564 lines
22 KiB
Python
564 lines
22 KiB
Python
import os
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import re
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import sys
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import time
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import pandas as pd
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import matplotlib.pyplot as plt
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from datetime import datetime
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from matplotlib.lines import Line2D
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from typing import Optional, Tuple, List, Dict, Any, Union
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from pathlib import Path
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import numpy as np
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from colorama import Fore, Style, init
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# 避免 SettingWithCopy 警告影响输出可读性
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pd.options.mode.chained_assignment = None
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# 设置中文字体支持
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plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'Arial Unicode MS', 'Microsoft YaHei']
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plt.rcParams['axes.unicode_minus'] = False
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class TestReportScatterPlotter:
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def __init__(self):
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self.file_path: Optional[str] = None
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self.df: Optional[pd.DataFrame] = None
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self.output_dir: Optional[str] = None
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self.required_columns = ["Test Name New", "SN", "Measurement", "Test Time"]
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self.col_lower: Optional[str] = None
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self.col_upper: Optional[str] = None
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# 缓存处理过的数据
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self._processed_data_cache: Dict[str, Any] = {}
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def _print_stage(self, msg: str) -> None:
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"""统一的阶段信息输出"""
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print(f"\n{'=' * 30}\n{msg}\n{'=' * 30}")
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def _print_progress(self, current: int, total: int, prefix: str = "进度") -> None:
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"""改进的进度条显示"""
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if total <= 0:
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return
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percent = (current / total) * 100
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bar_len = 30
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filled = int(bar_len * current / total)
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bar = "█" * filled + "-" * (bar_len - filled)
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sys.stdout.write(f"\r{prefix}: [{bar}] {current}/{total} ({percent:.1f}%)")
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sys.stdout.flush()
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if current == total:
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print() # 换行
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def get_file_path(self) -> None:
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"""改进的文件路径获取，支持路径补全"""
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self._print_stage("输入文件路径")
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while True:
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print(f"{Fore.WHITE}请输入测试报告文件路径(.xlsx): ")
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file_path = input("> ").strip()
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# 尝试路径补全和验证
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if not file_path:
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continue
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path_obj = Path(file_path)
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if path_obj.exists():
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self.file_path = str(path_obj.resolve())
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print(f"已选择文件: {self.file_path}")
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break
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else:
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print(f"文件不存在: {file_path}，请重新输入")
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def _find_column_case_insensitive(self, candidates: List[str]) -> Optional[str]:
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"""优化的大小写不敏感列查找"""
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if self.df is None:
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return None
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columns_lower = {col.lower().strip(): col for col in self.df.columns}
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for candidate in candidates:
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key = candidate.lower().strip()
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if key in columns_lower:
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return columns_lower[key]
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return None
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def load_data(self) -> None:
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"""优化的数据加载方法"""
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self._print_stage("加载数据")
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start_time = time.time()
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# try:
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# # 使用更高效的数据读取方式
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# self.df = pd.read_excel(
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# self.file_path,
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# sheet_name="Merged All Tests",
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# engine='openpyxl' # 指定引擎提高性能
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# )
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# except Exception as e:
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# raise RuntimeError(
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# f"读取 Excel 失败，请确认工作表名为 'Merged All Tests'。错误: {type(e).__name__}: {e}"
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# )
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# 检查文件是否存在
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if not os.path.exists(self.file_path):
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raise FileNotFoundError(f"文件不存在: {self.file_path}")
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# 检查文件扩展名是否为Excel支持的格式
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if not self.file_path.lower().endswith(('.xls', '.xlsx')):
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raise ValueError("输入文件不是有效的 Excel 文件（应为 .xls 或 .xlsx 格式）")
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try:
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# 打开Excel文件并获取所有sheet名称
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excel_file = pd.ExcelFile(self.file_path, engine='openpyxl')
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sheet_names = excel_file.sheet_names
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except Exception as e:
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raise RuntimeError(f"无法打开 Excel 文件，请确认该文件未被损坏或占用。错误: {type(e).__name__}: {e}")
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# 定义优先查找的工作表名
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target_sheets = ["Merged All Tests", "All Tests"]
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selected_sheet = None
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for sheet in target_sheets:
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if sheet in sheet_names:
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selected_sheet = sheet
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break
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if selected_sheet is None:
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raise ValueError(
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f"未找到指定的工作表: {' 或 '.join(target_sheets)}。"
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f"当前文件包含的工作表有: {sheet_names}"
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)
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try:
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# 使用更高效的方式读取指定sheet
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self.df = pd.read_excel(
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self.file_path,
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sheet_name=selected_sheet,
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engine='openpyxl'
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)
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except Exception as e:
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raise RuntimeError(
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f"读取 Excel 失败，工作表: '{selected_sheet}'。错误: {type(e).__name__}: {e}"
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)
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if self.df.empty:
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raise ValueError("工作表为空，无法处理")
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# 校验必要列
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missing_columns = [col for col in self.required_columns if col not in self.df.columns]
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if missing_columns:
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raise KeyError(f"缺少必要列: {missing_columns}")
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# 记录上下限列名
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self.col_lower = self._find_column_case_insensitive([
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"Lower Limit", "lower limit", "lower_limit", "ll", "lower"
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])
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self.col_upper = self._find_column_case_insensitive([
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"Upper Limit", "upper limit", "upper_limit", "ul", "upper"
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])
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loading_time = time.time() - start_time
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print(f"数据加载完成: {len(self.df)} 行 × {self.df.shape[1]} 列")
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print(f"耗时: {loading_time:.2f}s")
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# 显示列信息摘要
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print(f"检测到下限列: {self.col_lower or '无'}")
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print(f"检测到上限列: {self.col_upper or '无'}")
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def get_keyword(self) -> Tuple[pd.DataFrame, str, List[str]]:
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"""获取用户输入的关键词并筛选数据"""
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self._print_stage("筛选关键词")
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while True:
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keyword = input("请输入筛选关键词(匹配 'Test Name New'): ").strip()
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if not keyword:
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print("关键词不能为空，请重新输入")
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continue
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break
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mask = self.df["Test Name New"].astype(str).str.contains(keyword, case=False, na=False)
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filtered_df = self.df.loc[mask].copy()
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if filtered_df.empty:
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raise ValueError(f"没有找到包含关键词 '{keyword}' 的测试项")
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unique_tests = filtered_df["Test Name New"].unique().tolist()
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print(f"匹配到 {len(filtered_df)} 行数据，涉及 {len(unique_tests)} 个不同测试项")
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return filtered_df, keyword, unique_tests
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def create_output_dir(self) -> None:
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"""创建输出目录"""
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self._print_stage("创建输出目录")
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if not self.file_path:
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raise ValueError("文件路径未设置")
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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base_dir = os.path.dirname(self.file_path)
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self.output_dir = os.path.join(base_dir, f"scatter_plots_{timestamp}")
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os.makedirs(self.output_dir, exist_ok=True)
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print(f"输出目录: {self.output_dir}")
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@staticmethod
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def _safe_filename(name: str) -> str:
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"""生成安全的文件名"""
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safe = "".join(c for c in str(name) if c.isalnum() or c in (" ", "_", "-")).strip()
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return safe or "Unknown_Test"
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def _extract_limits(self, df_one_test: pd.DataFrame) -> Tuple[
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Optional[float], Optional[float], List[float], List[float]]:
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"""提取某个测试项的上下限数值"""
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lower_plot = upper_plot = None
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lower_set = []
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upper_set = []
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if self.col_lower and self.col_lower in df_one_test.columns:
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lower_vals = self._clean_and_convert_series(df_one_test[self.col_lower], 'numeric').dropna().unique()
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lower_set = sorted(lower_vals.tolist()) if len(lower_vals) > 0 else []
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if lower_set:
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lower_plot = min(lower_set)
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if self.col_upper and self.col_upper in df_one_test.columns:
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upper_vals = self._clean_and_convert_series(df_one_test[self.col_upper], 'numeric').dropna().unique()
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upper_set = sorted(upper_vals.tolist()) if len(upper_vals) > 0 else []
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if upper_set:
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upper_plot = max(upper_set)
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return lower_plot, upper_plot, lower_set, upper_set
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@staticmethod
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def _clean_and_convert_series(series: pd.Series, target_type: str = 'numeric') -> pd.Series:
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"""统一的系列清洗和转换方法"""
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if series.empty:
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return series
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if target_type == 'numeric':
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# 数值转换优化
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if pd.api.types.is_numeric_dtype(series):
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return series.astype(float)
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# 批量字符串处理
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cleaned = series.astype(str).str.replace(r'[, ]', '', regex=True).str.strip()
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return pd.to_numeric(cleaned, errors='coerce')
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elif target_type == 'datetime':
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return TestReportScatterPlotter._convert_to_datetime(series)
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return series
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@staticmethod
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def _convert_to_datetime(series: pd.Series) -> pd.Series:
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"""优化的日期时间转换"""
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if pd.api.types.is_datetime64_any_dtype(series):
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return series
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# 预处理：转换为数值和字符串两种形式
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numeric_series = pd.to_numeric(series, errors='coerce')
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string_series = series.astype(str).str.strip()
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result = pd.Series(pd.NaT, index=series.index, dtype='datetime64[ns]')
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# 数值时间戳处理
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masks = {
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'ms': numeric_series >= 1e11,
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's': (numeric_series >= 1e9) & (numeric_series < 1e11),
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'excel': (numeric_series > 20000) & (numeric_series < 60000)
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}
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for mask_type, mask in masks.items():
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if mask.any():
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if mask_type == 'ms':
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result.loc[mask] = pd.to_datetime(numeric_series.loc[mask], unit='ms')
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elif mask_type == 's':
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result.loc[mask] = pd.to_datetime(numeric_series.loc[mask], unit='s')
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elif mask_type == 'excel':
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origin = pd.Timestamp('1899-12-30')
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result.loc[mask] = origin + pd.to_timedelta(numeric_series.loc[mask], unit='D')
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# 字符串日期处理
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remaining_mask = result.isna()
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if remaining_mask.any():
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remaining_strings = string_series.loc[remaining_mask]
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# 特定格式优先处理
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format_patterns = [
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(r'^\d{4}-\d{2}-\d{2} \d{2}-\d{2}-\d{2}$', '%Y-%m-%d %H-%M-%S'),
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]
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for pattern, date_format in format_patterns:
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format_mask = remaining_strings.str.match(pattern)
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if format_mask.any():
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result.loc[remaining_mask[remaining_mask].index[format_mask]] = pd.to_datetime(
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remaining_strings.loc[format_mask], format=date_format, errors='coerce'
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)
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# 通用解析
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still_na_mask = result.isna() & remaining_mask
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if still_na_mask.any():
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result.loc[still_na_mask] = pd.to_datetime(
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string_series.loc[still_na_mask], errors='coerce'
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)
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return result
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def _preprocess_test_data(self, test_data: pd.DataFrame) -> pd.DataFrame:
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"""数据预处理"""
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# 数值转换
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test_data['Measurement_num'] = self._clean_and_convert_series(
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test_data['Measurement'], 'numeric'
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)
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test_data['TestTime_dt'] = self._clean_and_convert_series(
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test_data['Test Time'], 'datetime'
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)
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# 去除无效数据
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valid_data = test_data.dropna(subset=['Measurement_num', 'TestTime_dt'])
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return valid_data.sort_values('TestTime_dt')
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def _calculate_statistics(self, y_data: pd.Series) -> Dict[str, float]:
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"""计算统计信息"""
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stats = {
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'count': len(y_data),
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'mean': y_data.mean(),
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'median': y_data.median(),
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'min': y_data.min(),
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'max': y_data.max(),
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'std': y_data.std(),
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'q1': y_data.quantile(0.25),
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'q3': y_data.quantile(0.75)
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}
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return stats
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def _add_statistics_textbox(self, ax, stats: Dict[str, float],
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x_pos: float = 1.02, y_pos: float = 0.98) -> None:
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"""在图表右侧添加统计信息文本框"""
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# 使用英文标签避免中文显示问题
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stats_text = (
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f"Count: {stats['count']}\n"
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f"Mean: {stats['mean']:.4f}\n"
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f"Median: {stats['median']:.4f}\n"
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f"Min: {stats['min']:.4f}\n"
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f"Max: {stats['max']:.4f}\n"
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f"Std: {stats['std']:.4f}\n"
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f"Q1: {stats['q1']:.4f}\n"
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f"Q3: {stats['q3']:.4f}"
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)
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# 添加文本框到右侧，使用英文字体
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props = dict(boxstyle='round', facecolor='wheat', alpha=0.8)
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ax.text(x_pos, y_pos, stats_text, transform=ax.transAxes, fontsize=8,
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verticalalignment='top', horizontalalignment='left', # 左对齐
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bbox=props, fontfamily='monospace')
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def _add_statistics_lines(self, ax, stats: Dict[str, float],
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x_min: float, x_max: float) -> None:
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"""添加统计线到图表"""
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# 添加平均值线
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ax.hlines(y=stats['mean'], xmin=x_min, xmax=x_max,
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colors='orange', linestyles='-', linewidth=1.5, alpha=0.7, label='Mean')
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# 添加中位数线
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ax.hlines(y=stats['median'], xmin=x_min, xmax=x_max,
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colors='purple', linestyles='-.', linewidth=1.5, alpha=0.7, label='Median')
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# 添加Q1和Q3线
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ax.hlines(y=stats['q1'], xmin=x_min, xmax=x_max,
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colors='gray', linestyles=':', linewidth=1.0, alpha=0.7, label='Q1')
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ax.hlines(y=stats['q3'], xmin=x_min, xmax=x_max,
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colors='gray', linestyles=':', linewidth=1.0, alpha=0.7, label='Q3')
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def _configure_plot(self, ax, test_data: pd.DataFrame, test_name: str,
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lower_plot: Optional[float], upper_plot: Optional[float]) -> None:
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"""配置图形属性"""
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# 计算统计信息
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y_data = test_data['Measurement_num']
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stats = self._calculate_statistics(y_data)
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# 获取时间范围用于统计线
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x_min = test_data['TestTime_dt'].min()
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x_max = test_data['TestTime_dt'].max()
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# Y轴范围计算
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y_min, y_max = y_data.min(), y_data.max()
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y_candidates = [y_min, y_max]
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# 绘制限值线
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custom_lines = []
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if lower_plot is not None:
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y_candidates.append(lower_plot)
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ax.axhline(y=lower_plot, color='green', linestyle='--', linewidth=1.2)
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custom_lines.append(Line2D([0], [0], color='green', linestyle='--', label="Lower Limit"))
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if upper_plot is not None:
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y_candidates.append(upper_plot)
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ax.axhline(y=upper_plot, color='red', linestyle='--', linewidth=1.2)
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custom_lines.append(Line2D([0], [0], color='red', linestyle='--', label="Upper Limit"))
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# 添加统计线
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self._add_statistics_lines(ax, stats, x_min, x_max)
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# 设置范围
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valid_candidates = [y for y in y_candidates if pd.notna(y)]
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if valid_candidates:
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y_min_plot = min(valid_candidates)
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y_max_plot = max(valid_candidates)
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y_range = y_max_plot - y_min_plot
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if y_range == 0:
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y_range = abs(y_max_plot) * 0.1 if y_max_plot != 0 else 1.0
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y_min_plot = y_min_plot - y_range / 2
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y_max_plot = y_max_plot + y_range / 2
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ax.set_ylim(y_min_plot - 0.1 * y_range, y_max_plot + 0.1 * y_range)
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# 添加统计信息文本框到右侧
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self._add_statistics_textbox(ax, stats)
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# 设置标题和标签，使用英文避免中文问题
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ax.set_title(f"Scatter Plot - {test_name}\n"
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f"Mean: {stats['mean']:.4f}, Median: {stats['median']:.4f}, "
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f"Range: [{stats['min']:.4f}, {stats['max']:.4f}]",
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fontsize=10)
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ax.set_xlabel("Test Time")
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ax.set_ylabel("Measurement Value")
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ax.grid(True, alpha=0.3)
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ax.tick_params(axis='x', rotation=45)
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# 图例处理 - 优化位置在右侧
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handles, labels = ax.get_legend_handles_labels()
|
||
if custom_lines:
|
||
handles.extend(custom_lines)
|
||
labels.extend([line.get_label() for line in custom_lines])
|
||
|
||
if handles:
|
||
# 根据图例项数量决定图例位置和布局
|
||
if len(handles) > 10: # 如果图例项很多，使用两列布局
|
||
ncol = 2
|
||
# 调整图例位置，确保不遮挡数据
|
||
ax.legend(handles=handles, labels=labels, title="Legend",
|
||
fontsize=8, loc='center left', bbox_to_anchor=(1.05, 0.5),
|
||
ncol=ncol, frameon=True, fancybox=True, shadow=True)
|
||
else:
|
||
# 图例项较少时使用单列布局
|
||
ax.legend(handles=handles, labels=labels, title="Legend",
|
||
fontsize=8, loc='center left', bbox_to_anchor=(1.05, 0.5),
|
||
frameon=True, fancybox=True, shadow=True)
|
||
|
||
def _save_plot(self, fig, test_name: str) -> None:
|
||
"""保存图形"""
|
||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
safe_name = self._safe_filename(test_name)
|
||
filename = f"{safe_name}_{timestamp}.png"
|
||
output_path = os.path.join(self.output_dir, filename)
|
||
|
||
# 调整布局以确保图例完整显示
|
||
fig.savefig(output_path, dpi=300, bbox_inches='tight')
|
||
plt.close(fig)
|
||
print(f"已保存: {output_path}")
|
||
|
||
def plot_scatter(self, filtered_df: pd.DataFrame, unique_tests: List[str]) -> None:
|
||
"""优化的散点图绘制方法"""
|
||
self._print_stage("生成散点图")
|
||
total_tests = len(unique_tests)
|
||
start_time = time.time()
|
||
|
||
for i, test_name in enumerate(unique_tests, 1):
|
||
self._print_progress(i, total_tests, "测试项绘图")
|
||
|
||
# 使用缓存避免重复计算
|
||
cache_key = f"test_{hash(test_name)}"
|
||
if cache_key in self._processed_data_cache:
|
||
test_data = self._processed_data_cache[cache_key]
|
||
else:
|
||
test_data = filtered_df[filtered_df["Test Name New"] == test_name].copy()
|
||
# 预处理数据
|
||
test_data = self._preprocess_test_data(test_data)
|
||
self._processed_data_cache[cache_key] = test_data
|
||
|
||
if test_data.empty:
|
||
print(f"\n跳过 '{test_name}' - 无有效的 Measurement/Test Time 数据")
|
||
continue
|
||
|
||
# 提取限值信息
|
||
lower_plot, upper_plot, lower_set, upper_set = self._extract_limits(test_data)
|
||
|
||
# 输出限值信息
|
||
limit_info = []
|
||
if lower_set:
|
||
limit_info.append(f"Lower unique={len(lower_set)}, used={lower_plot}")
|
||
else:
|
||
limit_info.append("Lower N/A")
|
||
if upper_set:
|
||
limit_info.append(f"Upper unique={len(upper_set)}, used={upper_plot}")
|
||
else:
|
||
limit_info.append("Upper N/A")
|
||
|
||
# 计算并输出统计信息
|
||
y_data = test_data['Measurement_num']
|
||
stats = self._calculate_statistics(y_data)
|
||
stat_info = (
|
||
f"数据点: {stats['count']}, "
|
||
f"均值: {stats['mean']:.4f}, "
|
||
f"中位数: {stats['median']:.4f}, "
|
||
f"范围: [{stats['min']:.4f}, {stats['max']:.4f}]"
|
||
)
|
||
|
||
print(f"\n→ 绘制: '{test_name}' | {stat_info} | 限值: {', '.join(limit_info)}")
|
||
|
||
# 创建图形 - 增大图像尺寸以容纳图例和统计信息
|
||
sn_count = len(test_data["SN"].unique()) if "SN" in test_data.columns else 1
|
||
|
||
# 根据SN数量和预期图例项数量调整图形大小
|
||
base_width = 14 # 增加宽度以容纳统计信息
|
||
base_height = 9 # 增加高度以容纳更多信息
|
||
|
||
# 如果SN数量多，增加图形宽度以容纳图例
|
||
if sn_count > 5:
|
||
fig_width = base_width + min(sn_count / 5, 6) # 最大增加6个单位宽度
|
||
else:
|
||
fig_width = base_width
|
||
|
||
fig, ax = plt.subplots(figsize=(fig_width, base_height))
|
||
|
||
# 分组绘制
|
||
groups = list(test_data.groupby("SN")) if "SN" in test_data.columns else [("Unknown_SN", test_data)]
|
||
|
||
for j, (sn, group) in enumerate(groups, 1):
|
||
ax.scatter(group['TestTime_dt'], group['Measurement_num'],
|
||
label=str(sn), alpha=0.7, s=25)
|
||
if j % 10 == 0 or j == len(groups):
|
||
self._print_progress(j, len(groups), "SN分组绘制")
|
||
|
||
# 配置图形
|
||
self._configure_plot(ax, test_data, test_name, lower_plot, upper_plot)
|
||
|
||
# 调整布局，为右侧统计信息和图例留出空间
|
||
plt.tight_layout()
|
||
plt.subplots_adjust(right=0.8 if sn_count <= 10 else 0.7) # 为右侧统计信息留出更多空间
|
||
|
||
# 保存图像
|
||
self._save_plot(fig, test_name)
|
||
|
||
total_time = time.time() - start_time
|
||
print(f"\n全部绘图完成，总耗时: {total_time:.2f}s")
|
||
print(f"所有图表已保存到: {self.output_dir}")
|
||
|
||
def run(self) -> None:
|
||
"""运行主程序"""
|
||
try:
|
||
self.get_file_path()
|
||
self.load_data()
|
||
filtered_df, keyword, unique_tests = self.get_keyword()
|
||
self.create_output_dir()
|
||
self.plot_scatter(filtered_df, unique_tests)
|
||
|
||
except Exception as e:
|
||
print(f"\n❌ 发生错误: {type(e).__name__}: {str(e)}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
sys.exit(1)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
plotter = TestReportScatterPlotter()
|
||
plotter.run()
|