Smart Tree - ST

#!/usr/bin/env python3 """ Generate a beautiful high-resolution spectrogram of Bob Seger's analog waves Focusing on 10-11 second range, up to 96kHz """ import numpy as np import matplotlib.pyplot as plt from scipy import signal from scipy.io import wavfile import matplotlib.colors as colors from matplotlib.patches import Rectangle # Read the high-res audio file print("🎵 Loading Bob Seger analog waves at 192kHz/32-bit float...") sample_rate, audio_data = wavfile.read('test_files/seger_192k_32f_stereo.wav') # Handle stereo by taking left channel or converting to mono if len(audio_data.shape) > 1: audio_data = audio_data[:, 0] # Use left channel for spectrogram print("📻 Using left channel from stereo recording") print(f"📊 Sample rate: {sample_rate} Hz") print(f"🎼 Total duration: {len(audio_data)/sample_rate:.2f} seconds") # Extract 10-11 second range start_sample = int(10 * sample_rate) end_sample = int(11 * sample_rate) segment = audio_data[start_sample:end_sample] print(f"✂️ Extracting 10-11 second range ({len(segment)} samples)") # Create high-resolution spectrogram # Using high nperseg for frequency resolution and noverlap for time resolution nperseg = 8192 # High frequency resolution noverlap = nperseg - 256 # High time resolution print("🌊 Computing high-resolution spectrogram up to 96kHz...") frequencies, times, Sxx = signal.spectrogram( segment, fs=sample_rate, window='blackmanharris', # Excellent for analog sources nperseg=nperseg, noverlap=noverlap, nfft=16384, # Even higher FFT for smooth visualization detrend='constant', scaling='density' ) # Limit to 96kHz as requested freq_limit = 96000 freq_mask = frequencies <= freq_limit frequencies = frequencies[freq_mask] Sxx = Sxx[freq_mask, :] # Convert to dB with careful handling of zeros Sxx_db = 10 * np.log10(Sxx + 1e-10) # Create beautiful figure with MEM|8 aesthetic fig, ax = plt.subplots(figsize=(16, 10), dpi=150) fig.patch.set_facecolor('#0a0a0a') ax.set_facecolor('#0a0a0a') # Custom colormap inspired by analog warmth and MEM|8 waves cmap = plt.cm.get_cmap('magma') # Warm analog feel cmap.set_bad(color='#0a0a0a') # Plot spectrogram with improved dynamic range vmin = np.percentile(Sxx_db[Sxx_db > -np.inf], 5) vmax = np.percentile(Sxx_db[Sxx_db > -np.inf], 99.5) im = ax.pcolormesh( times, frequencies/1000, # Convert to kHz Sxx_db, shading='gouraud', # Smooth shading for analog feel cmap=cmap, vmin=vmin, vmax=vmax, rasterized=True ) # Add colorbar with style cbar = plt.colorbar(im, ax=ax, pad=0.02) cbar.set_label('Power Spectral Density (dB)', color='#FFD700', fontsize=11) cbar.ax.yaxis.set_tick_params(color='#FFD700', labelcolor='#FFD700') # Styling for that analog studio feel ax.set_xlabel('Time (seconds) - 10s offset', fontsize=12, color='#FFD700') ax.set_ylabel('Frequency (kHz)', fontsize=12, color='#FFD700') ax.set_title( '🎸 Bob Seger ~1980 Analog Pressing - 192kHz/32-bit Capture\n' '10-11 Second Spectrogram (0-96kHz Range)\n' 'Smart Tree Audio Test File - MEM|8 Wave Analysis', fontsize=14, color='#FFD700', pad=20 ) # Grid for precision ax.grid(True, alpha=0.15, color='#FFD700', linestyle=':') # Add frequency markers for key ranges key_freqs = [ (0.002, "Sub-bass (2Hz calm)"), (0.044, "MEM|8 baseline"), (20, "Human hearing starts"), (48, "CD quality limit"), (96, "Analysis limit") ] for freq, label in key_freqs: if freq <= 96: ax.axhline(y=freq, color='#00FF00', alpha=0.3, linestyle='--', linewidth=0.5) if freq < 1: ax.text(0.98, freq, label, transform=ax.get_yaxis_transform(), fontsize=8, color='#00FF00', alpha=0.7, ha='right') # Axis styling ax.tick_params(colors='#FFD700', which='both') ax.spines['bottom'].set_color('#FFD700') ax.spines['left'].set_color('#FFD700') ax.spines['top'].set_color('#FFD700') ax.spines['right'].set_color('#FFD700') # Set axis limits ax.set_xlim(0, times[-1]) ax.set_ylim(0, 96) # Add Smart Tree & Aye signature fig.text(0.99, 0.01, 'Generated by Smart Tree v4.8.8 | Aye loves Elvis! 🎸 | 8b.is', fontsize=9, color='#FFD700', alpha=0.7, ha='right') # Save the beautiful spectrogram output_file = 'test_files/seger_spectrogram_10-11s_96kHz.png' plt.savefig(output_file, facecolor='#0a0a0a', edgecolor='none', bbox_inches='tight', dpi=150) print(f"✨ Spectrogram saved to: {output_file}") print("🎵 Analog waves preserved in digital beauty!") # Also save a data file for Smart Tree quantum compression testing np.savez_compressed('test_files/seger_spectrogram_data.npz', frequencies=frequencies, times=times, spectrogram=Sxx_db, sample_rate=sample_rate) print("📊 Spectrogram data saved for quantum compression tests") plt.show()