ansys-acp-core

v0.3.2 safe
3.0
Low Risk

Python library for ACP - Ansys Composite PrepPost

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk indicators with no network calls, obfuscation, or credential harvesting. The shell execution patterns are slightly concerning but lack evidence of malicious intent.

  • No network calls detected
  • Low obfuscation risk
  • Shell execution patterns warrant further investigation
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution patterns may indicate interaction with remote systems, but without context, it's hard to determine if they're malicious.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account but no other red flags were raised.

📦 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://acp.docs.pyansys.com
  • Detailed PyPI description (8190 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

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

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in ansys/pyacp
  • 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 5 shell execution pattern(s)

  • w-remote-host" in subprocess.check_output( # nosec B603, B607: documented in 'security_considerations
  • f-8") self._process = subprocess.Popen( # nosec B603: documented in 'security_considerations.rst'
  • arse_version( subprocess.check_output( # nosec B603, B607: documented in 'security_considerations
  • wait") proc_res = subprocess.run( # nosec B603: documented in 'security_considerations.rst'
  • pend("--volumes") subprocess.check_call( # nosec B603: documented in 'security_considerations.rst'
✓ 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/pyacp 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-acp-core 
Create a mini-application named 'CompositeAnalyzer' using the 'ansys-acp-core' Python package. This application aims to simplify the process of analyzing composite materials for engineering students and professionals. It should allow users to input basic composite material properties and generate analysis reports based on these inputs. Here’s a step-by-step guide on what your application should achieve:

1. **User Interface Design**: Develop a simple, intuitive user interface using a Python GUI framework like PyQt5 or Tkinter. This UI will enable users to input data such as ply angles, laminate thicknesses, and material properties.
2. **Data Input Validation**: Implement robust validation checks to ensure that all inputs are within acceptable ranges and formats. For instance, ply angles should not exceed 90 degrees.
3. **Analysis Engine**: Utilize the 'ansys-acp-core' package to perform the composite analysis. Specifically, use its functionalities to calculate key mechanical properties such as stiffness matrices, strain-displacement relations, and stress-strain relationships under various loading conditions.
4. **Visualization**: Integrate matplotlib or another visualization library to plot graphs showing the results of the analysis, such as stress distribution across different layers of the composite laminate.
5. **Report Generation**: Enable the creation of PDF reports summarizing the analysis results. Include charts, tables, and a brief explanation of the findings.
6. **Saving & Loading Sessions**: Allow users to save their current session including all inputs and results to a file, and load previously saved sessions.
7. **Help Documentation**: Provide a comprehensive help section within the application explaining how to use each feature, common errors, and tips for optimal use.

This application will serve as an educational tool for understanding composite material behavior and a practical utility for preliminary design analysis.