Interpolation (scipy.interpolate)

Sub-package for objects used in interpolation.

As listed below, this sub-package contains spline functions and classes, one-dimensional and multi-dimensional (univariate and multivariate) interpolation classes, Lagrange and Taylor polynomial interpolators, and wrappers for FITPACK and DFITPACK functions.

Univariate interpolation

interp1d(x, y[, kind, axis, copy, ...]) Interpolate a 1-D function.
BarycentricInterpolator(xi[, yi, axis]) The interpolating polynomial for a set of points
KroghInterpolator(xi, yi[, axis]) Interpolating polynomial for a set of points.
PchipInterpolator(x, y[, axis, extrapolate]) PCHIP 1-d monotonic cubic interpolation.
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.
Akima1DInterpolator(x, y[, axis]) 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.

Multivariate interpolation

Unstructured data:

griddata(points, values, xi[, method, ...]) Interpolate unstructured D-dimensional data.
LinearNDInterpolator(points, values[, ...]) Piecewise linear interpolant in N dimensions.
NearestNDInterpolator(points, values) Nearest-neighbour interpolation in N dimensions.
CloughTocher2DInterpolator(points, values[, tol]) Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.
Rbf(*args) A class for radial basis function approximation/interpolation of n-dimensional scattered data.
interp2d(x, y, z[, kind, copy, ...]) Interpolate over a 2-D grid.

For data on a grid:

interpn(points, values, xi[, method, ...]) Multidimensional interpolation on regular grids.
RegularGridInterpolator(points, values[, ...]) Interpolation on a regular grid in arbitrary dimensions
RectBivariateSpline(x, y, z[, bbox, kx, ky, s]) Bivariate spline approximation over a rectangular mesh.

See also


Tensor product polynomials:


1-D Splines

UnivariateSpline(x, y[, w, bbox, k, s, ext, ...]) One-dimensional smoothing spline fit to a given set of data points.
InterpolatedUnivariateSpline(x, y[, w, ...]) One-dimensional interpolating spline for a given set of data points.
LSQUnivariateSpline(x, y, t[, w, bbox, k, ...]) One-dimensional spline with explicit internal knots.

Functional interface to FITPACK functions:

splrep(x, y[, w, xb, xe, k, task, s, t, ...]) Find the B-spline representation of 1-D curve.
splprep(x[, w, u, ub, ue, k, task, s, t, ...]) Find the B-spline representation of an N-dimensional 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.
sproot(tck[, mest]) Find the roots of a cubic B-spline.
spalde(x, tck) Evaluate all derivatives of a B-spline.
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.

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.

Additional tools

lagrange(x, w) Return a Lagrange interpolating polynomial.
approximate_taylor_polynomial(f, x, degree, ...) Estimate the Taylor polynomial of f at x by polynomial fitting.

Functions existing for backward compatibility (should not be used in new code):

ppform(coeffs, breaks[, fill, sort]) Deprecated piecewise polynomial class.
spleval(xck, xnew[, deriv]) Evaluate a fixed spline represented by the given tuple at the new x-values
spline(xk, yk, xnew[, order, kind, conds]) Interpolate a curve at new points using a spline fit
splmake(xk, yk[, order, kind, conds]) Return a representation of a spline given data-points at internal knots
spltopp(xk, cvals, k) Return a piece-wise polynomial object from a fixed-spline tuple.
pchip alias of PchipInterpolator