AI Analysis
The package is deemed safe based on the absence of network calls, shell executions, obfuscations, and credential risks. While there is an insecure link and the maintainer has only one package, these factors alone do not indicate a supply-chain attack.
- No network calls or shell executions detected.
- No signs of obfuscation or credential harvesting.
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 malicious activity or unexpected behavior.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The maintainer has only one package, and there's an insecure link, but no clear signs of typosquatting or other malicious intent.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (8828 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: jhu.edu
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://dx.doi.org/10.1007/s40870-024-00427-9},
No GitHub repository linked
No GitHub repository link found
1 maintainer concern(s) found
Author "Jake Diamond" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application using the Python package 'alpss' which automates the analysis of photonic Doppler velocimetry spall signals while providing uncertainty quantification. This application will serve as a tool for researchers and engineers working with high-speed impact experiments. The application should include the following features: 1. **Signal Input**: Allow users to upload PDV signal data files (e.g., CSV, TXT). 2. **Preprocessing Module**: Implement basic signal preprocessing steps such as filtering and normalization. 3. **Analysis Engine**: Utilize 'alpss' to perform automated analysis on the preprocessed signals, extracting key parameters like velocity, displacement, and acceleration. 4. **Uncertainty Quantification**: Provide uncertainty estimates for the extracted parameters based on the methods implemented in 'alpss'. 5. **Visualization**: Offer graphical representations of the original signal, analyzed results, and uncertainties. 6. **Report Generation**: Automatically generate a detailed report summarizing the analysis findings and including visualizations. Your task is to outline the development process from setting up the environment to deploying the final application. Include considerations for user interface design, backend processing, and any necessary documentation. Additionally, suggest ways to enhance the application's functionality in future iterations.