autoarray

v2026.5.29.4 safe
4.0
Medium Risk

PyAuto Data Structures

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all assessed categories with only a slight concern regarding metadata and maintainer details.

  • No network calls detected.
  • Maintainer account is new or inactive.
Per-check LLM notes
  • Network: No network calls detected, which is normal for many packages that don't require internet access.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no attempt to steal secrets or credentials.
  • Metadata: The maintainer has a new or inactive account and lacks detailed author information, which may indicate low activity or legitimacy issues.

📦 Package Quality Overall: Low (4.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 180 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in PyAutoLabs/PyAutoArray
  • 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: rghsoftware.co.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository PyAutoLabs/PyAutoArray 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 autoarray 
Create a mini-application called 'PyAutoArrayVisualizer' which leverages the 'autoarray' package to visualize and manipulate data structures commonly used in astronomical image processing. This tool will serve as an educational and practical resource for astronomers and data scientists who work with complex datasets from telescopes. Your application should include the following core functionalities:

1. **Data Visualization**: Allow users to input their astronomical data into predefined data structures supported by 'autoarray'. These data structures should include grids, arrays, and mappings relevant to astronomical imaging. Implement functions within your application to visualize these data structures using matplotlib or similar plotting libraries.
2. **Data Manipulation**: Enable basic operations such as filtering, resizing, and transforming these data structures. For instance, users should be able to apply transformations like rotations, translations, and scalings to their data.
3. **Interactive Exploration**: Integrate interactive elements where users can dynamically change parameters (such as filter thresholds, scaling factors) and immediately see the effects on their data visualization.
4. **Exporting Options**: Provide options for users to export their manipulated and visualized data back into formats that can be used in other scientific software or for publication purposes.

To utilize 'autoarray', focus on its capabilities in handling large-scale astronomical data efficiently. Use its built-in functionalities to streamline data manipulation tasks and ensure that your application is both performant and user-friendly. Remember to include documentation and examples within your project to help new users get started easily.

💬 Discussion Feed

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