autoplex

v0.3.1 suspicious
5.0
Medium Risk

Automated machine-learned Potential Landscape explorer

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network activity, shell execution, obfuscation, and credential handling. However, the incomplete author information and potential inactivity of the maintainer raise concerns about its authenticity and ongoing maintenance.

  • Incomplete author information
  • Potential inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution appears to be related to testing and dependency checks, which could be legitimate for a development package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The author's information is incomplete and the maintainer seems new or inactive, raising some suspicion.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10827 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

  • 124 type-annotated function signatures detected in source
○ 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 score 10.0

Found 6 shell execution pattern(s)

  • file.write(ace_text) os.system(f"export OMP_NUM_THREADS={num_processes_fit} && julia ace.jl
  • st of test cases result = subprocess.run( ["pytest", "--collect-only"], capture_outpu
  • } @requires( ( subprocess.run( 'julia -e "using Pkg; println(haskey(Pkg.depend
  • ) as file_err, ): subprocess.call(["julia", script_name], stdout=file_out, stderr=file_err)
  • ) as file_err, ): subprocess.call(["gap_fit", *parameters], stdout=file_std, stderr=file_err)
  • ) as file_err, ): subprocess.call("nep", stdout=file_out, stderr=file_err, env=env) def run_
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: bam.de>

✓ 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 autoplex 
Develop a comprehensive mini-application using the 'autoplex' package in Python that serves as a potential landscape explorer for scientific data analysis. This application will allow users to input their dataset, which could include various physical, chemical, or biological parameters, and automatically generate a potential energy landscape. Here’s a detailed breakdown of the project requirements:

1. **User Input Interface**: Create a simple yet effective user interface where users can upload their dataset in CSV format. Ensure the application supports basic data validation to check if the uploaded file is a valid CSV and contains numerical data.

2. **Data Preprocessing**: Implement a preprocessing module that cleans the data by handling missing values, normalizing numeric columns, and possibly performing dimensionality reduction techniques like PCA if the dataset is high-dimensional. This step is crucial for ensuring the accuracy of the potential landscape generation.

3. **Potential Landscape Generation**: Utilize the 'autoplex' package to analyze the preprocessed dataset and generate a potential energy landscape. The landscape should visually represent the relationships and interactions within the dataset, highlighting areas of high and low potential energy. Users should be able to interact with this visualization to explore different sections of the landscape.

4. **Analysis Tools**: Provide tools for analyzing specific regions of the potential landscape. For instance, users should be able to query the system to find local minima and maxima, calculate the gradient at any point, and understand the flow of potential energy across the landscape.

5. **Visualization Customization**: Allow users to customize the visualization of the potential landscape according to their needs. Options could include adjusting color schemes, adding labels, and changing the scale of the axes.

6. **Report Generation**: Enable users to generate a report summarizing the findings from their analysis. This report should include key insights derived from the potential landscape, such as critical points and significant trends in the data.

7. **Documentation and Help**: Include comprehensive documentation and a help section within the application to guide users through each step of the process, from uploading data to generating reports. This documentation should also highlight the importance of potential landscapes in scientific research and how they can be used to gain deeper insights into complex datasets.

By completing this project, you will create a powerful tool for scientists and researchers to explore and understand the underlying structures within their data using advanced machine learning techniques.

💬 Discussion Feed

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