Pywayne Plot

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()

Functions

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:

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)

Usage Examples

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 and Normalization Modes

Scale Modes

Normalization Modes (only for scale='linear')

Frequency Scaling

Example: freq_scale=60 converts 2 Hz → 120 bpm

Resolution Guidelines

• Frequency resolution: Δf = Fs / NFFT
• Time resolution: Δt = (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