airavata-cerebrum

v0.1.2 suspicious
4.0
Medium Risk

Apache Airavata Cerebrum : Integrated Data-driver Modeling for Computational Neuroscience

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential signs of being legitimate but raises concerns due to its novelty and lack of associated metadata. Further scrutiny is advised.

  • New package with limited maintainer history
  • No associated GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is not suspicious in itself.
  • Shell: Shell execution patterns may be legitimate if the package performs tasks requiring system commands, but further investigation is needed to ensure there's no abuse.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is new with limited maintainer history and no associated GitHub repository, which raises some suspicion but not enough to conclude it's malicious.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3829 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Separate author ("Sriram P Chockalingam") and maintainer ("Apache Airavata Developers") listed
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 419 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 4.0

Found 2 shell execution pattern(s)

  • to download file result = subprocess.run(command.split(" "), stdout=subprocess.PIPE) print("time
  • load directories result = subprocess.run(command.split(), stdout=subprocess.PIPE) print("time tak
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gatech.edu

✓ 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

  • Only one version has ever been released — brand new package
  • Author "Sriram P Chockalingam" 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 airavata-cerebrum 
Develop a mini-application that integrates computational neuroscience models using the 'airavata-cerebrum' package. This application will serve as a user-friendly interface for researchers to explore brain modeling scenarios based on real-world data inputs. Here’s a detailed step-by-step guide on how to create this application:

1. **Setup Environment**: Begin by setting up your Python development environment. Ensure you have Python installed, then install the 'airavata-cerebrum' package along with any other necessary dependencies.

2. **Design User Interface**: Design a simple yet effective GUI using a library like Tkinter or PyQt. This UI should allow users to upload their own datasets or select preloaded datasets for analysis.

3. **Data Preprocessing Module**: Implement a module within your application that preprocesses the uploaded data. This could include normalization, filtering, and other preprocessing steps essential for computational neuroscience studies.

4. **Model Integration**: Utilize the 'airavata-cerebrum' package to integrate various computational neuroscience models into your application. These models should be selectable by the user through the GUI and capable of being run with the provided dataset.

5. **Visualization Tools**: Include visualization tools that allow users to see the output of their model runs. This could range from simple plots to more complex visualizations depending on the complexity of the model outputs.

6. **Results Analysis**: Provide basic statistical analysis tools that help interpret the results of the model runs. This could include calculating key metrics, comparing different model outputs, etc.

7. **Documentation & Help Section**: Finally, ensure your application has a well-documented section explaining how to use each feature and what each model does. This will make it easier for new users to get started.

**Suggested Features**:
- A database integration feature where users can save their datasets and models for future reference.
- An option for users to export their results in various formats such as CSV, JSON, or image files.
- A tutorial section within the application that walks users through the process of running a model.

This project not only serves as a practical application of the 'airavata-cerebrum' package but also provides valuable insights into computational neuroscience modeling for educational and research purposes.

💬 Discussion Feed

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