---
id: numpy-rms
version: "0.7.0"
license: MIT License Copyright (c) 2024 Nomono Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# numpy-rms — A fast python library for calculating the RMS of a NumPy array

License: permissive · Maintenance: active · Downloads: 206.1K/mo

## 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 above — verify before relying.

Calculates Root Mean Square (RMS) values over sliding windows in NumPy arrays using vectorized C code with SIMD acceleration (AVX on x86-64, NEON on ARM).

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

pip install numpy-rms
uv add numpy-rms
poetry add numpy-rms

## Installing numpy-rms

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.

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)

Requires numpy and cffi; C compiler needed if prebuilt wheel unavailable for your platform/Python version.

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: MIT License Copyright (c) 2024 Nomono Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the… (full text in the JSON record) (permissive)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 206.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

rms calculation numpy, root mean square windowed, fast rms computation, simd accelerated rms, sliding window rms, audio signal rms, numpy array rms, simd-accelerated, audio-processing, signal-analysis

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