krogh_interpolate#
- scipy.interpolate.krogh_interpolate(xi, yi, x, der=0, axis=0)[source]#
- Convenience function for Krogh interpolation. - See - KroghInterpolatorfor more details.- Parameters:
- xiarray_like
- Interpolation points (known x-coordinates). 
- yiarray_like
- Known y-coordinates, of shape - (xi.size, R). Interpreted as vectors of length R, or scalars if R=1.
- xarray_like
- Point or points at which to evaluate the derivatives. 
- derint or list or None, optional
- How many derivatives to evaluate, or None for all potentially nonzero derivatives (that is, a number equal to the number of points), or a list of derivatives to evaluate. This number includes the function value as the ‘0th’ derivative. 
- axisint, optional
- Axis in the yi array corresponding to the x-coordinate values. 
 
- Returns:
- dndarray
- If the interpolator’s values are R-D then the returned array will be the number of derivatives by N by R. If x is a scalar, the middle dimension will be dropped; if the yi are scalars then the last dimension will be dropped. 
 
 - See also - KroghInterpolator
- Krogh interpolator 
 - Notes - Construction of the interpolating polynomial is a relatively expensive process. If you want to evaluate it repeatedly consider using the class KroghInterpolator (which is what this function uses). - Examples - We can interpolate 2D observed data using Krogh interpolation: - >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from scipy.interpolate import krogh_interpolate >>> x_observed = np.linspace(0.0, 10.0, 11) >>> y_observed = np.sin(x_observed) >>> x = np.linspace(min(x_observed), max(x_observed), num=100) >>> y = krogh_interpolate(x_observed, y_observed, x) >>> plt.plot(x_observed, y_observed, "o", label="observation") >>> plt.plot(x, y, label="krogh interpolation") >>> plt.legend() >>> plt.show() 