Access to multiple array elements

Question

Is there a way to get the elements of an array in one operation for the known rows and columns of these elements? In each row, I would like to access the elements from col_start to col_end (each row has different start and end indices). The number of elements is the same for each row, the elements are sequentially. Example:

[ . . . . | | | . . . . . ]
[ | | | . . . . . . . . . ]
[ . . | | | . . . . . . . ]
[ . . . . . . . . | | | . ]

One solution would be to get the indices (pairs of rows and columns) of the elements, rather than using my_array [row_list, col_list].

Is there any other (simpler) way without using for loops?

Answer 1

A = np.arange(40).reshape(4,10)*.1
startend = [[2,5],[3,6],[4,7],[5,8]]
index_list = [np.arange(v[0],v[1]) + i*A.shape[1] 
                 for i,v in enumerate(startend)]
# [array([2, 3, 4]), array([13, 14, 15]), array([24, 25, 26]), array([35, 36, 37])]
A.flat[index_list]

production

array([[ 0.2,  0.3,  0.4],
       [ 1.3,  1.4,  1.5],
       [ 2.4,  2.5,  2.6],
       [ 3.5,  3.6,  3.7]])

, . , 1d, A. np.take(A, index_list) .

, np.r_ . , .

A.flat[np.r_[tuple(index_list)]]
# array([ 0.2,  0.3,  0.4,  1.3,  1.4,  1.5,  2.4,  2.5,  2.6,  3.5,  3.6, 3.7])

---

idx, ajcr, choose:

idx = [np.arange(v[0], v[1]) for i,v in enumerate(startend)]
A[np.arange(A.shape[0])[:,None], idx]

idx my index_list, , .

np.array(idx)

array([[2, 3, 4],
       [3, 4, 5],
       [4, 5, 6],
       [5, 6, 7]])

arange , idx :

col_start = np.array([2,3,4,5])
idx = col_start[:,None] + np.arange(3)

- , idx.

np.arange(A.shape[0])[:,None] 
array([[0],
       [1],
       [2],
       [3]])

A idx :

In [515]: timeit np.choose(idx,A.T[:,:,None])
10000 loops, best of 3: 30.8 µs per loop

In [516]: timeit A[np.arange(A.shape[0])[:,None],idx]
100000 loops, best of 3: 10.8 µs per loop

In [517]: timeit A.flat[idx+np.arange(A.shape[0])[:,None]*A.shape[1]]
10000 loops, best of 3: 24.9 µs per loop

flat , fancier .

flat.

A=np.arange(4000).reshape(40,100)*.1
col_start=np.arange(20,60)
idx=col_start[:,None]+np.arange(30)

In [536]: timeit A[np.arange(A.shape[0])[:,None],idx]
10000 loops, best of 3: 108 µs per loop

In [537]: timeit A.flat[idx+np.arange(A.shape[0])[:,None]*A.shape[1]]
10000 loops, best of 3: 59.4 µs per loop

np.choose : Need between 2 and (32) array objects (inclusive).

---

idx?

col_start=np.array([2,4,6,8])
idx=col_start[:,None]+np.arange(3)
A[np.arange(A.shape[0])[:,None], idx]

, idx 10 .

clip idx

idx=idx.clip(0,A.shape[1]-1)

[ 3.8,  3.9,  3.9]

A . . np.pad.

np.pad(A,((0,0),(0,2)),'edge')[np.arange(A.shape[0])[:,None], idx]

- . idx ( ). flat , .

startend = [[2,5],[4,7],[6,9],[8,10]]
index_list = [np.arange(v[0],v[1]) + i*A.shape[1] 
                 for i,v in enumerate(startend)]
# [array([2, 3, 4]), array([14, 15, 16]), array([26, 27, 28]), array([38, 39])]

A.flat[np.r_[tuple(index_list)]]
# array([ 0.2,  0.3,  0.4,  1.4,  1.5,  1.6,  2.6,  2.7,  2.8,  3.8,  3.9])

Answer 2

np.choose.

NumPy arr:

array([[ 0,  1,  2,  3,  4,  5,  6],
       [ 7,  8,  9, 10, 11, 12, 13],
       [14, 15, 16, 17, 18, 19, 20]])

, [1, 2, 3] , [11, 12, 13] [17, 18, 19] .

, arr, idx:

array([[1, 2, 3],
       [4, 5, 6],
       [3, 4, 5]])

np.choose:

>>> np.choose(idx, arr.T[:,:,np.newaxis])
array([[ 1,  2,  3],
       [11, 12, 13],
       [17, 18, 19]])

---

, : arr.T[:,:,np.newaxis] , arr 3D- (7, 3, 1). 3D-, arr - . 3D- :

#  0       1       2       3       4       5       6
[[ 0]   [[ 1]   [[ 2]   [[ 3]   [[ 4]   [[ 5]   [[ 6]   # choose values from 1, 2, 3
 [ 7]    [ 8]    [ 9]    [10]    [11]    [12]    [13]   # choose values from 4, 5, 6
 [14]]   [15]]   [16]]   [17]]   [18]]   [19]]   [20]]  # choose values from 3, 4, 5

, choose 1, 2 2D 3.

, choose 2D- 4, 5,... .

Answer 3

, - . , , , .

indexes = [(4,6), (0,2), (2,4), (8, 10)]
arr = [
    [ . . . . | | | . . . . . ],
    [ | | | . . . . . . . . . ],
    [ . . | | | . . . . . . . ],
    [ . . . . . . . . | | | . ]
]

for x in zip(indexes, arr):
    index = x[0]
    row = x[1]
    print row[index[0]:index[1]+1]