flopscope.

flopscope.numpy.permute_dims

fnp.permute_dims(a, axes=None)[flopscope source][numpy source]

Returns an array with axes transposed.

Adapted from NumPy docs np.permute_dims

Areacore
Typefree
NumPy Refnp.permute_dims
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0
Flopscope Context

Permute dimensions (NumPy 2.x array API).

For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., flops.atleast_2d(a).T achieves this, as does a[:, flops.newaxis]. For a 2-D array, this is the standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided, then transpose(a).shape == a.shape[::-1].

Parameters

a:array_like

Input array.

axes:tuple or list of ints, optional

If specified, it must be a tuple or list which contains a permutation of [0, 1, ..., N-1] where N is the number of axes of a. Negative indices can also be used to specify axes. The i-th axis of the returned array will correspond to the axis numbered axes[i] of the input. If not specified, defaults to range(a.ndim)[::-1], which reverses the order of the axes.

Returns

p:ndarray

a with its axes permuted. A view is returned whenever possible.

See also

Notes

Use transpose(a, argsort(axes)) to invert the transposition of tensors when using the axes keyword argument.

Examples

>>> import flopscope.numpy as fnp
>>> a = flops.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> flops.transpose(a)
array([[1, 3],
       [2, 4]])
>>> a = flops.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> flops.transpose(a)
array([1, 2, 3, 4])
>>> a = flops.ones((1, 2, 3))
>>> flops.transpose(a, (1, 0, 2)).shape
(2, 1, 3)
>>> a = flops.ones((2, 3, 4, 5))
>>> flops.transpose(a).shape
(5, 4, 3, 2)
>>> a = flops.arange(3*4*5).reshape((3, 4, 5))
>>> flops.transpose(a, (-1, 0, -2)).shape
(5, 3, 4)