![]() ![]() We can compute a linear interpolation with numpy: yn = np.interp(xn, x, y) We'll do some examples with this values: x = np.linspace(0, 4, 12) ![]() The linear interpolation is easy to compute but not precise, due to the discontinuites at the points. Plt.plot(xn, yn, label='Interpolated values') Plt.plot(x, y, 'ok', label='Known points') If nothing about the plots is said, they will be generated as: plt.plot(xn, y0, '-k', label='True values') As a representation, y0 will be the true values, generated from the original function to show the interpolator behavior. We will need to obtain the interpolated values yn for xn. In the next examples, x and y represents the known points. Let's see some interpolation examples for one and two-dimensional data.įirst of all, the required modules: import numpy as np Interpolation methods in Scipy numerical-analysis interpolation python numpy scipyĪmong other numerical analysis modules, scipy covers some interpolation algorithms as well as a different approaches to use them to calculate an interpolation, evaluate a polynomial with the representation of the interpolation, calculate derivatives, integrals or roots with functional and class-based interfaces. Modesto Mas Numerical Computing, Python, Julia, Hadoop and more ![]()
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