後端代碼清理：移除冗餘註解和調試代碼
清理內容： - 移除所有開發元資訊（Author, Version, DocID, Rationale等） - 刪除註解掉的代碼片段（力導向演算法等24行） - 移除調試用的 logger.debug 語句 - 簡化冗餘的內聯註解（emoji、"重要"等標註） - 刪除 TDD 文件引用 清理檔案： - backend/main.py - 移除調試日誌和元資訊 - backend/importer.py - 移除詳細類型檢查調試 - backend/export.py - 簡化 docstring - backend/schemas.py - 移除元資訊 - backend/renderer.py - 移除 TDD 引用 - backend/renderer_timeline.py - 移除註解代碼和冗餘註解 - backend/path_planner.py - 簡化策略註解 保留內容： - 所有函數的 docstring（功能說明、參數、返回值） - 必要的業務邏輯註解 - 簡潔的模組功能說明 效果： - 刪除約 100+ 行冗餘註解 - 代碼更加簡潔專業 - 功能完整性 100% 保留 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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beabigegg
@@ -3,9 +3,6 @@
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使用BFS算法在網格化的繪圖區域中為連接線尋找最佳路徑，
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完全避開標籤障礙物。
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Author: AI Agent
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Version: 1.0.0
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"""
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import logging
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@@ -151,49 +148,35 @@ class GridMap:
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path_points: 路徑點列表 [(datetime, y), ...]
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width_expansion: 寬度擴展倍數
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策略：
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1. 標記所有線段（包括起點線段）
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2. 但是起點線段只標記離開時間軸的垂直部分
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3. 時間軸 y=0 本身不標記，避免阻擋其他起點
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"""
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if len(path_points) < 2:
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return
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# 標記所有線段
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for i in range(len(path_points) - 1):
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dt1, y1 = path_points[i]
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dt2, y2 = path_points[i + 1]
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# 如果是從時間軸（y=0）出發的第一段線段
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if i == 0 and abs(y1) < 0.1:
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# 只標記離開時間軸的部分（從 y=0.2 開始）
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# 避免阻擋其他事件的起點
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if abs(y2) > 0.2: # 確保終點不在時間軸上
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# 使用線性插值找到 y=0.2 的點
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if abs(y2) > 0.2:
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if abs(y2 - y1) > 0.01:
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t = (0.2 - y1) / (y2 - y1) if y2 > y1 else (-0.2 - y1) / (y2 - y1)
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if 0 < t < 1:
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# 計算 y=0.2 時的 datetime
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seconds_offset = (dt2 - dt1).total_seconds() * t
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dt_cutoff = dt1 + timedelta(seconds=seconds_offset)
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y_cutoff = 0.2 if y2 > 0 else -0.2
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# 只標記從 cutoff 點到終點的部分
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col1 = self.datetime_to_grid_x(dt_cutoff)
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row1 = self.y_to_grid_y(y_cutoff)
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col2 = self.datetime_to_grid_x(dt2)
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row2 = self.y_to_grid_y(y2)
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self._mark_line(row1, col1, row2, col2, int(width_expansion))
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else:
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# t 不在範圍內，標記整段
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col1 = self.datetime_to_grid_x(dt1)
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row1 = self.y_to_grid_y(y1)
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col2 = self.datetime_to_grid_x(dt2)
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row2 = self.y_to_grid_y(y2)
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self._mark_line(row1, col1, row2, col2, int(width_expansion))
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# 如果終點也在時間軸上，不標記
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else:
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# 非起點線段，全部標記
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col1 = self.datetime_to_grid_x(dt1)
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row1 = self.y_to_grid_y(y1)
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col2 = self.datetime_to_grid_x(dt2)
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@@ -308,15 +291,10 @@ def find_path_bfs(
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end_row: int,
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end_col: int,
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grid_map: GridMap,
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direction_constraint: str = "up" # "up" or "down"
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direction_constraint: str = "up"
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) -> Optional[List[Tuple[int, int]]]:
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"""
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使用BFS尋找路徑（改進版：優先離開時間軸）
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策略：
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1. 優先垂直移動（離開時間軸）
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2. 遇到障礙物才水平繞行
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3. 使用優先隊列，根據與時間軸的距離排序
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使用BFS尋找路徑
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Args:
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start_row, start_col: 起點網格座標
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@@ -338,23 +316,16 @@ def find_path_bfs(
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import heapq
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# 計算時間軸的Y座標（row）
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timeline_row = grid_map.y_to_grid_y(0)
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# 優先隊列：(優先度, row, col, path)
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# 優先度 = 與時間軸的距離（越遠越好）+ 路徑長度（越短越好）
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start_priority = 0
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heap = [(start_priority, start_row, start_col, [(start_row, start_col)])]
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visited = set()
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visited.add((start_row, start_col))
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# 方向優先順序（垂直優先於水平）
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if direction_constraint == "up":
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# 優先往上，然後才左右
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directions = [(-1, 0), (0, 1), (0, -1)] # 上、右、左
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else: # "down"
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# 優先往下，然後才左右
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directions = [(1, 0), (0, 1), (0, -1)] # 下、右、左
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directions = [(-1, 0), (0, 1), (0, -1)]
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else:
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directions = [(1, 0), (0, 1), (0, -1)]
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max_iterations = grid_map.grid_rows * grid_map.grid_cols * 2
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iterations = 0
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@@ -363,42 +334,30 @@ def find_path_bfs(
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iterations += 1
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_, current_row, current_col, path = heapq.heappop(heap)
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# 到達終點
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if current_row == end_row and current_col == end_col:
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logger.info(f"找到路徑，長度: {len(path)}，迭代: {iterations}")
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return path
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# 探索鄰居（按優先順序）
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for d_row, d_col in directions:
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next_row = current_row + d_row
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next_col = current_col + d_col
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# 檢查是否可通行
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if (next_row, next_col) in visited:
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continue
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if not grid_map.is_free(next_row, next_col):
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continue
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# 計算優先度
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# 1. 與時間軸的距離（主要因素）
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distance_from_timeline = abs(next_row - timeline_row)
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# 2. 曼哈頓距離到終點（次要因素）
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manhattan_to_goal = abs(next_row - end_row) + abs(next_col - end_col)
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# 3. 路徑長度（避免繞太遠）
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path_length = len(path)
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# 綜合優先度：離時間軸越遠越好，離目標越近越好
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# 權重調整：優先離開時間軸
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priority = (
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-distance_from_timeline * 100 + # 負數因為要最大化
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-distance_from_timeline * 100 +
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manhattan_to_goal * 10 +
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path_length
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)
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# 添加到優先隊列
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visited.add((next_row, next_col))
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new_path = path + [(next_row, next_col)]
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heapq.heappush(heap, (priority, next_row, next_col, new_path))
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