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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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1027024891 | I_kwDOAMm_X849Nyf7 | 5866 | Element-wise numpy operations using kwarg `where` are not supported for chunked DataArrays | ilopezgp 38085953 | closed | 0 | 3 | 2021-10-15T02:53:22Z | 2022-03-21T09:02:43Z | 2022-03-21T09:02:43Z | NONE | What happened: In general, element-wise operations work smoothly with DataArrays. However, I have found that using element-wise operations such as What you expected to happen: I expected the operation Minimal Complete Verifiable Example: ```python import numpy as np import xarray as xr toy_da = xr.DataArray( np.random.rand(50, 50), coords=([np.arange(50), np.arange(50)]), dims=['x', 'y'], name='var1') toy_da2 = toy_da.chunk({'x': 5}) This works as expectednp.divide(toy_da, toy_da, where=toy_da > 0.5) This, however, does notnp.divide(toy_da2, toy_da2, where=toy_da2 > 0.5) ``` The error message:
Anything else we need to know?: Environment: Output of <tt>xr.show_versions()</tt>>>> xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.11 (default, Jul 27 2021, 07:03:16) [Clang 10.0.0 ] python-bits: 64 OS: Darwin OS-release: 20.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.8.0 xarray: 0.19.0 pandas: 1.3.2 numpy: 1.18.5 scipy: 1.4.1 netCDF4: 1.5.7 pydap: None h5netcdf: 0.11.0 h5py: 2.10.0 Nio: None zarr: None cftime: 1.5.0 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: 0.9.9.0 iris: None bottleneck: 1.3.2 dask: 2021.08.1 distributed: 2021.08.1 matplotlib: 3.2.0 cartopy: None seaborn: 0.11.2 numbagg: None pint: None setuptools: 52.0.0.post20210125 pip: 21.2.2 conda: None pytest: None IPython: 7.26.0 sphinx: None |
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