增加赔付金额频率图

yyyy_js/python-server/data_analysis/analysis2.py

@@ -1,7 +1,11 @@
 import numpy as np
 import json
 import matplotlib.pyplot as plt
+from collections import defaultdict
 
+# 设置Matplotlib的默认字体
+plt.rcParams['font.sans-serif'] = ['SimHei']  # 'SimHei' 是一种支持中文的字体
+plt.rcParams['axes.unicode_minus'] = False  # 正确显示负号
 # 假设数据文件名为 'betting_data.txt'，每行为一个时间戳和JSON数据
 file_path = '开奖记录.log'
 
@@ -83,6 +87,8 @@ def save_and_clear(key):
 
 # 分析和绘图函数
 def analyze_group_positions(round_info_list, group1, group2):
+    # 存储每个中奖选项组合在排序后赔付列表中的位置
+    combination_positions = []
     # 存储每个中奖选项在排序后赔付列表中的位置
     positions_group1 = []
     positions_group2 = []
@@ -106,10 +112,26 @@ def analyze_group_positions(round_info_list, group1, group2):
             if res in sorted_keys_group2:
                 positions_group2.append(sorted_keys_group2.index(res) + 1)
 
+        # 创建所有可能的组合并计算赔付总额
+        combinations_payouts = {}
+        for key1, payout1 in group1_payouts.items():
+            for key2, payout2 in group2_payouts.items():
+                combinations_payouts[(key1, key2)] = payout1 + payout2
+
+        # 对组合赔付总额进行排序
+        sorted_combinations = sorted(combinations_payouts.items(), key=lambda x: x[1])
+        sorted_combination_keys = [item[0] for item in sorted_combinations]
+
+        # 获取实际开奖结果的组合位置
+        winning_combination = tuple(info.result_option)
+        if winning_combination in sorted_combination_keys:
+            position = sorted_combination_keys.index(winning_combination) + 1
+            combination_positions.append(position)
+
     # 绘制两个组的结果
     plt.figure(figsize=(12, 6))
 
-    plt.subplot(1, 2, 1)
+    plt.subplot(1, 3, 1)
     # plt.hist(positions_group1, bins=len(group1), alpha=0.75, edgecolor='black', align='left', rwidth=0.5)
     plt.hist(positions_group1, bins=np.arange(1, len(group1) + 2) - 0.5, alpha=0.75, edgecolor='black')
 
@@ -118,7 +140,7 @@ def analyze_group_positions(round_info_list, group1, group2):
     plt.title('Frequency of Winning Options in Group 1')
     plt.xticks(np.arange(1, len(group1) + 1))
 
-    plt.subplot(1, 2, 2)
+    plt.subplot(1, 3, 2)
     # plt.hist(positions_group2, bins=len(group2), alpha=0.75, edgecolor='black', align='left', rwidth=0.5)
     plt.hist(positions_group2, bins=np.arange(1, len(group2) + 2) - 0.5, alpha=0.75, edgecolor='black')
 
@@ -127,9 +149,35 @@ def analyze_group_positions(round_info_list, group1, group2):
     plt.title('Frequency of Winning Options in Group 2')
     plt.xticks(np.arange(1, len(group2) + 1))
 
+    # 统计各个位置的频率
+    position_counts = defaultdict(int)
+    for pos in combination_positions:
+        position_counts[pos] += 1
+
+    # 统计各个位置的频率
+    total_positions = len(combination_positions)
+    # 绘制频率图
+    positions, frequencies = zip(*sorted(position_counts.items()))
+    plt.subplot(1, 3, 3)
+    plt.bar(positions, frequencies, color='skyblue')
+    plt.xlabel('Position in Combined Payout Sorting')
+    plt.ylabel('频率')
+    plt.title('奖金排名中获胜组合位置的频率')
+    plt.xticks(positions)
+
+    # 绘制频率图
+    frequencies_percentage = [f / total_positions * 100 for f in frequencies]
+    bars = plt.bar(positions, frequencies_percentage, color='skyblue')
+
+    # 在柱状图上添加百分比标签
+    for bar, freq in zip(bars, frequencies_percentage):
+        yval = bar.get_height()
+        plt.text(bar.get_x() + bar.get_width() / 2, yval, f'{freq:.2f}%', ha='center', va='bottom', fontsize=8)
+
     plt.tight_layout()
     plt.show()
 
+
 def parse_data_from_file(_file_path):
     clean_data(_file_path)
     delete_wrong_data(_file_path)