issues

What is your issue?

Currently, experiencing an issue in writing NCZarr files that I try to write using xarray=='2023.11.0', when I read all the necessary data in netCDF format using xarray.open_mfdataset(...) function.

A simple dataset such as the following: console <xarray.Dataset> Dimensions: (subbasin: 197, time: 96) Coordinates: * time (time) datetime64[ns] 1980-01-01T13:00:00 ... 1980-0... * subbasin (subbasin) int32 71032409 71032292 ... 71027770 Data variables: RDRS_v2.1_P_UVC_10m (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> RDRS_v2.1_P_FI_SFC (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> RDRS_v2.1_P_FB_SFC (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> RDRS_v2.1_P_PR0_SFC (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> RDRS_v2.1_P_P0_SFC (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> RDRS_v2.1_P_TT_1.5m (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> RDRS_v2.1_P_HU_1.5m (subbasin, time) float64 dask.array<chunksize=(197, 1), meta=np.ndarray> lat (subbasin) float64 dask.array<chunksize=(197,), meta=np.ndarray> lon (subbasin) float64 dask.array<chunksize=(197,), meta=np.ndarray> crs int32 ...

Gives me the following error

```python

ds.to_netcdf("file://path/to/test.nczarr#mode=nczarr", engine='netcdf4')

AttributeError Traceback (most recent call last) File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/netCDF4-1.7.0-py3.10-linux-x86_64.egg/netCDF4/utils.py:39, in _find_dim(grp, dimname) 38 try: ---> 39 dim = group.dimensions[dimname] 40 break

AttributeError: 'NoneType' object has no attribute 'dimensions'

During handling of the above exception, another exception occurred:

AttributeError Traceback (most recent call last) File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/netCDF4-1.7.0-py3.10-linux-x86_64.egg/netCDF4/utils.py:43, in _find_dim(grp, dimname) 42 try: ---> 43 group = group.parent 44 except:

AttributeError: 'NoneType' object has no attribute 'parent'

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) Cell In[7], line 1 ----> 1 ds.to_netcdf("file:///home/user/test.nczarr#mode=nczarr,file", engine='netcdf4')

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/core/dataset.py:2280, in Dataset.to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf) 2277 encoding = {} 2278 from xarray.backends.api import to_netcdf -> 2280 return to_netcdf( # type: ignore # mypy cannot resolve the overloads:( 2281 self, 2282 path, 2283 mode=mode, 2284 format=format, 2285 group=group, 2286 engine=engine, 2287 encoding=encoding, 2288 unlimited_dims=unlimited_dims, 2289 compute=compute, 2290 multifile=False, 2291 invalid_netcdf=invalid_netcdf, 2292 )

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/api.py:1259, in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf) 1254 # TODO: figure out how to refactor this logic (here and in save_mfdataset) 1255 # to avoid this mess of conditionals 1256 try: 1257 # TODO: allow this work (setting up the file for writing array data) 1258 # to be parallelized with dask -> 1259 dump_to_store( 1260 dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims 1261 ) 1262 if autoclose: 1263 store.close()

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/api.py:1306, in dump_to_store(dataset, store, writer, encoder, encoding, unlimited_dims) 1303 if encoder: 1304 variables, attrs = encoder(variables, attrs) -> 1306 store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims)

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/common.py:356, in AbstractWritableDataStore.store(self, variables, attributes, check_encoding_set, writer, unlimited_dims) 354 self.set_attributes(attributes) 355 self.set_dimensions(variables, unlimited_dims=unlimited_dims) --> 356 self.set_variables( 357 variables, check_encoding_set, writer, unlimited_dims=unlimited_dims 358 )

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/common.py:394, in AbstractWritableDataStore.set_variables(self, variables, check_encoding_set, writer, unlimited_dims) 392 name = _encode_variable_name(vn) 393 check = vn in check_encoding_set --> 394 target, source = self.prepare_variable( 395 name, v, check, unlimited_dims=unlimited_dims 396 ) 398 writer.add(source, target)

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:500, in NetCDF4DataStore.prepare_variable(self, name, variable, check_encoding, unlimited_dims) 498 nc4_var = self.ds.variables[name] 499 else: --> 500 nc4_var = self.ds.createVariable( 501 varname=name, 502 datatype=datatype, 503 dimensions=variable.dims, 504 zlib=encoding.get("zlib", False), 505 complevel=encoding.get("complevel", 4), 506 shuffle=encoding.get("shuffle", True), 507 fletcher32=encoding.get("fletcher32", False), 508 contiguous=encoding.get("contiguous", False), 509 chunksizes=encoding.get("chunksizes"), 510 endian="native", 511 least_significant_digit=encoding.get("least_significant_digit"), 512 fill_value=fill_value, 513 ) 515 nc4_var.setncatts(attrs) 517 target = NetCDF4ArrayWrapper(name, self)

File src/netCDF4/_netCDF4.pyx:2839, in genexpr()

File src/netCDF4/_netCDF4.pyx:2839, in genexpr()

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/netCDF4-1.7.0-py3.10-linux-x86_64.egg/netCDF4/utils.py:45, in _find_dim(grp, dimname) 43 group = group.parent 44 except: ---> 45 raise ValueError("cannot find dimension %s in this group or parent groups" % dimname) 46 if dim is None: 47 raise KeyError("dimension %s not defined in group %s or any group in it's family tree" % (dimname, grp.path))

ValueError: cannot find dimension subbasin in this group or parent groups ```

The xarray.Dataset.to_zarr(...) function work perfectly fine.

If I read the files using xarray.open_mfdataset(..., engine='h5netcdf') I get the following error instead: ```python

ds.to_netcdf("file://path/to/test.nczarr#mode=nczarr", engine='netcdf4')

ImportError Traceback (most recent call last) Cell In[6], line 1 ----> 1 ds.to_netcdf("file:///home/user/test.nczarr#mode=nczarr,file", engine='netcdf4')

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/core/dataset.py:2280, in Dataset.to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf) 2277 encoding = {} 2278 from xarray.backends.api import to_netcdf -> 2280 return to_netcdf( # type: ignore # mypy cannot resolve the overloads:( 2281 self, 2282 path, 2283 mode=mode, 2284 format=format, 2285 group=group, 2286 engine=engine, 2287 encoding=encoding, 2288 unlimited_dims=unlimited_dims, 2289 compute=compute, 2290 multifile=False, 2291 invalid_netcdf=invalid_netcdf, 2292 )

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/api.py:1242, in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf) 1238 else: 1239 raise ValueError( 1240 f"unrecognized option 'invalid_netcdf' for engine {engine}" 1241 ) -> 1242 store = store_open(target, mode, format, group, **kwargs) 1244 if unlimited_dims is None: 1245 unlimited_dims = dataset.encoding.get("unlimited_dims", None)

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:367, in NetCDF4DataStore.open(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose) 353 @classmethod 354 def open( 355 cls, (...) 365 autoclose=False, 366 ): --> 367 import netCDF4 369 if isinstance(filename, os.PathLike): 370 filename = os.fspath(filename)

File ~/virtual-envs/zarrenv/lib/python3.10/site-packages/netCDF4-1.7.0-py3.10-linux-x86_64.egg/netCDF4/init.py:3 1 # init for netCDF4. package 2 # Docstring comes from extension module _netCDF4. ----> 3 from ._netCDF4 import * 4 # Need explicit imports for names beginning with underscores 5 from ._netCDF4 import doc

ImportError: /home/user/.local/lib64/libnetcdf.so.19: undefined symbol: H5Pset_fapl_mpio ```

Any ideas?

Let me know if a small .nc file for this example is needed. As far as I know, all the dependencies, are properly compiled. HDF5 is compiled with the --enable-parallel flag, netCDF-C is compiled with parallel turned on, and netCDF4-python is also compiled against these mentioned. I also have netcdf-fortran that is compiled in parallel mode if it helps.

Here are also the xarray.show_versions() details: ``` INSTALLED VERSIONS

commit: None python: 3.10.2 (main, Feb 4 2022, 19:10:35) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-1160.88.1.el7.x86_64 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.1-mpi libnetcdf: 4.9.3-development

xarray: 2023.11.0 pandas: 2.1.0 numpy: 1.25.2 scipy: 1.11.2 netCDF4: 1.7.0-development pydap: None h5netcdf: 1.3.0 h5py: 3.8.0 Nio: None zarr: 2.16.1 cftime: 1.6.3 nc_time_axis: None iris: None bottleneck: 1.3.7 dask: 2023.11.0 distributed: 2023.11.0 matplotlib: None cartopy: None seaborn: None numbagg: 0.6.4 fsspec: 2023.10.0 cupy: None pint: 0.22+computecanada sparse: 0.14.0 flox: None numpy_groupies: None setuptools: 68.2.2 pip: 23.3.1 conda: None pytest: None mypy: None IPython: 8.18.1 sphinx: None ```

What is your issue?

I am simply reading 366 small (~15MBs) NetCDF files to create one big NetCDF file at the end. Below is the relevant workflow:

```python-console In [1]: import os; import dask

In [2]: import xarray as xr

In [3]: from dask.distributed import Client, LocalCluster

In [4]: cluster = LocalCluster(n_workers=4, threads_per_worker=1) # 1 core to each worker

In [5]: client = Client(cluster)

In [6]: os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'

In [7]: ds = xr.open_mfdataset('./remapped/*.nc', chunks={'COMID': 1400}, parallel=True)

In [8]: ds.to_netcdf('./out2.nc')

```

And below, is the error I am getting:

The header of individual NetCDF ones are also in the following:

I am running xarray and Dask on an HPC, so the "modules" I have loaded are the following: ```console module list

Currently Loaded Modules: 1) CCconfig 6) ucx/1.8.0 11) netcdf-mpi/4.9.0 (io) 16) freexl/1.0.5 (t) 21) scipy-stack/2023a (math) 2) gentoo/2020 (S) 7) libfabric/1.10.1 12) hdf5-mpi/1.12.1 (io) 17) geos/3.10.2 (geo) 22) libspatialindex/1.8.5 (phys) 3) gcccore/.9.3.0 (H) 8) openmpi/4.0.3 (m) 13) libffi/3.3 18) librttopo-proj9/1.1.0 23) ipykernel/2023a 4) imkl/2020.1.217 (math) 9) StdEnv/2020 (S) 14) python/3.10.2 (t) 19) proj/9.0.1 (geo) 24) sqlite/3.38.5 5) intel/2020.1.217 (t) 10) mii/1.1.2 15) mpi4py/3.1.3 (t) 20) libspatialite-proj901/5.0.1 ```

Any suggestion is greatly appreciated!

What is your issue?

I have trouble getting values of variables inside Zarr stores created using XArray. The ncdump -v variable-name file://path/to/zarr/store#mode=zarr gives me the following error: ```console foo@bar:~$ ncdump -v variable-name 'file:///path/to/zarr/store#mode=zarr' %%% HEADER STUFF %%%

NetCDF: Filter error: undefined filter encountered Location: file /path/to/netcdf-c/ncdump/vardata.c; fcn print_rows line 478 variable-name = ```

Here is a typical header of such Zarr stores that I create:

console netcdf test3 { dimensions: rlat = 628 ; rlon = 655 ; bnds = 2 ; bounds = 4 ; variables: double lat(rlat, rlon) ; lat:long_name = "latitude" ; lat:standard_name = "latitude" ; lat:units = "degrees_north" ; double lon(rlat, rlon) ; lon:long_name = "longitude" ; lon:standard_name = "longitude" ; lon:units = "degrees_east" ; float pr(rlat, rlon) ; pr:_QuantizeBitRoundNumberOfSignificantDigits = 12 ; pr:cell_methods = "area: mean time: mean" ; pr:coordinates = "lon lat" ; pr:grid_mapping = "crs" ; pr:long_name = "Precipitation" ; pr:standard_name = "precipitation_flux" ; pr:units = "kg m-2 s-1" ; double rlat(rlat) ; rlat:actual_range = -33.625, 35.345 ; rlat:axis = "Y" ; rlat:long_name = "latitude in rotated pole grid" ; rlat:standard_name = "grid_latitude" ; rlat:units = "degrees" ; double rlon(rlon) ; rlon:actual_range = -34.045, 37.895 ; rlon:axis = "X" ; rlon:long_name = "longitude in rotated pole grid" ; rlon:standard_name = "grid_longitude" ; rlon:units = "degrees" ; float tas(rlat, rlon) ; tas:_QuantizeBitRoundNumberOfSignificantDigits = 12 ; tas:cell_methods = "area: mean time: point" ; tas:coordinates = "lon lat height" ; tas:grid_mapping = "crs" ; tas:long_name = "Near-Surface Air Temperature" ; tas:standard_name = "air_temperature" ; tas:units = "K" ; double time_bnds(bnds) ; time_bnds:calendar = "proleptic_gregorian" ; time_bnds:units = "days since 1950-01-01" ; double vertices_latitude(rlat, rlon, bounds) ; double vertices_longitude(rlat, rlon, bounds) ; I have compiled the netcdf-c v4.9.3-development version from the latest commit of the netcdf-c's GitHub repository. I assured to have the plugins installed during the compiling procedure, as I understood certain shared libraries need to be installed for HDF5.

I understand this issue does not have an MCVE, but let me know if a test case is needed. I would be more than happy to upload one here and share.

By the way, XArray has no problem reading these Zarr stores back again.

What is your issue?

Upon trying to use the to_netcdf method of the Dataset, the encoding (local attributes) of the time variable changes. More specifically, the units has changed into another format. Here is a reproducible example:

```python-console $ ipython Python 3.10.2 (main, Feb 4 2022, 19:10:35) [GCC 9.3.0] Type 'copyright', 'credits' or 'license' for more information IPython 8.10.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import xarray as xr imp
In [2]: import numpy as np

In [3]: import pandas as pd

In [4]: np.random.seed(0) ...: temperature = 15 + 8 * np.random.randn(2, 2, 25) ...: precipitation = 10 * np.random.rand(2, 2, 25) ...: lon = [[-99.83, -99.32], [-99.79, -99.23]] ...: lat = [[42.25, 42.21], [42.63, 42.59]] ...: time = pd.date_range("2014-09-06", "2014-09-07",freq='H') ...: reference_time = pd.Timestamp("2014-09-05")

In [5]: ds = xr.Dataset( ...: data_vars=dict( ...: temperature=(["x", "y", "time"], temperature), ...: precipitation=(["x", "y", "time"], precipitation), ...: ), ...: coords=dict( ...: lon=(["x", "y"], lon), ...: lat=(["x", "y"], lat), ...: time=time, ...: reference_time=reference_time, ...: ), ...: attrs=dict(description="Weather related data."), ...: ) ...: ds Out[5]: <xarray.Dataset> Dimensions: (x: 2, y: 2, time: 25) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 ... 2014-09-07 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 29.11 18.2 22.83 ... 29.29 16.02 18.22 precipitation (x, y, time) float64 4.239 6.064 0.1919 ... 8.727 2.735 7.98 Attributes: description: Weather related data.

In [6]: ds.time Out[6]: <xarray.DataArray 'time' (time: 25)> array(['2014-09-06T00:00:00.000000000', '2014-09-06T01:00:00.000000000', '2014-09-06T02:00:00.000000000', '2014-09-06T03:00:00.000000000', '2014-09-06T04:00:00.000000000', '2014-09-06T05:00:00.000000000', '2014-09-06T06:00:00.000000000', '2014-09-06T07:00:00.000000000', '2014-09-06T08:00:00.000000000', '2014-09-06T09:00:00.000000000', '2014-09-06T10:00:00.000000000', '2014-09-06T11:00:00.000000000', '2014-09-06T12:00:00.000000000', '2014-09-06T13:00:00.000000000', '2014-09-06T14:00:00.000000000', '2014-09-06T15:00:00.000000000', '2014-09-06T16:00:00.000000000', '2014-09-06T17:00:00.000000000', '2014-09-06T18:00:00.000000000', '2014-09-06T19:00:00.000000000', '2014-09-06T20:00:00.000000000', '2014-09-06T21:00:00.000000000', '2014-09-06T22:00:00.000000000', '2014-09-06T23:00:00.000000000', '2014-09-07T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2014-09-06 ... 2014-09-07 reference_time datetime64[ns] 2014-09-05

In [7]: ds.time.encoding Out[7]: {}

In [9]: ds.to_netcdf("./test.nc", encoding={'time': {'units': 'hours since 2014-09-01 12:00:00'}})

In [10]: !ncdump -h ./test.nc netcdf test { dimensions: x = 2 ; y = 2 ; time = 25 ; variables: double temperature(x, y, time) ; temperature:_FillValue = NaN ; temperature:coordinates = "lat lon reference_time" ; double precipitation(x, y, time) ; precipitation:_FillValue = NaN ; precipitation:coordinates = "lat lon reference_time" ; double lon(x, y) ; lon:_FillValue = NaN ; double lat(x, y) ; lat:_FillValue = NaN ; int64 time(time) ; time:units = "hours since 2014-09-01T12:00:00" ; <------- this is the problem time:calendar = "proleptic_gregorian" ; int64 reference_time ; reference_time:units = "days since 2014-09-05 00:00:00" ; reference_time:calendar = "proleptic_gregorian" ;

// global attributes: :description = "Weather related data." ; }

In [11]: ds.info() xarray.Dataset { dimensions: x = 2 ; y = 2 ; time = 25 ;

variables: float64 temperature(x, y, time) ; float64 precipitation(x, y, time) ; float64 lon(x, y) ; float64 lat(x, y) ; datetime64[ns] time(time) ; datetime64[ns] reference_time() ;

// global attributes: :description = Weather related data. ; } ```

The only thing that I am concerned about is the T value in the "hours since 2014-09-01T12:00:00" string in the final netCDF file. I would like to have control over it, however, even by providing an encoding dictionary for the units attribute, the T is placed in the attribute string.

The sample dataset is taken from here: https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html

How may I evade this issue? Any suggestions. I did my best to Google. Thanks.

What is your issue?

I try to open about ~10 files, each 5MB as a test case, using xarray's open_mfdataset method with the parallel=True option, however, it throws a "Segmentation fault" error as the following:

```python $ ipython Python 3.10.2 (main, Feb 4 2022, 19:10:35) [GCC 9.3.0] Type 'copyright', 'credits' or 'license' for more information IPython 8.10.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import xarray as xr

In [2]: ds = xr.open_mfdataset('./ab_models_198001*.nc', chunks={'time':10})

In [3]: ds Out[3]: <xarray.Dataset> Dimensions: (time: 744, rlat: 140, rlon: 105) Coordinates: * time (time) datetime64[ns] 1980-01-01T13:00:00 ... 1980-0... lon (rlat, rlon) float32 dask.array<chunksize=(140, 105), meta=np.ndarray> lat (rlat, rlon) float32 dask.array<chunksize=(140, 105), meta=np.ndarray> * rlon (rlon) float64 342.1 342.2 342.2 ... 351.2 351.3 351.4 * rlat (rlat) float64 -7.83 -7.74 -7.65 ... 4.5 4.59 4.68 Data variables: rotated_pole (time) int32 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 RDRS_v2.1_P_UVC_10m (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> RDRS_v2.1_P_FI_SFC (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> RDRS_v2.1_P_FB_SFC (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> RDRS_v2.1_A_PR0_SFC (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> RDRS_v2.1_P_P0_SFC (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> RDRS_v2.1_P_TT_1.5m (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> RDRS_v2.1_P_HU_1.5m (time, rlat, rlon) float32 dask.array<chunksize=(10, 140, 105), meta=np.ndarray> Attributes: CDI: Climate Data Interface version 2.0.4 (https://mpimet.mpg.de... Conventions: CF-1.6 product: RDRS_v2.1 Remarks: Variable names are following the convention <Product>_<Type... License: These data are provided by the Canadian Surface Prediction ... history: Mon Aug 28 13:44:02 2023: cdo -z zip -s -L -sellonlatbox,-1... NCO: netCDF Operators version 5.0.6 (Homepage = http://nco.sf.ne... CDO: Climate Data Operators version 2.0.4 (https://mpimet.mpg.de...

In [4]: type(ds) Out[4]: xarray.core.dataset.Dataset

In [5]: ds = xr.open_mfdataset('./ab_models_198001*.nc', chunks={'time':10}, parallel=True) [gra-login3:25527:0:6913] Caught signal 11 (Segmentation fault: address not mapped to object at address 0x8) [gra-login3:25527] *** Process received signal *** [gra-login3:25527] Signal: Segmentation fault (11) [gra-login3:25527] Signal code: (128) [gra-login3:25527] Failing at address: (nil) Segmentation fault

```

Here is the version of xarray:

```python In [5]: xr.show_versions() /home/user/virtual-envs/scienv/lib/python3.10/site-packages/_distutils_hack/init.py:36: UserWarning: Setuptools is replacing distutils. warnings.warn("Setuptools is replacing distutils.")

INSTALLED VERSIONS

commit: None python: 3.10.2 (main, Feb 4 2022, 19:10:35) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-1160.88.1.el7.x86_64 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: ('en_CA', 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.9.0

xarray: 2023.7.0 pandas: 1.4.0 numpy: 1.21.2 scipy: 1.8.0 netCDF4: 1.6.4 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None iris: None bottleneck: None dask: 2023.8.0 distributed: 2023.8.0 matplotlib: 3.5.1 cartopy: None seaborn: None numbagg: None fsspec: 2023.6.0 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 60.2.0 pip: 23.2.1 conda: None pytest: 7.4.0 mypy: None IPython: 8.10.0 sphinx: None ```

I'm working on an HPC, so if a list "modules" I have loaded helps, here it is: ```console $ module list

Currently Loaded Modules: 1) CCconfig 5) gcccore/.9.3.0 (H) 9) libfabric/1.10.1 13) ipykernel/2023a 17) sqlite/3.38.5 21) postgresql/12.4 (t) 25) gdal/3.5.1 (geo) 29) udunits/2.2.28 (t) 33) cdo/2.2.1 (geo) 2) gentoo/2020 (S) 6) imkl/2020.1.217 (math) 10) openmpi/4.0.3 (m) 14) scipy-stack/2023a (math) 18) jasper/2.0.16 (vis) 22) freexl/1.0.5 (t) 26) geos/3.10.2 (geo) 30) libaec/1.0.6 34) mpi4py/3.1.3 (t) 3) StdEnv/2020 (S) 7) gcc/9.3.0 (t) 11) libffi/3.3 15) hdf5/1.10.6 (io) 19) libgeotiff-proj901/1.7.1 23) librttopo-proj9/1.1.0 27) proj/9.0.1 (geo) 31) eccodes/2.25.0 (geo) 35) netcdf-fortran/4.5.2 (io) 4) mii/1.1.2 8) ucx/1.8.0 12) python/3.10.2 (t) 16) netcdf/4.7.4 (io) 20) cfitsio/4.1.0 (vis) 24) libspatialite-proj901/5.0.1 28) expat/2.4.1 (t) 32) yaxt/0.9.0 (t) 36) libspatialindex/1.8.5 (phys)

Where: S: Module is Sticky, requires --force to unload or purge m: MPI implementations / Implémentations MPI math: Mathematical libraries / Bibliothèques mathématiques io: Input/output software / Logiciel d'écriture/lecture t: Tools for development / Outils de développement vis: Visualisation software / Logiciels de visualisation geo: Geography libraries/apps / Logiciels de géographie phys: Physics libraries/apps / Logiciels de physique H: Hidden Module ```

Thanks.