convolve

Contents

convolve#

class brainunit.math.convolve(a, v, mode='full', *, precision=None, preferred_element_type=None, **kwargs)#

Return the discrete, linear convolution of two one-dimensional sequences.

The resulting unit is a.unit * v.unit.

Parameters:

  • a (saiunit.Quantity | Array | ndarray | bool | number | bool | int | float | complex) – First one-dimensional input array.

  • v (saiunit.Quantity | Array | ndarray | bool | number | bool | int | float | complex) – Second one-dimensional input array.

  • mode (str) – ‘full’ (default): output shape (N+M-1,). ‘same’: output length max(M, N). ‘valid’: output length max(M, N) - min(M, N) + 1.

  • precision (Any) – Precision for the computation.

  • preferred_element_type (str | type[Any] | dtype | SupportsDType | None) – Accumulation and result dtype.

Returns:

out – Discrete, linear convolution of a and v. The resulting unit is a.unit * v.unit.

Return type:

saiunit.Quantity | Array | ndarray | bool | number | bool | int | float | complex

Examples

>>> import saiunit as u
>>> a = u.math.array([1.0, 2.0, 3.0]) * u.meter
>>> v = u.math.array([0.5, 1.0]) * u.second
>>> u.math.convolve(a, v)  # unit is meter * second
Contents