Interpolation (scipy.interpolate) — SciPy v1.15.2 Manual (original) (raw)
Sub-package for objects used in interpolation.
As listed below, this sub-package contains spline functions and classes, 1-D and multidimensional (univariate and multivariate) interpolation classes, Lagrange and Taylor polynomial interpolators, and wrappers for FITPACKand DFITPACK functions.
Univariate interpolation#
| interp1d(x, y[, kind, axis, copy, ...]) | Interpolate a 1-D function. |
|---|---|
| BarycentricInterpolator(xi[, yi, axis, wi, rng]) | Interpolating polynomial for a set of points. |
| KroghInterpolator(xi, yi[, axis]) | Interpolating polynomial for a set of points. |
| barycentric_interpolate(xi, yi, x[, axis, ...]) | Convenience function for polynomial interpolation. |
| krogh_interpolate(xi, yi, x[, der, axis]) | Convenience function for polynomial interpolation. |
| pchip_interpolate(xi, yi, x[, der, axis]) | Convenience function for pchip interpolation. |
| CubicHermiteSpline(x, y, dydx[, axis, ...]) | Piecewise-cubic interpolator matching values and first derivatives. |
| PchipInterpolator(x, y[, axis, extrapolate]) | PCHIP 1-D monotonic cubic interpolation. |
| Akima1DInterpolator(x, y[, axis, method, ...]) | Akima interpolator |
| CubicSpline(x, y[, axis, bc_type, extrapolate]) | Cubic spline data interpolator. |
| PPoly(c, x[, extrapolate, axis]) | Piecewise polynomial in terms of coefficients and breakpoints |
| BPoly(c, x[, extrapolate, axis]) | Piecewise polynomial in terms of coefficients and breakpoints. |
| FloaterHormannInterpolator(points, values, *) | Floater-Hormann barycentric rational interpolation. |
Multivariate interpolation#
Unstructured data:
| griddata(points, values, xi[, method, ...]) | Interpolate unstructured D-D data. |
|---|---|
| LinearNDInterpolator(points, values[, ...]) | Piecewise linear interpolator in N > 1 dimensions. |
| NearestNDInterpolator(x, y[, rescale, ...]) | NearestNDInterpolator(x, y). |
| CloughTocher2DInterpolator(points, values[, ...]) | CloughTocher2DInterpolator(points, values, tol=1e-6). |
| RBFInterpolator(y, d[, neighbors, ...]) | Radial basis function (RBF) interpolation in N dimensions. |
| Rbf(*args, **kwargs) | A class for radial basis function interpolation of functions from N-D scattered data to an M-D domain. |
| interp2d(x, y, z[, kind, copy, ...]) | Removed in version 1.14.0. |
For data on a grid:
| interpn(points, values, xi[, method, ...]) | Multidimensional interpolation on regular or rectilinear grids. |
|---|---|
| RegularGridInterpolator(points, values[, ...]) | Interpolator on a regular or rectilinear grid in arbitrary dimensions. |
| RectBivariateSpline(x, y, z[, bbox, kx, ky, s]) | Bivariate spline approximation over a rectangular mesh. |
Tensor product polynomials:
| NdPPoly(c, x[, extrapolate]) | Piecewise tensor product polynomial |
|---|---|
| NdBSpline(t, c, k, *[, extrapolate]) | Tensor product spline object. |
1-D Splines#
| BSpline(t, c, k[, extrapolate, axis]) | Univariate spline in the B-spline basis. |
|---|---|
| make_interp_spline(x, y[, k, t, bc_type, ...]) | Compute the (coefficients of) interpolating B-spline. |
| make_lsq_spline(x, y, t[, k, w, axis, ...]) | Compute the (coefficients of) an LSQ (Least SQuared) based fitting B-spline. |
| make_smoothing_spline(x, y[, w, lam]) | Compute the (coefficients of) smoothing cubic spline function using lam to control the tradeoff between the amount of smoothness of the curve and its proximity to the data. |
| generate_knots(x, y, *[, w, xb, xe, k, s, nest]) | Replicate FITPACK's constructing the knot vector. |
| make_splrep(x, y, *[, w, xb, xe, k, s, t, nest]) | Find the B-spline representation of a 1D function. |
| make_splprep(x, *[, w, u, ub, ue, k, s, t, nest]) | Find a smoothed B-spline representation of a parametric N-D curve. |
Functional interface to FITPACK routines:
| splrep(x, y[, w, xb, xe, k, task, s, t, ...]) | Find the B-spline representation of a 1-D curve. |
|---|---|
| splprep(x[, w, u, ub, ue, k, task, s, t, ...]) | Find the B-spline representation of an N-D curve. |
| splev(x, tck[, der, ext]) | Evaluate a B-spline or its derivatives. |
| splint(a, b, tck[, full_output]) | Evaluate the definite integral of a B-spline between two given points. |
| sproot(tck[, mest]) | Find the roots of a cubic B-spline. |
| spalde(x, tck) | Evaluate a B-spline and all its derivatives at one point (or set of points) up to order k (the degree of the spline), being 0 the spline itself. |
| splder(tck[, n]) | Compute the spline representation of the derivative of a given spline |
| splantider(tck[, n]) | Compute the spline for the antiderivative (integral) of a given spline. |
| insert(x, tck[, m, per]) | Insert knots into a B-spline. |
Object-oriented FITPACK interface:
| UnivariateSpline(x, y[, w, bbox, k, s, ext, ...]) | 1-D smoothing spline fit to a given set of data points. |
|---|---|
| InterpolatedUnivariateSpline(x, y[, w, ...]) | 1-D interpolating spline for a given set of data points. |
| LSQUnivariateSpline(x, y, t[, w, bbox, k, ...]) | 1-D spline with explicit internal knots. |
2-D Splines#
For data on a grid:
| RectBivariateSpline(x, y, z[, bbox, kx, ky, s]) | Bivariate spline approximation over a rectangular mesh. |
|---|---|
| RectSphereBivariateSpline(u, v, r[, s, ...]) | Bivariate spline approximation over a rectangular mesh on a sphere. |
For unstructured data:
| BivariateSpline() | Base class for bivariate splines. |
|---|---|
| SmoothBivariateSpline(x, y, z[, w, bbox, ...]) | Smooth bivariate spline approximation. |
| SmoothSphereBivariateSpline(theta, phi, r[, ...]) | Smooth bivariate spline approximation in spherical coordinates. |
| LSQBivariateSpline(x, y, z, tx, ty[, w, ...]) | Weighted least-squares bivariate spline approximation. |
| LSQSphereBivariateSpline(theta, phi, r, tt, tp) | Weighted least-squares bivariate spline approximation in spherical coordinates. |
Low-level interface to FITPACK functions:
| bisplrep(x, y, z[, w, xb, xe, yb, ye, kx, ...]) | Find a bivariate B-spline representation of a surface. |
|---|---|
| bisplev(x, y, tck[, dx, dy]) | Evaluate a bivariate B-spline and its derivatives. |
Rational Approximation#
| pade(an, m[, n]) | Return Pade approximation to a polynomial as the ratio of two polynomials. |
|---|---|
| AAA(x, y, *[, rtol, max_terms, clean_up, ...]) | AAA real or complex rational approximation. |
Additional tools#
See also
scipy.ndimage.map_coordinates,scipy.ndimage.spline_filter,scipy.signal.resample,scipy.signal.bspline,scipy.signal.gauss_spline,scipy.signal.qspline1d,scipy.signal.cspline1d,scipy.signal.qspline1d_eval,scipy.signal.cspline1d_eval,scipy.signal.qspline2d,scipy.signal.cspline2d.
pchip is an alias of PchipInterpolator for backward compatibility (should not be used in new code).