strat-gameplay-webapp/backend/app/core/play_resolver.py
Cal Corum 1c32787195 CLAUDE: Refactor game models and modularize terminal client
This commit includes cleanup from model refactoring and terminal client
modularization for better code organization and maintainability.

## Game Models Refactor

**Removed RunnerState class:**
- Eliminated separate RunnerState model (was redundant)
- Replaced runners: List[RunnerState] with direct base references:
  - on_first: Optional[LineupPlayerState]
  - on_second: Optional[LineupPlayerState]
  - on_third: Optional[LineupPlayerState]
- Updated helper methods:
  - get_runner_at_base() now returns LineupPlayerState directly
  - get_all_runners() returns List[Tuple[int, LineupPlayerState]]
  - is_runner_on_X() simplified to direct None checks

**Benefits:**
- Matches database structure (plays table has on_first_id, etc.)
- Simpler state management (direct references vs list management)
- Better type safety (LineupPlayerState vs generic runner)
- Easier to work with in game engine logic

**Updated files:**
- app/models/game_models.py - Removed RunnerState, updated GameState
- app/core/play_resolver.py - Use get_all_runners() instead of state.runners
- app/core/validators.py - Updated runner access patterns
- tests/unit/models/test_game_models.py - Updated test assertions
- tests/unit/core/test_play_resolver.py - Updated test data
- tests/unit/core/test_validators.py - Updated test data

## Terminal Client Refactor

**Modularization (DRY principle):**
Created separate modules for better code organization:

1. **terminal_client/commands.py** (10,243 bytes)
   - Shared command functions for game operations
   - Used by both CLI (main.py) and REPL (repl.py)
   - Functions: submit_defensive_decision, submit_offensive_decision,
     resolve_play, quick_play_sequence
   - Single source of truth for command logic

2. **terminal_client/arg_parser.py** (7,280 bytes)
   - Centralized argument parsing and validation
   - Handles defensive/offensive decision arguments
   - Validates formats (alignment, depths, hold runners, steal attempts)

3. **terminal_client/completions.py** (10,357 bytes)
   - TAB completion support for REPL mode
   - Command completions, option completions, dynamic completions
   - Game ID completions, defensive/offensive option suggestions

4. **terminal_client/help_text.py** (10,839 bytes)
   - Centralized help text and command documentation
   - Detailed command descriptions
   - Usage examples for all commands

**Updated main modules:**
- terminal_client/main.py - Simplified by using shared commands module
- terminal_client/repl.py - Cleaner with shared functions and completions

**Benefits:**
- DRY: Behavior consistent between CLI and REPL modes
- Maintainability: Changes in one place affect both interfaces
- Testability: Can test commands module independently
- Organization: Clear separation of concerns

## Documentation

**New files:**
- app/models/visual_model_relationships.md
  - Visual documentation of model relationships
  - Helps understand data flow between models
- terminal_client/update_docs/ (6 phase documentation files)
  - Phased documentation for terminal client evolution
  - Historical context for implementation decisions

## Tests

**New test files:**
- tests/unit/terminal_client/__init__.py
- tests/unit/terminal_client/test_arg_parser.py
- tests/unit/terminal_client/test_commands.py
- tests/unit/terminal_client/test_completions.py
- tests/unit/terminal_client/test_help_text.py

**Updated tests:**
- Integration tests updated for new runner model
- Unit tests updated for model changes
- All tests passing with new structure

## Summary

-  Simplified game state model (removed RunnerState)
-  Better alignment with database structure
-  Modularized terminal client (DRY principle)
-  Shared command logic between CLI and REPL
-  Comprehensive test coverage
-  Improved documentation

Total changes: 26 files modified/created

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-28 14:16:38 -05:00

367 lines
11 KiB
Python

"""
Play Resolver - Resolves play outcomes based on dice rolls.
Uses our advanced dice system with AbRoll for at-bat resolution.
Simplified result charts for Phase 2 MVP.
Author: Claude
Date: 2025-10-24
"""
import logging
from dataclasses import dataclass
from typing import Optional, List
from enum import Enum
from app.core.dice import dice_system
from app.core.roll_types import AbRoll
from app.models.game_models import GameState, DefensiveDecision, OffensiveDecision
logger = logging.getLogger(f'{__name__}.PlayResolver')
class PlayOutcome(str, Enum):
"""Possible play outcomes"""
# Outs
STRIKEOUT = "strikeout"
GROUNDOUT = "groundout"
FLYOUT = "flyout"
LINEOUT = "lineout"
DOUBLE_PLAY = "double_play"
# Hits
SINGLE = "single"
DOUBLE = "double"
TRIPLE = "triple"
HOMERUN = "homerun"
# Other
WALK = "walk"
HIT_BY_PITCH = "hbp"
ERROR = "error"
WILD_PITCH = "wild_pitch"
PASSED_BALL = "passed_ball"
@dataclass
class PlayResult:
"""Result of a resolved play"""
outcome: PlayOutcome
outs_recorded: int
runs_scored: int
batter_result: Optional[int] # None = out, 1-4 = base reached
runners_advanced: List[tuple[int, int]] # [(from_base, to_base), ...]
description: str
ab_roll: AbRoll # Full at-bat roll for audit trail
# Statistics
is_hit: bool = False
is_out: bool = False
is_walk: bool = False
class SimplifiedResultChart:
"""
Simplified SBA result chart for Phase 2
Real implementation will load from config files and consider:
- Batter card stats
- Pitcher card stats
- Defensive alignment
- Offensive approach
This provides basic outcomes for MVP testing.
"""
@staticmethod
def get_outcome(ab_roll: AbRoll) -> PlayOutcome:
"""
Map AbRoll to outcome (simplified)
Uses the check_d20 value for outcome determination.
Checks for wild pitch/passed ball first.
"""
# Check for wild pitch/passed ball
if ab_roll.check_wild_pitch:
# check_d20 == 1, use resolution_d20 to confirm
if ab_roll.resolution_d20 <= 10: # 50% chance it actually happens
return PlayOutcome.WILD_PITCH
# Otherwise treat as ball/foul
return PlayOutcome.STRIKEOUT # Simplified
if ab_roll.check_passed_ball:
# check_d20 == 2, use resolution_d20 to confirm
if ab_roll.resolution_d20 <= 10: # 50% chance
return PlayOutcome.PASSED_BALL
# Otherwise treat as ball/foul
return PlayOutcome.STRIKEOUT # Simplified
# Normal at-bat resolution using check_d20
roll = ab_roll.check_d20
if roll <= 5:
return PlayOutcome.STRIKEOUT
elif roll <= 10:
return PlayOutcome.GROUNDOUT
elif roll <= 13:
return PlayOutcome.FLYOUT
elif roll <= 15:
return PlayOutcome.WALK
elif roll <= 17:
return PlayOutcome.SINGLE
elif roll <= 18:
return PlayOutcome.DOUBLE
elif roll == 19:
return PlayOutcome.TRIPLE
else: # 20
return PlayOutcome.HOMERUN
class PlayResolver:
"""Resolves play outcomes based on dice rolls and game state"""
def __init__(self):
self.result_chart = SimplifiedResultChart()
def resolve_play(
self,
state: GameState,
defensive_decision: DefensiveDecision,
offensive_decision: OffensiveDecision
) -> PlayResult:
"""
Resolve a complete play
Args:
state: Current game state
defensive_decision: Defensive team's choices
offensive_decision: Offensive team's choices
Returns:
PlayResult with complete outcome
"""
logger.info(f"Resolving play - Inning {state.inning} {state.half}, {state.outs} outs")
# Roll dice using our advanced AbRoll system
ab_roll = dice_system.roll_ab(
league_id=state.league_id,
game_id=state.game_id
)
logger.info(f"AB Roll: {ab_roll}")
# Get base outcome from chart
outcome = self.result_chart.get_outcome(ab_roll)
logger.info(f"Base outcome: {outcome}")
# Apply decisions (simplified for Phase 2)
# TODO: Implement full decision logic in Phase 3
# Resolve outcome details
result = self._resolve_outcome(outcome, state, ab_roll)
logger.info(f"Play result: {result.description}")
return result
def _resolve_outcome(
self,
outcome: PlayOutcome,
state: GameState,
ab_roll: AbRoll
) -> PlayResult:
"""Resolve specific outcome type"""
if outcome == PlayOutcome.STRIKEOUT:
return PlayResult(
outcome=outcome,
outs_recorded=1,
runs_scored=0,
batter_result=None,
runners_advanced=[],
description="Strikeout looking",
ab_roll=ab_roll,
is_out=True
)
elif outcome == PlayOutcome.GROUNDOUT:
# Simple groundout - runners don't advance
return PlayResult(
outcome=outcome,
outs_recorded=1,
runs_scored=0,
batter_result=None,
runners_advanced=[],
description="Groundout to shortstop",
ab_roll=ab_roll,
is_out=True
)
elif outcome == PlayOutcome.FLYOUT:
return PlayResult(
outcome=outcome,
outs_recorded=1,
runs_scored=0,
batter_result=None,
runners_advanced=[],
description="Flyout to center field",
ab_roll=ab_roll,
is_out=True
)
elif outcome == PlayOutcome.WALK:
# Walk - batter to first, runners advance if forced
runners_advanced = self._advance_on_walk(state)
runs_scored = sum(1 for (from_base, to_base) in runners_advanced if to_base == 4)
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=1,
runners_advanced=runners_advanced,
description="Walk",
ab_roll=ab_roll,
is_walk=True
)
elif outcome == PlayOutcome.SINGLE:
# Single - batter to first, runners advance 1-2 bases
runners_advanced = self._advance_on_single(state)
runs_scored = sum(1 for (from_base, to_base) in runners_advanced if to_base == 4)
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=1,
runners_advanced=runners_advanced,
description="Single to left field",
ab_roll=ab_roll,
is_hit=True
)
elif outcome == PlayOutcome.DOUBLE:
runners_advanced = self._advance_on_double(state)
runs_scored = sum(1 for (from_base, to_base) in runners_advanced if to_base == 4)
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=2,
runners_advanced=runners_advanced,
description="Double to right-center",
ab_roll=ab_roll,
is_hit=True
)
elif outcome == PlayOutcome.TRIPLE:
# All runners score
runners_advanced = [(base, 4) for base, _ in state.get_all_runners()]
runs_scored = len(runners_advanced)
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=3,
runners_advanced=runners_advanced,
description="Triple to right-center gap",
ab_roll=ab_roll,
is_hit=True
)
elif outcome == PlayOutcome.HOMERUN:
# Everyone scores
runners_advanced = [(base, 4) for base, _ in state.get_all_runners()]
runs_scored = len(runners_advanced) + 1
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=4,
runners_advanced=runners_advanced,
description="Home run to left field",
ab_roll=ab_roll,
is_hit=True
)
elif outcome == PlayOutcome.WILD_PITCH:
# Runners advance one base
runners_advanced = [(base, base + 1) for base, _ in state.get_all_runners()]
runs_scored = sum(1 for (from_base, to_base) in runners_advanced if to_base == 4)
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=None, # Batter stays at plate
runners_advanced=runners_advanced,
description="Wild pitch - runners advance",
ab_roll=ab_roll
)
elif outcome == PlayOutcome.PASSED_BALL:
# Runners advance one base
runners_advanced = [(base, base + 1) for base, _ in state.get_all_runners()]
runs_scored = sum(1 for (from_base, to_base) in runners_advanced if to_base == 4)
return PlayResult(
outcome=outcome,
outs_recorded=0,
runs_scored=runs_scored,
batter_result=None, # Batter stays at plate
runners_advanced=runners_advanced,
description="Passed ball - runners advance",
ab_roll=ab_roll
)
else:
raise ValueError(f"Unhandled outcome: {outcome}")
def _advance_on_walk(self, state: GameState) -> List[tuple[int, int]]:
"""Calculate runner advancement on walk"""
advances = []
# Only forced runners advance
if state.on_first:
# First occupied - check second
if state.on_second:
# Bases loaded scenario
if state.on_third:
# Bases loaded - force runner home
advances.append((3, 4))
advances.append((2, 3))
advances.append((1, 2))
return advances
def _advance_on_single(self, state: GameState) -> List[tuple[int, int]]:
"""Calculate runner advancement on single (simplified)"""
advances = []
if state.on_third:
# Runner on third scores
advances.append((3, 4))
if state.on_second:
# Runner on second scores (simplified - usually would)
advances.append((2, 4))
if state.on_first:
# Runner on first to third (simplified)
advances.append((1, 3))
return advances
def _advance_on_double(self, state: GameState) -> List[tuple[int, int]]:
"""Calculate runner advancement on double"""
advances = []
# All runners score on double (simplified)
for base, _ in state.get_all_runners():
advances.append((base, 4))
return advances
# Singleton instance
play_resolver = PlayResolver()