import math

def audit_bytes(raw_data):
 print(f"--- Raw Byte Data Audit ---")

 # 1. Measurement
 byte_count = len(raw_data)
 bit_count = byte_count * 8
 print(f"[*] Physical Size: {byte_count} bytes ({bit_count} bits)")

 # 2. Entropy Calculation (Simplified)
 # Checks how diverse the byte values are. 
 # High entropy = Random (Encrypted/Hashed), Low = Patterned
 if byte_count > 0:
 counts = {}
 for b in raw_data:
 counts[b] = counts.get(b, 0) + 1

 entropy = 0
 for count in counts.values():
 p = count / byte_count
 entropy -= p * math.log2(p)
 print(f"[*] Byte Entropy: {entropy:.2f} bits per byte")

 # 3. Network Cost Analysis (Assume 10 sats/vByte)
 # Note: 1 Physical Byte = 4 Weight Units
 cost_sats = byte_count * 10
 print(f"[*] Estimated Cost: {cost_sats} Satoshis (@ 10 sat/vB)")

 # 4. Hex Preview
 hex_preview = raw_data.hex(' ')[:47] # Show first 16 bytes
 print(f"[*] Hex Preview: {hex_preview}...")

# --- Simulation ---

# Case 1: Random Hashed Data (High Entropy)
print("Scenario: A SHA-256 Hash")
audit_bytes(bytes.fromhex("000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f"))

# Case 2: Patterned Data (Low Entropy)
print("\nScenario: Empty/Padded Data")
audit_bytes(b"\x00" * 32)

# Case 3: Text Data
print("\nScenario: ASCII Message")
audit_bytes(b"Satoshi Nakamoto sent 10 BTC to Hal Finney")