`scipy.interpolate._cubic:pchip_interpolate`

source: /scipy/interpolate/_cubic.py :339

Signature

def pchip_interpolate ( xi , yi , x , der = 0 , axis = 0 )

Summary

Convenience function for pchip interpolation.

Extended Summary

xi and yi are arrays of values used to approximate some function f, with yi = f(xi). The interpolant uses monotonic cubic splines to find the value of new points x and the derivatives there.

See scipy.interpolate.PchipInterpolator for details.

Parameters

xi : array_like: A sorted list of x-coordinates, of length N.
yi : array_like: A 1-D array of real values. yi's length along the interpolation axis must be equal to the length of xi. If N-D array, use axis parameter to select correct axis.
x : scalar or array_like: Of length M.
der : int or list, optional: Derivatives to extract. The 0th derivative can be included to return the function value.
axis : int, optional: Axis in the yi array corresponding to the x-coordinate values.

Returns

y : scalar or array_like: The result, of length R or length M or M by R.

Examples

We can interpolate 2D observed data using pchip interpolation:

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import pchip_interpolate
x_observed = np.linspace(0.0, 10.0, 11)
y_observed = np.sin(x_observed)
x = np.linspace(min(x_observed), max(x_observed), num=100)
y = pchip_interpolate(x_observed, y_observed, x)

✓

plt.plot(x_observed, y_observed, "o", label="observation")
plt.plot(x, y, label="pchip interpolation")
plt.legend()

✗

plt.show()

✓

Aliases

scipy.interpolate.pchip_interpolate