numpy.ma.atleast_3d()

numpy.ma.atleast_3d(*arys) =

View inputs as arrays with at least three dimensions.

Parameters:

Parameters:	arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved.
Returns:	res1, res2, ... : ndarray An array, or tuple of arrays, each with `a.ndim >= 3`. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape `(N,)` becomes a view of shape `(1, N, 1)`, and a 2-D array of shape `(M, N)` becomes a view of shape `(M, N, 1)`.

arys1, arys2, ... : array_like

One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved.

Returns:

res1, res2, ... : ndarray

An array, or tuple of arrays, each with a.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape (N,) becomes a view of shape (1, N, 1), and a 2-D array of shape (M, N) becomes a view of shape (M, N, 1).

Notes

The function is applied to both the _data and the _mask, if any.

Examples

>>> 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
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)

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.ma.atleast_3d.html

doc_NumPy

2025-01-10 15:47:30

Comments