numpy.core._asarray

module documentation

Functions in the as*array family that promote array-likes into arrays.

require fits this category despite its name not matching this pattern.

Function ​_require​_dispatcher Undocumented
Function require Return an ndarray of the provided type that satisfies requirements.
Variable ​_require​_with​_like Undocumented
def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):

Undocumented

@set_array_function_like_doc
@set_module('numpy')
def 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 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

${ARRAY_FUNCTION_LIKE}

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
  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
_require_with_like =

Undocumented