html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1329#issuecomment-709570971,https://api.github.com/repos/pydata/xarray/issues/1329,709570971,MDEyOklzc3VlQ29tbWVudDcwOTU3MDk3MQ==,1956032,2020-10-15T20:25:10Z,2020-10-15T20:25:10Z,CONTRIBUTOR,"I don't know if this issue is still relevant for xarray
But I encountered the same error with https://github.com/euroargodev/argopy 
and may be surprisingly, only with xarray version 0.16.1","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,217216935
https://github.com/pydata/xarray/issues/3268#issuecomment-539465775,https://api.github.com/repos/pydata/xarray/issues/3268,539465775,MDEyOklzc3VlQ29tbWVudDUzOTQ2NTc3NQ==,1956032,2019-10-08T11:13:25Z,2019-10-08T11:13:25Z,CONTRIBUTOR,"Alright, I think I get it, thanks for the clarification @crusaderky ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,485708282
https://github.com/pydata/xarray/issues/3268#issuecomment-539383066,https://api.github.com/repos/pydata/xarray/issues/3268,539383066,MDEyOklzc3VlQ29tbWVudDUzOTM4MzA2Ng==,1956032,2019-10-08T07:28:07Z,2019-10-08T07:28:07Z,CONTRIBUTOR,"Ok, I get it. Probably the accessor is not the best solution in my case.
And yes, an attribute was in fact my first implementation of the add/clean idea. But I was afraid it would be less reliable than the internal list over a long term perspective (but that was before getting in the troubles described above).

But why is asking accessor developers to define a copy method an issue ? That wouldn't be mandatory but only required in situations where propagating functional informations could be useful. Sorry if that's a naive question for you guys.

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,485708282
https://github.com/pydata/xarray/issues/3268#issuecomment-539174999,https://api.github.com/repos/pydata/xarray/issues/3268,539174999,MDEyOklzc3VlQ29tbWVudDUzOTE3NDk5OQ==,1956032,2019-10-07T19:49:41Z,2019-10-07T19:49:41Z,CONTRIBUTOR,"@crusaderky thanks for the explanation, that's a solution to my pb.

Although I understand that since accessor will be created from scratch, a dataset copy won't propagate the accessor properties (in this case the list of added variables):

```python
ds = xarray.Dataset()
ds['ext_data'] = xarray.DataArray(1.)

my_estimator = BaseEstimator() # With ""clean"" method from @crusaderky 
ds.my_accessor.fit(my_estimator, x=2.)
ds.my_accessor.transform(my_estimator, y=3.)

ds2 = ds.copy()

ds = ds.my_accessor.clean()
ds2 = ds2.my_accessor.clean()

print(ds.data_vars)
print(ds2.data_vars)
```
gives:
```python
Data variables:
    ext_data  float64 1.0
Data variables:
    ext_data  float64 1.0
    fit_data  float64 4.0
    trf_data  float64 7.0
```
""Cleaning"" the dataset works as expected, but the copy (ds2) has en empty list of added variables so the ""clean"" method doesn't have the expected result. We have the same behavior for deep copy.

Would that make any sense that the xr.DataSet.copy() method also return a copy of the accessors ?



","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,485708282
https://github.com/pydata/xarray/issues/3268#issuecomment-538461456,https://api.github.com/repos/pydata/xarray/issues/3268,538461456,MDEyOklzc3VlQ29tbWVudDUzODQ2MTQ1Ng==,1956032,2019-10-04T16:07:21Z,2019-10-04T16:09:00Z,CONTRIBUTOR,"Hi all,
I recently encountered an issue that look like this with accessor, but not sure.
Here is a peace of code that reproduces the issue.

Starting from a class with the core of the code and an accessor to implement the user API:

``` python
import xarray

class BaseEstimator():
    def fit(self, this_ds, x=None):
        # Do something with this_ds:
        x = x**2
        # and create a new array with results:
        da = xarray.DataArray(x).rename('fit_data')
        # Return results:
        return da
    
    def transform(self, this_ds, **kw):
        # Do something with this_ds:
        val = kw['y'] + this_ds['fit_data']
        # and create a new array with results:
        da = xarray.DataArray(val).rename('trf_data')
        # Return results:
        return da

@xarray.register_dataset_accessor('my_accessor')
class Foo:
    def __init__(self, obj):
        self.obj = obj
        self.added = list()
        
    def add(self, da):
        self.obj[da.name] = da
        self.added.append(da.name)
        return self.obj
    
    def clean(self):
        for v in self.added:
            self.obj = self.obj.drop(v)
            self.added.remove(v)
        return self.obj    
    
    def fit(self, estimator, **kw):
        this_da = estimator.fit(self, **kw)
        return self.add(this_da)
    
    def transform(self, estimator, **kw):
        this_da = estimator.transform(self.obj, **kw)
        return self.add(this_da)
        
```

Now if we consider this workflow: 
``` python

ds = xarray.Dataset()
ds['ext_data'] = xarray.DataArray(1.)

my_estimator = BaseEstimator()
ds = ds.my_accessor.fit(my_estimator, x=2.)

print(""Before clean:"")
print(""xr.DataSet var  :"", list(ds.data_vars))
print(""accessor.obj var:"", list(ds.my_accessor.obj.data_vars))

print(""\nAfter clean:"")
# ds.my_accessor.clean() # This does nothing to ds but clean the accessor.obj
# ds = ds.my_accessor.clean() # Cleaning ok for both ds and accessor.obj
ds_clean = ds.my_accessor.clean() # Cleaning ok on new ds, does nothing to ds as expected but clean in accessor.obj
print(""xr.DataSet var    :"", list(ds.data_vars))
print(""accessor.obj var  :"", list(ds.my_accessor.obj.data_vars))
print(""Cleaned xr.DataSet var:"", list(ds_clean.data_vars))
```
We have the following output:
```python
Before clean:
xr.DataSet var  : ['ext_data', 'fit_data']
accessor.obj var: ['ext_data', 'fit_data']

After clean:
xr.DataSet var    : ['ext_data', 'fit_data']
accessor.obj var  : ['ext_data']
Cleaned xr.DataSet var: ['ext_data']
```
The issue is clear here: the base space dataset has the 'fit_data' variable but not the accessor object: they've been ""disconnected"" **and it's not apparent to users**.

So if users later proceed to run the ""transform"":

```python
ds.my_accessor.transform(my_estimator, y=2.)
```
they get an KeyError raised because the 'fit_data' is not in the accessor, although it still appears on the list of the ds variables, which is more than confusing.

Sorry for this long post, I'm not sure it's relevant to this issue but it seems so to me.
I don't see a solution to this from the accessor developer side, except for not ""interfering"" with the content of the accessed object.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,485708282
https://github.com/pydata/xarray/issues/3361#issuecomment-537669216,https://api.github.com/repos/pydata/xarray/issues/3361,537669216,MDEyOklzc3VlQ29tbWVudDUzNzY2OTIxNg==,1956032,2019-10-02T20:35:26Z,2019-10-02T20:35:26Z,CONTRIBUTOR,"Thanks for the example @jthielen, this looks great !

May be it would be better to not show to readers the accessor class name, since it will never be seen on the API frontend, only the scope name will.  

So it would be great to be able to have the documentation to read something like:  

`xarray.DataSet.<scope_name>.<accessor_method_or_property>`  

instead of:  

`<package_name>.xarray.<accessor_class_name>.<accessor_method_or_property>`
(in the case where the accessor class is in xarray.py file on the package)

But I don't know how to manage that with sphinx documentation
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,500949040
https://github.com/pydata/xarray/issues/2699#issuecomment-537018647,https://api.github.com/repos/pydata/xarray/issues/2699,537018647,MDEyOklzc3VlQ29tbWVudDUzNzAxODY0Nw==,1956032,2019-10-01T12:43:46Z,2019-10-01T12:43:46Z,CONTRIBUTOR,I also recently encountered this bug and without user warnings it took me a while to identify its origin. I'll use this temporary fix. Thanks,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,402413097
https://github.com/pydata/xarray/issues/1732#issuecomment-347108958,https://api.github.com/repos/pydata/xarray/issues/1732,347108958,MDEyOklzc3VlQ29tbWVudDM0NzEwODk1OA==,1956032,2017-11-27T08:21:15Z,2017-11-27T08:21:15Z,CONTRIBUTOR,"The scipy backend has shown to be a good alternative as of now, if not I'll write a work around though. 
Thanks for your help !","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,275744315
https://github.com/pydata/xarray/issues/1732#issuecomment-346066403,https://api.github.com/repos/pydata/xarray/issues/1732,346066403,MDEyOklzc3VlQ29tbWVudDM0NjA2NjQwMw==,1956032,2017-11-21T15:42:02Z,2017-11-21T16:01:04Z,CONTRIBUTOR,"Sorry guys, just found out that the issue is still going on with some of the variables in the dataset:

Works ok for temperature TEMP for instance:
```python
ds = xr.open_dataset(argofile, autoclose=True, decode_cf=True)
ds['TEMP']
Out[89]: 
<xarray.DataArray 'TEMP' (N_PROF: 338, N_LEVELS: 51)>
array([[ 27.393   ,  27.392   ,  27.393   , ...,   3.597   ,   3.34    ,
               nan],
       [ 27.57    ,  27.572001,  27.570999, ...,   3.543   ,   3.265   ,
               nan],
       [ 28.094999,  28.091999,  28.096001, ...,   3.544   ,   3.287   ,
               nan],
       ..., 
       [ 27.157   ,  27.156   ,  27.159   , ...,   3.318   ,        nan,
               nan],
       [ 27.608999,  27.610001,  27.608999, ...,   3.419   ,        nan,
               nan],
       [ 27.569   ,  27.566999,  27.561001, ...,   3.422   ,        nan,
               nan]])
Dimensions without coordinates: N_PROF, N_LEVELS
Attributes:
    long_name:       Sea temperature in-situ ITS-90 scale
    standard_name:   sea_water_temperature
    units:           degree_Celsius
    valid_min:       -2.5
    valid_max:       40.0
    C_format:        %9.3f
    FORTRAN_format:  F9.3
    resolution:      0.001
```

but for the variable ""HISTORY_STEP"", I get the error:
```python
ds['HISTORY_STEP']
Out[90]: Traceback (most recent call last):
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/core/formatters.py"", line 190, in catch_format_error
    r = method(self, *args, **kwargs)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/core/formatters.py"", line 672, in __call__
    printer.pretty(obj)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/lib/pretty.py"", line 383, in pretty
    return _default_pprint(obj, self, cycle)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/lib/pretty.py"", line 503, in _default_pprint
    _repr_pprint(obj, p, cycle)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/lib/pretty.py"", line 701, in _repr_pprint
    output = repr(obj)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/common.py"", line 100, in __repr__
    return formatting.array_repr(self)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py"", line 393, in array_repr
    summary.append(short_array_repr(arr.values))
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/dataarray.py"", line 412, in values
    return self.variable.values
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py"", line 396, in values
    return _as_array_or_item(self._data)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py"", line 217, in _as_array_or_item
    data = np.asarray(data)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py"", line 482, in asarray
    return array(a, dtype, copy=False, order=order)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py"", line 557, in __array__
    self._ensure_cached()
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py"", line 554, in _ensure_cached
    self.array = NumpyIndexingAdapter(np.asarray(self.array))
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py"", line 482, in asarray
    return array(a, dtype, copy=False, order=order)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py"", line 538, in __array__
    return np.asarray(self.array, dtype=dtype)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py"", line 482, in asarray
    return array(a, dtype, copy=False, order=order)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py"", line 505, in __array__
    return np.asarray(array[self.key], dtype=None)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py"", line 388, in __getitem__
    return mask_and_scale(self.array[key], self.fill_value,
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py"", line 498, in __getitem__
    return char_to_bytes(self.array[key])
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py"", line 640, in char_to_bytes
    arr = np.array(arr, copy=False, order='C')
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py"", line 505, in __array__
    return np.asarray(array[self.key], dtype=None)
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py"", line 72, in __getitem__
    raise IndexError(msg)
IndexError: The indexing operation you are attempting to perform is not valid on netCDF4.Variable object. Try loading your data into memory first by calling .load().
Original traceback:
Traceback (most recent call last):
  File ""/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py"", line 61, in __getitem__
    data = getitem(self.get_array(), key)
  File ""netCDF4/_netCDF4.pyx"", line 3961, in netCDF4._netCDF4.Variable.__getitem__
  File ""netCDF4/_netCDF4.pyx"", line 4796, in netCDF4._netCDF4.Variable._get
IndexError
```

The new state of the versions:
```python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
xarray: 0.10.0
pandas: 0.21.0
numpy: 1.11.3
scipy: 0.18.1
netCDF4: 1.3.1
h5netcdf: 0.3.1
Nio: None
bottleneck: 1.2.0
cyordereddict: 1.0.0
dask: 0.16.0
matplotlib: 1.5.3
cartopy: 0.15.1
seaborn: 0.7.1
setuptools: 36.5.0
pip: 9.0.1
conda: None
pytest: None
IPython: 5.2.2
sphinx: 1.5.2
```

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,275744315
https://github.com/pydata/xarray/issues/1732#issuecomment-346058667,https://api.github.com/repos/pydata/xarray/issues/1732,346058667,MDEyOklzc3VlQ29tbWVudDM0NjA1ODY2Nw==,1956032,2017-11-21T15:18:37Z,2017-11-21T15:18:37Z,CONTRIBUTOR,"Ok, upgrading to 0.10.0 solve the issue ! Thanks
Should have tried this in the 1st place","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,275744315
https://github.com/pydata/xarray/issues/1662#issuecomment-340008407,https://api.github.com/repos/pydata/xarray/issues/1662,340008407,MDEyOklzc3VlQ29tbWVudDM0MDAwODQwNw==,1956032,2017-10-27T15:44:11Z,2017-10-27T15:44:11Z,CONTRIBUTOR,"Note that if the xarray decode_cf is given a NaT, in a datetime64, it works:

```python
attrs = {'units': 'days since 1950-01-01 00:00:00 UTC'} # Classic Argo data Julian Day reference
jd = [24658.46875, 24658.46366898, 24658.47256944, np.NaN]  # Sample

def dirtyfixNaNjd(ref,day):
    td = pd.NaT
    if not np.isnan(day):
        td = pd.Timedelta(days=day)
    return pd.Timestamp(ref) + td

jd = [dirtyfixNaNjd('1950-01-01',day) for day in jd]
print jd
```
```python
[Timestamp('2017-07-06 11:15:00'), Timestamp('2017-07-06 11:07:40.999872'), Timestamp('2017-07-06 11:20:29.999616'), NaT]
```
then:
```python
ds = xr.Dataset({'time': ('time', jd, {'units': 'ns'})}) # Update the units attribute appropriately
ds = xr.decode_cf(ds)
print ds['time'].values
```
```python
['2017-07-06T11:15:00.000000000' '2017-07-06T11:07:40.999872000'
 '2017-07-06T11:20:29.999616000' 'NaT']
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,268725471
https://github.com/pydata/xarray/issues/1662#issuecomment-339991529,https://api.github.com/repos/pydata/xarray/issues/1662,339991529,MDEyOklzc3VlQ29tbWVudDMzOTk5MTUyOQ==,1956032,2017-10-27T14:42:56Z,2017-10-27T14:42:56Z,CONTRIBUTOR,"Hi Ryan, never been very far, following/promoting xarray around here, and congrats for Pangeo !

Ok, I get the datatype being wrong, but about the issue from pandas TimedeltaIndex:
Does this means that a quick/dirty fix should be to decode value by value rather than on a vector ?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,268725471