numpy.ravel_multi_index()

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:

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`.

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

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.ravel_multi_index.html

doc_NumPy

2025-01-10 15:47:30

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