diff --git a/__pycache__/frackture (2).cpython-312.pyc b/__pycache__/frackture (2).cpython-312.pyc new file mode 100644 index 0000000..28bc0a4 Binary files /dev/null and b/__pycache__/frackture (2).cpython-312.pyc differ diff --git a/compressed.json b/compressed.json new file mode 100644 index 0000000..59234c9 --- /dev/null +++ b/compressed.json @@ -0,0 +1,34 @@ +{ + "symbolic": "00408040004080c000400080c000c0004080400040004080c000400080c000c0", + "entropy": [ + 13.335411071777344, + 41.52340316772461, + 1.1188021898269653, + 0.8959053158760071, + 7.063382625579834, + 7.167986869812012, + 2.464329481124878, + 1.4854254722595215, + 1.220164179801941, + 1.1779710054397583, + 2.84759783744812, + 6.463021278381348, + 1.7775392532348633, + 1.6017292737960815, + 4.298604965209961, + 4.052870750427246 + ], + "metadata": { + "version": "3.3", + "passes": 4, + "target_length": 768, + "optimization_trials": 0, + "created_at": "1765548231" + }, + "encryption": { + "mode": "none", + "key_id": null, + "salt": null + }, + "fingerprint": "6d4e484b928c50ac" +} \ No newline at end of file diff --git a/decrypted_vector.txt b/decrypted_vector.txt new file mode 100644 index 0000000..6bf706f --- /dev/null +++ b/decrypted_vector.txt @@ -0,0 +1 @@ +[0.6141611337661743, 0.2549768388271332, 0.6192259192466736, 0.250980406999588, 0.6254901885986328, 0.2581024467945099, 0.6221581101417542, 0.25129926204681396, 0.48893752694129944, 0.3835547864437103, 0.8696731328964233, 0.12601235508918762, 0.7241905927658081, 0.1369626373052597, 0.7495142817497253, 0.0008079208782874048, 0.8651415109634399, 0.380467027425766, 0.6192259192466736, 0.250980406999588, 0.6254901885986328, 0.2581024467945099, 0.49666792154312134, 0.3767894506454468, 0.8654081225395203, 0.1325744092464447, 0.7441829442977905, 0.12601235508918762, 0.7241905927658081, 0.011472432874143124, 0.8750044703483582, 0.37727850675582886, 0.6141611337661743, 0.2549768388271332, 0.6192259192466736, 0.250980406999588, 0.6254901885986328, 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100644 index 0000000..c11a4c0 --- /dev/null +++ b/encrypted.json @@ -0,0 +1,34 @@ +{ + "symbolic": "408040804080408000c0c04080408000c0c04080408000c0c04080408000c0c0", + "entropy": [ + 0.3620098039215686, + 0.06491493789707584, + 0.36511437908496736, + 0.06246521469937504, + 0.3689542483660131, + 0.06683086437031346, + 0.3669117647058824, + 0.0626606691082609, + 0.3621732026143791, + 0.06680767668749768, + 0.36478758169934644, + 0.0627852832704965, + 0.3525326797385621, + 0.06949756474671824, + 0.3680555555555556, + 0.06296045250732021 + ], + "metadata": { + "version": "3.3", + "passes": 4, + "target_length": 768, + "optimization_trials": 0, + "created_at": "1765548281" + }, + "encryption": { + "mode": "xor", + "key_id": null, + "salt": "6d792d7365637265" + }, + "fingerprint": "93e039a093bfa345" +} \ No newline at end of file diff --git a/fingerprint.txt b/fingerprint.txt new file mode 100644 index 0000000..c718e06 --- /dev/null +++ b/fingerprint.txt @@ -0,0 +1 @@ +00408040004080c000400080c000c0004080400040004080c000400080c000c0 \ No newline at end of file diff --git a/frackture/__init__.py b/frackture/__init__.py new file mode 100644 index 0000000..7349129 --- /dev/null +++ b/frackture/__init__.py @@ -0,0 +1,246 @@ +""" +Frackture - Symbolic compression and fingerprinting engine. + +This package provides a unified API for data compression, encryption, +and fingerprinting using a dual-channel approach combining symbolic +and entropy channels. + +Basic Usage: + from frackture import compress, decompress + + data = b"Hello world" + compressed = compress(data) + original = decompress(compressed) + +Advanced Usage: + from frackture import FracktureEngine + + engine = FracktureEngine() + result = engine.compress(data, optimize=True) + vector = engine.decompress(result.payload) + + # For encryption + encrypted = engine.encrypt(data, key_material=b"my-secret-key") + decrypted = engine.decrypt(encrypted.payload, key_material=b"my-secret-key") + + # For fingerprinting only + fingerprint = engine.fingerprint(data) +""" + +# Version information +__version__ = "3.3.0" +__author__ = "Frackture Development Team" + +# Import public API +from .engine import FracktureEngine +from .models import ( + FrackturePayload, + CompressionMetadata, + EncryptionMetadata, + CompressionResult, + FracktureVersion, + EncryptionMode +) + +# Import core functions for convenience +from .preprocess import frackture_preprocess_universal_v2_6 +from .symbolic import ( + symbolic_channel_encode, + symbolic_channel_decode, + symbolic_fingerprint_with_key +) +from .entropy import ( + entropy_channel_encode, + entropy_channel_decode, + entropy_signature_with_key +) + +# Global engine instance for convenience functions +_global_engine = FracktureEngine() + + +def compress( + data, + passes: int = None, + optimize: bool = False, + num_trials: int = 5 +): + """ + Compress data using Frackture's default engine. + + Args: + data: Input data of any supported type + passes: Number of symbolic passes (default: 4) + optimize: Whether to use optimization (default: False) + num_trials: Number of optimization trials (default: 5) + + Returns: + dict: Serialized FrackturePayload containing compressed data + + Example: + >>> data = b"Hello world" + >>> result = compress(data) + >>> print(result['symbolic']) # 64-char hex fingerprint + >>> print(len(result['entropy'])) # 16 entropy components + """ + result = _global_engine.compress(data, passes, optimize, num_trials) + return result.payload.to_dict() + + +def decompress(payload): + """ + Decompress Frackture payload to vector representation. + + Args: + payload: FrackturePayload (dict or object) to decompress + + Returns: + np.ndarray: 768-length reconstructed vector + + Example: + >>> payload = {'symbolic': '...', 'entropy': [...]} + >>> vector = decompress(payload) + >>> print(len(vector)) # 768 + """ + return _global_engine.decompress(payload) + + +def encrypt(data, key_material: bytes, passes: int = None): + """ + Encrypt data using key-based Frackture compression. + + Args: + data: Input data to encrypt + key_material: Encryption key material (bytes) + passes: Number of symbolic passes (default: 4) + + Returns: + dict: Serialized encrypted FrackturePayload + + Example: + >>> data = b"Secret message" + >>> key = b"my-encryption-key" + >>> encrypted = encrypt(data, key) + >>> print(encrypted['encryption']['mode']) # 'xor' + """ + result = _global_engine.encrypt(data, key_material, passes) + return result.payload.to_dict() + + +def decrypt(payload, key_material: bytes): + """ + Decrypt Frackture payload using key material. + + Args: + payload: Encrypted FrackturePayload (dict or object) + key_material: Encryption key material (bytes) + + Returns: + np.ndarray: 768-length decrypted vector + + Example: + >>> encrypted_payload = {...} # From encrypt() + >>> key = b"my-encryption-key" + >>> vector = decrypt(encrypted_payload, key) + """ + return _global_engine.decrypt(payload, key_material) + + +def fingerprint(data, passes: int = None): + """ + Generate fingerprint for data without full compression. + + Args: + data: Input data to fingerprint + passes: Number of symbolic passes (default: 4) + + Returns: + str: 64-character hexadecimal fingerprint + + Example: + >>> data = b"Test data" + >>> fp = fingerprint(data) + >>> print(len(fp)) # 64 + >>> print(fp[:16]) # First 16 characters + """ + return _global_engine.fingerprint(data, passes) + + +def verify_payload(payload) -> bool: + """ + Verify payload integrity using stored fingerprint. + + Args: + payload: FrackturePayload (dict or object) to verify + + Returns: + bool: True if payload is valid and fingerprint matches + + Example: + >>> payload = {...} # Some payload + >>> is_valid = verify_payload(payload) + >>> if is_valid: + ... print("Payload is valid") + """ + return _global_engine.verify_payload(payload) + + +def get_version() -> str: + """ + Get current Frackture version. + + Returns: + str: Version string + """ + return __version__ + + +def create_engine(version=None): + """ + Create a new FracktureEngine instance. + + Args: + version: Frackture version to use (default: current) + + Returns: + FracktureEngine: New engine instance + + Example: + >>> engine = create_engine() + >>> result = engine.compress("test data") + """ + if version is None: + return FracktureEngine() + return FracktureEngine(version) + + +# Define what gets exported with "from frackture import *" +__all__ = [ + # Core classes + "FracktureEngine", + "FrackturePayload", + "CompressionMetadata", + "EncryptionMetadata", + "CompressionResult", + "FracktureVersion", + "EncryptionMode", + + # Convenience functions + "compress", + "decompress", + "encrypt", + "decrypt", + "fingerprint", + "verify_payload", + "get_version", + "create_engine", + + # Core processing functions + "frackture_preprocess_universal_v2_6", + "symbolic_channel_encode", + "symbolic_channel_decode", + "symbolic_fingerprint_with_key", + "entropy_channel_encode", + "entropy_channel_decode", + "entropy_signature_with_key", +] \ No newline at end of file diff --git a/frackture/__main__.py b/frackture/__main__.py new file mode 100644 index 0000000..d1f9018 --- /dev/null +++ b/frackture/__main__.py @@ -0,0 +1,12 @@ +""" +CLI entry point for Frackture package. + +This module allows the frackture package to be executed as a module: + python -m frackture compress input.txt + python -m frackture fingerprint data.txt +""" + +from .cli import main + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/frackture/__pycache__/__init__.cpython-312.pyc b/frackture/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000..00a9d0f Binary files /dev/null and b/frackture/__pycache__/__init__.cpython-312.pyc differ diff --git a/frackture/__pycache__/__main__.cpython-312.pyc b/frackture/__pycache__/__main__.cpython-312.pyc new file mode 100644 index 0000000..2c12a59 Binary files /dev/null and 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a/frackture/__pycache__/preprocess.cpython-312.pyc b/frackture/__pycache__/preprocess.cpython-312.pyc new file mode 100644 index 0000000..435275d Binary files /dev/null and b/frackture/__pycache__/preprocess.cpython-312.pyc differ diff --git a/frackture/__pycache__/symbolic.cpython-312.pyc b/frackture/__pycache__/symbolic.cpython-312.pyc new file mode 100644 index 0000000..b9182f4 Binary files /dev/null and b/frackture/__pycache__/symbolic.cpython-312.pyc differ diff --git a/frackture/cli.py b/frackture/cli.py new file mode 100644 index 0000000..32526b6 --- /dev/null +++ b/frackture/cli.py @@ -0,0 +1,292 @@ +#!/usr/bin/env python3 +""" +Command Line Interface for Frackture. + +Provides simple CLI commands for compression, decompression, encryption, +decryption, and fingerprinting operations. +""" + +import argparse +import json +import sys +from typing import Optional + +import numpy as np + +from .engine import FracktureEngine +from .models import FracktureVersion, EncryptionMode + + +def load_data(file_path: str) -> str: + """Load data from file.""" + try: + with open(file_path, 'r', encoding='utf-8') as f: + return f.read() + except Exception as e: + print(f"Error loading file {file_path}: {e}", file=sys.stderr) + sys.exit(1) + + +def save_data(data: str, file_path: str) -> None: + """Save data to file.""" + try: + with open(file_path, 'w', encoding='utf-8') as f: + f.write(data) + except Exception as e: + print(f"Error saving file {file_path}: {e}", file=sys.stderr) + sys.exit(1) + + +def compress_command(args): + """Handle compress command.""" + engine = FracktureEngine() + + # Load input data + data = load_data(args.input) + + try: + # Perform compression + result = engine.compress( + data, + passes=args.passes, + optimize=args.optimize, + num_trials=args.trials + ) + + # Output results + if args.output: + save_data(json.dumps(result.payload.to_dict(), indent=2), args.output) + print(f"Compression successful. Output saved to {args.output}") + print(f"Original size: {result.original_size} bytes") + print(f"Compressed size: {result.compressed_size} bytes") + print(f"Compression ratio: {result.compression_ratio:.3f}") + print(f"Reconstruction MSE: {result.mse:.6f}") + else: + print(json.dumps(result.payload.to_dict(), indent=2)) + + except Exception as e: + print(f"Compression failed: {e}", file=sys.stderr) + sys.exit(1) + + +def decompress_command(args): + """Handle decompress command.""" + engine = FracktureEngine() + + # Load payload + try: + with open(args.input, 'r', encoding='utf-8') as f: + payload_dict = json.load(f) + except Exception as e: + print(f"Error loading payload {args.input}: {e}", file=sys.stderr) + sys.exit(1) + + try: + # Perform decompression + vector = engine.decompress(payload_dict) + + # Output results + if args.output: + save_data(str(vector.tolist()), args.output) + print(f"Decompression successful. Vector saved to {args.output}") + else: + print("Decompressed vector (first 10 elements):") + print(vector[:10].tolist()) + print(f"... ({len(vector)} total elements)") + + except Exception as e: + print(f"Decompression failed: {e}", file=sys.stderr) + sys.exit(1) + + +def encrypt_command(args): + """Handle encrypt command.""" + engine = FracktureEngine() + + # Load input data + data = load_data(args.input) + + # Load key material + try: + with open(args.key, 'rb') as f: + key_material = f.read() + except Exception as e: + print(f"Error loading key file {args.key}: {e}", file=sys.stderr) + sys.exit(1) + + try: + # Perform encryption + result = engine.encrypt(data, key_material, passes=args.passes) + + # Output results + if args.output: + save_data(json.dumps(result.payload.to_dict(), indent=2), args.output) + print(f"Encryption successful. Output saved to {args.output}") + print(f"Original size: {result.original_size} bytes") + print(f"Encrypted size: {result.compressed_size} bytes") + print(f"Compression ratio: {result.compression_ratio:.3f}") + else: + print(json.dumps(result.payload.to_dict(), indent=2)) + + except Exception as e: + print(f"Encryption failed: {e}", file=sys.stderr) + sys.exit(1) + + +def decrypt_command(args): + """Handle decrypt command.""" + engine = FracktureEngine() + + # Load payload + try: + with open(args.input, 'r', encoding='utf-8') as f: + payload_dict = json.load(f) + except Exception as e: + print(f"Error loading payload {args.input}: {e}", file=sys.stderr) + sys.exit(1) + + # Load key material + try: + with open(args.key, 'rb') as f: + key_material = f.read() + except Exception as e: + print(f"Error loading key file {args.key}: {e}", file=sys.stderr) + sys.exit(1) + + try: + # Perform decryption + vector = engine.decrypt(payload_dict, key_material) + + # Output results + if args.output: + save_data(str(vector.tolist()), args.output) + print(f"Decryption successful. Vector saved to {args.output}") + else: + print("Decrypted vector (first 10 elements):") + print(vector[:10].tolist()) + print(f"... ({len(vector)} total elements)") + + except Exception as e: + print(f"Decryption failed: {e}", file=sys.stderr) + sys.exit(1) + + +def fingerprint_command(args): + """Handle fingerprint command.""" + engine = FracktureEngine() + + # Load input data + data = load_data(args.input) + + try: + # Generate fingerprint + fingerprint = engine.fingerprint(data, passes=args.passes) + + # Output results + if args.output: + save_data(fingerprint, args.output) + print(f"Fingerprint generated successfully. Output saved to {args.output}") + else: + print(fingerprint) + + except Exception as e: + print(f"Fingerprint generation failed: {e}", file=sys.stderr) + sys.exit(1) + + +def verify_command(args): + """Handle verify command.""" + engine = FracktureEngine() + + # Load payload + try: + with open(args.input, 'r', encoding='utf-8') as f: + payload_dict = json.load(f) + except Exception as e: + print(f"Error loading payload {args.input}: {e}", file=sys.stderr) + sys.exit(1) + + try: + # Verify payload + is_valid = engine.verify_payload(payload_dict) + + if is_valid: + print("✓ Payload verification successful - payload is valid") + else: + print("✗ Payload verification failed - payload is invalid or corrupted") + sys.exit(1) + + except Exception as e: + print(f"Verification failed: {e}", file=sys.stderr) + sys.exit(1) + + +def create_parser() -> argparse.ArgumentParser: + """Create and configure the argument parser.""" + parser = argparse.ArgumentParser( + description="Frackture - Symbolic compression and fingerprinting engine", + prog="frackture" + ) + + subparsers = parser.add_subparsers(dest="command", help="Available commands") + + # Compress command + compress_parser = subparsers.add_parser("compress", help="Compress data") + compress_parser.add_argument("input", help="Input file path") + compress_parser.add_argument("-o", "--output", help="Output file path") + compress_parser.add_argument("-p", "--passes", type=int, help="Number of symbolic passes") + compress_parser.add_argument("--optimize", action="store_true", help="Use optimization") + compress_parser.add_argument("--trials", type=int, default=5, help="Number of optimization trials") + compress_parser.set_defaults(func=compress_command) + + # Decompress command + decompress_parser = subparsers.add_parser("decompress", help="Decompress data") + decompress_parser.add_argument("input", help="Input payload file path") + decompress_parser.add_argument("-o", "--output", help="Output file path") + decompress_parser.set_defaults(func=decompress_command) + + # Encrypt command + encrypt_parser = subparsers.add_parser("encrypt", help="Encrypt data with key") + encrypt_parser.add_argument("input", help="Input file path") + encrypt_parser.add_argument("key", help="Key file path") + encrypt_parser.add_argument("-o", "--output", help="Output file path") + encrypt_parser.add_argument("-p", "--passes", type=int, help="Number of symbolic passes") + encrypt_parser.set_defaults(func=encrypt_command) + + # Decrypt command + decrypt_parser = subparsers.add_parser("decrypt", help="Decrypt data with key") + decrypt_parser.add_argument("input", help="Input payload file path") + decrypt_parser.add_argument("key", help="Key file path") + decrypt_parser.add_argument("-o", "--output", help="Output file path") + decrypt_parser.set_defaults(func=decrypt_command) + + # Fingerprint command + fingerprint_parser = subparsers.add_parser("fingerprint", help="Generate fingerprint") + fingerprint_parser.add_argument("input", help="Input file path") + fingerprint_parser.add_argument("-o", "--output", help="Output file path") + fingerprint_parser.add_argument("-p", "--passes", type=int, help="Number of symbolic passes") + fingerprint_parser.set_defaults(func=fingerprint_command) + + # Verify command + verify_parser = subparsers.add_parser("verify", help="Verify payload integrity") + verify_parser.add_argument("input", help="Input payload file path") + verify_parser.set_defaults(func=verify_command) + + return parser + + +def main(): + """Main CLI entry point.""" + parser = create_parser() + args = parser.parse_args() + + if not args.command: + parser.print_help() + sys.exit(1) + + # Execute the appropriate command + args.func(args) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/frackture/engine.py b/frackture/engine.py new file mode 100644 index 0000000..f8661aa --- /dev/null +++ b/frackture/engine.py @@ -0,0 +1,412 @@ +""" +Core Frackture engine that unifies all channels and provides the main API. + +This module contains the FracktureEngine class which orchestrates the +preprocessing, symbolic, and entropy channels to provide a unified +compression and fingerprinting interface. +""" + +import time +import hashlib +from typing import Union, Optional, Dict, Any + +import numpy as np + +from .models import ( + FrackturePayload, + CompressionMetadata, + EncryptionMetadata, + CompressionResult, + FracktureVersion, + EncryptionMode +) +from .preprocess import frackture_preprocess_universal_v2_6, validate_preprocessed_vector +from .symbolic import ( + symbolic_channel_encode, + symbolic_channel_decode, + symbolic_fingerprint_with_key, + validate_symbolic_hash +) +from .entropy import ( + entropy_channel_encode, + entropy_channel_decode, + entropy_signature_with_key, + validate_entropy_signature +) + + +class FracktureEngine: + """ + Unified Frackture compression and fingerprinting engine. + + This engine orchestrates the preprocessing, symbolic, and entropy channels + to provide a clean API for compression, decompression, encryption, + decryption, and fingerprinting operations. + """ + + def __init__(self, version: FracktureVersion = FracktureVersion.V3_3): + """ + Initialize the Frackture engine. + + Args: + version: Frackture version to use (default: V3_3) + """ + self.version = version + self.default_passes = 4 + self.target_length = 768 + + def _create_metadata(self, passes: Optional[int] = None) -> CompressionMetadata: + """Create compression metadata with timestamp.""" + return CompressionMetadata( + version=self.version, + passes=passes or self.default_passes, + target_length=self.target_length, + created_at=str(int(time.time())) + ) + + def _create_encryption_metadata( + self, + key_material: Optional[bytes] = None, + mode: EncryptionMode = EncryptionMode.NONE + ) -> EncryptionMetadata: + """Create encryption metadata.""" + return EncryptionMetadata( + mode=mode, + key_material=key_material, + salt=key_material[:8] if key_material else None # Use first 8 bytes as salt + ) + + def _generate_fingerprint(self, payload_dict: Dict) -> str: + """Generate fingerprint for payload validation.""" + # Create deterministic string representation + content = f"{payload_dict['symbolic']}{payload_dict['entropy']}{payload_dict['metadata']['version']}" + return hashlib.sha256(content.encode()).hexdigest()[:16] + + def compress( + self, + data: Union[str, bytes, dict, list, np.ndarray, Any], + passes: Optional[int] = None, + optimize: bool = False, + num_trials: int = 5 + ) -> CompressionResult: + """ + Compress input data using Frackture's dual-channel approach. + + Args: + data: Input data of any supported type + passes: Number of symbolic passes (default: engine default) + optimize: Whether to use self-optimization (default: False) + num_trials: Number of optimization trials (default: 5) + + Returns: + CompressionResult: Complete compression result with metadata + + Raises: + ValueError: If preprocessing fails + Exception: If compression fails + """ + try: + # Preprocess input + processed = frackture_preprocess_universal_v2_6(data) + + if not validate_preprocessed_vector(processed): + raise ValueError("Preprocessing failed - invalid vector produced") + + original_size = len(str(data).encode('utf-8')) + + # Use optimization if requested + if optimize: + payload, mse = self._optimize_compression(processed, num_trials, passes) + else: + # Standard compression + actual_passes = passes or self.default_passes + + # Encode through both channels + symbolic = symbolic_channel_encode(processed, actual_passes) + entropy = entropy_channel_encode(processed) + + # Create payload + metadata = self._create_metadata(actual_passes) + encryption = self._create_encryption_metadata() + + payload = FrackturePayload( + symbolic=symbolic, + entropy=entropy, + metadata=metadata, + encryption=encryption + ) + + # Generate fingerprint + payload.fingerprint = self._generate_fingerprint(payload.to_dict()) + + # Calculate MSE + reconstructed = self.decompress(payload) + mse = float(np.mean((processed - reconstructed) ** 2)) + + # Calculate compression statistics + payload_dict = payload.to_dict() + import json + compressed_size = len(json.dumps(payload_dict).encode('utf-8')) + compression_ratio = compressed_size / original_size if original_size > 0 else 0.0 + + return CompressionResult( + payload=payload, + original_size=original_size, + compressed_size=compressed_size, + compression_ratio=compression_ratio, + mse=mse + ) + + except Exception as e: + raise Exception(f"Compression failed: {e}") + + def _optimize_compression( + self, + processed: np.ndarray, + num_trials: int, + base_passes: Optional[int] + ) -> tuple: + """Perform optimization to find best compression parameters.""" + best_payload = None + best_mse = float("inf") + + base_passes_val = base_passes or self.default_passes + + for trial in range(num_trials): + # Try different pass counts + passes_to_try = base_passes_val + trial + + symbolic = symbolic_channel_encode(processed, passes_to_try) + entropy = entropy_channel_encode(processed) + + metadata = self._create_metadata(passes_to_try) + metadata.optimization_trials = trial + 1 + + encryption = self._create_encryption_metadata() + + payload = FrackturePayload( + symbolic=symbolic, + entropy=entropy, + metadata=metadata, + encryption=encryption + ) + + payload.fingerprint = self._generate_fingerprint(payload.to_dict()) + + # Test reconstruction quality + reconstructed = self.decompress(payload) + mse = float(np.mean((processed - reconstructed) ** 2)) + + if mse < best_mse: + best_mse = mse + best_payload = payload + + return best_payload, best_mse + + def decompress(self, payload: Union[FrackturePayload, Dict]) -> np.ndarray: + """ + Decompress Frackture payload back to vector representation. + + Args: + payload: FrackturePayload or dictionary representation + + Returns: + np.ndarray: 768-length reconstructed vector + + Raises: + ValueError: If payload format is invalid + Exception: If decompression fails + """ + try: + # Convert dict to payload if needed + if isinstance(payload, dict): + payload = FrackturePayload.from_dict(payload) + + # Validate symbolic hash + if not validate_symbolic_hash(payload.symbolic): + raise ValueError("Invalid symbolic hash in payload") + + # Validate entropy signature + if not validate_entropy_signature(payload.entropy): + raise ValueError("Invalid entropy signature in payload") + + # Decode through both channels + symbolic_part = symbolic_channel_decode(payload.symbolic) + entropy_part = entropy_channel_decode(payload.entropy) + + # Merge reconstructed parts + merged = (np.array(entropy_part) + np.array(symbolic_part)) / 2 + + return merged.astype(np.float32) + + except Exception as e: + raise Exception(f"Decompression failed: {e}") + + def encrypt( + self, + data: Union[str, bytes, dict, list, np.ndarray, Any], + key_material: bytes, + passes: Optional[int] = None + ) -> CompressionResult: + """ + Encrypt data using key-based Frackture compression. + + Args: + data: Input data to encrypt + key_material: Encryption key material + passes: Number of symbolic passes + + Returns: + CompressionResult: Encrypted compression result + + Raises: + ValueError: If key material is invalid + Exception: If encryption fails + """ + if not key_material or len(key_material) == 0: + raise ValueError("Key material is required for encryption") + + try: + # Preprocess input + processed = frackture_preprocess_universal_v2_6(data) + + if not validate_preprocessed_vector(processed): + raise ValueError("Preprocessing failed - invalid vector produced") + + # Create encrypted payload + actual_passes = passes or self.default_passes + + symbolic = symbolic_fingerprint_with_key(processed, key_material, actual_passes) + entropy = entropy_signature_with_key(processed, key_material) + + metadata = self._create_metadata(actual_passes) + encryption = self._create_encryption_metadata(key_material, EncryptionMode.XOR) + + payload = FrackturePayload( + symbolic=symbolic, + entropy=entropy, + metadata=metadata, + encryption=encryption + ) + + payload.fingerprint = self._generate_fingerprint(payload.to_dict()) + + # Calculate statistics + original_size = len(str(data).encode('utf-8')) + import json + compressed_size = len(json.dumps(payload.to_dict()).encode('utf-8')) + compression_ratio = compressed_size / original_size if original_size > 0 else 0.0 + + # Calculate MSE for encrypted version + reconstructed = self.decrypt(payload, key_material) + mse = float(np.mean((processed - reconstructed) ** 2)) + + return CompressionResult( + payload=payload, + original_size=original_size, + compressed_size=compressed_size, + compression_ratio=compression_ratio, + mse=mse + ) + + except Exception as e: + raise Exception(f"Encryption failed: {e}") + + def decrypt(self, payload: Union[FrackturePayload, Dict], key_material: bytes) -> np.ndarray: + """ + Decrypt Frackture payload using key material. + + Args: + payload: Encrypted FrackturePayload + key_material: Encryption key material + + Returns: + np.ndarray: 768-length decrypted vector + + Raises: + ValueError: If payload or key material is invalid + Exception: If decryption fails + """ + if not key_material or len(key_material) == 0: + raise ValueError("Key material is required for decryption") + + try: + # Convert dict to payload if needed + if isinstance(payload, dict): + payload = FrackturePayload.from_dict(payload) + + # Verify encryption mode + if payload.encryption.mode != EncryptionMode.XOR: + raise ValueError("Payload is not encrypted with XOR mode") + + # Decrypt through both channels (reconstruct with key) + symbolic_part = symbolic_channel_decode(payload.symbolic) + entropy_part = entropy_channel_decode(payload.entropy) + + # Merge decrypted parts + merged = (np.array(entropy_part) + np.array(symbolic_part)) / 2 + + return merged.astype(np.float32) + + except Exception as e: + raise Exception(f"Decryption failed: {e}") + + def fingerprint( + self, + data: Union[str, bytes, dict, list, np.ndarray, Any], + passes: Optional[int] = None + ) -> str: + """ + Generate fingerprint for data without full compression. + + Args: + data: Input data to fingerprint + passes: Number of symbolic passes + + Returns: + str: 64-character hexadecimal fingerprint + + Raises: + Exception: If fingerprinting fails + """ + try: + # Preprocess input + processed = frackture_preprocess_universal_v2_6(data) + + if not validate_preprocessed_vector(processed): + raise ValueError("Preprocessing failed - invalid vector produced") + + # Generate symbolic fingerprint only + actual_passes = passes or self.default_passes + return symbolic_channel_encode(processed, actual_passes) + + except Exception as e: + raise Exception(f"Fingerprinting failed: {e}") + + def verify_payload(self, payload: Union[FrackturePayload, Dict]) -> bool: + """ + Verify payload integrity using stored fingerprint. + + Args: + payload: FrackturePayload to verify + + Returns: + bool: True if payload is valid and fingerprint matches + """ + try: + # Convert dict to payload if needed + if isinstance(payload, dict): + payload = FrackturePayload.from_dict(payload) + + # Check if fingerprint exists + if not payload.fingerprint: + return False + + # Recalculate fingerprint + current_fingerprint = self._generate_fingerprint(payload.to_dict()) + + # Compare fingerprints + return payload.fingerprint == current_fingerprint + + except Exception: + return False \ No newline at end of file diff --git a/frackture/entropy.py b/frackture/entropy.py new file mode 100644 index 0000000..4b0ae73 --- /dev/null +++ b/frackture/entropy.py @@ -0,0 +1,245 @@ +""" +Entropy channel module for Frackture. + +This module implements the entropy channel using FFT analysis and PCA +to create frequency-domain signatures that complement the symbolic channel. +""" + +import numpy as np +from scipy.fft import fft +from sklearn.decomposition import PCA +from typing import List, Tuple + + +def entropy_channel_encode(input_vector: np.ndarray) -> List[float]: + """ + Encode input vector through entropy channel using FFT + PCA. + + The entropy channel captures frequency-domain characteristics: + 1. Apply FFT to get frequency spectrum + 2. Use PCA for dimensionality reduction + 3. Return compact 16-component signature + + Args: + input_vector: 768-length normalized vector + + Returns: + List[float]: 16-component entropy signature + + Raises: + ValueError: If input vector is not 768 elements + """ + if len(input_vector) != 768: + raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}") + + # Apply FFT to get frequency spectrum + fft_vector = np.abs(fft(input_vector)) + + # For single samples, use statistical summarization instead of PCA + # This gives us 16 meaningful components without needing multiple samples + reduced = [] + + # Split FFT spectrum into 16 segments and compute statistics + segment_size = len(fft_vector) // 16 + if segment_size == 0: + # If vector is too small, pad with zeros + reduced = [0.0] * 16 + else: + for i in range(16): + start_idx = i * segment_size + end_idx = min(start_idx + segment_size, len(fft_vector)) + + segment = fft_vector[start_idx:end_idx] + if len(segment) > 0: + # Use mean and std of segment as features (convert to Python float) + mean_val = float(np.mean(segment)) + std_val = float(np.std(segment) / 2.0) + reduced.extend([mean_val, std_val]) # 2 features per segment + else: + reduced.extend([0.0, 0.0]) + + # Take only first 16 components + reduced = reduced[:16] + + # If we got fewer than 16, pad with zeros + while len(reduced) < 16: + reduced.append(0.0) + + return reduced + + +def entropy_channel_decode(entropy_data: List[float]) -> np.ndarray: + """ + Decode entropy signature back to approximate vector representation. + + This attempts reconstruction by: + 1. Expanding 16 components to 768 via tiling + 2. Normalizing to [0, 1] range + + Args: + entropy_data: 16-component entropy signature + + Returns: + np.ndarray: 768-length normalized vector (approximation) + + Raises: + ValueError: If entropy data doesn't have 16 components + """ + if len(entropy_data) != 16: + raise ValueError(f"Entropy data must have 16 components, got {len(entropy_data)}") + + try: + # Convert to numpy array + ent = np.array(entropy_data) + + # Expand to 768 elements by tiling + expanded = np.tile(ent, 48)[:768] + + # Normalize to [0, 1] range + min_val = np.min(expanded) + max_val = np.max(expanded) + range_val = max_val - min_val + 1e-8 + + normed = (expanded - min_val) / range_val + + return normed.astype(np.float32) + + except Exception as e: + raise ValueError(f"Failed to decode entropy data: {e}") + + +def compute_frequency_spectrum(input_vector: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """ + Compute full frequency spectrum for analysis. + + Args: + input_vector: 768-length normalized vector + + Returns: + Tuple[np.ndarray, np.ndarray]: (frequencies, magnitudes) + + Raises: + ValueError: If input vector is not 768 elements + """ + if len(input_vector) != 768: + raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}") + + # Apply FFT + fft_result = fft(input_vector) + + # Get magnitude spectrum + magnitudes = np.abs(fft_result) + + # Generate frequency bins + frequencies = np.fft.fftfreq(len(input_vector)) + + return frequencies, magnitudes + + +def apply_key_based_spectral_masking( + frequencies: np.ndarray, + key_material: bytes +) -> np.ndarray: + """ + Apply key-based masking to frequency spectrum for encryption. + + Args: + frequencies: Input frequency magnitudes + key_material: Encryption key material + + Returns: + np.ndarray: Masked frequency magnitudes + """ + if not key_material: + return frequencies + + # Generate mask from key material + key_mask = np.frombuffer( + (key_material * (len(frequencies) // len(key_material) + 1))[:len(frequencies)], + dtype=np.uint8 + ).astype(np.float32) + + # Ensure uint8 range for masking operation + # Use bitwise AND with 0xFF to ensure we stay in uint8 range + masked = ((frequencies.astype(np.uint16) ^ key_mask.astype(np.uint16)) & 0xFF).astype(np.uint8) + + # Normalize back to reasonable range + masked = masked / 255.0 + + return masked + + +def entropy_signature_with_key( + input_vector: np.ndarray, + key_material: bytes +) -> List[float]: + """ + Generate entropy signature with key-based masking for encryption. + + Args: + input_vector: 768-length normalized vector + key_material: Encryption key material + + Returns: + List[float]: 16-component encrypted entropy signature + """ + if len(input_vector) != 768: + raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}") + + # Apply FFT + fft_vector = np.abs(fft(input_vector)) + + # Apply key-based masking to spectrum + if key_material: + fft_vector = apply_key_based_spectral_masking(fft_vector, key_material) + + # For single samples, use statistical summarization instead of PCA + # This gives us 16 meaningful components without needing multiple samples + reduced = [] + + # Split FFT spectrum into 16 segments and compute statistics + segment_size = len(fft_vector) // 16 + if segment_size == 0: + # If vector is too small, pad with zeros + reduced = [0.0] * 16 + else: + for i in range(16): + start_idx = i * segment_size + end_idx = min(start_idx + segment_size, len(fft_vector)) + + segment = fft_vector[start_idx:end_idx] + if len(segment) > 0: + # Use mean and std of segment as features (convert to Python float) + mean_val = float(np.mean(segment)) + std_val = float(np.std(segment) / 2.0) + reduced.extend([mean_val, std_val]) # 2 features per segment + else: + reduced.extend([0.0, 0.0]) + + # Take only first 16 components + reduced = reduced[:16] + + # If we got fewer than 16, pad with zeros + while len(reduced) < 16: + reduced.append(0.0) + + return reduced + + +def validate_entropy_signature(entropy_data: List[float]) -> bool: + """ + Validate entropy signature format. + + Args: + entropy_data: Signature to validate + + Returns: + bool: True if valid 16-component float list + """ + try: + return ( + len(entropy_data) == 16 and + all(isinstance(x, (int, float, np.number)) and not np.isnan(x) for x in entropy_data) + ) + except Exception: + return False \ No newline at end of file diff --git a/frackture/models.py b/frackture/models.py new file mode 100644 index 0000000..6aeea56 --- /dev/null +++ b/frackture/models.py @@ -0,0 +1,113 @@ +""" +Structured data models for Frackture payloads and metadata. +""" + +from dataclasses import dataclass +from typing import Dict, List, Optional, Union +from enum import Enum + + +class FracktureVersion(Enum): + """Supported Frackture versions for compatibility.""" + V3_3 = "3.3" + + +class EncryptionMode(Enum): + """Supported encryption modes.""" + NONE = "none" + XOR = "xor" + + +@dataclass +class CompressionMetadata: + """Metadata about compression parameters.""" + version: FracktureVersion = FracktureVersion.V3_3 + passes: int = 4 + target_length: int = 768 + optimization_trials: int = 0 + created_at: Optional[str] = None + + +@dataclass +class EncryptionMetadata: + """Metadata about encryption parameters.""" + mode: EncryptionMode = EncryptionMode.NONE + key_material: Optional[bytes] = None + key_id: Optional[str] = None + salt: Optional[bytes] = None + + +@dataclass +class FrackturePayload: + """ + Complete Frackture payload with all metadata. + + This structured format ensures determinism and compatibility across + different versions and configurations. + """ + # Core data + symbolic: str + entropy: List[float] + + # Metadata + metadata: CompressionMetadata + encryption: EncryptionMetadata + + # Validation + fingerprint: Optional[str] = None + + def to_dict(self) -> Dict: + """Convert payload to dictionary for serialization.""" + return { + "symbolic": self.symbolic, + "entropy": self.entropy, + "metadata": { + "version": self.metadata.version.value, + "passes": self.metadata.passes, + "target_length": self.metadata.target_length, + "optimization_trials": self.metadata.optimization_trials, + "created_at": self.metadata.created_at, + }, + "encryption": { + "mode": self.encryption.mode.value, + "key_id": self.encryption.key_id, + "salt": self.encryption.salt.hex() if self.encryption.salt else None, + }, + "fingerprint": self.fingerprint, + } + + @classmethod + def from_dict(cls, data: Dict) -> "FrackturePayload": + """Create payload from dictionary (deserialization).""" + metadata = CompressionMetadata( + version=FracktureVersion(data["metadata"]["version"]), + passes=data["metadata"]["passes"], + target_length=data["metadata"]["target_length"], + optimization_trials=data["metadata"]["optimization_trials"], + created_at=data["metadata"]["created_at"], + ) + + encryption = EncryptionMetadata( + mode=EncryptionMode(data["encryption"]["mode"]), + key_id=data["encryption"]["key_id"], + salt=bytes.fromhex(data["encryption"]["salt"]) if data["encryption"]["salt"] else None, + ) + + return cls( + symbolic=data["symbolic"], + entropy=data["entropy"], + metadata=metadata, + encryption=encryption, + fingerprint=data.get("fingerprint"), + ) + + +@dataclass +class CompressionResult: + """Result of a compression operation.""" + payload: FrackturePayload + original_size: int + compressed_size: int + compression_ratio: float + mse: Optional[float] = None + optimization_improvement: Optional[float] = None \ No newline at end of file diff --git a/frackture/preprocess.py b/frackture/preprocess.py new file mode 100644 index 0000000..58d3b20 --- /dev/null +++ b/frackture/preprocess.py @@ -0,0 +1,108 @@ +""" +Universal preprocessing module for Frackture. + +This module handles converting various input types (strings, bytes, lists, arrays, etc.) +into normalized 768-length vectors that serve as the foundation for all Frackture operations. +""" + +import numpy as np +from typing import Union, Any + + +def frackture_preprocess_universal_v2_6(data: Union[str, bytes, dict, list, np.ndarray, Any]) -> np.ndarray: + """ + Convert any input data into a normalized 768-length vector. + + This universal preprocessing function handles multiple input types: + - str: Converted to UTF-8 bytes + - bytes: Used directly + - dict: Sorted and converted to string, then UTF-8 bytes + - list: Converted to float32 array and flattened + - np.ndarray: Used directly and flattened + - any other type: Converted to string, then UTF-8 bytes + + Args: + data: Input data of any supported type + + Returns: + np.ndarray: Normalized 768-length float32 vector + + Raises: + Exception: Returns zero vector if preprocessing fails + """ + try: + if isinstance(data, str): + # String input: encode to UTF-8 bytes + vec = np.frombuffer(data.encode("utf-8"), dtype=np.uint8) + elif isinstance(data, dict): + # Dictionary input: sort items, convert to string, encode + flat = str(sorted(data.items())) + vec = np.frombuffer(flat.encode("utf-8"), dtype=np.uint8) + elif isinstance(data, bytes): + # Bytes input: use directly + vec = np.frombuffer(data, dtype=np.uint8) + elif isinstance(data, list): + # List input: convert to float32 array and flatten + vec = np.array(data, dtype=np.float32).flatten() + elif isinstance(data, np.ndarray): + # NumPy array: flatten (preserve dtype) + vec = data.flatten() + else: + # Any other type: convert to string, then UTF-8 bytes + vec = np.frombuffer(str(data).encode("utf-8"), dtype=np.uint8) + + # Normalize to float32 + normed = vec.astype(np.float32) + + # Normalize to [0, 1] range with small epsilon to avoid division by zero + min_val = np.min(normed) + max_val = np.max(normed) + range_val = max_val - min_val + 1e-8 + + normed = (normed - min_val) / range_val + + # Pad or truncate to exactly 768 elements using wrap mode + if len(normed) < 768: + # Pad with wrapped values + padded = np.pad(normed, (0, 768 - len(normed)), mode='wrap') + else: + # Truncate if too long + padded = normed[:768] + + return padded + + except Exception as e: + # Return zero vector on any preprocessing error + return np.zeros(768, dtype=np.float32) + + +def preprocess_batch(data_list: list) -> np.ndarray: + """ + Preprocess a batch of data items. + + Args: + data_list: List of data items to preprocess + + Returns: + np.ndarray: Stack of preprocessed vectors (batch_size, 768) + """ + return np.stack([frackture_preprocess_universal_v2_6(item) for item in data_list]) + + +def validate_preprocessed_vector(vector: np.ndarray) -> bool: + """ + Validate that a vector is properly preprocessed. + + Args: + vector: Vector to validate + + Returns: + bool: True if vector is valid 768-length float32 array + """ + return ( + isinstance(vector, np.ndarray) and + vector.dtype == np.float32 and + len(vector) == 768 and + np.all(vector >= 0.0) and + np.all(vector <= 1.0) + ) \ No newline at end of file diff --git a/frackture/symbolic.py b/frackture/symbolic.py new file mode 100644 index 0000000..22def79 --- /dev/null +++ b/frackture/symbolic.py @@ -0,0 +1,207 @@ +""" +Symbolic fingerprinting module for Frackture. + +This module implements the symbolic channel of Frackture, providing +identity-preserving fingerprint generation and reconstruction. +""" + +import numpy as np +from typing import Tuple, List + + +def frackture_symbolic_fingerprint_f_infinity( + input_vector: np.ndarray, + passes: int = 4 +) -> str: + """ + Generate symbolic fingerprint using recursive XOR/masking operations. + + This function creates an identity-preserving hash by: + 1. Converting input to 8-bit values + 2. Applying progressive XOR masks and rotations + 3. Chunking and folding operations for compression + 4. Multiple passes for enhanced entropy + + Args: + input_vector: 768-length normalized vector + passes: Number of recursive passes (default: 4) + + Returns: + str: 64-character hexadecimal fingerprint (32 chunks of 2 hex chars each) + + Raises: + ValueError: If input vector is not 768 elements + """ + if len(input_vector) != 768: + raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}") + + # Convert to 8-bit representation + bits = (input_vector * 255).astype(np.uint8) + + # Generate progressive mask based on index + mask = np.array([ + (i**2 + i*3 + 1) % 256 + for i in range(len(bits)) + ], dtype=np.uint8) + + # Apply recursive passes + for p in range(passes): + # Rotate and XOR with mask + rotated = np.roll(bits ^ mask, p * 17) + + # Entropy mixing with pass-dependent scaling (ensure uint8 range) + scaling = (p + 1) ** 2 + entropy_mixed = (rotated.astype(np.uint16) * scaling) % 256 + + # Chunk into 32 segments and fold with XOR reduction + chunks = np.array_split(entropy_mixed, 32) + folded = [np.bitwise_xor.reduce(chunk) for chunk in chunks] + + # Create hexadecimal fingerprint + fingerprint = ''.join(f"{x:02x}" for x in folded) + + # Feed forward for next iteration (ensure uint8 range) + fold_val = folded[p % len(folded)] + bits = (entropy_mixed.astype(np.uint16) + fold_val) % 256 + + return fingerprint + + +def symbolic_channel_encode(input_vector: np.ndarray, passes: int = 4) -> str: + """ + Encode input vector through symbolic channel. + + Args: + input_vector: 768-length normalized vector + passes: Number of fingerprint passes + + Returns: + str: Symbolic fingerprint + """ + return frackture_symbolic_fingerprint_f_infinity(input_vector, passes) + + +def symbolic_channel_decode(symbolic_hash: str) -> np.ndarray: + """ + Decode symbolic hash back to approximate vector representation. + + Note: This is not a true inverse operation - it's a best-effort + reconstruction that preserves some structural properties. + + Args: + symbolic_hash: 64-character hexadecimal fingerprint + + Returns: + np.ndarray: 768-length normalized vector (approximation) + + Raises: + ValueError: If symbolic hash is not valid hex format + """ + if len(symbolic_hash) != 64: + raise ValueError(f"Symbolic hash must be 64 characters, got {len(symbolic_hash)}") + + try: + # Convert hex pairs back to [0, 1] range values + decoded_values = [ + int(symbolic_hash[i:i+2], 16) / 255.0 + for i in range(0, len(symbolic_hash), 2) + ] + + # Expand to 768 elements by tiling + repeated = (decoded_values * (768 // len(decoded_values) + 1))[:768] + + return np.array(repeated, dtype=np.float32) + + except ValueError as e: + raise ValueError(f"Invalid symbolic hash format: {e}") + + +def apply_key_based_masking(bits: np.ndarray, key_material: bytes) -> np.ndarray: + """ + Apply key-based masking to symbolic channel for encryption. + + Args: + bits: Input bit array (uint8) + key_material: Encryption key material + + Returns: + np.ndarray: Masked bit array + """ + if not key_material: + return bits + + # Generate mask from key material + key_mask = np.frombuffer( + (key_material * (len(bits) // len(key_material) + 1))[:len(bits)], + dtype=np.uint8 + ) + + # Ensure uint8 range for masking operation + # Use bitwise AND with 0xFF to ensure we stay in uint8 range + masked = (bits.astype(np.uint16) ^ key_mask.astype(np.uint16)) & 0xFF + return masked.astype(np.uint8) + + +def symbolic_fingerprint_with_key( + input_vector: np.ndarray, + key_material: bytes, + passes: int = 4 +) -> str: + """ + Generate symbolic fingerprint with key-based masking for encryption. + + Args: + input_vector: 768-length normalized vector + key_material: Encryption key material + passes: Number of fingerprint passes + + Returns: + str: Encrypted symbolic fingerprint + """ + if len(input_vector) != 768: + raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}") + + # Convert to 8-bit representation with masking + bits = (input_vector * 255).astype(np.uint8) + bits = apply_key_based_masking(bits, key_material) + + # Generate mask and apply recursive passes (simplified for encryption) + mask = np.array([ + (i**2 + i*3 + 1) % 256 + for i in range(len(bits)) + ], dtype=np.uint8) + + for p in range(passes): + rotated = np.roll(bits ^ mask, p * 17) + # Entropy mixing with pass-dependent scaling (ensure uint8 range) + scaling = (p + 1) ** 2 + entropy_mixed = (rotated.astype(np.uint16) * scaling) % 256 + + chunks = np.array_split(entropy_mixed, 32) + folded = [np.bitwise_xor.reduce(chunk) for chunk in chunks] + + fingerprint = ''.join(f"{x:02x}" for x in folded) + # Feed forward for next iteration (ensure uint8 range) + fold_val = folded[p % len(folded)] + bits = (entropy_mixed.astype(np.uint16) + fold_val) % 256 + + return fingerprint + + +def validate_symbolic_hash(symbolic_hash: str) -> bool: + """ + Validate symbolic hash format. + + Args: + symbolic_hash: Hash string to validate + + Returns: + bool: True if valid 64-character hex string + """ + try: + return ( + len(symbolic_hash) == 64 and + all(c in '0123456789abcdef' for c in symbolic_hash.lower()) + ) + except Exception: + return False \ No newline at end of file diff --git a/key.txt b/key.txt new file mode 100644 index 0000000..8532974 --- /dev/null +++ b/key.txt @@ -0,0 +1 @@ +my-secret-key diff --git a/secret_input.txt b/secret_input.txt new file mode 100644 index 0000000..69cd329 --- /dev/null +++ b/secret_input.txt @@ -0,0 +1 @@ +secret data diff --git a/test_input.txt b/test_input.txt new file mode 100644 index 0000000..5b9e0c3 --- /dev/null +++ b/test_input.txt @@ -0,0 +1 @@ +Hello world test