html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4079#issuecomment-1435715372,https://api.github.com/repos/pydata/xarray/issues/4079,1435715372,IC_kwDOAMm_X85Vk0cs,14808389,2023-02-18T16:51:14Z,2023-02-18T16:51:14Z,MEMBER,"> hmm... would `np.nan` work?
not sure about alignment, but at least `obj.sizes` would break with multiple dims: while it does not compare as equal, the `hash()` of `np.nan` stays constant (same with any other object, I guess).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-1434959378,https://api.github.com/repos/pydata/xarray/issues/4079,1434959378,IC_kwDOAMm_X85Vh74S,2448579,2023-02-17T17:16:14Z,2023-02-17T17:16:22Z,MEMBER,"This reminds me of the issue with `nan` shaped dimensions. Where again we want to avoid automatic alignment (well in that case you can't align).
For this particular issue could we define a `xr.UNNAMED` that doesn't equal itself (hmm... would `np.nan` work?). Would that skip alignment checks?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-1434932769,https://api.github.com/repos/pydata/xarray/issues/4079,1434932769,IC_kwDOAMm_X85Vh1Yh,1217238,2023-02-17T17:03:52Z,2023-02-17T17:03:52Z,MEMBER,"I agree, automatic dimension only ever really made sense for interactive usecases, where a user could see and fix the default names.
It's a little late to change the default now to raising an error instead, but maybe we could add a warning?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-1434805981,https://api.github.com/repos/pydata/xarray/issues/4079,1434805981,IC_kwDOAMm_X85VhWbd,14808389,2023-02-17T15:28:07Z,2023-02-17T15:28:07Z,MEMBER,"I'd probably use `itertools.count()` or the `uuid` module to generate globally unique dimension names... something like
```python
class _UnnamedDimensions:
def __init__(self):
self.dimension_names = (f""unnamed_dim_{number}"" for number in itertools.count())
def __call__(self, n):
return list(itertools.islice(dimension_names, None, n))
unnamed_dimensions = _UnnamedDimensions()
```
or using `uuid` (probably overkill):
```python
def unnamed_dimensions(n):
return [uuid.uuid4() for _ in range(n)]
```
you'd use it like this:
```python
d1 = xr.DataArray(data=[1, 2], dims=unnamed_dimensions(1))
d2 = xr.DataArray(data=[[1, 2]], dims=unnamed_dimensions(2))
```
which would make ""unnamed"" a bit more explicit.
Edit: that's probably not so different from what you meant with `d1_i`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-1434780029,https://api.github.com/repos/pydata/xarray/issues/4079,1434780029,IC_kwDOAMm_X85VhQF9,90008,2023-02-17T15:08:50Z,2023-02-17T15:08:50Z,CONTRIBUTOR,"I know it is ""stale"" but aligning to these ""surprise dimensions"" creates ""late stage"" bugs that are hard to pinpoint.
I'm not sure if it is possible to mark these dimensions as ""unnamed"" and as such, they should be ""merged"" into new ""unnamed"" dimensions that the user isn't tracking at this point in time.
Our workaround have included calling these dimensions something related to the datarray `d1_i`, or simply making small small ""arrays"" a countable number of scalar variables (`d1_min`, `d1_max`) instead of a single array containing two values `d1_limits`.
```python
import xarray as xr
d1 = xr.DataArray(data=[1, 2])
assert 'dim_0' in d1.dims
d2 = xr.DataArray(data=[1, 2, 3])
assert 'dim_0' in d2.dims
xr.Dataset({'d1': d1, 'd2': d2})
```
Stack trace
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 7
4 d2 = xr.DataArray(data=[1, 2, 3])
5 assert 'dim_0' in d2.dims
----> 7 xr.Dataset({'d1': d1, 'd2': d2})
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/dataset.py:612, in Dataset.__init__(self, data_vars, coords, attrs)
609 if isinstance(coords, Dataset):
610 coords = coords.variables
--> 612 variables, coord_names, dims, indexes, _ = merge_data_and_coords(
613 data_vars, coords, compat=""broadcast_equals""
614 )
616 self._attrs = dict(attrs) if attrs is not None else None
617 self._close = None
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/merge.py:564, in merge_data_and_coords(data_vars, coords, compat, join)
562 objects = [data_vars, coords]
563 explicit_coords = coords.keys()
--> 564 return merge_core(
565 objects,
566 compat,
567 join,
568 explicit_coords=explicit_coords,
569 indexes=Indexes(indexes, coords),
570 )
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/merge.py:741, in merge_core(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value)
738 _assert_compat_valid(compat)
740 coerced = coerce_pandas_values(objects)
--> 741 aligned = deep_align(
742 coerced, join=join, copy=False, indexes=indexes, fill_value=fill_value
743 )
744 collected = collect_variables_and_indexes(aligned, indexes=indexes)
745 prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat)
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/alignment.py:848, in deep_align(objects, join, copy, indexes, exclude, raise_on_invalid, fill_value)
845 else:
846 out.append(variables)
--> 848 aligned = align(
849 *targets,
850 join=join,
851 copy=copy,
852 indexes=indexes,
853 exclude=exclude,
854 fill_value=fill_value,
855 )
857 for position, key, aligned_obj in zip(positions, keys, aligned):
858 if key is no_key:
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/alignment.py:785, in align(join, copy, indexes, exclude, fill_value, *objects)
589 """"""
590 Given any number of Dataset and/or DataArray objects, returns new
591 objects with aligned indexes and dimension sizes.
(...)
775
776 """"""
777 aligner = Aligner(
778 objects,
779 join=join,
(...)
783 fill_value=fill_value,
784 )
--> 785 aligner.align()
786 return aligner.results
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/alignment.py:573, in Aligner.align(self)
571 self.assert_no_index_conflict()
572 self.align_indexes()
--> 573 self.assert_unindexed_dim_sizes_equal()
575 if self.join == ""override"":
576 self.override_indexes()
File ~/mambaforge/envs/dev/lib/python3.9/site-packages/xarray/core/alignment.py:472, in Aligner.assert_unindexed_dim_sizes_equal(self)
470 add_err_msg = """"
471 if len(sizes) > 1:
--> 472 raise ValueError(
473 f""cannot reindex or align along dimension {dim!r} ""
474 f""because of conflicting dimension sizes: {sizes!r}"" + add_err_msg
475 )
ValueError: cannot reindex or align along dimension 'dim_0' because of conflicting dimension sizes: {2, 3}
```
cc: @claydugo","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-1112749202,https://api.github.com/repos/pydata/xarray/issues/4079,1112749202,IC_kwDOAMm_X85CUzSS,26384082,2022-04-28T23:37:47Z,2022-04-28T23:37:47Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity
If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-631075010,https://api.github.com/repos/pydata/xarray/issues/4079,631075010,MDEyOklzc3VlQ29tbWVudDYzMTA3NTAxMA==,703554,2020-05-19T20:50:26Z,2020-05-19T20:50:51Z,CONTRIBUTOR,"> In the specific example from your notebook, where do the dimensions lengths `__variants/BaseCounts_dim1`, `__variants/MLEAC_dim1` and `__variants/MLEAF_dim1` come from?
>
> `BaseCounts_dim1` is length 4, so maybe that corresponds to DNA bases ATGC?
In this specific example, I do actually know where these dimension lengths come from. In fact I should've used the shared dimension `alt_alleles` instead of `__variants/MLEAC_dim1` and `__variants/MLEAF_dim1`. And yes `BaseCounts_dim1` does correspond to DNA bases.
But two points.
First, I don't care about these dimensions. The only dimensions I care about and will use are `variants`, `samples` and `ploidy`.
Second, more important, this kind of data can come from a number of different sources, each of which includes a different set of arrays with different names and semantics. While there are some common arrays and naming conventions where I can guess what the dimensions mean, in general I can't know all of those up front and bake them in as special cases. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-630936142,https://api.github.com/repos/pydata/xarray/issues/4079,630936142,MDEyOklzc3VlQ29tbWVudDYzMDkzNjE0Mg==,1217238,2020-05-19T16:33:49Z,2020-05-19T16:33:49Z,MEMBER,"In the specific example from your notebook, where do the dimensions lengths `__variants/BaseCounts_dim1`, `__variants/MLEAC_dim1` and `__variants/MLEAF_dim1` come from?
`BaseCounts_dim1` is length 4, so maybe that corresponds to DNA bases ATGC?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-630924754,https://api.github.com/repos/pydata/xarray/issues/4079,630924754,MDEyOklzc3VlQ29tbWVudDYzMDkyNDc1NA==,703554,2020-05-19T16:14:27Z,2020-05-19T16:14:27Z,CONTRIBUTOR,"Thanks @shoyer.
For reference, I'm exploring putting some genome variation data into xarray, here's an [initial experiment](https://nbviewer.jupyter.org/gist/alimanfoo/b74b08465727894538d5b161b3ced764) and [discussion here](https://discourse.smadstatgen.org/t/xarray-conventions-for-variation-data/44/2).
In general I will have some arrays where I won't know what some of the dimensions mean, and so cannot give them a meaningful name.
No worries if this is hard, was just wondering if it was supported already.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-630916682,https://api.github.com/repos/pydata/xarray/issues/4079,630916682,MDEyOklzc3VlQ29tbWVudDYzMDkxNjY4Mg==,1217238,2020-05-19T16:00:34Z,2020-05-19T16:00:34Z,MEMBER,"The main problem is that unnamed dimensions (in NumPy, at least) have different semantics than named dimensions, particular for how dimensions are matched up in broadcasting. For that reason, we really want all dimensions to always be named.
We could possibly add support for unnamed dimensions in the Dataset constructor, but on the other hand it also provides a gentle nudge to chose names.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-630913851,https://api.github.com/repos/pydata/xarray/issues/4079,630913851,MDEyOklzc3VlQ29tbWVudDYzMDkxMzg1MQ==,703554,2020-05-19T15:55:54Z,2020-05-19T15:55:54Z,CONTRIBUTOR,Thanks so much @rabernat for quick response.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539
https://github.com/pydata/xarray/issues/4079#issuecomment-630906095,https://api.github.com/repos/pydata/xarray/issues/4079,630906095,MDEyOklzc3VlQ29tbWVudDYzMDkwNjA5NQ==,1197350,2020-05-19T15:42:07Z,2020-05-19T15:42:07Z,MEMBER,"> Is there a way to create arrays and datasets with unnamed dimensions?
No. Xarray's data model requires all dimensions to have a name.
Xarray allows you to create DataArrays without specifying dimensions:
```python
import xarray as xr
import numpy as np
da = xr.DataArray(np.zeros((4, 5)))
```
Xarray will choose dimension names in this case (`dim_0`, `dim_1`, etc.)
This doesn't work with creating Datasets, because you *have* to specify all variables' dimensions in the Dataset constructor.
I imagine that unnamed dimensions would break lots of internal Xarray logic.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,621078539