issues

when i use Dataset.to_netcdf, it raise,

ValueError: cannot serialize coordinates because variable omega already has an attribute 'coordinates' <xarray.DataArray 'omega' (south_north: 252, west_east: 384)> Coordinates: XLONG (south_north, west_east) float32 75.7405 75.9333 76.1263 ... XLAT (south_north, west_east) float32 -1.63264 -1.56052 -1.48872 ... * south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ... * west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ... XTIME float32 16560.0 Time datetime64[ns] 1988-01-12T12:00:00 Attributes: FieldType: 104 MemoryOrder: XYZ description: omega units: Pa s-1 stagger: coordinates: XLONG XLAT XTIME projection: LambertConformal(stand_lon=116.0, moad_cen_lat=34.9999961853, truelat1=10.0, ruelat2=50.0, pole_lat=90.0, pole_lon=0.0) vert_interp_type: p

How can i drop attribute coordinates of omega ?

I have several WRFout files for 20-year climate simulations, when i use open_mfdataset to read them, it takes me 10 - 20 minutes to finish on my server.

Is there a way to speed up this process? Multiprocessing ??

when i read some netCDF4 file using xr.open_dataset, it seems this method would auto convert the variable type from float32 to float64, how to avoid it ?

Use xarry.open_dataset

python import xarray as xr xr.open_dataset('cat.20151003200633.nc')

will yield output as follow

<xarray.Dataset> Dimensions: (x: 461, y: 461, z: 9) Coordinates: * x (x) float32 -230.0 -229.0 -228.0 -227.0 -226.0 -225.0 -224.0 ... * y (y) float32 -230.0 -229.0 -228.0 -227.0 -226.0 -225.0 -224.0 ... * z (z) float32 0.4 1.4 2.3 3.2 4.3 5.8 9.7 14.5 19.3 Data variables: dbz (z, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ... vr (z, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ... sw (z, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...

Variables dtype of dbz, vr and sw in this file have been convert to float64, which actually is float32.

Use netCDF4.Dataset

python import netCDF4 as ncf ncf.Dataset('cat.20151003200633.nc')

will yield output as follow

<type 'netCDF4._netCDF4.Dataset'> root group (NETCDF4 data model, file format HDF5): .......... dimensions(sizes): x(461), y(461), z(9) variables(dimensions): float32 x(x), float32 y(y), float32 z(z), float32 dbz(z,y,x), float32 vr(z,y,x), float32 sw(z,y,x) groups:

The netCDF4.Dataset produce the right variable type, while the xarray.open_dataset not.

Hi, today i use xr.concat to concat my wrf model splitted netcdf datasets, in this model output data, it typically has two kinds of meshgrid, includes one stagged meshgrid and mass meshgrid, different variables have located on the different kind of grid.

For example, below is some variables in a dataset, some have dimension west_east and other take west_east_stag, MAPFAC_M (Time, south_north, west_east) float32 1.06697 ... MAPFAC_U (Time, south_north, west_east_stag) float32 1.06697 ... MAPFAC_V (Time, south_north_stag, west_east) float32 1.06698 ... MAPFAC_MX (Time, south_north, west_east) float32 1.06697 ... MAPFAC_MY (Time, south_north, west_east) float32 1.06697 ... MAPFAC_UX (Time, south_north, west_east_stag) float32 1.06697 ... MAPFAC_UY (Time, south_north, west_east_stag) float32 1.06697 ... MAPFAC_VX (Time, south_north_stag, west_east) float32 1.06698 ... MF_VX_INV (Time, south_north_stag, west_east) float32 0.937228 ... MAPFAC_VY (Time, south_north_stag, west_east) float32 1.06698 ... i want concat along dimension west_east and west_east_stag, so that these piece of datasets can be concatenate fully. When i use xr.concat to concatenate these datasets, the argument dim can just use one dimension ?

Is it possible to concat with multiple independent dimensions, like following, python xr.concat([ds_col_0, ds_col_1, ds_col_2], dim=['west_east', 'west_east_stag'])

Use netCDF4 package, one can modify the netCDF file variable as follow,

python import netCDF4 dset = netCDF4.Dataset('test.nc', 'r+') dset['varname'][:] = 0 dset.close()

Is it possible in xarray?