# scipy.interpolate.pchip_interpolate#

scipy.interpolate.pchip_interpolate(xi, yi, x, der=0, axis=0)[source]#

Convenience function for pchip interpolation.

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:
xiarray_like

A sorted list of x-coordinates, of length N.

yiarray_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.

Deprecated since version 1.13.0: Complex data is deprecated and will raise an error in SciPy 1.15.0. If you are trying to use the real components of the passed array, use `np.real` on yi.

xscalar or array_like

Of length M.

derint or list, optional

Derivatives to extract. The 0th derivative can be included to return the function value.

axisint, optional

Axis in the yi array corresponding to the x-coordinate values.

Returns:
yscalar or array_like

The result, of length R or length M or M by R.

See also

`PchipInterpolator`

PCHIP 1-D monotonic cubic interpolator.

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()
```