Attributes

x : ndarray, shape (n,)

Breakpoints. The same x which was passed to the constructor.

c : ndarray, shape (4, n-1, ...)

Coefficients of the polynomials on each segment. The trailing dimensions match the dimensions of y, excluding axis . For example, if y is 1-D, then c[k, i] is a coefficient for (x-x[i])**(3-k) on the segment between x[i] and x[i+1] .

axis : int

Interpolation axis. The same axis which was passed to the constructor.

The result is represented as a PPoly instance.

Methods

Notes

If you want to create a higher-order spline matching higher-order derivatives, use :None:None:`BPoly.from_derivatives`.

Parameters

x : array_like, shape (n,)

1-D array containing values of the independent variable. Values must be real, finite and in strictly increasing order.

y : array_like

Array containing values of the dependent variable. It can have arbitrary number of dimensions, but the length along axis (see below) must match the length of x . Values must be finite.

dydx : array_like

Array containing derivatives of the dependent variable. It can have arbitrary number of dimensions, but the length along axis (see below) must match the length of x . Values must be finite.

axis : int, optional

Axis along which y is assumed to be varying. Meaning that for x[i] the corresponding values are np.take(y, i, axis=axis) . Default is 0.

extrapolate : {bool, 'periodic', None}, optional

If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. If None (default), it is set to True.

Piecewise-cubic interpolator matching values and first derivatives.

See Also

Akima1DInterpolator

Akima 1D interpolator.

CubicSpline

Cubic spline data interpolator.

PPoly

Piecewise polynomial in terms of coefficients and breakpoints

PchipInterpolator

PCHIP 1-D monotonic cubic interpolator.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.interpolate._cubic.PchipInterpolator

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