numpy - ravel_multi_index

arr = np.array([[3,6,6],[4,5,1]])
np.ravel_multi_index(arr, (7,6))
## array([22, 41, 37])


r = 7
c = 6
print(np.arange(r*c))
print(np.arange(r*c).reshape(r,c))
print(np.arange(r*c).reshape(r,c)[[3,6,6],[4,5,1]])

## [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41]

## [[ 0  1  2  3  4  5]
## [ 6  7  8  9 10 11]
## [12 13 14 15 16 17]
## [18 19 20 21 22 23] (3,4)
## [24 25 26 27 28 29]
## [30 31 32 33 34 35]
## [36 37 38 39 40 41]] (6,5) (6,1)

## [22 41 37] (3*6+4, 6*6+5, 6*6+1)

 

https://numpy.org/doc/stable/reference/generated/numpy.ravel_multi_index.html

 

numpy.ravel_multi_index(multi_index, dims, mode='raise', order='C')
Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.

- Parameters :
> multi_index : tuple of array_like
   A tuple of integer arrays, one array for each dimension.
> dim : stuple of ints
   The shape of array into which the indices from multi_index apply.
> mode{‘raise’, ‘wrap’, ‘clip’}, optional
   Specifies how out-of-bounds indices are handled. Can specify either one mode or a tuple of modes, one mode per index.
   ‘raise’ – raise an error (default)
   ‘wrap’ – wrap around
   ‘clip’ – clip to the range
   In ‘clip’ mode, a negative index which would normally wrap will clip to 0 instead.
> order{‘C’, ‘F’}, optional
   Determines whether the multi-index should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order.

- Returns
> raveled_indices : ndarray
   An array of indices into the flattened version of an array of dimensions dims.