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1499473190 I_kwDOAMm_X85ZYCUm 7385 Unexpected NaNs in broadcast dopplershift 221526 open 0     4 2022-12-16T02:42:44Z 2023-03-14T20:43:00Z   CONTRIBUTOR      

What happened?

When running the broadcast in the sample code, I end up with nan in the output when there are not any in the original source array. While I know the construction is really odd (this came from user-submitted code), I'm shocked that it resulted in nans the resulting broadcasted data and honestly assumed MetPy's code was doing something dumb for quite awhile. I would have expected (regardless of the nature of the coordinates) that the result for broad_a be [[1, 2], [1, 2]].

What did you expect to happen?

No response

Minimal Complete Verifiable Example

```Python levs = np.array([100000, 85000]) a = xr.Dataset({'a': (('lev',), [1, 2])}, coords={'lev': levs}).to_array() b = xr.Dataset({'b': (('lev',), [3, 4])}, coords={'lev': levs}).to_array()

broad_a, broad_b = xr.broadcast(a, b) print(broad_a) ```

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

Python <xarray.DataArray (variable: 2, lev: 2)> array([[ 1., 2.], [nan, nan]]) Coordinates: * lev (lev) int64 100000 85000 * variable (variable) object 'a' 'b'

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:31:57) [Clang 14.0.6 ] python-bits: 64 OS: Darwin OS-release: 21.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2022.12.0 pandas: 1.5.2 numpy: 1.23.5 scipy: 1.9.3 netCDF4: 1.6.2 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.13.3 cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: 0.9.10.3 iris: None bottleneck: 1.3.5 dask: 2022.6.1 distributed: 2022.6.1 matplotlib: 3.6.2 cartopy: 0.21.0 seaborn: None numbagg: None fsspec: 2022.11.0 cupy: None pint: 0.20.1 sparse: None flox: None numpy_groupies: None setuptools: 65.5.1 pip: 22.3.1 conda: None pytest: 7.2.0 mypy: 0.991 IPython: 8.7.0 sphinx: 5.3.0 
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     xarray 13221727 issue

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