numpy.ascontiguousarray — NumPy v2.2 Manual (original) (raw)

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 dtypeif 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.