package scipy

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package scipy
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type tag = [
  1. | `LinearNDInterpolator
]
type t = [ `LinearNDInterpolator | `NDInterpolatorBase | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_nd_interpolator : t -> [ `NDInterpolatorBase ] Obj.t
val create : ?fill_value:float -> ?rescale:bool -> points:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Delaunay of Py.Object.t ] -> values:Py.Object.t -> unit -> t

LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False)

Piecewise linear interpolant in N dimensions.

.. versionadded:: 0.9

Methods ------- __call__

Parameters ---------- points : ndarray of floats, shape (npoints, ndims); or Delaunay Data point coordinates, or a precomputed Delaunay triangulation. values : ndarray of float or complex, shape (npoints, ...) Data values. fill_value : float, optional Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is ``nan``. rescale : bool, optional Rescale points to unit cube before performing interpolation. This is useful if some of the input dimensions have incommensurable units and differ by many orders of magnitude.

Notes ----- The interpolant is constructed by triangulating the input data with Qhull 1_, and on each triangle performing linear barycentric interpolation.

References ---------- .. 1 http://www.qhull.org/

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 : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.