astro-p3

v1.6.0 safe
2.0
Low Risk

(No description)

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

🤖 AI Analysis

Final verdict: SAFE

The package shows very little indication of risky behavior with no network calls, shell executions, obfuscation, or credential harvesting. The metadata suggests it may be from a newer maintainer with only one package.

  • No network calls
  • Single package from maintainer
Per-check LLM notes
  • Network: No network calls suggest normal behavior for most utility packages.
  • Shell: No shell executions indicate the package is not attempting to run external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The maintainer has only one package and lacks PyPI classifiers, indicating potential low effort or newness, but no clear signs of malice.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2334 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ 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

  • 3 unique contributor(s) across 100 commits in astro-tiptop/P3
  • 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository astro-tiptop/P3 appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Olivier Beltramo-Martin, Benoit Neichel, Fabio Rossi, Guido Agapito, Cédric Plantet" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astro-p3 
Create a Python-based mini-application that simulates and analyzes Adaptive Optics (AO) Point Spread Functions (PSFs) using the 'astro-p3' package. This application will allow astronomers and researchers to input various parameters related to AO systems and generate PSF models for different astronomical observations. The app should have the following functionalities:

1. **User Interface**: Develop a simple GUI using a library like PyQt or Tkinter where users can input system parameters such as wavelength, telescope diameter, atmospheric conditions, etc.
2. **PSF Simulation**: Utilize the 'aoSystem' library from 'astro-p3' to simulate AO PSFs based on user inputs. The application should support multiple simulation scenarios, such as ground-based telescopes under different atmospheric turbulence conditions.
3. **Visualization**: Implement a feature to visualize the simulated PSFs. Users should be able to see both the raw PSF data and processed versions (e.g., Fourier transform).
4. **Analysis Tools**: Include basic analysis tools within the application, allowing users to calculate key metrics from the PSFs, such as Full Width at Half Maximum (FWHM), Strehl ratio, etc.
5. **Save and Export**: Provide options for users to save their simulations and analysis results. Results can be saved as images, CSV files, or other formats suitable for scientific documentation.
6. **Documentation**: Ensure the application comes with comprehensive documentation explaining each component, usage instructions, and example use cases.

This project aims to provide a practical tool for researchers and students to explore AO PSFs in a controlled environment, facilitating better understanding and optimization of AO systems.

💬 Discussion Feed

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