issue_comments

Here's a small example to get things started:

python x = aesara.shared(np.random.standard_normal((3, 4))) xda = xr.DataArray(x, dims=('x', 'y')) this currently returns the following error: ```python traceback

ValueError Traceback (most recent call last) Cell In[138], line 2 1 x = aesara.shared(np.random.standard_normal((3, 4))) ----> 2 xda = xr.DataArray(x, dims=('x', 'y'))

File ~/miniforge3/envs/demo-env/lib/python3.10/site-packages/xarray/core/dataarray.py:428, in DataArray.init(self, data, coords, dims, name, attrs, indexes, fastpath) 426 data = _check_data_shape(data, coords, dims) 427 data = as_compatible_data(data) --> 428 coords, dims = _infer_coords_and_dims(data.shape, coords, dims) 429 variable = Variable(dims, data, attrs, fastpath=True) 430 indexes, coords = _create_indexes_from_coords(coords)

File ~/miniforge3/envs/demo-env/lib/python3.10/site-packages/xarray/core/dataarray.py:142, in _infer_coords_and_dims(shape, coords, dims) 140 dims = tuple(dims) 141 elif len(dims) != len(shape): --> 142 raise ValueError( 143 "different number of dimensions on data " 144 f"and dims: {len(shape)} vs {len(dims)}" 145 ) 146 else: 147 for d in dims:

ValueError: different number of dimensions on data and dims: 0 vs 2 ```

This tells me there is a bit of work to do at the core of Aesara's numpy compatibility. Xarray will make frequent references to attributes like data.shape, data.ndim, etc expecting to get numpy-like results.

Thanks all for the discussion. Welcome @brandonwillard, @twiecki, and @rlouf to the Xarray project. And thanks to @rabernat and @TomNicholas for helping orient the conversation.

As a next step, I think it would be fun to try putting an Aesara array into Xarray and see how it goes. In our experience, this process inevitably brings up a few issues where interaction between Xarray and Aesara developers is fruitful.