SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Classes

• interp1d - Interpolate a 1D function
• interp2d - Interpolate over a 2D grid
• ppform - The ppform of the piecewise polynomials is given in terms of coefficients and breaks

Function summary

• lagrange(x, w)
• reduce_sometrue(a)
• spleval((xj, cvals, k), xnew, deriv = 0)
• spline(xk, yk, xnew, order = 3, kind = 'smoothest', conds = None)
• splmake(xk, yk, order = 3, kind = 'smoothest', conds = None)
• spltopp(xk, cvals, k)

Functions

• lagrange(x, w)

  Return the Lagrange interpolating polynomial of the data-points (x,w)

• reduce_sometrue(a)
• spleval((xj, cvals, k), xnew, deriv = 0)

  Evaluate a fixed spline represented by the given tuple at the new x-values. The xj values are the interior knot points. The approximation region is xj[0] to xj[-1]. If N+1 is the length of xj, then cvals should have length N+k where k is the order of the spline.

  Internally, an additional k-1 knot points are added on either side of the spline.

  If cvals represents more than one curve (cvals.ndim > 1) and/or xnew is N-d, then the result is xnew.shape + cvals.shape[1:] providing the interpolation of multiple curves.

• spline(xk, yk, xnew, order = 3, kind = 'smoothest', conds = None)

  Interpolate a curve (xk,yk) at points xnew using a spline fit.

• splmake(xk, yk, order = 3, kind = 'smoothest', conds = None)

  Return a (xk, cvals, k) representation of a spline given data-points where the (internal) knots are at the data-points.

  yk can be an N-d array to represent more than one curve, through the same xk points. The first dimension is assumed to be the interpolating dimension.

  kind can be 'smoothest', 'not_a_knot', 'fixed',

  'clamped', 'natural', 'periodic', 'symmetric', 'user', 'mixed'

  it is ignored if order < 2

• spltopp(xk, cvals, k)

  Return a piece-wise polynomial object from a fixed-spline tuple.

Imported Names