API Reference

Lifecycle Functions

quickmp.initialize() → None

Initialize the quickmp backend.

Initializes all available devices and selects device 0.

quickmp.finalize() → None

Finalize the quickmp backend.

Device Management

quickmp.get_device_count() → int

Get the number of available devices.

Returns:

number of VE devices, CPU: always 1)

Return type:

Number of available devices (VE

quickmp.use_device(device: int) → None

Switch to the specified device.

Parameters:

device – Device ID to use

quickmp.get_current_device() → int

Get the currently selected device ID.

Returns:

Currently selected device ID

quickmp.get_stream_count() → int

Get the number of available streams for parallel execution.

Returns:

Number of available streams (CPU cores for CPU backend,

VE streams for VE backend)

Return type:

int

Matrix Profile Computation

quickmp.selfjoin(T: numpy.ndarray, m: int, stream: int = 0, normalize: bool = True) → numpy.ndarray

Compute the matrix profile for time series T.

Parameters:

  • T – Time series

  • m – Window size

  • stream – Stream number (default: 0). Only used for VE backend.

  • normalize – If True (default), use Z-normalized Euclidean distance. If False, use raw Euclidean distance.

Returns:

Matrix profile

quickmp.abjoin(T1: numpy.ndarray, T2: numpy.ndarray, m: int, stream: int = 0, normalize: bool = True) → numpy.ndarray

Compute the matrix profile between time series T1 and T2.

Parameters:

  • T1 – Time series

  • T2 – Time series

  • m – Window size

  • stream – Stream number (default: 0). Only used for VE backend.

  • normalize – If True (default), use Z-normalized Euclidean distance. If False, use raw Euclidean distance.

Returns:

Matrix profile

Low-Level Functions

quickmp.sliding_dot_product(T: numpy.ndarray, Q: numpy.ndarray, stream: int = 0) → numpy.ndarray

Compute the sliding dot product between time series T and Q.

Parameters:

  • T – Time series

  • Q – Time series

  • stream – Stream number (default: 0). Only used for VE backend.

Returns:

Sliding dot product

quickmp.compute_mean_std(T: numpy.ndarray, m: int, stream: int = 0) → tuple[numpy.ndarray, numpy.ndarray]

Compute the mean and standard deviation of every subsequence in time series T.

Parameters:

  • T – Time series

  • m – Window size

  • stream – Stream number (default: 0). Only used for VE backend.

Returns:

Tuple of mean and standard deviation