aa987adfb9
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>
492 lines
15 KiB
Python
492 lines
15 KiB
Python
"""
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CSV/XLSX 匯入模組
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本模組負責處理時間軸事件的資料匯入。
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支援 CSV 和 XLSX 格式，包含欄位自動對應與格式容錯功能。
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"""
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import csv
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import re
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import List, Dict, Any, Optional, Union
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import logging
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try:
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import pandas as pd
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PANDAS_AVAILABLE = True
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except ImportError:
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PANDAS_AVAILABLE = False
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from .schemas import Event, ImportResult, EventType
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# 設定日誌
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logger = logging.getLogger(__name__)
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class ImporterError(Exception):
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"""匯入器錯誤基礎類別"""
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pass
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class FieldMapper:
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"""
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欄位對應器
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負責將不同的欄位名稱映射到標準欄位。
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支援多語言和不同命名習慣。
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"""
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# 欄位對應字典
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FIELD_MAPPING = {
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'id': ['id', 'ID', '編號', '序號', 'identifier'],
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'title': ['title', 'Title', '標題', '名稱', 'name', 'event'],
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'start': ['start', 'Start', '開始', '開始時間', 'start_time', 'begin', 'time', 'Time', '時間', 'date', 'Date', '日期'],
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'group': ['group', 'Group', '群組', '分類', 'category', 'phase'],
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'description': ['description', 'Description', '描述', '說明', 'detail', 'note'],
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'color': ['color', 'Color', '顏色', 'colour'],
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}
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@classmethod
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def map_fields(cls, headers: List[str]) -> Dict[str, str]:
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"""
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將 CSV/XLSX 的欄位名稱映射到標準欄位
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Args:
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headers: 原始欄位名稱列表
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Returns:
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映射字典 {標準欄位: 原始欄位}
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"""
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mapping = {}
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headers_lower = [h.strip() for h in headers]
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for standard_field, variants in cls.FIELD_MAPPING.items():
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for header in headers_lower:
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if header in variants or header.lower() in [v.lower() for v in variants]:
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# 找到原始 header（保留大小寫）
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original_header = headers[headers_lower.index(header)]
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mapping[standard_field] = original_header
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break
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return mapping
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@classmethod
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def validate_required_fields(cls, mapping: Dict[str, str]) -> List[str]:
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"""
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驗證必要欄位是否存在
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Args:
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mapping: 欄位映射字典
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Returns:
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缺少的必要欄位列表
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"""
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required_fields = ['id', 'title', 'start']
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missing_fields = [f for f in required_fields if f not in mapping]
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return missing_fields
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class DateParser:
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"""
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日期解析器
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支援多種日期格式的容錯解析。
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"""
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# 支援的日期格式列表
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DATE_FORMATS = [
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'%Y-%m-%d %H:%M:%S',
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'%Y/%m/%d %H:%M:%S',
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'%Y-%m-%d',
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'%Y/%m/%d',
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'%d-%m-%Y',
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'%d/%m/%Y',
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'%Y年%m月%d日',
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'%Y年%m月%d日 %H:%M:%S',
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'%Y-%m-%dT%H:%M:%S',
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'%Y-%m-%dT%H:%M:%S.%f',
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]
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@classmethod
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def parse(cls, date_str: str) -> Optional[datetime]:
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"""
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解析日期字串
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Args:
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date_str: 日期字串或 Excel 日期序列號
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Returns:
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datetime 物件，解析失敗則回傳 None
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"""
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if not date_str or (isinstance(date_str, str) and not date_str.strip()):
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return None
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# 如果是數字（Excel 日期序列號），先轉換
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if isinstance(date_str, (int, float)):
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if PANDAS_AVAILABLE:
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try:
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# Excel 日期從 1899-12-30 開始計算
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return pd.to_datetime(date_str, origin='1899-12-30', unit='D')
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except Exception as e:
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logger.warning(f"無法解析 Excel 日期序列號 {date_str}: {str(e)}")
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return None
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else:
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# 沒有 pandas，使用標準庫手動計算
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try:
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excel_epoch = datetime(1899, 12, 30)
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return excel_epoch + timedelta(days=int(date_str))
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except Exception as e:
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logger.warning(f"無法解析 Excel 日期序列號 {date_str}: {str(e)}")
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return None
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date_str = str(date_str).strip()
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# 嘗試各種格式
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for fmt in cls.DATE_FORMATS:
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try:
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return datetime.strptime(date_str, fmt)
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except ValueError:
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continue
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# 嘗試使用 pandas 的智能解析（如果可用）
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if PANDAS_AVAILABLE:
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try:
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return pd.to_datetime(date_str)
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except Exception:
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pass
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logger.warning(f"無法解析日期: {date_str}")
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return None
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class ColorValidator:
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"""
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顏色格式驗證器
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"""
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# HEX 顏色正則表達式
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HEX_PATTERN = re.compile(r'^#[0-9A-Fa-f]{6}$')
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# 預設顏色
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DEFAULT_COLORS = [
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'#3B82F6', # 藍色
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'#10B981', # 綠色
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'#F59E0B', # 橙色
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'#EF4444', # 紅色
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'#8B5CF6', # 紫色
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'#EC4899', # 粉色
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'#14B8A6', # 青色
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'#F97316', # 深橙
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]
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@classmethod
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def validate(cls, color: str, index: int = 0) -> str:
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"""
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驗證顏色格式
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Args:
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color: 顏色字串
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index: 索引（用於選擇預設顏色）
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Returns:
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有效的 HEX 顏色代碼
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"""
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# 確保 index 是整數（防止 pandas 傳入 float）
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index = int(index) if index is not None else 0
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if not color:
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return cls.DEFAULT_COLORS[index % len(cls.DEFAULT_COLORS)]
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color = str(color).strip().upper()
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# 補充 # 符號
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if not color.startswith('#'):
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color = '#' + color
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# 驗證格式
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if cls.HEX_PATTERN.match(color):
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return color
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# 格式無效，使用預設顏色
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logger.warning(f"無效的顏色格式: {color}，使用預設顏色")
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return cls.DEFAULT_COLORS[index % len(cls.DEFAULT_COLORS)]
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class CSVImporter:
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"""
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CSV/XLSX 匯入器
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負責讀取 CSV 或 XLSX 檔案並轉換為 Event 物件列表。
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"""
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def __init__(self):
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self.field_mapper = FieldMapper()
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self.date_parser = DateParser()
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self.color_validator = ColorValidator()
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def import_file(self, file_path: Union[str, Path]) -> ImportResult:
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"""
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匯入 CSV 或 XLSX 檔案
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Args:
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file_path: 檔案路徑
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Returns:
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ImportResult 物件
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"""
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file_path = Path(file_path)
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if not file_path.exists():
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return ImportResult(
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success=False,
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errors=[f"檔案不存在: {file_path}"],
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total_rows=0,
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imported_count=0
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)
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# 根據副檔名選擇處理方式
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if file_path.suffix.lower() == '.csv':
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return self._import_csv(file_path)
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elif file_path.suffix.lower() in ['.xlsx', '.xls']:
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return self._import_xlsx(file_path)
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else:
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return ImportResult(
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success=False,
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errors=[f"不支援的檔案格式: {file_path.suffix}"],
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total_rows=0,
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imported_count=0
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)
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def _import_csv(self, file_path: Path) -> ImportResult:
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"""
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匯入 CSV 檔案
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Args:
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file_path: CSV 檔案路徑
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Returns:
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ImportResult 物件
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"""
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events = []
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errors = []
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try:
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with open(file_path, 'r', encoding='utf-8-sig') as f:
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reader = csv.DictReader(f)
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headers = reader.fieldnames
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if not headers:
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return ImportResult(
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success=False,
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errors=["CSV 檔案為空"],
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total_rows=0,
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imported_count=0
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)
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# 欄位映射
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field_mapping = self.field_mapper.map_fields(headers)
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logger.info(f"CSV 欄位映射結果: {field_mapping}")
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logger.info(f"原始欄位: {headers}")
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missing_fields = self.field_mapper.validate_required_fields(field_mapping)
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if missing_fields:
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logger.error(f"缺少必要欄位: {missing_fields}")
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return ImportResult(
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success=False,
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errors=[f"缺少必要欄位: {', '.join(missing_fields)}"],
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total_rows=0,
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imported_count=0
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)
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# 逐行處理
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row_num = 1
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for row in reader:
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row_num += 1
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try:
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logger.debug(f"處理第 {row_num} 行: {row}")
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event = self._parse_row(row, field_mapping, row_num)
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if event:
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events.append(event)
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logger.debug(f"成功匯入第 {row_num} 行")
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else:
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logger.warning(f"第 {row_num} 行返回 None")
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except Exception as e:
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error_msg = f"第 {row_num} 行錯誤: {str(e)}"
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errors.append(error_msg)
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logger.error(error_msg)
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return ImportResult(
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success=True,
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events=events,
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errors=errors,
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total_rows=int(row_num - 1),
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imported_count=int(len(events))
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)
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except Exception as e:
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logger.error(f"CSV 匯入失敗: {str(e)}")
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return ImportResult(
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success=False,
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errors=[f"CSV 匯入失敗: {str(e)}"],
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total_rows=0,
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imported_count=0
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)
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def _import_xlsx(self, file_path: Path) -> ImportResult:
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"""
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匯入 XLSX 檔案
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Args:
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file_path: XLSX 檔案路徑
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Returns:
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ImportResult 物件
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"""
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if not PANDAS_AVAILABLE:
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return ImportResult(
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success=False,
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errors=["需要安裝 pandas 和 openpyxl 以支援 XLSX 匯入"],
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total_rows=0,
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imported_count=0
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)
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try:
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# 讀取第一個工作表
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df = pd.read_excel(file_path, sheet_name=0)
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if df.empty:
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return ImportResult(
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success=False,
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errors=["XLSX 檔案為空"],
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total_rows=0,
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imported_count=0
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)
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# 轉換為字典列表
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records = df.to_dict('records')
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headers = df.columns.tolist()
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# 欄位映射
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field_mapping = self.field_mapper.map_fields(headers)
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logger.info(f"XLSX 欄位映射結果: {field_mapping}")
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logger.info(f"原始欄位: {headers}")
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missing_fields = self.field_mapper.validate_required_fields(field_mapping)
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if missing_fields:
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logger.error(f"缺少必要欄位: {missing_fields}")
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return ImportResult(
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success=False,
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errors=[f"缺少必要欄位: {', '.join(missing_fields)}"],
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total_rows=0,
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imported_count=0
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)
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# 逐行處理
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events = []
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errors = []
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for idx, row in enumerate(records, start=2): # Excel 從第 2 行開始（第 1 行是標題）
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try:
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event = self._parse_row(row, field_mapping, idx)
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if event:
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events.append(event)
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except Exception as e:
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errors.append(f"第 {idx} 行錯誤: {str(e)}")
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return ImportResult(
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success=True,
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events=events,
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errors=errors,
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total_rows=int(len(records)),
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imported_count=int(len(events))
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)
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except Exception as e:
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logger.error(f"XLSX 匯入失敗: {str(e)}")
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return ImportResult(
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success=False,
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errors=[f"XLSX 匯入失敗: {str(e)}"],
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total_rows=0,
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imported_count=0
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)
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def _parse_row(self, row: Dict[str, Any], field_mapping: Dict[str, str], row_num: int) -> Optional[Event]:
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"""
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解析單行資料
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Args:
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row: 行資料字典
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field_mapping: 欄位映射
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row_num: 行號
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Returns:
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Event 物件或 None
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"""
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# 輔助函數：安全地轉換為字串（處理 NaN、None、float 等）
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def safe_str(value):
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if pd.isna(value) if PANDAS_AVAILABLE else (value is None or value == ''):
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return ''
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# 如果是 float 且接近整數，轉為整數後再轉字串
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if isinstance(value, float):
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if value == int(value):
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return str(int(value))
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return str(value).strip()
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# 提取欄位值
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event_id = safe_str(row.get(field_mapping['id'], ''))
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title = safe_str(row.get(field_mapping['title'], ''))
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start_str = safe_str(row.get(field_mapping['start'], ''))
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group = safe_str(row.get(field_mapping.get('group', ''), '')) or None
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description = safe_str(row.get(field_mapping.get('description', ''), '')) or None
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color = safe_str(row.get(field_mapping.get('color', ''), ''))
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# 驗證必要欄位
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if not event_id or not title:
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raise ValueError("缺少 ID 或標題")
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if not start_str:
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raise ValueError("缺少時間欄位")
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# 解析時間（只有一個時間欄位）
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start = self.date_parser.parse(start_str)
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if not start:
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raise ValueError(f"無效的時間: {start_str}")
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# 將 pandas Timestamp 轉換為標準 datetime
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if PANDAS_AVAILABLE:
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if isinstance(start, pd.Timestamp):
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start = start.to_pydatetime()
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# 驗證顏色
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color = self.color_validator.validate(color, int(row_num))
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if not color:
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color = self.color_validator.DEFAULT_COLORS[0]
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# 所有事件都是時間點類型
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event_type = EventType.POINT
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end = None
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# 建立 Event 物件
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try:
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event = Event(
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id=event_id,
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title=title,
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start=start,
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end=end,
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group=group,
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description=description,
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color=color,
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event_type=event_type
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)
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return event
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except Exception as e:
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logger.error(f"創建 Event 失敗: {str(e)}")
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raise
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# 匯出主要介面
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__all__ = ['CSVImporter', 'ImportResult', 'ImporterError']
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