issue_comments

Thanks you @keewis and @Illviljan! I made comment to deprecate cdms2 in xarray in another issue/PR last year and didn't get around to it. I linked this PR in an xCDAT discussion post here.

Thank you @jhamman!

With netcdf4 1.5.4

Encountered problems while solving: - package cdms2-3.1.0-py27h6091dcd_0 requires python >=2.7,<2.8.0a0, but none of the providers can be installed @tomvothecoder, I see you're committing to cdms. Is this something you've run in to?

since all CI runs are currently broken due to new flox requiring numpy>=1.20

sorry! trying to fix this in conda-forge/flox-feedstock#22

I haven't ran into this issue since I've only committed documentation and README updates for the repo.

@jasonb5 maintains the repo dependencies, so let's see if he can help. Jason, can you try taking a look at this issue? It looks like cdms2 is breaking the xarray builds.

As a side-note, cdms2 (and CDAT as a whole) is now in maintenance-only mode and support will cease around the end of CY 2023. Just an FYI to plan for possibly dropping cdms2 from xarray in the future. CDAT's successor is xCDAT (Xarray Climate Data Analysis Tools) .

I was able to isolate the parts of my code that causes this numpy warning to be thrown.

Here's how the warning was thrown: 1. Chunk a Dataset using Dask 2. Calculate length of time by subtracting the upper and lower bounds of each time coordinate (dtype is datetime64[ns]). This produces a DataArray with a dtype of timedelta64[ns]. 3. Calculate the weights using xarray's grouped arithmetic. 4. numpy warning is thrown after xarray's groupby.sum() call - https://github.com/numpy/numpy/blob/c63f2c2bab48e0446b34f3bba5574729327d68d1/numpy/core/fromnumeric.py#L86

My workaround is to convert the dtype of the underlying dask.array from timedelta[64] to float64 before performing the grouped arithmetic. I'm still unclear as to why this only affects dask.array though.

Related code: ``` def _get_weights(self, data_var: xr.DataArray) -> xr.DataArray: """Calculates weights for a data variable using time bounds."""

    with xr.set_options(keep_attrs=True):
        time_lengths: xr.DataArray = self._time_bounds[:, 1] - self._time_bounds[:, 0]

    # Must be convert dtype from timedelta64[ns] to float64, specifically
    # when chunking DataArrays using Dask. Otherwise, the numpy warning
    # below is thrown: `DeprecationWarning: The `dtype` and `signature`
    # arguments to ufuncs only select the general DType and not details such
    # as the byte order or time unit (with rare exceptions see release
    # notes). To avoid this warning please use the scalar types
    # `np.float64`, or string notation.`
    time_lengths = time_lengths.astype(np.float64)
    time_grouped = self._groupby_multiindex(time_lengths)
    weights: xr.DataArray = time_grouped / time_grouped.sum()  # type: ignore

    self._validate_weights(data_var, weights)
    return weights

def _groupby_multiindex(self, data_var: xr.DataArray) -> DataArrayGroupBy:
    """Adds the MultiIndex to the data variable and groups by it."""
    dv = data_var.copy()
    dv.coords[self._multiindex_name] = ("time", self._multiindex)
    dv_gb = dv.groupby(self._multiindex_name)
    return dv_gb

```