Numpy array manipulation: Changing number of dimensions#

Convert inputs to arrays with at least one dimension: numpy.atleast_1d#

npm.atleast_1d(*arys)#

Convert inputs to arrays with at least one dimension.

See https://numpy.org/doc/stable/reference/generated/numpy.atleast_1d.html#numpy.atleast_1d for details

Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.

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

>>> np.atleast_1d(1.0)
array([1.])

>>> x = np.arange(9.0).reshape(3,3)
>>> np.atleast_1d(x)
array([[0., 1., 2.],
       [3., 4., 5.],
       [6., 7., 8.]])
>>> np.atleast_1d(x) is x
True

>>> np.atleast_1d(1, [3, 4])
[array([1]), array([3, 4])]

Convert inputs to arrays with at least two dimensions: numpy.atleast_2d#

npm.atleast_2d(*arys)#

Convert inputs as arrays with at least two dimensions.

See https://numpy.org/doc/stable/reference/generated/numpy.atleast_2d.html#numpy.atleast_2d for details

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

>>> np.atleast_2d(3.0)
array([[3.]])

>>> x = np.arange(3.0)
>>> np.atleast_2d(x)
array([[0., 1., 2.]])
>>> np.atleast_2d(x).base is x
True

>>> np.atleast_2d(1, [1, 2], [[1, 2]])
[array([[1]]), array([[1, 2]]), array([[1, 2]])]

Convert inputs to arrays with at least two dimensions: numpy.atleast_3d#

npm.atleast_3d(*arys)#

Convert inputs as arrays with at least three dimensions.

See https://numpy.org/doc/stable/reference/generated/numpy.atleast_3d.html#numpy.atleast_3d for details.

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

>>> np.atleast_3d(3.0)
array([[[3.]]])

>>> x = np.arange(3.0)
>>> np.atleast_3d(x).shape
(1, 3, 1)

>>> x = np.arange(12.0).reshape(4,3)
>>> np.atleast_3d(x).shape
(4, 3, 1)
>>> np.atleast_3d(x).base is x.base  # x is a reshape, so not base itself
True

for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]): print(arr, arr.shape)
[[[1]
  [2]]] (1, 2, 1)
[[[1]
  [2]]] (1, 2, 1)
[[[1 2]]] (1, 1, 2)

Broadcast an array to a new shape: numpy.broadcast_to#

npm.broadcast_to(array, shape, subok=False)#

Broadcast an array to a new shape.

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

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

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

Broadcast any number of arrays against each other: numpy.broadcast_arrays#

npm.broadcast_arrays(*args, subok=False)#

Broadcast any number of arrays against each other.

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

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

>>> x = np.array([[1,2,3]])
>>> y = np.array([[4],[5]])
>>> np.broadcast_arrays(x, y)
[array([[1, 2, 3],
       [1, 2, 3]]), array([[4, 4, 4],
       [5, 5, 5]])]

Here is a useful idiom for getting contiguous copies instead of non-contiguous views.

>>> [np.array(a) for a in np.broadcast_arrays(x, y)]
[array([[1, 2, 3],
       [1, 2, 3]]), array([[4, 4, 4],
       [5, 5, 5]])]

Expand the shape of an array: numpy.expand_dims#

npm.expand_dims(a, axis)#

Expand the shape of an array. Insert a new axis that will appear at the axis position in the expanded array shape.

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

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

>>> x = np.array([1, 2])
>>> x.shape
(2,)

The following is equivalent to x[np.newaxis, :] or x[np.newaxis]:

>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)

The following is equivalent to x[:, np.newaxis]:

>>> y = np.expand_dims(x, axis=1)
>>> y
array([[1],
       [2]])
>>> y.shape
(2, 1)

axis may also be a tuple:

>>> y = np.expand_dims(x, axis=(0, 1))
>>> y
array([[[1, 2]]])
>>> y = np.expand_dims(x, axis=(2, 0))
>>> y
array([[[1],
        [2]]])

Note that some examples may use None instead of np.newaxis. These are the same objects:

>>> np.newaxis is None
True

Remove axes of length one from an array: numpy.squeeze#

npm.squeeze(a, axis=None)#

Remove axes of length one from a.

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

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

>>> x = np.array([[[0], [1], [2]]])
>>> x.shape
(1, 3, 1)
>>> np.squeeze(x).shape
(3,)
>>> np.squeeze(x, axis=0).shape
(3, 1)
>>> np.squeeze(x, axis=1).shape
Traceback (most recent call last):
...
ValueError: cannot select an axis to squeeze out which has size not equal to one

>>> np.squeeze(x, axis=2).shape
(1, 3)
>>> x = np.array([[1234]])
>>> x.shape
(1, 1)
>>> np.squeeze(x)
array(1234)  # 0d array
>>> np.squeeze(x).shape
()
>>> np.squeeze(x)[()]
1234

Numpy array manipulation: Changing number of dimensions

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