`numpy:require`

source: /numpy/_core/_asarray.py :22

Signature

   def     require (    a    ,    dtype    =  None   ,    requirements    =  None   ,  * ,     like    =  None     )

Summary

Return an ndarray of the provided type that satisfies requirements.

Extended Summary

This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes).

Parameters

a : array_like

The object to be converted to a type-and-requirement-satisfying array.

dtype : data-type

The required data-type. If None preserve the current dtype. If your application requires the data to be in native byteorder, include a byteorder specification as a part of the dtype specification.

requirements : str or sequence of str

The requirements list can be any of the following

'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
'ALIGNED' ('A') - ensure a data-type aligned array
'WRITEABLE' ('W') - ensure a writable array
'OWNDATA' ('O') - ensure an array that owns its own data
'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass

like : array_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.

Returns

out : ndarray: Array with specified requirements and type if given.

Notes

The returned array will be guaranteed to have the listed requirements by making a copy if needed.

Examples

import numpy as np
x = np.arange(6).reshape(2,3)

✓

x.flags

✗

y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])

✓

y.flags

✗

Aliases

numpy.require
numpy._core._asarray._require_with_like