id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 280942467,MDU6SXNzdWUyODA5NDI0Njc=,1774,xarray mean generates unstable dask name (hash),27647769,closed,0,,,2,2017-12-11T09:11:31Z,2017-12-11T20:43:35Z,2017-12-11T20:43:35Z,NONE,,,,"#### Code Sample ```python import dask.array as da import xarray as xr import numpy as np # create dask array x = da.ones((5, 5), chunks=(2, 2)) # create xarray array x2 = xr.DataArray(x, dims=['d1', 'd2']) # print dask name after taking mean x = x.mean(axis=0) print(x.name) # print dask name after taking mean x2 = x2.mean(dim='d1') print(x2.data.name) # confirm both functions do the same print(np.allclose(x.compute(), x2.data.compute())) ``` #### Problem Description Running the above sample three times outputs: ``` mean_agg-aggregate-9716da6e38d695dbff18f713d787e614 mean_agg-aggregate-02c33c19e6209edbe409749388d2f9f0 True mean_agg-aggregate-9716da6e38d695dbff18f713d787e614 mean_agg-aggregate-2f59be8ef8c35336717fdcd7744bd167 True mean_agg-aggregate-9716da6e38d695dbff18f713d787e614 mean_agg-aggregate-822994428d6b4cdea8e5c134711e5609 True ``` which shows the dask name (hash) generated using the xarray mean is unstable. #### Expected Output For processing large datasets, it's convenient to have a stable hash name in order to save intermediate results or compare them among developers. > Current workaround: unwrap dask array and recreate xarray array. #### Output of ``xr.show_versions()``
INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.8.0-58-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 xarray: 0.10.0 pandas: 0.21.0 numpy: 1.13.3 scipy: 0.19.1 netCDF4: None h5netcdf: None Nio: None bottleneck: None cyordereddict: None dask: 0.16.0 matplotlib: 2.1.0 cartopy: None seaborn: None setuptools: 38.2.3 pip: 9.0.1 conda: None pytest: None IPython: 6.2.1 sphinx: None
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