krogh_interpolate(xi, yi, x, der=0, axis=0)
See KroghInterpolator
for more details.
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).
Known x-coordinates.
Known y-coordinates, of shape (xi.size, R)
. Interpreted as vectors of length R, or scalars if R=1.
Point or points at which to evaluate the derivatives.
How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative.
Axis in the yi array corresponding to the x-coordinate values.
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 :None:None:`yi` are scalars then the last dimension will be dropped.
Convenience function for polynomial interpolation.
KroghInterpolator
Krogh interpolator
We can interpolate 2D observed data using krogh interpolation:
>>> 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.interpolate._polyint.krogh_interpolate
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