Agfdhyk

I've tried and searched for a few days, I've come closer but need your help.

I have a 3d array in python,

shape(files)

>> (31,2049,2)

which corresponds to 31 input files with 2 columns of data with 2048 rows and a header.

I'd like to sort this array based on the header, which is a number, in each file.

I tried to follow NumPy: sorting 3D array but keeping 2nd dimension assigned to first , but i'm incredibly confused.

First I try to setup get my headers for the argsort, I thought I could do

sortval=files[:][0][0]

but this does not work..

Then I simply did a for loop to iterate and get my headers

for i in xrange(shape(files)[0]:

    sortval.append([i][0][0])

Then

sortedIdx = np.argsort(sortval)

This works, however I dont understand whats happening in the last line..

files = files[np.arange(len(deck))[:,np.newaxis],sortedIdx]

Help would be appreciated.

Here we can use a simple example to demonstrate what your code is doing:

First we create a random 3D numpy matrix:

a = (np.random.rand(3,3,2)*10).astype(int)

array([[[3, 1],

        [3, 7],

        [0, 3]],

       [[2, 9],

        [1, 0],

        [9, 2]],

       [[9, 2],

        [8, 8],

        [8, 0]]])

Then a[:] will gives a itself, and a[:][0][0] is just the first row in first 2D array in a, which is:

a[:][0]

# array([[3, 1],

#        [3, 7],

#        [0, 3]])



a[:][0][0]

# array([3, 1])

What you want is the header which are 3,2,9 in this example, so we can use a[:, 0, 0] to extract them:

a[:,0,0]

# array([3, 2, 9])

Now we sort the above list and get an index array:

np.argsort(a[:,0,0])

# array([1, 0, 2])

In order to rearrange the entire 3D array, we need to slice the array with correct order. And np.arange(len(a))[:,np.newaxis] is equal to np.arange(len(a)).reshape(-1,1) which creates a sequential 2D index array:

np.arange(len(a))[:,np.newaxis]

# array([[0],

#        [1],

#        [2]])

Without the 2D array, we will slice the array to 2 dimension

a[np.arange(3), np.argsort(a[:,0,0])]

# array([[3, 7],

#        [2, 9],

#        [8, 0]])

With the 2D array, we can perform 3D slicing and keeps the shape:

a[np.arange(3).reshape(-1,1), np.argsort(a[:,0,0])]

array([[[3, 7],

        [3, 1],

        [0, 3]],

       [[1, 0],

        [2, 9],

        [9, 2]],

       [[8, 8],

        [9, 2],

        [8, 0]]])

And above is the final result you want.

Edit:

To arange the 2D arrays:, one could use:

a[np.argsort(a[:,0,0])]

array([[[2, 9],

        [1, 0],

        [9, 2]],

       [[3, 1],

        [3, 7],

        [0, 3]],

       [[9, 2],

        [8, 8],

        [8, 0]]])

Another way to do this is with np.take

header = a[:,0,0]

sorted = np.take(a, np.argsort(header), axis=0)