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
163414759,MDU6SXNzdWUxNjM0MTQ3NTk=,894,Dataset variable reference fails after renaming,17951292,closed,0,,,4,2016-07-01T15:04:15Z,2016-08-02T17:39:11Z,2016-08-02T17:39:11Z,NONE,,,,"Using xarray v0.7.2, I've encountered what looks to be a bug whereby referencing a re-named variable raises a KeyError:
# compute anomalies
climo = ds.groupby('time.dayofyear').mean('time')
anom = ds.groupby('time.dayofyear') - climo
anom.rename({'air': 't2manom'})
**# Output**
_<xarray.Dataset>
Dimensions: (lat: 37, level: 1, lon: 192, time: 13514)
Coordinates:
- lat (lat) float32 88.542 86.6531 84.7532 82.8508 80.9473 79.0435 ...
- level (level) float32 2.0
- lon (lon) float32 0.0 1.875 3.75 5.625 7.5 9.375 11.25 13.125 ...
dayofyear (time) int32 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
- time (time) datetime64[ns] 1979-01-01 1979-01-02 1979-01-03 ...
Data variables:
t2manom (time, level, lat, lon) float64 -6.342 -6.476 -6.522 -6.579 ..._
anom['t2manom'].values[0:5]
**Output:**
_---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-10-05206d711de3> in <module>()
8 #del normalized['dayofyear']
9 #normalized
---> 10 anom['t2manom'].values[0:5]
/usr/local/lib/python2.7/dist-packages/xarray/core/dataset.pyc in **getitem**(self, key)
527
528 if hashable(key):
--> 529 return self._construct_dataarray(key)
530 else:
531 return self._copy_listed(np.asarray(key))
/usr/local/lib/python2.7/dist-packages/xarray/core/dataset.pyc in _construct_dataarray(self, name)
476 variable = self._variables[name]
477 except KeyError:
--> 478 _, name, variable = _get_virtual_variable(self._variables, name)
479
480 coords = OrderedDict()
/usr/local/lib/python2.7/dist-packages/xarray/core/dataset.pyc in _get_virtual_variable(variables, key)
43 split_key = key.split('.', 1)
44 if len(split_key) != 2:
---> 45 raise KeyError(key)
46
47 ref_name, var_name = split_key
KeyError: 't2manom'_
However, referencing the OLD variable name ('air' in this case) produces the output expected when referencing the NEW variable name:
anom['air'].values[0:5]
**Output:**
array([[[[ -6.34243229, -6.47621607, -6.52189175, ..., -6.3105404 ,
-6.33972959, -6.30999986],
[ -9.18891871, -9.32432412, -9.57216195, ..., -8.67081062,
-8.89675656, -9.09081061],
[ -8.8072971 , -9.27054033, -9.69972951, ..., -7.53378362,
-7.93783766, -8.3737836 ],
...,
[ -4.30864855, -3.5656756 , -1.10594592, ..., -1.70405402,
-2.53135129, -2.92999993],
[ -1.197027 , -1.34837835, -0.5481081 , ..., 0.01810811,
0.01540541, -0.62756755],
[ 0.47135134, 0.22297297, -0.50756756, ..., 0.63297296,
0.65837836, 0.58891891]]],
[...omitted values...]
Am I doing something wrong here?
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167684282,MDU6SXNzdWUxNjc2ODQyODI=,919,ValueError: encountered unexpected variable nbnds,17951292,closed,0,,,3,2016-07-26T18:59:02Z,2016-07-27T22:37:02Z,2016-07-27T22:37:02Z,NONE,,,,"Using the recipe presented near the bottom of http://xarray.pydata.org/en/stable/io.html for reading in multiple files via opendap, I encounter the error referenced in the subject line.
Here is the call to read_netcdfs:
base = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface_gauss/air.2m.gauss.%4u.nc'
files = [base % d for d in range(1948,2016,1)]
ds_combined = read_netcdfs(files,dim='time',transform_func=None)
I am using option decode_cf=False in xr.open_dataset
Here is the entire content of the error message:
ValueError Traceback (most recent call last)
<ipython-input-5-024a3f9ac990> in <module>()
2 base = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface_gauss/air.2m.gauss.%4u.nc'
3 files = [base % d for d in range(1948,2016,1)]
----> 4 ds_combined = read_netcdfs(files,dim='time',transform_func=None)
<ipython-input-4-36b274dcd63c> in read_netcdfs(files, dim, transform_func)
18 paths = files
19 datasets = [process_one_path(p) for p in paths]
---> 20 combined = xr.concat(datasets, dim)
21 return combined
/usr/local/lib/python2.7/dist-packages/xarray/core/combine.pyc in concat(objs, dim, data_vars, coords, compat, positions, indexers, mode, concat_over)
112 raise TypeError('can only concatenate xarray Dataset and DataArray '
113 'objects, got %s' % type(first_obj))
--> 114 return f(objs, dim, data_vars, coords, compat, positions)
115
116
/usr/local/lib/python2.7/dist-packages/xarray/core/combine.pyc in _dataset_concat(datasets, dim, data_vars, coords, compat, positions)
231 for k, v in iteritems(ds.variables):
232 if k not in result_vars and k not in concat_over:
--> 233 raise ValueError('encountered unexpected variable %r' % k)
234 elif (k in result_coord_names) != (k in ds.coords):
235 raise ValueError('%r is a coordinate in some datasets but not '
ValueError: encountered unexpected variable u'nbnds'
Any ideas as to how I can avoid triggering this error?
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162726984,MDU6SXNzdWUxNjI3MjY5ODQ=,891,BUG: Test for dask version erroneously fails when calling xr.mfdataset(),17951292,closed,0,,,2,2016-06-28T16:17:16Z,2016-07-20T04:29:34Z,2016-07-20T04:29:34Z,NONE,,,,"I have latest version of dask installed but get the following exception when using xr.mfdataset():
'ImportError: xarray requires dask version 0.6 or newer'
Problem appears to be due to the fact that the latest version is 0.10 (i.e., dask.**version** = '0.10.0') but exception is raised since 0.1 < 0.6
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166195300,MDU6SXNzdWUxNjYxOTUzMDA=,900,How to apply function to two (or more) variables simultaneously,17951292,closed,0,,,3,2016-07-18T21:14:57Z,2016-07-19T01:31:46Z,2016-07-19T01:31:40Z,NONE,,,,"I have two datasets: fcst and obs, each with dimensions (time, lat, lon). The first contains predicted values on a lat x lon grid for a given lead time and the second the corresponding verifying observations. I want to compute skill scores at each time but this obviously involves applying a function involving variables from both datasets. Moreover, the two fields (fcst and obs) need to be cosine weighted first (involves coordinate 'lat'). Furthermore, I wish to align the forecasts and obs in time; there may be missing values of each at different times. I tried aligning the datasets using 'xr.align' but got all kinds of errors when trying to use the resulting new dataset. I suppose I could extract the values from each dataset, taking care to use indexing to extract the common times and then using standard numpy operations and the like but before I go that route is there a methodology for using xarray to do such a computation? If I were doing a single variable computation, it would be easy to use the groupby and apply methods. TIA for any advice!
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