Replace inefficient list comprehensions with for loops

[ptone]

Replace inefficient list comprehensions with for loops

Summary

This PR improves memory efficiency in the BirdFish lighting control framework by replacing list comprehensions used only for side effects with regular for loops. The codebase had 12 instances where list comprehensions were creating temporary list objects that were immediately discarded, consuming unnecessary memory and CPU cycles in performance-critical rendering code.

Changes:

• birdfish/lights.py: Converted 9 list comprehensions to for loops in BaseLightElement.update(), BaseLightElement.trigger(), LightGroup.trigger(), LightGroup.set_intensity(), Chase.render(), and Spawner.spawn()
• birdfish/effects.py: Converted 2 list comprehensions to for loops in Twinkle.update() and Twinkle._off_trigger()
• birdfish/output/base.py: Converted 1 list comprehension to for loop in BaseNetwork.update_data()
• Added efficiency_report.md documenting this and 5 other potential efficiency improvements

All changes are purely mechanical transformations from [expr for x in items] to for x in items: expr with no logic changes.

Review & Testing Checklist for Human

• [ ] Run existing test suite - I couldn't run pytest in the environment, so runtime behavior is unverified
• [ ] Test live lighting control - Verify the system still works correctly at 40 FPS with actual hardware/simulation
• [ ] Spot check the transformations - Review a few of the changes to confirm they're semantically equivalent (e.g., lines 181-182, 355-358, 583-587 in lights.py)

Test Plan

1. Run python -m pytest tests/ to verify existing tests pass
2. If possible, test with actual lighting hardware or simulation to ensure rendering still works correctly
3. Monitor memory usage to confirm the optimization has the expected positive impact

Notes

• All 12 transformations follow the same pattern and should be safe, but untested changes in performance-critical code warrant careful review
• The efficiency report identifies 5 additional optimization opportunities for future consideration
• Session: https://app.devin.ai/sessions/e0ad765cdb26445bb7a1238dadd30d17
• Requested by: Preston Holmes (@ptone)