package scipy

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package scipy
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Class type
type tag = [
  1. | `SmoothBivariateSpline
]
type t = [ `Object | `SmoothBivariateSpline ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val create : ?w:[> `Ndarray ] Np.Obj.t -> ?bbox:[> `Ndarray ] Np.Obj.t -> ?kx:Py.Object.t -> ?ky:Py.Object.t -> ?s:float -> ?eps:float -> x:Py.Object.t -> y:Py.Object.t -> z:Py.Object.t -> unit -> t

Smooth bivariate spline approximation.

Parameters ---------- x, y, z : array_like 1-D sequences of data points (order is not important). w : array_like, optional Positive 1-D sequence of weights, of same length as `x`, `y` and `z`. bbox : array_like, optional Sequence of length 4 specifying the boundary of the rectangular approximation domain. By default, ``bbox=min(x), max(x), min(y), max(y)``. kx, ky : ints, optional Degrees of the bivariate spline. Default is 3. s : float, optional Positive smoothing factor defined for estimation condition: ``sum((wi*(zi-s(xi, yi)))**2, axis=0) <= s`` Default ``s=len(w)`` which should be a good value if ``1/wi`` is an estimate of the standard deviation of ``zi``. eps : float, optional A threshold for determining the effective rank of an over-determined linear system of equations. `eps` should have a value within the open interval ``(0, 1)``, the default is 1e-16.

See Also -------- bisplrep : an older wrapping of FITPACK bisplev : an older wrapping of FITPACK UnivariateSpline : a similar class for univariate spline interpolation LSQBivariateSpline : to create a BivariateSpline using weighted least-squares fitting

Notes ----- The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.

val ev : ?dx:int -> ?dy:int -> xi:Py.Object.t -> yi:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

Evaluate the spline at points

Returns the interpolated value at ``(xii, yii), i=0,...,len(xi)-1``.

Parameters ---------- xi, yi : array_like Input coordinates. Standard Numpy broadcasting is obeyed. dx : int, optional Order of x-derivative

.. versionadded:: 0.14.0 dy : int, optional Order of y-derivative

.. versionadded:: 0.14.0

val get_coeffs : [> tag ] Obj.t -> Py.Object.t

Return spline coefficients.

val get_knots : [> tag ] Obj.t -> Py.Object.t

Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. The position of interior and additional knots are given as tk+1:-k-1 and t:k+1=b, t-k-1:=e, respectively.

val get_residual : [> tag ] Obj.t -> Py.Object.t

Return weighted sum of squared residuals of the spline approximation: sum ((wi*(zi-s(xi,yi)))**2,axis=0)

val integral : xa:Py.Object.t -> xb:Py.Object.t -> ya:Py.Object.t -> yb:Py.Object.t -> [> tag ] Obj.t -> float

Evaluate the integral of the spline over area xa,xb x ya,yb.

Parameters ---------- xa, xb : float The end-points of the x integration interval. ya, yb : float The end-points of the y integration interval.

Returns ------- integ : float The value of the resulting integral.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

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