🤖 AI Analysis

Final verdict: SAFE

The package I2MC v2.2.8 shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The metadata risk is slightly elevated due to the maintainer's limited package history.

No network calls detected
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, suggesting low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other red flags were raised.

🔬 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: humlab.lu.se

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository dcnieho/I2MC_Python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Diederick Niehorster" 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 I2MC

Create a Python-based eye-tracking data analysis tool named 'EyeAnalyzer' that leverages the 'I2MC' package to classify fixations from noisy eye-tracking data. This tool will be designed to help researchers and developers analyze eye movement patterns more accurately, thereby enhancing our understanding of visual attention and cognitive processes. Here are the key steps and features to include in your project:

1. **Data Importation**: Design a feature that allows users to import eye-tracking data files (CSV or JSON format). Ensure that the data includes timestamps, x-y coordinates, and pupil size measurements.

2. **Preprocessing Module**: Implement a preprocessing module that cleans the imported data by filtering out noise using techniques like smoothing and outlier removal. This step is crucial as it prepares the data for accurate fixation classification.

3. **Fixation Classification**: Utilize the 'I2MC' package to classify fixations from the preprocessed data. Ensure that you explore different parameters within the I2MC library to find the optimal settings for your dataset. Document any adjustments made and their impact on the results.

4. **Visualization Tool**: Develop a visualization component that plots the eye movements over time and highlights the classified fixations. This could be achieved using libraries such as Matplotlib or Seaborn.

5. **Report Generation**: Create a report generation feature that summarizes the findings from the analysis. This report should include key metrics such as the total number of fixations, average fixation duration, and areas of highest visual attention.

6. **User Interface**: Optionally, consider building a simple GUI using Tkinter or PyQt that allows users to interact with the tool more intuitively. This interface should allow users to easily load data, view the analysis process, and generate reports.

7. **Documentation and Testing**: Write comprehensive documentation explaining how to use each feature of 'EyeAnalyzer'. Also, implement unit tests to ensure that the tool functions correctly across various types of eye-tracking datasets.

By completing these steps, you'll have developed a robust tool that not only utilizes the 'I2MC' package effectively but also provides valuable insights into eye-tracking data.