A specialized digital signal processing toolkit for Python designed to handle noise suppression, peak detection, and complex time-series analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dsp-2
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install dsp-2 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is pywayne-dsp?
pywayne-dsp is a robust library for digital signal processing, providing developers with essential tools for filtering, detrending, and signal feature extraction. As a key component of the Openclaw Skills library, it enables AI agents to process raw sensor data and time-series information with mathematical precision. The toolkit includes high-performance implementations of Butterworth filters, One-Euro filters for low-latency smoothing, and Dynamic Time Warping for curve comparison.
This skill is particularly valuable for developers who need to bridge the gap between raw data streams and actionable insights. By offering both offline batch processing and real-time streaming capabilities, pywayne-dsp ensures that your data pipelines are clean, stabilized, and ready for advanced analysis. Whether you are dealing with industrial sensors, medical devices, or financial trends, this library provides the algorithmic foundation required for professional-grade signal manipulation.
pywayne-dsp Use Cases
- Preprocessing medical signals like ECG or PPG using bandpass filters and peak detection to identify heart rates.
- Smoothing noisy input from motion sensors or human-computer interaction devices using the One-Euro filter.
- Removing linear or non-linear drift from sensor data through multi-method detrending techniques.
- Comparing signal patterns for gesture recognition or anomaly detection using Dynamic Time Warping (DTW).
- Implementing real-time statistical monitoring on data streams using the memory-efficient Welford algorithm.
How pywayne-dsp Works
- Define the signal processing requirements by selecting a specific filter or analysis tool from the pywayne-dsp module.
- Configure parameters such as sampling frequency (fs), cutoff frequencies, or window sizes to match the physical characteristics of the input signal.
- Input the raw data sequence—either as a batch array for historical analysis or as a stream of single values for real-time applications.
- Execute the processing method, which applies mathematical transformations like zero-phase filtering (filtfilt) or local weighted regression (LOESS).
- Retrieve the processed output, such as filtered signal values, peak indices, or similarity scores, for further decision-making by the AI agent.
pywayne-dsp Setup
To utilize this skill within your environment, ensure you have the core library installed. This is a standard requirement for signal processing tasks involving Openclaw Skills.
pip install pywayne
Basic usage in your script:
from pywayne.dsp import butter_bandpass_filter, SignalDetrend
# Example: Apply a Butterworth filter
filtered = butter_bandpass_filter(data, order=4, lo=1, hi=50, fs=250)
pywayne-dsp Data Schema & Taxonomy
The skill organizes data primarily through NumPy-compatible arrays and structured return types for easy integration into data science workflows.
| Functionality | Input Structure | Output Structure | Metadata/Parameters |
|---|---|---|---|
| Peak Detection | 1D Array | (maxima_idx, minima_idx) | Delta threshold for sensitivity |
| Signal Detrending | 1D Array | 1D Array | Method (linear, poly, loess, etc.) |
| Curve Similarity | Two 1D Arrays | Float (Distance) | Global or Local DTW modes |
| Online Stats | Single Float | Current Std Dev | Window size (win) |
pywayne-dsp Advanced Features
- Support for Zero-Phase Filtering (filtfilt) to prevent phase distortion in offline signal analysis.
- Real-time stateful processing via the One-Euro Filter and sliding window extrema detectors.
- Comprehensive detrending suite including Wavelet, EMD (Empirical Mode Decomposition), and CEEMDAN algorithms.
- Memory-efficient online standard deviation calculation using the Welford algorithm for infinite or windowed streams.
- High-performance Dynamic Time Warping (DTW) for quantifying similarity between signals of different lengths.
SKILL.md
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