Comments (3)
Ah.. I guess that's:
Automate pre-computed filter generation. Regenerated and improved kaiser_fast and kaiser_best filters.
Cool. Thanks. Sorry to bother you with this.
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We just stumbled over this by accident and it's (probably) completely inconsequential, but v0.2.2 produces slightly different results from v0.3.0.
To reproduce, unzip the archive and run:
conda env create -f resampy022.yml; conda env create -f resampy030.yml; conda activate resampy022; python resampy_test.py; conda activate resampy030; python resampy_test.py;
Perhaps this is an intentional side-effect of some other change.
Yes, this is known and expected. Per the changelog, the filters have been revised in the 0.3 release to reduce aliasing.
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No worries! Documentation probably could have been clearer here.
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Related Issues (20)
- Resampy 0.3.0 is slow for multi-dimensional input HOT 5
- Issues with Numba 0.55.2 HOT 4
- Resampy 0.3.1 slows down other libraries HOT 43
- Use parallel=True by default can be problematic in multiprocessing applications HOT 8
- Bringing import time back down
- Edge Case, rounding error in length calculation HOT 3
- Missing argument in guvectorize decorator ? HOT 2
- Quality issues? HOT 5
- resampling to the same hz HOT 1
- Incompatible with latest numba HOT 3
- Best quality resampling?
- PyPI status HOT 2
- Add linting CI
- Add pypi packaging workflow HOT 1
- Drop legacy python support
- Fix documentation builds HOT 1
- Update README badges
- Improve default filters and make the whole process reproducible HOT 14
- Cache filters on load
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