TraceForest

High-performance Python profiler that generates interactive call tree visualizations to identify execution bottlenecks.

TraceForest is a blazing-fast Python profiler that captures function calls in real-time and presents them as beautiful, interactive tree visualizations. Perfect for identifying performance bottlenecks and understanding code execution flow.

Features

• Real-time profiling - Captures function calls as they happen
• Interactive trees - Beautiful hierarchical visualizations
• Modern web interface - Clean, responsive design with dark theme
• Detailed metrics - Precise timing information for each function
• Deep call analysis - See the complete execution path
• Persistent storage - Save and share profiling sessions
• Zero configuration - Start profiling with just a few lines of code

Quick Start

Installation

pip install traceforest

Basic Usage

from traceforest import Profiler

# Create profiler instance
profiler = Profiler()

# Start profiling
profiler.start()

# Your code here
def slow_function():
    import time
    time.sleep(1)
    return "done"

result = slow_function()

# Stop profiling and view results
profiler.stop()
profiler.export()

Web Visualization

After running export(), TraceForest will:

1. Generate a unique profiling session
2. Open your browser to view the interactive tree

Detailed Usage

Profiling Classes and Methods

from traceforest import Profiler

class MyClass:
    def method_a(self):
        time.sleep(0.5)
        self.method_b()
    
    def method_b(self):
        time.sleep(0.2)

profiler = Profiler()
profiler.start()

obj = MyClass()
obj.method_a()

profiler.stop()
profiler.export()

Web Interface

The web interface provides:

• Interactive tree navigation - Click to expand/collapse branches
• Visual performance bars - See relative execution times at a glance
• Detailed timing information - Precise millisecond measurements

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.