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We could potentially add a from_memory() constructor to NetCDF4DataStore to simplify this process. On Thu, Jan 11, 2018 at 6:27 PM Michael Delgado notifications@github.com wrote:

yes! Thanks @jhamman https://github.com/jhamman and @shoyer https://github.com/shoyer. I hadn't tried it yet, but just did. worked great!

In [1]: import xarray as xr ...: import requests ...: import netCDF4 ...: ...: %matplotlib inline

In [2]: res = requests.get( ...: 'http://nasanex.s3.amazonaws.com/NEX-GDDP/BCSD/rcp45/day/atmos/tasmin/' + ...: 'r1i1p1/v1.0/tasmin_day_BCSD_rcp45_r1i1p1_CESM1-BGC_2073.nc')

In [3]: res.status_code Out [3]: 200

In [4]: res.headers['content-type'] Out [4]: 'application/x-netcdf'

In [5]: nc4_ds = netCDF4.Dataset('tasmin_day_BCSD_rcp45_r1i1p1_CESM1-BGC_2073', memory=res.content)

In [6]: store = xr.backends.NetCDF4DataStore(nc4_ds)

In [7]: ds = xr.open_dataset(store)

In [8]: ds.tasmin.isel(time=0).plot() /global/home/users/mdelgado/git/public/xarray/xarray/plot/utils.py:51: FutureWarning: 'pandas.tseries.converter.register' has been moved and renamed to 'pandas.plotting.register_matplotlib_converters'. converter.register() Out [8]: <matplotlib.collections.QuadMesh at 0x2aede3c922b0>

[image: output_7_2] https://user-images.githubusercontent.com/3698640/34856943-f82619f4-f6fc-11e7-831d-f5d4032a338a.png

In [9]: ds Out [9]: <xarray.Dataset> Dimensions: (lat: 720, lon: 1440, time: 365) Coordinates: * time (time) datetime64[ns] 2073-01-01T12:00:00 2073-01-02T12:00:00 ... * lat (lat) float32 -89.875 -89.625 -89.375 -89.125 -88.875 -88.625 ... * lon (lon) float32 0.125 0.375 0.625 0.875 1.125 1.375 1.625 1.875 ... Data variables: tasmin (time, lat, lon) float64 ... Attributes: parent_experiment: historical parent_experiment_id: historical parent_experiment_rip: r1i1p1 Conventions: CF-1.4 institution: NASA Earth Exchange, NASA Ames Research C... institute_id: NASA-Ames realm: atmos modeling_realm: atmos version: 1.0 downscalingModel: BCSD experiment_id: rcp45 frequency: day realization: 1 initialization_method: 1 physics_version: 1 tracking_id: 1865ff49-b20c-4268-852a-a9503efec72c driving_data_tracking_ids: N/A driving_model_ensemble_member: r1i1p1 driving_experiment_name: historical driving_experiment: historical model_id: BCSD references: BCSD method: Thrasher et al., 2012, Hydro... DOI: http://dx.doi.org/10.7292/W0MW2F2G experiment: RCP4.5 title: CESM1-BGC global downscaled NEX CMIP5 Cli... contact: Dr. Rama Nemani: rama.nemani@nasa.gov, Dr... disclaimer: This data is considered provisional and s... resolution_id: 0.25 degree project_id: NEXGDDP table_id: Table day (12 November 2010) source: BCSD 2014 creation_date: 2015-01-07T19:18:31Z forcing: N/A product: output

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@delgadom Yes, that should work (I haven't tested it, but yes in principle it should all work now).

Just to clarify: I wrote about that we use could support initializing a Dataset from a netCDF4 file image. But this wouldn't help yet for streaming access.

Initializing a Dataset from a netCDF4 file image should actually work with the latest versions of xarray and netCDF4-python: python nc4_ds = netCDF4.Dataset('arbitrary-name', memory=netcdf_bytes) store = xarray.backends.NetCDF4DataStore(nc4_ds) ds = xarray.open_dataset(store)

Yes, we could support initializing a Dataset from netCDF4 file image in a bytes object.

This does work for netCDF3 files, if you provide a file-like object (e.g., wrapped in BytesIO) or set engine='scipy'.

Unfortunately, this is a netCDF4/HDF5 file:

```

data.raw.read(8) '\x89HDF\r\n\x1a\n' ```

And as yet, there is no support for reading from file-like objects in either h5py (https://github.com/h5py/h5py/issues/552) or python-netCDF4 (https://github.com/Unidata/netcdf4-python/issues/295). So we're currently stuck :(.

One possibility is to use the new HDF5 library pyfive with h5netcdf (https://github.com/shoyer/h5netcdf/issues/25). But pyfive doesn't have enough features yet to read netCDF files.