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require(a, dtype=None, requirements=None, *, like=None)

This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes).

Notes

The returned array will be guaranteed to have the listed requirements by making a copy if needed.

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

like : array_like

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.

versionadded

Returns

out : ndarray: Array with specified requirements and type if given.

Return an ndarray of the provided type that satisfies requirements.

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ascontiguousarray numpy.asfortranarray numpy.core._asarray

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