
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

For more information on include and search types, see Section IV D and IV E

Examples

>>> import stumpy
>>> import numpy as np
>>> mps, indices = stumpy.mstump(
...     np.array([[584., -11., 23., 79., 1001., 0., -19.],
...               [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...     m=3)
>>> stumpy.mmotifs(
...     np.array([[584., -11., 23., 79., 1001., 0., -19.],
...               [  1.,   2.,  4.,  8.,   16., 0.,  32.]]),
...     mps,
...     indices)
(array([[4.47034836e-08, 4.47034836e-08]]),  array([[0, 2]]), [array([1])],
 [array([ 80.      , 111.509775])])

snippets#

stumpy.snippets(T, m, k, percentage=1.0, s=None, mpdist_percentage=0.05, mpdist_k=None, normalize=True, p=2.0, mpdist_T_subseq_isconstant=None)[source]#

Identify the top k snippets that best represent the time series, T

Parameters:

Tnumpy.ndarray: The time series or sequence for which to find the snippets.
mint: The snippet window size.
kint: The desired number of snippets.
percentagefloat, default 1.0: With the length of each non-overlapping subsequence, S[i], set to m, this is the percentage of S[i] (i.e., percentage * m) to set s (the sub-subsequence length) to. When percentage == 1.0, then the full length of S[i] is used to compute the mpdist_vect. When percentage < 1.0, then a shorter sub-subsequence length of s = min(math.ceil(percentage * m), m) from each S[i] is used to compute mpdist_vect. When s is not None, then the percentage parameter is ignored.
sint, default None: With the length of each non-overlapping subsequence, S[i], set to m, this is essentially the sub-subsequence length (i.e., a shorter part of S[i]). When s == m, then the full length of S[i] is used to compute the mpdist_vect. When s < m, then shorter subsequences with length s from each S[i] is used to compute mpdist_vect. When s is not None, then the percentage parameter is ignored.
mpdist_percentagefloat, default 0.05: The percentage of distances that will be used to report mpdist. The value is between 0.0 and 1.0.
mpdist_kint: Specify the k-th value in the concatenated matrix profiles to return. When mpdist_k is not None, then the mpdist_percentage parameter is ignored.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
mpdist_T_subseq_isconstantnumpy.ndarray or function, default None: A boolean array that indicates whether a subsequence (of length equal to len(s)) in T is constant (True). Alternatively, a custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Returns:

snippetsnumpy.ndarray: The top k snippets.
snippets_indicesnumpy.ndarray: The index locations for each of top k snippets.
snippets_profilesnumpy.ndarray: The MPdist profiles for each of the top k snippets.
snippets_fractionsnumpy.ndarray: The fraction of data that each of the top k snippets represents.
snippets_areasnumpy.ndarray: The area under the curve corresponding to each profile for each of the top k snippets.
snippets_regimes: numpy.ndarray: The index slices corresponding to the set of regimes for each of the top k snippets. The first column is the (zero-based) snippet index while the second and third columns correspond to the (inclusive) regime start indices and the (exclusive) regime stop indices, respectively.

Notes

DOI: 10.1109/ICBK.2018.00058

See Table I

Examples

>>> import stumpy
>>> import numpy as np
>>> stumpy.snippets(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3, k=2)
(array([[ 584.,  -11.,   23.],
        [  79., 1001.,    0.]]),
 array([0, 3]),
 array([[0.        , 3.2452632 , 3.00009263, 2.982409  , 0.11633857],
        [2.982409  , 2.69407392, 3.01719586, 0.        , 2.92154586]]),
array([0.6, 0.4]),
array([9.3441034 , 5.81050512]),
array([[0, 0, 1],
       [0, 2, 3],
       [0, 4, 5],
       [1, 1, 2],
       [1, 3, 4]]))

stimp#

stumpy.stimp(T, min_m=3, max_m=None, step=1, percentage=0.01, pre_scrump=True, normalize=True, p=2.0, T_subseq_isconstant_func=None)[source]#

A class to compute the Pan Matrix Profile

This is based on the SKIMP algorithm.

Parameters:

Tnumpy.ndarray: The time series or sequence for which to compute the pan matrix profile.
min_mint, default 3: The starting (or minimum) subsequence window size for which a matrix profile may be computed.
max_mint, default None: The stopping (or maximum) subsequence window size for which a matrix profile may be computed. When max_m = None, this is set to the maximum allowable subsequence window size.
stepint, default 1: The step between subsequence window sizes.
percentagefloat, default 0.01: The percentage of the full matrix profile to compute for each subsequence window size. When percentage < 1.0, then the scrump algorithm is used. Otherwise, the stump algorithm is used when the exact matrix profile is requested.
pre_scrumpbool, default True: A flag for whether or not to perform the PreSCRIMP calculation prior to computing SCRIMP. If set to True, this is equivalent to computing SCRIMP++. This parameter is ignored when percentage = 1.0.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isconstant_funcfunction, default None: A custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Attributes:

PAN_numpy.ndarray: The transformed (i.e., normalized, contrasted, binarized, and repeated) pan matrix profile.
M_numpy.ndarray: The full list of (breadth first search (level) ordered) subsequence window sizes.

Methods

update():

Compute the next matrix profile using the next available (breadth-first-search (level) ordered) subsequence window size and update the pan matrix profile

See also

stumpy.stimped: Compute the Pan Matrix Profile with a dask/ray cluster
stumpy.gpu_stimp: Compute the Pan Matrix Profile with with one or more GPU devices

Notes

DOI: 10.1109/ICBK.2019.00031

See Table 2

Examples

>>> import stumpy
>>> import numpy as np
>>> pmp = stumpy.stimp(np.array([584., -11., 23., 79., 1001., 0., -19.]))
>>> pmp.update()
>>> pmp.PAN_
array([[0., 1., 1., 1., 1., 1., 1.],
       [0., 1., 1., 1., 1., 1., 1.]])

stimped#

stumpy.stimped(client, T, min_m=3, max_m=None, step=1, normalize=True, p=2.0, T_subseq_isconstant_func=None)[source]#

A class to compute the Pan Matrix Profile with a dask/ray cluster

This is based on the SKIMP algorithm.

Parameters:

clientclient: A dask/ray client. Setting up a dask/ray cluster is beyond the scope of this library. Please refer to the dask/ray documentation.
Tnumpy.ndarray: The time series or sequence for which to compute the pan matrix profile.
min_mint, default 3: The starting (or minimum) subsequence window size for which a matrix profile may be computed.
max_mint, default None: The stopping (or maximum) subsequence window size for which a matrix profile may be computed. When max_m = None, this is set to the maximum allowable subsequence window size
stepint, default 1: The step between subsequence window sizes.
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isconstant_funcfunction, default None: A custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Attributes:

PAN_numpy.ndarray: The transformed (i.e., normalized, contrasted, binarized, and repeated) pan matrix profile.
M_numpy.ndarray: The full list of (breadth first search (level) ordered) subsequence window sizes.

Methods

update():

Compute the next matrix profile using the next available (breadth-first-search (level) ordered) subsequence window size and update the pan matrix profile.

See also

stumpy.stimp: Compute the Pan Matrix Profile
stumpy.gpu_stimp: Compute the Pan Matrix Profile with with one or more GPU devices

Notes

DOI: 10.1109/ICBK.2019.00031

See Table 2

Examples

>>> import stumpy
>>> import numpy as np
>>> from dask.distributed import Client
>>> if __name__ == "__main__":
...     with Client() as dask_client:
...         pmp = stumpy.stimped(
...             dask_client,
...             np.array([584., -11., 23., 79., 1001., 0., -19.]))
...         pmp.update()
...         pmp.PAN_
array([[0., 1., 1., 1., 1., 1., 1.],
       [0., 1., 1., 1., 1., 1., 1.]])

Alternatively, you can also use ray

>>> import ray
>>> if __name__ == "__main__":
>>>     ray.init()
>>>     pmp = stumpy.stimped(
...         ray,
...         np.array([584., -11., 23., 79., 1001., 0., -19.]))
>>>     ray.shutdown()

gpu_stimp#

stumpy.gpu_stimp(T, min_m=3, max_m=None, step=1, device_id=0, normalize=True, p=2.0, T_subseq_isconstant_func=None)#

A class to compute the Pan Matrix Profile with with one or more GPU devices

This is based on the SKIMP algorithm.

Parameters:

Tnumpy.ndarray: The time series or sequence for which to compute the pan matrix profile.
min_mint, default 3: The starting (or minimum) subsequence window size for which a matrix profile may be computed.
max_mint, default None: The stopping (or maximum) subsequence window size for which a matrix profile may be computed. When m_stop = None, this is set to the maximum allowable subsequence window size.
stepint, default 1: The step between subsequence window sizes.
device_idint or list, default 0: The (GPU) device number to use. The default value is 0. A list of valid device ids (int) may also be provided for parallel GPU-STUMP computation. A list of all valid device ids can be obtained by executing [device.id for device in numba.cuda.list_devices()].
normalizebool, 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.
pfloat, default 2.0: The p-norm to apply for computing the Minkowski distance. Minkowski distance is typically used with p being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. This parameter is ignored when normalize == True.
T_subseq_isconstant_funcfunction, default None: A custom, user-defined function that returns a boolean array that indicates whether a subsequence in T is constant (True). The function must only take two arguments, a, a 1-D array, and w, the window size, while additional arguments may be specified by currying the user-defined function using functools.partial. Any subsequence with at least one np.nan/np.inf will automatically have its corresponding value set to False in this boolean array.

Attributes:

PAN_numpy.ndarray: The transformed (i.e., normalized, contrasted, binarized, and repeated) pan matrix profile.
M_numpy.ndarray: The full list of (breadth first search (level) ordered) subsequence window sizes.

Methods

update():

Compute the next matrix profile using the next available (breadth-first-search (level) ordered) subsequence window size and update the pan matrix profile.

See also

stumpy.stimp: Compute the Pan Matrix Profile
stumpy.stimped: Compute the Pan Matrix Profile with a dask/ray cluster

Notes

DOI: 10.1109/ICBK.2019.00031

See Table 2

Examples

>>> import stumpy
>>> import numpy as np
>>> from numba import cuda
>>> if __name__ == "__main__":
...     all_gpu_devices = [device.id for device in cuda.list_devices()]
...     pmp = stumpy.gpu_stimp(
...         np.array([584., -11., 23., 79., 1001., 0., -19.]),
...         device_id=all_gpu_devices)
...     pmp.update()
...     pmp.PAN_
array([[0., 1., 1., 1., 1., 1., 1.],
       [0., 1., 1., 1., 1., 1., 1.]])

STUMPY API#

Have A Question?#

stump#

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scrump#

stumpi#

mstump#

mstumped#

subspace#

mdl#

atsc#

allc#

fluss#

floss#

ostinato#

ostinatoed#

gpu_ostinato#

mpdist#

mpdisted#

gpu_mpdist#

motifs#

match#

mmotifs#

snippets#

stimp#

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