pyscamp API documentation

pyscamp: Python bindings for SCAMP

selfjoin(a, m, **kwargs)

Computes the matrix profile for time series A.

abjoin(a, b, m, **kwargs)

For each subsequence in time series A, finds the nearest neighbor in time series B.

selfjoin_sum(a, m, **kwargs)

Returns the sum of the correlations above specified threshold (default 0) for each subsequence in a time series.

abjoin_sum(a, b, m, **kwargs)

For each subsequence in time series a, returns the sum of the correlations to subsequences in time series b above specified threshold (default 0).

selfjoin_knn(a, m, k, **kwargs)

[GPU ONLY, EXPERIMENTAL] Returns the approximate k nearest neighbors for each subsequence in a time series

abjoin_knn(a, b, m, k, **kwargs)

[GPU ONLY, EXPERIMENTAL] For each subsequence in time series A, returns its Approximate K nearest neighbors in time series B

selfjoin_matrix(a, m, **kwargs)

[EXPERIMENTAL] Returns a pooled version of the distance matrix with HxW of [mheight x mwidth], pooling operation is max() for Pearson Correlation and min() for Euclidian Distance

abjoin_matrix(a, b, m, **kwargs)

[EXPERIMENTAL] Returns a pooled version of the distance matrix with HxW of [mheight x mwidth], pooling operation is max() for Pearson Correlation and min() for Euclidian Distance
pyscamp.abjoin(a: List[float], b: List[float], m: int, **kwargs) → Tuple[numpy.ndarray[numpy.float32], numpy.ndarray[numpy.int32]]

For each subsequence in time series A, finds the nearest neighbor in time series B.

Parameters:

  • a (1D array) – Time series, b will be queried for subsequences in a.

  • b (1D array) – Time series in which to search for matches for subsequences in a.

  • m (int) – Subsequence length to use for computing the matrix profile.

Returns:

A tuple. First element: The nearest neighbor distance of subsequences in a to time series b. Second element: The index (in b) of each nearest neighbor.

Return type:

Tuple of np.ndarray[float32] and np.ndarray[int32]

pyscamp.abjoin_knn(a: List[float], b: List[float], m: int, k: int, **kwargs) → List[Tuple[int, int, float]]

[GPU ONLY, EXPERIMENTAL] For each subsequence in time series A, returns its Approximate K nearest neighbors in time series B

Parameters:

  • a (1D array) – Time series to compute the KNN matrix profile for.`

  • b (1D array) – Time series in which to search for matches.

  • m (int) – Subsequence length to use for computing the matrix profile.

  • k (int) – Number of neighbors to return for each subsequence

  • threshold (float, optional) – Correlation threshold [0,1] (Default 0), matches which have a correlation less than the threshold will be ignored

Returns:

List of tuples (col, row, distance) containing the matches (up to K) for each column of the distance matrix, col is the index in A, row is the index in B of the match, and d is the distance between the two subsequences

Return type:

List of tuple[int, int, float]

pyscamp.abjoin_matrix(a: List[float], b: List[float], m: int, **kwargs) → numpy.ndarray[numpy.float32]

[EXPERIMENTAL] Returns a pooled version of the distance matrix with HxW of [mheight x mwidth], pooling operation is max() for Pearson Correlation and min() for Euclidian Distance

Parameters:

  • a (1D array) – Time series corresponding to the columns of the distance matrix.

  • b (1D array) – Time series corresponding to the rows of the distance matrix.

  • m (int) – Subsequence length to use for computing the matrix profile.

  • mheight (int, optional) – Height of the pooled distance matrix to output. Default 50

  • mwidth (int, optional) – Width of the pooled distance matrix to output. Default 50

  • threshold (float, optional) – Correlation threshold [0,1] (Default 0), matches which have a correlation less than the threshold will be ignored

Returns:

A 2D array of height of mheight and width of mwidth. This is a pooled version of the full distance matrix.

Return type:

2D array

pyscamp.abjoin_sum(a: List[float], b: List[float], m: int, **kwargs) → numpy.ndarray[numpy.float64]

For each subsequence in time series a, returns the sum of the correlations to subsequences in time series b above specified threshold (default 0).

Parameters:

  • a (1D array) – Time series to compute matrix profile for.

  • b (1D array) – Time series to search for matches.

  • m (int) – Subsequence length to use for computing the matrix profile.

  • threshold (float, optional) – Correlation threshold [0,1] (Default 0), matches which have a correlation less than the threshold will be ignored

Returns:

For each subsequence in A, returns the sum of correlations above the the specified threshold in B.

Return type:

np.ndarray[float64]

pyscamp.gpu_supported() → bool

Returns true if both 1) The module was compiled with GPU support and 2) GPUs are available.

pyscamp.selfjoin(a: List[float], m: int, **kwargs) → Tuple[numpy.ndarray[numpy.float32], numpy.ndarray[numpy.int32]]

Computes the matrix profile for time series A.

Parameters:

  • a (1D array) – Time series to compute matrix profile for.

  • m (int) – Subsequence length to use for computing the matrix profile.

Returns:

A tuple containing the matrix profile as the first element and the indices as a the second element.

Return type:

Tuple of np.ndarray[float32] and np.ndarray[int32]

pyscamp.selfjoin_knn(a: List[float], m: int, k: int, **kwargs) → List[Tuple[int, int, float]]

[GPU ONLY, EXPERIMENTAL] Returns the approximate k nearest neighbors for each subsequence in a time series

Parameters:

  • a (1D array) – Time series to compute the KNN matrix profile for.

  • m (int) – Subsequence length to use for computing the matrix profile.

  • k (int) – Number of neighbors to return for each subsequence

  • threshold (float, optional) – Correlation threshold [0,1] (Default 0), matches which have a correlation less than the threshold will be ignored

Returns:

List of tuples (col, row, distance) containing the matches (up to K) for each column of the distance matrix, row is the index of the match, and d is the distance between the two subsequences

Return type:

List of tuple[int, int, float]

pyscamp.selfjoin_matrix(a: List[float], m: int, **kwargs) → numpy.ndarray[numpy.float32]

[EXPERIMENTAL] Returns a pooled version of the distance matrix with HxW of [mheight x mwidth], pooling operation is max() for Pearson Correlation and min() for Euclidian Distance

Parameters:

  • a (1D array) – Time series to compute matrix profile for.

  • m (int) – Subsequence length to use for computing the matrix profile.

  • mheight (int, optional) – Height of the pooled distance matrix to output. Default 50

  • mwidth (int, optional) – Width of the pooled distance matrix to output. Default 50

  • threshold (float, optional) – Correlation threshold [0,1] (Default 0), matches which have a correlation less than the threshold will be ignored

Returns:

A 2D array of height of mheight and width of mwidth. This is a pooled version of the full distance matrix.

Return type:

2D array

pyscamp.selfjoin_sum(a: List[float], m: int, **kwargs) → numpy.ndarray[numpy.float64]

Returns the sum of the correlations above specified threshold (default 0) for each subsequence in a time series.

Parameters:

  • a (1D array) – Time series to compute matrix profile for.

  • m (int) – Subsequence length to use for computing the matrix profile.

  • threshold (float, optional) – Correlation threshold [0,1] (Default 0), matches which have a correlation less than the threshold will be ignored

Returns:

For each subsequence in A, returns the sum of correlations above the the specified threshold to other subesequences in A.

Return type:

np.ndarray[float64]