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
869180122,MDU6SXNzdWU4NjkxODAxMjI=,5225,python3.9 dask/array/slicing.py in slice_wrap_lists Don't yet support nd fancy indexing ,34353851,closed,0,,,5,2021-04-27T19:12:48Z,2021-04-28T09:31:22Z,2021-04-28T09:08:48Z,NONE,,,,"The code in the line 411, returns Don't yet support nd fancy indexing from dask.
https://github.com/JavierRuano/ASI_Steady/blob/main/ASI_Datase_RACKt.py#L411
that was working well with python3.7 .
ASI_Datase_RACKt.py in refresh_Graphics
saveFile = self.xarray[indices_maps[str(self.typi)]].where(self.xarray.mask == 1).where( …
▼ Local vars
Variable Value
NETCDF_FILES_FOLDER
'/var/www/stream/stream/data/'
end
'2020-11-02'
indices_maps
{'horton': 'ASI_Horton_2012',
'huang': 'ASI_Huang_2018',
'wang': 'ASI_Wang_2017'}
lat_dos
48.75
lat_uno
48.0
lng_dos
-5.75
lng_uno
-5.0
self
<polls.ASI_Datase_RACKt.ASI object at 0x7f585899e1f0>
start
'2020-11-01'
/usr/local/lib/python3.9/dist-packages/xarray/core/common.py in where
self = self.isel(**indexers) …
▼ Local vars
Variable Value
DataArray
<class 'xarray.core.dataarray.DataArray'>
Dataset
<class 'xarray.core.dataset.Dataset'>
align
<function align at 0x7f5878522af0>
clipcond
<xarray.DataArray (latitude: 68, longitude: 81)>
array([[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]])
Coordinates:
* latitude (latitude) float32 75.0 74.25 73.5 72.75 ... 26.25 25.5 24.75
* longitude (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 38.5 39.25 40.0
cond
<xarray.DataArray (latitude: 68, longitude: 81)>
array([[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]])
Coordinates:
* latitude (latitude) float32 75.0 74.25 73.5 72.75 ... 26.25 25.5 24.75
* longitude (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 38.5 39.25 40.0
drop
True
indexers
{'latitude': array([], dtype=int64), 'longitude': array([], dtype=int64)}
nonzeros
<zip object at 0x7f584bfc08c0>
other
<NA>
self
<xarray.DataArray 'ASI_Horton_2012' (time: 15310, latitude: 68, longitude: 81)>
dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>
Coordinates:
* time (time) datetime64[ns] 1979-01-01T12:00:00 ... 2020-11-30T12:00:00
* latitude (latitude) float32 75.0 74.25 73.5 72.75 ... 26.25 25.5 24.75
* longitude (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 38.5 39.25 40.0
mask (latitude, longitude) float64 1.0 2.0 2.0 2.0 ... 1.0 1.0 1.0 1.0
Attributes:
long_name: air_stagnation_index
/usr/local/lib/python3.9/dist-packages/xarray/core/dataarray.py in isel
variable = self._variable.isel(indexers, missing_dims=missing_dims) …
▼ Local vars
Variable Value
drop
False
indexers
{'latitude': array([], dtype=int64), 'longitude': array([], dtype=int64)}
indexers_kwargs
{'latitude': array([], dtype=int64), 'longitude': array([], dtype=int64)}
missing_dims
'raise'
self
<xarray.DataArray 'ASI_Horton_2012' (time: 15310, latitude: 68, longitude: 81)>
dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>
Coordinates:
* time (time) datetime64[ns] 1979-01-01T12:00:00 ... 2020-11-30T12:00:00
* latitude (latitude) float32 75.0 74.25 73.5 72.75 ... 26.25 25.5 24.75
* longitude (longitude) float32 -20.0 -19.25 -18.5 -17.75 ... 38.5 39.25 40.0
mask (latitude, longitude) float64 1.0 2.0 2.0 2.0 ... 1.0 1.0 1.0 1.0
Attributes:
long_name: air_stagnation_index
/usr/local/lib/python3.9/dist-packages/xarray/core/variable.py in isel
return self[key] …
▼ Local vars
Variable Value
indexers
{'latitude': array([], dtype=int64), 'longitude': array([], dtype=int64)}
indexers_kwargs
{}
key
(slice(None, None, None), array([], dtype=int64), array([], dtype=int64))
missing_dims
'raise'
self
<xarray.Variable (time: 15310, latitude: 68, longitude: 81)>
dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>
Attributes:
long_name: air_stagnation_index
/usr/local/lib/python3.9/dist-packages/xarray/core/variable.py in __getitem__
data = as_indexable(self._data)[indexer] …
▼ Local vars
Variable Value
dims
('time', 'latitude', 'longitude')
indexer
OuterIndexer((slice(None, None, None), array([], dtype=int64), array([], dtype=int64)))
key
(slice(None, None, None), array([], dtype=int64), array([], dtype=int64))
new_order
None
self
<xarray.Variable (time: 15310, latitude: 68, longitude: 81)>
dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>
Attributes:
long_name: air_stagnation_index
/usr/local/lib/python3.9/dist-packages/xarray/core/indexing.py in __getitem__
return array[key] …
▼ Local vars
Variable Value
array
dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>
key
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
self
NdArrayLikeIndexingAdapter(array=dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>)
/usr/local/lib/python3.9/dist-packages/dask/array/core.py in __getitem__
dsk, chunks = slice_array(out, self.name, self.chunks, index2, self.itemsize) …
▼ Local vars
Variable Value
dependencies
{'copy-60d43625606b762db38f4c336bfabf09'}
i
array([], shape=(1, 1, 0), dtype=int64)
index
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
index2
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
normalize_index
<function normalize_index at 0x7f585a1b7940>
out
'getitem-33275cee9730d05017ed841f8e82e286'
self
dask.array<copy, shape=(15310, 68, 81), dtype=float64, chunksize=(15310, 68, 81), chunktype=numpy.ndarray>
slice_with_bool_dask_array
<function slice_with_bool_dask_array at 0x7f585a1b7b80>
slice_with_int_dask_array
<function slice_with_int_dask_array at 0x7f585a1b7a60>
/usr/local/lib/python3.9/dist-packages/dask/array/slicing.py in slice_array
dsk_out, bd_out = slice_with_newaxes(out_name, in_name, blockdims, index, itemsize) …
▼ Local vars
Variable Value
blockdims
((15310,), (68,), (81,))
in_name
'copy-60d43625606b762db38f4c336bfabf09'
index
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
itemsize
8
missing
0
not_none_count
3
out_name
'getitem-33275cee9730d05017ed841f8e82e286'
/usr/local/lib/python3.9/dist-packages/dask/array/slicing.py in slice_with_newaxes
dsk, blockdims2 = slice_wrap_lists(out_name, in_name, blockdims, index2, itemsize) …
▼ Local vars
Variable Value
blockdims
((15310,), (68,), (81,))
in_name
'copy-60d43625606b762db38f4c336bfabf09'
index
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
index2
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
itemsize
8
out_name
'getitem-33275cee9730d05017ed841f8e82e286'
where_none
[]
where_none_orig
[]
**/usr/local/lib/python3.9/dist-packages/dask/array/slicing.py in slice_wrap_lists
raise NotImplementedError(""Don't yet support nd fancy indexing"") …**
▼ Local vars
Variable Value
blockdims
((15310,), (68,), (81,))
in_name
'copy-60d43625606b762db38f4c336bfabf09'
index
(array([[[ 0]],
[[ 1]],
[[ 2]],
...,
[[15307]],
[[15308]],
[[15309]]]),
array([], shape=(1, 0, 1), dtype=int64),
array([], shape=(1, 1, 0), dtype=int64))
itemsize
8
out_name
'getitem-33275cee9730d05017ed841f8e82e286'
where_list
[0, 1, 2]","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5225/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
627356505,MDU6SXNzdWU2MjczNTY1MDU=,4110,Feature request: merge compat overlap,34353851,open,0,,,1,2020-05-29T15:36:29Z,2021-04-19T14:46:46Z,,NONE,,,,"`xarray.merge` should have an option compat overlap.
is that option is combine_first, but it has more sense inside of merge.
```python
import xarray as xr
x1=xr.DataArray([1,2,3,4,11],dims=['time'],coords=[[4,5,6,7,3]]).to_dataset(name='fusionfria')
x2=xr.DataArray([5,6,7,8],dims=['time'],coords=[[0,1,2,3]]).to_dataset(name='fusionfria')
x1.combine_first(x2)
```
It could be useful to avoid the error in xarray.open_mfdataset, if the time index is overlapped between netcdf files.
ValueError: Resulting object does not have monotonic global indexes along dimension time","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4110/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
842615316,MDExOlB1bGxSZXF1ZXN0NjAyMTUwMzAx,5086,Update examples.rst,34353851,closed,0,,,0,2021-03-27T22:32:25Z,2021-04-19T09:31:22Z,2021-04-19T09:31:22Z,NONE,,0,pydata/xarray/pulls/5086,"Add an external example of xarray uses
<!-- Feel free to remove check-list items aren't relevant to your change -->
- [x] Closes #xxxx
- [ ] Tests added
- [ ] Passes `pre-commit run --all-files`
- [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst`
- [ ] New functions/methods are listed in `api.rst`
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5086/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
852809756,MDExOlB1bGxSZXF1ZXN0NjExMDA0Njkz,5129,Closes #5085,34353851,closed,0,,,0,2021-04-07T21:01:16Z,2021-04-19T09:31:00Z,2021-04-19T09:31:00Z,NONE,,0,pydata/xarray/pulls/5129,"<!-- Feel free to remove check-list items aren't relevant to your change -->
[x] Closes #5086
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5129/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
569806418,MDU6SXNzdWU1Njk4MDY0MTg=,3795,Dataset problem with chunk DataArray.,34353851,closed,0,,,8,2020-02-24T11:49:26Z,2021-04-19T09:28:21Z,2021-04-19T09:28:21Z,NONE,,,,"When i create a xr.Dataset with two variable which are a DataArray, i only obtain a chunk part of the DataArray.
should i do a compute or something similar?
How to control the number of the chunk of the DataArray to operate one to one them.
I mean::
/* DataArray */
dask.array<shape=(14610, 47, 68, 81), dtype=float32, chunksize=(365, 47, 68, 81)>
/* Dataset */
dask.array<shape=(365, 47, 68, 81), dtype=float32, chunksize=(365, 47, 68, 81)>
:-) Perhaps is the answer set chunksize to the total? In my case is .chunk(14610), isn't it?
Which is the difference with compute()? the lazy operation is maintained...","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3795/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
842610988,MDU6SXNzdWU4NDI2MTA5ODg=,5085,Add example in your wiki.,34353851,closed,0,,,10,2021-03-27T22:02:56Z,2021-04-19T08:52:19Z,2021-04-18T21:59:47Z,NONE,,,,"Add example in wiki our calculation Stagnation index program repository, It is based on xarray with complex functions as ufunc and integration with django and cartopy. It is a Physics faculty UCM project 2020 and the researchers have articles based on that copernicus era5 data.
https://github.com/JavierRuano/ASI_Steady
The website is http://steady-ucm.org
I have created a Example with all necessary dataset
https://github.com/JavierRuano/ASI_Steady/tree/main/Examples
The example could be here
http://xarray.pydata.org/en/stable/examples.html
Regards
Javier Ruano.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5085/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue