95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
import copy
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import random
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from typing import Dict, List, Optional
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from sgf_parser import SGFNode
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class GameNode(SGFNode):
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"""Represents a single game node, with one or more moves and placements."""
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def __init__(self, parent=None, properties=None, move=None):
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super().__init__(parent=parent, properties=properties, move=move)
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self.analysis = None
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self.ownership = None
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self.auto_undid = False
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self.move_number = 0
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self.undo_threshold = random.random() # for fractional undos, store the random threshold in the move itself for consistency
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@property
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def sgf_properties(self):
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best_sq = []
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properties = copy.copy(super().sgf_properties)
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if best_sq and "SQ" not in properties:
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properties["SQ"] = best_sq
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comment = self.comment(sgf=True)
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if comment:
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properties["C"] = [properties.get("C", "") + comment]
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return properties
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# various analysis functions
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def analyze(self, engine, priority=0):
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engine.request_analysis(self, lambda result: self.set_analysis(result), priority=priority)
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def set_analysis(self, analysis_blob):
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self.analysis = analysis_blob["moveInfos"] # TODO: fix when rootInfos comes in
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self.ownership = analysis_blob["ownership"]
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@property
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def analysis_ready(self):
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return self.analysis is not None
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def format_score(self, score=None):
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score = score or self.score
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return f"{'B' if score >= 0 else 'W'}+{abs(score):.1f}"
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def format_win_rate(self, win_rate=None):
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win_rate = win_rate or self.analysis[0]["winrate"]
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return f"{'B' if win_rate > 0.5 else 'W'} {max(win_rate,1-win_rate):.1%}"
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def comment(self, sgf=False, eval=False, hints=False):
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single_move = self.single_move
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if not self.parent or not single_move: # root
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return ""
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text = f"Move {self.depth}: {single_move.player} {single_move.gtp()}\n"
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if self.analysis_ready:
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score = self.score
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if sgf:
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text += f"Score: {self.format_score(score)}\n"
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if self.parent and self.parent.analysis_ready:
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if sgf or hints and self.parent.analysis[0]["move"] != single_move.gtp():
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text += f"Top move was {self.parent.analysis[0]['move']} ({self.format_score(self.parent.analysis[0]['scoreLead'])})\n"
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points_lost = self.points_lost
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if sgf and points_lost > 0.5:
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text += f"Estimated point loss: {points_lost:.1f}\n"
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else:
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text = "No analysis available" if sgf else "Analyzing move..."
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return text
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@property
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def points_lost(self) -> Optional[float]:
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single_move = self.single_move
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if single_move and self.parent and self.analysis_ready and self.parent.analysis_ready:
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parent_score = self.parent.score
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score = self.score
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return self.player_sign(single_move.player) * (parent_score - score)
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@property
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def score(self):
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return self.analysis[0]["scoreLead"] # TODO: update for rootInfo
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@staticmethod
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def player_sign(player):
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return {"B": 1, "W": -1, None: 0}[player]
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@property
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def ai_moves(self) -> List[Dict]:
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if not self.analysis_ready:
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return []
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analysis = copy.copy(self.analysis) # not deep, so eval is saved, but avoids race conditions
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for d in analysis:
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d["pointsLost"] = self.player_sign(self.next_player) * (analysis[0]["scoreLead"] - d["scoreLead"]) # TODO: update for rootInfo
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return analysis
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