github: issues: 7 rows where comments = 8 and user = 14371165 sorted by updated

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id	node_id	number	title	user	state	comments	created_at	updated_at ▲	closed_at	author_association	draft	pull_request	body	reactions	repo	type
1680031454	I_kwDOAMm_X85kIz7e	7780	mypy does not understand output of binary operations	Illviljan 14371165	open	8	2023-04-23T13:38:55Z	2024-04-28T20:07:04Z		MEMBER			What happened? When doing operations on numpy arrays and xarray variables mypy does not understand that the output is always a xarray variable regardless of the order. See example. What did you expect to happen? mypy to pass for the example code. Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr x = np.array([1, 2, 4]) v = xr.Variable(["x"], x) numpy first: xv = x * v xv.values # error: "ndarray[Any, dtype[bool_]]" has no attribute "values" [attr-defined] if isinstance(xv, xr.Variable): xv.values variable first: vx = v * x vx.values if isinstance(vx, xr.Variable): vx.values ``` 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, 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? Seen in #7741 Environment xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.16 (main, Mar 8 2023, 10:39:24) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en libhdf5: 1.10.6 libnetcdf: None xarray: 2023.4.2 pandas: 2.0.0 numpy: 1.23.5 scipy: 1.10.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None iris: None bottleneck: None dask: 2023.4.0 distributed: 2023.4.0 matplotlib: 3.5.3 cartopy: None seaborn: 0.12.2 numbagg: None fsspec: 2023.4.0 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 67.7.1 pip: 23.1.1 conda: 23.3.1 pytest: 7.3.1 mypy: 1.2.0 IPython: 8.12.0 sphinx: 6.1.3	{ "url": "https://api.github.com/repos/pydata/xarray/issues/7780/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	xarray 13221727	issue
1953053810	PR_kwDOAMm_X85dURGi	8344	Add mean to NamedArray._array_api	Illviljan 14371165	open	8	2023-10-19T21:05:06Z	2023-12-19T17:49:22Z		MEMBER	1	pydata/xarray/pulls/8344	[ ] Closes #xxxx [ ] Tests added [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` [ ] New functions/methods are listed in `api.rst`	{ "url": "https://api.github.com/repos/pydata/xarray/issues/8344/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	xarray 13221727	pull
970245117	MDExOlB1bGxSZXF1ZXN0NzEyMjIzNzc2	5704	Allow in-memory arrays with open_mfdataset	Illviljan 14371165	open	8	2021-08-13T09:50:26Z	2023-04-29T06:58:26Z		MEMBER	0	pydata/xarray/pulls/5704	The docstring seems to imply that it's possible to get in-memory arrays: https://github.com/pydata/xarray/blob/4bb9d9c6df77137f05e85c7cc6508fe7a93dc0e4/xarray/backends/api.py#L732 But it doesn't seem possible because of: https://github.com/pydata/xarray/blob/4bb9d9c6df77137f05e85c7cc6508fe7a93dc0e4/xarray/backends/api.py#L899 This PR removes that `or` check, changes the default to `chunk={}`, and fixes the failing tests. [x] Noticed in #5689 [ ] Closes #7792 [x] Tests added [x] Passes `pre-commit run --all-files` [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst`	{ "url": "https://api.github.com/repos/pydata/xarray/issues/5704/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	xarray 13221727	pull
931016490	MDExOlB1bGxSZXF1ZXN0Njc4NTc5MjIx	5542	Do not transpose 1d arrays during interpolation	Illviljan 14371165	open	8	2021-06-27T20:56:13Z	2022-10-12T20:12:11Z		MEMBER	0	pydata/xarray/pulls/5542	Seems a waste of time to transpose 1d arrays. [ ] Closes #xxxx [ ] Tests added [ ] Passes `pre-commit run --all-files` [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` [ ] New functions/methods are listed in `api.rst`	{ "url": "https://api.github.com/repos/pydata/xarray/issues/5542/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	xarray 13221727	pull
957432870	MDExOlB1bGxSZXF1ZXN0NzAwODYwMzY4	5661	Speed up _mapping_repr	Illviljan 14371165	closed	8	2021-08-01T08:44:17Z	2022-08-12T09:07:44Z	2021-08-02T19:45:16Z	MEMBER	0	pydata/xarray/pulls/5661	Creating a ordered list for filtering purposes using `.items()` turns out being rather slow. Use `.keys()` instead as that doesn't trigger a bunch of dataarray initializations. [x] Passes `pre-commit run --all-files` Test case: ```python import numpy as np import xarray as xr a = np.arange(0, 2000) data_vars = dict() for i in a: data_vars[f"long_variable_name_{i}"] = xr.DataArray( name=f"long_variable_name_{i}", data=np.arange(0, 20), dims=[f"long_coord_name_{i}x"], coords={f"long_coord_name{i}x": np.arange(0, 20) 2}, ) ds0 = xr.Dataset(data_vars) ds0.attrs = {f"attr*{k}": 2 for k in a} ``` Before: `python %timeit print(ds0) 14.6 s ± 215 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)` After: `python %timeit print(ds0) 120 ms ± 2.06 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)`	{ "url": "https://api.github.com/repos/pydata/xarray/issues/5661/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	xarray 13221727	pull
830918156	MDExOlB1bGxSZXF1ZXN0NTkyMzc2Mzk2	5031	Keep coord attrs when interpolating	Illviljan 14371165	closed	8	2021-03-13T15:05:39Z	2021-05-18T18:16:10Z	2021-04-27T07:00:08Z	MEMBER	0	pydata/xarray/pulls/5031	[x] Closes #4239 [x] Closes #4839 [x] Tests added [x] Passes `pre-commit run --all-files`	{ "url": "https://api.github.com/repos/pydata/xarray/issues/5031/reactions", "total_count": 2, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 1, "eyes": 0 }	xarray 13221727	pull
777526340	MDExOlB1bGxSZXF1ZXN0NTQ3Nzk5MDk2	4750	Limit number of data rows shown in repr	Illviljan 14371165	closed	8	2021-01-02T21:14:50Z	2021-01-04T02:13:52Z	2021-01-04T02:13:52Z	MEMBER	0	pydata/xarray/pulls/4750	[x] Closes #4736 [x] Tests added [x] Passes `isort . && black . && mypy . && flake8` [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` [ ] New functions/methods are listed in `api.rst` Test example: `python a = np.arange(0, 2000) b = np.core.defchararray.add("long_variable_name", a.astype(str)) c = np.arange(0, 30) d = np.core.defchararray.add("attr_", c.astype(str)) e = {k: 2 for k in d} coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=da.array([3, 4]), dims=["time"], coords=coords, ) ds0 = xr.Dataset(data_vars) ds0.attrs = e` Looks like this with 24 max rows of interesting data: python print(ds0) Out[15]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name2 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name3 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name4 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name5 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name6 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name7 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name8 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name9 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name10 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name11 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> ... ... long_variable_name1988 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1989 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1990 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1991 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1992 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1993 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1994 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1995 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1996 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1997 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1998 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1999 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> Attributes: attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 attr_6: 2 attr_7: 2 attr_8: 2 attr_9: 2 attr_10: 2 attr_11: 2 ... ... attr_18: 2 attr_19: 2 attr_20: 2 attr_21: 2 attr_22: 2 attr_23: 2 attr_24: 2 attr_25: 2 attr_26: 2 attr_27: 2 attr_28: 2 attr_29: 2 With 20 rows of interesting data: python xr.set_options(display_max_rows=20) ds0 Out[26]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name2 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name3 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name4 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name5 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name6 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name7 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name8 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name9 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> ... long_variable_name1990 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1991 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1992 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1993 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1994 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1995 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1996 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1997 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1998 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1999 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> Attributes: attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 attr_6: 2 attr_7: 2 attr_8: 2 attr_9: 2 ... attr_20: 2 attr_21: 2 attr_22: 2 attr_23: 2 attr_24: 2 attr_25: 2 attr_26: 2 attr_27: 2 attr_28: 2 attr_29: 2 With 16 rows of interesting data: python xr.set_options(display_max_rows=16) ds0 Out[28]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name2 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name3 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name4 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name5 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name6 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name7 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> ... long_variable_name1992 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1993 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1994 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1995 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1996 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1997 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1998 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1999 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> Attributes: attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 attr_6: 2 attr_7: 2 ... attr_22: 2 attr_23: 2 attr_24: 2 attr_25: 2 attr_26: 2 attr_27: 2 attr_28: 2 attr_29: 2 With 12 rows of interesting data: ```python xr.set_options(display_max_rows=12) print(ds0) Out[79]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name2 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name3 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name4 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name5 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> ... long_variable_name1994 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1995 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1996 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1997 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1998 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> long_variable_name1999 (time) int32 dask.array<chunksize=(2,), meta=np.ndarray> Attributes: attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_24: 2 attr_25: 2 attr_26: 2 attr_27: 2 attr_28: 2 attr_29: 2 ```	{ "url": "https://api.github.com/repos/pydata/xarray/issues/4750/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	xarray 13221727	pull

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issues

7 rows where comments = 8 and user = 14371165 sorted by updated_at descending

What happened?

What did you expect to happen?

Minimal Complete Verifiable Example

numpy first:

variable first:

MVCE confirmation

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