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scipy.interpolate.LSQSphereBivariateSpline.

# __call__

LSQSphereBivariateSpline.__call__(theta, phi, dtheta=0, dphi=0, grid=True)

  Evaluate the spline or its derivatives at given positions.

  Parameters:
    theta, phi array_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 with `np.meshgrid(..., indexing="ij")` and inconsistent with the default ordering `np.meshgrid(..., indexing="xy")`.

    dtheta int, optional

      Order of theta-derivative

      Added in version 0.14.0.

    dphi int

      Order of phi-derivative

      Added in version 0.14.0.

    grid bool

      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()