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It is well known that IDW(Inverse Distance Weighted Interpolation) will not yield exactly the same value if we use the known locations as the input. Write a Python program to test this property.
The following is a Python implementation of IDW. This function takes two input variables. Z is a list of lists where each element list contains four values: X, Y, Value, and Distance to target point. Z can also be a NumPy 2-D array. Variable b is the power of distance. It returns the estimated value at the target location. Note that we don't need to specify the target location per se. Instead in Z we have the distance from each known location to the target point.
def IDW(Z, b):zw = 0.0 # sum of weighted zsw = 0.0 # sum of weightsN = len(Z) # number of points in the datafor i in range(N): d = Z[i][3] if d == 0: return Z[i][2] w = 1.0/d**b sw += w zw += w*Z[i][2]return zw/sw
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