STUMPY API#
Overview
Compute the z-normalized matrix profile |
|
Compute the z-normalized matrix profile with a distributed dask/ray cluster |
|
Compute the z-normalized matrix profile with one or more GPU devices |
|
Compute the distance profile using the MASS algorithm |
|
Compute an approximate z-normalized matrix profile |
|
Compute an incremental z-normalized matrix profile for streaming data |
|
Compute the multi-dimensional z-normalized matrix profile |
|
Compute the multi-dimensional z-normalized matrix profile with a distributed dask/ray cluster |
|
Compute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index |
|
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) |
|
Compute the anchored time series chain (ATSC) |
|
Compute the all-chain set (ALLC) |
|
Compute the Fast Low-cost Unipotent Semantic Segmentation (FLUSS) for static data (i.e., batch processing) |
|
Compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for streaming data |
|
Find the z-normalized consensus motif of multiple time series |
|
Find the z-normalized consensus motif of multiple time series with a distributed cluster |
|
Find the z-normalized consensus motif of multiple time series with one or more GPU devices |
|
Compute the z-normalized matrix profile distance (MPdist) measure between any two time series |
|
Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with a distributed dask/ray cluster |
|
Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices |
|
Discover the top motifs for time series T |
|
Find all matches of a query Q in a time series T |
|
Discover the top motifs for the multi-dimensional time series T |
|
Identify the top k snippets that best represent the time series, T |
|
Compute the Pan Matrix Profile |
|
Compute the Pan Matrix Profile with a distributed dask/ray cluster |
|
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_A (numpy.ndarray) – The time series or sequence for which to compute the matrix profile
m (int) – Window size
T_B (numpy.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_trivial (bool, default True) – Set to True if this is a self-join. Otherwise, for AB-join, set this to False. Default is True.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
k (int, 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_isconstant (numpy.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_isconstant (numpy.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
out – 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:2k]) consists of the corresponding top-k matrix profile indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+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.
- Return type
numpy.ndarray
See also
stumpy.stumpedCompute the z-normalized matrix profile with a distributed dask cluster
stumpy.gpu_stumpCompute the z-normalized matrix profile with one or more GPU devices
stumpy.scrumpCompute 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.
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.
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 >>> stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3) array([[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)
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 distributed 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
client (client) – A Dask or Ray Distributed client. Setting up a distributed cluster is beyond the scope of this library. Please refer to the Dask or Ray Distributed documentation.
T_A (numpy.ndarray) – The time series or sequence for which to compute the matrix profile
m (int) – Window size
T_B (numpy.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_trivial (bool, default True) – Set to True if this is a self-join. Otherwise, for AB-join, set this to False. Default is True.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
k (int, 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_isconstant (numpy.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_isconstant (numpy.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
out – 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:2k]) consists of the corresponding top-k matrix profile indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+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.
- Return type
numpy.ndarray
See also
stumpy.stumpCompute the z-normalized matrix profile cluster
stumpy.gpu_stumpCompute the z-normalized matrix profile with one or more GPU devices
stumpy.scrumpCompute 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.
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.
See Table II
This is a Dask distributed 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.
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 >>> 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) array([[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)
gpu_stump#
- stumpy.gpu_stump(T_A, m, T_B=None, ignore_trivial=True, device_id=0, normalize=True, p=2.0)#
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_A (numpy.ndarray) – The time series or sequence for which to compute the matrix profile
m (int) – Window size
T_B (numpy.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_trivial (bool, default True) – Set to True if this is a self-join. Otherwise, for AB-join, set this to False. Default is True.
device_id (int 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()].
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
k (int, 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_isconstant (numpy.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_isconstant (numpy.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
out – 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:2k]) consists of the corresponding top-k matrix profile indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+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.
- Return type
numpy.ndarray
See also
stumpy.stumpCompute the z-normalized matrix profile
stumpy.stumpedCompute the z-normalized matrix profile with a distributed dask cluster
stumpy.scrumpCompute an approximate z-normalized matrix profile
Notes
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.
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 >>> from numba import cuda >>> if __name__ == "__main__": ... all_gpu_devices = [device.id for device in cuda.list_devices()] ... stumpy.gpu_stump( ... np.array([584., -11., 23., 79., 1001., 0., -19.]), ... m=3, ... device_id=all_gpu_devices) array([[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)
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)[source]#
Compute the distance profile using the MASS algorithm
This is a convenience wrapper around the Numba JIT compiled _mass function.
- Parameters
Q (numpy.ndarray) – Query array or subsequence
T (numpy.ndarray) – Time series or sequence
M_T (numpy.ndarray, default None) – Sliding mean of T
Σ_T (numpy.ndarray, default None) – Sliding standard deviation of T
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
T_subseq_isfinite (numpy.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_isconstant (numpy.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_isconstant (numpy.ndarray or function, default None) – A boolean array that indicates whether a subsequence in 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
distance_profile – Distance profile
- Return type
numpy.ndarray
See also
stumpy.motifsDiscover the top motifs for time series T
stumpy.matchFind all matches of a query Q in a time series T``
Notes
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)[source]#
Compute 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_A (numpy.ndarray) – The time series or sequence for which to compute the matrix profile
T_B (numpy.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.
m (int) – Window size
ignore_trivial (bool) – Set to True if this is a self-join. Otherwise, for AB-join, set this to False. Default is True.
percentage (float) – Approximate percentage completed. The value is between 0.0 and 1.0.
pre_scrump (bool) – 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
s (int) – 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)), the size of the exclusion zone.
normalize (bool, 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.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
k (int, 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.
- stumpy.P_#
The updated (top-k) matrix profile. When k=1 (default), this output is a 1D array consisting of the matrix profile. When k > 1, the output is a 2D array that has exactly k columns consisting of the top-k matrix profile.
- Type
numpy.ndarray
- stumpy.I_#
The updated (top-k) matrix profile indices. When k=1 (default), this output is a 1D array consisting of the matrix profile indices. When k > 1, the output is a 2D array that has exactly k columns consisting of the top-k matrix profile indiecs.
- Type
numpy.ndarray
- stumpy.left_I_#
The updated left (top-1) matrix profile indices
- Type
numpy.ndarray
- stumpy.right_I_#
The updated right (top-1) matrix profile indices
- Type
numpy.ndarray
- stumpy.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.stumpCompute the z-normalized matrix profile
stumpy.stumpedCompute the z-normalized matrix profile with a distributed dask cluster
stumpy.gpu_stumpCompute the z-normalized matrix profile with one or more GPU devices
Notes
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)[source]#
Compute an incremental z-normalized matrix profile for streaming data
This is based on the on-line STOMPI and STAMPI algorithms.
- Parameters
T (numpy.ndarray) – The time series or sequence for which the matrix profile and matrix profile indices will be returned
m (int) – Window size
egress (bool, 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
normalize (bool, 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.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
k (int, 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.
- stumpy.P_#
The updated (top-k) matrix profile for T. When k=1 (default), the first (and only) column in this 2D array consists of the matrix profile. When k > 1, the output has exactly k columns consisting of the top-k matrix profile.
- Type
numpy.ndarray
- stumpy.I_#
The updated (top-k) matrix profile indices for T. When k=1 (default), the first (and only) column in this 2D array consists of the matrix profile indices. When k > 1, the output has exactly k columns consisting of the top-k matrix profile indices.
- Type
numpy.ndarray
- stumpy.left_P_#
The updated left (top-1) matrix profile for T
- Type
numpy.ndarray
- stumpy.left_I_#
The updated left (top-1) matrix profile indices for T
- Type
numpy.ndarray
- stumpy.T_#
The updated time series or sequence for which the matrix profile and matrix profile indices are computed
- Type
numpy.ndarray
- stumpy.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)[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
T (numpy.ndarray) – The time series or sequence for which to compute the multi-dimensional matrix profile. Each row in T represents data from a different dimension while each column in T represents data from the same dimension.
m (int) – Window size
include (list, numpy.ndarray, default None) –
A list of (zero-based) indices corresponding to the dimensions in T that must be included in the constrained multidimensional motif search. For more information, see Section IV D in:
discords (bool, default False) – When set to True, this reverses the distance matrix which results in a multi-dimensional matrix profile that favors larger matrix profile values (i.e., discords) rather than smaller values (i.e., motifs). Note that indices in include are still maintained and respected.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
P (numpy.ndarray) – The multi-dimensional matrix profile. Each row of the array corresponds to each matrix profile for a given dimension (i.e., the first row is the 1-D matrix profile and the second row is the 2-D matrix profile).
I (numpy.ndarray) – The multi-dimensional matrix profile index where each row of the array corresponds to each matrix profile index for a given dimension.
See also
stumpy.mstumpedCompute the multi-dimensional z-normalized matrix profile with a distributed dask cluster
stumpy.subspaceCompute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index
stumpy.mdlCompute the number of bits needed to compress one array with another using the minimum description length (MDL)
Notes
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, normalize=True)[source]#
Compute the multi-dimensional z-normalized matrix profile with a distributed 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
client (client) – A Dask Distributed client that is connected to a Dask scheduler and Dask workers. Setting up a Dask distributed cluster is beyond the scope of this library. Please refer to the Dask Distributed documentation.
T (numpy.ndarray) – The time series or sequence for which to compute the multi-dimensional matrix profile. Each row in T represents data from a different dimension while each column in T represents data from the same dimension.
m (int) – Window size
include (list, numpy.ndarray, default None) –
A list of (zero-based) indices corresponding to the dimensions in T that must be included in the constrained multidimensional motif search. For more information, see Section IV D in:
discords (bool, default False) – When set to True, this reverses the distance matrix which results in a multi-dimensional matrix profile that favors larger matrix profile values (i.e., discords) rather than smaller values (i.e., motifs). Note that indices in include are still maintained and respected.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
- Returns
P (numpy.ndarray) – The multi-dimensional matrix profile. Each row of the array corresponds to each matrix profile for a given dimension (i.e., the first row is the 1-D matrix profile and the second row is the 2-D matrix profile).
I (numpy.ndarray) – The multi-dimensional matrix profile index where each row of the array corresponds to each matrix profile index for a given dimension.
See also
stumpy.mstumpCompute the multi-dimensional z-normalized matrix profile
stumpy.subspaceCompute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index
stumpy.mdlCompute the number of bits needed to compress one array with another using the minimum description length (MDL)
Notes
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( ... 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]]))
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)[source]#
Compute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index
- Parameters
T (numpy.ndarray) – The time series or sequence for which the multi-dimensional matrix profile, multi-dimensional matrix profile indices were computed
m (int) – Window size
subseq_idx (int) – The subsequence index in T
nn_idx (int) – The nearest neighbor index in T
k (int) – The subset number of dimensions out of D = T.shape[0]-dimensions to return the subspace for. Note that zero-based indexing is used.
include (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:
discords (bool, 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_func (func, 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_bit (int, default 8) –
The number of bits used for discretization. For more information on an appropriate value, see Figure 4 in:
and Figure 2 in:
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
S – 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.
- Return type
numpy.ndarray
See also
stumpy.mstumpCompute the multi-dimensional z-normalized matrix profile
stumpy.mstumpedCompute the multi-dimensional z-normalized matrix profile with a distributed dask cluster
stumpy.mdlCompute 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)[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
T (numpy.ndarray) – The time series or sequence for which the multi-dimensional matrix profile, multi-dimensional matrix profile indices were computed
m (int) – Window size
subseq_idx (numpy.ndarray) – The multi-dimensional subsequence indices in T
nn_idx (numpy.ndarray) – The multi-dimensional nearest neighbor index in T
include (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:
discords (bool, 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_func (func, 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_bit (int, 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:
and Figure 2 in:
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
bit_sizes (numpy.ndarray) – The total number of bits computed from MDL for representing each pair of multidimensional subsequences.
S (list) – A list of numpy.ndarrays that contain the `k`th-dimensional subspaces
See also
stumpy.mstumpCompute the multi-dimensional z-normalized matrix profile
stumpy.mstumpedCompute the multi-dimensional z-normalized matrix profile with a distributed dask cluster
stumpy.subspaceCompute 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
IL (numpy.ndarray) – Left matrix profile indices
IR (numpy.ndarray) – Right matrix profile indices
j (int) – The index value for which to compute the ATSC
- Returns
out – Anchored time series chain for index, j
- Return type
numpy.ndarray
See also
stumpy.allcCompute the all-chain set (ALLC)
Notes
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])
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
IL (numpy.ndarray) – Left matrix profile indices
IR (numpy.ndarray) – Right matrix profile indices
- Returns
S (list(numpy.ndarray)) – All-chain set
C (numpy.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.atscCompute the anchored time series chain (ATSC)
Notes
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]))
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
I (numpy.ndarray) – The matrix profile indices for the time series of interest
L (int) – 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_regimes (int) – The number of regimes to search for. This is one more than the number of regime changes as denoted in the original paper.
excl_factor (int, default 5) – The multiplying factor for the regime exclusion zone
custom_iac (numpy.ndarray, default None) – A custom idealized arc curve (IAC) that will used for correcting the arc curve
- Returns
cac (numpy.ndarray) – A corrected arc curve (CAC)
regime_locs (numpy.ndarray) – The locations of the regimes
See also
stumpy.flossCompute the Fast Low-Cost Online Semantic Segmentation (FLOSS) for streaming data
Notes
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]))
floss#
- stumpy.floss(mp, T, m, L, excl_factor=5, n_iter=1000, n_samples=1000, custom_iac=None, normalize=True, p=2.0)[source]#
Compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for streaming data
- Parameters
mp (numpy.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.
T (numpy.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.
m (int) – 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.
L (int) – 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_factor (int, 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_iter (int, default 1000) – Number of iterations to average over when determining the parameters for the IAC beta distribution
n_samples (int, default 1000) – Number of distribution samples to draw during each iteration when computing the IAC
custom_iac (numpy.ndarray, default None) – A custom idealized arc curve (IAC) that will used for correcting the arc curve
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances
- stumpy.cac_1d_#
A 1-dimensional corrected arc curve (CAC) updated as a result of ingressing a single new data point and egressing a single old data point.
- Type
numpy.ndarray
- stumpy.P_#
The matrix profile updated as a result of ingressing a single new data point and egressing a single old data point.
- Type
numpy.ndarray
- stumpy.I_#
The (right) matrix profile indices updated as a result of ingressing a single new data point and egressing a single old data point.
- Type
numpy.ndarray
- stumpy.T_#
The updated time series, T
- Type
numpy.ndarray
- stumpy.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.flussCompute the Fast Low-cost Unipotent Semantic Segmentation (FLUSS) for static data (i.e., batch processing)
Notes
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)[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
Ts (list) – A list of time series for which to find the most central consensus motif
m (int) – Window size
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
central_radius (float) – Radius of the most central consensus motif
central_Ts_idx (int) – The time series index in Ts which contains the most central consensus motif
central_subseq_idx (int) – The subsequence index within time series Ts[central_motif_Ts_idx] the contains most central consensus motif
See also
stumpy.ostinatoedFind the z-normalized consensus motif of multiple time series with a distributed cluster
stumpy.gpu_ostinatoFind the z-normalized consensus motif of multiple time series with one or more GPU devices
Notes
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)[source]#
Find the z-normalized consensus motif of multiple time series with a distributed 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
client (client) – A Dask or Ray Distributed client. Setting up a distributed cluster is beyond the scope of this library. Please refer to the Dask or Ray Distributed documentation.
Ts (list) – A list of time series for which to find the most central consensus motif
m (int) – Window size
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
central_radius (float) – Radius of the most central consensus motif
central_Ts_idx (int) – The time series index in Ts which contains the most central consensus motif
central_subseq_idx (int) – The subsequence index within time series Ts[central_motif_Ts_idx] the contains most central consensus motif
See also
stumpy.ostinatoFind the z-normalized consensus motif of multiple time series
stumpy.gpu_ostinatoFind the z-normalized consensus motif of multiple time series with one or more GPU devices
Notes
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 >>> 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)
gpu_ostinato#
- stumpy.gpu_ostinato(Ts, m, device_id=0, normalize=True, p=2.0)#
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
Ts (list) – A list of time series for which to find the most central consensus motif
m (int) – Window size
device_id (int 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()].
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
central_radius (float) – Radius of the most central consensus motif
central_Ts_idx (int) – The time series index in Ts which contains the most central consensus motif
central_subseq_idx (int) – The subsequence index within time series Ts[central_motif_Ts_idx] the contains most central consensus motif
See also
stumpy.ostinatoFind the z-normalized consensus motif of multiple time series
stumpy.ostinatoedFind the z-normalized consensus motif of multiple time series with a distributed dask cluster
Notes
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 >>> 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)[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_A (numpy.ndarray) – The first time series or sequence for which to compute the matrix profile
T_B (numpy.ndarray) – The second time series or sequence for which to compute the matrix profile
m (int) – Window size
percentage (float, default 0.05) – The percentage of distances that will be used to report mpdist. The value is between 0.0 and 1.0.
k (int) – Specify the k`th value in the concatenated matrix profiles to return. When `k is not None, then the percentage parameter is ignored.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
MPdist – The matrix profile distance
- Return type
See also
mpdistedCompute the z-normalized matrix profile distance (MPdist) measure between any two time series with a distributed dask cluster
gpu_mpdistCompute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices
Notes
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)[source]#
Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with a distributed dask/ray cluster
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
client (client) – A Dask or Ray Distributed client. Setting up a distributed cluster is beyond the scope of this library. Please refer to the Dask or Ray Distributed documentation.
T_A (numpy.ndarray) – The first time series or sequence for which to compute the matrix profile
T_B (numpy.ndarray) – The second time series or sequence for which to compute the matrix profile
m (int) – Window size
percentage (float, 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.
k (int) – Specify the k`th value in the concatenated matrix profiles to return. When `k is not None, then the percentage parameter is ignored.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
MPdist – The matrix profile distance
- Return type
See also
mpdistCompute the z-normalized matrix profile distance (MPdist) measure between any two time series
gpu_mpdistCompute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices
Notes
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
gpu_mpdist#
- stumpy.gpu_mpdist(T_A, T_B, m, percentage=0.05, k=None, device_id=0, normalize=True, p=2.0)#
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_A (numpy.ndarray) – The first time series or sequence for which to compute the matrix profile
T_B (numpy.ndarray) – The second time series or sequence for which to compute the matrix profile
m (int) – Window size
percentage (float, 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.
k (int, 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_id (int 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()].
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
MPdist – The matrix profile distance
- Return type
Notes
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)[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
T (numpy.ndarray) – The time series or sequence
P (numpy.ndarray) – Matrix Profile of T
min_neighbors (int, 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_distance (float 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)]).
cutoff (float, 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_matches (int, 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_motifs (int, default 1) – The maximum number of motifs to return
atol (float, default 1e-8) – The absolute tolerance parameter. This value will be added to max_distance when comparing distances between subsequences.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
motif_distances (numpy.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_indices (numpy.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.matchFind all matches of a query Q in a time series T
stumpy.mmotifsDiscover the top motifs for the multi-dimensional time series T
stumpy.stumpCompute the z-normalized matrix profile
stumpy.stumpedCompute the z-normalized matrix profile with a distributed dask cluster
stumpy.gpu_stumpCompute the z-normalized matrix profile with one or more GPU devices
stumpy.scrumpCompute 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]]))
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)[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
Q (numpy.ndarray) – The query sequence. It doesn’t have to be a subsequence of T
T (numpy.ndarray) – The time series of interest
M_T (numpy.ndarray, default None) – Sliding mean of time series, T
Σ_T (numpy.ndarray, default None) – Sliding standard deviation of time series, T
max_distance (float or function, default None) – Maximum distance between Q and a subsequence S for S to be considered a match. If a function, then it has to be a function of one argument D, which will be the distance profile of Q with 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_matches (int, 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.
atol (float, default 1e-8) – The absolute tolerance parameter. This value will be added to max_distance when comparing distances between subsequences.
query_idx (int, 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.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
T_subseq_isfinite (numpy.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_isconstant (numpy.ndarray) – A boolean array that indicates whether a subsequence in T is constant (True)
- Returns
out – 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.
- Return type
numpy.ndarray
See also
stumpy.motifsDiscover the top motifs for time series T
stumpy.mmotifsDiscover the top motifs for the multi-dimensional time series T
stumpy.stumpCompute the z-normalized matrix profile
stumpy.stumpedCompute the z-normalized matrix profile with a distributed dask cluster
stumpy.gpu_stumpCompute the z-normalized matrix profile with one or more GPU devices
stumpy.scrumpCompute 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)[source]#
Discover the top motifs for the multi-dimensional time series T
- Parameters
T (numpy.ndarray) – The multi-dimensional time series or sequence
P (numpy.ndarray) – Multi-dimensional Matrix Profile of T
I (numpy.ndarray) – Multi-dimensional Matrix Profile indices
min_neighbors (int, 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_distance (flaot, 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).
cutoffs (numpy.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_matches (int, 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_motifs (int, default 1) – The maximum number of motifs to return
atol (float, default 1e-8) – The absolute tolerance parameter. This value will be added to max_distance when comparing distances between subsequences.
k (int, 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).
include (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.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- 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.motifsFind the top motifs for time series T
stumpy.matchFind all matches of a query Q in a time series T
stumpy.mstumpCompute the multi-dimensional z-normalized matrix profile
stumpy.mstumpedCompute the multi-dimensional z-normalized matrix profile with a distributed dask cluster
stumpy.subspaceCompute the k-dimensional matrix profile subspace for a given subsequence index and its nearest neighbor index
stumpy.mdlCompute the number of bits needed to compress one array with another using the minimum description length (MDL)
Notes
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)[source]#
Identify the top k snippets that best represent the time series, T
- Parameters
T (numpy.ndarray) – The time series or sequence for which to find the snippets
m (int) – The snippet window size
k (int) – The desired number of snippets
percentage (float, 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.
s (int, 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_percentage (float, default 0.05) – The percentage of distances that will be used to report mpdist. The value is between 0.0 and 1.0.
mpdist_k (int) – 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.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- Returns
snippets (numpy.ndarray) – The top k snippets
snippets_indices (numpy.ndarray) – The index locations for each of top k snippets
snippets_profiles (numpy.ndarray) – The MPdist profiles for each of the top k snippets
snippets_fractions (numpy.ndarray) – The fraction of data that each of the top k snippets represents
snippets_areas (numpy.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
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)[source]#
Compute the Pan Matrix Profile
This is based on the SKIMP algorithm.
- Parameters
T (numpy.ndarray) – The time series or sequence for which to compute the pan matrix profile
m_start (int, default 3) – The starting (or minimum) subsequence window size for which a matrix profile may be computed
m_stop (int, default None) – The stopping (or maximum) subsequence window size for which a matrix profile may be computed. When m_stop = Non, this is set to the maximum allowable subsequence window size
m_step (int, default 1) – The step between subsequence window sizes
percentage (float, 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_scrump (bool, 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.
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- stumpy.PAN_#
The transformed (i.e., normalized, contrasted, binarized, and repeated) pan matrix profile
- Type
numpy.ndarray
- stumpy.M_#
The full list of (breadth first search (level) ordered) subsequence window sizes
- Type
numpy.ndarray
- 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.stimpedCompute the Pan Matrix Profile with a distributed dask cluster
stumpy.gpu_stimpCompute the Pan Matrix Profile with with one or more GPU devices
Notes
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)[source]#
Compute the Pan Matrix Profile with a distributed dask/ray cluster
This is based on the SKIMP algorithm.
- Parameters
client (client) – A Dask or Ray Distributed client. Setting up a distributed cluster is beyond the scope of this library. Please refer to the Dask or Ray Distributed documentation.
T (numpy.ndarray) – The time series or sequence for which to compute the pan matrix profile
m_start (int, default 3) – The starting (or minimum) subsequence window size for which a matrix profile may be computed
m_stop (int, default None) – The stopping (or maximum) subsequence window size for which a matrix profile may be computed. When m_stop = Non, this is set to the maximum allowable subsequence window size
m_step (int, default 1) – The step between subsequence window sizes
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- stumpy.PAN_#
The transformed (i.e., normalized, contrasted, binarized, and repeated) pan matrix profile
- Type
numpy.ndarray
- stumpy.M_#
The full list of (breadth first search (level) ordered) subsequence window sizes
- Type
numpy.ndarray
- 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.stimpCompute the Pan Matrix Profile
stumpy.gpu_stimpCompute the Pan Matrix Profile with with one or more GPU devices
Notes
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.]])
gpu_stimp#
- stumpy.gpu_stimp(T, min_m=3, max_m=None, step=1, device_id=0, normalize=True, p=2.0)#
Compute the Pan Matrix Profile with with one or more GPU devices
This is based on the SKIMP algorithm.
- Parameters
T (numpy.ndarray) – The time series or sequence for which to compute the pan matrix profile
m_start (int, default 3) – The starting (or minimum) subsequence window size for which a matrix profile may be computed
m_stop (int, default None) – The stopping (or maximum) subsequence window size for which a matrix profile may be computed. When m_stop = Non, this is set to the maximum allowable subsequence window size
m_step (int, default 1) – The step between subsequence window sizes
device_id (int 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()].
normalize (bool, default True) – When set to True, this z-normalizes subsequences prior to computing distances. Otherwise, this function gets re-routed to its complementary non-normalized equivalent set in the @core.non_normalized function decorator.
p (float, default 2.0) – The p-norm to apply for computing the Minkowski distance. This parameter is ignored when normalize == True.
- stumpy.PAN_#
The transformed (i.e., normalized, contrasted, binarized, and repeated) pan matrix profile
- Type
numpy.ndarray
- stumpy.M_#
The full list of (breadth first search (level) ordered) subsequence window sizes
- Type
numpy.ndarray
- 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.stimpCompute the Pan Matrix Profile
stumpy.stimpedCompute the Pan Matrix Profile with a distributed dask cluster
Notes
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.]])