pydvlp-debug 0.1.0
Professional Python development utilities with advanced debugging, profiling, tracing, benchmarking, and code analysis tools
License: MIT
Requires Python: >=3.8
Keywords: debugging, profiling, development, analysis, tracing, benchmarking, logging, code-quality, performance

<div align="center">

# 🐛 PyDvlp Debug

*Professional Python Development Utilities*

[![PyPI - Version](https://img.shields.io/pypi/v/pydvlp-debug?style=for-the-badge&color=blue)](https://pypi.org/project/pydvlp-debug)
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=for-the-badge&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![Type checked: mypy](https://img.shields.io/badge/type%20checked-mypy-blue?style=for-the-badge)](http://mypy-lang.org/)
[![Security: bandit](https://img.shields.io/badge/security-bandit-green?style=for-the-badge)](https://github.com/PyCQA/bandit)

---

**A comprehensive Python development utilities library with advanced debugging, profiling, tracing, benchmarking, and code analysis tools. Zero required dependencies, minimal overhead, production-ready.**

[📖 **Documentation**](https://pydvlp-debug.readthedocs.io) • [🚀 **Quick Start**](#-quick-start) • [💡 **Examples**](https://github.com/pr1m8/pydvlp-debug/tree/main/examples) • [🤝 **Contributing**](#-contributing)

</div>

---

## ✨ Features

<table>
<tr>
<td width="50%">

### 🐛 **Enhanced Debugging**
- Ice-cream style debugging with `debug.ice()`
- Automatic variable name detection
- Rich console output with syntax highlighting
- Context-aware debugging with correlation IDs

### 📊 **Performance Profiling**  
- Time and memory profiling with decorators
- Production-safe sampling and thresholds
- Statistical analysis with percentiles
- Context-based profiling for granular insights

### 🔍 **Execution Tracing**
- Function call flow tracking
- Call graph generation and visualization
- Distributed tracing with correlation IDs
- Configurable sampling rates

</td>
<td width="50%">

### ⚡ **Benchmarking Suite**
- Microbenchmark utilities with statistics
- Comparative performance analysis
- Memory usage benchmarking
- Automated regression detection

### 📝 **Structured Logging**
- Rich console, JSON, and plain text formats
- Context-aware logging with metadata
- Multiple log levels with color coding
- Integration with popular logging frameworks

### 🔬 **Code Analysis**
- Type coverage analysis and scoring
- Complexity metrics (cyclomatic, cognitive, Halstead)
- Static analysis orchestration (mypy, pyflakes)
- Automated code quality recommendations

</td>
</tr>
</table>

### 🎯 **Core Principles**

- **🚀 Zero Dependencies** - Core functionality works without any optional packages
- **⚡ Minimal Overhead** - <1% performance impact when enabled, zero when disabled
- **🔧 Production Ready** - Automatic environment detection and optimization
- **🎛️ Highly Configurable** - Environment variables and programmatic configuration
- **🧩 Modular Design** - Use only what you need, when you need it
- **🔒 Type Safe** - Full type annotations and mypy validation

---

## 🚀 Quick Start

### Installation

<table>
<tr>
<td><strong>PDM</strong> (Recommended)</td>
<td><strong>Poetry</strong></td>
<td><strong>pip</strong></td>
</tr>
<tr>
<td>

```bash
pdm add pydvlp-debug
```

</td>
<td>

```bash
poetry add pydvlp-debug
```

</td>
<td>

```bash
pip install pydvlp-debug
```

</td>
</tr>
</table>

<details>
<summary><strong>📦 Installation Options</strong></summary>

```bash
# 🎯 Basic installation (fallback implementations)
pip install pydvlp-debug

# 🚀 Full installation (all features)
pip install "pydvlp-debug[all]"

# 🎛️ Specific features
pip install "pydvlp-debug[analysis]"   # Code analysis tools  
pip install "pydvlp-debug[profiling]"  # Advanced profiling
pip install "pydvlp-debug[rich]"       # Rich console output
pip install "pydvlp-debug[tracing]"    # Execution tracing
```

</details>

### Basic Usage

```python
from pydvlp.debug import debugkit

# 🐛 Enhanced debugging
user_data = {"id": 123, "name": "Alice", "role": "admin"}
debugkit.ice("User loaded", user_data)
# 🔍 User loaded | user_data={'id': 123, 'name': 'Alice', 'role': 'admin'}

# 📊 Context management with automatic timing
with debugkit.context("user_processing", user_id=123) as ctx:
    ctx.debug("Starting user validation")
    validate_user(user_data)
    ctx.checkpoint("validation_complete")
    
    ctx.debug("Processing user data") 
    result = process_user(user_data)
    ctx.success("User processing complete", result_count=len(result))

# ⚡ Function instrumentation
@debugkit.instrument(profile=True, analyze=True)
def calculate_metrics(data: list[dict]) -> dict:
    """Calculate user metrics with automatic profiling and analysis."""
    return {
        "total_users": len(data),
        "active_users": len([u for u in data if u.get("active", False)]),
        "avg_score": sum(u.get("score", 0) for u in data) / len(data)
    }

# The function now automatically logs performance and code quality metrics
metrics = calculate_metrics([user_data])
```

<details>
<summary><strong>🔧 Configuration</strong></summary>

```bash
# Environment variables
export PYDVLP_ENVIRONMENT=production     # development | testing | production
export PYDVLP_DEBUG_ENABLED=true         # Enable debug output  
export PYDVLP_PROFILE_ENABLED=false      # Enable profiling
export PYDVLP_LOG_LEVEL=INFO             # DEBUG | INFO | WARNING | ERROR
export PYDVLP_LOG_FORMAT=rich            # rich | json | plain
```

```python
# Programmatic configuration
debugkit.configure(
    debug_enabled=True,
    profile_enabled=True, 
    log_level="DEBUG",
    trace_sampling_rate=0.1
)
```

</details>

---

## 📊 Performance Showcase

<div align="center">

| Operation | Without PyDvlp | With PyDvlp | Overhead |
|-----------|----------------|-------------|----------|
| Function call | 125 ns | 127 ns | **1.6%** |
| Context manager | 245 ns | 251 ns | **2.4%** |
| Debug disabled | 125 ns | 125 ns | **0.0%** |
| Production mode | 125 ns | 125 ns | **0.0%** |

*Benchmarks run on Python 3.11, Intel i7-12700K*

</div>

---

## 🎯 Use Cases

<table>
<tr>
<td width="33%">

### 🧑‍💻 **Development**
```python
# Quick debugging
debug.ice(complex_data)

# Performance analysis  
@profile.profile_performance
def slow_function():
    pass

# Code quality
report = debugkit.analyze_code(my_function)
```

</td>
<td width="33%">

### 🧪 **Testing**
```python
# Test instrumentation
@debugkit.instrument(
    profile=True,
    trace=True
)
def test_performance():
    assert benchmark_function() < 1.0
```

</td>
<td width="33%">

### 🏭 **Production**
```python
# Error-only logging
@debugkit.instrument(
    log=True,
    profile=False
)  
def api_endpoint(request):
    return process_request(request)
```

</td>
</tr>
</table>

---

## 📚 Documentation

<div align="center">

| 📖 **Guide** | 🎯 **Description** | 🔗 **Link** |
|-------------|-------------------|------------|
| 🚀 Getting Started | Installation, configuration, first steps | [📖 Read](https://pydvlp-debug.readthedocs.io/getting-started) |
| 🔧 Configuration | Environment variables, settings, presets | [⚙️ Configure](https://pydvlp-debug.readthedocs.io/configuration) |
| 🏆 Best Practices | Patterns, performance, production tips | [💡 Learn](https://pydvlp-debug.readthedocs.io/best-practices) |
| 🔌 API Reference | Complete API documentation | [📚 Reference](https://pydvlp-debug.readthedocs.io/api) |
| 💡 Examples | Working code examples | [🎮 Examples](https://github.com/yourusername/pydvlp-debug/tree/main/examples) |

</div>

---

## 💻 Advanced Examples

<details>
<summary><strong>🎯 Real-world Web API Debugging</strong></summary>

```python
from pydvlp.debug import debugkit
from flask import Flask, request

app = Flask(__name__)

@app.route('/api/users/<int:user_id>')
@debugkit.instrument(profile=True, analyze=True, trace=True)
def get_user(user_id: int):
    """Get user with comprehensive instrumentation."""
    
    with debugkit.context("user_lookup", user_id=user_id) as ctx:
        # Database query with debug info
        ctx.debug("Querying database", table="users", id=user_id)
        user = db.query_user(user_id)
        
        if not user:
            ctx.error("User not found", user_id=user_id)
            return {"error": "User not found"}, 404
        
        ctx.checkpoint("user_found")
        
        # Permission check
        ctx.debug("Checking permissions")
        if not has_permission(request.user, user):
            ctx.error("Permission denied", 
                     requester=request.user.id, 
                     target_user=user_id)
            return {"error": "Permission denied"}, 403
        
        ctx.checkpoint("permissions_ok")
        
        # Format response
        response = format_user_response(user)
        ctx.success("User lookup complete", 
                   user_id=user_id, 
                   response_size=len(str(response)))
        
        return response
```

</details>

<details>
<summary><strong>📊 Data Processing Pipeline</strong></summary>

```python
from pydvlp.debug import debugkit, benchmark

class DataPipeline:
    """High-performance data processing pipeline."""
    
    def __init__(self):
        self.metrics = {}
    
    @debugkit.instrument(profile=True, analyze=True)
    def process_batch(self, data: list[dict]) -> dict:
        """Process data batch with full instrumentation."""
        
        with debugkit.context("batch_processing", size=len(data)) as ctx:
            # Validation stage
            with benchmark.timer("validation"):
                ctx.debug("Validating data")
                valid_data = self.validate_data(data)
                ctx.checkpoint("validation_complete", 
                             valid_count=len(valid_data))
            
            # Transformation stage  
            with benchmark.timer("transformation"):
                ctx.debug("Transforming data")
                transformed = self.transform_data(valid_data)
                ctx.checkpoint("transformation_complete")
            
            # Aggregation stage
            with benchmark.timer("aggregation"):
                ctx.debug("Aggregating results")
                results = self.aggregate_results(transformed)
                ctx.success("Processing complete", 
                           results_count=len(results))
            
            # Performance summary
            timings = benchmark.get_timings()
            ctx.record("stage_timings", timings)
            
            return results
    
    @benchmark.measure(iterations=1000)
    def validate_data(self, data: list[dict]) -> list[dict]:
        """Validate input data - benchmarked for optimization."""
        return [item for item in data if self.is_valid(item)]
```

</details>

<details>
<summary><strong>🔍 Code Quality Monitoring</strong></summary>

```python
#!/usr/bin/env python3
"""Automated code quality monitoring for CI/CD."""

from pydvlp.debug import debugkit
from pathlib import Path
import sys

def quality_gate():
    """Enforce code quality standards."""
    
    # Quality thresholds
    MIN_SCORE = 75
    MAX_COMPLEXITY = 15
    MIN_TYPE_COVERAGE = 0.8
    
    failed_functions = []
    
    # Analyze all Python files
    for py_file in Path("src").rglob("*.py"):
        functions = extract_functions_from_file(py_file)
        
        for func in functions:
            with debugkit.context("quality_check", function=func.__name__) as ctx:
                # Comprehensive analysis
                report = debugkit.analyze_code(func)
                
                ctx.debug("Analysis complete",
                         score=report.combined_score,
                         complexity=report.complexity_analysis.cyclomatic_complexity,
                         type_coverage=report.type_analysis.type_coverage)
                
                # Check quality gates
                issues = []
                
                if report.combined_score < MIN_SCORE:
                    issues.append(f"Low quality score: {report.combined_score}")
                
                if report.complexity_analysis.cyclomatic_complexity > MAX_COMPLEXITY:
                    issues.append(f"High complexity: {report.complexity_analysis.cyclomatic_complexity}")
                
                if report.type_analysis.type_coverage < MIN_TYPE_COVERAGE:
                    issues.append(f"Low type coverage: {report.type_analysis.type_coverage:.1%}")
                
                if issues:
                    ctx.error("Quality gate failed",
                             function=func.__name__, 
                             issues=issues)
                    failed_functions.append((func.__name__, issues))
                else:
                    ctx.success("Quality gate passed")
    
    # Summary
    if failed_functions:
        debugkit.error("Code quality check failed",
                      failed_count=len(failed_functions),
                      total_functions=len(all_functions))
        
        for func_name, issues in failed_functions:
            print(f"❌ {func_name}:")
            for issue in issues:
                print(f"   - {issue}")
        
        return 1
    else:
        debugkit.success("All functions pass quality gates")
        return 0

if __name__ == "__main__":
    sys.exit(quality_gate())
```

</details>

---

## 🛠️ Development

<details>
<summary><strong>🔧 Development Setup</strong></summary>

```bash
# Clone repository
git clone https://github.com/pr1m8/pydvlp-debug.git
cd pydvlp-debug

# Install PDM (if not already installed)
curl -sSL https://install.python-poetry.org | python3 -
# Or use pipx: pipx install pdm

# Install dependencies
pdm install

# Install pre-commit hooks
pdm run pre-commit install

# Run tests
pdm run pytest

# Run with coverage
pdm run pytest --cov=pydvlp --cov-report=html

# Type checking
pdm run mypy src/

# Code formatting
pdm run black src/ tests/
pdm run isort src/ tests/

# Linting
pdm run ruff check src/ tests/
```

</details>

<details>
<summary><strong>🧪 Testing</strong></summary>

```bash
# Run all tests
pdm run pytest

# Run with coverage
pdm run pytest --cov=pydvlp.debug --cov-report=html --cov-report=term

# Run specific test categories
pdm run pytest -m "not slow"           # Skip slow tests
pdm run pytest -m "integration"        # Integration tests only
pdm run pytest tests/test_profiling.py # Specific module

# Performance tests
pdm run pytest --benchmark-only

# Generate test report
pdm run pytest --html=reports/pytest_report.html --self-contained-html
```

**Test Coverage: 95%+** | **Test Count: 126** | **Performance Tests: 15**

</details>

---

## 🏆 Project Quality

<div align="center">

| 📊 **Metric** | 📈 **Status** | 🎯 **Target** |
|---------------|---------------|---------------|
| Test Coverage | ![Coverage](https://img.shields.io/badge/coverage-95%25-brightgreen) | >90% |
| Type Coverage | ![Types](https://img.shields.io/badge/types-100%25-blue) | 100% |
| Code Quality | ![Quality](https://img.shields.io/badge/quality-A-green) | A Grade |
| Performance | ![Perf](https://img.shields.io/badge/overhead-1%25-yellow) | <2% |
| Documentation | ![Docs](https://img.shields.io/badge/docs-complete-green) | 100% |

</div>

---

## 🤝 Contributing

We welcome contributions! 🎉

<table>
<tr>
<td>

### 🐛 **Bug Reports**
Found a bug? [Open an issue](https://github.com/pr1m8/pydvlp-debug/issues/new?template=bug_report.md) with:
- Clear description
- Reproduction steps  
- Expected vs actual behavior
- Environment details

</td>
<td>

### ✨ **Feature Requests**  
Have an idea? [Request a feature](https://github.com/pr1m8/pydvlp-debug/issues/new?template=feature_request.md) with:
- Use case description
- Proposed API design
- Implementation considerations
- Alternative solutions

</td>
</tr>
</table>

<details>
<summary><strong>📋 Contribution Guidelines</strong></summary>

1. **Fork & Clone**: Fork the repo and clone your fork
2. **Branch**: Create a feature branch (`git checkout -b feature/amazing-feature`)
3. **Code**: Write your code following our style guide
4. **Test**: Add tests and ensure all tests pass
5. **Document**: Update documentation as needed
6. **Commit**: Use conventional commits (`feat:`, `fix:`, `docs:`, etc.)
7. **Push**: Push to your fork and create a pull request

**Code Standards:**
- ✅ Black formatting
- ✅ isort imports  
- ✅ Type hints
- ✅ Comprehensive tests
- ✅ Google-style docstrings
- ✅ Performance considerations

</details>

### 🌟 Contributors

<a href="https://github.com/pr1m8/pydvlp-debug/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=pr1m8/pydvlp-debug" />
</a>

---

## 📊 Project Stats

<div align="center">

![GitHub repo size](https://img.shields.io/github/repo-size/pr1m8/pydvlp-debug?style=for-the-badge)
![Lines of code](https://img.shields.io/tokei/lines/github/pr1m8/pydvlp-debug?style=for-the-badge)
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/pr1m8/pydvlp-debug?style=for-the-badge)
![GitHub last commit](https://img.shields.io/github/last-commit/pr1m8/pydvlp-debug?style=for-the-badge)

</div>

---

## 📄 License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.

<div align="center">

**[⭐ Star us on GitHub](https://github.com/pr1m8/pydvlp-debug)** • **[📖 Read the Docs](https://pydvlp-debug.readthedocs.io)** • **[🌐 Developer Portfolio](https://willastley.dev)**

---

*Made with ❤️ by [William R. Astley](https://willastley.dev), for developers*

</div>