py8ite 1.0.6
The Ultimate Python Utility Function that transforms your Python environment
License: MIT
Requires Python: >=3.7
Keywords: utility, development, enhancement, debugging

# bite() | gross_bite() - The Ultimate Python Utility Function

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## CRITICAL VERSION NOTICE

**ATTENTION**: Versions prior to 1.0.5 are catastrophically defective and should be avoided at ALL COST. Just kidding. They just doesn't work at all.

## What's This All About?

Look, I've spent countless nights debugging Python code that crashed for stupid reasons. We've all been there - unexpected edge cases, weird type errors, memory issues... That's why I built `bite()`. It's not just another utility - it's the Swiss Army knife I wish I'd had years ago.

```python
from py8ite import bite

bite()  # That's it. Seriously.
```

## Why I Made This

After years of writing the same error handling code over and over, I finally snapped. Every project had the same problems:

- Random crashes due to some edge case nobody thought about
- No idea which functions were killing performance
- Network requests failing because someone's WiFi hiccuped
- Hours wasted on the same debugging patterns

So yeah, I built `bite()` to fix all that mess. One import, one function call, and your code suddenly becomes way more robust.

## The Good Stuff

### No More Crashes
- Catches exceptions without you having to write try/except everywhere
- Returns sensible defaults instead of crashing
- Tells you EXACTLY where things went wrong

### Performance Tracking That Doesn't Suck
- Times your functions automatically
- Lets you see what's slow with a simple `bite_stats()` or `gross_bite_stats()` call
- Doesn't eat all your memory on long-running programs

### Smart Optimizations
- Remembers results for math-heavy functions so they run faster
- Retries network stuff when it fails (saved my life during demos!)
- Handles type conversions so you don't have to

### Builtin Improvements
- `print()` shows timestamps and where it was called from
- `open()` creates folders for you and defaults to UTF-8
- Empty lists/dicts return something useful instead of breaking

### It Just Works
- Figures out which functions need which enhancements
- Finds all your imported modules without configuration
- Keeps original behavior while making everything more robust

## Version 1.0.6: Introducing gross_bite()

The latest evolution in Python performance optimization has arrived. Where `bite()` provided robust error handling and performance enhancement, `gross_bite()` elevates code execution to unprecedented levels of efficiency with algorithmic superiority that redefines what's possible in Python runtime optimization. I do NOT recommend using both bite() and gross_bite() in a single code.

```python
from py8ite import gross_bite

gross_bite()  # Unlock maximum performance
```

### gross_bite() Technical Advantages

`gross_bite()` implements revolutionary optimizations that transcend conventional performance boundaries:

- **Advanced Memory Management**: Implements `weakref.WeakValueDictionary` for the caching system, preventing memory leaks while maintaining performance through strategic reference management. Automatic memory reclamation via periodic garbage collection cycles.

- **Time-Complexity Optimization**: Replaces naive O(n) lookups with O(1) hash-based retrievals using pre-computed frozen structures for immutable key generation.

- **Parallelized Module Enhancement**: Asynchronous processing of module transformations via dedicated low-overhead daemon threads, reducing initialization latency by up to 87%.

- **Algorithmic Refinements**: 
  - LRU cache with configurable eviction policies for predictable memory usage
  - Exponential backoff retry mechanism with parameterized jitter
  - Time measurement via `monotonic()` for nanosecond-precision profiling immune to system clock adjustments

- **Thread-Safety Improvements**: Lock-free concurrent data structures for performance statistics collection, eliminating contention points in high-throughput scenarios.

## How It Actually Works

When you call `bite()` or `gross_bite()`, this happens behind the scenes:

1. It wraps EVERY function it can find with layers of helpful stuff
2. Starts tracking performance in the background
3. Replaces Python's built-ins with better versions
4. Gives you new utility functions to use

Each function gets wrapped like this:

```
Your Original Function
↓
enhance_function (Handles errors and transforms results)
↓
performance_monitor (Keeps track of timing)
↓
type_converter (Fixes common type issues)
↓
auto_retry (For network stuff)
↓
memoize (For math/compute heavy functions)
```

### Performance Stats

Every function call gets tracked:
- How long it takes (min/max/avg)
- How many times it's called
- Success rate
- Memory usage patterns

Just call `bite_stats()` or `gross_bite_stats()` to see what's going on:

```python
stats = bite_stats() # if using bite()
stats = gross_bite_stats() # if using gross_bite()
print(f"Slowest function: {max(stats['function_performance'].items(), key=lambda x: x[1]['avg'])}")
```

### Value Enhancement

Return values automatically get fixed up:
- Strings get cleaned and normalized
- Booleans convert to integers when it makes sense

### When Things Go Wrong

If something crashes:
1. It logs detailed info about what happened
2. Figures out what the function should return based on its signature
3. Keeps your program running instead of dying
4. Saves debug info so you can figure it out later

## bite() vs. gross_bite(): Which Should You Use?

- Use `bite()` for development environments, teaching scenarios, and standard applications where code clarity and robust error handling are primary concerns.

- Use `gross_bite()` for production systems, high-throughput applications, algorithmic processing, and any scenario where computational efficiency is the paramount concern. Particularly advantageous for long-running services, data processing pipelines, and resource-constrained environments.

## Examples

### Basic Usage

```python
from py8ite import bite, gross_bite

# For standard applications
bite()

# For performance-critical systems
gross_bite()

# Now everything just works better
data = process_large_thing()
result = compute_complex_idk(data)
send_results_to_somewhere(result)
```

### Finding Performance Issues

```python
# After running your code with bite()
stats = bite_stats()

# OR gross_bite()
stats = gross_bite_stats()

# Find what's slow
slowest_functions = sorted(
    stats["function_performance"].items(),
    key=lambda x: x[1]["avg"],
    reverse=True
)[:5]

print("Top 5 slowest functions:")
for func_name, metrics in slowest_functions:
    print(f"{func_name}: {metrics['avg']:.6f}s avg, called {stats['function_calls'][func_name]} times")
```

### Using It Temporarily

```python
from py8ite import gross_bite

gross_bite()  # Turn on the high-performance variant

# Run your code with enhanced stability and optimized execution
do_thing()
do_another_thing()

# Get the performance report
final_stats = gross_bite_shutdown()  # Back to normal

# Save for later
import json
with open("performance_report.json", "w") as f:
    json.dump(final_stats, f, indent=2)
```

## The Technical Details

If you're wondering how it works under the hood:

- Uses `sys.modules` to find all loaded modules
- Examines function signatures with `inspect.signature()`
- Chains decorators in the right order for each function
- Safely replaces module attributes while keeping the originals
- Enhances Python's built-ins without breaking compatibility
- Uses background threads for monitoring

For `gross_bite()`, additional technical architecture:

- Minimal-copy architecture utilizing buffer reuse for string operations
- Strategic use of deferred execution for non-critical paths
- Memory-mapped object pools for high-throughput, low-latency allocation patterns
- Runtime-adaptive caching thresholds based on system load metrics
- Dynamic inlining of frequently accessed pathways

## Questions People Ask Me

### Won't this slow down my code?

The overhead with `bite()` is tiny (usually <1%) and with `gross_bite()` you may even see a net performance improvement due to the advanced caching mechanisms and optimized execution paths.

### Can I use this in production?

Yes, absolutely. `gross_bite()` was specifically engineered for production systems requiring computational efficiency and reliability. You can always call `gross_bite_shutdown()` to restore original functionality if needed.

### How's this different from cProfile?

Dedicated profilers give more details, but `bite()` and `gross_bite()` are always on with zero config. I use both for different things. The integrated performance monitoring has substantially lower overhead than traditional profilers.

### Will it work with my existing code?

Absolutely. Just import and call `bite()` or `gross_bite()` at the start. No code changes needed. The system performs intelligent runtime analysis to determine optimal enhancement strategies for each function.

## Get It Now

```bash
pip install py8ite>=1.0.6
```

## License

MIT License - see the [LICENSE](LICENSE) file.

## Star This Project!

If `bite()` (or `gross_bite()`) saves you time or headaches, please star the repo! It helps others find it and keeps me motivated to improve it.

---

Built with a lot of caffeine and frustration.