numpy.require

numpy.require(a, dtype=None, requirements=None)[源代码]

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).

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 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

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.

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