Averaging n elements along 1st axis of 4D array with numpy

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I have a 4D array containing daily time-series of gridded data for different years with shape (year, day, x-coordinate, y-coordinate). The actual shape of my array is (19, 133, 288, 620), so I have 19 years of data with 133 days per year over a 288 x 620 grid. I want to take the weekly average of each grid cell over the period of record. The shape of the weekly averaged array should be (19, 19, 288, 620), or (year, week, x-coordinate, y-coordinate). I would like to use numpy to achieve this.

Here I construct some dummy data to work with and an array of what the solution should be:

import numpy as np

a1 = np.arange(1, 10).reshape(3, 3)
a1days = np.repeat(a1[np.newaxis, ...], 7, axis=0)

b1 = np.arange(10, 19).reshape(3, 3)
b1days = np.repeat(b1[np.newaxis, ...], 7, axis=0)

c1year = np.concatenate((a1days, b1days), axis=0)

a2 = np.arange(19, 28).reshape(3, 3)
a2days = np.repeat(a2[np.newaxis, ...], 7, axis=0)

b2 = np.arange(29, 38).reshape(3, 3)
b2days = np.repeat(b2[np.newaxis, ...], 7, axis=0)
c2year = np.concatenate((a2days, b2days), axis=0)

dummy_data = np.concatenate((c1year, c2year), axis=0).reshape(2, 14, 3, 3)

solution = np.concatenate((a1, b1, a2, b2), axis=0).reshape(2, 2, 3, 3)

The shape of the dummy_data is (2, 14, 3, 3). Per the dummy data, I have two years of data, 14 days per year, over a 3 X 3 grid. I want to return the weekly average of the grid for both years, resulting in a solution with shape (2, 2, 3, 3).

python-3.x numpy
1 Answers

You can reshape and take mean:

week_mean = dummy_data.reshape(2,-1,7,3,3).mean(axis=2)
# in your case .reshape(year, -1, 7, x_coord, y_coord)

# check:
(dummy_data.reshape(2,2,7,3,3).mean(axis=2) == solution).all()
# True
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