Package: python3-numpy-rms (0.6.0-1 and others)
Links for python3-numpy-rms
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Download Source Package numpy-rms:
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- Homepage [github.com]
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fast root mean square calculation for NumPy arrays
This library provides a fast implementation of root mean square (RMS) calculations for NumPy arrays. It computes RMS values over fixed-size windows and returns the resulting series.
The function operates on one-dimensional or two-dimensional float32 arrays stored in contiguous memory. Given an input array and a window size, it calculates the RMS value for each window, producing a sequence of RMS values. This is useful for summarising signal magnitude over time or across grouped samples.
The implementation is written in C and includes architecture-specific optimisations for x86-64 (AVX) and ARM (NEON) systems, allowing efficient processing of large arrays where repeated RMS calculations would otherwise be costly.
The module focuses on a single task: efficient RMS computation for NumPy array data. It does not attempt to provide a full signal-processing framework.
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Download python3-numpy-rms
| Architecture | Version | Package Size | Installed Size | Files |
|---|---|---|---|---|
| alpha (unofficial port) | 0.6.0-1 | 9.1 kB | 93.0 kB | [list of files] |
| amd64 | 0.6.0-1+b1 | 9.9 kB | 41.0 kB | [list of files] |
| arm64 | 0.6.0-1 | 9.3 kB | 93.0 kB | [list of files] |
| hppa (unofficial port) | 0.6.0-1 | 9.3 kB | 36.0 kB | [list of files] |
| i386 | 0.6.0-1 | 9.3 kB | 40.0 kB | [list of files] |
| loong64 | 0.6.0-1 | 9.4 kB | 92.0 kB | [list of files] |
| m68k (unofficial port) | 0.6.0-1 | 8.7 kB | 36.0 kB | [list of files] |
| ppc64 (unofficial port) | 0.6.0-1 | 9.5 kB | 93.0 kB | [list of files] |
| ppc64el | 0.6.0-1 | 9.6 kB | 93.0 kB | [list of files] |
| riscv64 | 0.6.0-1 | 8.9 kB | 36.0 kB | [list of files] |
| s390x | 0.6.0-1 | 9.1 kB | 36.0 kB | [list of files] |
| sh4 (unofficial port) | 0.6.0-1 | 9.3 kB | 92.0 kB | [list of files] |
| sparc64 (unofficial port) | 0.6.0-1 | 8.9 kB | 1,053.0 kB | [list of files] |