xtensor-python
provides two container types wrapping numpy arrays: pyarray
and pytensor
. They are the counterparts to xarray
and xtensor
containers.
Like xarray
, pyarray
has a dynamic shape. This means that you can reshape the numpy array on the C++ side and see this change reflected on the python side. pyarray
doesn't make a copy of the shape or the strides, but reads them each time it is needed. Therefore, if a reference on a pyarray
is kept in the C++ code and the corresponding numpy array is then reshaped in the python code, this modification will reflect in the pyarray
.
Like xtensor
, pytensor
has a static stack-allocated shape. This means that the shape of the numpy array is copied into the shape of the pytensor
upon creation. As a consequence, reshapes are not reflected across languages. However, this drawback is offset by a more effective computation of shape and broadcast.