xorbits.numpy.require#
- xorbits.numpy.require(a, dtype=None, requirements=None, *, like=None)#
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).
- 参数
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.1.20.0(numpy) 新版功能.
- 返回
out – Array with specified requirements and type if given.
- 返回类型
ndarray
参见
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.
提示
The returned array will be guaranteed to have the listed requirements by making a copy if needed.
实际案例
>>> x = np.arange(6).reshape(2,3) >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : 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
警告
This method has not been implemented yet. Xorbits will try to execute it with numpy.
This docstring was copied from numpy.