Interpolation (scipy.interpolate)¶
Univariate interpolation¶
| interp1d(x, y[, kind, axis, copy, ...]) | Interpolate a 1D function. |
| BarycentricInterpolator(xi[, yi]) | The interpolating polynomial for a set of points |
| KroghInterpolator(xi, yi) | The interpolating polynomial for a set of points |
| PiecewisePolynomial(xi, yi[, orders, direction]) | Piecewise polynomial curve specified by points and derivatives |
| barycentric_interpolate(xi, yi, x) | Convenience function for polynomial interpolation |
| krogh_interpolate(xi, yi, x[, der]) | Convenience function for polynomial interpolation. |
| piecewise_polynomial_interpolate(xi, yi, x) | Convenience function for piecewise polynomial interpolation |
Multivariate interpolation¶
| interp2d(x, y, z[, kind, copy, ...]) | Interpolate over a 2D grid. |
| Rbf(*args) | A class for radial basis function approximation/interpolation of n-dimensional scattered data. |
1-D Splines¶
| UnivariateSpline(x, y[, w, bbox, k, s]) | One-dimensional smoothing spline fit to a given set of data points. |
| InterpolatedUnivariateSpline(x, y[, w, bbox, k]) | 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. |
The above univariate spline classes have the following methods:
| UnivariateSpline.__call__(x[, nu]) | Evaluate spline (or its nu-th derivative) at positions x. |
| UnivariateSpline.derivatives(x) | Return all derivatives of the spline at the point x. |
| UnivariateSpline.integral(a, b) | Return definite integral of the spline between two |
| UnivariateSpline.roots() | Return the zeros of the spline. |
| UnivariateSpline.get_coeffs() | Return spline coefficients. |
| UnivariateSpline.get_knots() | Return the positions of (boundary and interior) |
| UnivariateSpline.get_residual() | Return weighted sum of squared residuals of the spline |
| UnivariateSpline.set_smoothing_factor(s) | Continue spline computation with the given smoothing |
Low-level 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]) | Evaluate a B-spline and 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. |
| 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. |
2-D Splines¶
See also
scipy.ndimage.map_coordinates
| BivariateSpline | Bivariate spline s(x,y) of degrees kx and ky on the rectangle [xb,xe] x [yb, ye] calculated from a given set of data points (x,y,z). |
| SmoothBivariateSpline(x, y, z, None, None[, ...]) | Smooth bivariate spline approximation. |
| LSQBivariateSpline(x, y, z, tx, ty, None, None) | Weighted least-squares spline approximation. |
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 the Lagrange interpolating polynomial of the data-points (x,w) |
| approximate_taylor_polynomial(f, x, degree, ...) | Estimate the Taylor polynomial of f at x by polynomial fitting. |
