issue_comments: 1469467986
This data as json
| 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 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
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