numpy-rms: a fast function for calculating a series of Root Mean Square (RMS) values
- Written in C and takes advantage of AVX (on x86-64) or NEON (on ARM) for speed
- The fast implementation is tailored for C-contiguous 1-dimensional and 2-dimensional float32 arrays
Installation
$ pip install numpy-rms
Usage
import numpy_rms
import numpy as np
arr = np.arange(40, dtype=np.float32)
rms_series = numpy_rms.rms(arr, window_size=10)
print(rms_series.shape) # (4,)
Changelog
[0.7.0] - 2025-12-27
Added
- Add support for Python 3.14
Removed
- Remove support for Python 3.9
For the complete changelog, go to CHANGELOG.md
Development
- Install dev/build/test dependencies as denoted in pyproject.toml
CC=clang pip install -e .pytest
Acknowledgements
This library is maintained/backed by Nomono, a Norwegian audio AI startup.
Release files for numpy-rms 0.7.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 647.5 kB
Release files / numpy_rms-0.7.0-cp314-cp314t-win_amd64.whl
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Release files / numpy_rms-0.7.0-cp312-cp312-macosx_11_0_arm64.whl
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Release files / numpy_rms-0.7.0-cp311-cp311-win_amd64.whl
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Release files / numpy_rms-0.7.0-cp311-cp311-musllinux_1_2_x86_64.whl
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Release files / numpy_rms-0.7.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
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Release files / numpy_rms-0.7.0-cp311-cp311-macosx_11_0_arm64.whl
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Release files / numpy_rms-0.7.0-cp311-cp311-macosx_10_9_x86_64.whl
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Release files / numpy_rms-0.7.0-cp310-cp310-win_amd64.whl
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Release files / numpy_rms-0.7.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
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