numpy.require¶

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 flagsis 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 yourapplication requires the data to be in native byteorder, includea byteorder specification as a part of the dtype specification. requirements:str or list 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

See also

asarray

Convert input to an ndarray.

asanyarray

Convert to an ndarray, but pass through ndarray subclasses.

ascontiguousarray

Convert input to a contiguous array.

asfortranarray

Convert input to an ndarray with column-major memory order.

ndarray.flags

Information about the memory layout of the array.

Notes

The returned array will be guaranteed to have the listed requirementsby 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