Tessero\Interpolate

apply(string $name, mixed ...$args): mixed

Call a function by its SciPy/NumPy name: Interpolate::apply('log_ndtr', $x).

Akima1DInterpolator(mixed $x, mixed $y, mixed $xnew): mixed

Akima piecewise-cubic interpolation evaluated at xnew (scipy.interpolate.Akima1DInterpolator).

BPoly(mixed $c, mixed $x, mixed $xnew): mixed

Piecewise Bernstein polynomial evaluated at xnew (scipy.interpolate.BPoly).

BSpline(mixed $t, mixed $c, mixed $k, mixed $x): mixed

Evaluate a B-spline (knots t, coefficients c, degree k) at x via de Boor's algorithm (scipy.interpolate.BSpline).

BarycentricInterpolator(mixed $xi, mixed $yi, mixed $x): mixed

Barycentric Lagrange interpolation evaluated at x (scipy.interpolate.BarycentricInterpolator).

CubicHermiteSpline(mixed $x, mixed $y, mixed $dydx, mixed $xnew): mixed

Cubic Hermite spline with given derivatives evaluated at xnew (scipy.interpolate.CubicHermiteSpline).

CubicSpline(mixed $x, mixed $y, mixed $xnew): mixed

Not-a-knot cubic spline evaluated at xnew (scipy.interpolate.CubicSpline).

FloaterHormannInterpolator(mixed $xi, mixed $yi, mixed $x, mixed $d = 3): mixed

Floater-Hormann barycentric rational interpolation evaluated at x (scipy.interpolate.FloaterHormannInterpolator).

KroghInterpolator(mixed $xi, mixed $yi, mixed $x): mixed

Polynomial interpolation (Newton divided differences) evaluated at x (scipy.interpolate.KroghInterpolator).

NdBSpline(mixed $tx, mixed $ty, mixed $c, mixed $kx, mixed $ky, mixed $xi): mixed

2-D tensor B-spline evaluated at points xi (scipy.interpolate.NdBSpline).

NdPPoly(mixed $c, mixed $xb, mixed $yb, mixed $xi): mixed

2-D tensor piecewise polynomial evaluated at points xi (scipy.interpolate.NdPPoly).

PPoly(mixed $c, mixed $x, mixed $xnew): mixed

Piecewise polynomial (local power basis) evaluated at xnew (scipy.interpolate.PPoly).

PchipInterpolator(mixed $x, mixed $y, mixed $xnew): mixed

PCHIP monotone cubic interpolation evaluated at xnew (scipy.interpolate.PchipInterpolator).

RBFInterpolator(mixed $y, mixed $d, mixed $xi, mixed $kernel = 'thin_plate_spline'): mixed

Radial basis function interpolation (thin_plate_spline or linear kernel) evaluated at xi (scipy.interpolate.RBFInterpolator).

RegularGridInterpolator(mixed $points, mixed $values, mixed $xi): mixed

Multilinear interpolation on a regular n-D grid, evaluated at xi (scipy.interpolate.RegularGridInterpolator).

barycentricInterpolate(mixed $xi, mixed $yi, mixed $x): mixed

Barycentric Lagrange polynomial interpolation evaluated at x (scipy.interpolate.barycentric_interpolate).

bisplrep(mixed $x, mixed $y, mixed $z, mixed $w, mixed $kx, mixed $ky, mixed $s, mixed $eps): mixed

FITPACK auto-knot bivariate smoothing spline (surfit): knots tx, ty and coefficients c for degrees kx, ky, smoothing s>=0 (scipy.interpolate.bisplrep / SmoothBivariateSpline).

insert(mixed $x, mixed $t, mixed $c, mixed $k, mixed $m = 1): mixed

Insert a knot into a B-spline m times by Boehm's algorithm (scipy.interpolate.insert).

interpn(mixed $points, mixed $values, mixed $xi): mixed

Multilinear interpolation on a regular n-D grid at points xi (scipy.interpolate.interpn).

kroghInterpolate(mixed $xi, mixed $yi, mixed $x): mixed

Polynomial interpolation (Newton divided differences) evaluated at x (scipy.interpolate.krogh_interpolate).

lagrange(mixed $x, mixed $w): mixed

Lagrange interpolating polynomial through (x, w) as poly1d coefficients, highest-degree first (scipy.interpolate.lagrange).

makeInterpSpline(mixed $x, mixed $y, mixed $k, mixed $xnew): mixed

Interpolating spline (k=1 linear or k=3 not-a-knot cubic) evaluated at xnew (scipy.interpolate.make_interp_spline).

makeLsqSpline(mixed $x, mixed $y, mixed $t, mixed $k, mixed $xnew): mixed

Least-squares B-spline fit with given knots t and degree k, evaluated at xnew (scipy.interpolate.make_lsq_spline).

pade(mixed $an, mixed $m, mixed $n = null): mixed

Pade rational approximant (numerator p, denominator q) from Taylor coefficients (scipy.interpolate.pade).

pchipInterpolate(mixed $xi, mixed $yi, mixed $x): mixed

Monotone piecewise-cubic (PCHIP) interpolation evaluated at x (scipy.interpolate.pchip_interpolate).

spalde(mixed $t, mixed $c, mixed $k, mixed $x): mixed

All derivatives 0..k of a B-spline at each x (scipy.interpolate.spalde).

spgrid(mixed $u, mixed $v, mixed $r, mixed $s): mixed

FITPACK smoothing spline on a rectangular grid over a sphere: knots tu, tv and coefficients c (scipy.interpolate.RectSphereBivariateSpline).

spherfit(mixed $teta, mixed $phi, mixed $r, mixed $w, mixed $s, mixed $eps): mixed

FITPACK sphere smoothing spline (sphere): knots tt, tp and coefficients c for smoothing s>=0 (scipy.interpolate.SmoothSphereBivariateSpline).

spherfitLsq(mixed $teta, mixed $phi, mixed $r, mixed $w, mixed $tt, mixed $tp, mixed $eps): mixed

FITPACK sphere least-squares spline with given knots tt, tp: coefficients c (scipy.interpolate.LSQSphereBivariateSpline).

splantider(mixed $t, mixed $c, mixed $k, mixed $n = 1): mixed

Antiderivative of a B-spline in (t, c, k) form (scipy.interpolate.splantider).

splder(mixed $t, mixed $c, mixed $k, mixed $n = 1): mixed

Derivative of a B-spline in (t, c, k) form (scipy.interpolate.splder).

splev(mixed $t, mixed $c, mixed $k, mixed $x): mixed

Evaluate a B-spline (knots t, coefficients c, degree k) at x via de Boor's algorithm (scipy.interpolate.splev).

splint(mixed $a, mixed $b, mixed $t, mixed $c, mixed $k): mixed

Definite integral of a B-spline over [a, b], taking the spline as zero outside its base interval (scipy.interpolate.splint).

splrep(mixed $x, mixed $y, mixed $w, mixed $k, mixed $s): mixed

FITPACK auto-knot smoothing spline (curfit): knots t and coefficients c for degree k, smoothing s>=0 (scipy.interpolate.splrep / UnivariateSpline).

sproot(mixed $t, mixed $c, mixed $k, mixed $mest = 10): mixed

Roots of a cubic (k=3) B-spline, sorted ascending, at most mest of them (scipy.interpolate.sproot).