Install & Compatibility
Where this runs
tested against v0.7.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
installs and imports cleanly · install 0.0s · import 0.236s · 90.9MB
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 3.9s · import 0.236s · 87MB
91MB installed
● package 91MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
rms
✓ from numpy_rms import rms
✗ import numpy_rms
NDArray
✓ from numpy_rms import NDArray
np
✓ from numpy_rms import np
This quickstart demonstrates how to import `numpy_rms` and calculate a series of RMS values for a 1D NumPy array with a specified window size. Ensure your input array is `float32` for optimal performance.
import numpy_rms
import numpy as np
arr = np.arange(40, dtype=np.float32)
rms_series = numpy_rms.rms(arr, window_size=10)
print(f"Original array shape: {arr.shape}")
print(f"RMS series shape: {rms_series.shape}")
print(f"First few RMS values: {rms_series[:5]}")
Debug
Known issues
gotchaThe library is specifically optimized for C-contiguous 1-dimensional and 2-dimensional NumPy arrays of `float32` dtype. Using other data types (e.g., `float64`) or non-contiguous array layouts might lead to reduced performance or unexpected behavior compared to its advertised speed.fixEnsure input arrays are `np.float32` and, if performance is critical, check array contiguity using `array.flags['C_CONTIGUOUS']`.
affects: All versions
gotchaAs `numpy-rms` is implemented in C and interacts with NumPy's C API, major NumPy version updates (e.g., NumPy 2.0 and above) can introduce Application Binary Interface (ABI) incompatibilities. This may require `numpy-rms` to be rebuilt or updated by its maintainers to ensure compatibility, potentially causing `ImportError` or runtime issues if using an older `numpy-rms` binary with a new NumPy version.fixAfter upgrading NumPy to a major new version, always check for a `numpy-rms` release compatible with that NumPy version. Reinstalling `numpy-rms` (e.g., `pip install --upgrade --force-reinstall numpy-rms`) might resolve some ABI issues by forcing a rebuild against the current NumPy.
affects: All versions (especially with NumPy 2.0+)
gotchaThe project is currently classified with a 'Development Status :: 3 - Alpha' on PyPI. This indicates that while functional, the API may not be entirely stable and could undergo breaking changes in future minor releases, even if semver is generally followed.fixReview the `CHANGELOG.md` or release notes before updating `numpy-rms` to new minor versions, and ensure your code is adequately tested.
affects: All versions
Upgrade
Version history
0.7.0latest on PyPI · released Jun 26, 2026
Audit
Dependencies
numpyrequiredCore functionality relies on NumPy arrays and its C API.