issues

In pandas, you can pas a pandas.TimeGrouper object to a .groupby() call, and it allows you to group by month, year, day, or other times, without manually creating a new index with those values first. It would be great if you could do this with xray, but at the moment, I get:

`` /usr/local/lib/python3.4/dist-packages/xray/core/groupby.py in __init__(self, obj, group, squeeze) 66 if the dimension is squeezed out. 67 """ ---> 68 if group.ndim != 1: 69 # TODO: remove this limitation? 70 raise ValueError('group` must be 1 dimensional')

AttributeError: 'TimeGrouper' object has no attribute 'ndim' ```

Not sure how this will work though, because pandas.TimeGrouper doesn't appear to work with multi-index dataframes yet anyway, so maybe there needs to be a feature request over there too, or maybe it's better to implement something from scratch...

Code Sample, a copy-pastable example if possible

A "Minimal, Complete and Verifiable Example" will make it much easier for maintainers to help you: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

```python ds.attrs = dict(a=dict(b=2)) ds.to_netcdf(outfile)

...

~/miniconda3/envs/ana/lib/python3.6/site-packages/xarray/backends/api.py in check_attr(name, value) 158 'a string, an ndarray or a list/tuple of ' 159 'numbers/strings for serialization to netCDF ' --> 160 'files'.format(value)) 161 162 # Check attrs on the dataset itself

TypeError: Invalid value for attr: {'b': 2} must be a number, a string, an ndarray or a list/tuple of numbers/strings for serialization to netCDF files

```

Problem description

I'm not entirely sure if this should be possible, but it seems like it should be from this email: https://www.unidata.ucar.edu/support/help/MailArchives/netcdf/msg10502.html

Nested attributes would be nice as a way to namespace metadata.

Expected Output

Netcdf with nested global attributes.

Output of xr.show_versions()

I have a multifile dataset made up of month-long 8-hourly netcdf datasets over nearly 30 years. The files are available from ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/GLOBAL/, and I'm spcifically looking at e.g. 1990_CFSR/hs/ww3.199001_hs.nc for each year and month. Each file is about 45Mb, for about 15Gb total.

I want to calculate some lognormal distribution parameters of the Hs variable at each grid point (actually, only a smallish subset of points, using a mask). However, if I load the data with open_mfdataset and try to read a single lat/lon grid cell, my computer tanks, and python gets killed due to running out of memory (I have 16Gb, but even if I only try to open 1 year of data - ~500Mb, python ends up using 27% of my memory).

Is there a way in xarray/dask to force dask to only read single sub-arrays at a time? I have tried using lat/lon chunking, e.g.

python mfdata_glob = '/home/nedcr/cr/data/wave/*1990*.nc' global_ds = xr.open_mfdataset( mfdata_glob, chunks={'latitude': 1, 'longitude': 1}) but that doesn't seem to improve things.

Is there any way around this problem? I guess I could try using preprocess= to sub-select grid cells, and loop over that, but that seems like it would require opening and reading each file 317*720 times, which sounds like a recipe for a long wait.