aspect-stable

v0.5.1 safe
1.0
Low Risk

Automatic SPEctra Components Tagging

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity or unusual behavior, with very low risks across all checked categories.

  • No network calls detected
  • No shell execution patterns found
  • No obfuscation or credential harvesting detected
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, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_tools.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (445 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
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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: stsci.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 aspect-stable 
Create a fully-functional mini-application that leverages the 'aspect-stable' package to analyze and tag spectral components from given datasets. Your application should be designed to import spectral data, process it using 'aspect-stable', and then provide visualizations and tags for the identified components. Here’s a detailed step-by-step guide on what your application should accomplish:

1. **Project Setup**: Begin by setting up your Python environment. Ensure you have Python installed, along with necessary libraries such as Matplotlib for plotting and Pandas for data manipulation. Install 'aspect-stable' via pip.
2. **Data Importation**: Develop a feature that allows users to upload their spectral data files (CSV, TXT). This data should include wavelength and corresponding intensity values.
3. **Spectral Analysis**: Utilize 'aspect-stable' to perform automatic tagging of spectral components within the imported dataset. This involves identifying peaks, valleys, and other significant features within the spectrum.
4. **Visualization**: Implement a visualization module that plots the original spectrum alongside the tagged components. Use different colors or markers to highlight the tagged areas.
5. **Tagging Interface**: Provide an interface where users can review and adjust the tags manually if needed. Allow them to add, remove, or modify tags based on their analysis.
6. **Export Functionality**: Enable users to export the analyzed data including the tagged components back into a file format of their choice (CSV, TXT).
7. **Documentation & Help**: Include a brief user guide explaining how to use each feature of the application. Also, provide links to the 'aspect-stable' documentation for more detailed information.

Your goal is to create an intuitive, user-friendly tool that simplifies the complex task of spectral component tagging, making it accessible to researchers and analysts without deep expertise in spectroscopy.

💬 Discussion Feed

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