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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 |
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2196272235 | PR_kwDOAMm_X85qKODl | 8856 | Migrate indexing and broadcasting logic to `xarray.namedarray` (Part 1) | andersy005 13301940 | open | 0 | 0 | 2024-03-19T23:51:46Z | 2024-05-03T17:08:11Z | MEMBER | 1 | pydata/xarray/pulls/8856 | This pull request is the first part of migrating the indexing and broadcasting logic from
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xarray 13221727 | pull | ||||||
2231711080 | PR_kwDOAMm_X85sCbN- | 8921 | Revert `.oindex` and `.vindex` additions in `_ElementwiseFunctionArray`, `NativeEndiannessArray`, and `BoolTypeArray` classes | andersy005 13301940 | open | 0 | 9 | 2024-04-08T17:11:08Z | 2024-04-30T06:49:46Z | MEMBER | 0 | pydata/xarray/pulls/8921 | As noted in https://github.com/pydata/xarray/issues/8909, the use of
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xarray 13221727 | pull | ||||||
2115621781 | I_kwDOAMm_X85-GdOV | 8696 | 🐛 compatibility issues with ArrayAPI and SparseAPI Protocols in `namedarray` | andersy005 13301940 | open | 0 | 2 | 2024-02-02T19:27:07Z | 2024-02-03T10:55:04Z | MEMBER | What happened?i'm experiencing compatibility issues when using What did you expect to happen?i expected that since COO arrays from the sparse library provide the necessary attributes and methods, they would pass the ```python In [56]: from xarray.namedarray._typing import _arrayfunction_or_api, _sparsearrayfunc ...: tion_or_api In [57]: import xarray as xr, sparse, numpy as np, sparse, pandas as pd ```
```python In [58]: x = np.random.random((10)) In [59]: x[x < 0.9] = 0 In [60]: s = sparse.COO(x) In [61]: isinstance(s, _arrayfunction_or_api) Out[61]: True In [62]: s Out[62]: <COO: shape=(10,), dtype=float64, nnz=0, fill_value=0.0> ```
```python In [63]: p = sparse.COO(np.array(['a', 'b'])) In [64]: p Out[64]: <COO: shape=(2,), dtype=<U1, nnz=2, fill_value=> In [65]: isinstance(s, _arrayfunction_or_api) Out[65]: True ``` - object dtype doesn't work ```python In [66]: q = sparse.COO(np.array(['a', 'b']).astype(object)) In [67]: isinstance(s, _arrayfunction_or_api) Out[67]: True In [68]: isinstance(q, _arrayfunction_or_api)TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:606, in _Elemwise._get_func_coords_data(self, mask) 605 try: --> 606 func_data = self.func(func_args, dtype=self.dtype, *self.kwargs) 607 except TypeError: TypeError: real() got an unexpected keyword argument 'dtype' During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:611, in _Elemwise._get_func_coords_data(self, mask) 610 out = np.empty(func_args[0].shape, dtype=self.dtype) --> 611 func_data = self.func(func_args, out=out, *self.kwargs) 612 except TypeError: TypeError: real() got an unexpected keyword argument 'out' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) Cell In[68], line 1 ----> 1 isinstance(q, _arrayfunction_or_api) File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in _ProtocolMeta.instancecheck(cls, instance) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance) File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in <genexpr>(.0) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:900, in SparseArray.real(self) 875 @property 876 def real(self): 877 """The real part of the array. 878 879 Examples (...) 898 numpy.real : NumPy equivalent function. 899 """ --> 900 return self.array_ufunc(np.real, "call", self) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:340, in SparseArray.array_ufunc(self, ufunc, method, inputs, kwargs) 337 inputs = tuple(reversed(inputs_transformed)) 339 if method == "call": --> 340 result = elemwise(ufunc, inputs, kwargs) 341 elif method == "reduce": 342 result = SparseArray._reduce(ufunc, *inputs, kwargs) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:49, in elemwise(func, args, kwargs)
12 def elemwise(func, args, kwargs):
13 """
14 Apply a function to any number of arguments.
15
(...)
46 it is necessary to convert Numpy arrays to :obj: File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:480, in _Elemwise.get_result(self) 477 if not any(mask): 478 continue --> 480 r = self._get_func_coords_data(mask) 482 if r is not None: 483 coords_list.append(r[0]) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:613, in _Elemwise._get_func_coords_data(self, mask) 611 func_data = self.func(func_args, out=out, self.kwargs) 612 except TypeError: --> 613 func_data = self.func(func_args, **self.kwargs).astype(self.dtype) 615 unmatched_mask = ~equivalent(func_data, self.fill_value) 617 if not unmatched_mask.any(): ValueError: invalid literal for int() with base 10: 'a' In [69]: q Out[69]: <COO: shape=(2,), dtype=object, nnz=2, fill_value=0> ``` the failing case appears to be a well know issue Minimal Complete Verifiable Example```Python In [69]: q Out[69]: <COO: shape=(2,), dtype=object, nnz=2, fill_value=0> In [70]: n = xr.NamedArray(data=q, dims=['x']) ``` MVCE confirmation
Relevant log output```Python In [71]: n.data Out[71]: <COO: shape=(2,), dtype=object, nnz=2, fill_value=0> In [72]: n Out[72]: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:606, in _Elemwise._get_func_coords_data(self, mask) 605 try: --> 606 func_data = self.func(func_args, dtype=self.dtype, *self.kwargs) 607 except TypeError: TypeError: real() got an unexpected keyword argument 'dtype' During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:611, in _Elemwise._get_func_coords_data(self, mask) 610 out = np.empty(func_args[0].shape, dtype=self.dtype) --> 611 func_data = self.func(func_args, out=out, *self.kwargs) 612 except TypeError: TypeError: real() got an unexpected keyword argument 'out' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/IPython/core/formatters.py:708, in PlainTextFormatter.call(self, obj) 701 stream = StringIO() 702 printer = pretty.RepresentationPrinter(stream, self.verbose, 703 self.max_width, self.newline, 704 max_seq_length=self.max_seq_length, 705 singleton_pprinters=self.singleton_printers, 706 type_pprinters=self.type_printers, 707 deferred_pprinters=self.deferred_printers) --> 708 printer.pretty(obj) 709 printer.flush() 710 return stream.getvalue() File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/IPython/lib/pretty.py:410, in RepresentationPrinter.pretty(self, obj) 407 return meth(obj, self, cycle) 408 if cls is not object \ 409 and callable(cls.dict.get('repr')): --> 410 return _repr_pprint(obj, self, cycle) 412 return _default_pprint(obj, self, cycle) 413 finally: File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/IPython/lib/pretty.py:778, in repr_pprint(obj, p, cycle) 776 """A pprint that just redirects to the normal repr function.""" 777 # Find newlines and replace them with p.break() --> 778 output = repr(obj) 779 lines = output.splitlines() 780 with p.group(): File ~/devel/pydata/xarray/xarray/namedarray/core.py:987, in NamedArray.repr(self) 986 def repr(self) -> str: --> 987 return formatting.array_repr(self) File ~/mambaforge/envs/xarray-tests/lib/python3.9/reprlib.py:21, in recursive_repr.<locals>.decorating_function.<locals>.wrapper(self) 19 repr_running.add(key) 20 try: ---> 21 result = user_function(self) 22 finally: 23 repr_running.discard(key) File ~/devel/pydata/xarray/xarray/core/formatting.py:665, in array_repr(arr) 658 name_str = "" 660 if ( 661 isinstance(arr, Variable) 662 or _get_boolean_with_default("display_expand_data", default=True) 663 or isinstance(arr.variable._data, MemoryCachedArray) 664 ): --> 665 data_repr = short_data_repr(arr) 666 else: 667 data_repr = inline_variable_array_repr(arr.variable, OPTIONS["display_width"]) File ~/devel/pydata/xarray/xarray/core/formatting.py:633, in short_data_repr(array) 631 if isinstance(array, np.ndarray): 632 return short_array_repr(array) --> 633 elif isinstance(internal_data, _arrayfunction_or_api): 634 return limit_lines(repr(array.data), limit=40) 635 elif getattr(array, "_in_memory", None): File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in _ProtocolMeta.instancecheck(cls, instance) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance) File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in <genexpr>(.0) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:900, in SparseArray.real(self) 875 @property 876 def real(self): 877 """The real part of the array. 878 879 Examples (...) 898 numpy.real : NumPy equivalent function. 899 """ --> 900 return self.array_ufunc(np.real, "call", self) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:340, in SparseArray.array_ufunc(self, ufunc, method, inputs, kwargs) 337 inputs = tuple(reversed(inputs_transformed)) 339 if method == "call": --> 340 result = elemwise(ufunc, inputs, kwargs) 341 elif method == "reduce": 342 result = SparseArray._reduce(ufunc, *inputs, kwargs) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:49, in elemwise(func, args, kwargs)
12 def elemwise(func, args, kwargs):
13 """
14 Apply a function to any number of arguments.
15
(...)
46 it is necessary to convert Numpy arrays to :obj: File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:480, in _Elemwise.get_result(self) 477 if not any(mask): 478 continue --> 480 r = self._get_func_coords_data(mask) 482 if r is not None: 483 coords_list.append(r[0]) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:613, in _Elemwise._get_func_coords_data(self, mask) 611 func_data = self.func(func_args, out=out, self.kwargs) 612 except TypeError: --> 613 func_data = self.func(func_args, **self.kwargs).astype(self.dtype) 615 unmatched_mask = ~equivalent(func_data, self.fill_value) 617 if not unmatched_mask.any(): ValueError: invalid literal for int() with base 10: 'a' ``` Anything else we need to know?i was trying to replace instances of @Illviljan, are there any changes that could be made to both protocols without making them too complex? Environment
```python
INSTALLED VERSIONS
------------------
commit: 541049f45edeb518a767cb3b23fa53f6045aa508
python: 3.9.18 | packaged by conda-forge | (main, Dec 23 2023, 16:35:41)
[Clang 16.0.6 ]
python-bits: 64
OS: Darwin
OS-release: 23.2.0
machine: arm64
processor: arm
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2
xarray: 2024.1.2.dev50+g78dec61f
pandas: 2.2.0
numpy: 1.26.3
scipy: 1.12.0
netCDF4: 1.6.5
pydap: installed
h5netcdf: 1.3.0
h5py: 3.10.0
Nio: None
zarr: 2.16.1
cftime: 1.6.3
nc_time_axis: 1.4.1
iris: 3.7.0
bottleneck: 1.3.7
dask: 2024.1.1
distributed: 2024.1.1
matplotlib: 3.8.2
cartopy: 0.22.0
seaborn: 0.13.2
numbagg: 0.7.1
fsspec: 2023.12.2
cupy: None
pint: 0.23
sparse: 0.15.1
flox: 0.9.0
numpy_groupies: 0.9.22
setuptools: 67.7.2
pip: 23.3.2
conda: None
pytest: 8.0.0
mypy: 1.8.0
IPython: 8.14.0
sphinx: None
```
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xarray 13221727 | issue | ||||||||
775502974 | MDU6SXNzdWU3NzU1MDI5NzQ= | 4738 | ENH: Compute hash of xarray objects | andersy005 13301940 | open | 0 | 11 | 2020-12-28T17:18:57Z | 2023-12-06T18:24:59Z | MEMBER | Is your feature request related to a problem? Please describe. I'm working on some caching/data-provenance functionality for xarray objects, and I realized that there's no standard/efficient way of computing hashes for xarray objects. Describe the solution you'd like It would be useful to have a configurable, reliable/standard ```python In [16]: import zarr In [17]: z = zarr.zeros(shape=(10000, 10000), chunks=(1000, 1000)) In [18]: z.hexdigest() # uses sha1 by default for speed Out[18]: '7162d416d26a68063b66ed1f30e0a866e4abed60' In [20]: z.hexdigest(hashname='sha256') Out[20]: '46fc6e52fc1384e37cead747075f55201667dd539e4e72d0f372eb45abdcb2aa' ``` I'm thinking that an xarray's built-in hashing mechanism would provide a more reliable way to treat metadata such as global attributes, encoding, etc... during the hash computation... Describe alternatives you've considered So far, I am using joblib's default hasher: ```python In [1]: import joblib In [2]: import xarray as xr In [3]: ds = xr.tutorial.open_dataset('rasm') In [5]: joblib.hash(ds, hash_name='sha1') Out[5]: '3e5e3f56daf81e9e04a94a3dff9fdca9638c36cf' In [8]: ds.attrs = {} In [9]: joblib.hash(ds, hash_name='sha1') Out[9]: 'daab25fe735657e76514040608fadc67067d90a0' ``` Additional context Add any other context about the feature request here. |
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xarray 13221727 | issue | ||||||||
1916603412 | PR_kwDOAMm_X85bZS7E | 8244 | Migrate VariableArithmetic to NamedArrayArithmetic | andersy005 13301940 | open | 0 | 6 | 2023-09-28T02:29:15Z | 2023-10-11T17:03:02Z | MEMBER | 1 | pydata/xarray/pulls/8244 |
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xarray 13221727 | pull | ||||||
1903416932 | I_kwDOAMm_X85xc9Zk | 8210 | Inconsistent Type Hinting for dims Parameter in xarray Methods | andersy005 13301940 | open | 0 | 7 | 2023-09-19T17:15:43Z | 2023-09-20T15:03:45Z | MEMBER |
I want Then we can easily go through our xarray methods and easily replace
But it wouldn't be easy in xarray because we use Another idea is to try and make a HashableExcludingNone:
Another idea is drop the idea of Hashable and just allow a few common ones that are used:
No easy paths, and trying to be backwards compatible is very demotivating. Originally posted by @Illviljan in https://github.com/pydata/xarray/pull/8075#discussion_r1330437962 |
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xarray 13221727 | issue | ||||||||
576502871 | MDU6SXNzdWU1NzY1MDI4NzE= | 3834 | encode_cf_datetime() casts dask arrays to NumPy arrays | andersy005 13301940 | open | 0 | 2 | 2020-03-05T20:11:37Z | 2022-04-09T03:10:49Z | MEMBER | Currently, when ```python In [46]: import numpy as np In [47]: import xarray as xr In [48]: import pandas as pd In [49]: times = pd.date_range("2000-01-01", "2001-01-01", periods=11) In [50]: time_bounds = np.vstack((times[:-1], times[1:])).T In [51]: arr = xr.DataArray(time_bounds).chunk() In [52]: arr In [53]: xr.coding.times.encode_cf_datetime(arr) ``` Cc @jhamman |
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xarray 13221727 | issue | ||||||||
653442225 | MDU6SXNzdWU2NTM0NDIyMjU= | 4209 | `xr.save_mfdataset()` doesn't honor `compute=False` argument | andersy005 13301940 | open | 0 | 4 | 2020-07-08T16:40:11Z | 2022-04-09T01:25:56Z | MEMBER | What happened: While using What you expected to happen: I expect the datasets to be written when I explicitly call Minimal Complete Verifiable Example: ```python In [2]: import xarray as xr In [3]: ds = xr.tutorial.open_dataset('rasm', chunks={}) In [4]: ds Out[4]: <xarray.Dataset> Dimensions: (time: 36, x: 275, y: 205) Coordinates: * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00 xc (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray> yc (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray> Dimensions without coordinates: x, y Data variables: Tair (time, y, x) float64 dask.array<chunksize=(36, 205, 275), meta=np.ndarray> Attributes: title: /workspace/jhamman/processed/R1002RBRxaaa01a/l... institution: U.W. source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged convention: CF-1.4 references: Based on the initial model of Liang et al., 19... comment: Output from the Variable Infiltration Capacity... nco_openmp_thread_number: 1 NCO: "4.6.0" history: Tue Dec 27 14:15:22 2016: ncatted -a dimension... In [5]: path = "test.nc" In [7]: ls -ltrh test.nc ls: cannot access test.nc: No such file or directory In [8]: tasks = xr.save_mfdataset(datasets=[ds], paths=[path], compute=False) In [9]: tasks Out[9]: Delayed('list-aa0b52e0-e909-4e65-849f-74526d137542') In [10]: ls -ltrh test.nc -rw-r--r-- 1 abanihi ncar 14K Jul 8 10:29 test.nc ``` Anything else we need to know?: Environment: Output of <tt>xr.show_versions()</tt>```python INSTALLED VERSIONS ------------------ commit: None python: 3.7.6 | packaged by conda-forge | (default, Jun 1 2020, 18:57:50) [GCC 7.5.0] python-bits: 64 OS: Linux OS-release: 3.10.0-693.21.1.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.4 xarray: 0.15.1 pandas: 0.25.3 numpy: 1.18.5 scipy: 1.5.0 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: 1.2.0 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.20.0 distributed: 2.20.0 matplotlib: 3.2.1 cartopy: None seaborn: None numbagg: None setuptools: 49.1.0.post20200704 pip: 20.1.1 conda: None pytest: None IPython: 7.16.1 sphinx: None ``` |
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xarray 13221727 | issue | ||||||||
726020233 | MDU6SXNzdWU3MjYwMjAyMzM= | 4527 | Refactor `xr.save_mfdataset()` to automatically save an xarray object backed by dask arrays to multiple files | andersy005 13301940 | open | 0 | 2 | 2020-10-20T23:48:21Z | 2020-10-22T17:06:46Z | MEMBER | Is your feature request related to a problem? Please describe. Currently, when a user wants to write multiple netCDF files in parallel with xarray and dask, they can take full advantage of A few months ago, I wrote a blog post showing how to save an xarray dataset backed by dask into multiple netCDF files, and since then I've been meaning to request a new feature to make this process convenient for users. Describe the solution you'd like Would it be useful to actually refactor the existing ```python ds.save_mfdataset(prefix="directory/my-dataset") orxr.save_mfdataset(ds, prefix="directoy/my-dataset") ``` ----> ```bash directory/my-dataset-chunk-1.nc directory/my-dataset-chunk-2.nc directory/my-dataset-chunk-3.nc .... ``` |
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xarray 13221727 | issue |
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