9fc6b83bba
- Extract WebSocket handlers from main.py into handlers.py - Add V3 feature docs (dealer rotation, dealing animation, round end reveal, column pair celebration, final turn urgency, opponent thinking, score tallying, card hover/selection, knock early drama, column pair indicator, swap animation improvements, draw source distinction, card value tooltips, active rules context, discard pile history, realistic card sounds) - Add V3 refactoring docs (ai.py, main.py/game.py, misc improvements) - Add installation guide with Docker, systemd, and nginx setup - Add helper scripts (install.sh, dev-server.sh, docker-build.sh) - Add animation flow diagrams documentation - Add test files for handlers, rooms, and V3 features - Add e2e test specs for V3 features - Update README with complete project structure and current tech stack - Update CLAUDE.md with full architecture tree and server layer descriptions - Update .env.example to reflect PostgreSQL (remove SQLite references) - Update .gitignore to exclude virtualenv files, .claude/, and .db files - Remove tracked virtualenv files (bin/, lib64, pyvenv.cfg) - Remove obsolete game_log.py (SQLite) and games.db Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
43 lines
1.7 KiB
Markdown
43 lines
1.7 KiB
Markdown
# Remaining Refactor Tasks
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Leftover items from the v3 refactor plans that are functional but could benefit from further cleanup.
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---
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## R1. Decompose `calculate_swap_score()` (from Plan 2, Step 4)
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**File:** `server/ai.py` (~236 lines)
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Scores a single position for swapping. Still long with inline pair calculations, point gain logic, reveal bonuses, and comeback bonuses. Could extract:
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- `_pair_improvement(player, position, new_card, options)` — pair-related benefit of swapping into a position
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- `_standings_pressure(player, game)` — how much standings position should affect decisions (shared with `should_take_discard`)
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**Validation:** `python server/simulate.py 500` before and after — stats should match within normal variance.
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---
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## R2. Decompose `should_take_discard()` (from Plan 2, Step 5)
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**File:** `server/ai.py` (~148 lines)
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Decides whether to take from discard pile. Contains a nested `has_good_swap_option()` helper. After R1's extracted utilities exist, this should shrink since `project_score()` and `known_score()` handle the repeated estimation logic.
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**Validation:** Same simulation approach as R1.
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---
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## R3. New Test Files (from Plan 3, M6)
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After Plans 1 and 2, the extracted handlers and AI sub-functions are much easier to unit test. Add:
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- **`server/test_handlers.py`** — Test each message handler with mock WebSocket/Room
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- **`server/test_ai_decisions.py`** — Test individual AI sub-functions (go-out logic, denial, etc.)
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- **`server/test_room.py`** — Test Room/RoomManager CRUD operations
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---
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## Priority
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R1 and R2 are pure structural refactors — no behavior changes, low risk, but also low urgency since the code works fine. R3 adds safety nets for future changes.
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