{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Sound/Audio","url":"https://skillfed.io/packages/category/multimedia-sound-audio"}],"enrichment":{"capability":"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).","skillfed_tags":["simd-accelerated","audio-processing","signal-analysis"],"use_cases":["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."],"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.\n\nThe package wraps a fast C implementation via cffi and provides prebuilt wheels for modern Python versions (3.10\u20133.14) 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.","worth_installing":"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\u2014standard NumPy is sufficient."},"id":"numpy-rms","links":{"html":"https://skillfed.io/packages/numpy-rms","md":"https://skillfed.io/packages/numpy-rms.md","pypi":"https://pypi.org/project/numpy-rms/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-26","license_spdx":null,"license_treatment":"permissive","name":"numpy-rms","python_support":"unspecified","summary":"A fast python library for calculating the RMS of a NumPy array"},"popularity":{"monthly_downloads":206054,"position":9578,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.0"}