I have 4-dimensional array. I am going to turn it into a 1-dim array. I use numpy ravel and it works fin with the default parameters.

However I would also like the positions/indices in the 4-dim array.

I want something like this as row in my output.

x,y,z,w,value

With x being the first dimension of my initial array and so on.

The obvious approach is iteration, however I was told to avoid it when I can.

for i in range(test.shape[0]):
    for j in range(test.shape[1]):
        for k in range(test.shape[2]):
            for l in range(test.shape[3]):
                print(i,j,k,l,test[(i,j,k,l)])

It will be to slow when I use a larger dataset.

Is there a way to configure ravel to do this or any other approach faster than iteration.

CodePudding user response：

Use np.indices with sparse=False, combined with np.concatenate to build the array. np.indices provides the first n columns, and np.concatenate appends the last one:

test = np.random.randint(10, size=(3, 5, 4, 2))
index = np.indices(test.shape, sparse=False)  # shape: (4, 3, 5, 4, 2)
data = np.concatenate((index, test[None, ...]), axis=0).reshape(test.ndim   1, -1).T

A more detailed breakdown:

1. index is a (4, *test.shape) array, with one element per dimension.
2. To make test concatenatable with index, you need to prepend a unit dimension, which is what test[None, ...] does. None is synonymous with np.newaxis, and Ellipsis, or ..., means "all the remaining dimensions".
3. When you concatenate along axis=0, you are appending test to the array of indices. Each element of index along the first axis is now a 5-element array containing the index followed by the value. The remaining axes reflect the shape of test, but besides that, you have what you want.
4. The goal is to flatten out the trailing dimensions, so you get a (5, N) array, where N = np.prod(test.shape). Thats what the final reshape does. test.ndim 1 is the size of the index 1 for the value. -1 can appear exactly once in a reshape. It means "product of all the remaining dimensions".