| """Before/after waveform and spectrum comparison plots.""" |
|
|
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
|
|
|
|
| def _to_mono(audio): |
| """Collapse to mono for plotting.""" |
| if audio.ndim > 1 and audio.shape[1] > 1: |
| return audio.mean(axis=1) |
| return audio.ravel() |
|
|
|
|
| def _downsample_for_plot(signal, time, max_points=500_000): |
| """Reduce sample count so matplotlib stays responsive.""" |
| if len(signal) > max_points: |
| step = len(signal) // max_points |
| return signal[::step], time[::step] |
| return signal, time |
|
|
|
|
| def plot_waveform_comparison(original, mastered, sample_rate): |
| """Create a stacked before/after waveform plot. |
| |
| Returns a matplotlib Figure. |
| """ |
| fig, axes = plt.subplots(2, 1, figsize=(8, 4), sharex=True) |
|
|
| duration = len(original) / sample_rate |
| time_o = np.linspace(0, duration, len(original)) |
| time_m = np.linspace(0, duration, len(mastered)) |
|
|
| orig_mono = _to_mono(original) |
| mast_mono = _to_mono(mastered) |
|
|
| orig_mono, time_o = _downsample_for_plot(orig_mono, time_o) |
| mast_mono, time_m = _downsample_for_plot(mast_mono, time_m) |
|
|
| axes[0].plot(time_o, orig_mono, color="#4a90d9", linewidth=0.3) |
| axes[0].set_ylabel("Amplitude") |
| axes[0].set_title("Original") |
| axes[0].set_ylim(-1.05, 1.05) |
|
|
| axes[1].plot(time_m, mast_mono, color="#d94a4a", linewidth=0.3) |
| axes[1].set_ylabel("Amplitude") |
| axes[1].set_title("Mastered") |
| axes[1].set_xlabel("Time (seconds)") |
| axes[1].set_ylim(-1.05, 1.05) |
|
|
| plt.tight_layout() |
| return fig |
|
|
|
|
| def plot_spectrum_comparison(original, mastered, sample_rate): |
| """Create an overlaid frequency spectrum comparison. |
| |
| Returns a matplotlib Figure. |
| """ |
| fig, ax = plt.subplots(1, 1, figsize=(8, 3)) |
|
|
| orig_mono = _to_mono(original) |
| mast_mono = _to_mono(mastered) |
|
|
| n_fft = 8192 |
|
|
| def avg_spectrum(signal, n_fft, sr): |
| hop = n_fft // 2 |
| n_windows = max(1, (len(signal) - n_fft) // hop) |
| spectra = [] |
| for i in range(min(n_windows, 100)): |
| start = i * hop |
| window = signal[start : start + n_fft] * np.hanning(n_fft) |
| spectrum = np.abs(np.fft.rfft(window)) |
| spectra.append(spectrum) |
| avg = np.mean(spectra, axis=0) |
| freqs = np.fft.rfftfreq(n_fft, 1.0 / sr) |
| avg_db = 20.0 * np.log10(avg + 1e-10) |
| return freqs, avg_db |
|
|
| freqs_o, spec_o = avg_spectrum(orig_mono, n_fft, sample_rate) |
| freqs_m, spec_m = avg_spectrum(mast_mono, n_fft, sample_rate) |
|
|
| ax.plot(freqs_o, spec_o, color="#4a90d9", alpha=0.7, linewidth=1, label="Original") |
| ax.plot(freqs_m, spec_m, color="#d94a4a", alpha=0.7, linewidth=1, label="Mastered") |
| ax.set_xscale("log") |
| ax.set_xlim(20, sample_rate / 2) |
| ax.set_xlabel("Frequency (Hz)") |
| ax.set_ylabel("Magnitude (dB)") |
| ax.set_title("Frequency Spectrum Comparison") |
| ax.legend() |
| ax.grid(True, alpha=0.3) |
|
|
| plt.tight_layout() |
| return fig |
|
|
