jax.numpy.interp#

jax.numpy.interp(x, xp, fp, left=None, right=None, period=None)[source]#

One-dimensional linear interpolation for monotonically increasing sample points.

LAX-backend implementation of numpy.interp().

In addition to constant interpolation supported by NumPy, jnp.interp also supports left=’extrapolate’ and right=’extrapolate’ to indicate linear extrapolation instead.

Original docstring below.

Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.

Parameters:

  • x (array_like) – The x-coordinates at which to evaluate the interpolated values.

  • xp (1-D sequence of floats) – The x-coordinates of the data points, must be increasing if argument period is not specified. Otherwise, xp is internally sorted after normalizing the periodic boundaries with xp = xp % period.

  • fp (1-D sequence of float or complex) – The y-coordinates of the data points, same length as xp.

  • left (optional float or complex corresponding to fp) – Value to return for x < xp[0], default is fp[0].

  • right (optional float or complex corresponding to fp) – Value to return for x > xp[-1], default is fp[-1].

  • period (None or float, optional) – A period for the x-coordinates. This parameter allows the proper interpolation of angular x-coordinates. Parameters left and right are ignored if period is specified.

Returns:

y – The interpolated values, same shape as x.

Return type:

float or complex (corresponding to fp) or ndarray