dim handling issues

[rob-taggart]

I did some testing of dim handling using mse from main in a jupyter notebook.

Here is some code and problems I found.

fcst = xr.DataArray(
    data=[[1., 2, 3], [2, 3, 4]],
    dims=['stn', 'date'],
    coords=dict(stn=[101, 102], date=['2022-01-01', '2022-01-02', '2022-01-03'])
)
obs = xr.DataArray(
    data=[[0., 2, 4], [.5, 2.2, 3.5]],
    dims=['source', 'date'],
    coords=dict(source=['a', 'b'], date=['2022-01-01', '2022-01-02', '2022-01-03'])
)

1. mse(fcst, obs, reduce_dims=[]) returns a single value (i.e. reduces all dimensions). It would be preferable if it reduced no dimensions.
2. mse(fcst, obs, preserve_dims=['source', 'date', 'stn']) returns a single value (i.e. reduces all dimensions) rather than an array with all dimensions preserved. Same as mse(fcst, fcst, preserve_dims=fcst.dims)
3. The docstring for mse says, for 'preserve_dims', that "the forecast and observed dimensions must match precisely". This can be removed as it works perfectly fine if they don't match (using usual xarray broadcasting) and I don't think it is even desirable that fcst and obs have matching dimensions.

[rob-taggart]

@tennlee , @aidanjgriffiths

[tennlee]

Thank you very much for your investigations. I will start on fixes for each point and raise new issues for each individually, and then link those back here.

[tennlee]

All three of these points ended up being addressed through the response to issue #19, and so this can now be closed.

[tennlee]

Closing