anesthetic

v2.14.8 safe
3.0
Low Risk

nested sampling post-processing

🤖 AI Analysis

Final verdict: SAFE

The package anesthetic v2.14.8 shows very low risk indicators such as no network calls, shell executions, obfuscations, or credential harvesting attempts. The only concern is the metadata risk due to sparse maintainer information.

  • No network calls
  • No shell execution patterns
  • Sparse maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected unless the package requires internet access to function.
  • Shell: No shell execution patterns detected, which aligns with typical benign package behavior.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer information is sparse and the author appears to be new or inactive, raising some suspicion, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • 14 test file(s) detected (e.g. test_boundary.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://anesthetic.readthedocs.io/en/latest/
  • Detailed PyPI description (8487 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

  • 3 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 12 unique contributor(s) across 100 commits in handley-lab/anesthetic
  • Active community — 5 or more distinct 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: cam.ac.uk>

⚠ Suspicious Page Links score 8.0

Found 4 suspicious link(s) on the package page

  • Non-HTTPS external link: http://joss.theoj.org/papers/8c51bffda75d122cf4a8b991e18d3e45/status.svg
  • Non-HTTPS external link: http://joss.theoj.org/papers/8c51bffda75d122cf4a8b991e18d3e45
  • Non-HTTPS external link: http://dx.doi.org/10.21105/joss.01414},
  • Non-HTTPS external link: http://baudren.github.io/montepython.html
✓ Git Repository History

Repository handley-lab/anesthetic 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 anesthetic 
Create a mini-application that leverages the 'anesthetic' Python package to process and visualize results from nested sampling algorithms. This tool will serve as a valuable resource for researchers and data scientists who work with complex statistical models and Bayesian inference problems. The application should include the following features:

1. **Data Input**: Allow users to upload their nested sampling output files or input the data directly through a CSV file format.
2. **Model Visualization**: Utilize 'anesthetic' to plot the posterior distributions of parameters inferred from the nested sampling process. Ensure these plots are interactive and allow users to zoom in/out and pan across the graphs.
3. **Parameter Analysis**: Implement functionality within the app to calculate key statistics such as mean, median, standard deviation, and credible intervals for each parameter from the nested sampling results.
4. **Customization Options**: Provide users with the ability to customize the appearance of the plots (e.g., colors, line styles) and the specific parameters they wish to analyze.
5. **Export Results**: Enable users to export the processed data and visualizations in various formats (PDF, PNG, CSV).
6. **Documentation & Help**: Include comprehensive documentation within the app to guide users on how to use the tool effectively, especially for those new to nested sampling and Bayesian analysis.

The 'anesthetic' package will be utilized throughout the application to handle the heavy lifting of processing nested sampling outputs and generating accurate, high-quality visualizations and statistical analyses. Your task is to design and implement this mini-application using modern web technologies (such as Flask for backend and JavaScript libraries like Plotly for frontend visualization), ensuring it is user-friendly and accessible to a wide range of users.

💬 Discussion Feed

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