interpolate / downsample 2D array in Python

Question

I have 2 separate arrays with different sizes:

len(range_data) = 4320
len(az1) = 385
len(az2) = 347

data1.shape = (385,4320)
data2.shape = (347,4320)

I would like for the dimensions of data2 to equal that of data1, such that data2.shape should be (385,4320). I have tried scipy interpolate such as:

f = interpolate.interp2d(az1,range_data,data1,kind='cubic')
znew = f(az2,range_data)

print(znew.shape)
(347,4320)

znew.shape should be (385,4320), any ideas why this is happening and/or what might need to be done to fix this?

Answer 1

I don't think that interp2d actually generates more points for you, it defines an interpolation function over a grid. That means that what you've created is a way to interpolate points within the grid defined by your first set of data points. znew will return an interpolated grid with the same number of values as the x and y passed to it.

See the source code .

Returns
        -------
        z : 2-D array with shape (len(y), len(x))
            The interpolated values.

If you want to add extra data points, I would suggest deriving a regression function (or whatever ML technique you want, NNs if you're so inclined) on the second data set and use that function to produce the extra 38 datapoints you need.