TCAMpy

v0.2.0 suspicious
4.0
Medium Risk

Cellular Automaton Model for Tumor Growth

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low individual risk factors but the metadata risk score is elevated due to the maintainer's new or inactive account and lack of community engagement, raising suspicion about its legitimacy.

  • Metadata risk due to new/inactive maintainer
  • Lack of community engagement
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands without user interaction.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and the repository lacks community engagement.

🔬 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: gmail.com>

✓ 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 2.0

1 maintainer concern(s) found

  • Author "Richárd Bence Rózsa" 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 TCAMpy 
Develop a tumor growth simulation app using the TCAMpy package. This application will allow users to model and visualize the growth of tumors under various conditions. Here’s a detailed breakdown of the requirements and functionalities:

1. **User Interface**: Create a simple yet intuitive graphical user interface (GUI) using Python's Tkinter library. This GUI should enable users to input parameters and view the simulation results.
2. **Simulation Parameters**:
   - Allow users to set initial cell density and type distribution.
   - Enable selection of different types of cellular automata rules (e.g., Conway's Game of Life, but adapted for tumor growth).
   - Provide options to adjust environmental factors such as nutrient availability and oxygen levels.
3. **Visualization**:
   - Display the current state of the tumor as a grid or heatmap in real-time.
   - Include playback controls to step through the simulation or run it continuously.
4. **Data Export**:
   - Implement functionality to export simulation data as CSV files for further analysis.
5. **Advanced Features**:
   - Integrate a feature that allows users to save and load custom simulation scenarios.
   - Add a statistics panel that shows key metrics like tumor size over time, cell survival rate, etc.
6. **Utilization of TCAMpy**:
   - Use TCAMpy to define and simulate the cellular automata models for tumor growth.
   - Leverage TCAMpy’s capabilities to handle complex interactions between cells and their environment.
7. **Testing and Validation**:
   - Ensure the application runs smoothly and accurately simulates tumor growth based on user inputs.
   - Validate the simulation against known biological data or theoretical models to ensure accuracy.
8. **Documentation**:
   - Provide clear documentation explaining how to use the app, including setup instructions and a guide to interpreting the results.

This project aims to create an educational and research tool that helps understand tumor growth dynamics in a controlled, customizable environment.