take

mdtools.numpy_helper_functions.take(a, start=None, stop=None, step=None, axis=None)

Take a slice from an array along an axis.

For example, take(a, axis=2, start, stop, step) is equivalent to a[:,:,start:stop:step,...].

Parameters:

• a (array_like) – The input array.

• start, stop, step (int or None, optional) – Start, stop and step of the slice. See the built-in function slice() for more details.

• axis (int or None, optional) – The axis over which to select values. By default, the flattened input array is used.

Returns:

a_slice (numpy.ndarray) – A view of the requested slice along the requested axis.

See also

numpy.take()

Similar function but supports fancy indexing and therefore always returns a copy instead of a view.

Notes

This function is similar to numpy.take(). The difference is that this function only supports slicing and not fancy indexing but therefore always returns a view instead of a copy.

References

Inspired by Leland Hepworth (https://stackoverflow.com/a/64436208)

Examples

>>> a = np.arange(6).reshape(2,3)
>>> x = mdt.nph.take(a)
>>> y = np.take(a, indices=np.arange(a.size))
>>> np.array_equal(x, y)
True
>>> x
array([0, 1, 2, 3, 4, 5])
>>> x[0] = 6  # x is a view of a, i.e. changing x changes a.
>>> x
array([6, 1, 2, 3, 4, 5])
>>> a
array([[6, 1, 2],
       [3, 4, 5]])
>>> y[0] = 7  # y is a copy of a, i.e. changing y does not affect a.
>>> y
array([7, 1, 2, 3, 4, 5])
>>> a
array([[6, 1, 2],
       [3, 4, 5]])
>>> x = mdt.nph.take(a, axis=0)
>>> y = np.take(a, indices=np.arange(a.shape[0]), axis=0)
>>> np.array_equal(x, y)
True
>>> x
array([[6, 1, 2],
       [3, 4, 5]])

>>> a = np.arange(24).reshape(2,3,4)
>>> mdt.nph.take(a, axis=0)
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],

       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])
>>> mdt.nph.take(a, start=1, axis=1)
array([[[ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],

       [[16, 17, 18, 19],
        [20, 21, 22, 23]]])
>>> mdt.nph.take(a, stop=3, axis=2)
array([[[ 0,  1,  2],
        [ 4,  5,  6],
        [ 8,  9, 10]],

       [[12, 13, 14],
        [16, 17, 18],
        [20, 21, 22]]])
>>> np.array_equal(
...     mdt.nph.take(a, stop=3, axis=2),
...     mdt.nph.take(a, stop=-1, axis=2)
... )
True
>>> np.array_equal(
...     mdt.nph.take(a, stop=3, axis=2),
...     mdt.nph.take(a, stop=3, axis=-1)
... )
True
>>> np.array_equal(
...     mdt.nph.take(a, axis=2, stop=3),
...     np.take(a, indices=np.arange(a.shape[2] - 1), axis=2)
... )
True
>>> mdt.nph.take(a, step=2, axis=2)
array([[[ 0,  2],
        [ 4,  6],
        [ 8, 10]],

       [[12, 14],
        [16, 18],
        [20, 22]]])