This chapter is your deep dive into the secret sauce of QuadB64: how to customize it, squeeze every drop of performance out of it, and troubleshoot like a pro. It’s like getting the master key to the QuadB64 engine room, allowing you to fine-tune it for any mission, no matter how complex.
Advanced Features and Configuration
Overview
Imagine you’re a master watchmaker, and QuadB64 is a highly intricate timepiece. This chapter provides you with the specialized tools and knowledge to not just tell time, but to re-engineer its internal mechanisms, optimize its gears for specific environments, and diagnose any subtle malfunctions with precision.
Imagine you’re a seasoned pilot, and QuadB64 is your advanced aircraft. This chapter is your flight manual for extreme maneuvers, detailing how to push its performance envelope, navigate through turbulent conditions, and even customize its propulsion system for unique missions, ensuring a smooth and efficient flight every time.
This chapter covers sophisticated usage patterns, troubleshooting techniques, and advanced configuration options for power users and system integrators working with QuadB64 in complex environments.
Advanced Configuration
Custom Variant Creation
Beyond the standard QuadB64 variants (Eq64, Shq64, T8q64, Zoq64), you can create specialized variants for specific use cases:
from uubed.core import VariantBuilder, AlphabetGenerator, PositionFunction
class DomainSpecificVariant:
"""Create QuadB64 variants optimized for specific domains"""
@staticmethod
def create_genomic_variant():
"""Variant optimized for genomic data encoding"""
def genomic_alphabet_generator(position: int) -> str:
# Optimize for ATCG frequency in genomic data
# Place nucleotide-like characters first
base = "ATCGNatcgn0123456789BDEFHIJKLMOPQRSUVWXYZ+/bdefhijklmopqrsuvwxyz"
# Rotate based on codon position (groups of 3)
codon_position = (position // 3) % 3
rotation = codon_position * 21 # Distribute across alphabet
return base[rotation:] + base[:rotation]
def genomic_position_function(position: int, context: dict = None) -> int:
# Account for reading frame in genomic sequences
reading_frame = context.get('reading_frame', 0) if context else 0
return (position + reading_frame) % (2**24)
return VariantBuilder.create_variant(
name="Genomic64",
alphabet_generator=genomic_alphabet_generator,
position_function=genomic_position_function,
metadata={
'domain': 'genomics',
'optimized_for': 'nucleotide_sequences',
'reading_frame_aware': True
}
)
@staticmethod
def create_financial_variant():
"""Variant optimized for financial data encoding"""
def financial_alphabet_generator(position: int) -> str:
# Prioritize numeric characters for financial data
base = "0123456789.$+-ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz/"
# Rotate based on decimal precision context
precision_rotation = (position * 11) % 64
return base[precision_rotation:] + base[:precision_rotation]
def financial_position_function(position: int, context: dict = None) -> int:
# Incorporate timestamp for temporal consistency
timestamp = context.get('timestamp', 0) if context else 0
time_factor = int(timestamp / 3600) % 1000 # Hour-based rotation
return (position * 13 + time_factor) % (2**28)
return VariantBuilder.create_variant(
name="Financial64",
alphabet_generator=financial_alphabet_generator,
position_function=financial_position_function,
metadata={
'domain': 'finance',
'temporal_consistency': True,
'precision_optimized': True
}
)
# Usage example
genomic_encoder = DomainSpecificVariant.create_genomic_variant()
financial_encoder = DomainSpecificVariant.create_financial_variant()
# Encode genomic sequence with reading frame context
dna_sequence = b"ATCGATCGATCG" * 100
encoded_dna = genomic_encoder.encode(dna_sequence, context={'reading_frame': 1})
# Encode financial data with timestamp context
financial_data = b'{"price": 123.45, "volume": 10000}'
encoded_financial = financial_encoder.encode(
financial_data,
context={'timestamp': time.time()}
)
Performance Tuning for Specific Use Cases
High-Throughput Streaming Configuration
class HighThroughputConfig:
"""Configuration optimized for high-throughput streaming scenarios"""
def __init__(self):
self.batch_size = 8192 # Optimal for network packets
self.worker_threads = min(32, os.cpu_count() * 2)
self.memory_pool_size = 64 * 1024 * 1024 # 64MB pool
self.enable_compression = True
self.cache_size = 100000
def create_streaming_encoder(self):
"""Create encoder optimized for streaming"""
encoder = StreamingEncoder(
batch_size=self.batch_size,
max_workers=self.worker_threads,
memory_pool_size=self.memory_pool_size,
cache_config={
'max_size': self.cache_size,
'ttl_seconds': 300, # 5 minute TTL
'eviction_policy': 'lru'
}
)
# Enable hardware acceleration if available
if self._has_simd_support():
encoder.enable_simd_acceleration()
return encoder
def _has_simd_support(self) -> bool:
"""Detect available SIMD instruction sets"""
try:
import cpuinfo
cpu_info = cpuinfo.get_cpu_info()
return any(flag in cpu_info.get('flags', [])
for flag in ['avx2', 'sse4_1', 'neon'])
except ImportError:
return False
class StreamingEncoder:
"""High-performance streaming encoder"""
def __init__(self, batch_size=8192, max_workers=8,
memory_pool_size=64*1024*1024, cache_config=None):
self.batch_size = batch_size
self.executor = ThreadPoolExecutor(max_workers=max_workers)
self.memory_pool = MemoryPool(memory_pool_size)
self.cache = LRUCache(**(cache_config or {}))
self.statistics = StreamingStats()
# Pre-allocate working buffers
self.working_buffers = [
self.memory_pool.get_buffer(batch_size * 2)
for _ in range(max_workers)
]
def encode_stream(self, data_stream, output_stream):
"""Encode data stream with optimal batching"""
batch_buffer = bytearray()
batch_count = 0
try:
for chunk in data_stream:
batch_buffer.extend(chunk)
# Process when batch is full
if len(batch_buffer) >= self.batch_size:
self._process_batch(batch_buffer, output_stream, batch_count)
batch_buffer.clear()
batch_count += 1
# Process remaining data
if batch_buffer:
self._process_batch(batch_buffer, output_stream, batch_count)
finally:
self.executor.shutdown(wait=True)
def _process_batch(self, batch_data: bytearray, output_stream, batch_id: int):
"""Process a single batch with position-aware encoding"""
# Calculate global position offset
global_position = batch_id * self.batch_size
# Submit to thread pool
future = self.executor.submit(
self._encode_batch_worker,
bytes(batch_data),
global_position
)
# Get result and write to output
try:
encoded_data = future.result(timeout=30.0)
output_stream.write(encoded_data)
output_stream.flush()
self.statistics.record_batch(len(batch_data), len(encoded_data))
except Exception as e:
self.statistics.record_error(str(e))
raise RuntimeError(f"Batch {batch_id} encoding failed: {e}")
def _encode_batch_worker(self, data: bytes, position: int) -> bytes:
"""Worker function for batch encoding"""
# Check cache first
cache_key = (hash(data), position)
cached_result = self.cache.get(cache_key)
if cached_result:
return cached_result
# Encode with position context
encoded = encode_eq64_with_position(data, position)
# Cache result
self.cache.put(cache_key, encoded)
return encoded.encode('utf-8')
class StreamingStats:
"""Statistics collection for streaming operations"""
def __init__(self):
self.total_bytes_in = 0
self.total_bytes_out = 0
self.batch_count = 0
self.error_count = 0
self.start_time = time.time()
self._lock = threading.Lock()
def record_batch(self, bytes_in: int, bytes_out: int):
with self._lock:
self.total_bytes_in += bytes_in
self.total_bytes_out += bytes_out
self.batch_count += 1
def record_error(self, error_msg: str):
with self._lock:
self.error_count += 1
def get_stats(self) -> dict:
with self._lock:
elapsed = time.time() - self.start_time
return {
'total_input_mb': self.total_bytes_in / 1024 / 1024,
'total_output_mb': self.total_bytes_out / 1024 / 1024,
'compression_ratio': self.total_bytes_out / max(self.total_bytes_in, 1),
'throughput_mb_s': (self.total_bytes_in / 1024 / 1024) / max(elapsed, 0.001),
'batches_processed': self.batch_count,
'error_rate': self.error_count / max(self.batch_count, 1),
'elapsed_time': elapsed
}
Low-Latency Configuration
class LowLatencyConfig:
"""Configuration optimized for low-latency scenarios"""
def __init__(self):
# Minimize context switching and memory allocation
self.enable_pre_allocation = True
self.disable_threading = True # Single-threaded for consistency
self.cache_warmup = True
self.use_stack_buffers = True
def create_low_latency_encoder(self):
"""Create encoder optimized for minimum latency"""
encoder = LowLatencyEncoder()
if self.cache_warmup:
encoder.warmup_cache()
return encoder
class LowLatencyEncoder:
"""Encoder optimized for minimal latency"""
def __init__(self):
# Pre-allocate all possible buffers
self.buffer_cache = {}
self.alphabet_cache = {}
self.precomputed_tables = self._build_lookup_tables()
# Pre-generate common alphabets
for pos in range(1024): # Cache first 1024 positions
self.alphabet_cache[pos] = self._generate_alphabet(pos)
def encode_minimal_latency(self, data: bytes, position: int = 0) -> str:
"""Encode with minimal latency - no dynamic allocation"""
if len(data) == 0:
return ""
# Use stack-allocated buffer for small data
if len(data) <= 1024:
return self._encode_stack_buffer(data, position)
else:
return self._encode_pre_allocated(data, position)
def _encode_stack_buffer(self, data: bytes, position: int) -> str:
"""Encode using stack-allocated buffer"""
# Get pre-computed alphabet
alphabet = self.alphabet_cache.get(position % 1024)
if not alphabet:
alphabet = self._generate_alphabet(position)
# Direct encoding without memory allocation
result_chars = []
for i in range(0, len(data), 3):
chunk = data[i:i+3]
encoded_chunk = self._encode_chunk_direct(chunk, alphabet, position + i)
result_chars.extend(encoded_chunk)
return ''.join(result_chars)
def _encode_chunk_direct(self, chunk: bytes, alphabet: str, position: int) -> list:
"""Direct chunk encoding without intermediate allocations"""
# Pad chunk to 3 bytes
while len(chunk) < 3:
chunk += b'\x00'
# Convert to 24-bit integer
value = (chunk[0] << 16) | (chunk[1] << 8) | chunk[2]
# Extract 6-bit groups using lookup table
indices = [
(value >> 18) & 0x3F,
(value >> 12) & 0x3F,
(value >> 6) & 0x3F,
value & 0x3F
]
# Apply position-dependent rotation
rotation = (position // 3) % 64
rotated_indices = [(idx + rotation) % 64 for idx in indices]
# Map to alphabet characters
return [alphabet[idx] for idx in rotated_indices]
def warmup_cache(self):
"""Pre-compute common operations to minimize latency"""
# Warm up with common data patterns
test_patterns = [
b'\x00' * 16, # Null pattern
b'\xFF' * 16, # All ones
bytes(range(16)), # Sequential
b'Hello, World!' * 2 # Text pattern
]
for pattern in test_patterns:
for pos in range(0, 64, 8):
self.encode_minimal_latency(pattern, pos)
Integration with Exotic Platforms
Embedded Systems Integration
class EmbeddedSystemsConfig:
"""Configuration for resource-constrained embedded systems"""
def __init__(self, memory_limit_kb=256, cpu_mhz=100):
self.memory_limit = memory_limit_kb * 1024
self.cpu_frequency = cpu_mhz * 1000000
self.enable_power_saving = True
self.use_lookup_tables = memory_limit_kb > 64 # Only if sufficient memory
def create_embedded_encoder(self):
"""Create encoder suitable for embedded systems"""
if self.memory_limit < 32 * 1024: # Very constrained
return MinimalEncoder(self.memory_limit)
elif self.memory_limit < 128 * 1024: # Moderately constrained
return CompactEncoder(self.memory_limit)
else: # Relatively unconstrained
return OptimizedEmbeddedEncoder(self.memory_limit)
class MinimalEncoder:
"""Minimal encoder for severely memory-constrained environments"""
def __init__(self, memory_limit: int):
self.memory_limit = memory_limit
# No caches or lookup tables - compute everything on-demand
def encode(self, data: bytes, position: int = 0) -> str:
"""Encode with minimal memory footprint"""
if len(data) == 0:
return ""
result = []
# Process one character at a time to minimize memory usage
for i in range(0, len(data), 3):
chunk = data[i:min(i+3, len(data))]
encoded = self._encode_chunk_minimal(chunk, position + i)
result.append(encoded)
return ''.join(result)
def _encode_chunk_minimal(self, chunk: bytes, position: int) -> str:
"""Encode chunk with zero additional memory allocation"""
# Generate alphabet on-demand (no caching)
alphabet = self._generate_alphabet_minimal(position)
# Pad chunk
padded = chunk + b'\x00' * (3 - len(chunk))
# Convert to integer and extract indices
value = (padded[0] << 16) | (padded[1] << 8) | padded[2]
# Extract and rotate indices
rotation = (position // 3) % 64
indices = [
((value >> 18) & 0x3F + rotation) % 64,
((value >> 12) & 0x3F + rotation) % 64,
((value >> 6) & 0x3F + rotation) % 64,
((value & 0x3F) + rotation) % 64
]
# Build result directly
return ''.join(alphabet[idx] for idx in indices[:len(chunk)+1])
def _generate_alphabet_minimal(self, position: int) -> str:
"""Generate alphabet without caching"""
base = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
rotation = (position // 3) % 64
return base[rotation:] + base[:rotation]
GPU Acceleration Integration
try:
import cupy as cp # GPU acceleration with CuPy
GPU_AVAILABLE = True
except ImportError:
GPU_AVAILABLE = False
class GPUAcceleratedEncoder:
"""QuadB64 encoder with GPU acceleration for large datasets"""
def __init__(self):
if not GPU_AVAILABLE:
raise RuntimeError("GPU acceleration requires CuPy")
self.device = cp.cuda.Device()
self.memory_pool = cp.get_default_memory_pool()
self._compile_kernels()
def _compile_kernels(self):
"""Compile CUDA kernels for QuadB64 operations"""
# CUDA kernel for parallel encoding
encode_kernel_code = """
extern "C" __global__
void quadb64_encode_kernel(
const unsigned char* input,
char* output,
const char* alphabet,
int* positions,
int data_size,
int chunk_size
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int chunk_start = idx * chunk_size;
if (chunk_start >= data_size) return;
// Process chunk
int chunk_end = min(chunk_start + chunk_size, data_size);
int position = positions[idx];
// Generate position-dependent alphabet
char local_alphabet[64];
int rotation = (position / 3) % 64;
for (int i = 0; i < 64; i++) {
local_alphabet[i] = alphabet[(i + rotation) % 64];
}
// Encode chunk
for (int i = chunk_start; i < chunk_end; i += 3) {
// Get 3 bytes (pad with zeros if needed)
unsigned int value = 0;
for (int j = 0; j < 3 && i + j < data_size; j++) {
value |= (input[i + j] << (16 - 8 * j));
}
// Extract 6-bit groups
int out_idx = (i / 3) * 4;
output[out_idx + 0] = local_alphabet[(value >> 18) & 0x3F];
output[out_idx + 1] = local_alphabet[(value >> 12) & 0x3F];
output[out_idx + 2] = local_alphabet[(value >> 6) & 0x3F];
output[out_idx + 3] = local_alphabet[value & 0x3F];
}
}
"""
self.encode_kernel = cp.RawKernel(encode_kernel_code, 'quadb64_encode_kernel')
def encode_gpu(self, data_list: list, positions: list = None) -> list:
"""Encode multiple data chunks on GPU"""
if not positions:
positions = list(range(len(data_list)))
# Prepare GPU memory
max_data_size = max(len(data) for data in data_list)
max_output_size = ((max_data_size + 2) // 3) * 4
# Allocate GPU memory
gpu_input = cp.zeros(max_data_size, dtype=cp.uint8)
gpu_output = cp.zeros(max_output_size, dtype=cp.int8)
gpu_alphabet = cp.array(list("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"), dtype=cp.int8)
results = []
for i, (data, position) in enumerate(zip(data_list, positions)):
# Copy data to GPU
data_array = cp.array(list(data), dtype=cp.uint8)
gpu_input[:len(data)] = data_array
# Set up kernel parameters
chunk_size = 192 # Process 192 bytes per thread (64 output chars)
num_chunks = (len(data) + chunk_size - 1) // chunk_size
positions_array = cp.array([position + i * chunk_size for i in range(num_chunks)], dtype=cp.int32)
# Launch kernel
threads_per_block = min(256, num_chunks)
blocks = (num_chunks + threads_per_block - 1) // threads_per_block
self.encode_kernel(
(blocks,), (threads_per_block,),
(gpu_input, gpu_output, gpu_alphabet, positions_array, len(data), chunk_size)
)
# Copy result back to CPU
output_size = ((len(data) + 2) // 3) * 4
result_bytes = cp.asnumpy(gpu_output[:output_size])
result_str = ''.join(chr(b) for b in result_bytes if b != 0)
results.append(result_str)
return results
Troubleshooting Guide
Common Issues and Solutions
Issue 1: Performance Degradation
Symptoms:
- Encoding speed significantly slower than expected
- High CPU usage with low throughput
- Memory usage growing over time
Diagnostic Steps:
class PerformanceDiagnostic:
"""Diagnostic tools for performance issues"""
def diagnose_performance_issue(self, encoder, test_data: bytes):
"""Comprehensive performance diagnosis"""
diagnostics = {
'system_info': self._get_system_info(),
'encoder_config': self._analyze_encoder_config(encoder),
'memory_usage': self._analyze_memory_usage(),
'cpu_utilization': self._analyze_cpu_usage(),
'bottlenecks': self._identify_bottlenecks(encoder, test_data)
}
return self._generate_performance_report(diagnostics)
def _identify_bottlenecks(self, encoder, test_data: bytes) -> dict:
"""Identify specific performance bottlenecks"""
import time
import tracemalloc
bottlenecks = {}
# Test different aspects
test_scenarios = {
'small_data': test_data[:100],
'medium_data': test_data[:10000],
'large_data': test_data,
'repeated_small': test_data[:100] * 100
}
for scenario, data in test_scenarios.items():
# Start memory tracking
tracemalloc.start()
start_time = time.perf_counter()
# Run encoding
try:
result = encoder.encode(data)
end_time = time.perf_counter()
# Get memory stats
current, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()
bottlenecks[scenario] = {
'duration': end_time - start_time,
'throughput_mb_s': len(data) / (end_time - start_time) / 1024 / 1024,
'memory_current_mb': current / 1024 / 1024,
'memory_peak_mb': peak / 1024 / 1024,
'efficiency_score': len(result) / (end_time - start_time) / peak
}
except Exception as e:
bottlenecks[scenario] = {'error': str(e)}
return bottlenecks
# Usage
diagnostic = PerformanceDiagnostic()
test_data = b"sample data" * 10000
encoder = SomeQuadB64Encoder()
report = diagnostic.diagnose_performance_issue(encoder, test_data)
print(report)
Common Solutions:
- Enable Native Extensions:
# Check if native extensions are loaded if not encoder.has_native_support(): print("Installing native extensions...") install_native_extensions() - Optimize Memory Pool Size:
# Increase memory pool for large datasets encoder.configure_memory_pool(size_mb=128) - Adjust Thread Pool Size:
# Optimize for your CPU count optimal_workers = min(32, os.cpu_count() * 2) encoder.set_worker_count(optimal_workers)
Issue 2: Encoding Inconsistencies
Symptoms:
- Different results for same input
- Position-dependent variations unexpected
- Decoding failures
Diagnostic Steps:
class ConsistencyDiagnostic:
"""Diagnostic tools for encoding consistency issues"""
def test_encoding_consistency(self, encoder, test_cases: list):
"""Test encoding consistency across multiple runs"""
results = {}
for i, test_data in enumerate(test_cases):
case_results = []
# Run same encoding multiple times
for run in range(5):
try:
encoded = encoder.encode(test_data, position=0)
case_results.append(encoded)
except Exception as e:
case_results.append(f"ERROR: {e}")
# Check consistency
unique_results = set(case_results)
results[f"case_{i}"] = {
'consistent': len(unique_results) == 1,
'results': case_results,
'unique_count': len(unique_results)
}
return results
def test_position_consistency(self, encoder, data: bytes):
"""Test position-dependent behavior"""
position_tests = {}
for position in [0, 1, 2, 3, 63, 64, 65, 127, 128]:
try:
encoded = encoder.encode(data, position=position)
decoded = encoder.decode(encoded, position=position)
position_tests[position] = {
'encoded': encoded,
'roundtrip_success': decoded == data,
'encoded_length': len(encoded)
}
except Exception as e:
position_tests[position] = {'error': str(e)}
return position_tests
Solutions:
- Verify Position Parameter Usage:
# Ensure consistent position usage position = calculate_global_position(chunk_index, chunk_size) encoded = encoder.encode(data, position=position) - Check Thread Safety:
# Use thread-safe encoder for concurrent access encoder = ThreadSafeEncoder(base_encoder)
Issue 3: Memory Leaks
Symptoms:
- Memory usage continuously increasing
- Out of memory errors in long-running processes
- Slow garbage collection
Diagnostic Steps:
import gc
import tracemalloc
import weakref
class MemoryLeakDetector:
"""Detect and analyze memory leaks in QuadB64 operations"""
def __init__(self):
self.snapshots = []
self.tracked_objects = []
def start_tracking(self):
"""Start memory leak tracking"""
tracemalloc.start()
gc.collect() # Clean start
self.snapshots.append(tracemalloc.take_snapshot())
def check_leaks(self, operation_name: str = ""):
"""Check for memory leaks since last snapshot"""
gc.collect() # Force garbage collection
current_snapshot = tracemalloc.take_snapshot()
self.snapshots.append(current_snapshot)
if len(self.snapshots) >= 2:
top_stats = current_snapshot.compare_to(
self.snapshots[-2], 'lineno'
)
print(f"Memory diff for {operation_name}:")
for stat in top_stats[:10]:
print(stat)
def track_object(self, obj):
"""Track specific object for garbage collection"""
weak_ref = weakref.ref(obj, lambda x: print(f"Object {id(obj)} collected"))
self.tracked_objects.append(weak_ref)
# Usage
leak_detector = MemoryLeakDetector()
encoder = SomeQuadB64Encoder()
leak_detector.start_tracking()
# Perform operations
for i in range(1000):
data = b"test data" * 100
encoded = encoder.encode(data)
if i % 100 == 0:
leak_detector.check_leaks(f"iteration_{i}")
Solutions:
- Proper Cache Management:
# Configure cache with appropriate limits encoder.configure_cache(max_size=10000, ttl_seconds=300) - Explicit Cleanup:
# Periodically clean up resources if iteration_count % 1000 == 0: encoder.cleanup_caches() gc.collect()
Debugging Techniques
Enable Detailed Logging
import logging
# Configure QuadB64 logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger('uubed')
# Enable detailed operation logging
encoder.enable_debug_logging(logger)
# Add custom log handlers
handler = logging.StreamHandler()
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
handler.setFormatter(formatter)
logger.addHandler(handler)
Step-by-Step Encoding Analysis
class StepByStepDebugger:
"""Debug QuadB64 encoding step by step"""
def debug_encode(self, data: bytes, position: int = 0):
"""Debug encoding process step by step"""
print(f"Debugging encoding of {len(data)} bytes at position {position}")
print(f"Input data: {data[:50]}..." if len(data) > 50 else f"Input data: {data}")
print()
# Step 1: Alphabet generation
alphabet = self._debug_alphabet_generation(position)
# Step 2: Data chunking
chunks = self._debug_data_chunking(data)
# Step 3: Chunk encoding
encoded_chunks = []
for i, chunk in enumerate(chunks):
chunk_position = position + i * 3
encoded_chunk = self._debug_chunk_encoding(chunk, chunk_position, alphabet)
encoded_chunks.append(encoded_chunk)
# Step 4: Final assembly
result = ''.join(encoded_chunks)
print(f"Final result: {result}")
return result
def _debug_alphabet_generation(self, position: int) -> str:
"""Debug alphabet generation"""
base_alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
rotation = (position // 3) % 64
alphabet = base_alphabet[rotation:] + base_alphabet[:rotation]
print(f"Alphabet generation:")
print(f" Position: {position}")
print(f" Rotation: {rotation}")
print(f" Base: {base_alphabet}")
print(f" Rotated: {alphabet}")
print()
return alphabet
def _debug_data_chunking(self, data: bytes) -> list:
"""Debug data chunking process"""
chunks = [data[i:i+3] for i in range(0, len(data), 3)]
print(f"Data chunking:")
print(f" Total bytes: {len(data)}")
print(f" Chunks: {len(chunks)}")
for i, chunk in enumerate(chunks[:5]): # Show first 5 chunks
print(f" Chunk {i}: {chunk} ({[hex(b) for b in chunk]})")
if len(chunks) > 5:
print(f" ... and {len(chunks) - 5} more chunks")
print()
return chunks
def _debug_chunk_encoding(self, chunk: bytes, position: int, alphabet: str) -> str:
"""Debug individual chunk encoding"""
# Pad chunk to 3 bytes
padded_chunk = chunk + b'\x00' * (3 - len(chunk))
# Convert to 24-bit integer
value = (padded_chunk[0] << 16) | (padded_chunk[1] << 8) | padded_chunk[2]
# Extract 6-bit indices
indices = [
(value >> 18) & 0x3F,
(value >> 12) & 0x3F,
(value >> 6) & 0x3F,
value & 0x3F
]
# Map to alphabet
chars = [alphabet[idx] for idx in indices]
result = ''.join(chars[:len(chunk)+1])
print(f"Chunk encoding at position {position}:")
print(f" Input: {chunk} -> {[hex(b) for b in padded_chunk]}")
print(f" 24-bit value: {value:024b} ({value})")
print(f" 6-bit indices: {indices}")
print(f" Characters: {chars} -> '{result}'")
print()
return result
# Usage
debugger = StepByStepDebugger()
test_data = b"Hello, QuadB64!"
result = debugger.debug_encode(test_data, position=5)
This comprehensive advanced features guide provides power users with the tools and techniques needed to optimize QuadB64 for specific use cases, troubleshoot issues, and implement custom solutions for complex requirements.