🌍 Eco-Code Analyzer v0.4.0

Eco-Code Analyzer is a Python library that analyzes code for its ecological impact, providing developers with insights and recommendations to write more environmentally friendly and efficient code. By optimizing code for energy efficiency, resource usage, I/O operations, and algorithm selection, we can collectively reduce the carbon footprint of our software.

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🛠️ Installation

Install Eco-Code Analyzer via pip:

pip install eco-code-analyzer

For development:

pip install eco-code-analyzer[dev]

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✨ Features

• ♻️ Analyzes Python code for ecological impact with research-based rules
• 📊 Provides an overall eco-score and a detailed breakdown across multiple categories
• 💡 Offers improvement suggestions with examples, references, and environmental impact estimates
• 🔍 Analyzes entire projects or individual files with context-aware analysis
• 🌱 Estimates potential energy savings and CO2 reduction
• 🌍 Calculates project carbon footprint
• ⏳ Analyzes Git history to track eco-score over time
• 📈 Generates visualizations of eco-score trends
• ⚙️ Supports custom configuration and rules with an extensible rule system
• 🌳 Allows users to contribute to tree planting based on analysis results
• 🔬 Provides detailed analysis across five key categories:
  • Energy Efficiency: Loop constructs, lazy evaluation, redundant computations
  • Resource Usage: Memory management, context managers, data structures
  • I/O Efficiency: File, network, and database operations
  • Algorithm Efficiency: Time/space complexity, data structure selection
  • Custom Rules: Extensible framework for project-specific rules

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🚀 Usage

As a Library

from eco_code_analyzer import analyze_code, get_eco_score, get_improvement_suggestions, estimate_energy_savings

code = """
def example_function():
    result = []
    for i in range(100):
        result.append(i * 2)
    return result
"""

# Analyze code with optional file path and configuration
analysis_result = analyze_code(code, file_path="example.py", config={
    "thresholds": {"category_score": 0.6}
})
eco_score = get_eco_score(analysis_result)
suggestions = get_improvement_suggestions(analysis_result)
energy_savings = estimate_energy_savings({'overall_score': eco_score})

print(f"Eco-Score: {eco_score}")
print("Improvement Suggestions:")
for suggestion in suggestions:
    print(f"- {suggestion['category']}: {suggestion['suggestion']}")
    print(f"  Impact: {suggestion['impact']}")
    print(f"  Example: {suggestion['example']}")
    print(f"  Environmental Impact: {suggestion['environmental_impact']}")

print("
Estimated Environmental Impact if Optimized:")
print(f"Potential Energy Savings: {energy_savings['energy_kwh_per_year']:.2f} kWh/year")
print(f"Potential CO2 Reduction: {energy_savings['co2_kg_per_year']:.2f} kg CO2/year")
print(f"Equivalent to planting: {energy_savings['trees_equivalent']:.2f} trees")

As a Command-Line Tool

Analyze a single file:

eco-code-analyzer path/to/your/python_file.py

Analyze a project directory:

eco-code-analyzer path/to/your/project/directory -v

Generate a detailed report:

eco-code-analyzer path/to/your/project/directory -o report.json

Analyze Git history and visualize eco-score trend:

eco-code-analyzer path/to/your/project/directory -g -n 10 --visualize

Use a custom configuration:

eco-code-analyzer path/to/your/project/directory -c config.json

Contribute to tree planting based on analysis results:

eco-code-analyzer path/to/your/project/directory --contribute

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🌿 Environmental Impact and Tree Planting

The Eco-Code Analyzer helps developers understand the environmental impact of their code by:

1. Estimating energy consumption and CO2 emissions for different code constructs
2. Providing an overall eco-score that reflects the code's environmental friendliness
3. Offering specific suggestions to improve code efficiency and reduce energy consumption
4. Calculating potential energy savings and CO2 reduction if the code is optimized
5. Tracking the project's eco-score over time to encourage continuous improvement
6. Estimating the equivalent number of trees that need to be planted to offset the code's environmental impact

By using the Eco-Code Analyzer, developers can:

• Reduce the energy consumption of their applications
• Lower the carbon footprint of their software
• Improve code performance and efficiency
• Raise awareness about the environmental impact of code
• Actively contribute to reforestation efforts based on their code's impact

The new tree planting feature allows users to take immediate action to offset their code's environmental impact. When using the --contribute flag, the tool will:

1. Calculate the number of trees equivalent to the potential CO2 reduction
2. Provide an estimated cost for planting these trees
3. Offer the user an option to contribute to a tree planting organization directly from the command line

Remember, every small optimization and contribution counts. By collectively improving our code's eco-friendliness and supporting reforestation efforts, we can make a significant impact on reducing the IT industry's carbon footprint and promoting a healthier planet.

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⚙️ Configuration

You can customize the behavior of the Eco-Code Analyzer by providing a JSON configuration file. This includes the ability to adjust weights for different aspects of the analysis and configure the coefficients used in the calculations.

Here's an example configuration file:

{
  "weights": {
    "energy_efficiency": 0.25,
    "resource_usage": 0.25,
    "io_efficiency": 0.2,
    "algorithm_efficiency": 0.2,
    "custom_rules": 0.1
  },
  "thresholds": {
    "eco_score": 0.7,
    "category_score": 0.6
  },
  "custom_rules": [
    {
      "name": "check_api_call_efficiency",
      "weight": 0.05
    }
  ],
  "coefficients": {
    "energy_consumption_per_cpu_cycle": 1.2e-9,
    "co2_emissions_per_kwh": 0.5,
    "base_energy_consumption_per_year": 100,
    "base_co2_emissions_per_year": 50,
    "trees_equivalent_factor": 2
  }
}

In this configuration:

• weights: Adjust the importance of different categories in the overall eco-score:
  • energy_efficiency: Efficiency of code execution and CPU usage
  • resource_usage: Memory and resource management
  • io_efficiency: File, network, and database operations
  • algorithm_efficiency: Time and space complexity of algorithms
  • custom_rules: User-defined rules for specific needs
• thresholds: Set the levels at which warnings or suggestions are triggered.
• custom_rules: Add your own rules or adjust the weight of existing ones.
• coefficients: Configure the key assumptions used in environmental impact calculations:
  • energy_consumption_per_cpu_cycle: Energy consumed per CPU cycle (in joules)
  • co2_emissions_per_kwh: CO2 emitted per kWh of energy (varies by region)
  • base_energy_consumption_per_year: Assumed baseline energy consumption for a typical project (in kWh)
  • base_co2_emissions_per_year: Assumed baseline CO2 emissions for a typical project (in kg)
  • trees_equivalent_factor: Factor used to convert CO2 reduction to equivalent number of trees planted

By adjusting these coefficients, you can tailor the analysis to better match your specific environment or to reflect more recent data on energy consumption and emissions.

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🧠 Enhanced Rules System

Version 0.4.0 introduces a completely redesigned rules system with:

• Research-Based Rules: Each rule includes references to academic research and best practices
• Context-Aware Analysis: The analyzer maintains context between rule checks for more accurate results
• Pattern Detection Engine: Sophisticated pattern matching for complex code structures
• Extensible Architecture: Easily add new rules and categories
• Detailed Suggestions: More specific and actionable recommendations

Rule Categories

1. Energy Efficiency

   • Loop optimization (list comprehensions, generator expressions)
   • Lazy evaluation techniques
   • Redundant computation detection
   • Nested loop analysis

2. Resource Usage

   • Memory management and leak detection
   • Context manager usage
   • Efficient data structure selection
   • Global variable usage

3. I/O Efficiency

   • File operation optimization
   • Network request batching
   • Database query efficiency (N+1 query detection)
   • Caching strategies
   • Bulk operation usage

4. Algorithm Efficiency

   • Time complexity analysis
   • Space complexity analysis
   • Data structure selection
   • Recursive algorithm optimization

5. Custom Rules

   • Extensible framework for project-specific rules

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Here are some ways you can contribute:

1. Add new rules for detecting eco-unfriendly code patterns
2. Improve the accuracy of energy consumption and CO2 emission estimates
3. Enhance the visualization capabilities
4. Add support for more programming languages
5. Improve documentation and provide usage examples
6. Refine the assumptions and coefficients used in the analysis
7. Expand the tree planting contribution feature with more options and partnerships
8. Add more research references to existing rules
9. Implement new rule categories
10. Improve the pattern detection engine

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📄 License

This project is licensed under the MIT License.

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🌱 Let's Build a Greener Future, One Line of Code at a Time!

By using the Eco-Code Analyzer, you're not just improving your code – you're contributing to a more sustainable future for software development and our planet. Together, we can make a significant impact on reducing the environmental footprint of the IT industry and supporting global reforestation efforts. Happy eco-coding!