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What are these ordering code doing
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What are these ordering code doing
#1
Hello, I am trying to understand what these ordering code does. They order a matrix, however I don't know what it is to order a matrix. My first guess is that it treats each row as an independent array. It is not that. Other option is that [i][j] should be bigger or smaller than [i+1][j]. Or finally that [i][j] has to be bigger/smaller[i][j+1].

However, in the following code, none of the above happens.

order = mothFit.argsort()

mothPos = mothPos[order, :]
I printed the values for a small matrix, 7x3, and yet it does not say anything to me:

Quote:('Matrix position:', 0, 0, 'it is: ', -29.387393693113182)
('Matrix position:', 0, 1, 'it is: ', -83.440206190738877)
('Matrix position:', 0, 2, 'it is: ', -10.309922902484161)
('Matrix position:', 1, 0, 'it is: ', -33.361882990330002)
('Matrix position:', 1, 1, 'it is: ', 72.39308962330756)
('Matrix position:', 1, 2, 'it is: ', -75.510119686670876)
('Matrix position:', 2, 0, 'it is: ', 40.051772638850878)
('Matrix position:', 2, 1, 'it is: ', -4.0235637181012862)
('Matrix position:', 2, 2, 'it is: ', -52.411468781539973)
('Matrix position:', 3, 0, 'it is: ', 89.862128975930261)
('Matrix position:', 3, 1, 'it is: ', -43.815001282979168)
('Matrix position:', 3, 2, 'it is: ', -93.485652785167318)
('Matrix position:', 4, 0, 'it is: ', 13.619610433388289)
('Matrix position:', 4, 1, 'it is: ', -27.184963891837128)
('Matrix position:', 4, 2, 'it is: ', -96.080991854199056)
('Matrix position:', 5, 0, 'it is: ', -81.052774094944553)
('Matrix position:', 5, 1, 'it is: ', -23.33498710449237)
('Matrix position:', 5, 2, 'it is: ', -19.97294189498686)
('Matrix position:', 6, 0, 'it is: ', 37.162577502915894)
('Matrix position:', 6, 1, 'it is: ', -4.6313634770747569)
('Matrix position:', 6, 2, 'it is: ', 46.449244269539577)

Quote:('Matrix position:', 0, 0, 'it is: ', 37.162577502915894)
('Matrix position:', 0, 1, 'it is: ', -4.6313634770747569)
('Matrix position:', 0, 2, 'it is: ', 46.449244269539577)
('Matrix position:', 1, 0, 'it is: ', 40.051772638850878)
('Matrix position:', 1, 1, 'it is: ', -4.0235637181012862)
('Matrix position:', 1, 2, 'it is: ', -52.411468781539973)
('Matrix position:', 2, 0, 'it is: ', -81.052774094944553)
('Matrix position:', 2, 1, 'it is: ', -23.33498710449237)
('Matrix position:', 2, 2, 'it is: ', -19.97294189498686)
('Matrix position:', 3, 0, 'it is: ', -29.387393693113182)
('Matrix position:', 3, 1, 'it is: ', -83.440206190738877)
('Matrix position:', 3, 2, 'it is: ', -10.309922902484161)
('Matrix position:', 4, 0, 'it is: ', 13.619610433388289)
('Matrix position:', 4, 1, 'it is: ', -27.184963891837128)
('Matrix position:', 4, 2, 'it is: ', -96.080991854199056)
('Matrix position:', 5, 0, 'it is: ', -33.361882990330002)
('Matrix position:', 5, 1, 'it is: ', 72.39308962330756)
('Matrix position:', 5, 2, 'it is: ', -75.510119686670876)
('Matrix position:', 6, 0, 'it is: ', 89.862128975930261)
('Matrix position:', 6, 1, 'it is: ', -43.815001282979168)
('Matrix position:', 6, 2, 'it is: ', -93.485652785167318)

So what the above code does?
Reply
#2
order = mothFit.argsort() creates an array or matrix the same size as mothFit which contains indices that will result in a sorted list. Since there is no "axis" argument specified, mothFit is sorted along the "last" axis. Axis 0 sorts the values in colomns (along the rows axis), Axis 1 sorts values in rows (along the columns axis). The "last" axis is Axis 1.

This is a complete, though short example.
import numpy as np
x = np.array([[1,6,3],[2,5,4]])
i = x.argsort()
y = np.take_along_axis(x, i, axis=1)
print('Sort each row\nIndices\n', i)
print('Sorted Values\n', y)
Output:
Sort each row Indices [[0 2 1] # In first row, the middle number is moved to the end [0 2 1]] # Same for the second row Sorted Values [[1 3 6] # Values in a row are sorted in increasing value [2 4 5]]
You can specify the sorting axis = 0 and sort the values in a column.
i = x.argsort(axis=0)
y = np.take_along_axis(x, i, axis=0)
print('\nSort each column\nIndices\n', i)
print('Sorted Values\n', y)
Output:
Sort each column Indices [[0 1 0] # Need to flip middle column [1 0 1]] Sorted Values [[1 5 3] # All columns sorted in increasing order [2 6 4]]
If you set axis = None, the matrix is sorted as a flat list.
i = x.argsort(axis=None)
y = np.take_along_axis(x, i, axis=None)
print('\nFlat sort\nIndices\n', i)
print('Sorted Values\n', y)
Output:
Flat sort Indices [0 3 2 5 4 1] Sorted Values [1 2 3 4 5 6]
Sorting the values to to still be in matrix form requires the additional step of splitting the array into two parts.
i = x.argsort(axis=None)
y = np.take_along_axis(x, i, axis=None)
z = np.array(np.array_split(y, 2))
print('\nFlat sort\nIndices\n', i)
print('Sorted Values\n', z)
Output:
Flat sort Indices [0 3 2 5 4 1] Sorted Values [[1 2 3] [4 5 6]]
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