CODEWITHSUNDEEP
pythonnumpypython-xarray
ainetamf
ainetamf

Reputation: 33

Covariance between a 1-d and a 3-d array in Python

I have a dataset created through xarray with its assigned coordinates and dimensions. Obtained from that, I also have two variables: a 1-dimensional array and a 3-dimensional one, with the same coordinate as the first one and two additional ones. I want to obtain the covariance of both in their shared coordinate "memb" for each point in the 2-d space defined by the two coordinates which aren't shared by both and make that a matrix.

In other words, a variable is defined by "memb" and another one is defined by "memb", "north_south" and "west_east". I want to find the memb covariance for each north_south and west_east point and assign it to a variable with a value assigned to each north_south and west_east value.

To obtain it at one point I can run the following code and obtain the desired result:

numpy.cov(var_1,var_2.isel(north_south=1,west_east=1)[0][1]

I want to assign this to a variable which will have the dimensions north_south and west_east. I think I know how to make it work with for blocks, but how can i assign it to a variable with the two dimensions at each point?

Upvotes: 2

Views: 712

Answers (1)

user6655984
user6655984

Reputation:

The method apply_along_axis seems appropriate. Example:

import numpy as np
a = np.random.uniform(size=(5,))
b = np.random.uniform(size=(5, 3, 2))
c = np.apply_along_axis(lambda x: np.cov(a, x)[0][1], 0, b)

Here c is a 2D array of size 3 by 2. The second parameter of apply_along_axis specifies that the axis of b along which to work is 0th axis (could be another one, as long as it matches the size of 1D array a). The lambda just computes the covariance, returning the scalar value of interest.

Upvotes: 3

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