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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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811321550 | MDU6SXNzdWU4MTEzMjE1NTA= | 4922 | Bottleneck and dask objects ignore `min_periods` on `rolling` | bradyrx 8881170 | open | 0 | 5 | 2021-02-18T17:43:50Z | 2021-12-19T15:18:45Z | CONTRIBUTOR | What happened: When What you expected to happen: When using
Minimal Complete Verifiable Example: With
Without ```python import xarray as xr ds = xr.DataArray([1], dims='time') ds.rolling(time=2, center=True, min_periods=1).mean()
Anything else we need to know?: In an applied case, this came up while working on Environment: Output of <tt>xr.show_versions()</tt>INSTALLED VERSIONS ------------------ commit: None python: 3.8.6 | packaged by conda-forge | (default, Jan 25 2021, 23:22:12) [Clang 11.0.1 ] python-bits: 64 OS: Darwin OS-release: 19.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.2 pandas: 1.2.1 numpy: 1.19.5 scipy: 1.6.0 netCDF4: 1.5.5.1 pydap: None h5netcdf: 0.8.1 h5py: 3.1.0 Nio: None zarr: 2.6.1 cftime: 1.3.1 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.1.8 cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.01.1 distributed: 2021.01.1 matplotlib: 3.3.3 cartopy: 0.18.0 seaborn: 0.11.1 numbagg: None pint: 0.16.1 setuptools: 49.6.0.post20210108 pip: 21.0 conda: None pytest: 6.2.2 IPython: 7.18.1 sphinx: 3.4.3 |
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xarray 13221727 | issue | ||||||||
445175953 | MDU6SXNzdWU0NDUxNzU5NTM= | 2969 | `where` function mis-broadcasts and alters data type on dataset | bradyrx 8881170 | closed | 0 | 2 | 2019-05-16T21:52:58Z | 2019-05-20T16:30:02Z | 2019-05-20T16:30:02Z | CONTRIBUTOR | Code Sample, a copy-pastable example if possible```python import numpy as np generate datadateVar = np.arange('2005-02', '2005-06', dtype='datetime64[D]') t = len(dateVar) floatVar = np.random.rand(t, 100) indexVar = np.arange(100) intVar = np.random.randint(1, high=10, size=(t, 100)) create datasetA = xr.DataArray(floatVar, dims=['time', 'N']) A.name = 'floatVar' B = xr.DataArray(indexVar, dims=['N']) B.name = 'indexVar' C = xr.DataArray(intVar, dims=['time', 'N']) C.name = 'intVar' D = xr.DataArray(dateVar, dims=['time']) D.name = 'dateVar' ds = xr.merge([A,B,C,D]) print(ds) <xarray.Dataset> Dimensions: (N: 100, time: 120) Dimensions without coordinates: N, time Data variables: floatVar (time, N) float64 0.4223 0.5019 0.8522 ... 0.9338 0.5833 0.09859 indexVar (N) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99 intVar (time, N) int64 9 2 3 6 8 4 8 7 6 4 2 6 ... 3 1 8 3 8 3 5 3 1 6 7 dateVar (time) datetime64[ns] 2005-02-01 2005-02-02 ... 2005-05-31 apply where functionds.where(ds.indexVar > 50, drop=True) <xarray.Dataset> Dimensions: (N: 49, time: 120) Dimensions without coordinates: N, time Data variables: floatVar (time, N) float64 0.3381 0.04735 0.464 ... 0.5571 0.5297 0.8106 indexVar (N) float64 51.0 52.0 53.0 54.0 55.0 ... 95.0 96.0 97.0 98.0 99.0 intVar (time, N) float64 5.0 2.0 9.0 5.0 5.0 1.0 ... 1.0 6.0 5.0 4.0 3.0 dateVar (time, N) datetime64[ns] 2005-02-01 2005-02-01 ... 2005-05-31 ``` Problem descriptionThis is motivated by a use-case of dimensions (Time, nParticle) for a Lagrangian particle simulation. In the above code snippet, I filter by some condition on For variables that contain the same dimension as the one in Further, data-types are changed ( So the two major issues here:
1. Expected Output
Output of
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