interpolation between arrays in python


What is the easiest and fastest way to interpolate between two arrays to get new array.

For example, I have 3 arrays:

x = np.array([0,1,2,3,4,5])
y = np.array([5,4,3,2,1,0])
z = np.array([0,5])

x,y corresponds to data-points and z is an argument. So at z=0 x array is valid, and at z=5 y array valid. But I need to get new array for z=1. So it could be easily solved by:

a = (y-x)/(z[1]-z[0])*1+x

Problem is that data is not linearly dependent and there are more than 2 arrays with data. Maybe it is possible to use somehow spline interpolation?

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| numpy   | python   | arrays   | interpolation   | scipy   2017-01-06 12:01 1 Answers

Answers to interpolation between arrays in python ( 1 )

  1. 2017-01-07 10:01

    This is a univariate to multivariate regression problem. Scipy supports univariate to univariate regression, and multivariate to univariate regression. But you can instead iterate over the outputs, so this is not such a big problem. Below is an example of how it can be done. I've changed the variable names a bit and added a new point:

    import numpy as np
    from scipy.interpolate import interp1d
    X = np.array([0, 5, 10])
    Y = np.array([[0, 1, 2, 3, 4, 5],
                  [5, 4, 3, 2, 1, 0],
                  [8, 6, 5, 1, -4, -5]])
    XX = np.array([0, 1, 5])  # Find YY for these
    YY = np.zeros((len(XX), Y.shape[1]))
    for i in range(Y.shape[1]):
        f = interp1d(X, Y[:, i])
        for j in range(len(XX)):
            YY[j, i] = f(XX[j])

    So YY are the result for XX. Hope it helps.

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