Numpy array manipulation: Transpose-like operations

Numpy array manipulation: Transpose-like operations#

Move axes of an array to new positions: numpy.flatten#

npm.moveaxis(a, source, destination)#

Move axes of an array to new positions. Other axes remain in their original order.

See https://numpy.org/doc/stable/reference/generated/numpy.moveaxis.html#numpy.moveaxis for details.

>>> import numpy as np; from mpfunlab import mpm, dpm

>>> x = np.zeros((3, 4, 5))
>>> np.moveaxis(x, 0, -1).shape
(4, 5, 3)
>>> np.moveaxis(x, -1, 0).shape
(5, 3, 4)

These all achieve the same result:

>>> np.transpose(x).shape
(5, 4, 3)
>>> np.swapaxes(x, 0, -1).shape
(5, 4, 3)
>>> np.moveaxis(x, [0, 1], [-1, -2]).shape
(5, 4, 3)
>>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
(5, 4, 3)

Interchange two axes of an array: numpy.swapaxes#

npm.swapaxes(a, axis1, axis2)#

Interchange two axes of an array.

See https://numpy.org/doc/stable/reference/generated/numpy.swapaxes.html#numpy.swapaxes for details.

>>> import numpy as np; from mpfunlab import mpm, dpm

>>> x = np.array([[1,2,3]])
>>> np.swapaxes(x,0,1)
array([[1],
       [2],
       [3]])

>>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
>>> x
array([[[0, 1],
        [2, 3]],
       [[4, 5],
        [6, 7]]])

>>> np.swapaxes(x,0,2)
array([[[0, 4],
        [2, 6]],
       [[1, 5],
        [3, 7]]])

Get an array with axes transposed: numpy.transpose#

npm.transpose(a, axes=None)#

Returns an array with axes transposed.

See https://numpy.org/doc/stable/reference/generated/numpy.transpose.html#numpy.transpose for details.

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., np.atleast2d(a).T achieves this, as does a[:, np.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].

>>> import numpy as np; from mpfunlab import mpm, dpm

>>> a = np.array([[1, 2], [3, 4]])
a
array([[1, 2],
       [3, 4]])
>>> np.transpose(a)
array([[1, 3],
       [2, 4]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> np.transpose(a)
array([1, 2, 3, 4])

>>> a = np.ones((1, 2, 3))
>>> np.transpose(a, (1, 0, 2)).shape
(2, 1, 3)

>>> a = np.ones((2, 3, 4, 5))
>>> np.transpose(a).shape
(5, 4, 3, 2)

ndarray.T

https://numpy.org/doc/stable/reference/generated/numpy.ndarray.T.html#numpy.ndarray.T

View of the transposed array. Same as self.transpose().

>>> import numpy as np; from mpfunlab import mpm, dpm

>>> a = np.array([[1, 2], [3, 4]])
a
array([[1, 2],
       [3, 4]])
>>> a.T
array([[1, 3],
       [2, 4]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.T
array([1, 2, 3, 4])
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