scipy.interpolate.PchipInterpolator

class scipy.interpolate.PchipInterpolator(x, y, axis=0, extrapolate=None)

PCHIP 1-D monotonic cubic interpolation.

x and y are arrays of values used to approximate some function f, with y = f(x). The interpolant uses monotonic cubic splines to find the value of new points. (PCHIP stands for Piecewise Cubic Hermite Interpolating Polynomial).

Parameters

xndarray

A 1-D array of monotonically increasing real values. x cannot include duplicate values (otherwise f is overspecified)

yndarray

A 1-D array of real values. y’s length along the interpolation axis must be equal to the length of x. If N-D array, use axis parameter to select correct axis.

axisint, optional

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

extrapolatebool, optional

Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs.

See also

CubicHermiteSpline

Piecewise-cubic interpolator.

Akima1DInterpolator

Akima 1D interpolator.

CubicSpline

Cubic spline data interpolator.

PPoly

Piecewise polynomial in terms of coefficients and breakpoints.

Notes

The interpolator preserves monotonicity in the interpolation data and does not overshoot if the data is not smooth.

The first derivatives are guaranteed to be continuous, but the second derivatives may jump at \(x_k\).

Determines the derivatives at the points \(x_k\), \(f'_k\), by using PCHIP algorithm [1].

Let \(h_k = x_{k+1} - x_k\), and \(d_k = (y_{k+1} - y_k) / h_k\) are the slopes at internal points \(x_k\). If the signs of \(d_k\) and \(d_{k-1}\) are different or either of them equals zero, then \(f'_k = 0\). Otherwise, it is given by the weighted harmonic mean

\[\frac{w_1 + w_2}{f'_k} = \frac{w_1}{d_{k-1}} + \frac{w_2}{d_k}\]

where \(w_1 = 2 h_k + h_{k-1}\) and \(w_2 = h_k + 2 h_{k-1}\).

The end slopes are set using a one-sided scheme [2].

References

1

F. N. Fritsch and R. E. Carlson, Monotone Piecewise Cubic Interpolation, SIAM J. Numer. Anal., 17(2), 238 (1980). DOI:10.1137/0717021.

2

see, e.g., C. Moler, Numerical Computing with Matlab, 2004. DOI:10.1137/1.9780898717952

Attributes

axis

c

extrapolate

x

Methods

__call__(self, x[, nu, extrapolate])	Evaluate the piecewise polynomial or its derivative.
derivative(self[, nu])	Construct a new piecewise polynomial representing the derivative.
antiderivative(self[, nu])	Construct a new piecewise polynomial representing the antiderivative.
roots(self[, discontinuity, extrapolate])	Find real roots of the the piecewise polynomial.