ansys-pyensight-core

v0.11.12 safe
4.0
Medium Risk

A python wrapper for Ansys EnSight

🤖 AI Analysis

Final verdict: SAFE

The package shows some signs of obfuscation and shell execution, which are moderately concerning, but there is no evidence of malicious activity or credential risk. Overall, the package appears safe.

  • moderate network and shell execution risks
  • potential obfuscation techniques used
Per-check LLM notes
  • Network: Network calls are likely used for legitimate purposes such as fetching configuration or data from external sources.
  • Shell: Shell execution might be intended for system checks or compatibility verification but could pose risks if not properly sanitized or controlled.
  • Obfuscation: The use of base64 decoding and eval() suggests potential obfuscation, but without further context, it's hard to determine malicious intent.
  • Credentials: No clear patterns indicating credential harvesting were found.

📦 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://ensight.docs.pyansys.com/
  • Detailed PyPI description (9099 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

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

Active multi-contributor project

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

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • try: _ = requests.get(url) except requests.exceptions.ConnectionError:
  • l = None with requests.get(uri) as r: data = r.json()
  • uri {uri}") with requests.get(correct_url, stream=True) as r: with open(pa
  • else: with requests.get(uri) as r: data = r.json() o
  • ) r = requests.get(file_url, stream=True) with open(fil
  • e, filename) with requests.get(url, stream=True) as r: with open(outpath, "
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • a, bytes): data = base64.b64decode(data) self._buffer = io.BytesIO(data) the_fi
  • will be the returned string (eval() will not be performed). If json is True, the retu
  • ne, a string ready for Python eval() or a JSON string. Raises ------
  • N_PYTHON: # return eval(response.value) return response.value def prefi
  • e) cei_home = eval(self.command("enve.home()")) self.command("i
  • cei_version = eval(self.command("ceiversion.version_suffix")) s
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • pragma: no cover subprocess.check_output("nvidia-smi") return True except (subpro
  • 008 # self._pid = subprocess.Popen(cmd, creationflags=f, close_fds=True, env=my_env).pid
  • pid # self._pid = subprocess.Popen(cmd, startupinfo=si, close_fds=True, env=my_env).pid
  • n\n") self._pid = subprocess.Popen(cmd, close_fds=True, env=my_env).pid else:
  • else: self._pid = subprocess.Popen(cmd, close_fds=True, env=my_env).pid return self._pi
  • self._server_process = subprocess.Popen( cmd, close_fds=True
✓ 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/pyensight 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 ansys-pyensight-core 
Create a Python-based visualization tool called 'EnSightVisualizer' using the 'ansys-pyensight-core' package. This tool will enable users to load, manipulate, and visualize complex engineering simulation data from various sources, such as CFD (Computational Fluid Dynamics) and FEA (Finite Element Analysis). The goal is to provide a user-friendly interface for engineers and researchers to analyze their simulation results more effectively.

Step 1: Set up the Project Environment
- Initialize a new Python virtual environment.
- Install the 'ansys-pyensight-core' package and any other necessary dependencies.

Step 2: Design the User Interface
- Create a simple command-line interface (CLI) for users to interact with the tool.
- Consider adding a basic GUI using a library like Tkinter or PyQt if desired.

Step 3: Implement Core Functionality
- Develop functions to load different types of simulation data files supported by EnSight.
- Implement methods to perform basic operations on the loaded data, such as filtering, slicing, and extracting specific variables.

Step 4: Visualization Capabilities
- Utilize 'ansys-pyensight-core' to render 2D and 3D plots of the simulation data.
- Allow users to customize plot settings, including color maps, contour levels, and camera angles.

Step 5: Advanced Features
- Integrate support for real-time data streaming if applicable.
- Add functionality for exporting visualizations to image files or video formats.
- Implement tools for comparing multiple datasets side-by-side.

Step 6: Documentation and Testing
- Write comprehensive documentation for the tool, explaining how to install it, use its commands, and interpret the visualizations.
- Conduct thorough testing to ensure all features work correctly and efficiently.

By following these steps, you'll create a powerful yet accessible tool for analyzing and presenting complex simulation data, leveraging the capabilities of 'ansys-pyensight-core'.

💬 Discussion Feed

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