flopscope.

flopscope.numpy.ravel_multi_index

fnp.ravel_multi_index(*args, **kwargs)[flopscope source]

Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.

Adapted from NumPy docs np.ravel_multi_index

Areacore
Typefree
NumPy Refnp.ravel_multi_index
Cost
0
Flopscope Context

Convert multi-dimensional index to flat 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.

See also

Examples

>>> import flopscope.numpy as fnp
>>> arr = flops.array([[3,6,6],[4,5,1]])
>>> flops.ravel_multi_index(arr, (7,6))
array([22, 41, 37])
>>> flops.ravel_multi_index(arr, (7,6), order='F')
array([31, 41, 13])
>>> flops.ravel_multi_index(arr, (4,6), mode='clip')
array([22, 23, 19])
>>> flops.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
array([12, 13, 13])
>>> flops.ravel_multi_index((3,1,4,1), (6,7,8,9))
1621