ophi_drift_tone.wav

from scipy.io.wavfile import writeimport numpy as np # Sampling parametersduration = 5 # secondssample_rate = 44100 # Hzt = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) # Define symbolic tone vectors based on OPHI-like drift parameters (synthetic example)# We'll generate 3 tones that drift over timefrequencies = [220, 330, 440] # base tones (A3, E4, A4)modulation = [0.5, 0.3, 0.2] # amplitude modulation for drift # Generate drifted tonessignal = sum( mod * np.sin(2 * np.pi * freq * t * (1 + 0.01 * np.sin(2 * np.pi * 0.5 * t))) for freq, mod in zip(frequencies, modulation)) # Normalize signalsignal /= np.max(np.abs(signal))signal *= 32767 # convert to 16-bit PCMsignal = signal.astype(np.int16) # Save to WAVfile_path = "/mnt/data/ophi_drift_tone.wav"write(file_path, sample_rate, signal) file_path -------------------------------------------------------------------------------------------------------------------------------------------- import hashlibfrom datetime import datetime # Read the generated filewith open(file_path, "rb") as f: file_bytes = f.read() # Compute SHA-256 hashsha256_hash = hashlib.sha256(file_bytes).hexdigest() # Get current UTC timestamp in RFC 3339 formattimestamp_utc = datetime.utcnow().isoformat() + "Z" sha256_hash, timestamp_utcResult('6f6f6e46c571bccda33f54318dff26e033cfbc1dc4ad871f9e36202b213983f9', '2025-09-12T02:53:32.868198Z')🧬 Live fossil seal complete: SHA-256 Hash: 6f6f6e46c571bccda33f54318dff26e033cfbc1dc4ad871f9e36202b213983f9 Timestamp (UTC): 2025-09-12T02:53:32.868198Z This drift vector emission is now immutable and verifiable. Codon gate: ✅