numpy.require(a, dtype=None, requirements=None)[source]¶Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes).
The object to be converted to a type-and-requirement-satisfying array.
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.
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
Array with specified requirements and type if given.
See also
asarrayConvert input to an ndarray.
asanyarrayConvert to an ndarray, but pass through ndarray subclasses.
ascontiguousarrayConvert input to a contiguous array.
asfortranarrayConvert input to an ndarray with column-major memory order.
ndarray.flagsInformation about the memory layout of the array.
Notes
The returned array will be guaranteed to have the listed requirements by making a copy if needed.
Examples
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False