Interactive tools

ERLab’s interactive applications cover the complete workflow from exploratory slicing to managing large batches of ImageTool windows and specialized tools. These applications are designed to be used alongside Jupyter notebooks, allowing you to combine interactive exploration with reproducible analysis.

If you are moving back and forth between notebook code and the GUI, start with GUI and Python Side by Side. It maps the most common interactive workflows to the corresponding public Python APIs and explains how to round-trip between them.

• ImageTool is the primary GUI for inspecting multidimensional xarray.DataArray objects. It includes navigation with multiple cursors, colormap controls, destructive and non-destructive edits, and quick access to a variety of analysis and plotting routines.

• ImageTool manager sits on top of ImageTool and provides a high-level interface for organizing large numbers of ImageTools, and also keeps track of data provenance and analysis steps for reproducibility.

• Specialized tools such as ktool (momentum conversion), dtool (derivative plotting), and the data explorer integrate with both ImageTool and the manager for specific analysis tasks. These are described in Other interactive tools. Each tool can be launched directly or from the relevant ImageTool menu or context menu. When a tool is opened from an ImageTool in the manager, the manager can keep that tool as a child row of the ImageTool and keep ImageTool windows opened from the tool as child rows of the tool.

• Experienced users who want to build or contribute a new GUI should continue to Authoring interactive tools.

Table of Contents

• ImageTool
  • Key capabilities
  • Launching ImageTool
  • Round-trip
  • Interface tour
  • Working with dimensions and coordinates
  • Slicing, binning, and linking
  • Cursor control and context menus
  • Color and normalization
  • Editing and filtering data
  • Regions of Interest (ROIs)
  • Exporting and settings
  • Keyboard shortcuts
• ImageTool Manager
  • Why use the manager?
  • Starting the manager
  • Opening and replacing ImageTool windows
  • Multiple manager instances
  • Navigating and organizing tools
  • Creating Matplotlib figures
  • Saving and loading
  • Nested windows
  • Choosing where new data opens
  • Automatic updates
  • Results from several ImageTools
  • Operation history
  • Data Explorer and Console
  • Notebook integration
  • Automation APIs
  • Installing as a standalone application
• Figure Composer
  • Opening Figure Composer
  • Layout
  • Recipe steps
  • Toolbar controls
  • Reproducibility
  • Stylesheets and export
• Other interactive tools
  • ktool
  • dtool
  • goldtool
  • ftool
  • restool
  • meshtool
  • Data explorer
  • Periodic table
  • BZPlotter
  • Notebook shortcuts
• Configuration
• Authoring interactive tools
  • Start with the right shape
  • Map capabilities to hooks
  • Build two concrete examples
  • Add manager-facing metadata
  • Support updates from ImageTool data
  • Expose the tool cleanly
  • Test and document the contribution
  • Next steps