🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all categories with no network calls, shell executions, obfuscations, or credential harvesting. However, the incomplete metadata suggests a potential new or less active maintainer, warranting further observation.

Incomplete author information
Single package maintained

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is incomplete and the maintainer has a single package, which may indicate a new or less active account.

🔬 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: wustl.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository WUSTL-ORL/NeuroDOT_py 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 NeuroDOT-py

Your task is to create a mini-application using the 'NeuroDOT-py' Python package, which is designed for efficient optical brain mapping. This application will serve as a user-friendly interface for researchers and scientists to process and visualize brain activity data obtained through optical methods. Here are the key steps and features your application should include:

1. **Data Importation**: Allow users to upload their optical brain mapping data in common formats such as .csv or .txt files. Ensure that the data includes timestamps and intensity values for different regions of interest in the brain.
2. **Preprocessing Module**: Implement a preprocessing module using 'NeuroDOT-py' functionalities to clean and normalize the imported data. This should include steps like background subtraction, noise reduction, and normalization to ensure accurate analysis.
3. **Mapping Visualization**: Utilize 'NeuroDOT-py' to generate real-time visualizations of the brain activity maps based on the processed data. Users should be able to interactively explore these maps, zoom in/out, and highlight specific regions of interest.
4. **Analysis Tools**: Provide tools for analyzing the brain activity patterns, such as identifying peaks in activity, calculating average activity levels over time, and comparing activity across different experimental conditions.
5. **Report Generation**: Enable users to generate comprehensive reports summarizing their findings. These reports should include visual representations of the brain maps, statistical analyses, and interpretations of the data.

Your application should be designed with ease-of-use in mind, providing clear instructions and feedback at each step. Additionally, consider adding features like saving/loading sessions, exporting visualizations and reports, and integrating with other neuroimaging software packages.