Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Pywayne Plot
Enhanced spectrogram visualization tools for time-frequency analysis. Use when creating spectrograms, spectral analysis, or time-frequency plots for signals...
skill openclawclawhub Free
Enhanced spectrogram visualization tools for time-frequency analysis. Use when creating spectrograms, spectral analysis, or time-frequency plots for signals...
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- Tencent SkillHub
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- SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
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- Version
- 0.1.0
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- wangyendt
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Documentation
Pywayne Plot
Enhanced spectrogram visualization tools for professional time-frequency analysis.
Quick Start
import matplotlib.pyplot as plt from pywayne.plot import regist_projection, parula_map import numpy as np # Register custom projection regist_projection() # Create spectrogram fig, ax = plt.subplots(subplot_kw={'projection': 'z_norm'}) spec, freqs, t, im = ax.specgram( x=signal_data, Fs=100, NFFT=128, noverlap=96, cmap=parula_map, scale='dB' ) ax.set_ylabel('Frequency (Hz)') plt.colorbar(im, label='Magnitude (dB)') plt.show()
regist_projection
Register the custom SpecgramAxes projection. Must be called before using the enhanced specgram functionality. from pywayne.plot import regist_projection regist_projection()
SpecgramAxes.specgram
Enhanced spectrogram with advanced features. Key Parameters: ParameterDescriptionDefaultNFFTFFT window length (points)256FsSampling frequency (Hz)2noverlapOverlap points between windows128cmapColormap (use parula_map)-mode'psd', 'magnitude', 'angle', 'phase''psd'scale'dB' or 'linear''dB'normalize'global', 'local', 'none''global'freq_scaleFrequency scaling factor1.0FcCenter frequency offset (Hz)0 Returns: spec - 2D spectrogram array (n_freqs, n_times) freqs - Frequency axis array t - Time axis array im - matplotlib image object (for colorbar)
get_specgram_params
Auto-recommend STFT parameters based on signal characteristics. from pywayne.plot import get_specgram_params params = get_specgram_params( signal_length=10000, sampling_rate=100, time_resolution=0.1 # or freq_resolution=0.5 ) # Returns: NFFT, noverlap, actual_freq_res, actual_time_res, n_segments
parula_map
MATLAB-style perceptually uniform colormap for scientific visualization. from pywayne.plot import parula_map plt.imshow(data, cmap=parula_map)
IMU Signal Analysis
fs = 100 # Sampling rate win_time, step_time = 1, 0.1 fig, ax = plt.subplots(subplot_kw={'projection': 'z_norm'}) spec, freqs, t, im = ax.specgram( x=acc_data, Fs=fs, NFFT=int(win_time * fs), noverlap=int((win_time - step_time) * fs), scale='dB', cmap=parula_map ) ax.set_ylabel('Frequency (Hz)') ax.set_ylim(0, 30)
Physiological Signals (PPG - Heart Rate)
# Convert Hz to bpm for heart rate visualization fig, ax = plt.subplots(subplot_kw={'projection': 'z_norm'}) spec, freqs, t, im = ax.specgram( x=ppg_signal, Fs=100, NFFT=400, noverlap=300, freq_scale=60, # Hz -> bpm scale='dB' ) ax.set_ylabel('Heart Rate (bpm)') ax.set_ylim(40, 180)
Vibration Analysis with Global Normalization
fig, ax = plt.subplots(subplot_kw={'projection': 'z_norm'}) spec, freqs, t, im = ax.specgram( x=vibration_data, Fs=1000, NFFT=1024, noverlap=512, scale='linear', normalize='global' ) plt.colorbar(im, label='Normalized Magnitude')
High-Resolution Analysis with Zero-Padding
fig, ax = plt.subplots(subplot_kw={'projection': 'z_norm'}) spec, freqs, t, im = ax.specgram( x=signal, Fs=100, NFFT=100, pad_to=512, # Zero-pad for smoother spectrum noverlap=80, scale='dB' )
Scale Modes
ModeDescriptionUse CasedBLogarithmic (10log10 for PSD, 20log10 for magnitude)Large dynamic range signalslinearLinear amplitudeDirect amplitude comparison
Normalization Modes (only for scale='linear')
ModeDescriptionUse CaseglobalZ/max(Z), preserves relative intensityCompare intensity across timelocalPer-column normalization to [0,1]Focus on frequency content over timenoneNo normalizationRaw spectrogram values
Frequency Scaling
freq_scaleUnitUse Case1.0HzDefault, most signals60bpmHeart rate, respiration rate0.001kHzAudio signals Example: freq_scale=60 converts 2 Hz β 120 bpm
Resolution Guidelines
Frequency resolution: Ξf = Fs / NFFT Time resolution: Ξt = (NFFT - noverlap) / Fs Trade-off: Cannot simultaneously achieve high frequency and time resolution Use get_specgram_params() to auto-calculate optimal parameters.
Interactive Analysis
spec, freqs, t, im = ax.specgram(...) def on_click(event): if event.xdata and event.inaxes == ax: time_idx = np.argmin(np.abs(t - event.xdata)) plt.figure() plt.plot(freqs, spec[:, time_idx]) plt.title(f'FFT at t={event.xdata:.2f}s') plt.show() fig.canvas.mpl_connect('button_press_event', on_click)
Application Areas
IMU data: Accelerometer and gyroscope analysis Physiological signals: PPG (heart rate), ECG, respiration Vibration analysis: Machinery fault diagnosis Audio processing: Speech and audio spectrum analysis
Notes
Always call regist_projection() before using projection='z_norm' parula_map is recommended for best perceptual uniformity dB mode automatically handles log(0) issues For better FFT efficiency, set NFFT to power of 2
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md