autowisp

v1.0.21 suspicious
3.0
Low Risk

Pipeline for extracting photometry from wide-field night sky images

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low individual risk scores across all categories except metadata, where there is some concern due to incomplete author information and a potentially new or inactive account. This combination warrants further scrutiny.

  • Incomplete author information
  • Potentially new or inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://kpenev.github.io/AutoWISP/
  • Detailed PyPI description (15190 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in kpenev/AutoWISP
  • Small but multi-author team (3–4 contributors)

🔬 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: utdallas.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository kpenev/AutoWISP 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 autowisp 
Create a Python-based astronomy tool called 'SkyMapper' which leverages the 'autowisp' package to process and analyze wide-field night sky images. This tool will enable amateur astronomers and researchers to automatically extract photometric data from images captured by telescopes or DSLR cameras equipped with astrophotography lenses. Here’s a step-by-step guide on how to develop this application:

1. **Project Setup**: Start by setting up a virtual environment and installing the necessary packages including 'autowisp'. Ensure you have a way to input image files, possibly through a user-friendly GUI or command-line interface.
2. **Image Preprocessing**: Implement functionality to preprocess images before feeding them into 'autowisp'. This could include adjusting brightness levels, removing noise, and aligning multiple exposures if required.
3. **Photometry Extraction**: Use 'autowisp' to extract photometric data from these images. This involves identifying stars, measuring their brightness across different wavelengths, and recording these measurements.
4. **Data Visualization**: Develop a feature that visualizes the extracted photometric data in real-time or post-processing. This could include plotting light curves, histograms of star brightness, and interactive graphs.
5. **Database Integration**: Integrate a database system where users can save their processed images and associated photometric data. This allows for long-term storage and analysis of astronomical observations.
6. **Reporting Tool**: Create a reporting module that generates comprehensive reports summarizing the findings from the photometric analysis. These reports should be customizable, allowing users to highlight specific aspects of their data.
7. **User Interface**: Design an intuitive user interface that guides users through the process of uploading images, selecting parameters for processing, and viewing results. Consider incorporating tutorials and help sections to assist new users.
8. **Testing and Validation**: Rigorously test your application using a variety of astronomical images to ensure accuracy and reliability. Validate the photometric data against known star catalogs or professional-grade astronomical software.
9. **Documentation**: Write detailed documentation covering installation instructions, usage guidelines, and troubleshooting tips. Include examples and case studies to demonstrate the capabilities of 'SkyMapper'.
10. **Deployment**: Once the application is thoroughly tested and validated, prepare it for deployment. This might involve packaging it as a standalone executable or making it available via a web service.

By following these steps, you'll create a powerful yet accessible tool for the astronomical community, leveraging the advanced capabilities of 'autowisp' to democratize access to sophisticated photometric analysis techniques.

💬 Discussion Feed

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