import numpy as np
[docs]
def spline_interp(x, y, num=100, knots=3):
"""Interpolate using B-splines.
Fits a B-spline representation to the data ``(x, y)`` and evaluates it on
a uniformly spaced grid.
Args:
x: Independent variable values (1D array, must be monotonically
increasing).
y: Dependent variable values (same length as *x*).
num: Number of evaluation points on the output grid.
knots: Degree of the spline (passed as ``k`` to
``scipy.interpolate.splrep``).
Returns:
tuple: ``(x_fine, y_fine)`` where *x_fine* is a uniform grid of length
*num* spanning ``[x.min(), x.max()]`` and *y_fine* is the spline
evaluated on that grid.
.. versionadded:: 1.0.0
"""
from rgpycrumbs._aux import ensure_import
scipy_interp = ensure_import("scipy.interpolate")
spl = scipy_interp.splrep(x, y, k=knots)
x_fine = np.linspace(x.min(), x.max(), num=num)
y_fine = scipy_interp.splev(x_fine, spl)
return x_fine, y_fine
