Add tutorial notebook: XRT intensity calculation and ROI analysis for DEM preparation

[joyvelasquez]

Feature description

Add a new Jupyter notebook example to the XRTpy documentation that guides users through a complete workflow for XRT intensity analysis, from data download through ROI-based intensity calculation, error estimation, understand XRT meta data - building toward DEM input preparation.

The notebook would cover:

• Downloading XRT data (referencing existing download notebook)
• Selecting a Point of Interest (POI) for context/visualization
• Defining a Region of Interest (ROI) and understanding what the selection parameters mean
• Calculating intensities over the ROI
• Extracting relevant metadata (e.g., exposure time)
• Converting to DN/pix/sec for quick intensity error estimation
• Describing how the output connects to downstream DEM analysis

Motivation

Currently, XRTpy documentation covers data download but does not walk users through the full analysis pipeline needed before performing a DEM.

• How to spatially select data (POI vs. ROI distinction)
• What the intensity calculation outputs represent physically (mention XRT CCD saturation limit)
• How to propagate or estimate intensity errors from exposure metadata
• How the calculated intensities map to DEM inputs

This notebook bridges the gap between raw data access and DEM analysis, and would
serve as a natural companion to the forthcoming DEM tutorial!!!

Implementation strategy

No response

Additional context

• Planned as a post-DEM-release follow-up (tracked separately)
• Companion to PR XRT DEM Iterative Solver Module #367 (DEM solver) - this notebook would demonstrate how to prepare inputs for that solver or Kernel (might change solver to match "Kernel" as in SunKit-DEM