[Bug]: RuntimeError: NetCDF: Not a valid ID appears randomly when running operations on the same datasets repeatedly #561
Description
What happened?
For some reason, the RuntimeError: NetCDF: Not a valid ID is being thrown at random times when running operations on the same dataset.
This has happened when I call the 1) spatial averaging and 2) temporal averaging APIs.
1) Spatial Averaging API
Example:
ds = xc.open_dataset("some_dataset")
for _ in range(0, 5):
ds_res = ds.spatial.average(axis=["X", "Y"], weighted=True)
ds_res_comp = ds_res.compute()2) Temporal Averaging API
For this API, the RuntimeError stacktrace points to the logic in TemporalAccessor._get_weights().
Temporal averaging should work fine across multiple copies of the same dataset object.
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/home/vo13/xCDAT/xcdat_test/validation/v0.6.0/xcdat-cdat-perf-metrics/Untitled-1.py in line 2
58 #%%
----> 59 ds2 = ds.copy().temporal.departures("tas", freq='month', weighted=True)
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xcdat/temporal.py:687, in TemporalAccessor.departures(self, data_var, freq, weighted, keep_weights, reference_period, season_config)
685 inferred_freq = _infer_freq(ds[self.dim])
686 if inferred_freq != freq:
--> 687 ds_obs = ds_obs.temporal.group_average(
688 data_var,
689 freq,
690 weighted,
691 keep_weights,
692 season_config,
693 )
695 ds_climo = ds.temporal.climatology(
696 data_var,
697 freq,
(...)
701 season_config,
702 )
704 # 4. Group the averaged data variable values by the time `freq`.
705 # ----------------------------------------------------------------------
706 # This step allows us to perform xarray's grouped arithmetic to
707 # calculate departures.
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xcdat/temporal.py:353, in TemporalAccessor.group_average(self, data_var, freq, weighted, keep_weights, season_config)
225 """Returns a Dataset with average of a data variable by time group.
226
227 Time bounds are used for generating weights to calculate weighted group
(...)
349 }
350 """
351 self._set_data_var_attrs(data_var)
--> 353 return self._averager(
354 data_var,
355 "group_average",
356 freq,
357 weighted=weighted,
358 keep_weights=keep_weights,
359 season_config=season_config,
360 )
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xcdat/temporal.py:767, in TemporalAccessor._averager(self, data_var, mode, freq, weighted, keep_weights, reference_period, season_config)
765 dv = self._average(dv, time_bounds)
766 elif self._mode in ["group_average", "climatology", "departures"]:
--> 767 dv = self._group_average(dv, time_bounds)
769 # The original time dimension is dropped from the dataset because
770 # it becomes obsolete after the data variable is averaged. When the
771 # averaged data variable is added to the dataset, the new time dimension
772 # and its associated coordinates are also added.
773 ds = ds.drop_dims(self.dim) # type: ignore
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xcdat/temporal.py:1132, in TemporalAccessor._group_average(self, data_var, time_bounds)
1129 self._labeled_time = self._label_time_coords(dv[self.dim])
1131 if self._weighted:
-> 1132 self._weights = self._get_weights(time_bounds)
1133 # Weight the data variable.
1134 dv *= self._weights
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xcdat/temporal.py:1216, in TemporalAccessor._get_weights(self, time_bounds)
1209 # Must be cast dtype from "timedelta64[ns]" to "float64", specifically
1210 # when using Dask arrays. Otherwise, the numpy warning below is thrown:
1211 # `DeprecationWarning: The `dtype` and `signature` arguments to ufuncs
1212 # only select the general DType and not details such as the byte order
1213 # or time unit (with rare exceptions see release notes). To avoid this
1214 # warning please use the scalar types `np.float64`, or string notation.`
1215 if isinstance(time_lengths.data, Array):
-> 1216 time_lengths.load()
1218 time_lengths = time_lengths.astype(np.float64)
1220 grouped_time_lengths = self._group_data(time_lengths)
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xarray/core/dataarray.py:1122, in DataArray.load(self, **kwargs)
1104 def load(self, **kwargs) -> Self:
1105 """Manually trigger loading of this array's data from disk or a
1106 remote source into memory and return this array.
1107
(...)
1120 dask.compute
1121 """
-> 1122 ds = self._to_temp_dataset().load(**kwargs)
1123 new = self._from_temp_dataset(ds)
1124 self._variable = new._variable
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xarray/core/dataset.py:847, in Dataset.load(self, **kwargs)
844 chunkmanager = get_chunked_array_type(*lazy_data.values())
846 # evaluate all the chunked arrays simultaneously
--> 847 evaluated_data = chunkmanager.compute(*lazy_data.values(), **kwargs)
849 for k, data in zip(lazy_data, evaluated_data):
850 self.variables[k].data = data
File ~/mambaforge/envs/xcdat_test_stable/lib/python3.10/site-packages/xarray/core/daskmanager.py:70, in DaskManager.compute(self, *data, **kwargs)
67 def compute(self, *data: DaskArray, **kwargs) -> tuple[np.ndarray, ...]:
68 from dask.array import compute
...
File src/netCDF4/_netCDF4.pyx:4496, in netCDF4._netCDF4.Variable._getdims()
File src/netCDF4/_netCDF4.pyx:2028, in netCDF4._netCDF4._ensure_nc_success()
RuntimeError: NetCDF: Not a valid IDPossible answers:
- In
_get_weights(), make a copy oftime_lengthsvariable and load that into memory instead of the originaltime_lengthsvariable (which might be closed after the first API call, resulting in theRuntime: NetCDFerror.
import xarray
import xcdat as xc
dir_path = "/some/dir/path/"
ds = xc.open_mfdataset(dir_path, chunks={"time": "auto"}, add_bounds=['X', 'Y', 'T'], parallel=True)
# `RuntimeError: NetCDF: Not a valid ID`
ds2 = ds.copy().temporal.departures("tas", freq='month', weighted=True)There is a line of code that calculates time_lengths using time bounds. The time_lengths are then loaded into memory.
Lines 1209 to 1216 in c83f46e
What did you expect to happen? Are there are possible answers you came
APIs should run work consistently across multiple copies of the same dataset.
@jasonb5 suggests that it could be the LLNL climate filesystem not behaving well with Xarray dataset I/O.
- Try the same code out on local machine
Minimal Complete Verifiable Example (MVCE)
Provided above.
Relevant log output
Provided above.
Anything else we need to know?
Related GitHub issues:
- "RuntimeError: NetCDF: Not a valid ID" error when trying to operate on a netCDF file using netCDF4 pydata/xarray#1001
- open_mfdataset: Not a valid ID pydata/xarray#5276
Environment
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readline 8.2 h8228510_1 conda-forge
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zlib 1.2.13 hd590300_5 conda-forge
zstd 1.5.5 hfc55251_0 conda-forge