issue_comments

I think this actually comes from a problem with the data. The data files do not all have the same calendar system: some are 'proleptic_gregorian' and others are 'standard'. I have raised this with the data provider.

The nested version still fails when you try to subset based on dates, but the error message is much more informative.

It would still be good if there were a more informative error message. Perhaps I'm the only person this has ever happened to.

Here is a wget script, generated by the ESGF web interface.

Using combine='nested', concat_dim='time' works fine. Would be good to get a more informative error message.

I'm going to share a code snippet that might be useful to people reading this issue. I wanted to group my data by month and year, and take the mean for each group.

I did not want to use resample, as I wanted the dimensions to be ('month', 'year'), rather than ('time'). The obvious way of doing this is to use a pd.MultiIndex to create a 'year_month' stacked coordinate: I found this did not have good perfomance.

My solution was to use xr.apply_ufunc, as suggested above. I think it should be OK with dask chunked data, provided it is not chunked in time.

Here is the code:

``` def _grouped_mean( data: np.ndarray, months: np.ndarray, years: np.ndarray) -> np.ndarray: """similar to grouping year_month MultiIndex, but faster.

    Should be used wrapped by _wrapped_grouped_mean"""
    unique_months = np.sort(np.unique(months))
    unique_years = np.sort(np.unique(years))
    old_shape = list(data.shape)
    new_shape = old_shape[:-1]
    new_shape.append(unique_months.shape[0])
    new_shape.append(unique_years.shape[0])

    output = np.zeros(new_shape)

    for i_month, j_year in np.ndindex(output.shape[2:]):
        indices = np.intersect1d(
            (months == unique_months[i_month]).nonzero(),
            (years == unique_years[j_year]).nonzero()
        )

        output[:, :, i_month, j_year] =\
            np.mean(data[:, :, indices], axis=-1)

    return output

def _wrapped_grouped_mean(da: xr.DataArray) -> xr.DataArray: """similar to grouping by a year_month MultiIndex, but faster.

    Wraps a numpy-style function with xr.apply_ufunc
    """
    Y = xr.apply_ufunc(
        _grouped_mean,
        da,
        da.time.dt.month,
        da.time.dt.year,
        input_core_dims=[['lat', 'lon', 'time'], ['time'], ['time']],
        output_core_dims=[['lat', 'lon', 'month', 'year']],
    )
    Y = Y.assign_coords(
        {'month': np.sort(np.unique(da.time.dt.month)),
         'year': np.sort(np.unique(da.time.dt.year))})
    return Y

```