PlotPy

v2.10.0 suspicious
4.0
Medium Risk

Curve and image plotting tools for Python/Qt applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation techniques, and the maintainer's metadata raises concerns due to a lack of detailed information. These factors suggest a need for closer scrutiny.

  • High obfuscation risk
  • Incomplete maintainer metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires online functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: The code pattern suggests dynamic importation which can be used for obfuscation to hide the actual module and function names.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The maintainer has a new or inactive account with limited package history and lacks an author name, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • s: return getattr(__import__(modname, fromlist=[name]), name) def item_name_from_object(obj: Any) -> str | None:
✓ 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: codra.fr>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository PlotPyStack/plotpy 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 PlotPy 
Create a scientific data visualization tool using the Python package 'PlotPy' that allows users to easily plot curves and images. This application will serve as a user-friendly interface for scientists and researchers to analyze their data visually. Here are the key steps and features you need to implement:

1. **Setup**: Install necessary Python packages including PlotPy and any other dependencies such as NumPy and PyQt.
2. **User Interface Design**: Develop a clean and intuitive GUI using PyQt where users can input their data. Include options for file upload, direct data entry, and settings for curve/image plotting preferences.
3. **Data Handling**: Implement functionality to handle both numerical datasets for curve plotting and image files for image plotting. Ensure that the application can process different file formats commonly used in scientific research.
4. **Plotting Functions**: Utilize PlotPy’s core features to generate high-quality plots. Allow customization of plot styles, axes labels, titles, legends, and color schemes. Provide real-time preview updates as users adjust settings.
5. **Export Options**: Enable users to export their plots in various formats (PNG, PDF, SVG) with the option to include or exclude specific elements like axes and legends.
6. **Interactive Features**: Add interactive elements such as zooming, panning, and tooltips displaying data points on hover over the plotted curves/images.
7. **Help and Documentation**: Integrate a help section within the app that provides quick tips and usage guides for each feature. Additionally, create a comprehensive README file explaining installation and setup instructions.

Your task is to design and develop this mini-application from scratch, ensuring it adheres to best coding practices and is scalable for future enhancements. Use PlotPy effectively to showcase its capabilities while making the application accessible and appealing to a broad audience of scientists and researchers.