call — SciPy v1.15.3 Manual (original) (raw)

scipy.interpolate.LSQSphereBivariateSpline.

LSQSphereBivariateSpline.__call__(theta, phi, dtheta=0, dphi=0, grid=True)[source]#

Evaluate the spline or its derivatives at given positions.

Parameters:

theta, phiarray_like

Input coordinates.

If grid is False, evaluate the spline at points(theta[i], phi[i]), i=0, ..., len(x)-1. Standard Numpy broadcasting is obeyed.

If grid is True: evaluate spline at the grid points defined by the coordinate arrays theta, phi. The arrays must be sorted to increasing order. The ordering of axes is consistent withnp.meshgrid(..., indexing="ij") and inconsistent with the default ordering np.meshgrid(..., indexing="xy").

dthetaint, optional

Order of theta-derivative

Added in version 0.14.0.

dphiint

Order of phi-derivative

Added in version 0.14.0.

gridbool

Whether to evaluate the results on a grid spanned by the input arrays, or at points specified by the input arrays.

Added in version 0.14.0.

Examples

Suppose that we want to use splines to interpolate a bivariate function on a sphere. The value of the function is known on a grid of longitudes and colatitudes.

import numpy as np from scipy.interpolate import RectSphereBivariateSpline def f(theta, phi): ... return np.sin(theta) * np.cos(phi)

We evaluate the function on the grid. Note that the default indexing=”xy” of meshgrid would result in an unexpected (transposed) result after interpolation.

thetaarr = np.linspace(0, np.pi, 22)[1:-1] phiarr = np.linspace(0, 2 * np.pi, 21)[:-1] thetagrid, phigrid = np.meshgrid(thetaarr, phiarr, indexing="ij") zdata = f(thetagrid, phigrid)

We next set up the interpolator and use it to evaluate the function on a finer grid.

rsbs = RectSphereBivariateSpline(thetaarr, phiarr, zdata) thetaarr_fine = np.linspace(0, np.pi, 200) phiarr_fine = np.linspace(0, 2 * np.pi, 200) zdata_fine = rsbs(thetaarr_fine, phiarr_fine)

Finally we plot the coarsly-sampled input data alongside the finely-sampled interpolated data to check that they agree.

import matplotlib.pyplot as plt fig = plt.figure() ax1 = fig.add_subplot(1, 2, 1) ax2 = fig.add_subplot(1, 2, 2) ax1.imshow(zdata) ax2.imshow(zdata_fine) plt.show()