purple-titanium 0.1.5

Pipeline framework for task-based data processing workflows.

Purple Titanium

A Python pipeline framework that provides a structured way to define and execute task-based data processing workflows.

Example

Here's a comprehensive example demonstrating the pipeline framework's features:

import purple_titanium as pt
from typing import List, Dict, Optional

# Define tasks with type hints for better clarity
@pt.task()
def fetch_data(api_key: pt.Injectable[str]) -> List[int]:
    """Simulates fetching data from a source using an API key."""
    # In a real implementation, use api_key to authenticate
    return [1, 2, 3, 4, 5]

@pt.task()
def validate_data(
    data: List[int],
    max_value: pt.Injectable[int],
    min_value: pt.Injectable[int]
) -> List[int]:
    """Validates the input data against configured limits."""
    if not data:
        raise ValueError("Data cannot be empty")
    if not all(isinstance(x, int) for x in data):
        raise TypeError("All elements must be integers")
    if any(x > max_value for x in data):
        raise ValueError(f"Values cannot exceed {max_value}")
    if any(x < min_value for x in data):
        raise ValueError(f"Values cannot be less than {min_value}")
    return data

@pt.task()
def process_data(
    data: List[int],
    multiplier: pt.Injectable[int]
) -> List[int]:
    """Processes the data by applying transformations."""
    return [x * multiplier for x in data]

@pt.task()
def analyze_data(
    data: List[int],
    include_sum: pt.Injectable[bool]
) -> Dict[str, float]:
    """Analyzes the processed data and returns statistics."""
    result = {
        "average": sum(data) / len(data),
        "count": len(data)
    }
    if include_sum:
        result["sum"] = sum(data)
    return result

# Event listeners for monitoring pipeline execution
@pt.listen(pt.TASK_STARTED)
def on_task_started(event):
    print(f"Task {event.task.name} started")

@pt.listen(pt.TASK_FINISHED)
def on_task_finished(event):
    print(f"Task {event.task.name} finished successfully")

@pt.listen(pt.TASK_FAILED)
def on_task_failed(event):
    print(f"Task {event.task.name} failed: {event.task.exception}")

# Create the pipeline with error handling and context management
try:
    # Define the pipeline with context
    with pt.Context(
        api_key="secret123",
        max_value=100,
        min_value=0,
        multiplier=2,
        include_sum=True
    ):
        # Define the pipeline
        raw_data = fetch_data()
        validated_data = validate_data(raw_data)
        processed_data = process_data(validated_data)
        analysis = analyze_data(processed_data)

        # Execute the pipeline
        result = analysis.resolve()
        print("Pipeline completed successfully!")
        print(f"Analysis results: {result}")
        # Expected output:
        # Analysis results: {'average': 6.0, 'count': 5, 'sum': 30}

except ValueError as e:
    print(f"Validation error: {e}")
except TypeError as e:
    print(f"Type error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

# Example of checking task existence
print(f"Raw data task exists: {raw_data.exists()}")  # True
print(f"Analysis task exists: {analysis.exists()}")  # True

# Example of nested contexts with parameter overrides
with pt.Context(
    api_key="secret123",
    max_value=100,
    min_value=0,
    multiplier=2,
    include_sum=True
):
    # Override multiplier for this scope
    with pt.Context(multiplier=3):
        raw_data = fetch_data()
        validated_data = validate_data(raw_data)
        processed_data = process_data(validated_data)
        analysis = analyze_data(processed_data)
        result = analysis.resolve()
        print(f"Analysis with multiplier=3: {result}")
        # Expected output:
        # Analysis with multiplier=3: {'average': 9.0, 'count': 5, 'sum': 45}

This example demonstrates several key features of the framework:

1. Task Definition

   • Tasks are created using the @pt.task() decorator
   • Type hints provide clear interface definitions
   • Each task has a single responsibility

2. Dependency Management

   • Tasks can depend on other tasks through their parameters
   • Dependencies are automatically resolved
   • The framework ensures correct execution order

3. Error Handling

   • Tasks can raise exceptions that propagate through the pipeline
   • Exceptions can be caught at any level
   • Failed tasks maintain their error state

4. Event System

   • Event listeners can monitor pipeline execution
   • Events are emitted for task start, completion, and failure
   • Custom event handling enables monitoring and logging

5. Task State

   • The exists() method checks if a task has been executed
   • Task state persists after execution
   • Failed tasks maintain their error state

6. Pipeline Execution

   • The resolve() method executes the pipeline
   • Only one resolve() call is allowed in the call stack
   • Results are cached for efficiency

Code Contribution

Prerequisites

• Python 3.8 or higher
• uv for dependency management

Development Setup

1. Clone the repository:

   git clone https://github.com/yourusername/purple-titanium.git
   cd purple-titanium
   

2. Install uv if you don't have it already:

   pip install uv
   

3. Set up the development environment:

   uv venv
   uv pip install -e ".[dev]"  # Installs package in editable mode with dev dependencies
   

Development Tools

• Testing: Run the test suite

  pytest
  

• Code Quality:

  # Run linting checks
  ruff check .
  
  # Format code
  ruff format .
  

Contributing Guidelines

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

License

MIT License

Copyright (c) 2024 Purple Titanium Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.