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4 rows where "created_at" is on date 2023-10-15, type = "pull" and user = 14371165 sorted by updated_at descending
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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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1944068053 | PR_kwDOAMm_X85c1sC5 | 8314 | Align MemoryCachedArray and PandasIndexingAdapter more | Illviljan 14371165 | closed | 0 | 1 | 2023-10-15T21:42:27Z | 2023-10-16T20:01:21Z | 2023-10-16T20:01:20Z | MEMBER | 0 | pydata/xarray/pulls/8314 | Seen in #8294. The issue is the IndexVariable, ExplicitlyIndexedNDArrayMixin lacks |
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xarray 13221727 | pull | |||||
1944059054 | PR_kwDOAMm_X85c1qR0 | 8312 | Fix typing issues in tests | Illviljan 14371165 | closed | 0 | 1 | 2023-10-15T21:11:12Z | 2023-10-16T15:09:57Z | 2023-10-16T15:09:57Z | MEMBER | 0 | pydata/xarray/pulls/8312 | Seen in #8294. These tests implicitly made sure the type was correct in a way that type checkers wont understand. Make it explicit instead. |
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xarray 13221727 | pull | |||||
1944067022 | PR_kwDOAMm_X85c1r1z | 8313 | Reverse type checks for better type inheritance | Illviljan 14371165 | closed | 0 | 0 | 2023-10-15T21:38:59Z | 2023-10-16T06:30:55Z | 2023-10-16T06:30:55Z | MEMBER | 0 | pydata/xarray/pulls/8313 | Seen in #8294. It's usually better to switch if-checks so that the wider type is defined first. Especially when relying on the implicit type casting like we do here. |
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xarray 13221727 | pull | |||||
1944083743 | PR_kwDOAMm_X85c1vGB | 8315 | Handle numpy missing the array api function astype | Illviljan 14371165 | closed | 0 | 1 | 2023-10-15T22:32:17Z | 2023-10-16T06:25:20Z | 2023-10-16T06:25:19Z | MEMBER | 0 | pydata/xarray/pulls/8315 | This is how our get_array_namespace works: https://github.com/pydata/xarray/blob/dafd726c36e24ac77427513a4a149a6933353b66/xarray/core/duck_array_ops.py#L44-L48 Which usually works. But not for astype. Using np.array_api doesn't work because you have to use np.array_api.Array instead of np.ndarray: ```python import numpy.array_api as nxp nxp.astype(np.array([1, 2,]), np.dtype(float)) Traceback (most recent call last): File "C:\Users\J.W\AppData\Local\Temp\ipykernel_8616\23329947.py", line 1, in <cell line: 1> nxp.astype(np.array([1, 2,]), np.dtype(float)) File "C:\Users\J.W\anaconda3\envs\xarray-tests\lib\site-packages\numpy\array_api_data_type_functions.py", line 20, in astype return Array._new(x._array.astype(dtype=dtype, copy=copy)) AttributeError: 'numpy.ndarray' object has no attribute '_array' ``` I found it simpler to just change astype here. An alternative solution would be to use: https://github.com/data-apis/array-api-compat https://github.com/tomwhite/cubed/pull/317 Seen in #8294. |
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xarray 13221727 | pull |
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