🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with a low risk score due to the presence of normal network calls and shell executions typical in Python development, and no signs of obfuscation or overt credential misuse.

Normal network calls and shell executions
No obfuscation detected
Potential credential risk from environment variable usage

Per-check LLM notes

Network: The detected network calls are likely for fetching data or resources required by the package, which seems normal but should be reviewed for URL legitimacy and data handling.
Shell: Shell executions observed are possibly related to code formatting tools like 'isort' and 'black', which are common practices in Python development. However, direct execution capabilities can pose risks if not properly sanitized.
Obfuscation: No obfuscation patterns were detected in the provided code snippet.
Credentials: The use of os.getenv to retrieve environment variables like 'AWS_DEFAULT_REGION' could indicate legitimate use for configuring application settings, but it also poses a risk if not properly secured.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 27 test file(s) found

27 test file(s) detected (e.g. test_genbank_integration.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (2320 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

26 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 score 4.5

Found 3 network call pattern(s)

ge_size=10000" resp = requests.get(url, headers=headers) resp.raise_for_status()
a CSV file.""" response = requests.get(url, allow_redirects=True, timeout=5) content = response
mode": "text"} response = requests.get(url, params=params) response.raise_for_status() text

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

d black if isort: subprocess.run(["isort", str(output_file)]) if black: subproce
ile)]) if black: subprocess.run(["black", str(output_file)]) if __name__ == "__main__":

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

MODELS") AWS_DEFAULT_REGION = os.getenv("AWS_DEFAULT_REGION") def csv_to_json(csv_file_path: str) -> It

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Allen Institute for Neural Dynamics" 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 aind-data-schema-models

Create a Python-based mini-application named 'NeuroDataExplorer' that leverages the 'aind-data-schema-models' package to manage and analyze neuroimaging data efficiently. Your application should allow users to import, visualize, and perform basic statistical analyses on neuroimaging datasets. Here’s a step-by-step guide to building your application:

1. **Setup Project Environment**: Start by setting up a virtual environment for your project and installing necessary packages including 'aind-data-schema-models'.
2. **Data Importation**: Implement functionality to import various types of neuroimaging data formats supported by 'aind-data-schema-models', such as NIfTI files.
3. **Data Visualization**: Utilize 'aind-data-schema-models' to define schemas for visualizing imported data. Integrate visualization libraries like Matplotlib or Plotly to display brain images and statistical maps.
4. **Statistical Analysis**: Incorporate basic statistical analysis tools within the app using 'aind-data-schema-models'. Users should be able to perform operations such as calculating mean intensity values across different brain regions.
5. **User Interface**: Develop a simple GUI using Tkinter or PyQt to make the application more user-friendly. This interface should allow users to easily navigate through their data and view analysis results.
6. **Documentation**: Write comprehensive documentation detailing how to install, run, and use 'NeuroDataExplorer'. Include examples of input data and expected outputs.

Suggested Features:
- Support for importing multiple file formats (e.g., NIfTI, DICOM).
- Interactive brain region selection for localized data analysis.
- Export options for visualizations and statistical results.
- Basic data cleaning and preprocessing steps before analysis.

How 'aind-data-schema-models' is utilized:
- Define data models and schemas for different types of neuroimaging data.
- Validate imported data against these predefined schemas to ensure data integrity.
- Use schemas to structure output data for visualization and analysis purposes.

This project will not only demonstrate the capabilities of 'aind-data-schema-models' but also provide a practical tool for researchers working with neuroimaging data.