numpy.ravel_multi_index — NumPy v1.11 Manual (original) (raw)

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. 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 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.

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 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.

Notes

New in version 1.6.0.

Examples

arr = np.array([[3,6,6],[4,5,1]]) np.ravel_multi_index(arr, (7,6)) array([22, 41, 37]) np.ravel_multi_index(arr, (7,6), order='F') array([31, 41, 13]) np.ravel_multi_index(arr, (4,6), mode='clip') array([22, 23, 19]) np.ravel_multi_index(arr, (4,4), mode=('clip','wrap')) array([12, 13, 13])

np.ravel_multi_index((3,1,4,1), (6,7,8,9)) 1621