issues

I am not sure this is a bug - but it clearly doesn't give the results the user would expect.

The rolling sum of zeros gives me values that are not zeros

```python var = np.array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.31 , 0.91999996, 8.3 , 1.42 , 0.03 , 1.22 , 0.09999999, 0.14 , 0.13 , 0. , 0.12 , 0.03 , 2.53 , 0. , 0.19999999, 0.19999999, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], dtype='float32')

timet = np.array([ 43200000000000, 129600000000000, 216000000000000, 302400000000000, 388800000000000, 475200000000000, 561600000000000, 648000000000000, 734400000000000, 820800000000000, 907200000000000, 993600000000000, 1080000000000000, 1166400000000000, 1252800000000000, 1339200000000000, 1425600000000000, 1512000000000000, 1598400000000000, 1684800000000000, 1771200000000000, 1857600000000000, 1944000000000000, 2030400000000000, 2116800000000000, 2203200000000000, 2289600000000000, 2376000000000000, 2462400000000000, 2548800000000000, 2635200000000000, 2721600000000000, 2808000000000000, 2894400000000000, 2980800000000000], dtype='timedelta64[ns]')

ds_ex = xr.Dataset(data_vars=dict( pr=(["time"], var), ), coords=dict( time=("time", timet) ), )

ds_ex.rolling(time=3).sum().pr.values

``` it gives me this result:

array([ nan, nan, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.1000000e-01, 1.2300000e+00, 9.5300007e+00, 1.0640000e+01, 9.7500000e+00, 2.6700001e+00, 1.3500001e+00, 1.4600002e+00, 3.7000012e-01, 2.7000013e-01, 2.5000012e-01, 1.5000013e-01, 2.6800001e+00, 2.5600002e+00, 2.7300003e+00, 4.0000033e-01, 4.0000033e-01, 2.0000035e-01, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07, 3.5762787e-07], dtype=float32)

Note the non zero values - the non zero value changes depending on whether i use float64 or float32 as precision of my data. So this seems to be a precision related issue (although the first values are correctly set to zero), in fact other sums of values are not exactly what they should be.

The small difference at the 8th/9th decimal position can be expected due to precision, but the fact that the 0s become non zeros is problematic imho, especially if not documented. Oftentimes zero in geoscience data can mean a very specific thing (i.e. zero rainfall will be characterized differently than non-zero).

in pandas this instead works:

python df_ex = ds_ex.to_dataframe() df_ex.rolling(window=3).sum().values.T gives me

array([[ nan, nan, 0. , 0. , 0. , 0. , 0. , 0.31 , 1.22999996, 9.53000015, 10.6400001 , 9.75000015, 2.66999999, 1.35000001, 1.46000002, 0.36999998, 0.27 , 0.24999999, 0.15 , 2.67999997, 2.55999997, 2.72999996, 0.39999998, 0.39999998, 0.19999999, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])

What you expected to happen:

the sum of zeros should be zero. If this cannot be achieved/expected because of precision issues, it should be documented.

Anything else we need to know?:

I discovered this behavior in my old environments, but I created a new ad hoc environment with the latest versions, and it does the same thing.

Environment:

INSTALLED VERSIONS

commit: None python: 3.9.7 (default, Sep 16 2021, 08:50:36) [Clang 10.0.0 ] python-bits: 64 OS: Darwin OS-release: 17.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: None libnetcdf: None

xarray: 0.19.0 pandas: 1.3.3 numpy: 1.21.2 scipy: None netCDF4: None pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None pint: None setuptools: 58.0.4 pip: 21.2.4 conda: None pytest: None IPython: 7.28.0 sphinx: None

Is your feature request related to a problem? Please describe. I have explored a bit the object returned by faceting a plot (usually identified like p in the tutorial). It clearly stores tons of stuff that can be manipulated and make the plots more flexible.

I have an example here which I was planning to add somewhere to the tutorial for plotting.

Would this be of interest? or not since it makes use of i.e. matplotlib methods?

This issue is also intended to call for people that might have been playing with obscure attributes/method/whatever stored in p and have come out with some interesting manipulation. xarray faceting is very powerful, imho, and it is a great starting point for more complicated figures.

For example, in my notebook linked above, I add some axes to the side of the facetgrid to add a meridional average, and it used to take me a second to match the location of the added axes to the location of the axes in the faceted plot. But I figured that: for oa in p.axes.flat: print(oa.get_position().bounds) gets me the position.

I am sure tons of people have come up with similar stuff - so it would be amazing to put it all together in one spot!

Describe the solution you'd like If there is interest, I will open a PR with an example on how to manipulate faceted plots.