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 2019566184,I_kwDOAMm_X854YCJo,8494,Filter expected warnings in the test suite,35968931,closed,0,,,1,2023-11-30T21:50:15Z,2024-04-29T16:57:07Z,2024-04-29T16:56:16Z,MEMBER,,,,"FWIW one thing I'd be keen for to do generally — though maybe this isn't the place to start it — is handle warnings in the test suite when we add a new warning — i.e. filter them out where we expect them. In this case, that would be the loading the netCDF files that have duplicate dims. Otherwise warnings become a huge block of text without much salience. I mostly see the 350 lines of them and think ""meh mostly units & cftime"", but then something breaks on a new upstream release that was buried in there, or we have a supported code path that is raising warnings internally. (I'm not sure whether it's possible to generally enforce that — maybe we could raise on any warnings coming from within xarray? Would be a non-trivial project to get us there though...) _Originally posted by @max-sixty in https://github.com/pydata/xarray/issues/8491#issuecomment-1834615826_ ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8494/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2224036575,I_kwDOAMm_X86EkBrf,8905,Variable doesn't have an .expand_dims method,35968931,closed,0,,,4,2024-04-03T22:19:10Z,2024-04-28T19:54:08Z,2024-04-28T19:54:08Z,MEMBER,,,,"### Is your feature request related to a problem? `DataArray` and `Dataset` have an `.expand_dims` method, but it looks like `Variable` doesn't. ### Describe the solution you'd like Variable should also have this method, the only difference being that it wouldn't create any coordinates or indexes. ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8905/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2198196326,I_kwDOAMm_X86DBdBm,8860,Ugly error in constructor when no data passed,35968931,closed,0,,,2,2024-03-20T17:55:52Z,2024-04-10T22:46:55Z,2024-04-10T22:46:54Z,MEMBER,,,,"### What happened? Passing no data to the `Dataset` constructor can result in a very unhelpful ""tuple index out of range"" error when this is a clear case of malformed input that we should be able to catch. ### What did you expect to happen? An error more like ""tuple must be of form (dims, data[, attrs])"" ### Minimal Complete Verifiable Example ```Python xr.Dataset({""t"": ()}) ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. - [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output ```Python --------------------------------------------------------------------------- IndexError Traceback (most recent call last) Cell In[2], line 1 ----> 1 xr.Dataset({""t"": ()}) File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:693, in Dataset.__init__(self, data_vars, coords, attrs) 690 if isinstance(coords, Dataset): 691 coords = coords._variables --> 693 variables, coord_names, dims, indexes, _ = merge_data_and_coords( 694 data_vars, coords 695 ) 697 self._attrs = dict(attrs) if attrs else None 698 self._close = None File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:422, in merge_data_and_coords(data_vars, coords) 418 coords = create_coords_with_default_indexes(coords, data_vars) 420 # exclude coords from alignment (all variables in a Coordinates object should 421 # already be aligned together) and use coordinates' indexes to align data_vars --> 422 return merge_core( 423 [data_vars, coords], 424 compat=""broadcast_equals"", 425 join=""outer"", 426 explicit_coords=tuple(coords), 427 indexes=coords.xindexes, 428 priority_arg=1, 429 skip_align_args=[1], 430 ) File ~/Documents/Work/Code/xarray/xarray/core/merge.py:718, in merge_core(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value, skip_align_args) 715 for pos, obj in skip_align_objs: 716 aligned.insert(pos, obj) --> 718 collected = collect_variables_and_indexes(aligned, indexes=indexes) 719 prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat) 720 variables, out_indexes = merge_collected( 721 collected, prioritized, compat=compat, combine_attrs=combine_attrs 722 ) File ~/Documents/Work/Code/xarray/xarray/core/merge.py:358, in collect_variables_and_indexes(list_of_mappings, indexes) 355 indexes_.pop(name, None) 356 append_all(coords_, indexes_) --> 358 variable = as_variable(variable, name=name, auto_convert=False) 359 if name in indexes: 360 append(name, variable, indexes[name]) File ~/Documents/Work/Code/xarray/xarray/core/variable.py:126, in as_variable(obj, name, auto_convert) 124 obj = obj.copy(deep=False) 125 elif isinstance(obj, tuple): --> 126 if isinstance(obj[1], DataArray): 127 raise TypeError( 128 f""Variable {name!r}: Using a DataArray object to construct a variable is"" 129 "" ambiguous, please extract the data using the .data property."" 130 ) 131 try: IndexError: tuple index out of range ``` ### Anything else we need to know? _No response_ ### Environment Xarray `main` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8860/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2212186122,I_kwDOAMm_X86D20gK,8883,Coordinates object permits invalid state,35968931,closed,0,,,2,2024-03-28T01:49:21Z,2024-03-28T16:28:11Z,2024-03-28T16:28:11Z,MEMBER,,,,"### What happened? It is currently possible to create a `Coordinates` object where a variable shares a name with a dimension, but the variable is not 1D. This is explicitly forbidden by the xarray data model. ### What did you expect to happen? If you try to pass the resulting object into the `Dataset` constructor you get the expected error telling you that this is forbidden, but that error should have been raised by `Coordinates.__init__`. ### Minimal Complete Verifiable Example ```Python In [1]: from xarray.core.coordinates import Coordinates In [2]: from xarray.core.variable import Variable In [4]: import numpy as np In [5]: var = Variable(data=np.arange(6).reshape(2, 3), dims=['x', 'y']) In [6]: var Out[6]: Size: 48B array([[0, 1, 2], [3, 4, 5]]) In [7]: coords = Coordinates(coords={'x': var}, indexes={}) In [8]: coords Out[8]: Coordinates: x (x, y) int64 48B 0 1 2 3 4 5 In [10]: import xarray as xr In [11]: ds = xr.Dataset(coords=coords) --------------------------------------------------------------------------- MergeError Traceback (most recent call last) Cell In[11], line 1 ----> 1 ds = xr.Dataset(coords=coords) File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:693, in Dataset.__init__(self, data_vars, coords, attrs) 690 if isinstance(coords, Dataset): 691 coords = coords._variables --> 693 variables, coord_names, dims, indexes, _ = merge_data_and_coords( 694 data_vars, coords 695 ) 697 self._attrs = dict(attrs) if attrs else None 698 self._close = None File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:422, in merge_data_and_coords(data_vars, coords) 418 coords = create_coords_with_default_indexes(coords, data_vars) 420 # exclude coords from alignment (all variables in a Coordinates object should 421 # already be aligned together) and use coordinates' indexes to align data_vars --> 422 return merge_core( 423 [data_vars, coords], 424 compat=""broadcast_equals"", 425 join=""outer"", 426 explicit_coords=tuple(coords), 427 indexes=coords.xindexes, 428 priority_arg=1, 429 skip_align_args=[1], 430 ) File ~/Documents/Work/Code/xarray/xarray/core/merge.py:731, in merge_core(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value, skip_align_args) 729 coord_names.intersection_update(variables) 730 if explicit_coords is not None: --> 731 assert_valid_explicit_coords(variables, dims, explicit_coords) 732 coord_names.update(explicit_coords) 733 for dim, size in dims.items(): File ~/Documents/Work/Code/xarray/xarray/core/merge.py:577, in assert_valid_explicit_coords(variables, dims, explicit_coords) 575 for coord_name in explicit_coords: 576 if coord_name in dims and variables[coord_name].dims != (coord_name,): --> 577 raise MergeError( 578 f""coordinate {coord_name} shares a name with a dataset dimension, but is "" 579 ""not a 1D variable along that dimension. This is disallowed "" 580 ""by the xarray data model."" 581 ) MergeError: coordinate x shares a name with a dataset dimension, but is not a 1D variable along that dimension. This is disallowed by the xarray data model. ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [x] New issue — a search of GitHub Issues suggests this is not a duplicate. - [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output _No response_ ### Anything else we need to know? I noticed this whilst working on #8872 ### Environment `main`","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8883/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2117248281,I_kwDOAMm_X85-MqUZ,8704,Currently no way to create a Coordinates object without indexes for 1D variables,35968931,closed,0,,,4,2024-02-04T18:30:18Z,2024-03-26T13:50:16Z,2024-03-26T13:50:15Z,MEMBER,,,,"### What happened? The workaround described in https://github.com/pydata/xarray/pull/8107#discussion_r1311214263 does not seem to work on `main`, meaning that I think there is currently no way to create an `xr.Coordinates` object without 1D variables being coerced to indexes. This means there is no way to create a `Dataset` object without 1D variables becoming `IndexVariables` being coerced to indexes. ### What did you expect to happen? I expected to at least be able to use the workaround described in https://github.com/pydata/xarray/pull/8107#discussion_r1311214263, i.e. ```python xr.Coordinates({'x': ('x', uarr)}, indexes={}) ``` where `uarr` is an un-indexable array-like. ### Minimal Complete Verifiable Example ```Python class UnindexableArrayAPI: ... class UnindexableArray: """""" Presents like an N-dimensional array but doesn't support changes of any kind, nor can it be coerced into a np.ndarray or pd.Index. """""" _shape: tuple[int, ...] _dtype: np.dtype def __init__(self, shape: tuple[int, ...], dtype: np.dtype) -> None: self._shape = shape self._dtype = dtype self.__array_namespace__ = UnindexableArrayAPI @property def dtype(self) -> np.dtype: return self._dtype @property def shape(self) -> tuple[int, ...]: return self._shape @property def ndim(self) -> int: return len(self.shape) @property def size(self) -> int: return np.prod(self.shape) @property def T(self) -> Self: raise NotImplementedError() def __repr__(self) -> str: return f""UnindexableArray(shape={self.shape}, dtype={self.dtype})"" def _repr_inline_(self, max_width): """""" Format to a single line with at most max_width characters. Used by xarray. """""" return self.__repr__() def __getitem__(self, key, /) -> Self: """""" Only supports extremely limited indexing. I only added this method because xarray will apparently attempt to index into its lazy indexing classes even if the operation would be a no-op anyway. """""" from xarray.core.indexing import BasicIndexer if isinstance(key, BasicIndexer) and key.tuple == ((slice(None),) * self.ndim): # no-op return self else: raise NotImplementedError() def __array__(self) -> np.ndarray: raise NotImplementedError(""UnindexableArrays can't be converted into numpy arrays or pandas Index objects"") ``` ```python uarr = UnindexableArray(shape=(3,), dtype=np.dtype('int32')) xr.Variable(data=uarr, dims=['x']) # works fine xr.Coordinates({'x': ('x', uarr)}, indexes={}) # works in xarray v2023.08.0 ``` but in versions after that it triggers the NotImplementedError in `__array__`: ```python --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[59], line 1 ----> 1 xr.Coordinates({'x': ('x', uarr)}, indexes={}) File ~/Documents/Work/Code/xarray/xarray/core/coordinates.py:301, in Coordinates.__init__(self, coords, indexes) 299 variables = {} 300 for name, data in coords.items(): --> 301 var = as_variable(data, name=name) 302 if var.dims == (name,) and indexes is None: 303 index, index_vars = create_default_index_implicit(var, list(coords)) File ~/Documents/Work/Code/xarray/xarray/core/variable.py:159, in as_variable(obj, name) 152 raise TypeError( 153 f""Variable {name!r}: unable to convert object into a variable without an "" 154 f""explicit list of dimensions: {obj!r}"" 155 ) 157 if name is not None and name in obj.dims and obj.ndim == 1: 158 # automatically convert the Variable into an Index --> 159 obj = obj.to_index_variable() 161 return obj File ~/Documents/Work/Code/xarray/xarray/core/variable.py:572, in Variable.to_index_variable(self) 570 def to_index_variable(self) -> IndexVariable: 571 """"""Return this variable as an xarray.IndexVariable"""""" --> 572 return IndexVariable( 573 self._dims, self._data, self._attrs, encoding=self._encoding, fastpath=True 574 ) File ~/Documents/Work/Code/xarray/xarray/core/variable.py:2642, in IndexVariable.__init__(self, dims, data, attrs, encoding, fastpath) 2640 # Unlike in Variable, always eagerly load values into memory 2641 if not isinstance(self._data, PandasIndexingAdapter): -> 2642 self._data = PandasIndexingAdapter(self._data) File ~/Documents/Work/Code/xarray/xarray/core/indexing.py:1481, in PandasIndexingAdapter.__init__(self, array, dtype) 1478 def __init__(self, array: pd.Index, dtype: DTypeLike = None): 1479 from xarray.core.indexes import safe_cast_to_index -> 1481 self.array = safe_cast_to_index(array) 1483 if dtype is None: 1484 self._dtype = get_valid_numpy_dtype(array) File ~/Documents/Work/Code/xarray/xarray/core/indexes.py:469, in safe_cast_to_index(array) 459 emit_user_level_warning( 460 ( 461 ""`pandas.Index` does not support the `float16` dtype."" (...) 465 category=DeprecationWarning, 466 ) 467 kwargs[""dtype""] = ""float64"" --> 469 index = pd.Index(np.asarray(array), **kwargs) 471 return _maybe_cast_to_cftimeindex(index) Cell In[55], line 63, in UnindexableArray.__array__(self) 62 def __array__(self) -> np.ndarray: ---> 63 raise NotImplementedError(""UnindexableArrays can't be converted into numpy arrays or pandas Index objects"") NotImplementedError: UnindexableArrays can't be converted into numpy arrays or pandas Index objects ``` ### MVCE confirmation - [x] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [x] Complete example — the example is self-contained, including all data and the text of any traceback. - [x] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [x] New issue — a search of GitHub Issues suggests this is not a duplicate. - [x] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output _No response_ ### Anything else we need to know? Context is #8699 ### Environment Versions described above ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8704/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2098882374,I_kwDOAMm_X859GmdG,8660,dtype encoding ignored during IO?,35968931,closed,0,,,3,2024-01-24T18:50:47Z,2024-02-05T17:35:03Z,2024-02-05T17:35:02Z,MEMBER,,,,"### What happened? When I set the `.encoding['dtype']` attribute before saving a to disk, the actual on-disk representation appears to store a record of the dtype encoding, but when opening it back up in xarray I get the same dtype I had before, not the one specified in the encoding. Is that what's supposed to happen? How does this work? (This happens with both zarr and netCDF.) ### What did you expect to happen? I expected that setting `.encoding['dtype']` would mean that once I open the data back up, it would be in the new dtype that I set in the encoding. ### Minimal Complete Verifiable Example ```Python air = xr.tutorial.open_dataset('air_temperature') air['air'].dtype # returns dtype('float32') air['air'].encoding['dtype'] # returns dtype('int16'), which already seems weird air.to_zarr('air.zarr') # I would assume here that the encoding actually does something during IO # now if I check the zarr `.zarray` metadata for the `air` variable it says `""dtype"": `"" 1 var * var.isel(x=0) File ~/Documents/Work/Code/xarray/xarray/core/_typed_ops.py:487, in VariableOpsMixin.__mul__(self, other) 486 def __mul__(self, other: VarCompatible) -> Self | T_DataArray: --> 487 return self._binary_op(other, operator.mul) File ~/Documents/Work/Code/xarray/xarray/core/variable.py:2406, in Variable._binary_op(self, other, f, reflexive) 2404 other_data, self_data, dims = _broadcast_compat_data(other, self) 2405 else: -> 2406 self_data, other_data, dims = _broadcast_compat_data(self, other) 2407 keep_attrs = _get_keep_attrs(default=False) 2408 attrs = self._attrs if keep_attrs else None File ~/Documents/Work/Code/xarray/xarray/core/variable.py:2922, in _broadcast_compat_data(self, other) 2919 def _broadcast_compat_data(self, other): 2920 if all(hasattr(other, attr) for attr in [""dims"", ""data"", ""shape"", ""encoding""]): 2921 # `other` satisfies the necessary Variable API for broadcast_variables -> 2922 new_self, new_other = _broadcast_compat_variables(self, other) 2923 self_data = new_self.data 2924 other_data = new_other.data File ~/Documents/Work/Code/xarray/xarray/core/variable.py:2899, in _broadcast_compat_variables(*variables) 2893 """"""Create broadcast compatible variables, with the same dimensions. 2894 2895 Unlike the result of broadcast_variables(), some variables may have 2896 dimensions of size 1 instead of the size of the broadcast dimension. 2897 """""" 2898 dims = tuple(_unified_dims(variables)) -> 2899 return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables) File ~/Documents/Work/Code/xarray/xarray/core/variable.py:2899, in (.0) 2893 """"""Create broadcast compatible variables, with the same dimensions. 2894 2895 Unlike the result of broadcast_variables(), some variables may have 2896 dimensions of size 1 instead of the size of the broadcast dimension. 2897 """""" 2898 dims = tuple(_unified_dims(variables)) -> 2899 return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables) File ~/Documents/Work/Code/xarray/xarray/core/variable.py:1479, in Variable.set_dims(self, dims, shape) 1477 expanded_data = duck_array_ops.broadcast_to(self.data, tmp_shape) 1478 else: -> 1479 expanded_data = self.data[(None,) * (len(expanded_dims) - self.ndim)] 1481 expanded_var = Variable( 1482 expanded_dims, expanded_data, self._attrs, self._encoding, fastpath=True 1483 ) 1484 return expanded_var.transpose(*dims) File ~/miniconda3/envs/dev3.11/lib/python3.12/site-packages/numpy/array_api/_array_object.py:555, in Array.__getitem__(self, key) 550 """""" 551 Performs the operation __getitem__. 552 """""" 553 # Note: Only indices required by the spec are allowed. See the 554 # docstring of _validate_index --> 555 self._validate_index(key) 556 if isinstance(key, Array): 557 # Indexing self._array with array_api arrays can be erroneous 558 key = key._array File ~/miniconda3/envs/dev3.11/lib/python3.12/site-packages/numpy/array_api/_array_object.py:348, in Array._validate_index(self, key) 344 elif n_ellipsis == 0: 345 # Note boolean masks must be the sole index, which we check for 346 # later on. 347 if not key_has_mask and n_single_axes < self.ndim: --> 348 raise IndexError( 349 f""{self.ndim=}, but the multi-axes index only specifies "" 350 f""{n_single_axes} dimensions. If this was intentional, "" 351 ""add a trailing ellipsis (...) which expands into as many "" 352 ""slices (:) as necessary - this is what np.ndarray arrays "" 353 ""implicitly do, but such flat indexing behaviour is not "" 354 ""specified in the Array API."" 355 ) 357 if n_ellipsis == 0: 358 indexed_shape = self.shape IndexError: self.ndim=1, but the multi-axes index only specifies 0 dimensions. If this was intentional, add a trailing ellipsis (...) which expands into as many slices (:) as necessary - this is what np.ndarray arrays implicitly do, but such flat indexing behaviour is not specified in the Array API. ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. - [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment main branch of xarray, numpy 1.26.0","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8665/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2099550299,I_kwDOAMm_X859JJhb,8666,Error unstacking array API compliant class,35968931,closed,0,,,0,2024-01-25T04:35:09Z,2024-01-26T16:06:02Z,2024-01-26T16:06:02Z,MEMBER,,,,"### What happened? Unstacking fails for array types that strictly follow the array API standard. ### What did you expect to happen? This obviously works fine with a normal numpy array. ### Minimal Complete Verifiable Example ```Python import numpy.array_api as nxp arr = nxp.asarray([[1, 2, 3], [4, 5, 6]], dtype=np.dtype('float32')) da = xr.DataArray( arr, coords=[(""x"", [""a"", ""b""]), (""y"", [0, 1, 2])], ) da stacked = da.stack(z=(""x"", ""y"")) stacked.indexes[""z""] stacked.unstack() --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[65], line 8 6 stacked = da.stack(z=(""x"", ""y"")) 7 stacked.indexes[""z""] ----> 8 roundtripped = stacked.unstack() 9 arr.identical(roundtripped) File ~/Documents/Work/Code/xarray/xarray/util/deprecation_helpers.py:115, in _deprecate_positional_args.._decorator..inner(*args, **kwargs) 111 kwargs.update({name: arg for name, arg in zip_args}) 113 return func(*args[:-n_extra_args], **kwargs) --> 115 return func(*args, **kwargs) File ~/Documents/Work/Code/xarray/xarray/core/dataarray.py:2913, in DataArray.unstack(self, dim, fill_value, sparse) 2851 @_deprecate_positional_args(""v2023.10.0"") 2852 def unstack( 2853 self, (...) 2857 sparse: bool = False, 2858 ) -> Self: 2859 """""" 2860 Unstack existing dimensions corresponding to MultiIndexes into 2861 multiple new dimensions. (...) 2911 DataArray.stack 2912 """""" -> 2913 ds = self._to_temp_dataset().unstack(dim, fill_value=fill_value, sparse=sparse) 2914 return self._from_temp_dataset(ds) File ~/Documents/Work/Code/xarray/xarray/util/deprecation_helpers.py:115, in _deprecate_positional_args.._decorator..inner(*args, **kwargs) 111 kwargs.update({name: arg for name, arg in zip_args}) 113 return func(*args[:-n_extra_args], **kwargs) --> 115 return func(*args, **kwargs) File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:5581, in Dataset.unstack(self, dim, fill_value, sparse) 5579 for d in dims: 5580 if needs_full_reindex: -> 5581 result = result._unstack_full_reindex( 5582 d, stacked_indexes[d], fill_value, sparse 5583 ) 5584 else: 5585 result = result._unstack_once(d, stacked_indexes[d], fill_value, sparse) File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:5474, in Dataset._unstack_full_reindex(self, dim, index_and_vars, fill_value, sparse) 5472 if name not in index_vars: 5473 if dim in var.dims: -> 5474 variables[name] = var.unstack({dim: new_dim_sizes}) 5475 else: 5476 variables[name] = var File ~/Documents/Work/Code/xarray/xarray/core/variable.py:1684, in Variable.unstack(self, dimensions, **dimensions_kwargs) 1682 result = self 1683 for old_dim, dims in dimensions.items(): -> 1684 result = result._unstack_once_full(dims, old_dim) 1685 return result File ~/Documents/Work/Code/xarray/xarray/core/variable.py:1574, in Variable._unstack_once_full(self, dim, old_dim) 1571 reordered = self.transpose(*dim_order) 1573 new_shape = reordered.shape[: len(other_dims)] + new_dim_sizes -> 1574 new_data = reordered.data.reshape(new_shape) 1575 new_dims = reordered.dims[: len(other_dims)] + new_dim_names 1577 return type(self)( 1578 new_dims, new_data, self._attrs, self._encoding, fastpath=True 1579 ) AttributeError: 'Array' object has no attribute 'reshape' ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. - [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output _No response_ ### Anything else we need to know? It fails on the `arr.reshape` call, because the array API standard has reshape be a function, not a method. We do in fact have an array API-compatible version of `reshape` defined in `duck_array_ops.py`, it just apparently isn't yet used everywhere we call reshape. https://github.com/pydata/xarray/blob/037a39e249e5387bc15de447c57bfd559fd5a574/xarray/core/duck_array_ops.py#L363 ### Environment main branch of xarray, numpy 1.26.0","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8666/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 552500673,MDU6SXNzdWU1NTI1MDA2NzM=,3709,Feature Proposal: `xarray.interactive` module,35968931,closed,0,,,36,2020-01-20T20:42:22Z,2023-10-27T18:24:49Z,2021-07-29T15:37:21Z,MEMBER,,,,"## Feature proposal: `xarray.interactive` module I've been experimenting with [ipython widgets](https://github.com/jupyter-widgets/ipywidgets) in jupyter notebooks, and I've been [working on](https://github.com/TomNicholas/xarray-interactive) how we might use them to make xarray more interactive. ### Motivation: For most users who are exploring their data, it will be common to find themselves rerunning the same cells repeatedly but with slightly different values. In `xarray`'s case that will often be in an `.isel()` or `.sel()` call, or selecting variables from a dataset. IPython widgets allow you to interact with your functions in a very intuitive way, which we could exploit. There are lots of tutorials on how to interact with `pandas` data (e.g. [this great one](https://towardsdatascience.com/interactive-controls-for-jupyter-notebooks-f5c94829aee6)), but I haven't seen any for interacting with `xarray` objects. ### Relationship to other libraries: Some downstream plotting libaries (such as @hvplot) [already use widgets](https://hvplot.holoviz.org/user_guide/Gridded_Data.html) when interactively plotting xarray-derived data structures, but they don't seem to go the full N dimensions. This also isn't something that should be confined to plotting functions - you often choose slices or variables at the start of analysis, not just at the end. I'll come back to this idea later. The default ipython widgets are pretty good, but we could write an `xarray.interactive` module in such a way that downstream developers can easily replace them with [their own widgets](https://hvplot.holoviz.org/user_guide/Widgets.html). ### Usage examples: ```python # imports import ipywidgets as widgets import xarray.plot as xplot import xarray.interactive as interactive # Load tutorial data ds = xr.tutorial.open_dataset('air_temperature')['air'] ``` Plotting against multiple dimensions interactively ```python interactive.isel(da, xplot.plot, lat=10, lon=50) ``` ![isel_lat_and_lon](https://user-images.githubusercontent.com/35968931/72755645-e632bb00-3bc2-11ea-8056-eb448e957bb0.gif) Interactively select a range from a dimension ```python def plot_mean_over_time(da): da.mean(dim=time) interactive.isel(da, plot_mean_over_time, time=slice(100, 500)) ``` ![mean_over_time_slice](https://user-images.githubusercontent.com/35968931/72755638-e337ca80-3bc2-11ea-9d66-efb8dd0d4fca.gif) Animate over one dimension ```python from ipywidgets import Play interactive.isel(da, xplot.plot, time=Play()) ``` ![Play](https://user-images.githubusercontent.com/35968931/72755630-de731680-3bc2-11ea-9d0f-46da96d6efda.gif) ### API ideas: We can write a function like this ```python interactive.isel(da, func=xplot.plot, time=10) ``` which could also be used as a decorator something like this ```python @interactive.isel(da, time=10) def plot(da) return xplot.plot(da) ``` It would be nicer to be able to do this ```python @Interactive(da).isel(time=10) def plot(da) return xplot.plot(da) ``` but [Guido forbade it](https://seriously.dontusethiscode.com/2013/04/21/lambda-decorators.html). But we can attach these functions to an accessor to get ```python da.interactive.isel(xplot.plot, time=10) ``` ### Other ideas Select variables from datasets ```python @interactive.data_vars(da1=ds['n'], da2=ds['T'], ...) def correlation(da1, da2, ...) ... # Would produce a dropdown list of variables for each dataset ``` Choose dimensions to apply functions over ```python @interactive.dims(dim='time') def mean(da, dim) ... # Would produce a dropdown list of dimensions in the dataarray ``` General `interactive.explore()` method to see variation over any number of dimensions, the default being all of them. What do people think about this? Is it something that makes sense to include within xarray itself? (Dependencies aren't a problem because it's fine to have `ipywidgets` as an optional dependency just for this module.)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3709/reactions"", ""total_count"": 6, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 3, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 663235664,MDU6SXNzdWU2NjMyMzU2NjQ=,4243,Manually drop DataArray from memory?,35968931,closed,0,,,3,2020-07-21T18:54:40Z,2023-09-12T16:17:12Z,2023-09-12T16:17:12Z,MEMBER,,,,"Is it possible to deliberately drop data associated with a particular DataArray from memory? Obviously `da.close()` exists, but what happens if you did for example ```python ds = open_dataset(file) da = ds[var] da.compute() # something that loads da into memory da.close() # is the memory freed up again now? ds.something() # what about now? ``` Also does calling python's built-in garbage collector (i.e. `gc.collect()`) do anything in this instance? The context of this question is that I'm trying to resave some massive variables (~65GB each) that were loaded from thousands of files into just a few files for each variable. I would love to use @rabernat 's new [rechunker package](https://github.com/pangeo-data/rechunker) but I'm not sure how easily I can convert my current netCDF data to Zarr, and I'm interested in this question no matter how I end up solving the problem. I don't currently have a particularly good understanding of file I/O and memory management in xarray, but would like to improve it. Can anyone recommend a tool I can use to answer this kind of question myself on my own machine? I suppose it would need to be able to tell me the current memory usage of specific objects, not just the total memory usage. (@johnomotani you might be interested)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4243/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1801849622,I_kwDOAMm_X85rZgsW,7982,Use Meilisearch in our docs,35968931,closed,0,,,1,2023-07-12T22:29:45Z,2023-07-19T19:49:53Z,2023-07-19T19:49:53Z,MEMBER,,,,"### Is your feature request related to a problem? Just saw this cool search thing for sphinx in a lightning talk at SciPy called Meilisearch Cc @dcherian ### Describe the solution you'd like Read about it here https://sphinxdocs.ansys.com/version/stable/user_guide/options.html ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7982/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1807782455,I_kwDOAMm_X85rwJI3,7996,Stable docs build not showing latest changes after release,35968931,closed,0,,,3,2023-07-17T13:24:58Z,2023-07-17T20:48:19Z,2023-07-17T20:48:19Z,MEMBER,,,,"### What happened? I released xarray version v2023.07.0 last night, but I'm not seeing changes to the documentation reflected in the [`https://docs.xarray.dev/en/stable/`](https://docs.xarray.dev/en/stable/) build. (In particular the Internals section now should have an entire extra page on wrapping chunked arrays.) I can however see the newest additions on [`https://docs.xarray.dev/en/latest/`](https://docs.xarray.dev/en/latest/) build. Is that how it's supposed to work? ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example _No response_ ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7996/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1308715638,I_kwDOAMm_X85OAWp2,6807,Alternative parallel execution frameworks in xarray,35968931,closed,0,,,12,2022-07-18T21:48:10Z,2023-05-18T17:34:33Z,2023-05-18T17:34:33Z,MEMBER,,,,"### Is your feature request related to a problem? Since early on the project xarray has supported wrapping `dask.array` objects in a first-class manner. However recent work on flexible array wrapping has made it possible to wrap all sorts of array types (and with #6804 we should support wrapping any array that conforms to the [array API standard](https://data-apis.org/array-api/latest/index.html)). Currently though the only way to parallelize array operations with xarray ""automatically"" is to use dask. (You could use [xarray-beam](https://github.com/google/xarray-beam) or other options too but they don't ""automatically"" generate the computation for you like dask does.) When dask is the only type of parallel framework exposing an array-like API then there is no need for flexibility, but now we have nascent projects like [cubed](https://github.com/tomwhite/cubed) to consider too. @tomwhite ### Describe the solution you'd like Refactor the internals so that dask is one option among many, and that any newer options can plug in in an extensible way. In particular cubed deliberately uses the same API as `dask.array`, exposing: 1) the methods needed to conform to the array API standard 2) a `.chunk` and `.compute` method, which we could dispatch to 3) dask-like functions to create computation graphs including [`blockwise`](https://github.com/tomwhite/cubed/blob/400dc9adcf21c8b468fce9f24e8d4b8cb9ef2f11/cubed/core/ops.py#L43), [`map_blocks`](https://github.com/tomwhite/cubed/blob/400dc9adcf21c8b468fce9f24e8d4b8cb9ef2f11/cubed/core/ops.py#L221), and [`rechunk`](https://github.com/tomwhite/cubed/blob/main/cubed/primitive/rechunk.py) I would like to see xarray able to wrap any array-like object which offers this set of methods / functions, and call the corresponding version of that method for the correct library (i.e. dask vs cubed) automatically. That way users could try different parallel execution frameworks simply via a switch like ```python ds.chunk(**chunk_pattern, manager=""dask"") ``` and see which one works best for their particular problem. ### Describe alternatives you've considered If we leave it the way it is now then xarray will not be truly flexible in this respect. Any library can wrap (or subclass if they are really brave) xarray objects to provide parallelism but that's not the same level of flexibility. ### Additional context [cubed repo](https://github.com/tomwhite/cubed) [PR](https://github.com/pydata/xarray/pull/6804) about making xarray able to wrap objects conforming to the new [array API standard](https://data-apis.org/array-api/latest/index.html) cc @shoyer @rabernat @dcherian @keewis ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6807/reactions"", ""total_count"": 6, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 3, ""rocket"": 2, ""eyes"": 1}",,completed,13221727,issue 1426383543,I_kwDOAMm_X85VBOK3,7232,ds.Coarsen.construct demotes non-dimensional coordinates to variables,35968931,closed,0,,,0,2022-10-27T23:39:32Z,2022-10-28T17:46:51Z,2022-10-28T17:46:51Z,MEMBER,,,,"### What happened? `ds.Coarsen.construct` demotes non-dimensional coordinates to variables ### What did you expect to happen? All variables that were coordinates before the coarsen.construct stay as coordinates afterwards. ### Minimal Complete Verifiable Example ```Python In [3]: da = xr.DataArray(np.arange(24), dims=[""time""]) ...: da = da.assign_coords(day=365 * da) ...: ds = da.to_dataset(name=""T"") In [4]: ds Out[4]: Dimensions: (time: 24) Coordinates: day (time) int64 0 365 730 1095 1460 1825 ... 6935 7300 7665 8030 8395 Dimensions without coordinates: time Data variables: T (time) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 In [5]: ds.coarsen(time=12).construct(time=(""year"", ""month"")) Out[5]: Dimensions: (year: 2, month: 12) Coordinates: day (year, month) int64 0 365 730 1095 1460 ... 7300 7665 8030 8395 Dimensions without coordinates: year, month Data variables: T (year, month) int64 0 1 2 3 4 5 6 7 8 ... 16 17 18 19 20 21 22 23 ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment `main` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7232/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1034238626,I_kwDOAMm_X849pTqi,5889,Release v0.20?,35968931,closed,0,,,13,2021-10-23T19:31:01Z,2021-11-02T18:38:50Z,2021-11-02T18:38:50Z,MEMBER,,,,"We should do another release soon. The last one was v0.19 on July 23rd, so it's been 3 months. (In particular I personally want to get some small pint compatibility fixes released such as https://github.com/pydata/xarray/pull/5571 and https://github.com/pydata/xarray/pull/5886, so that the code in [this blog post](https://github.com/xarray-contrib/pint-xarray/pull/142) advertising pint-xarray integration all works.) There's been plenty of changes since then, and there are more we could merge quite quickly. It's a breaking release because we changed some dependencies, so should be called `v0.20.0`. @benbovy how does the ongoing index refactor stuff affect this release? Do we need to wait so it can all be announced? Can we release with merged index refactor stuff just silently sitting there? Small additions we could merge, feel free to suggest more @pydata/xarray : - https://github.com/pydata/xarray/pull/5834 - https://github.com/pydata/xarray/pull/5662 - #5233 - #5900 - #5365 - #5845 - #5904 - #5911 - #5905 - #5847 - #5916 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5889/reactions"", ""total_count"": 5, ""+1"": 5, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1020282789,I_kwDOAMm_X8480Eel,5843,Why are `da.chunks` and `ds.chunks` properties inconsistent?,35968931,closed,0,,,6,2021-10-07T17:21:01Z,2021-10-29T18:12:22Z,2021-10-29T18:12:22Z,MEMBER,,,,"Basically the title, but what I'm referring to is this: ```python In [2]: da = xr.DataArray([[0, 1], [2, 3]], name='foo').chunk(1) In [3]: ds = da.to_dataset() In [4]: da.chunks Out[4]: ((1, 1), (1, 1)) In [5]: ds.chunks Out[5]: Frozen({'dim_0': (1, 1), 'dim_1': (1, 1)}) ``` Why does `DataArray.chunks` return a tuple and `Dataset.chunks` return a frozen dictionary? This seems a bit silly, for a few reasons: 1) it means that some perfectly reasonable code might fail unnecessarily if passed a DataArray instead of a Dataset or vice versa, such as ```python def is_core_dim_chunked(obj, core_dim): return len(obj.chunks[core_dim]) > 1 ``` which will work as intended for a dataset but raises a `TypeError` for a dataarray. 2) it breaks the pattern we use for `.sizes`, where ```python In [14]: da.sizes Out[14]: Frozen({'dim_0': 2, 'dim_1': 2}) In [15]: ds.sizes Out[15]: Frozen({'dim_0': 2, 'dim_1': 2}) ``` 3) if you want the chunks as a tuple they are always accessible via `da.data.chunks`, which is a more sensible place to look to find the chunks without dimension names. 4) It's an undocumented difference, as the docstrings for `ds.chunks` and `da.chunks` both only say `""""""Block dimensions for this dataset’s data or None if it’s not a dask array.""""""` which doesn't tell me anything about the return type, or warn me that the return types are different. EDIT: In fact `DataArray.chunk` doesn't even appear to be listed on the API docs page at all. In our codebase this difference is mostly washed out by us using `._to_temp_dataset()` all the time, and also by the way that the `.chunk()` method accepts both the tuple and dict form, so both of these invariants hold (but in different ways): ``` ds == ds.chunk(ds.chunks) da == da.chunk(da.chunks) ``` I'm not sure whether making this consistent is worth the effort of a significant breaking change though :confused: (Sort of related to https://github.com/pydata/xarray/issues/2103)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5843/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 935062144,MDU6SXNzdWU5MzUwNjIxNDQ=,5559,UserWarning when wrapping pint & dask arrays together,35968931,closed,0,,,4,2021-07-01T17:25:03Z,2021-09-29T17:48:39Z,2021-09-29T17:48:39Z,MEMBER,,,,"With `pint-xarray` you can create a chunked, unit-aware xarray object, but calling a calculation method and then computing doesn't appear to behave as hoped. ```python da = xr.DataArray([1,2,3], attrs={'units': 'metres'}) chunked = da.chunk(1).pint.quantify() ``` ```python print(chunked.compute()) ``` ``` Dimensions without coordinates: dim_0 ``` So far this is fine, but if we try to take a mean before computing we get ```python print(chunked.mean().compute()) ``` ``` , 'meter')> /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/array/core.py:3139: UserWarning: Passing an object to dask.array.from_array which is already a Dask collection. This can lead to unexpected behavior. warnings.warn( ``` This is not correct: as well as the UserWarning, the return value of compute is a dask array, meaning we need to compute a second time to actually get the answer: ```python print(chunked.mean().compute().compute()) ``` ``` /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/array/core.py:3139: UserWarning: Passing an object to dask.array.from_array which is already a Dask collection. This can lead to unexpected behavior. warnings.warn( ``` If we try chunking the other way (`chunked = da.pint.quantify().pint.chunk(1)`) then we get all the same results. xref https://github.com/xarray-contrib/pint-xarray/issues/116 and https://github.com/pydata/xarray/pull/4972 @keewis ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5559/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 940054482,MDU6SXNzdWU5NDAwNTQ0ODI=,5588,Release v0.19?,35968931,closed,0,,,15,2021-07-08T17:00:26Z,2021-07-23T23:15:39Z,2021-07-23T21:12:53Z,MEMBER,,,,"Yesterday in the dev call we discussed the need for another release. Not sure if this should be a bugfix release (i.e. v0.18.3) or a full release (i.e. v0.19). Last release (v0.18.2) was 19th May, with v0.18.0 on 6th May. @pydata/xarray Bug fixes: - #5581 and the fix #5359 (this one needs to be released soon really) - #5528 - Probably various smaller ones New features: - #4696 - #5514 - #5476 - #5464 - #5445 Internal: - `master` -> `main` #5520 - #5506 Nice to merge first?: - [x] #5568 and #5561 - [ ] #5571 - [x] #5586 - [ ] #5493 - [x] #4909 - [ ] #5580 - [ ] #4863 - [ ] #5501","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5588/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 911663002,MDU6SXNzdWU5MTE2NjMwMDI=,5438,Add Union Operators for Dataset,35968931,closed,0,,,2,2021-06-04T16:21:06Z,2021-06-04T16:35:36Z,2021-06-04T16:35:36Z,MEMBER,,,,"As of python 3.9, python dictionaries now support being merged via ```python c = a | b ``` and updated via ```python c = a |= b ``` see [PEP 584](https://www.python.org/dev/peps/pep-0584/#abstract). `xarray.Dataset` is dict-like, so it would make sense to support the same syntax for merging. The way to achieve that is by adding new dunder methods to `xarray.Dataset`, something like ```python def __or__(self, other): if not isinstance(other, xr.Dataset): return NotImplemented new = xr.merge(self, other) return new def __ror__(self, other): if not isinstance(other, xr.Dataset): return NotImplemented new = xr.merge(self, other) return new def __ior__(self, other): self.merge(other) return self ``` The distinction between the intent of these different operators is whether a new object is returned or the original object is updated. This would allow things like `(ds1 | ds2).to_netcdf()` (This feature doesn't require python 3.9, it merely echoes a feature that is only available in 3.9+) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5438/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 871111282,MDU6SXNzdWU4NzExMTEyODI=,5236,Error collecting tests due to optional pint import,35968931,closed,0,,,2,2021-04-29T15:01:13Z,2021-04-29T15:32:08Z,2021-04-29T15:32:08Z,MEMBER,,,,"When I try to run xarray's test suite locally with pytest I've suddenly started getting this weird error: ``` (xarray-dev) tegn500@fusion192:~/Documents/Work/Code/xarray$ pytest xarray/tests/test_backends.py ==================================================================================== test session starts ===================================================================================== platform linux -- Python 3.9.2, pytest-6.2.3, py-1.10.0, pluggy-0.13.1 rootdir: /home/tegn500/Documents/Work/Code/xarray, configfile: setup.cfg collected 0 items / 1 error =========================================================================================== ERRORS =========================================================================================== _______________________________________________________________________ ERROR collecting xarray/tests/test_backends.py _______________________________________________________________________ ../../../../anaconda3/envs/xarray-dev/lib/python3.9/importlib/__init__.py:127: in import_module return _bootstrap._gcd_import(name[level:], package, level) :1030: in _gcd_import ??? :1007: in _find_and_load ??? :972: in _find_and_load_unlocked ??? :228: in _call_with_frames_removed ??? :1030: in _gcd_import ??? :1007: in _find_and_load ??? :986: in _find_and_load_unlocked ??? :680: in _load_unlocked ??? :790: in exec_module ??? :228: in _call_with_frames_removed ??? xarray/tests/__init__.py:84: in has_pint_0_15, requires_pint_0_15 = _importorskip(""pint"", minversion=""0.15"") xarray/tests/__init__.py:46: in _importorskip if LooseVersion(mod.__version__) < LooseVersion(minversion): E AttributeError: module 'pint' has no attribute '__version__' ================================================================================== short test summary info =================================================================================== ERROR xarray/tests/test_backends.py - AttributeError: module 'pint' has no attribute '__version__' !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ====================================================================================== 1 error in 0.88s ====================================================================================== ``` I'm not sure whether this is my fault or a problem with xarray somehow. @keewis have you seen this happen before? This is with a fresh conda environment, running locally on my laptop, and on python 3.9.2. Pint isn't even in this environment. I can force it to proceed with the tests by also catching the attribute error, i.e. ```python def _importorskip(modname, minversion=None): try: mod = importlib.import_module(modname) has = True if minversion is not None: if LooseVersion(mod.__version__) < LooseVersion(minversion): raise ImportError(""Minimum version not satisfied"") except (ImportError, AttributeError): has = False ``` but I obviously shouldn't need to do that. Any ideas? **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: a5e72c9aacbf26936844840b75dd59fe7d13f1e6 python: 3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 05:02:46) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 4.8.10-040810-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.8.0 xarray: 0.15.2.dev545+ga5e72c9 pandas: 1.2.4 numpy: 1.20.2 scipy: 1.6.3 netCDF4: 1.5.6 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.8.1 cftime: 1.4.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.04.1 distributed: 2021.04.1 matplotlib: 3.4.1 cartopy: installed seaborn: None numbagg: None pint: installed setuptools: 49.6.0.post20210108 pip: 21.1 conda: None pytest: 6.2.3 IPython: None sphinx: None
**Conda Environment**:
Output of conda list # packages in environment at /home/tegn500/anaconda3/envs/xarray-dev: # # Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 1_gnu conda-forge alsa-lib 1.2.3 h516909a_0 conda-forge asciitree 0.3.3 py_2 conda-forge attrs 20.3.0 pyhd3deb0d_0 conda-forge bokeh 2.3.1 py39hf3d152e_0 conda-forge bottleneck 1.3.2 py39hce5d2b2_3 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.17.1 h7f98852_1 conda-forge ca-certificates 2020.12.5 ha878542_0 conda-forge certifi 2020.12.5 py39hf3d152e_1 conda-forge cftime 1.4.1 py39hce5d2b2_0 conda-forge click 7.1.2 pyh9f0ad1d_0 conda-forge cloudpickle 1.6.0 py_0 conda-forge curl 7.76.1 h979ede3_1 conda-forge cycler 0.10.0 py_2 conda-forge cytoolz 0.11.0 py39h3811e60_3 conda-forge dask 2021.4.1 pyhd8ed1ab_0 conda-forge dask-core 2021.4.1 pyhd8ed1ab_0 conda-forge dbus 1.13.6 h48d8840_2 conda-forge distributed 2021.4.1 py39hf3d152e_0 conda-forge expat 2.3.0 h9c3ff4c_0 conda-forge fasteners 0.14.1 py_3 conda-forge fontconfig 2.13.1 hba837de_1005 conda-forge freetype 2.10.4 h0708190_1 conda-forge fsspec 2021.4.0 pyhd8ed1ab_0 conda-forge gettext 0.19.8.1 h0b5b191_1005 conda-forge glib 2.68.1 h9c3ff4c_0 conda-forge glib-tools 2.68.1 h9c3ff4c_0 conda-forge gst-plugins-base 1.18.4 hf529b03_2 conda-forge gstreamer 1.18.4 h76c114f_2 conda-forge hdf4 4.2.13 h10796ff_1005 conda-forge hdf5 1.10.6 nompi_h6a2412b_1114 conda-forge heapdict 1.0.1 py_0 conda-forge icu 68.1 h58526e2_0 conda-forge iniconfig 1.1.1 pyh9f0ad1d_0 conda-forge jinja2 2.11.3 pyh44b312d_0 conda-forge jpeg 9d h36c2ea0_0 conda-forge kiwisolver 1.3.1 py39h1a9c180_1 conda-forge krb5 1.17.2 h926e7f8_0 conda-forge lcms2 2.12 hddcbb42_0 conda-forge ld_impl_linux-64 2.35.1 hea4e1c9_2 conda-forge libblas 3.9.0 8_openblas conda-forge libcblas 3.9.0 8_openblas conda-forge libclang 11.1.0 default_ha53f305_0 conda-forge libcurl 7.76.1 hc4aaa36_1 conda-forge libedit 3.1.20191231 he28a2e2_2 conda-forge libev 4.33 h516909a_1 conda-forge libevent 2.1.10 hcdb4288_3 conda-forge libffi 3.3 h58526e2_2 conda-forge libgcc-ng 9.3.0 h2828fa1_19 conda-forge libgfortran-ng 9.3.0 hff62375_19 conda-forge libgfortran5 9.3.0 hff62375_19 conda-forge libglib 2.68.1 h3e27bee_0 conda-forge libgomp 9.3.0 h2828fa1_19 conda-forge libiconv 1.16 h516909a_0 conda-forge liblapack 3.9.0 8_openblas conda-forge libllvm11 11.1.0 hf817b99_2 conda-forge libnetcdf 4.8.0 nompi_hfa85936_101 conda-forge libnghttp2 1.43.0 h812cca2_0 conda-forge libogg 1.3.4 h7f98852_1 conda-forge libopenblas 0.3.12 pthreads_h4812303_1 conda-forge libopus 1.3.1 h7f98852_1 conda-forge libpng 1.6.37 h21135ba_2 conda-forge libpq 13.2 hfd2b0eb_2 conda-forge libssh2 1.9.0 ha56f1ee_6 conda-forge libstdcxx-ng 9.3.0 h6de172a_19 conda-forge libtiff 4.2.0 hdc55705_1 conda-forge libuuid 2.32.1 h7f98852_1000 conda-forge libvorbis 1.3.7 h9c3ff4c_0 conda-forge libwebp-base 1.2.0 h7f98852_2 conda-forge libxcb 1.13 h7f98852_1003 conda-forge libxkbcommon 1.0.3 he3ba5ed_0 conda-forge libxml2 2.9.10 h72842e0_4 conda-forge libzip 1.7.3 h4de3113_0 conda-forge locket 0.2.0 py_2 conda-forge lz4-c 1.9.3 h9c3ff4c_0 conda-forge markupsafe 1.1.1 py39h3811e60_3 conda-forge matplotlib 3.4.1 py39hf3d152e_0 conda-forge matplotlib-base 3.4.1 py39h2fa2bec_0 conda-forge monotonic 1.5 py_0 conda-forge more-itertools 8.7.0 pyhd8ed1ab_1 conda-forge msgpack-python 1.0.2 py39h1a9c180_1 conda-forge mysql-common 8.0.23 ha770c72_1 conda-forge mysql-libs 8.0.23 h935591d_1 conda-forge ncurses 6.2 h58526e2_4 conda-forge netcdf4 1.5.6 nompi_py39hc6dca20_103 conda-forge nspr 4.30 h9c3ff4c_0 conda-forge nss 3.64 hb5efdd6_0 conda-forge numcodecs 0.7.3 py39he80948d_0 conda-forge numpy 1.20.2 py39hdbf815f_0 conda-forge olefile 0.46 pyh9f0ad1d_1 conda-forge openjpeg 2.4.0 hf7af979_0 conda-forge openssl 1.1.1k h7f98852_0 conda-forge packaging 20.9 pyh44b312d_0 conda-forge pandas 1.2.4 py39hde0f152_0 conda-forge partd 1.2.0 pyhd8ed1ab_0 conda-forge pcre 8.44 he1b5a44_0 conda-forge pillow 8.1.2 py39hf95b381_1 conda-forge pip 21.1 pyhd8ed1ab_0 conda-forge pluggy 0.13.1 py39hf3d152e_4 conda-forge psutil 5.8.0 py39h3811e60_1 conda-forge pthread-stubs 0.4 h36c2ea0_1001 conda-forge py 1.10.0 pyhd3deb0d_0 conda-forge pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge pyqt 5.12.3 py39hf3d152e_7 conda-forge pyqt-impl 5.12.3 py39h0fcd23e_7 conda-forge pyqt5-sip 4.19.18 py39he80948d_7 conda-forge pyqtchart 5.12 py39h0fcd23e_7 conda-forge pyqtwebengine 5.12.1 py39h0fcd23e_7 conda-forge pytest 6.2.3 py39hf3d152e_0 conda-forge python 3.9.2 hffdb5ce_0_cpython conda-forge python-dateutil 2.8.1 py_0 conda-forge python_abi 3.9 1_cp39 conda-forge pytz 2021.1 pyhd8ed1ab_0 conda-forge pyyaml 5.4.1 py39h3811e60_0 conda-forge qt 5.12.9 hda022c4_4 conda-forge readline 8.1 h46c0cb4_0 conda-forge scipy 1.6.3 py39hee8e79c_0 conda-forge setuptools 49.6.0 py39hf3d152e_3 conda-forge six 1.15.0 pyh9f0ad1d_0 conda-forge sortedcontainers 2.3.0 pyhd8ed1ab_0 conda-forge sqlite 3.35.5 h74cdb3f_0 conda-forge tblib 1.7.0 pyhd8ed1ab_0 conda-forge tk 8.6.10 h21135ba_1 conda-forge toml 0.10.2 pyhd8ed1ab_0 conda-forge toolz 0.11.1 py_0 conda-forge tornado 6.1 py39h3811e60_1 conda-forge typing_extensions 3.7.4.3 py_0 conda-forge tzdata 2021a he74cb21_0 conda-forge wheel 0.36.2 pyhd3deb0d_0 conda-forge xorg-libxau 1.0.9 h7f98852_0 conda-forge xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge xz 5.2.5 h516909a_1 conda-forge yaml 0.2.5 h516909a_0 conda-forge zarr 2.8.1 pyhd8ed1ab_0 conda-forge zict 2.0.0 py_0 conda-forge zlib 1.2.11 h516909a_1010 conda-forge zstd 1.4.9 ha95c52a_0 conda-forge
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5236/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 671609109,MDU6SXNzdWU2NzE2MDkxMDk=,4300,General curve fitting method,35968931,closed,0,,,9,2020-08-02T12:35:49Z,2021-03-31T16:55:53Z,2021-03-31T16:55:53Z,MEMBER,,,,"Xarray should have a general curve-fitting function as part of its main API. ## Motivation Yesterday I wanted to fit a simple decaying exponential function to the data in a DataArray and realised there currently isn't an immediate way to do this in xarray. You have to either pull out the `.values` (losing the power of dask), or use `apply_ufunc` (complicated). This is an incredibly common, domain-agnostic task, so although I don't think we should support various kinds of unusual optimisation procedures (which could always go in an extension package instead), I think a basic fitting method is within scope for the main library. There are [SO questions](https://stackoverflow.com/questions/62987617/using-scipy-curve-fit-with-dask-xarray) asking how to achieve this. We already have [`.polyfit` and `polyval` anyway](https://github.com/pydata/xarray/pull/3733/files#), which are more specific. (@AndrewWilliams3142 and @aulemahal I expect you will have thoughts on how implement this generally.) ## Proposed syntax I want something like this to work: ```python def exponential_decay(xdata, A=10, L=5): return A*np.exp(-xdata/L) # returns a dataset containing the optimised values of each parameter fitted_params = da.fit(exponential_decay) fitted_line = exponential_decay(da.x, A=fitted_params['A'], L=fitted_params['L']) # Compare da.plot(ax) fitted_line.plot(ax) ``` It would also be nice to be able to fit in multiple dimensions. That means both for example fitting a 2D function to 2D data: ```python def hat(xdata, ydata, h=2, r0=1): r = xdata**2 + ydata**2 return h*np.exp(-r/r0) fitted_params = da.fit(hat) fitted_hat = hat(da.x, da.y, h=fitted_params['h'], r0=fitted_params['r0']) ``` but also repeatedly fitting a 1D function to 2D data: ```python # da now has a y dimension too fitted_params = da.fit(exponential_decay, fit_along=['x']) # As fitted_params now has y-dependence, broadcasting means fitted_lines does too fitted_lines = exponential_decay(da.x, A=fitted_params.A, L=fitted_params.L) ``` The latter would be useful for fitting the same curve to multiple model runs, but means we need some kind of `fit_along` or `dim` argument, which would default to all dims. So the method docstring would end up like ```python def fit(self, f, fit_along=None, skipna=None, full=False, cov=False): """""" Fits the function f to the DataArray. Expects the function f to have a signature like `result = f(*coords, **params)` for example `result_da = f(da.xcoord, da.ycoord, da.zcoord, A=5, B=None)` The names of the `**params` kwargs will be used to name the output variables. Returns ------- fit_results - A single dataset which contains the variables (for each parameter in the fitting function): `param1` The optimised fit coefficients for parameter one. `param1_residuals` The residuals of the fit for parameter one. ... """""" ``` ## Questions 1) Should it wrap `scipy.optimise.curve_fit`, or reimplement it? Wrapping it is simpler, but as it just calls `least_squares` [under the hood](https://github.com/scipy/scipy/blob/v1.5.2/scipy/optimize/minpack.py#L532-L834) then reimplementing it would mean we could use the dask-powered version of `least_squares` (like [`da.polyfit does`](https://github.com/pydata/xarray/blob/9058114f70d07ef04654d1d60718442d0555b84b/xarray/core/dataset.py#L5987)). 2) What form should we expect the curve-defining function to come in? `scipy.optimize.curve_fit` expects the curve to act as `ydata = f(xdata, *params) + eps`, but in xarray then `xdata` could be one or multiple coords or dims, not necessarily a single array. Might it work to require a signature like `result_da = f(da.xcoord, da.ycoord, da.zcoord, ..., **params)`? Then the `.fit` method would be work out how many coords to pass to `f` based on the dimension of the `da` and the `fit_along` argument. But then the order of coord arguments in the signature of `f` would matter, which doesn't seem very xarray-like. 3) Is it okay to inspect parameters of the curve-defining function? If we tell the user the curve-defining function has to have a signature like `da = func(*coords, **params)`, then we could read the names of the parameters by inspecting the function kwargs. Is that a good idea or might it end up being unreliable? Is the `inspect` standard library module the right thing to use for that? This could also be used to provide default guesses for the fitting parameters.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4300/reactions"", ""total_count"": 4, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 1}",,completed,13221727,issue 604218952,MDU6SXNzdWU2MDQyMTg5NTI=,3992,"DataArray.integrate has a 'dim' arg, but Dataset.integrate has a 'coord' arg",35968931,closed,0,,,1,2020-04-21T19:12:03Z,2021-01-29T22:59:30Z,2021-01-29T22:59:30Z,MEMBER,,,,"This is just a minor gripe but I think it should be fixed. The API syntax is inconsistent: ```python ds.differentiate(coord='x') da.differentiate(coord='x') ds.integrate(coord='x') da.integrate(dim='x') # why dim?? ``` It should definitely be `coord` - IMO it doesn't make sense to integrate or differentiate over a dim because a dim by definition has no information about the distance between grid points. I think because the distinction between dims and coords is one of the things that new users have to learn about, we should be strict to not confuse up the meanings in the documentation/API. The discussion on the original PR [seems to agree](https://github.com/pydata/xarray/pull/2653#discussion_r246164990), so I think this was just an small oversight. The only question is whether it requires a deprecation cycle? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3992/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 453126577,MDU6SXNzdWU0NTMxMjY1Nzc=,3002,plot.pcolormesh fails with shading='gouraud',35968931,closed,0,,,5,2019-06-06T16:27:00Z,2020-11-29T16:28:32Z,2019-06-06T22:26:35Z,MEMBER,,,,"`xarray.plot.pcolormesh()` fails when you pass the `matplotlib.pyplot.pcolormesh()` keyword argument `shading='gouraud'` to it. #### Code Sample, a copy-pastable example if possible ```python import matplotlib.pyplot as plt import numpy as np import xarray as xr lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4)) lon += lat/10 lat += lon/10 da = xr.DataArray(np.arange(20).reshape(4, 5), dims=['y', 'x'], coords = {'lat': (('y', 'x'), lat), 'lon': (('y', 'x'), lon)}) da.plot.pcolormesh('lon', 'lat', shading='gouraud') plt.show() ``` #### Problem description This gives an error: ``` Traceback (most recent call last): File ""mwe.py"", line 17, in da.plot.pcolormesh('lon', 'lat', shading='gouraud') File ""/home/tegn500/Documents/Work/Code/xarray/xarray/plot/plot.py"", line 721, in plotmethod return newplotfunc(**allargs) File ""/home/tegn500/Documents/Work/Code/xarray/xarray/plot/plot.py"", line 662, in newplotfunc **kwargs) File ""/home/tegn500/Documents/Work/Code/xarray/xarray/plot/plot.py"", line 864, in pcolormesh primitive = ax.pcolormesh(x, y, z, **kwargs) File ""/home/tegn500/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/__init__.py"", line 1805, in inner return func(ax, *args, **kwargs) File ""/home/tegn500/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/axes/_axes.py"", line 5971, in pcolormesh X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch) File ""/home/tegn500/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/axes/_axes.py"", line 5559, in _pcolorargs C.shape, Nx, Ny, funcname)) TypeError: Dimensions of C (4, 5) are incompatible with X (6) and/or Y (5); see help(pcolormesh) ``` #### Expected Output This should give almost the same image as in the documentation, just with smoother shading: ![Figure_1](https://user-images.githubusercontent.com/35968931/59049474-f68a6580-887f-11e9-83db-697c38acdf5e.png) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3002/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 349026158,MDU6SXNzdWUzNDkwMjYxNTg=,2355,Animated plots - a suggestion for implementation,35968931,closed,0,,,9,2018-08-09T08:23:17Z,2020-08-16T08:07:12Z,2020-08-16T08:07:12Z,MEMBER,,,,"**It'd be awesome if one could animate the plots xarray creates using matplotlib just by specifying the dimension over which to animate the plot.** This would allow for rapid visualisation of time-evolving data and could potentially be very powerful (imagine a grid of faceted 2d plots, all evolving together over time). I know that there are already some libraries which can create animated plots of xarray data (e.g. Holoviews), but I think that it's within xarray's scope (#2030) to add another dimension to its default matplotlib-style plotting capabilities. **How?** I saw this new package for making it easier to animate matplotlib plots using the funcanimation module: [animatplot](https://github.com/t-makaro/animatplot). It essentially works by wrapping matplotlib commands like `plt.imshow()` to instead return ""blocks"". These blocks can then be animated by feeding them into an `animation` class. An introductory script to plot line data can be found [here](https://animatplot.readthedocs.io/en/latest/tutorial/getting_started..html), but basically has the form ```python import animatplot as amp import matplotlib.pyplot as plt X, Y = load_data_somehow block = amp.blocks.Line(X, Y) anim = amp.Animation([block]) anim.save_gif(""animated_line"") plt.show() ``` which creates a basic gif like this: ![animated line gif](https://user-images.githubusercontent.com/35968931/43885402-a3373002-9b6d-11e8-9b3d-f4e588a71a22.gif) I think that it might be possible to integrate this kind of animation-plotting tool by adding an optional dimension argument to xarray's plotting methods, which if given causes the function to call the wrapped animatplot plotting command instead of the bare matplotlib one. It would then return the corresponding ""block"" ready to be animated. Using the resulting code might only require a few lines to create an impressive visualisation: ```python turb2d = xr.load_dataset(""turbulent_fluid_data.nc"") block = turb2d[""density""].plot.imshow(animate_over='time') anim = Animation([block]) anim.save_gif(""fluid_density.gif"") plt.show() ``` ![n_over_time](https://user-images.githubusercontent.com/35968931/43887058-83d4161c-9b72-11e8-978d-fcb8e071a37a.gif) **What would need changing?** If we take the `da.plot.imshow()` example, then the way I'm imagining this would be done is to add the optional argument `animate_over` to the `plot_2d` decorator, and use it to choose between returning the matplotlib artist (as it does currently) or the ""block"". It would also mean altering the logic inside `plot_2d` and `imshow` to account for the fact you would be calling this on a 3D dataarray instead of a 2D one. I wanted to ask about this before delving into the code too much or submitting a pull request, in case there is some problem with the idea. What do you think?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2355/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 332987740,MDU6SXNzdWUzMzI5ODc3NDA=,2235,Adding surface plot for 2D data,35968931,closed,0,,,2,2018-06-16T13:36:10Z,2020-06-17T04:49:50Z,2020-06-17T04:49:50Z,MEMBER,,,,"I am interested in adding the ability to plot [surface plots](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html#surface-plots) of 2D xarray data using matplotlib's 3D plotting function `plot_surface()`. This would be nice because a surface in 3D is much more useful for showing certain features of 2D data then color plots are. For example an outlier would appear as an obvious spike rather than just a single bright point as it would when using `plot.imshow()`. I'm not suggesting adding full 3D plotting capability, just the ability to visualise 2D data as a surface in 3D. The code would end up allowing you to just call `xr.Dataarray.plot.surface()` to create something like this example from [here](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html#surface-plots) ([code here](https://matplotlib.org/mpl_examples/mplot3d/surface3d_demo.py)): ![Example surface plot](https://matplotlib.org/mpl_examples/mplot3d/surface3d_demo.png) Obviously xarray would be used to automatically set the axes labels and title and so on. As far as I can tell it wouldn't be too difficult to do, it would just be implemented as another 2D plotting method the same way as the `Dataarray.plot.imshow()`, `Dataarray.plot.contour()` etc methods currently are. It would require the imports ```python import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D ``` but these would only need to be imported if this type of plot was chosen. I would be interested in trying to add this myself, but I've never contributed to an open-source project before. Is this a reasonable thing for me to try? Can anyone see any immediate difficulties with this? Would I just need to have a go and then submit a pull request?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2235/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 594688816,MDU6SXNzdWU1OTQ2ODg4MTY=,3939,Why don't we allow indexing with keyword args via __call__?,35968931,closed,0,,,4,2020-04-05T22:44:18Z,2020-04-09T05:14:46Z,2020-04-09T05:14:46Z,MEMBER,,,,"Reading about [PEP472](https://www.python.org/dev/peps/pep-0472/), which would have allowed indexing with keyword arguments like ```python da[x=10] ``` made me wonder: why don't we use `__call__` to get the same effect but just with curved brackets instead of square ones? i.e. ```python da(x=10) ``` We don't currently use `__call__` on `DataArray` or `Dataset` for anything else. I presume there is some good reason why this design decision was taken, but I'm just wondering what it is. (Also has the [ship permanently sailed](https://mail.python.org/pipermail/python-dev/2019-March/156693.html) on PEP472 now?)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3939/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 474247717,MDU6SXNzdWU0NzQyNDc3MTc=,3168,apply_ufunc erroneously operating on an empty array when dask used,35968931,closed,0,,,3,2019-07-29T20:44:23Z,2020-03-30T15:08:16Z,2020-03-30T15:08:15Z,MEMBER,,,,"#### Problem description `apply_ufunc` with `dask='parallelized'` appears to be trying to act on an empty numpy array when the computation is specified, but before `.compute()` is called. In other words, a ufunc which just prints the shape of its argument will print `(0,0)` then print the correct shape once `.compute()` is called. #### Minimum working example ```python import numpy as np import xarray as xr def example_ufunc(x): print(x.shape) return np.mean(x, axis=-1) def new_mean(da, dim): result = xr.apply_ufunc(example_ufunc, da, input_core_dims=[[dim]], dask='parallelized', output_dtypes=[da.dtype]) return result shape = {'t': 2, 'x':3} data = xr.DataArray(data=np.random.rand(*shape.values()), dims=shape.keys()) unchunked = data chunked = data.chunk(shape) actual = new_mean(chunked, dim='x') # raises the warning print(actual) print(actual.compute()) # does the computation correctly ``` #### Result ``` (0, 0) /home/tnichol/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice. out=out, **kwargs) dask.array Dimensions without coordinates: t (2, 3) array([0.147205, 0.402913]) Dimensions without coordinates: t ``` #### Expected result Same thing without the `(0,0)` or the numpy warning. #### Output of ``xr.show_versions()`` (my xarray is up-to-date with master)
INSTALLED VERSIONS ------------------ commit: None python: 3.6.6 |Anaconda, Inc.| (default, Oct 9 2018, 12:34:16) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-862.14.4.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.2 libnetcdf: 4.6.1 xarray: 0.12.3+23.g1d7bcbd pandas: 0.24.2 numpy: 1.16.4 scipy: 1.3.0 netCDF4: 1.4.2 pydap: None h5netcdf: None h5py: 2.8.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.2.1 dask: 2.1.0 distributed: 2.1.0 matplotlib: 3.1.0 cartopy: None seaborn: 0.9.0 numbagg: None setuptools: 40.6.2 pip: 18.1 conda: None pytest: 4.0.0 IPython: 7.1.1 sphinx: 1.8.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3168/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 547523622,MDU6SXNzdWU1NDc1MjM2MjI=,3676,Merging dataArray into dataset using dataset method fails,35968931,closed,0,,,0,2020-01-09T14:46:49Z,2020-01-12T13:04:02Z,2020-01-12T13:04:02Z,MEMBER,,,,"While it's possible to merge a dataset and a dataarray object using the top-level `merge()` function, if you try the same thing with the `ds.merge()` method it fails. ```python import xarray as xr ds = xr.Dataset({'a': 0}) da = xr.DataArray(1, name='b') expected = xr.merge([ds, da]) # works fine print(expected) ds.merge(da) # fails ``` Output: ``` Dimensions: () Data variables: a int64 0 b int64 1 Traceback (most recent call last): File ""mwe.py"", line 6, in actual = ds.merge(da) File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/dataset.py"", line 3591, in merge fill_value=fill_value, File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/merge.py"", line 835, in dataset_merge_method objs, compat, join, priority_arg=priority_arg, fill_value=fill_value File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/merge.py"", line 548, in merge_core coerced = coerce_pandas_values(objects) File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/merge.py"", line 394, in coerce_pandas_values for k, v in obj.items(): File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/common.py"", line 233, in __getattr__ ""{!r} object has no attribute {!r}"".format(type(self).__name__, name) AttributeError: 'DataArray' object has no attribute 'items' ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3676/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 497184021,MDU6SXNzdWU0OTcxODQwMjE=,3334,plot.line fails when plot axis is a 1D coordinate,35968931,closed,0,,,3,2019-09-23T15:52:48Z,2019-09-26T08:51:59Z,2019-09-26T08:51:59Z,MEMBER,,,,"#### MCVE Code Sample ```python import xarray as xr import numpy as np x_coord = xr.DataArray(data=[0.1, 0.2], dims=['x']) t_coord = xr.DataArray(data=[10, 20], dims=['t']) da = xr.DataArray(data=np.array([[0, 1], [5, 9]]), dims=['x', 't'], coords={'x': x_coord, 'time': t_coord}) print(da) da.transpose('time', 'x') ``` Output: ``` array([[0, 1], [5, 9]]) Coordinates: * x (x) float64 0.1 0.2 time (t) int64 10 20 Traceback (most recent call last): File ""mwe.py"", line 22, in da.transpose('time', 'x') File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/dataarray.py"", line 1877, in transpose ""permuted array dimensions (%s)"" % (dims, tuple(self.dims)) ValueError: arguments to transpose (('time', 'x')) must be permuted array dimensions (('x', 't')) ``` As `'time'` is a coordinate with only one dimension, this is an unambiguous operation that I want to perform. However, because `.transpose()` currently only accepts dimensions, this fails with that error. This causes bug in other parts of the code - for example I found this by trying to plot this type of dataarray: ```python da.plot(x='time', hue='x') ``` which gives the same error. (You can get a similar error also with `da.plot(y='time', hue='x')`.) If the [code which explicitly checks](https://github.com/pydata/xarray/pull/2556/files?file-filters%5B%5D=.py#diff-ffd3597671590bab245b3193affa62b8R1437) that the arguments to transpose are dims and not just coordinate dimensions is removed, then both of these examples work as expected. I would like to generalise the transpose function to also accept dimension coordinates, is there any reason not to do this? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3334/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 324350248,MDU6SXNzdWUzMjQzNTAyNDg=,2159,Concatenate across multiple dimensions with open_mfdataset,35968931,closed,0,,,27,2018-05-18T10:10:49Z,2019-09-16T18:54:39Z,2019-06-25T15:50:33Z,MEMBER,,,,"#### Code Sample ```python # Create 4 datasets containing sections of contiguous (x,y) data for i, x in enumerate([1, 3]): for j, y in enumerate([10, 40]): ds = xr.Dataset({'foo': (('x', 'y'), np.ones((2, 3)))}, coords={'x': [x, x+1], 'y': [y, y+10, y+20]}) ds.to_netcdf('ds.' + str(i) + str(j) + '.nc') # Try to open them all in one go ds_read = xr.open_mfdataset('ds.*.nc') print(ds_read) ``` #### Problem description Currently ``xr.open_mfdataset`` will detect a single common dimension and concatenate DataSets along that dimension. However a common use case is a set of NetCDF files which have two or more common dimensions that need to be concatenated along simultaneously (for example collecting the output of any large-scale simulation which parallelizes in more than one dimension simultaneously). For the behaviour of ``xr.open_mfdataset`` to be n-dimensional it should automatically recognise and concatenate along all common dimensions. #### Expected Output ``` Dimensions: (x: 4, y: 6) Coordinates: * x (x) int64 1 2 3 4 * y (y) int64 10 20 30 40 50 60 Data variables: foo (x, y) float64 dask.array ``` #### Current output of ``xr.open_mfdataset()`` ``` Dimensions: (x: 4, y: 12) Coordinates: * x (x) int64 1 2 3 4 * y (y) int64 10 20 30 40 50 60 10 20 30 40 50 60 Data variables: foo (x, y) float64 dask.array ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2159/reactions"", ""total_count"": 4, ""+1"": 4, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 463096652,MDU6SXNzdWU0NjMwOTY2NTI=,3073,Accidentally left a print statement,35968931,closed,0,,,0,2019-07-02T08:38:40Z,2019-07-02T14:16:43Z,2019-07-02T14:16:43Z,MEMBER,,,,"Somehow a rogue debugging print statement managed to sneak through to master in #2616! Line 121 of combine.py https://github.com/pydata/xarray/blob/e2c2264833ce7e861bbb930be44356e1510e13c3/xarray/core/combine.py#L121 should be deleted. @shoyer @dcherian","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3073/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 409854736,MDU6SXNzdWU0MDk4NTQ3MzY=,2768,[Bug] Reduce fails when no axis given,35968931,closed,0,,,1,2019-02-13T15:16:45Z,2019-02-19T06:13:00Z,2019-02-19T06:12:59Z,MEMBER,,,,"`DataArray.reduce()` fails if you try to reduce using a function which doesn't accept any axis arguments. ```python import numpy as np from xarray import DataArray da = DataArray(np.array([[1, 3, 3], [2, 1, 5]])) def total_sum(data): return np.sum(data.flatten()) sum = da.reduce(total_sum) print(sum) ``` This should print a dataarray with just the number 15 in it, but instead it throws the error ``` Traceback (most recent call last): File ""mwe.py"", line 9, in sum = da.reduce(total_sum) File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/dataarray.py"", line 1605, in reduce var = self.variable.reduce(func, dim, axis, keep_attrs, **kwargs) File ""/home/tegn500/Documents/Work/Code/xarray/xarray/core/variable.py"", line 1365, in reduce axis=axis, **kwargs) TypeError: total_sum() got an unexpected keyword argument 'axis' ``` This contradicts what the docstring of `.reduce()` says: ``` axis: int or sequence of int, optional Axis(es) over which to repeatedly apply func. Only one of the ‘dim’ and ‘axis’ arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling f(x) without an axis argument). ``` The problem is that in `variable.py` an `axis=None` kwarg is always passed to func, even if no axis argument is given by the user in `reduce`. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2768/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 404383025,MDU6SXNzdWU0MDQzODMwMjU=,2725,Line plot with x=coord putting wrong variables on axes,35968931,closed,0,,,3,2019-01-29T16:43:18Z,2019-01-30T02:02:22Z,2019-01-30T02:02:22Z,MEMBER,,,,"When I try to plot the values in a 1D DataArray against the values in one of its coordinates, it does not behave at all as expected: ```python import numpy as np import matplotlib.pyplot as plt from xarray import DataArray current = DataArray(name='current', data=np.array([5, 8, 14, 22, 30]), dims=['time'], coords={'time': (['time'], np.array([0.1, 0.2, 0.3, 0.4, 0.5])), 'voltage': (['time'], np.array([100, 200, 300, 400, 500]))}) print(current) # Try to plot current against voltage current.plot.line(x='voltage') plt.show() ``` Output: ``` array([ 5, 8, 14, 22, 30]) Coordinates: * time (time) float64 0.1 0.2 0.3 0.4 0.5 voltage (time) int64 100 200 300 400 500 ``` ![incorrect_current_plot](https://user-images.githubusercontent.com/35968931/51924149-683f3800-23e4-11e9-8957-81d32da43117.png) #### Problem description Not only is `'voltage'` not on the x axis, but `'current'` isn't on the y axis either! #### Expected Output Based on the documentation (and common sense) I would have expected it to plot voltage on the x axis and current on the y axis. (using a branch of xarray which is up-to-date with master) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2725/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 367763373,MDU6SXNzdWUzNjc3NjMzNzM=,2473,Recommended way to extend xarray Datasets using accessors?,35968931,closed,0,,,6,2018-10-08T12:19:21Z,2018-10-31T09:58:05Z,2018-10-31T09:58:05Z,MEMBER,,,,"Hi, I'm now regularly using xarray (& dask) for organising and analysing the output of the simulation code I use ([BOUT++](https://boutproject.github.io/)) and it's very helpful, thank you!. However my current approach is quite clunky at dealing the extra information and functionality that's specific to the simulation code I'm using, and I have questions about what the recommended way to extend the xarray Dataset class is. This seems like a general enough problem that I thought I would make an issue for it. ### Desired What I ideally want to do is extend the xarray.Dataset class to accommodate extra attributes and methods, while retaining as much xarray functionality as possible, but avoiding reimplementing any of the API. This might not be possible, but ideally I want to make a `BoutDataset` class which contains extra attributes to hold information about the run which doesn't naturally fit into the xarray data model, extra methods to perform analysis/plotting which only users of this code would require, but also be able to use xarray-specific methods and top-level functions: ```python bd = BoutDataset('/path/to/data') ds = bd.data # access the wrapped xarray dataset extra_data = bd.extra_data # access the BOUT-specific data bd.isel(time=-1) # use xarray dataset methods bd2 = BoutDataset('/path/to/other/data') concatenated_bd = xr.concat([bd, bd2]) # apply top-level xarray functions to the data bd.plot_tokamak() # methods implementing bout-specific functionality ``` ### Problems with my current approach I have read the documentation about [extending xarray](http://xarray.pydata.org/en/stable/internals.html#extending-xarray), and the issue threads about subclassing Datasets (#706) and accessors (#1080), but I wanted to check that what I'm doing is the recommended approach. Right now I'm [trying](https://github.com/TomNicholas/xcollect/blob/master/boutdataset.py) to do something like ```python @xr.register_dataset_accessor('bout') class BoutDataset: def __init__(self, path): self.data = collect_data(path) # collect all my numerical data from output files self.extra_data = read_extra_data(path) # collect extra data about the simulation def plot_tokamak(): plot_in_bout_specific_way(self.data, self.extra_data) ``` which works in the sense that I can do ```python bd = BoutDataset('/path/to/data') ds = bd.bout.data # access the wrapped xarray dataset extra_data = bd.bout.extra_data # access the BOUT-specific data bd.bout.plot_tokamak() # methods implementing bout-specific functionality ``` but not so well with ```python bd.isel(time=-1) # AttributeError: 'BoutDataset' object has no attribute 'isel' bd.bout.data.isel(time=-1) # have to do this instead, but this returns an xr.Dataset not a BoutDataset concatenated_bd = xr.concat([bd1, bd2]) # TypeError: can only concatenate xarray Dataset and DataArray objects, got concatenated_ds = xr.concat([bd1.bout.data, bd2.bout.data]) # again have to do this instead, which again returns an xr.Dataset not a BoutDataset ``` If I have to reimplement the APl for methods like `.isel()` and top-level functions like `concat()`, then why should I not just subclass `xr.Dataset`? There aren't very many top-level xarray functions so reimplementing them would be okay, but there are loads of Dataset methods. However I think I know how I want my `BoutDataset` class to behave when an `xr.Dataset` method is called on it: I want it to implement that method on the underlying dataset and return the full BoutDatset with extra data and attributes still attached. Is it possible to do something like: ""if calling an `xr.Dataset` method on an instance of `BoutDataset`, call the corresponding method on the wrapped dataset and return a BoutDataset that has the extra BOUT-specific data propagated through""? Thanks in advance, apologies if this is either impossible or relatively trivial, I just thought other xarray users might have the same questions.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2473/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 354923742,MDU6SXNzdWUzNTQ5MjM3NDI=,2388,Test equality of DataArrays up to transposition,35968931,closed,0,,,2,2018-08-28T22:13:01Z,2018-10-08T12:25:46Z,2018-10-08T12:25:46Z,MEMBER,,,,"While writing some unit tests to check I had wrapped `np.gradient` correctly with `xr.apply_ufunc`, I came unstuck because my results were equivalent except for transposed dimensions. It took me a while to realise that `xarray.testing.asset_equal` considers two DataArrays equal only if their dimensions are in the same order, because intuitively that shouldn't matter in the context of xarray's data model. A simple example to demonstrate what I mean: ```python # Create two functionally-equivalent dataarrays data = np.random.randn(4, 3) da1 = xr.DataArray(data, dims=('x', 'y')) da2 = xr.DataArray(data.T, dims=('y', 'x')) # This test will fail xarray.tests.assert_equal(da1, da2) ``` This test fails, with output ``` E AssertionError: E array([[ 0.761038, 0.121675, 0.443863], E [ 0.333674, 1.494079, -0.205158], E [ 0.313068, -0.854096, -2.55299 ], E [ 0.653619, 0.864436, -0.742165]]) E Coordinates: E * x (x) int64 5 7 9 11 E * y (y) int64 1 4 6 E E array([[ 0.761038, 0.333674, 0.313068, 0.653619], E [ 0.121675, 1.494079, -0.854096, 0.864436], E [ 0.443863, -0.205158, -2.55299 , -0.742165]]) E Coordinates: E * x (x) int64 5 7 9 11 E * y (y) int64 1 4 6 ``` even though these two DataArrays are functionally-equivalent for all xarray operations you could perform with them. It would make certain types of unit tests simpler and clearer to have a function like ```python xarray.tests.assert_equivalent(da1, da2) ``` which would return true if one DataArray can be formed from the other by transposition. I would have thought that a test that does this would just transpose one into the shape of the other before comparison?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2388/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue