numpy-rms
A fast python library for calculating the RMS of a NumPy array
Decision gist · record as of 2026-08-14
Yes, if you need fast windowed RMS calculations on NumPy float32 arrays in audio, signal, or scientific workflows. The compiled SIMD implementation and wide platform/Python coverage make it a low-friction drop-in for performance-critical RMS loops. Skip it if you only need occasional RMS calls or work with non-float32 dtypes—standard NumPy is sufficient.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy and cffi; C compiler needed if prebuilt wheel unavailable for your platform/Python version.
- Medium install friction due to compiled C extension with cffi dependency; however, prebuilt wheels cover common platforms (x86-64, ARM64, Windows, macOS, Linux variants) and Python 3.10–3.14, reducing build-from-source scenarios.
- Active maintenance with recent release.
License · maintenance · safety
permissive license (permissive) — MIT License (permissive) allows unrestricted use, modification, and distribution in both open and closed projects with minimal obligations—only attribution and license inclusion required.
last release 2026-06-26 (49 days) · last repo commit 2026-07-20 · 5 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 206,054 downloads/mo, #9,578 on PyPI
Alternatives
Verify before relying
pip install numpy-rms
import numpy_rms
import numpy as np
arr = np.arange(40, dtype=np.float32)
rms_series = numpy_rms.rms(arr, window_size=10)- Whether the package supports non-float32 dtypes or only float32 arrays.
- Performance characteristics and typical speedup vs. pure-NumPy implementations.
- Whether window_size parameter supports stride/overlap configuration.
What it is and what it does
numpy-rms is a specialized NumPy extension that computes Root Mean Square values over sliding windows in arrays using hand-optimized C code with SIMD instructions. It targets audio and signal-processing workflows where RMS calculations are a bottleneck, offering speed gains by leveraging CPU vector instructions (AVX on x86-64, NEON on ARM) and C-level efficiency.
The package wraps a fast C implementation via cffi and provides prebuilt wheels for modern Python versions (3.10–3.14) and common platforms, reducing installation friction. It is tailored for C-contiguous 1-D and 2-D float32 arrays, making it most useful in audio processing, signal analysis, and scientific computing where batch RMS calculations are frequent.
Use it for
- Audio signal analysis: compute RMS energy levels over time windows for loudness metering or normalization.
- Signal processing pipelines: calculate windowed RMS as a feature for machine learning on time-series data.
- Real-time audio monitoring: efficiently compute RMS metrics in streaming or batch audio processing workflows.
- Accelerometer/sensor data: extract RMS statistics from high-frequency motion or vibration sensor streams.
- Quality assurance in audio production: measure signal integrity and noise floor via windowed RMS metrics.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fast windowed RMS calculations on NumPy float32 arrays in audio, signal, or scientific workflows.
The compiled SIMD implementation and wide platform/Python coverage make it a low-friction drop-in for performance-critical RMS loops. Skip it if you only need occasional RMS calls or work with non-float32 dtypes—standard NumPy is sufficient.
Install
numpy-rms on PyPI
Before you install
Medium install friction due to compiled C extension with cffi dependency; however, prebuilt wheels cover common platforms (x86-64, ARM64, Windows, macOS, Linux variants) and Python 3.10–3.14, reducing build-from-source scenarios. Active maintenance with recent release.
Requires numpy and cffi; C compiler needed if prebuilt wheel unavailable for your platform/Python version.
License in practice
MIT License (permissive) allows unrestricted use, modification, and distribution in both open and closed projects with minimal obligations—only attribution and license inclusion required.
Quickstart
pip install numpy-rms
import numpy_rms
import numpy as np
arr = np.arange(40, dtype=np.float32)
rms_series = numpy_rms.rms(arr, window_size=10)Verify before relying
- Whether the package supports non-float32 dtypes or only float32 arrays.
- Performance characteristics and typical speedup vs. pure-NumPy implementations.
- Whether window_size parameter supports stride/overlap configuration.
Package facts
| License | permissive license permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagescffinumpy |
| Maintenance | Actively maintained 49 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 206,054 / month, #9,578 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Multimedia :: Sound/AudioTopic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: numpy_rms-0.7.0-cp310-cp310-macosx_10_9_x86_64.whl; numpy_rms-0.7.0-cp310-cp310-macosx_11_0_arm64.whl; numpy_rms-0.7.0-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl; numpy_rms-0.7.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; numpy_rms-0.7.0-cp310-cp310-musllinux_1_2_aarch64.whl; numpy_rms-0.7.0-cp310-cp310-musllinux_1_2_x86_64.whl; numpy_rms-0.7.0-cp310-cp310-win_amd64.whl; numpy_rms-0.7.0-cp311-cp311-macosx_10_9_x86_64.whl; numpy_rms-0.7.0-cp311-cp311-macosx_11_0_arm64.whl; numpy_rms-0.7.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl; numpy_rms-0.7.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; numpy_rms-0.7.0-cp311-cp311-musllinux_1_2_aarch64.whl; numpy_rms-0.7.0-cp311-cp311-musllinux_1_2_x86_64.whl; numpy_rms-0.7.0-cp311-cp311-win_amd64.whl; numpy_rms-0.7.0-cp312-cp312-macosx_10_13_x86_64.whl; numpy_rms-0.7.0-cp312-cp312-macosx_11_0_arm64.whl; numpy_rms-0.7.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl; numpy_rms-0.7.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; numpy_rms-0.7.0-cp312-cp312-musllinux_1_2_aarch64.whl; numpy_rms-0.7.0-cp312-cp312-musllinux_1_2_x86_64.whl
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