aperta

v0.2.0a0 suspicious
4.0
Medium Risk

Python toolkit for cross-modal accessibility analysis on transport networks.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to incomplete metadata and network calls, but no evidence of malicious activities such as shell execution or obfuscation.

  • Incomplete author metadata
  • Network calls with unknown purpose
Per-check LLM notes
  • Network: The package makes network calls, which could be for legitimate purposes like fetching updates or data. Further investigation is needed to confirm the purpose.
  • Shell: No shell execution patterns detected, suggesting low risk of direct system command misuse.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The author's details are incomplete, indicating potential low credibility.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

  • 13 test file(s) detected (e.g. test_accessibility.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aperta.readthedocs.io/
  • Detailed PyPI description (10214 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 170 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 61 commits in mmiotti/aperta
  • Single author but highly active (61 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • , flush=True) r = requests.get(url, timeout=120) r.raise_for_status()
✓ 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: miotti.me>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository mmiotti/aperta appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aperta 
Your task is to develop a mini-application called 'AccessibleRoutes' using the Python package 'aperta'. This application will serve as a tool to help individuals with disabilities plan their travel routes by analyzing transport networks for accessibility features such as wheelchair ramps, audio announcements, and visual signage. Here are the steps and features you should include:

1. **Setup**: Install necessary packages including 'aperta', 'pandas', and 'matplotlib'. Ensure your environment is set up to handle geographical data.
2. **Data Input**: Allow users to input transport network data in CSV format which includes stops, routes, and accessibility features. This data should be parsed into a structured format that 'aperta' can process.
3. **Network Analysis**: Utilize 'aperta' to perform cross-modal accessibility analysis on the transport network. Identify key nodes (stops) that are accessible and those that lack necessary facilities.
4. **Route Planning**: Implement functionality to plan routes from a starting point to a destination, ensuring that the chosen route includes only accessible stops and transit modes. Highlight any gaps in accessibility along the planned route.
5. **Visualization**: Use 'matplotlib' to create visual representations of the transport network and highlighted accessible routes. This will help users better understand the accessibility status of different parts of the network.
6. **Report Generation**: Generate a report summarizing the accessibility analysis results, including suggestions for improving accessibility at identified problematic points.
7. **User Interface**: Develop a simple command-line interface for users to interact with the application easily. Provide options for uploading new datasets, planning routes, and viewing reports.
8. **Testing**: Conduct thorough testing with various datasets to ensure the application functions correctly and provides accurate accessibility information.

By following these steps, you'll create a powerful tool that leverages 'aperta's capabilities to enhance accessibility in public transportation systems.