asdex

v0.3.3 suspicious
4.0
Medium Risk

Automatic Sparse Differentiation in JAX

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct signs of malicious activity, but the missing maintainer information and potential inactivity raise concerns about its provenance and maintenance.

  • Missing maintainer's author name
  • Potential inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical for most non-administrative packages.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing and they seem to be new or inactive, which raises some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://adrianhill.de/asdex
  • Detailed PyPI description (4722 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

  • 282 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in adrhill/asdex
  • 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: adrianhill.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository adrhill/asdex 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 asdex 
Develop a small, yet powerful, Python application that leverages the 'asdex' package for performing automatic sparse differentiation on mathematical functions. This application will serve as a tool for researchers and students interested in understanding the behavior of complex functions by analyzing their gradients and Hessians efficiently. Here's a detailed guide on how to approach this project:

1. **Project Overview**: Your goal is to create an application that takes a user-defined mathematical function and computes its gradient and Hessian using sparse matrices to optimize memory usage and computation time.

2. **Core Features**:
   - **Function Input**: Allow users to input any differentiable mathematical function, e.g., f(x) = x^3 + 2x^2 + 3x + 4.
   - **Sparse Gradient Calculation**: Use 'asdex' to compute the gradient of the given function using sparse differentiation techniques.
   - **Sparse Hessian Calculation**: Similarly, calculate the Hessian matrix using 'asdex', ensuring it is represented sparsely to improve performance.
   - **Visualization**: Provide visual representations of the function, its gradient, and Hessian. Utilize libraries like Matplotlib for plotting.
   - **User Interface**: Implement a simple command-line interface for ease of use, though a graphical user interface could be an advanced feature.

3. **Implementation Steps**:
   - Start by installing 'asdex' and other necessary packages like JAX, NumPy, and Matplotlib.
   - Define a function to parse user inputs for mathematical functions.
   - Integrate 'asdex' to perform sparse differentiation. Experiment with different levels of sparsity to observe performance improvements.
   - Develop functions to plot the original function, its gradient, and Hessian. Ensure these plots are clear and informative.
   - Implement error handling for invalid function inputs or other potential issues.

4. **Advanced Features (Optional)**:
   - Add support for multi-variable functions.
   - Incorporate a comparison between dense and sparse differentiation methods to showcase the benefits of sparsity.
   - Allow users to save plots or results to files.

5. **Testing and Documentation**:
   - Write comprehensive tests to ensure your application works correctly across various scenarios.
   - Document your code thoroughly, explaining each part and how 'asdex' is utilized.
   - Prepare a README file detailing installation instructions, usage examples, and how to contribute to the project.

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