html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/7621#issuecomment-1469467986,https://api.github.com/repos/pydata/xarray/issues/7621,1469467986,IC_kwDOAMm_X85Xlk1S,5821660,2023-03-15T07:15:47Z,2023-03-15T07:15:47Z,MEMBER,"@RY4GIT Glad you can use the code. And good there's a way to use the data as is without tampering.
Regarding the different rasterio-approaches, just to be on the same page. The code I provided above (with the `fn_NASA_Earthdata_download` file) works for you too, but with `fn_NSIDC_output` it does not? Out of the box, the second also doesn't work for me. Didn't check this.
I've no immediate idea, what's going on in that case. The only thing I can think of is, that in the conversion process something has gone wrong with the georeferencing and/or the image origin. I hope you can figure that out, eventually.
Here for reference my package versions:
INSTALLED VERSIONS
------------------
commit: None
python: 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:06:46) [GCC 10.3.0]
python-bits: 64
OS: Linux
OS-release: 5.14.21-150400.24.46-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: ('de_DE', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.9.1
xarray: 2023.2.1.dev22+g49ae0f8d.d20230310
pandas: 1.4.2
numpy: 1.23.5
scipy: 1.9.1
netCDF4: 1.6.2
pydap: None
h5netcdf: 1.1.0
h5py: 3.8.0
Nio: None
zarr: 2.12.0
cftime: 1.5.1.1
nc_time_axis: 1.4.1
PseudoNetCDF: None
rasterio: 1.3.6
cfgrib: None
iris: 3.2.1
bottleneck: 1.3.5
dask: 2022.10.0
distributed: 2022.10.0
matplotlib: 3.6.3
cartopy: 0.21.1
seaborn: None
numbagg: None
fsspec: 2023.1.0
cupy: None
pint: 0.17
sparse: None
flox: None
numpy_groupies: None
setuptools: 59.2.0
pip: 21.3.1
conda: None
pytest: 7.1.2
mypy: None
IPython: 8.2.0
sphinx: None
rioxarray: 0.13.3
gdal: 3.6.2
pyproj: 3.4.1
PROJ: 9.1.1
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1622197017
https://github.com/pydata/xarray/issues/7621#issuecomment-1468375177,https://api.github.com/repos/pydata/xarray/issues/7621,1468375177,IC_kwDOAMm_X85XhaCJ,5821660,2023-03-14T15:58:20Z,2023-03-14T15:58:20Z,MEMBER,@RY4GIT I forgot to add my package versions. I'll add them the next day. But I'm pretty sure to have the most recent one's installed in a Python 3.10 conda-forge environment.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1622197017
https://github.com/pydata/xarray/issues/7621#issuecomment-1467552710,https://api.github.com/repos/pydata/xarray/issues/7621,1467552710,IC_kwDOAMm_X85XeRPG,5821660,2023-03-14T07:40:28Z,2023-03-14T07:41:24Z,MEMBER,"For the rasterio-approach:
```python
ds_NASA_download_rasterio = xr.open_dataset(os.path.join(input_path, fn_NASA_Earthdata_download), engine='rasterio')
ds_NASA_download_rasterio = ds_NASA_download_rasterio.set_coords([""cell_lat"", ""cell_lon""])
ds_NASA_download_rasterio.Geophysical_Data_precipitation_total_surface_flux[0].plot(y=""cell_lat"", x=""cell_lon"")
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1622197017
https://github.com/pydata/xarray/issues/7621#issuecomment-1467520091,https://api.github.com/repos/pydata/xarray/issues/7621,1467520091,IC_kwDOAMm_X85XeJRb,5821660,2023-03-14T07:21:30Z,2023-03-14T07:35:37Z,MEMBER,"First, I've found this on SO:
https://gis.stackexchange.com/questions/454543/fixing-the-flipped-inverted-y-axis-in-the-xarray-with-rasterio
For your data reading method 1, it works for me like this:
```python
# load root group with coordinates
ds_NSIDC_root = xr.open_dataset(os.path.join(input_path, fn_NSIDC_output), group=""/"", engine='netcdf4')
# load data from Geophysical_Data group
ds_NSIDC_precip = xr.open_dataset(os.path.join(input_path, fn_NSIDC_output), group=""Geophysical_Data"", engine='netcdf4')
# merge groups
ds_NSIDC = xr.merge([ds_NSIDC_root, ds_NSIDC_precip])
# plot
ds_NSIDC.precipitation_total_surface_flux.plot()
# the above is essentially the same as
# ds_NSIDC.precipitation_total_surface_flux.plot(x=""x"", y=""y"")
```
This can be expanded to use the cell_lon/cell_lat:
```
# set lat/lon as coords
ds_NSIDC = ds_NSIDC.set_coords([""cell_lon"", ""cell_lat""])
ds_NSIDC.precipitation_total_surface_flux.plot(x=""cell_lon"", y=""cell_lat"")
```
Update: This should also work like above when directly using NASA Earth Data.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1622197017
https://github.com/pydata/xarray/issues/7621#issuecomment-1466920659,https://api.github.com/repos/pydata/xarray/issues/7621,1466920659,IC_kwDOAMm_X85Xb27T,5821660,2023-03-13T20:36:54Z,2023-03-13T20:36:54Z,MEMBER,"@RY4GIT You would need to assign cell_lat/cell_lon as coordinates of the dataset and then use x=""cell_lon"" and y=""cell_lat"" in the call to .plot().
Plotting without that, will use the dimension coordinates (x,y) and there might be a difference in origin (upper vs. lower).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1622197017