Jupyter Notebooks for Python SDK
Flywheel provides a collection of Jupyter notebooks that demonstrate various use cases for the Python software development kit (SDK). These notebooks are available in the Flywheel Tutorials repository and cover a wide range of applications, including:
- Data and Image Curation — Notebooks demonstrating how to organize, clean, and prepare imaging data
- Search and Query — Examples of using FlyQL and advanced search capabilities
- Metadata Management — How to access, modify, and work with metadata across the Flywheel hierarchy
- Workflow Automation — Examples of automating data processing pipelines
- Analytics and Reporting — Creating reports and visualizations from Flywheel data
- Machine Learning Integration — Using Flywheel data with ML/AI frameworks
These notebooks provide practical examples and code snippets that you can adapt for your own projects. They are especially useful for researchers and developers who want to integrate Flywheel with their Python-based workflows.
JupyterLab Workspace
Flywheel also offers a JupyterLab Workspace feature that allows you to run Jupyter notebooks directly within the Flywheel platform, with pre-configured access to the Python SDK and your Flywheel data.