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:

Parameter	Description	Default
NFFT	FFT window length (points)	256
Fs	Sampling frequency (Hz)	2
noverlap	Overlap points between windows	128
cmap	Colormap (use parula_map)	-
mode	'psd', 'magnitude', 'angle', 'phase'	'psd'
scale	'dB' or 'linear'	'dB'
normalize	'global', 'local', 'none'	'global'
freq_scale	Frequency scaling factor	1.0
Fc	Center 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.