Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé

Module « scipy.interpolate »

Fonction griddata - module scipy.interpolate

Signature de la fonction griddata

def griddata(points, values, xi, method='linear', fill_value=nan, rescale=False) 

Description

help(scipy.interpolate.griddata)

Interpolate unstructured D-D data.

Parameters
----------
points : 2-D ndarray of floats with shape (n, D), or length D tuple of 1-D ndarrays with shape (n,).
    Data point coordinates.
values : ndarray of float or complex, shape (n,)
    Data values.
xi : 2-D ndarray of floats with shape (m, D), or length D tuple of ndarrays broadcastable to the same shape.
    Points at which to interpolate data.
method : {'linear', 'nearest', 'cubic'}, optional
    Method of interpolation. One of

    ``nearest``
      return the value at the data point closest to
      the point of interpolation. See `NearestNDInterpolator` for
      more details.

    ``linear``
      tessellate the input point set to N-D
      simplices, and interpolate linearly on each simplex. See
      `LinearNDInterpolator` for more details.

    ``cubic`` (1-D)
      return the value determined from a cubic
      spline.

    ``cubic`` (2-D)
      return the value determined from a
      piecewise cubic, continuously differentiable (C1), and
      approximately curvature-minimizing polynomial surface. See
      `CloughTocher2DInterpolator` for more details.
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``. This option has no effect for the
    'nearest' method.
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.

    .. versionadded:: 0.14.0

Returns
-------
ndarray
    Array of interpolated values.

See Also
--------
LinearNDInterpolator :
    Piecewise linear interpolator in N dimensions.
NearestNDInterpolator :
    Nearest-neighbor interpolator in N dimensions.
CloughTocher2DInterpolator :
    Piecewise cubic, C1 smooth, curvature-minimizing interpolator in 2D.
interpn : Interpolation on a regular grid or rectilinear grid.
RegularGridInterpolator : Interpolator on a regular or rectilinear grid
                          in arbitrary dimensions (`interpn` wraps this
                          class).

Notes
-----

.. versionadded:: 0.9

.. note:: For data on a regular grid use `interpn` instead.

Examples
--------

Suppose we want to interpolate the 2-D function

>>> import numpy as np
>>> def func(x, y):
...     return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2

on a grid in [0, 1]x[0, 1]

>>> grid_x, grid_y = np.mgrid[0:1:100j, 0:1:200j]

but we only know its values at 1000 data points:

>>> rng = np.random.default_rng()
>>> points = rng.random((1000, 2))
>>> values = func(points[:,0], points[:,1])

This can be done with `griddata` -- below we try out all of the
interpolation methods:

>>> from scipy.interpolate import griddata
>>> grid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest')
>>> grid_z1 = griddata(points, values, (grid_x, grid_y), method='linear')
>>> grid_z2 = griddata(points, values, (grid_x, grid_y), method='cubic')

One can see that the exact result is reproduced by all of the
methods to some degree, but for this smooth function the piecewise
cubic interpolant gives the best results:

>>> import matplotlib.pyplot as plt
>>> plt.subplot(221)
>>> plt.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin='lower')
>>> plt.plot(points[:,0], points[:,1], 'k.', ms=1)
>>> plt.title('Original')
>>> plt.subplot(222)
>>> plt.imshow(grid_z0.T, extent=(0,1,0,1), origin='lower')
>>> plt.title('Nearest')
>>> plt.subplot(223)
>>> plt.imshow(grid_z1.T, extent=(0,1,0,1), origin='lower')
>>> plt.title('Linear')
>>> plt.subplot(224)
>>> plt.imshow(grid_z2.T, extent=(0,1,0,1), origin='lower')
>>> plt.title('Cubic')
>>> plt.gcf().set_size_inches(6, 6)
>>> plt.show()

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé