UQPyL

v2.1.6 suspicious
5.0
Medium Risk

A python package for parameter uncertainty quantification and optimization

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of immediate malicious activity but has incomplete metadata and a potentially new or inactive author account, which raises some concerns.

  • Incomplete author information
  • New or inactive author account
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion.

🔬 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: hhu.edu.cn>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://dx.doi.org/10.2139/ssrn.5393295
✓ Git Repository History

Repository smasky/UQPyL 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 UQPyL 
Create a mini-application named 'UncertaintyAnalyzer' using the Python package 'UQPyL'. This application will serve as a tool for engineers and scientists to analyze the uncertainty in their models and optimize parameters based on input data. Here's a detailed plan on how to develop this application:

1. **Introduction**: Explain the importance of understanding uncertainties in predictive models and the role of optimization in improving model accuracy.
2. **Setup**: Guide the user through installing necessary packages including 'UQPyL', numpy, pandas, matplotlib, and scipy.
3. **Data Input**: Design a user-friendly interface where users can upload their dataset in CSV format. Ensure the dataset includes both input variables and output variables.
4. **Model Setup**: Allow users to define their mathematical model function or choose from predefined functions provided by 'UQPyL'. The model should take inputs and return outputs.
5. **Parameter Uncertainty Quantification**: Utilize 'UQPyL' to quantify the uncertainty in model parameters. Users should be able to specify which parameters they want to quantify uncertainty for and set confidence intervals.
6. **Optimization**: Implement an optimization feature using 'UQPyL' to find the optimal values of the parameters that minimize the error between the model predictions and actual data.
7. **Results Visualization**: Provide visualizations of the results, including histograms of parameter distributions, scatter plots comparing model predictions vs. actual data, and line plots showing the convergence of optimization.
8. **Export Results**: Allow users to export the results in various formats such as CSV, Excel, or PDF.
9. **Documentation**: Write comprehensive documentation explaining each feature of the application, how to install and use it, and examples of its application in real-world scenarios.

Ensure that the application is modular, easy to extend, and well-documented. Focus on making the application accessible to non-experts while still providing advanced features for experts.