Replace plot.py

plot.py

-import argparse, pandas as pd, matplotlib
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+需求：
+1. 生成 1 张「跨-Sheet 5 指标汇总」柱状图（保留）  
+2. 额外生成 5 张「单指标跨-Sheet 对比柱状图」  
+3. 全部输出到指定文件夹
+python six_plots.py your.xlsx -o output_folder
+"""
+
+import argparse, pandas as pd, matplotlib, os, re
 matplotlib.use('Agg')
-import matplotlib.pyplot as plt, re, os
+import matplotlib.pyplot as plt
 
+# ---------- 工具 ----------
 def find_avg_row(df):
     for idx, row in df.iterrows():
         if any(isinstance(cell, str) and re.search(r'平均值|Average', str(cell)) for cell in row):
@@ -10,90 +21,96 @@ def find_avg_row(df):
 
 def extract_values(df):
     idx = find_avg_row(df)
-    vals = [float(v) for v in df.iloc[idx].dropna().iloc[-4:] if str(v).replace('.', '').isdigit()]
-    if len(vals) != 4:
-        vals = [float(v) for v in df.iloc[-1].dropna().iloc[-4:] if str(v).replace('.', '').isdigit()]
-    if len(vals) != 4:
-        raise ValueError("无法提取4个百分比")
-    return {
-        "Total_throughput": vals[3],
-        "generate_throughput": vals[2],
-        "singel_road_generate_throughput": vals[1],
-        "generate_throughput_without_ttft": vals[0]
-    }
+    vals = [float(v) for v in df.iloc[idx].dropna().iloc[-5:] if str(v).replace('.', '').isdigit()]
+    if len(vals) != 5:
+        vals = [float(v) for v in df.iloc[-1].dropna().iloc[-5:] if str(v).replace('.', '').isdigit()]
+    return dict(zip(['total_throughput', 'generate_throughput', 'singel_road_generate_throughput', 'generate_throughput_without_ttft', 'ttft'], vals))
+
+# ---------- 绘图 ----------
+def draw_bar(data, labels, colors, title, out_path, xlabels):
+    fig, ax = plt.subplots(figsize=(max(len(xlabels) * 0.8, 12), 6))
+    fig.patch.set_facecolor('#ffffff')
+    ax.set_facecolor('#ffffff')
+    bars = ax.bar(xlabels, data, color=colors, width=0.6)
+    for bar, v in zip(bars, data):
+        height = bar.get_height()
+        ax.annotate(f"{v:.1f}%",
+                    xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3), textcoords='offset points',
+                    ha='center', va='bottom', fontsize=7, rotation=60,
+                    bbox=dict(boxstyle="round,pad=0.15", facecolor='white', edgecolor='none'))
+    ax.set_ylabel("Percentage")
+    ax.set_title(title, fontsize=13, weight='bold')
+    ax.grid(axis='y', linestyle='-', linewidth=0.25, alpha=0.2)
+    ax.tick_params(axis='x', rotation=30)
+    plt.tight_layout()
+    for ext in ['png', 'pdf']:
+        plt.savefig(f"{out_path}.{ext}", dpi=300 if ext == 'png' else None, bbox_inches='tight')
+    plt.close()
+
+# ---------- 主流程 ----------
+def main():
+    parser = argparse.ArgumentParser(description='生成 6 张柱状图：1 张汇总 + 5 张单指标')
+    parser.add_argument("xlsx_path", help="输入 Excel 文件")
+    parser.add_argument("-o", "--output", default="output", help="输出文件夹")
+    args = parser.parse_args()
 
-def plot_summary(file_path, output_prefix):
-    xls = pd.ExcelFile(file_path)
-    summary = {}
-    for sheet in xls.sheet_names:
-        try:
-            df = pd.read_excel(xls, sheet_name=sheet, header=None)
-            summary[sheet] = extract_values(df)
-        except Exception:
-            continue
-    if not summary:
-        print("❌ 无数据可绘图")
-        return
+    xls = pd.ExcelFile(args.xlsx_path)
+    summary = {sheet: extract_values(pd.read_excel(xls, sheet_name=sheet, header=None))
+               for sheet in xls.sheet_names}
 
     labels = [
-        "Total_throughput",
+        "total_throughput",
         "generate_throughput",
         "singel_road_generate_throughput",
-        "generate_throughput_without_ttft"
+        "generate_throughput_without_ttft",
+        "ttft"
     ]
-    # 大师级配色（Material 2024）
-    colors = ['#005F73', '#0A9396', '#94D2BD', '#E9D8A6']
-
-
-
+    colors = ['#005F73', '#0A9396', '#94D2BD', '#E9D8A6', '#d62728']
+    os.makedirs(args.output, exist_ok=True)
 
+    # 1. 跨-Sheet 5 指标汇总（原图保持不变）
     models = list(summary.keys())
     x = range(len(models))
-    bar_width = 0.18
-
+    bar_width = 0.14
     fig, ax = plt.subplots(figsize=(max(len(models) * 1.1, 14), 8))
     fig.patch.set_facecolor('#F8F9FA')
     ax.set_facecolor('#FFFFFF')
-
     for i, lab in enumerate(labels):
         vals = [summary[m][lab] for m in models]
         offset = (i - 1.5) * bar_width
         bars = ax.bar([p + offset for p in x], vals, width=bar_width,
                       label=lab.replace('_', ' ').title(), color=colors[i])
-
-        # 保持倾斜 60° + 虚线指引
         for bar, v in zip(bars, vals):
             height = bar.get_height()
-            # 虚线起点（柱顶中心）
             line_start = (bar.get_x() + bar.get_width() / 2, height)
-            # 文本终点（再向上 5%）
             text_pos   = (line_start[0], height + max(vals) * 0.05)
-            # 画虚线
             ax.plot([line_start[0], text_pos[0]], [line_start[1], text_pos[1]],
                     color='#555555', linestyle='--', linewidth=0.8, alpha=0.7)
-            # 倾斜 60° 文字
             ax.text(text_pos[0], text_pos[1], f"{v:.1f}%",
-                    ha='center', va='bottom', fontsize=7,
-                    rotation=60, color='#000000', weight='bold')
-
-    # 轴 & 网格
+                    ha='center', va='bottom', fontsize=7, rotation=60,
+                    color='#000000', weight='bold')
     ax.set_xticks(x)
     ax.set_xticklabels(models, rotation=25, ha='right', fontsize=10, weight='bold')
     ax.set_ylabel("Percentage", fontsize=12, weight='bold')
     ax.set_title("Throughput Comparison", fontsize=16, weight='bold', pad=20)
     ax.grid(axis='y', linestyle='-', linewidth=0.3, alpha=0.3, color='#DEE2E6')
     ax.legend(frameon=False, loc='upper left', bbox_to_anchor=(1.02, 1), fontsize=9)
-    plt.subplots_adjust(left=0.05, right=0.85, bottom=0.12, top=0.92)
-
+    plt.tight_layout()
     for ext in ['png', 'pdf']:
-        plt.savefig(f"{output_prefix}.{ext}", dpi=300 if ext == 'png' else None,
+        plt.savefig(os.path.join(args.output, f"summary.{ext}"), dpi=300 if ext == 'png' else None,
                     bbox_inches='tight')
-    print(f"✅ 已保存：{output_prefix}.png / .pdf")
+    plt.close()
 
+    # 2-6. 5 张单指标跨-Sheet 对比图
+    for lab, color in zip(labels, colors):
+        vals = [summary[s][lab] for s in models]
+        draw_bar(vals, [lab], [color],
+                 f"{lab.replace('_', ' ').title()} - All Sheets",
+                 os.path.join(args.output, lab),
+                 models)
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("xlsx_path")
-    parser.add_argument("-o", "--output", default="summary_chart")
-    args = parser.parse_args()
-    plot_summary(args.xlsx_path, args.output)
\ No newline at end of file
+    print(f"✅ 6 张图已生成：{args.output}")
+
+if __name__ == '__main__':
+    main()