bottleneck : Wrong mean for float32 array
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Description
I think it is better to have this discussion here instead of on the dask page https://github.com/dask/dask/issues/2095
This is the replicable "bug":
ds = xarray.open_dataset('/opt/data/ERAIN/ERAIN-t2m-1983-2012.seasmean.nc')
ds.var167.mean()
Out[14]:
<xarray.DataArray 'var167' ()>
array(261.6441345214844)
ds.var167.data.mean()
Out[15]: 278.62466
The dataset is ~65 MB, here the file https://www.dropbox.com/s/xtj3fm7ihtbwd5r/ERAIN-t2m-1983-2012.seasmean.nc?dl=0
It is a quite normal NetCDF (no NaN), just processed with CDO as you can see on the dask issue.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No source file or test is named. Start by reproducing the discrepancy with the linked NetCDF dataset, comparing ds.var167.mean() with ds.var167.data.mean(), and tracing the reduction path involved in the float32 calculation. Done means identifying and correcting the cause so both results agree appropriately.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- data
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100