vectorized linear interpolation in numpy

linear_interpolation.py

import numpy
from matplotlib import pyplot


def interpolate(x_fix, y_fix, x_var):
    x_repeat = numpy.tile(x_var[:,None], (len(x_fix),))
    distances = numpy.abs(x_repeat - x_fix)

    x_indices = numpy.searchsorted(x_fix, x_var)

    weights = numpy.zeros_like(distances)
    idx = numpy.arange(len(x_indices))
    weights[idx,x_indices] = distances[idx,x_indices-1]
    weights[idx,x_indices-1] = distances[idx,x_indices]
    weights /= numpy.sum(weights, axis=1)[:,None]
    print(weights.round(2))

    y_var = numpy.dot(weights, y_fix.T)
    return y_var

x_fix = numpy.arange(0,10,1.1)              # x for which y is available
y_fix = x_fix**2
x_var = numpy.array([1.2,2.3,6.7,7.2])      # x for which y is desired
y_var = interpolate(x_fix, y_fix, x_var)

print("x_fix: {}".format(x_fix))
print("y_fix: {}".format(y_fix))
print("x_var: {}".format(x_var))
print("y_var: {}".format(y_var))

pyplot.figure()
pyplot.plot(x_fix, y_fix, label="original")
pyplot.plot(x_var, y_var, label="interpolation")
pyplot.legend()
pyplot.show()