issues
5 rows where type = "issue" and user = 6420873 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: state_reason, created_at (date), updated_at (date), closed_at (date)
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1888573893 | I_kwDOAMm_X85wkVnF | 8161 | groupby bug | Yefee 6420873 | closed | 0 | 2 | 2023-09-09T04:38:48Z | 2023-09-13T20:03:52Z | 2023-09-13T20:03:52Z | NONE | What happened?Sometimes, when performing a groupby operation on a multidimensional data array, it can return unexpected results. A copy of the test data could be found here. Code to reproduce the bug:
What did you expect to happen?No response Minimal Complete Verifiable ExampleNo response MVCE confirmation
Relevant log outputNo response Anything else we need to know?No response Environment
INSTALLED VERSIONS
------------------
commit: None
python: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-1127.18.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: 4.8.1
xarray: 2023.7.0
pandas: 1.5.3
numpy: 1.24.4
scipy: 1.10.1
netCDF4: 1.6.2
pydap: installed
h5netcdf: 1.0.0
h5py: 3.7.0
Nio: None
zarr: 2.12.0
cftime: 1.6.2
nc_time_axis: 1.4.1
PseudoNetCDF: None
iris: None
bottleneck: 1.3.7
dask: 2023.7.1
distributed: 2023.7.1
matplotlib: 3.4.3
cartopy: 0.20.2
seaborn: 0.11.2
numbagg: None
fsspec: 2022.11.0
cupy: None
pint: 0.19.2
sparse: None
flox: None
numpy_groupies: None
setuptools: 68.0.0
pip: 22.1.2
conda: 23.3.1
pytest: None
mypy: None
IPython: 7.33.0
sphinx: 5.0.1
|
{ "url": "https://api.github.com/repos/pydata/xarray/issues/8161/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
not_planned | xarray 13221727 | issue | ||||||
187393785 | MDU6SXNzdWUxODczOTM3ODU= | 1081 | Transpose some but not all dimensions | Yefee 6420873 | closed | 0 | 17 | 2016-11-04T17:31:38Z | 2019-10-29T19:16:58Z | 2019-10-29T19:16:58Z | NONE | Hi, all Sorry to bother. Maybe it is a kind of stupid question for others, but I cannot figure it out at this moment. I want to swap dims in xarray, like swapaxes in numpy. I found both dataarray and dataset has method Here is my example:
ValueError Traceback (most recent call last) <ipython-input-47-c8aa4311b27e> in <module>() ----> 1 foo.swap_dims({'lat':'lon'}) /glade/u/home/che43/miniconda2/lib/python2.7/site-packages/xarray/core/dataarray.pyc in swap_dims(self, dims_dict) 794 Dataset.swap_dims 795 """ --> 796 ds = self._to_temp_dataset().swap_dims(dims_dict) 797 return self._from_temp_dataset(ds) 798 /glade/u/home/che43/miniconda2/lib/python2.7/site-packages/xarray/core/dataset.pyc in swap_dims(self, dims_dict, inplace) 1293 raise ValueError('replacement dimension %r is not a 1D ' 1294 'variable along the old dimension %r' -> 1295 % (v, k)) 1296 1297 result_dims = set(dims_dict.get(dim, dim) for dim in self.dims) ValueError: replacement dimension 'lon' is not a 1D variable along the old dimension 'lat' ``` Sorry to bother. |
{ "url": "https://api.github.com/repos/pydata/xarray/issues/1081/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
completed | xarray 13221727 | issue | ||||||
227858207 | MDU6SXNzdWUyMjc4NTgyMDc= | 1403 | Lost coords after multiplication | Yefee 6420873 | closed | 0 | 4 | 2017-05-11T02:00:59Z | 2019-04-11T17:53:16Z | 2019-04-11T17:53:16Z | NONE | Recently, I occurred a bug: multiplication discards coords of dsarray. ``` In [1]: import xarray as xr In [2]: xr.version Out[2]: '0.9.5' In [3]: tarea = xr.open_dataarray('tarea.nc') In [4]: tarea Out[4]: <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> [122880 values with dtype=float64] Coordinates: TLAT (nlat, nlon) float64 -79.22 -79.22 -79.22 -79.22 -79.22 -79.22 ... TLONG (nlat, nlon) float64 320.6 321.7 322.8 323.9 325.1 326.2 327.3 ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2 In [6]: advt = xr.open_dataarray('advt.nc') In [7]: advt Out[7]: <xarray.DataArray 'ADVT' (nlat: 384, nlon: 320)> [122880 values with dtype=float64] Coordinates: TLAT (nlat, nlon) float64 -79.22 -79.22 -79.22 -79.22 -79.22 -79.22 ... TLONG (nlat, nlon) float64 320.6 321.7 322.8 323.9 325.1 326.2 327.3 ... Dimensions without coordinates: nlat, nlon In [8]: advt * tarea Out[8]: <xarray.DataArray (nlat: 384, nlon: 320)> array([[ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ 8091417.091781, 15948194.682816, -49201736.790674, ..., nan, nan, nan], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]) Dimensions without coordinates: nlat, nlon ``` TLAT and TLONG are gone. Any suggestion? Here I provide my test data. |
{ "url": "https://api.github.com/repos/pydata/xarray/issues/1403/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
completed | xarray 13221727 | issue | ||||||
221855729 | MDU6SXNzdWUyMjE4NTU3Mjk= | 1374 | indexing error in sel subsets | Yefee 6420873 | closed | 0 | 6 | 2017-04-14T17:45:01Z | 2017-06-04T07:03:48Z | 2017-06-04T07:03:48Z | NONE | ``` import xarray as xr xr.version '0.9.1' ds = xr.open_dataset('lgm2co2.nc') ds <xarray.Dataset> Dimensions: (lat_aux_grid: 105, moc_comp: 1, moc_z: 26, time: 2204, transport_reg: 2) Coordinates: * time (time) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ... * lat_aux_grid (lat_aux_grid) float32 -80.2602 -78.7338 -77.2176 ... * moc_z (moc_z) float32 0.0 800.0 1644.05 2573.71 3627.36 ... moc_components (moc_comp) |S512 b'Eulerian Mean' transport_regions (transport_reg) |S512 b'Global Ocean - Marginal Seas' ... Dimensions without coordinates: moc_comp, transport_reg Data variables: MOC (time, transport_reg, moc_comp, moc_z, lat_aux_grid) float64 0.0 ... moc = ds.MOC.isel(transport_reg=1,moc_comp=0) moc TypeError Traceback (most recent call last) /Users/Yefee/miniconda3/lib/python3.6/site-packages/IPython/core/formatters.py in call(self, obj) 670 type_pprinters=self.type_printers, 671 deferred_pprinters=self.deferred_printers) --> 672 printer.pretty(obj) 673 printer.flush() 674 return stream.getvalue() /Users/Yefee/miniconda3/lib/python3.6/site-packages/IPython/lib/pretty.py in pretty(self, obj) 381 if callable(meth): 382 return meth(obj, self, cycle) --> 383 return _default_pprint(obj, self, cycle) 384 finally: 385 self.end_group() /Users/Yefee/miniconda3/lib/python3.6/site-packages/IPython/lib/pretty.py in default_pprint(obj, p, cycle) 501 if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs: 502 # A user-provided repr. Find newlines and replace them with p.break() --> 503 _repr_pprint(obj, p, cycle) 504 return 505 p.begin_group(1, '<') /Users/Yefee/miniconda3/lib/python3.6/site-packages/IPython/lib/pretty.py in repr_pprint(obj, p, cycle) 699 """A pprint that just redirects to the normal repr function.""" 700 # Find newlines and replace them with p.break() --> 701 output = repr(obj) 702 for idx,output_line in enumerate(output.splitlines()): 703 if idx: /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/common.py in repr(self) 152 153 def repr(self): --> 154 return formatting.array_repr(self) 155 156 def _iter(self): /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in array_repr(arr) 380 if hasattr(arr, 'coords'): 381 if arr.coords: --> 382 summary.append(repr(arr.coords)) 383 384 unindexed_dims_str = unindexed_dims_repr(arr.dims, arr.coords) /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in repr(self) 58 """Mixin that defines repr for a class that already has unicode.""" 59 def repr(self): ---> 60 return ensure_valid_repr(self.unicode()) 61 62 /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/coordinates.py in unicode(self) 44 45 def unicode(self): ---> 46 return formatting.coords_repr(self) 47 48 @property /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in coords_repr(coords, col_width) 309 col_width = _calculate_col_width(_get_col_items(coords)) 310 return _mapping_repr(coords, title=u'Coordinates', --> 311 summarizer=summarize_coord, col_width=col_width) 312 313 /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in _mapping_repr(mapping, title, summarizer, col_width) 291 summary = [u'%s:' % title] 292 if mapping: --> 293 summary += [summarizer(k, v, col_width) for k, v in mapping.items()] 294 else: 295 summary += [EMPTY_REPR] /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in <listcomp>(.0) 291 summary = [u'%s:' % title] 292 if mapping: --> 293 summary += [summarizer(k, v, col_width) for k, v in mapping.items()] 294 else: 295 summary += [EMPTY_REPR] /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in summarize_coord(name, var, col_width) 251 [_summarize_coord_multiindex(coord, col_width, marker), 252 _summarize_coord_levels(coord, col_width)]) --> 253 return _summarize_var_or_coord(name, var, col_width, show_values, marker) 254 255 /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in _summarize_var_or_coord(name, var, col_width, show_values, marker, max_width) 205 front_str = u'%s%s%s ' % (first_col, dims_str, var.dtype) 206 if show_values: --> 207 values_str = format_array_flat(var, max_width - len(front_str)) 208 else: 209 values_str = u'...' /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in format_array_flat(items_ndarray, max_width) 178 # print at least one item 179 max_possibly_relevant = max(int(np.ceil(max_width / 2.0)), 1) --> 180 relevant_items = first_n_items(items_ndarray, max_possibly_relevant) 181 pprint_items = format_items(relevant_items) 182 /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/formatting.py in first_n_items(x, n_desired) 86 if n_desired < x.size: 87 indexer = _get_indexer_at_least_n_items(x.shape, n_desired) ---> 88 x = x[indexer] 89 return np.asarray(x).flat[:n_desired] 90 /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/dataarray.py in getitem(self, key) 467 else: 468 # orthogonal array indexing --> 469 return self.isel(**self._item_key_to_dict(key)) 470 471 def setitem(self, key, value): /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/dataarray.py in isel(self, drop, indexers) 655 DataArray.sel 656 """ --> 657 ds = self._to_temp_dataset().isel(drop=drop, indexers) 658 return self._from_temp_dataset(ds) 659 /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/dataset.py in isel(self, drop, indexers) 1117 for name, var in iteritems(self._variables): 1118 var_indexers = dict((k, v) for k, v in indexers if k in var.dims) -> 1119 new_var = var.isel(var_indexers) 1120 if not (drop and name in var_indexers): 1121 variables[name] = new_var /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/variable.py in isel(self, **indexers) 545 if dim in indexers: 546 key[i] = indexers[dim] --> 547 return self[tuple(key)] 548 549 def squeeze(self, dim=None): /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/variable.py in getitem(self, key) 375 dims = tuple(dim for k, dim in zip(key, self.dims) 376 if not isinstance(k, (int, np.integer))) --> 377 values = self._indexable_data[key] 378 # orthogonal indexing should ensure the dimensionality is consistent 379 if hasattr(values, 'ndim'): /Users/Yefee/miniconda3/lib/python3.6/site-packages/xarray/core/indexing.py in getitem(self, key) 419 420 def getitem(self, key): --> 421 return type(self)(self.array[key]) 422 423 def setitem(self, key, value): TypeError: byte indices must be integers or slices, not tuple ``` But using copy method makes it work. ``` moc = ds.MOC.isel(transport_reg=1,moc_comp=0).copy() moc <xarray.DataArray 'MOC' (time: 2204, moc_z: 26, lat_aux_grid: 105)> array([[[ 2.859555e-03, 2.859555e-03, ..., 3.184585e-06, -1.938138e-07], [ 7.209966e-01, 7.209966e-01, ..., 5.836686e-03, -2.183406e-07], ..., [ 0.000000e+00, 0.000000e+00, ..., 8.159353e-08, 8.159353e-08], [ 0.000000e+00, 0.000000e+00, ..., 0.000000e+00, 0.000000e+00]],
Coordinates: * time (time) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ... * lat_aux_grid (lat_aux_grid) float32 -80.2602 -78.7338 -77.2176 ... * moc_z (moc_z) float32 0.0 800.0 1644.05 2573.71 3627.36 ... moc_components |S13 b'Eulerian Mean' transport_regions |S54 b'Atlantic Ocean + Labrador Sea + GIN Sea + Arctic Ocean' ... Attributes: long_name: Meridional Overturning Circulation units: Sverdrups ``` |
{ "url": "https://api.github.com/repos/pydata/xarray/issues/1374/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
completed | xarray 13221727 | issue | ||||||
154924822 | MDU6SXNzdWUxNTQ5MjQ4MjI= | 848 | Decode time error in CESM POP output | Yefee 6420873 | closed | 0 | 5 | 2016-05-15T19:18:50Z | 2016-05-16T17:11:53Z | 2016-05-16T17:11:53Z | NONE | Hi, all Recently, I found a error about time decoding. The .nc file is POP output. ``` Python In [29]: ds = xr.open_mfdataset('EXAMPLE_CASE.pop.h.0001-01.nc') ValueError Traceback (most recent call last) <ipython-input-29-576c2580c841> in <module>() ----> 1 ds = xr.open_mfdataset('EXAMPLE_CASE.pop.h.0001-01.nc') /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/backends/api.pyc in open_mfdataset(paths, chunks, concat_dim, preprocess, engine, lock, kwargs) 300 lock = _default_lock(paths[0], engine) 301 datasets = [open_dataset(p, engine=engine, chunks=chunks or {}, lock=lock, --> 302 kwargs) for p in paths] 303 file_objs = [ds._file_obj for ds in datasets] 304 /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/backends/api.pyc in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, engine, chunks, lock, drop_variables) 225 lock = _default_lock(filename_or_obj, engine) 226 with close_on_error(store): --> 227 return maybe_decode_store(store, lock) 228 else: 229 if engine is not None and engine != 'scipy': /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/backends/api.pyc in maybe_decode_store(store, lock) 156 store, mask_and_scale=mask_and_scale, decode_times=decode_times, 157 concat_characters=concat_characters, decode_coords=decode_coords, --> 158 drop_variables=drop_variables) 159 160 if chunks is not None: /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/conventions.pyc in decode_cf(obj, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables) 888 vars, attrs, coord_names = decode_cf_variables( 889 vars, attrs, concat_characters, mask_and_scale, decode_times, --> 890 decode_coords, drop_variables=drop_variables) 891 ds = Dataset(vars, attrs=attrs) 892 ds = ds.set_coords(coord_names.union(extra_coords)) /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/conventions.pyc in decode_cf_variables(variables, attributes, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables) 823 new_vars[k] = decode_cf_variable( 824 v, concat_characters=concat, mask_and_scale=mask_and_scale, --> 825 decode_times=decode_times) 826 if decode_coords: 827 var_attrs = new_vars[k].attrs /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/conventions.pyc in decode_cf_variable(var, concat_characters, mask_and_scale, decode_times, decode_endianness) 764 units = pop_to(attributes, encoding, 'units') 765 calendar = pop_to(attributes, encoding, 'calendar') --> 766 data = DecodedCFDatetimeArray(data, units, calendar) 767 elif attributes['units'] in TIME_UNITS: 768 # timedelta /Users/Yefee/miniconda2/lib/python2.7/site-packages/xarray/conventions.pyc in init(self, array, units, calendar) 389 if not PY3: 390 msg += ' Full traceback:\n' + traceback.format_exc() --> 391 raise ValueError(msg) 392 else: 393 self._dtype = getattr(result, 'dtype', np.dtype('object')) ValueError: unable to decode time units u'days since 0000-01-01 00:00:00' with the default calendar. Try opening your dataset with decode_times=False.` ``` The actual time is: ``` 31 double time(time) ; 32 time:long_name = "time" ; 33 time:units = "days since 0000-01-01 00:00:00" ; 34 time:bounds = "time_bound" ; 35 time:calendar = "noleap" ; ``` I can set 'decode_times=False' to open the file but the time is not right.
Is there any suggestions? `````` |
{ "url": "https://api.github.com/repos/pydata/xarray/issues/848/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
completed | xarray 13221727 | issue |
Advanced export
JSON shape: default, array, newline-delimited, object
CREATE TABLE [issues] ( [id] INTEGER PRIMARY KEY, [node_id] TEXT, [number] INTEGER, [title] TEXT, [user] INTEGER REFERENCES [users]([id]), [state] TEXT, [locked] INTEGER, [assignee] INTEGER REFERENCES [users]([id]), [milestone] INTEGER REFERENCES [milestones]([id]), [comments] INTEGER, [created_at] TEXT, [updated_at] TEXT, [closed_at] TEXT, [author_association] TEXT, [active_lock_reason] TEXT, [draft] INTEGER, [pull_request] TEXT, [body] TEXT, [reactions] TEXT, [performed_via_github_app] TEXT, [state_reason] TEXT, [repo] INTEGER REFERENCES [repos]([id]), [type] TEXT ); CREATE INDEX [idx_issues_repo] ON [issues] ([repo]); CREATE INDEX [idx_issues_milestone] ON [issues] ([milestone]); CREATE INDEX [idx_issues_assignee] ON [issues] ([assignee]); CREATE INDEX [idx_issues_user] ON [issues] ([user]);