astroARIADNE

v1.5.0 suspicious
4.0
Medium Risk

Bayesian Model Averaging SED fitter

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score primarily due to the maintainer's limited number of published packages and the presence of non-HTTPS links, suggesting potential vulnerabilities or lack of maturity.

  • Maintainer has only one published package
  • Several non-HTTPS links present
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The package shows some signs of potential risk due to the maintainer having only one published package and several non-HTTPS links, but no clear evidence of typosquatting or severe malicious intent.

📦 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 (30025 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
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in jvines/astroARIADNE
  • Two distinct contributors found

🔬 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 score 3.0

Suspicious email domain flags: Very short email domain: ug.uchile.cl>

  • Very short email domain: ug.uchile.cl>
⚠ Suspicious Page Links score 10.0

Found 6 suspicious link(s) on the package page

  • Non-HTTPS external link: http://numba.pydata.org/
  • Non-HTTPS external link: http://mfouesneau.github.io/docs/pyphot/
  • Non-HTTPS external link: http://svo2.cab.inta-csic.es/theory/newov2/
  • Non-HTTPS external link: http://ssb.stsci.edu/cdbs/tarfiles/synphot3.tar.gz
  • Non-HTTPS external link: http://ssb.stsci.edu/cdbs/tarfiles/synphot4.tar.gz
  • Non-HTTPS external link: http://adsabs.harvard.edu/abs/2014A%26A...571A..11P
✓ Git Repository History

Repository jvines/astroARIADNE appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Jose Vines" 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 astroARIADNE 
Create a mini-application that allows astronomers to fit Spectral Energy Distributions (SEDs) using Bayesian Model Averaging techniques. Your application should utilize the 'astroARIADNE' Python package, which is designed specifically for this purpose. The application will serve as a user-friendly interface where users can upload their astronomical data, select from various models available in 'astroARIADNE', and obtain a fitted SED along with statistical uncertainties. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Start by setting up a Python environment and installing necessary packages including 'astroARIADNE'. Ensure all dependencies are listed in a requirements.txt file.
2. **User Interface Design**: Design a simple yet effective web-based UI using Flask or Django. The UI should allow users to upload their data files (e.g., CSV or FITS), choose from a dropdown of available models provided by 'astroARIADNE', and submit their request for fitting.
3. **Data Handling**: Implement functionality within your app to handle the uploaded data appropriately. This includes reading different types of files and preparing them for input into 'astroARIADNE'.
4. **Model Fitting**: Utilize 'astroARIADNE' to perform the actual fitting process based on the selected model and uploaded data. Ensure the application captures both the fitted SED and any relevant statistical outputs provided by 'astroARIADNE'.
5. **Result Visualization**: Develop a feature within the application to visualize the fitted SED alongside the original data. Use matplotlib or similar libraries to generate plots that clearly show the fit results.
6. **Output Presentation**: Provide users with options to download the fitted SED and statistical results in various formats (CSV, PDF, etc.). Additionally, offer a summary page that presents key findings from the fitting process in a digestible format.
7. **Documentation & Support**: Write comprehensive documentation for your application, detailing how to use it effectively and any limitations of the current version. Include FAQs and support resources for users who encounter issues.

Suggested Features:
- Real-time progress indicators during the fitting process.
- Integration with popular cloud storage services for data uploads.
- Interactive plot adjustments post-fitting, allowing users to customize visualizations.
- Automated email notifications upon completion of a job.

💬 Discussion Feed

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