amplpower

v0.0.45 safe
4.0
Medium Risk

AMPL package for power systems

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with a moderate risk score due to potential obfuscation and the maintainer's limited presence on PyPI.

  • No network or shell risks detected
  • Potential obfuscation to obscure logic or protect IP
  • Single package from the maintainer, raising slight suspicion
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Subprocess calls appear to be related to tox environment management and not indicative of malicious shell execution.
  • Obfuscation: The code shows signs of obfuscation, possibly to obscure the logic or protect intellectual property, but does not clearly indicate malicious intent.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has only one package on PyPI, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_core.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Changelog" -> https://amplpower.readthedocs.io/en/latest/changelog.html
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5128 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in salvapineda/amplpower
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • nnectivity) self.ampl.eval("minimize objective: sum {g in G} (COST_2[g] * (BASEMVA*Pg[g
  • ST_0[g]);") self.ampl.eval("option presolve_eps 1e-10;") self.solve_model(solve
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • s): print("+", *args) subprocess.check_call(args) def exec_in_env(): env_path = base_path / ".tox"
  • y itself. for line in subprocess.check_output([sys.executable, "-m", "tox", "--listenvs"], universal_newli
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: uma.es

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository salvapineda/amplpower appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Salvador Pineda Morente" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amplpower 
Create a mini-application that helps engineers optimize the operation of a small-scale power grid using the 'amplpower' Python package. This application will serve as a tool for both educational purposes and practical problem-solving in power system management. Here’s a step-by-step guide on how to develop this application:

1. **Project Setup**: Begin by setting up your development environment with Python and installing the necessary packages including 'amplpy', 'pandas', and 'matplotlib'. Ensure you have 'amplpower' installed as well.
2. **Data Input**: Allow users to input data related to their power grid, such as generator capacities, load demands, transmission lines, and any constraints they might have. Use 'pandas' to manage this data effectively.
3. **Model Creation**: Utilize 'amplpower' to create a mathematical model of the power grid based on the user inputs. This model should include all relevant variables like power flows, voltages, and generator outputs.
4. **Optimization**: Implement optimization algorithms provided by 'amplpower' to find the most efficient way to operate the power grid while meeting all specified constraints. The goal could be minimizing operational costs, maximizing efficiency, or ensuring reliability.
5. **Visualization**: Use 'matplotlib' to visualize the results of your optimization. Display graphs showing power flows, voltage levels, and other key metrics across the network. This visualization should help users understand the optimized state of their power grid.
6. **Report Generation**: Develop a feature that generates a comprehensive report summarizing the optimization process and its outcomes. Include details like initial conditions, applied constraints, optimization results, and any recommendations for improving performance.
7. **User Interface**: Create a simple but intuitive GUI using a library like 'tkinter' or 'PyQt'. This interface should allow users to easily input their data, run the optimization, view results, and generate reports without needing to write code.
8. **Testing and Validation**: Before releasing the application, thoroughly test it with various scenarios to ensure accuracy and reliability. Validate the results against known benchmarks or real-world data if available.
9. **Documentation and Deployment**: Write clear documentation explaining how to use the application, what each feature does, and any limitations. Consider deploying the application online so others can access and use it too.

This project not only leverages the powerful capabilities of 'amplpower' for complex calculations but also provides a practical tool for anyone involved in managing power systems.

💬 Discussion Feed

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