ansys-turbogrid-core

v0.8.0 safe
3.0
Low Risk

A python wrapper for Ansys TurboGrid

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators with no network calls and appropriate use of shell commands for its intended functionality. The metadata suggests a potentially new maintainer but does not raise significant red flags.

  • No network calls detected.
  • Shell execution appears appropriate for Docker commands and logging.
Per-check LLM notes
  • Network: No network calls detected, indicating no direct risk from network activity.
  • Shell: Shell execution is primarily used for Docker commands and logging purposes, which seems appropriate for a package related to Ansys TurboGrid, but could indicate potential execution risks if not properly sanitized.
  • Metadata: The maintainer has only one package, suggesting a new or less active account which could be suspicious but not necessarily malicious.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://turbogrid.docs.pyansys.com/version/stable/
  • Detailed PyPI description (6083 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 64 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in ansys/pyturbogrid
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • print("\n") # subprocess.run( # "env" # if self.is_linux
  • elf.container_name}") subprocess.run( f"{self.prepend_command} docker container rm -f
  • print(f"start tg...") subprocess.run(f"{docker_command}", shell=True) print(f"sleep...")
  • le Output #########") subprocess.run(f"{self.prepend_command} docker logs {self.container_name}",
  • container #########") subprocess.run( f"{self.prepend_command} docker container stop
  • ntainer == False: subprocess.run( f"{self.prepend_command} docker container r
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ansys.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ansys/pyturbogrid appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ANSYS, Inc." 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 ansys-turbogrid-core 
Your task is to develop a fully-functional mini-application named 'TurboFlowDesigner' which leverages the capabilities of the 'ansys-turbogrid-core' Python package to design and optimize turbomachinery blade geometries. This application will serve as a user-friendly interface for engineers to create, modify, and analyze blade designs using TurboGrid's advanced meshing algorithms. Here's a detailed outline of the application's functionality and suggested features:

1. **User Interface**: Design a simple yet intuitive graphical user interface (GUI) using a library like Tkinter or PyQt. This GUI should allow users to input various parameters related to blade geometry, such as chord length, twist angle, and spanwise position.

2. **Blade Geometry Creation**: Implement functionality to generate initial blade geometries based on user inputs. This involves utilizing the 'ansys-turbogrid-core' package to define blade profiles and distribute them along the span.

3. **Mesh Generation**: Integrate TurboGrid's mesh generation capabilities to automatically create high-quality meshes for the designed blades. Users should have options to adjust mesh density and type (structured/unstructured).

4. **Visualization**: Provide tools within the application to visualize the blade geometries and their corresponding meshes. Consider using libraries like Matplotlib or PyVista for visualization purposes.

5. **Optimization Module**: Develop an optimization module that allows users to refine their blade designs based on performance criteria. This could include adjusting blade shapes to minimize drag or maximize lift, all while ensuring structural integrity.

6. **Export Functionality**: Ensure the application has the capability to export the final blade geometries and meshes in standard file formats (e.g., STL, CGNS) for use in CFD simulations or further engineering analysis.

7. **Documentation and User Guide**: Create comprehensive documentation and a user guide to assist new users in effectively using TurboFlowDesigner. Include examples and tutorials to demonstrate key functionalities.

The 'ansys-turbogrid-core' package is utilized throughout the application for its robust meshing algorithms and ability to handle complex turbomachinery geometries. Your goal is to showcase the package's power while providing a valuable tool for turbomachinery designers.

💬 Discussion Feed

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