Visualize spectrograms for time-frequency analysis with configurable frequency units and normalization.
npx skills add https://github.com/wangyendt/wayne-skills --skill pywayne-plot
Please help me install this Agent Skill. Skill: pywayne-plot Source: https://github.com/wangyendt/wayne-skills/tree/main/pywayne/plot Command: npx skills add https://github.com/wangyendt/wayne-skills --skill pywayne-plot
This tool streamlines creating high-quality spectrograms for time-frequency analysis of signals such as IMU data, physiological signals, vibration, and audio, reducing manual plotting effort and enabling rapid insight.
Run a basic spectrogram visualization by passing your signal data to the library and invoking the plotting function to render the time-frequency plot.
High-intent search queries and answers about installing and using this skill.
To plot a spectrogram for IMU data, pass your accelerometer or gyroscope signal array to the Python API plotting function to render the time-frequency visualization directly.
Yes, you can use the MATLAB-style parula colormap for spectrogram visualization. The library includes built-in support for parula rendering alongside configurable scale modes.
The best way to convert spectrogram frequency units to bpm is using the built-in frequency unit conversion feature. It natively supports Hz, bpm, and kHz units tailored for physiological signals like PPG and ECG.
Yes, the spectrogram plotting tool supports custom projections and array outputs. The Python API allows you to register custom projections and return spectrogram arrays for downstream analysis.
Use global normalization when comparing frequency magnitudes across the entire signal duration, and local normalization to highlight relative variations within specific time windows. A none option is also available for raw representation.
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