asteca

v0.6.9 safe
3.0
Low Risk

Stellar cluster analysis package

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across multiple categories including network, shell, obfuscation, and credential risks. While there is some concern regarding metadata and maintenance efforts, there are no clear indicators of malicious intent.

  • Low network risk
  • No shell execution detected
  • No obfuscation or credential harvesting patterns
Per-check LLM notes
  • Network: Making network calls to PyPI is generally expected for fetching package metadata or updates, indicating no immediate suspicious activity.
  • Shell: No shell execution patterns detected, which is normal and indicates no direct system command execution risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The package shows low maintenance effort and a new maintainer, but no clear signs of malicious intent.

📦 Package Quality Overall: Low (4.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://asteca.readthedocs.io/
  • Detailed PyPI description (1303 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 105 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in asteca/ASteCA
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • a/json" # pypi_response = requests.get(pypi_url, timeout=3) # pypi_data = pypi_response.json()
✓ 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: gmail.com>

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://dx.doi.org/10.1051/0004-6361/201424946
  • Non-HTTPS external link: http://www.aanda.org/articles/aa/abs/2015/04/aa24946-14/aa24946-14.html
  • Non-HTTPS external link: http://asteca.github.io
✓ Git Repository History

Repository asteca/ASteCA appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 asteca 
Create a Python-based mini-application named 'StellarClusterAnalyzer' that leverages the 'asteca' package for analyzing stellar clusters. This application should allow users to input astronomical data about stars, such as their positions and magnitudes, and then perform various analyses to identify and characterize potential stellar clusters within the dataset.

### Key Features:
1. **Data Input:** Users should be able to upload CSV files containing star data, which includes columns for right ascension, declination, and magnitude.
2. **Cluster Detection:** Implement a function that uses Astecca's clustering algorithms to detect potential stellar clusters from the uploaded data. The algorithm should be configurable based on user preferences regarding the minimum number of stars per cluster and the maximum radius of influence between stars.
3. **Visualization:** Provide a visualization feature where detected clusters are plotted on a scatter plot, with each cluster color-coded and labeled with its ID.
4. **Statistics Output:** For each detected cluster, generate a report that includes basic statistics like the number of stars, average magnitude, and spatial distribution characteristics.
5. **Interactive Interface:** Develop a simple web interface using Flask or Django to make the application accessible through a browser. The interface should include options for file uploads, parameter settings for cluster detection, and display areas for visualizations and reports.
6. **User Guide:** Include a brief user guide that explains how to use the application, what kind of data it expects, and how to interpret the results.

### Utilizing Astecca:
- Use Astecca's `cluster` module to load and preprocess the star data.
- Apply the `find_clusters` method to detect clusters, adjusting parameters like the minimum number of stars and the maximum distance between stars.
- Leverage Astecca's plotting capabilities to visualize the clusters on a scatter plot.
- Employ Astecca's statistical functions to compute and present key metrics about each detected cluster.

This project aims to provide astronomers and astronomy enthusiasts with a powerful yet easy-to-use tool for analyzing stellar data and identifying significant clusters within large datasets.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!