bundles / scipy 1.17.1 / scipy / interpolate / _rgi / RegularGridInterpolator

class

`scipy.interpolate._rgi:RegularGridInterpolator`

source: /scipy/interpolate/_rgi.py :59

Signature

   class     RegularGridInterpolator (    points    ,    values    ,    method    =  linear   ,    bounds_error    =  True   ,    fill_value    =  nan   ,  * ,     solver    =  None   ,    solver_args    =  None     )

Members

Summary

Interpolator of specified order on a rectilinear grid in N ≥ 1 dimensions.

Extended Summary

The data must be defined on a rectilinear grid; that is, a rectangular grid with even or uneven spacing. Linear, nearest-neighbor, spline interpolations are supported. After setting up the interpolator object, the interpolation method may be chosen at each evaluation.

Parameters

points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, ): The points defining the regular grid in n dimensions. The points in each dimension (i.e. every elements of the points tuple) must be strictly ascending or descending.
values : array_like, shape (m1, ..., mn, ...): The data on the regular grid in n dimensions. Complex data is accepted.
method : str, optional: The method of interpolation to perform. Supported are "linear", "nearest", "slinear", "cubic", "quintic" and "pchip". This parameter will become the default for the object's __call__ method. Default is "linear".
bounds_error : bool, optional: If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then fill_value is used. Default is True.
fill_value : float or None, optional: The value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Default is np.nan.
solver : callable, optional: Only used for methods "slinear", "cubic" and "quintic". Sparse linear algebra solver for construction of the NdBSpline instance. Default is the iterative solver scipy.sparse.linalg.gcrotmk.
versionadded 1.13
solver_args: dict, optional: Additional arguments to pass to solver, if any.
versionadded 1.13

Attributes

grid : tuple of ndarrays: The points defining the regular grid in n dimensions. This tuple defines the full grid via np.meshgrid(*grid, indexing='ij')
values : ndarray: Data values at the grid.
method : str: Interpolation method.
fill_value : float or ``None``: Use this value for out-of-bounds arguments to __call__.
bounds_error : bool: If True, out-of-bounds argument raise a ValueError.

Methods

__call__

Notes

Contrary to LinearNDInterpolator and NearestNDInterpolator, this class avoids expensive triangulation of the input data by taking advantage of the regular grid structure.

In other words, this class assumes that the data is defined on a rectilinear grid.

The 'slinear'(k=1), 'cubic'(k=3), and 'quintic'(k=5) methods are tensor-product spline interpolators, where k is the spline degree, If any dimension has fewer points than k + 1, an error will be raised.

If the input data is such that dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating.

Choosing a solver for spline methods

Spline methods, "slinear", "cubic" and "quintic" involve solving a large sparse linear system at instantiation time. Depending on data, the default solver may or may not be adequate. When it is not, you may need to experiment with an optional solver argument, where you may choose between the direct solver (scipy.sparse.linalg.spsolve) or iterative solvers from scipy.sparse.linalg. You may need to supply additional parameters via the optional solver_args parameter (for instance, you may supply the starting value or target tolerance). See the scipy.sparse.linalg documentation for the full list of available options.

Alternatively, you may instead use the legacy methods, "slinear_legacy", "cubic_legacy" and "quintic_legacy". These methods allow faster construction but evaluations will be much slower.

Rounding rule at half points with `nearest` method

The rounding rule with the nearest method at half points is rounding down.

Array API Standard Support

RegularGridInterpolator has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

**Evaluate a function on the points of a 3-D grid** As a first example, we evaluate a simple example function on the points of a 3-D grid:

from scipy.interpolate import RegularGridInterpolator
import numpy as np
def f(x, y, z):
    return 2 * x**3 + 3 * y**2 - z
x = np.linspace(1, 4, 11)
y = np.linspace(4, 7, 22)
z = np.linspace(7, 9, 33)
xg, yg ,zg = np.meshgrid(x, y, z, indexing='ij', sparse=True)
data = f(xg, yg, zg)

✓

``data`` is now a 3-D array with ``data[i, j, k] = f(x[i], y[j], z[k])``. Next, define an interpolating function from this data:

interp = RegularGridInterpolator((x, y, z), data)

✓

Evaluate the interpolating function at the two points ``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``:

pts = np.array([[2.1, 6.2, 8.3],
                [3.3, 5.2, 7.1]])

✓

interp(pts)

✗

which is indeed a close approximation to

f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)

✓

**Interpolate and extrapolate a 2D dataset** As a second example, we interpolate and extrapolate a 2D data set:

x, y = np.array([-2, 0, 4]), np.array([-2, 0, 2, 5])
def ff(x, y):
    return x**2 + y**2

✓

xg, yg = np.meshgrid(x, y, indexing='ij')
data = ff(xg, yg)
interp = RegularGridInterpolator((x, y), data,
                                 bounds_error=False, fill_value=None)

✓

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(projection='3d')

✓

ax.scatter(xg.ravel(), yg.ravel(), data.ravel(),
           s=60, c='k', label='data')

✗

Evaluate and plot the interpolator on a finer grid

xx = np.linspace(-4, 9, 31)
yy = np.linspace(-4, 9, 31)
X, Y = np.meshgrid(xx, yy, indexing='ij')

✓

ax.plot_wireframe(X, Y, interp((X, Y)), rstride=3, cstride=3,
                  alpha=0.4, color='m', label='linear interp')

✗

ax.plot_wireframe(X, Y, ff(X, Y), rstride=3, cstride=3,
                  alpha=0.4, label='ground truth')
plt.legend()

✗

plt.show()

✓

Other examples are given :ref:`in the tutorial <tutorial-interpolate_regular_grid_interpolator>`.

Aliases

scipy.interpolate.RegularGridInterpolator