Interpolation (scipy.interpolate)¶
Univariate interpolation¶
| interp1d | Interpolate a 1D function. |
| BarycentricInterpolator | The interpolating polynomial for a set of points |
| KroghInterpolator | The interpolating polynomial for a set of points |
| PiecewisePolynomial | 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[, orders, der]) | Convenience function for piecewise polynomial interpolation |
Multivariate interpolation¶
| interp2d (x, y, z[, kind, copy, bounds_error, ...]) | Interpolate over a 2D grid. |
| Rbf (*args) | A class for radial basis function approximation/interpolation of n-dimensional scattered data. |
1-D Splines¶
| UnivariateSpline | Univariate spline s(x) of degree k on the interval [xb,xe] calculated from a given set of data points (x,y). |
| InterpolatedUnivariateSpline | Interpolated univariate spline approximation. Identical to UnivariateSpline with less error checking. |
| LSQUnivariateSpline | Weighted least-squares univariate spline approximation. Appears to be identical to UnivariateSpline with more error checking. |
The above univariate spline classes have the following methods:
| UnivariateSpline.__call__ (self, x[, nu]) | Evaluate spline (or its nu-th derivative) at positions x. Note: x can be unordered but the evaluation is more efficient if x is (partially) ordered. |
| UnivariateSpline.derivatives (self, x) | Return all derivatives of the spline at the point x. |
| UnivariateSpline.integral (self, a, b) | Return definite integral of the spline between two given points. |
| UnivariateSpline.roots (self) | Return the zeros of the spline. |
| UnivariateSpline.get_coeffs (self) | Return spline coefficients. |
| UnivariateSpline.get_knots (self) | Return the positions of (boundary and interior) knots of the spline. |
| UnivariateSpline.get_residual (self) | Return weighted sum of squared residuals of the spline approximation: sum ((w[i]*(y[i]-s(x[i])))**2,axis=0) |
| UnivariateSpline.set_smoothing_factor (self, s) | Continue spline computation with the given smoothing factor s and with the knots found at the last call. |
Low-level interface to FITPACK functions:
| splrep (x, y[, w, xb, xe, k, task, ...]) | Find the B-spline representation of 1-D curve. |
| splprep (x[, w, u, ub, ue, k, ...]) | Find the B-spline representation of an N-dimensional curve. |
| splev (x, tck[, der]) | Evaulate 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, ...]) | 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 | Smooth bivariate spline approximation. |
| LSQBivariateSpline | Weighted least-squares spline approximation. See also: |
Low-level interface to FITPACK functions:
| bisplrep (x, y, z[, w, xb, xe, yb, ye, ...]) | 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, scale[, order]) | Estimate the Taylor polynomial of f at x by polynomial fitting |
