eco-code-analyzer 0.3.1

A Python library that analyzes code for ecological impact and provides optimization suggestions

Eco-Code Analyzer

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 and resource usage, we can collectively reduce the carbon footprint of our software.

Installation

You can install Eco-Code Analyzer using pip:

pip install eco-code-analyzer

For development, you can install additional dependencies:

pip install eco-code-analyzer[dev]

Features

• Analyzes Python code for ecological impact
• Provides an overall eco-score and detailed breakdown
• Offers improvement suggestions with examples and environmental impact
• Analyzes entire projects or individual files
• 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

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
"""

analysis_result = analyze_code(code)
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("\nEstimated 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

Environmental Impact

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

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

Remember, every small optimization counts. By collectively improving our code's eco-friendliness, we can make a significant impact on reducing the IT industry's carbon footprint.

Assumptions and Coefficients

It's important to note that the Eco-Code Analyzer uses various assumptions and coefficients to estimate the environmental impact of code. These are based on general patterns and simplified models, and may not perfectly reflect the actual impact in all scenarios. Some key assumptions include:

1. Energy consumption per CPU cycle
2. CO2 emissions per kWh of energy used
3. Impact of different code constructs on energy consumption
4. Baseline energy consumption for typical projects

We strive to make these assumptions as accurate as possible, but they should be treated as estimates rather than precise measurements. The primary goal is to provide relative comparisons and highlight areas for potential improvement.

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": 0.4,
    "resource_usage": 0.3,
    "code_optimizations": 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": 1e-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.
• 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.

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

License

This project is licensed under the MIT License.

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. Together, we can make a significant impact on reducing the environmental footprint of the IT industry. Happy eco-coding!