I have a grib file containing reanalysis (an), ensemble mean (em), and ensemble spread (es) data. The dataset I access is the analysis data,
import xarray as xr
import cfgrib
file = 'C:/Users/Downloads/04292020_rean_ensmean_ensspread.grib'
ds = xr.open_dataset(file,engine = "cfgrib",filter_by_keys={'dataType': 'an'}) # an accesses analysis data
time = 5;
res = 2; #2 = half degree
[phi,theta] = np.meshgrid(ds.variables['longitude'][::res].values,ds.variables['latitude'][::res].values)
divergence = ds.variables['d'][time,:,::res,::res].values
I'm able to access longitude and latitude values just fine, but if I try to access one of the field variable's values like divergence, it returns this error every time. Here's the head data assuming it's useful:
# <bound method Dataset.head of <xarray.Dataset> Size: 20GB
# Dimensions: (time: 16, isobaricInhPa: 23, latitude: 721, longitude: 1440)
# Coordinates:
# number int32 4B ...
# * time (time) datetime64[ns] 128B 2020-04-29 ... 2020-04-29T23:00:00
# step timedelta64[ns] 8B ...
# * isobaricInhPa (isobaricInhPa) float64 184B 1e+03 975.0 ... 225.0 200.0
# * latitude (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
# * longitude (longitude) float64 12kB 0.0 0.25 0.5 ... 359.2 359.5 359.8
# valid_time (time) datetime64[ns] 128B ...
# Data variables: (12/13)
# d (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# cc (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# z (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# pv (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# r (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# clwc (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# ...
# crwc (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# t (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# u (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# v (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# w (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# vo (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# Attributes:
# GRIB_edition: 1
# GRIB_centre: ecmf
# GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
# GRIB_subCentre: 0
# Conventions: CF-1.7
# institution: European Centre for Medium-Range Weather Forecasts
# history: 2025-03-23T18:13 GRIB to CDM+CF via cfgrib-0.9.1...>
EDIT: It actually works for time indices 0, 1, and 2. Anything past 2, and it fails with [Errno 22]. I've downloaded this data twice now on suspicion that the other dataset was corrupted, but it's still returning the same error.
EDIT: I went to check again today, and all of the sudden times 0-12 work now. Last night it was only times 0-2. Times 13-48 are still returning this error. I have no idea how, I didn't do anything to the data since last night. More of the data just randomly became accessible today.
Here's the full traceback:
divergence = ds.isel(time=13).d.data
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
Cell In[30], line 1
----> 1 divergence = ds.isel(time=13).d.data
File ~\anaconda3\Lib\site-packages\xarray\core\dataarray.py:795, in DataArray.data(self)
783 @property
784 def data(self) -> Any:
785 """
786 The DataArray's data as an array. The underlying array type
787 (e.g. dask, sparse, pint) is preserved.
(...)
793 DataArray.values
794 """
--> 795 return self.variable.data
File ~\anaconda3\Lib\site-packages\xarray\core\variable.py:474, in Variable.data(self)
472 return self._data
473 elif isinstance(self._data, indexing.ExplicitlyIndexed):
--> 474 return self._data.get_duck_array()
475 else:
476 return self.values
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:840, in MemoryCachedArray.get_duck_array(self)
839 def get_duck_array(self):
--> 840 self._ensure_cached()
841 return self.array.get_duck_array()
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:837, in MemoryCachedArray._ensure_cached(self)
836 def _ensure_cached(self):
--> 837 self.array = as_indexable(self.array.get_duck_array())
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:794, in CopyOnWriteArray.get_duck_array(self)
793 def get_duck_array(self):
--> 794 return self.array.get_duck_array()
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:657, in LazilyIndexedArray.get_duck_array(self)
653 array = apply_indexer(self.array, self.key)
654 else:
655 # If the array is not an ExplicitlyIndexedNDArrayMixin,
656 # it may wrap a BackendArray so use its __getitem__
--> 657 array = self.array[self.key]
659 # self.array[self.key] is now a numpy array when
660 # self.array is a BackendArray subclass
661 # and self.key is BasicIndexer((slice(None, None, None),))
662 # so we need the explicit check for ExplicitlyIndexed
663 if isinstance(array, ExplicitlyIndexed):
File ~\anaconda3\Lib\site-packages\cfgrib\xarray_plugin.py:163, in CfGribArrayWrapper.__getitem__(self, key)
159 def __getitem__(
160 self,
161 key: xr.core.indexing.ExplicitIndexer,
162 ) -> np.ndarray:
--> 163 return xr.core.indexing.explicit_indexing_adapter(
164 key, self.shape, xr.core.indexing.IndexingSupport.BASIC, self._getitem
165 )
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:1018, in explicit_indexing_adapter(key, shape, indexing_support, raw_indexing_method)
996 """Support explicit indexing by delegating to a raw indexing method.
997
998 Outer and/or vectorized indexers are supported by indexing a second time
(...)
1015 Indexing result, in the form of a duck numpy-array.
1016 """
1017 raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
-> 1018 result = raw_indexing_method(raw_key.tuple)
1019 if numpy_indices.tuple:
1020 # index the loaded np.ndarray
1021 indexable = NumpyIndexingAdapter(result)
File ~\anaconda3\Lib\site-packages\cfgrib\xarray_plugin.py:172, in CfGribArrayWrapper._getitem(self, key)
167 def _getitem(
168 self,
169 key: T.Tuple[T.Any, ...],
170 ) -> np.ndarray:
171 with self.datastore.lock:
--> 172 return self.array[key]
File ~\anaconda3\Lib\site-packages\cfgrib\dataset.py:373, in OnDiskArray.__getitem__(self, item)
371 continue
372 # NOTE: fill a single field as found in the message
--> 373 message = self.index.get_field(message_ids[0]) # type: ignore
374 values = get_values_in_order(message, array_field[tuple(array_field_indexes)].shape)
375 array_field.__getitem__(tuple(array_field_indexes)).flat[:] = values
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:488, in FieldsetIndex.get_field(self, message_id)
487 def get_field(self, message_id: T.Any) -> abc.Field:
--> 488 return ComputedKeysAdapter(self.fieldset[message_id], selfputed_keys)
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:345, in FileStream.__getitem__(self, item)
343 def __getitem__(self, item: T.Optional[OffsetType]) -> Message:
344 with open(self.path, "rb") as file:
--> 345 return self.message_from_file(file, offset=item)
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:341, in FileStream.message_from_file(self, file, offset, **kwargs)
339 def message_from_file(self, file, offset=None, **kwargs):
340 # type: (T.IO[bytes], T.Optional[OffsetType], T.Any) -> Message
--> 341 return Message.from_file(file, offset, **kwargs)
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:94, in Message.from_file(cls, file, offset, **kwargs)
92 offset, field_in_message = offset
93 if offset is not None:
---> 94 file.seek(offset)
95 codes_id = None
96 if field_in_message == 0:
OSError: [Errno 22] Invalid argument
I have a grib file containing reanalysis (an), ensemble mean (em), and ensemble spread (es) data. The dataset I access is the analysis data,
import xarray as xr
import cfgrib
file = 'C:/Users/Downloads/04292020_rean_ensmean_ensspread.grib'
ds = xr.open_dataset(file,engine = "cfgrib",filter_by_keys={'dataType': 'an'}) # an accesses analysis data
time = 5;
res = 2; #2 = half degree
[phi,theta] = np.meshgrid(ds.variables['longitude'][::res].values,ds.variables['latitude'][::res].values)
divergence = ds.variables['d'][time,:,::res,::res].values
I'm able to access longitude and latitude values just fine, but if I try to access one of the field variable's values like divergence, it returns this error every time. Here's the head data assuming it's useful:
# <bound method Dataset.head of <xarray.Dataset> Size: 20GB
# Dimensions: (time: 16, isobaricInhPa: 23, latitude: 721, longitude: 1440)
# Coordinates:
# number int32 4B ...
# * time (time) datetime64[ns] 128B 2020-04-29 ... 2020-04-29T23:00:00
# step timedelta64[ns] 8B ...
# * isobaricInhPa (isobaricInhPa) float64 184B 1e+03 975.0 ... 225.0 200.0
# * latitude (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
# * longitude (longitude) float64 12kB 0.0 0.25 0.5 ... 359.2 359.5 359.8
# valid_time (time) datetime64[ns] 128B ...
# Data variables: (12/13)
# d (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# cc (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# z (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# pv (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# r (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# clwc (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# ...
# crwc (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# t (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# u (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# v (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# w (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# vo (time, isobaricInhPa, latitude, longitude) float32 2GB ...
# Attributes:
# GRIB_edition: 1
# GRIB_centre: ecmf
# GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
# GRIB_subCentre: 0
# Conventions: CF-1.7
# institution: European Centre for Medium-Range Weather Forecasts
# history: 2025-03-23T18:13 GRIB to CDM+CF via cfgrib-0.9.1...>
EDIT: It actually works for time indices 0, 1, and 2. Anything past 2, and it fails with [Errno 22]. I've downloaded this data twice now on suspicion that the other dataset was corrupted, but it's still returning the same error.
EDIT: I went to check again today, and all of the sudden times 0-12 work now. Last night it was only times 0-2. Times 13-48 are still returning this error. I have no idea how, I didn't do anything to the data since last night. More of the data just randomly became accessible today.
Here's the full traceback:
divergence = ds.isel(time=13).d.data
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
Cell In[30], line 1
----> 1 divergence = ds.isel(time=13).d.data
File ~\anaconda3\Lib\site-packages\xarray\core\dataarray.py:795, in DataArray.data(self)
783 @property
784 def data(self) -> Any:
785 """
786 The DataArray's data as an array. The underlying array type
787 (e.g. dask, sparse, pint) is preserved.
(...)
793 DataArray.values
794 """
--> 795 return self.variable.data
File ~\anaconda3\Lib\site-packages\xarray\core\variable.py:474, in Variable.data(self)
472 return self._data
473 elif isinstance(self._data, indexing.ExplicitlyIndexed):
--> 474 return self._data.get_duck_array()
475 else:
476 return self.values
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:840, in MemoryCachedArray.get_duck_array(self)
839 def get_duck_array(self):
--> 840 self._ensure_cached()
841 return self.array.get_duck_array()
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:837, in MemoryCachedArray._ensure_cached(self)
836 def _ensure_cached(self):
--> 837 self.array = as_indexable(self.array.get_duck_array())
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:794, in CopyOnWriteArray.get_duck_array(self)
793 def get_duck_array(self):
--> 794 return self.array.get_duck_array()
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:657, in LazilyIndexedArray.get_duck_array(self)
653 array = apply_indexer(self.array, self.key)
654 else:
655 # If the array is not an ExplicitlyIndexedNDArrayMixin,
656 # it may wrap a BackendArray so use its __getitem__
--> 657 array = self.array[self.key]
659 # self.array[self.key] is now a numpy array when
660 # self.array is a BackendArray subclass
661 # and self.key is BasicIndexer((slice(None, None, None),))
662 # so we need the explicit check for ExplicitlyIndexed
663 if isinstance(array, ExplicitlyIndexed):
File ~\anaconda3\Lib\site-packages\cfgrib\xarray_plugin.py:163, in CfGribArrayWrapper.__getitem__(self, key)
159 def __getitem__(
160 self,
161 key: xr.core.indexing.ExplicitIndexer,
162 ) -> np.ndarray:
--> 163 return xr.core.indexing.explicit_indexing_adapter(
164 key, self.shape, xr.core.indexing.IndexingSupport.BASIC, self._getitem
165 )
File ~\anaconda3\Lib\site-packages\xarray\core\indexing.py:1018, in explicit_indexing_adapter(key, shape, indexing_support, raw_indexing_method)
996 """Support explicit indexing by delegating to a raw indexing method.
997
998 Outer and/or vectorized indexers are supported by indexing a second time
(...)
1015 Indexing result, in the form of a duck numpy-array.
1016 """
1017 raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
-> 1018 result = raw_indexing_method(raw_key.tuple)
1019 if numpy_indices.tuple:
1020 # index the loaded np.ndarray
1021 indexable = NumpyIndexingAdapter(result)
File ~\anaconda3\Lib\site-packages\cfgrib\xarray_plugin.py:172, in CfGribArrayWrapper._getitem(self, key)
167 def _getitem(
168 self,
169 key: T.Tuple[T.Any, ...],
170 ) -> np.ndarray:
171 with self.datastore.lock:
--> 172 return self.array[key]
File ~\anaconda3\Lib\site-packages\cfgrib\dataset.py:373, in OnDiskArray.__getitem__(self, item)
371 continue
372 # NOTE: fill a single field as found in the message
--> 373 message = self.index.get_field(message_ids[0]) # type: ignore
374 values = get_values_in_order(message, array_field[tuple(array_field_indexes)].shape)
375 array_field.__getitem__(tuple(array_field_indexes)).flat[:] = values
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:488, in FieldsetIndex.get_field(self, message_id)
487 def get_field(self, message_id: T.Any) -> abc.Field:
--> 488 return ComputedKeysAdapter(self.fieldset[message_id], selfputed_keys)
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:345, in FileStream.__getitem__(self, item)
343 def __getitem__(self, item: T.Optional[OffsetType]) -> Message:
344 with open(self.path, "rb") as file:
--> 345 return self.message_from_file(file, offset=item)
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:341, in FileStream.message_from_file(self, file, offset, **kwargs)
339 def message_from_file(self, file, offset=None, **kwargs):
340 # type: (T.IO[bytes], T.Optional[OffsetType], T.Any) -> Message
--> 341 return Message.from_file(file, offset, **kwargs)
File ~\anaconda3\Lib\site-packages\cfgrib\messages.py:94, in Message.from_file(cls, file, offset, **kwargs)
92 offset, field_in_message = offset
93 if offset is not None:
---> 94 file.seek(offset)
95 codes_id = None
96 if field_in_message == 0:
OSError: [Errno 22] Invalid argument
- Don't make us guess where the error happens. Please edit your question and add the full error message traceback. – John Gordon Commented Mar 24 at 21:46
- @JohnGordon Just added it – Researcher R Commented Mar 24 at 22:01
1 Answer
Reset to default 0Can you explain what you are trying to do? Why using the np.meshgrid?
Where are you obtaining the error? When calculating [phi, theta] or when obtainting the divergence ?
Are you trying to change the resolution from 0.25 to 0.5 and obtain the divergence (d variable) at a specific time?
I think should be better to access the data at the time=time with the function .isel as:
divergence = ds.isel(time=time).d.data
But still I'm not sure what you are doing, probably there is some `xarray` function that will do what you're expecting.
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