ev#
- SmoothBivariateSpline.ev(xi, yi, dx=0, dy=0)[source]#
- Evaluate the spline at points - Returns the interpolated value at - (xi[i], yi[i]), i=0,...,len(xi)-1.- Parameters:
- xi, yiarray_like
- Input coordinates. Standard Numpy broadcasting is obeyed. The ordering of axes is consistent with - np.meshgrid(..., indexing="ij")and inconsistent with the default ordering- np.meshgrid(..., indexing="xy").
- dxint, optional
- Order of x-derivative - Added in version 0.14.0. 
- dyint, optional
- Order of y-derivative - Added in version 0.14.0. 
 
 - Examples - Suppose that we want to bilinearly interpolate an exponentially decaying function in 2 dimensions. - >>> import numpy as np >>> from scipy.interpolate import RectBivariateSpline >>> def f(x, y): ... return np.exp(-np.sqrt((x / 2) ** 2 + y**2)) - We sample the function on a coarse grid and set up the interpolator. Note that the default - indexing="xy"of meshgrid would result in an unexpected (transposed) result after interpolation.- >>> xarr = np.linspace(-3, 3, 21) >>> yarr = np.linspace(-3, 3, 21) >>> xgrid, ygrid = np.meshgrid(xarr, yarr, indexing="ij") >>> zdata = f(xgrid, ygrid) >>> rbs = RectBivariateSpline(xarr, yarr, zdata, kx=1, ky=1) - Next we sample the function along a diagonal slice through the coordinate space on a finer grid using interpolation. - >>> xinterp = np.linspace(-3, 3, 201) >>> yinterp = np.linspace(3, -3, 201) >>> zinterp = rbs.ev(xinterp, yinterp) - And check that the interpolation passes through the function evaluations as a function of the distance from the origin along the slice. - >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax1 = fig.add_subplot(1, 1, 1) >>> ax1.plot(np.sqrt(xarr**2 + yarr**2), np.diag(zdata), "or") >>> ax1.plot(np.sqrt(xinterp**2 + yinterp**2), zinterp, "-b") >>> plt.show() 