astro-brutus

v1.1.0 safe
3.0
Low Risk

Brute-force Bayesian inference for photometric distances, reddenings, and stellar properties

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious behavior based on the provided analysis notes. The metadata risk is slightly elevated but does not indicate any clear malicious intent.

  • Low network and shell risks
  • No obfuscation or credential harvesting detected
  • Minor red flags in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has some minor red flags but no clear indicators of being malicious.

📦 Package Quality Overall: Medium (6.0/10)

◈ 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://brutus.readthedocs.io
  • Detailed PyPI description (2344 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

  • 4 type-annotated function signatures (partial)
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in joshspeagle/brutus
  • 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: utoronto.ca>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://waps.cfa.harvard.edu/MIST/
✓ Git Repository History

Repository joshspeagle/brutus 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 astro-brutus 
Create a mini-application named 'StellarExplorer' that utilizes the 'astro-brutus' Python package to perform Bayesian inference on stellar data. This application will enable astronomers and astrophysics enthusiasts to input their own photometric data and receive predictions about stellar distances, reddening effects, and other properties based on Bayesian analysis. Here are the key steps and features for developing this application:

1. **Setup Environment**: Ensure that the 'astro-brutus' package is installed along with necessary dependencies such as numpy, scipy, and matplotlib.
2. **Data Input Interface**: Develop a simple GUI where users can upload their photometric data in CSV format. The app should validate the uploaded data to ensure it contains the correct columns (e.g., magnitude measurements, filters used).
3. **Bayesian Inference Processing**: Use 'astro-brutus' to process the uploaded data through its Bayesian inference algorithms. Allow users to select from predefined models or customize parameters for more advanced users.
4. **Result Visualization**: Display the results of the Bayesian inference in an interactive plot using matplotlib or another plotting library. Include options to zoom, pan, and highlight specific data points.
5. **Report Generation**: Provide functionality to generate a report summarizing the Bayesian analysis results, including charts, tables, and explanations of the inferred stellar properties.
6. **User Guide**: Include a comprehensive user guide explaining how to use the application, interpret the results, and understand the underlying Bayesian inference process.

By completing this project, you'll create a valuable tool for the astronomical community that leverages the power of Bayesian inference to analyze photometric data, making complex analyses accessible to a broader audience.

💬 Discussion Feed

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