Apply 1D array representing index to element translation over 2D array of index values?-CodePudding

I have a 2D array

arr = np.array([
   [ 1,  2, -1, -1],
   [ 0,  1, -1, -1],
   [ 3,  5, -1, -1],
   [ 7,  8, -1, -1],
   [ 6,  7, -1, -1],
   [ 9, 11, -1, -1]])

Its elements are related to the indices of some other array. A -1 value represent "no index". I also have a translation of the elements in arr to some other value (indices of a different array) in the form of

trans = np.array([[ 0.],
   [-1.],
   [ 1.],
   [-1.],
   [ 2.],
   [-1.],
   [ 3.],
   [-1.],
   [ 4.],
   [-1.],
   [ 5.],
   [-1.]])

Here the nth element of trans denotes the mapping of the element values in arr to the element value of trans. For example, a 8 in arr should be translated to a value of 4 (trans[8] == 4).

How can I apply trans to translate the values of arr?

CodePudding user response：

Just flatten trans, and index it with arr. Note that this results in the entries that were -1 in arr being translated to the last entry in trans. To fix this, you can manually assign -1 to all entries that were -1 in arr:

result = trans.flat[arr]
result[arr == -1] = -1

print(repr(result))

outputs

array([[-1.,  1., -1., -1.],
       [ 0., -1., -1., -1.],
       [-1., -1., -1., -1.],
       [-1.,  4., -1., -1.],
       [ 3., -1., -1., -1.],
       [-1., -1., -1., -1.]])

Note that the result will have the dtype of trans.

CodePudding user response：

If you want to avoid doing unnecessary lookups into the last element of trans for the -1 entries in arr (as in this answer), you can instead create a copy of arr and then use similar indexing to only update the non--1 entries:

result = arr.copy()

has_index = arr != -1
result[has_index] = trans.flat[arr[has_index]].flat

print(repr(result))

which outputs

array([[-1,  1, -1, -1],
       [ 0, -1, -1, -1],
       [-1, -1, -1, -1],
       [-1,  4, -1, -1],
       [ 3, -1, -1, -1],
       [-1, -1, -1, -1]])

Note that the result will have the dtype of arr.