NumpyTensorSpace.__contains__

NumpyTensorSpace.__contains__(self, other)

Return other in self.

Returns

containsbool

True if other has a space attribute that is equal to this space, False otherwise.

Examples

Elements created with the TensorSpace.element method are guaranteed to be contained in the same space:

>>> spc = odl.tensor_space((2, 3), dtype='uint64')
>>> spc.element() in spc
True
>>> x = spc.element([[0, 1, 2],
...                  [3, 4, 5]])
>>> x in spc
True

Sizes, data types and other essential properties characterize spaces and decide about membership:

>>> smaller_spc = odl.tensor_space((2, 2), dtype='uint64')
>>> y = smaller_spc.element([[0, 1],
...                          [2, 3]])
>>> y in spc
False
>>> x in smaller_spc
False
>>> other_dtype_spc = odl.tensor_space((2, 3), dtype='uint32')
>>> z = other_dtype_spc.element([[0, 1, 2],
...                              [3, 4, 5]])
>>> z in spc
False
>>> x in other_dtype_spc
False

On the other hand, spaces are not unique:

>>> spc2 = odl.tensor_space((2, 3), dtype='uint64')
>>> spc2 == spc
True
>>> x2 = spc2.element([[5, 4, 3],
...                    [2, 1, 0]])
>>> x2 in spc
True
>>> x in spc2
True

Of course, random garbage is not in the space:

>>> spc = odl.tensor_space((2, 3), dtype='uint64')
>>> None in spc
False
>>> object in spc
False
>>> False in spc
False