numpy.ascontiguousarray#

numpy.ascontiguousarray(a, dtype=None, *, like=None)#

Return a contiguous array (ndim >= 1) in memory (C order).

Parameters:

aarray_like: Input array.
dtypestr or dtype object, optional: Data-type of returned array.
likearray_like, optional: Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

New in version 1.20.0.

Returns:

outndarray: Contiguous array of same shape and content as a, with type dtype if specified.

See also

asfortranarray: Convert input to an ndarray with column-major memory order.
require: Return an ndarray that satisfies requirements.
ndarray.flags: Information about the memory layout of the array.

Examples

Starting with a Fortran-contiguous array:

>>> import numpy as np
>>> x = np.ones((2, 3), order='F')
>>> x.flags['F_CONTIGUOUS']
True

Calling ascontiguousarray makes a C-contiguous copy:

>>> y = np.ascontiguousarray(x)
>>> y.flags['C_CONTIGUOUS']
True
>>> np.may_share_memory(x, y)
False

Now, starting with a C-contiguous array:

>>> x = np.ones((2, 3), order='C')
>>> x.flags['C_CONTIGUOUS']
True

Then, calling ascontiguousarray returns the same object:

>>> y = np.ascontiguousarray(x)
>>> x is y
True

Note: This function returns an array with at least one-dimension (1-d) so it will not preserve 0-d arrays.