accelerometry-annotator

v3.5.3 safe
3.0
Low Risk

Web-based tool for visualizing and annotating accelerometry data from physical performance assessments.

🤖 AI Analysis

Final verdict: SAFE

The package metadata risk is low with no apparent red flags. The license and version badges are correctly formatted, indicating active maintenance and transparency.

  • Sparse author information
  • No red flags detected
Per-check LLM notes
  • Metadata: The author's information is sparse, but there are no other red flags.

🔬 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: uchicago.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository TavoloPerUno/py_visualize_accelerometry 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 accelerometry-annotator 
Your task is to develop a fully functional mini-application that leverages the 'accelerometry-annotator' package to visualize and annotate accelerometry data from physical performance assessments. This application will serve as a powerful tool for athletes, coaches, and researchers to analyze and understand movement patterns and performance metrics.

The application should include the following core functionalities:
1. Data Import: Users should be able to upload their own accelerometry datasets in CSV format. The application will parse these files and display them in a user-friendly manner.
2. Visualization: Implement real-time visualization of the imported data, allowing users to see acceleration values over time. This could include graphs showing X, Y, and Z axis movements separately or combined.
3. Annotation Tool: Provide an intuitive interface for users to manually annotate specific segments of the data. Annotations could include labels such as 'rest', 'walking', 'running', etc., and users should be able to add notes or descriptions for each annotation.
4. Export Functionality: Allow users to export annotated data back into CSV format, including the original data along with the added annotations and notes.
5. Customizable Interface: Offer options for users to customize the appearance of the graphs and the layout of the annotation tool according to their preferences.
6. Data Analysis Tools: Integrate basic statistical analysis tools to provide insights into the annotated data, such as calculating average speed, distance traveled, or identifying peak activity periods.

To achieve these goals, you'll need to utilize the 'accelerometry-annotator' package effectively. Start by exploring its documentation and understanding how it handles data import/export, visualization, and annotation processes. Consider building your application using Flask or Django for the backend, and integrate JavaScript libraries like Plotly or D3.js for dynamic graphing capabilities.

Remember to focus on usability and ensure that the application is responsive and accessible across different devices.