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 1550569947,I_kwDOAMm_X85ca9Hb,7462,xarray add coordinates to mapping variables,46776466,closed,0,,,2,2023-01-20T09:47:39Z,2023-01-24T07:13:45Z,2023-01-24T07:13:45Z,NONE,,,,"### What happened? When a netcdf file containing a mapping variable and a height coordinates is opened, the height coordinates is added to the mapping variable. The minimal example generates a netcdf similar to CMIP6 data remapped on a curvilinear grid (for this example in fact it is also a lat / lon grid), where the curvilinear coordinates x / y are connected to lat / lon and tas with the PlateCarree variable. The PlateCarree variable doesn't contains any coordinates when the netcdf is created, and it is also the case when the netcdf is read with netCDF4. But when it is read with xr.open_dataset, the height coordinates is added to the PlateCarree variable. ### What did you expect to happen? Variables without coordinates should not have any after reading with xarray ### Minimal Complete Verifiable Example ```Python import sys,os import numpy as np import pandas as pd import xarray as xr import netCDF4 ############### ## Functions ## ############### def build_data(): ## Start by build the coordinates x = np.arange( -5 , 10 + 0.1 , 1 ) y = np.arange( 41 , 52 + 0.1 , 1 ) lon,lat = np.meshgrid(x,y) time = pd.date_range( ""2000-01-01"" , ""2005-12-31"" ).values lon = xr.DataArray( lon , dims = [""y"",""x""] , coords = [y,x] ) lat = xr.DataArray( lat , dims = [""y"",""x""] , coords = [y,x] ) ## Variables T = xr.DataArray( np.random.normal( size = (time.size,y.size,x.size) ) , dims = [""time"",""y"",""x""] , coords = { ""time"" : time , ""y"" : y , ""x"" : x , ""lat"" : lat , ""lon"" : lon } ) ## Dataset odata = xr.Dataset( { ""tas"" : T , ""height"" : 2. , ""PlateCarree"" : 1 } ) ## Add attributes odata.lon.attrs[""axis""] = ""x"" odata.lon.attrs[""long_name""] = ""longitude coordinate"" odata.lon.attrs[""standard_name""] = ""longitude"" odata.lon.attrs[""units""] = ""degrees_east"" odata.lat.attrs[""axis""] = ""y"" odata.lat.attrs[""long_name""] = ""latitude coordinate"" odata.lat.attrs[""standard_name""] = ""latitude"" odata.lat.attrs[""units""] = ""degrees_north"" odata.x.attrs[""units""] = ""degrees_north"" odata.x.attrs[""long_name""] = ""x coordinate of projection"" odata.x.attrs[""standard_name""] = ""projection_x_coordinate"" odata.y.attrs[""units""] = ""degrees_east"" odata.y.attrs[""long_name""] = ""y coordinate of projection"" odata.y.attrs[""standard_name""] = ""projection_y_coordinate"" odata.time.attrs[""axis""] = ""T"" odata.time.attrs[""standard_name""] = ""time"" odata.time.attrs[""long_name""] = ""Time axis"" odata.PlateCarree.attrs[""grid_mapping_name""] = ""plate_carree"" odata.PlateCarree.attrs[""epsg""] = ""4326"" odata.tas.attrs[""grid_mapping""] = ""PlateCarree"" odata.tas.attrs[""standard_name""] = ""air_temperature"" odata.tas.attrs[""long_name""] = ""Daily Mean Near-Surface Air Temperature"" odata.tas.attrs[""units""] = ""Kelvin"" odata.tas.attrs[""coordinates""] = ""height lat lon"" odata.height.attrs[""standard_name""] = ""height"" odata.height.attrs[""long_name""] = ""height"" odata.height.attrs[""units""] = ""m"" odata.height.attrs[""positive""] = ""up"" odata.height.attrs[""axis""] = ""Z"" odata.height.attrs[""name""] = ""height"" ## Encoding ny = y.size nx = x.size encoding = { ""time"" : { ""dtype"" : ""double"" , ""zlib"" : True , ""complevel"" : 5 , ""chunksizes"" : (1,) , ""units"" : ""days since 1950-01-01"" , ""calendar"" : ""standard"" } , ""y"" : { ""dtype"" : ""double"" , ""zlib"" : True , ""complevel"" : 5 , ""chunksizes"" : (ny,) } , ""x"" : { ""dtype"" : ""double"" , ""zlib"" : True , ""complevel"" : 5 , ""chunksizes"" : (nx,) } , ""lat"" : { ""dtype"" : ""double"" , ""zlib"" : True , ""complevel"" : 5 , ""chunksizes"" : (ny,nx) } , ""lon"" : { ""dtype"" : ""double"" , ""zlib"" : True , ""complevel"" : 5 , ""chunksizes"" : (ny,nx) } , ""tas"" : { ""dtype"" : ""float32"" , ""zlib"" : True , ""complevel"" : 5 , ""chunksizes"" : (1,ny,nx) } , ""height"" : { ""dtype"" : ""double"" } , ""PlateCarree"" : { ""dtype"" : ""int32"" } } odata.to_netcdf( ""test.nc"" , encoding = encoding ) ########## ## main ## ########## if __name__ == ""__main__"": ## Start by generate data build_data() ## Open with xarray data = xr.open_dataset(""test.nc"").load() print(""With xarray:"") print(data.PlateCarree) ## height is a coordinate of 'PlateCarree' ## And open with netCDF4 print(""=""*80) print(""With netCDF4"") with netCDF4.Dataset( ""test.nc"" , mode = ""r"" ) as ncfile: print(ncfile.variables[""PlateCarree""].get_dims()) ## height is not a coordinate of 'PlateCarree' ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment
INSTALLED VERSIONS ------------------ commit: None python: 3.9.12 | packaged by conda-forge | (main, Mar 24 2022, 23:24:38) [Clang 12.0.1 ] python-bits: 64 OS: Darwin OS-release: 22.2.0 machine: arm64 processor: arm byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 LOCALE: ('fr_FR', 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.8.1 xarray: 2022.9.0 pandas: 1.4.2 numpy: 1.23.2 scipy: 1.8.1 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.11.3 cftime: 1.6.0 nc_time_axis: 1.4.1 PseudoNetCDF: None rasterio: None cfgrib: 0.9.10.1 iris: None bottleneck: 1.3.5 dask: 2022.11.0 distributed: 2022.11.0 matplotlib: 3.5.2 cartopy: 0.20.2 seaborn: 0.11.2 numbagg: None fsspec: 2022.5.0 cupy: None pint: 0.19.2 sparse: None flox: None numpy_groupies: None setuptools: 62.3.2 pip: 22.1.1 conda: None pytest: None IPython: 7.31.1 sphinx: 4.5.0
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