arc-actris

v1.1.0 suspicious
4.0
Medium Risk

Algorithm for Rayleigh and Raman Calculations for atmospheric lidar applications.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk for network and shell activities, but its recent release and lack of community engagement raise concerns about potential supply-chain risks.

  • Low network and shell risks
  • Newly released with no community interaction
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Metadata: The package is newly released with no activity or community engagement, raising some suspicion.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_arc.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://nikolaos-siomos.github.io/arc-actris/
  • Detailed PyPI description (2273 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
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 42 commits in nikolaos-siomos/arc-actris
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: lmu.de>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Ioannis Binietoglou, Michael Haimerl, Volker Freudenthaler, Mariana Adam, Giuseppe D'Amico, Benedikt Gast, Moritz Haarig, Ulla Wandinger" 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 arc-actris 
Create a Python-based mini-application that leverages the 'arc-actris' package to simulate atmospheric conditions using Rayleigh and Raman scattering models. This application will serve as a tool for atmospheric scientists and researchers to understand light scattering in different atmospheric scenarios. Here are the steps and features you should include:

1. **Project Setup**: Initialize a Python environment and install necessary packages including 'arc-actris'. Ensure that all dependencies are properly managed.
2. **User Interface**: Develop a simple but intuitive command-line interface (CLI) for users to input parameters such as wavelength of light, atmospheric pressure, temperature, and altitude. These parameters should allow for customization of various atmospheric conditions.
3. **Core Functionality**: Utilize 'arc-actris' to calculate the intensity of Rayleigh and Raman scattering under the given atmospheric conditions. Ensure that the calculations are accurate and efficient.
4. **Visualization**: Implement basic visualization capabilities to display the calculated scattering intensities graphically. Users should be able to see how changes in atmospheric conditions affect the scattering patterns.
5. **Documentation and Help**: Provide comprehensive documentation within the CLI to guide users on how to use the application effectively. Include examples and explanations of key concepts related to Rayleigh and Raman scattering.
6. **Testing and Validation**: Conduct thorough testing to ensure the accuracy of the scattering calculations. Validate your results against known data sets or theoretical predictions where possible.
7. **Customization and Extensibility**: Allow users to customize their analysis further by providing options to save output data, modify calculation parameters beyond the default settings, and integrate additional atmospheric data sources if available.

This mini-application will not only serve as a practical tool for research purposes but also as an educational resource for those interested in understanding atmospheric optics.

💬 Discussion Feed

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