[jgostick]

Replace the removed snow2 keyword with parallel_kw=None so the AI-network tutorial can extract its network using the current API. Adds a regression test for the tutorial's image and extraction cells.

Addresses #1224. All 16 focused tests pass. Extraction and AI inference were also verified during validation.

The final comparison remains blocked by a separate mismatch: 745 current throats versus 744 saved DNS values. Reference data and saved outputs are unchanged; this does not establish end-to-end validation.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 73.0%. Comparing base (372b40a) to head (dd114df).

Additional details and impacted files

@@          Coverage Diff          @@
##             dev   #1248   +/-   ##
=====================================
  Coverage   73.0%   73.0%           
=====================================
  Files         48      48           
  Lines       6961    6961           
=====================================
  Hits        5087    5087           
  Misses      1874    1874           

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[Copilot]

🔵 Needs a closer look

End-to-end notebook validation remains unresolved because current extraction yields 745 throats versus 744 in the saved outputs, requiring human judgment on whether to regenerate or retain those artifacts.

No open issues 🎉

What changed in this PR

Updates the AI-network tutorial for the current snow2 API and adds focused regression coverage for image generation and network extraction.

Changes:

• Replaces the obsolete parallelization argument with parallel_kw=None.
• Tags the relevant notebook cells for targeted testing.
• Adds assertions for image dimensions, pore data, and throat connectivity.
• The notebook still contains stale 744-throat output while current extraction produces 745 throats.

This pull request is too complex or risky for automated approval.

File	Summary
examples/​networks/​tutorials/​using_diffusive_size_factor_AI_with_snow.ipynb	Updates the snow2 call and marks the image and extraction cells for regression testing.
test/​unit/​test_ai_snow_example.py	Adds regression coverage for the tutorial’s image generation and extracted network structure.

_{Review effort: Lite}