# numpy.require

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).

  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.

      New in version 1.20.0.

  Returns:
    out ndarray

      Array with specified requirements and type if given.

  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 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

      >>> 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