AI Analysis
The package attractor-tools v0.3.0 has a moderate risk score due to potential interactive shell use and low package activity, raising suspicion about its origin and intentions.
- Use of os.system for console clearing
- Low package activity and new maintainer
Per-check LLM notes
- Network: No network calls detected, indicating low risk of data exfiltration or C2 communication.
- Shell: Use of os.system for clearing the console is generally benign but may indicate interactive usage; careful review of its context is advised.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The low activity and new maintainer suggest potential risk, but no concrete evidence of malicious intent.
Package Quality Overall: Low (3.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (3262 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
69 type-annotated function signatures detected in source
Limited contributor diversity
2 unique contributor(s) across 55 commits in beasty79/attractor_apiTwo distinct contributors found
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
Found 2 shell execution pattern(s)
t(y_val), 4))) os.system("cls") for i, (x, y) in enumerate(currentPath):def start(self): os.system('cls' if os.name == 'nt' else 'clear') self._update
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
1 maintainer concern(s) found
Author "Silas Schimpeler" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a captivating Python-based desktop application named 'Simon Attractor Explorer' that leverages the 'attractor-tools' package to visualize and interact with the Simon attractor. This application should provide users with an engaging experience by allowing them to explore various parameters of the Simon attractor, observe its dynamic behavior, and save or share their favorite visualizations. ### Application Requirements: 1. **User Interface**: Design an intuitive GUI using a library such as PyQt5 or Tkinter. The UI should include sliders or input fields for adjusting parameters like time step, number of iterations, initial conditions, and color schemes. 2. **Visualization**: Utilize 'attractor-tools' to generate animated plots of the Simon attractor based on user-defined parameters. Ensure smooth animations and real-time updates as users adjust parameters. 3. **Parameter Exploration**: Implement a feature that allows users to easily switch between predefined sets of parameters to explore different behaviors of the Simon attractor. 4. **Save & Share**: Enable users to save their visualizations as image files or share them via social media platforms directly from the app. 5. **Educational Content**: Include tooltips or a help section explaining key concepts related to the Simon attractor and dynamical systems theory. ### Utilizing 'attractor-tools': - Use the 'animate_simon_attractor()' function from 'attractor-tools' to generate animated plots based on user inputs. - Leverage other functions provided by the package to enhance the visualization capabilities of your application, such as changing the animation speed, color gradients, and plot styles. - Consider integrating additional functionalities from 'attractor-tools' to offer advanced customization options for the Simon attractor visualization.
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