One-dimensional linear interpolation.
JAX implementation of numpy.interp().
x (ArrayLike) – N-dimensional array of x coordinates at which to evaluate the interpolation.
xp (ArrayLike) – one-dimensional sorted array of points to be interpolated.
fp (ArrayLike) – array of shape xp.shape containing the function values associated with xp.
left (ArrayLike | str | None) – specify how to handle points x < xp[0]. Default is to return fp[0]. If left is a scalar value, it will return this value. if left is the string "extrapolate", then the value will be determined by linear extrapolation. left is ignored if period is specified.
right (ArrayLike | str | None) – specify how to handle points x > xp[-1]. Default is to return fp[-1]. If right is a scalar value, it will return this value. if right is the string "extrapolate", then the value will be determined by linear extrapolation. right is ignored if period is specified.
period (ArrayLike | None) – optionally specify the period for the x coordinates, for e.g. interpolation in angular space.
an array of shape x.shape containing the interpolated function at values x.
Examples
>>> xp = jnp.arange(10) >>> fp = 2 * xp >>> x = jnp.array([0.5, 2.0, 3.5]) >>> interp(x, xp, fp) Array([1., 4., 7.], dtype=float32)
Unless otherwise specified, extrapolation will be constant:
>>> x = jnp.array([-10., 10.]) >>> interp(x, xp, fp) Array([ 0., 18.], dtype=float32)
Use "extrapolate" mode for linear extrapolation:
>>> interp(x, xp, fp, left='extrapolate', right='extrapolate') Array([-20., 20.], dtype=float32)
For periodic interpolation, specify the period:
>>> xp = jnp.array([0, jnp.pi / 2, jnp.pi, 3 * jnp.pi / 2]) >>> fp = jnp.sin(xp) >>> x = 2 * jnp.pi # note: not in input array >>> jnp.interp(x, xp, fp, period=2 * jnp.pi) Array(0., dtype=float32)
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