VTKio

v0.3.1 safe
2.0
Low Risk

A simple package for converting data between VTK files (XML and HDF5) and NumPy arrays.

🤖 AI Analysis

Final verdict: SAFE

The package VTKio appears to be legitimate with no signs of malicious activity. It does not make network calls and shows no indication of credential harvesting or obfuscation intended for malicious purposes.

  • No network calls detected
  • No evidence of credential harvesting
  • Legitimate use of compression/decompression techniques
Per-check LLM notes
  • Network: No network calls detected, which is normal.
  • Shell: Git commands are likely used for version control and may not indicate malicious intent, but could be exploited if improperly used.
  • Obfuscation: The observed patterns suggest legitimate data compression and decompression rather than malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • ta decoded_data = base64.b64decode(self.appended_data_bstr) # Split data by offset
  • decompressed_parts.append(zlib.decompress(block)) elif compressor == COMPRESSOR_LZMA:
  • decompressed = zlib.decompress(data) elif compression_type == CompressionType.L
  • decompressed = zlib.decompress(compressed_block) elif self._detected_compre
  • decompressed = zlib.decompress(compressed_data) elif self._detected_compression
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • ecutable?""" try: subprocess.check_output(['git', '--help']) return True except subprocess
  • git repository.""" return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=dir).decode('ut
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 VTKio 
Create a Python-based mini-application named 'VTKDataConverter' that leverages the VTKio package to convert medical imaging data between VTK XML and HDF5 formats and NumPy arrays. This tool will serve as a bridge for researchers and developers who need to interchangeably work with different file types while maintaining the integrity of their data. Here’s a detailed breakdown of what your application should accomplish:

1. **Setup Environment**: Ensure all necessary packages including VTKio, NumPy, and any other dependencies are installed.
2. **User Interface**: Develop a simple command-line interface where users can input commands to either convert from VTK to NumPy or vice versa.
3. **File Handling**: Implement robust file handling mechanisms to read and write both VTK XML and HDF5 files as well as NumPy arrays.
4. **Conversion Process**: Utilize the core functionalities of VTKio to perform seamless conversions between these formats. Specifically, demonstrate how to load a VTK XML file into a NumPy array, modify the array if needed, and then save it back as an HDF5 file or vice versa.
5. **Error Handling**: Incorporate comprehensive error handling to manage issues like incorrect file paths, unsupported file formats, and conversion errors.
6. **Documentation**: Provide clear documentation on how to install and use the application, along with examples of common use cases.
7. **Testing**: Include unit tests to verify the correctness of the conversion processes and ensure the application functions as expected under various scenarios.

Suggested Features:
- Support for multiple VTK file versions (e.g., .vtp, .vtu).
- Option to specify output precision when converting to NumPy arrays.
- Ability to visualize the converted data using matplotlib or another plotting library after conversion.
- Command-line options for specifying input/output directories and filenames.

By completing this project, you will have built a versatile tool that simplifies the workflow for those working with medical imaging data across different formats.