numpy.ravel_multi_index() function converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.
Syntax : numpy.ravel_multi_index(multi_index, dims, mode = ‘raise’, order = ‘C)
Parameters :
multi_index : [tuple of array_like] A tuple of integer arrays, one array for each dimension.
dims : [tuple 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 that 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.Return : [ndarray] An array of indices into the flattened version of an array of dimensions dims.
Code #1 :
# Python program explaining # numpy.ravel_multi_index() function # importing numpy as geek import numpy as geek arr = geek.array([[3, 6, 6], [4, 5, 1]]) gfg = geek.ravel_multi_index(arr, (7, 6)) print(gfg) |
Output :
[22 41 37]
Code #2 :
# Python program explaining # numpy.ravel_multi_index() function # importing numpy as geek import numpy as geek arr = geek.array([[3, 6, 6], [4, 5, 1]]) gfg = geek.ravel_multi_index(arr, (7, 6), order = 'F') print(gfg) |
Output :
[31 41 13]
Code #3 :
# Python program explaining # numpy.ravel_multi_index() function # importing numpy as geek import numpy as geek arr = geek.array([[3, 6, 6], [4, 5, 1]]) gfg = geek.ravel_multi_index(arr, (7, 6), mode = 'clip') print(gfg) |
Output :
[22 41 37]
