cunumeric.linspace#

cunumeric.linspace(start: ndarray, stop: ndarray, num: int = 50, endpoint: bool = True, retstep: bool = False, dtype: npt.DTypeLike | None = None, axis: int = 0) ndarray | tuple[ndarray, float | ndarray]#

Return evenly spaced numbers over a specified interval.

Returns num evenly spaced samples, calculated over the interval [start, stop].

The endpoint of the interval can optionally be excluded.

Parameters:
  • start (array_like) – The starting value of the sequence.

  • stop (array_like) – The end value of the sequence, unless endpoint is set to False. In that case, the sequence consists of all but the last of num + 1 evenly spaced samples, so that stop is excluded. Note that the step size changes when endpoint is False.

  • num (int, optional) – Number of samples to generate. Default is 50. Must be non-negative.

  • endpoint (bool, optional) – If True, stop is the last sample. Otherwise, it is not included. Default is True.

  • retstep (bool, optional) – If True, return (samples, step), where step is the spacing between samples.

  • dtype (data-type, optional) – The type of the output array. If dtype is not given, infer the data type from the other input arguments.

  • axis (int, optional) – The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end.

Returns:

  • samples (ndarray) – There are num equally spaced samples in the closed interval [start, stop] or the half-open interval [start, stop) (depending on whether endpoint is True or False).

  • step (float or ndarray, optional) – Only returned if retstep is True

    Size of spacing between samples.

See also

numpy.linspace

Availability:

Multiple GPUs, Multiple CPUs