The equation for k-order B-spline with n+1 control points (P0 , P1 , ... , Pn ) is P(t) = âi=0,n Ni,k(t) Pi ,   tk-1 <= t <= tn+1 . In a B-spline each control point is associated with a basis function Ni,k which is given by the recurrence relations (see Bspline.java) Ni,k(t) = Ni,k-1(t) (t - ti)/(ti+k-1 - ti) + Ni+1,k-1(t) (ti+k - t)/(ti+k - ti+1) , Ni,1 = {1  if  ti <= t <= ti+1 ,  0  othe
This article includes a list of general references, but it lacks sufficient corresponding inline citations. Please help to improve this article by introducing more precise citations. (July 2021) (Learn how and when to remove this message) In the mathematical field of numerical analysis, spline interpolation is a form of interpolation where the interpolant is a special type of piecewise polynomial
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is a B-spline. Specific types include the nonperiodic B-spline (first knots equal 0 and last equal to 1; illustrated above) and uniform B-spline (internal knots are equally spaced). A B-spline with no internal knots is a Bézier curve. A curve is times differentiable at a point where duplicate knot values occur. The knot values determine the extent of the control of the control points. -splines are
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interp2d# class scipy.interpolate.interp2d(x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=None)[source]# Removed in version 1.14.0: interp2d has been removed in SciPy 1.14.0. For legacy code, nearly bug-for-bug compatible replacements are RectBivariateSpline on regular grids, and bisplrep/bisplev for scattered 2D data. In new code, for regular grids use RegularGridInterpolator i
Interpolation (scipy.interpolate)# 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 FITPACK and DFITPACK functions. Univariate interpolation#
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