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
1785599886,I_kwDOAMm_X85qbheO,7957,`FacetGrid` plot overlaying multiple variables from same dataset? ,12760310,open,0,,,1,2023-07-03T08:15:42Z,2024-01-01T13:50:52Z,,NONE,,,,"### What is your issue?
I'm trying to produce a facet plot which contains maps with different overlaid layers (e.g. a `pcolormesh` and `streamplot`).
At the moment I'm creating the plot and then iterating over the axes to add the plots manuallay
```python
p = dss['LH'].plot.pcolormesh(
x='lon',
y='lat',
col=""exp"",
)
for i, ax in enumerate(p.axes.flat):
ax.coastlines()
ax.streamplot(
dss.isel(exp=i).lon.values,
dss.isel(exp=i).lat.values,
dss.isel(exp=i)['u_10m_gr'].values,
dss.isel(exp=i)['v_10m_gr'].values,
)
```
This is far from optimal and doesn't really look clean to me. Also, I'm not entirely sure the order of `p.axes.flat` correspond to the one of the `exp` dimension I'm using to facet.
All examples in the doc (https://docs.xarray.dev/en/stable/user-guide/plotting.html) refer to the `plot` method of `DataArray`, so it seems that, once created the `p` object, no other variable from the dataset can be accessed.
However, on the doc it is mentioned
> TODO: add an example of using the map method to plot dataset variables (e.g., with plt.quiver).
It is not clear to me whether the `xarray.plot.FacetGrid.map` method can indeed be used to plot another dataset variable or not. If that's not the case, is there any way to achieve what I'm doing without manually looping through the axes? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7957/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
1333650265,I_kwDOAMm_X85PfeNZ,6904,`sel` behaving randomly when applying to a dataset with multiprocessing,12760310,open,0,,,12,2022-08-09T18:43:06Z,2022-08-10T16:48:53Z,,NONE,,,,"### What happened?
I have a script structured like this
```python
def main():
global ds
ds = xr.open_dataset(file)
for point in points:
compute(point)
def compute(point):
ds_point = ds.sel(lat=point['latitude'],
lon=point['longitude'],
method='nearest')
print(ds_point.var.mean())
# do something with ds_point and other data...
if __name__ == ""__main__"":
main()
```
This works as expected. However, if I try to parallelize `compute` by calling it with
```python
process_map(compute, points, max_workers=5, chunksize=1)
```
The results of the print are completely different from the serial example and they change every time that I run the script. it seems that the `sel` is giving back a different part of the dataset when there are multiple processes running in parallel.
If I move the `open_dataset` statement inside `compute` then everything works also in the parallel case in the same way as in the serial one. Also, if I load the dataset at the beginning, i.e. `ds = xr.open_dataset(file).load()`, I also have reproducible results.
Is this supposed to happen? I really don't understand how.
### What did you expect to happen?
The behaviour of `sel` should be the same in parallel or serial execution.
### Minimal Complete Verifiable Example
_No response_
### MVCE confirmation
- [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [ ] Complete example — the example is self-contained, including all data and the text of any traceback.
- [ ] 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.
- [ ] 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: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:10)
[GCC 10.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.1.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.10.6
libnetcdf: 4.7.4
xarray: 2022.3.0
pandas: 1.2.3
numpy: 1.20.3
scipy: 1.8.1
netCDF4: 1.5.6
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.6.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.1
cfgrib: None
iris: None
bottleneck: None
dask: 2022.7.1
distributed: 2022.7.1
matplotlib: 3.5.2
cartopy: 0.18.0
seaborn: 0.11.2
numbagg: None
fsspec: 2022.5.0
cupy: None
pint: 0.19.2
sparse: None
setuptools: 59.8.0
pip: 22.2
conda: 4.13.0
pytest: None
IPython: 8.4.0
sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6904/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
932444037,MDU6SXNzdWU5MzI0NDQwMzc=,5549,Time is not correctly saved to disk netcdf ,12760310,open,0,,,0,2021-06-29T10:00:36Z,2021-06-29T10:00:36Z,,NONE,,,,"**What happened**:
When trying to write a dataset to netcdf file using the netcdf4 engine time is not saved correctly.
**What you expected to happen**:
Time to be saved correctly as in the original dataset.
**Minimal Complete Verifiable Example**:
```python
ds.to_netcdf(filename,
encoding={product_type: {'zlib': True, 'complevel': 9}},
engine='netcdf4')
```
is giving me the warning
```
SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs
```
xarray Dataset
![]()
saved on disk (notice time values)
![]()
I cannot see anything special in the time array...is there a limitation because of the compression?
**Environment**:
Output of xr.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)
[GCC 9.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.1.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8
libhdf5: 1.10.6
libnetcdf: 4.7.4
xarray: 0.17.0
pandas: 1.2.3
numpy: 1.20.1
scipy: 1.6.3
netCDF4: 1.5.6
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.5.0
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.2
cfgrib: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: 3.3.4
cartopy: 0.18.0
seaborn: 0.11.1
numbagg: None
pint: 0.17
setuptools: 49.6.0.post20210108
pip: 21.1.1
conda: 4.10.2
pytest: None
IPython: 7.21.0
sphinx: None