jax.numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)[source]#

Return evenly spaced numbers over a specified interval.

LAX-backend implementation of numpy.linspace().

Original docstring below.

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

The endpoint of the interval can optionally be excluded.

Changed in version 1.16.0: Non-scalar start and stop are now supported.

Changed in version 1.20.0: Values are rounded towards -inf instead of 0 when an integer dtype is specified. The old behavior can still be obtained with np.linspace(start, stop, num).astype(int)

  • 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 (dtype, optional) – The type of the output array. If dtype is not given, the data type is inferred from start and stop. The inferred dtype will never be an integer; float is chosen even if the arguments would produce an array of integers.

  • 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.

Return type:

Array | tuple[Array, Array]


  • 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, optional) – Only returned if retstep is True

    Size of spacing between samples.