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# numpy.require

`numpy.``require`(a, dtype=None, requirements=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, the default data-type is float64). 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

  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
        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
        UPDATEIFCOPY : False