FIX: scaling factor for window with negative value #25122
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Simply drop the `np.abs()` on window to fix the wrong scaling factor for window with negative value. For more detail refer to matplotlib#24821 **Caution**: With this fix, the behavior would change for window with complex value. With `np.abs()` on window, it seems can handle complex value, but I don't think it's the right way to do it. As it can't fall back to real value case for complex value with zero imaginary part and negative real part (for example -1 + 0j). Also, I didn't understand the need for complex window, so here I simply ignore the complex case. And this is consistent with the implementation of [scipy](https://github.com/scipy/scipy/blob/d9f75db82fdffef06187c9d8d2f0f5b36c7a791b/scipy/signal/_spectral_py.py#L1854-L1859).
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I think these are correct. Can you add some tests, particularly if you can somehow create a flattop w/o using scipy (we don't use it for our tests, unfortunately). For tests, I'd calculate the spectrum of random numbers and just show that its average equals an expected value, perhaps with a bit of floating point slop (np.testing.allclose)? I don't think this needs a what's new or anything - it only applies to windows with negative co-efficients, which are relatively rare. |
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Add new test function for scale factor of flattop window. Also remove the unnecessary `np.abs()` on window in other functions.
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I have add the new function |
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Thanks @gapplef and congratulations on your first merged PR! Hope to see you around! |
PR Summary
Simply drop the
np.abs()on window to fix the wrong scaling factor for window with negative value. For more detail refer to #24821Caution: With this fix, the behavior would change for window with complex value.
With
np.abs()on window, it seems can handle complex value, but I don't think it's the right way to do it. As it can't fall back to real value case for complex value with zero imaginary part and negative real part (for example -1 + 0j).Also, I didn't understand the need for complex window, so here I simply ignore the complex case. And this is consistent with the implementation of scipy.
Closes #24821
Related to #22828 (not sure it can be closed, that seems to try to deal with the complex case as well, but is there one?).
PR Checklist
Documentation and Tests
pytestpasses)Release Notes
.. versionadded::directive in the docstring and documented indoc/users/next_whats_new/.. versionchanged::directive in the docstring and documented indoc/api/next_api_changes/next_whats_new/README.rstornext_api_changes/README.rst