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日志处理文件.py

@@ -1,6 +1,5 @@
 import json
 import math
-
 import pandas as pd
 import matplotlib.pyplot as plt
 from matplotlib import rcParams
@@ -21,7 +20,6 @@ log_ones = []
 start_idx = -1
 for idx, one in enumerate(data):
     if 'Acar' == one['设备'] and '2' == one['类型']:
-        # print(one)
         if 1 == one['位置'] and 1 == one['方向']:
             start_idx = idx
             continue
@@ -31,6 +29,7 @@ for idx, one in enumerate(data):
             log_ones.append(data[start_idx:idx+1])
             start_idx = -1
             print(idx)
+
 # 筛选出只有 "Acar" 的日志片段
 log_ones_new = []
 for one in log_ones:
@@ -46,39 +45,51 @@ for one_one in log_ones_new:
     df['时间差'] = df['时间戳'].diff()
     time_diff = df[['位置', '方向', '时间差']].dropna().reset_index(drop=True)
     time_diffs.append(time_diff)
-# print(time_diffs)
+
-# 找出行数最多的时间差数据集
+# 找出行数最多的时间差数据集，以它的 (位置-方向) 组合作为标准 X 轴标签
-max_len_dataset = max(time_diffs, key=len)  # 找到行数最多的 DataFrame
+max_len_dataset = max(time_diffs, key=len)
-# print(max_len_dataset)
+all_x_labels = max_len_dataset['位置'].astype(str) + '-' + max_len_dataset['方向'].astype(str)
-all_x_labels = max_len_dataset['位置'].astype(str) + '-' + max_len_dataset['方向'].astype(str)  # 提取 X 轴标签
+
-# all_x_labels=pd.DataFrame({'位置-方向': all_x_labels})
+# 标准化所有数据集（没有对应的“位置-方向”就补 0）
-# 标准化所有数据集，缺少的补 0
 standardized_time_diffs = []
-time_diffs_new = []
 for time_diff in time_diffs:
     time_diff['位置-方向'] = time_diff['位置'].astype(str) + '-' + time_diff['方向'].astype(str)
-    # print(time_diff)
     standardized_time_diff = []
 
-    # 手动补零
+    data_idx = 0
-    data_idx=0
+    last_is_null = 0
-    last_is_null=0
+    for idx_label, label in enumerate(all_x_labels):
-    for idx,label in enumerate(all_x_labels):
+        row = time_diff.iloc[data_idx] if data_idx < len(time_diff) else None
-        first_row = time_diff.iloc[data_idx]
+        if row is not None and row['位置-方向'] == label:
-        if first_row['位置-方向']==label:
+            # 命中当前 label
-            if last_is_null==1:
+            if last_is_null == 1:
-                last_is_null=0
+                last_is_null = 0
-                standardized_time_diff.append(0)
+                standardized_time_diff.append(0)  # 这里保持你原先的逻辑，先补 0，后面再统一填充
             else:
-                standardized_time_diff.append(first_row['时间差'])
+                standardized_time_diff.append(row['时间差'])
-            data_idx+=1
+            data_idx += 1
         else:
+            # 没有命中当前 label
             standardized_time_diff.append(0)
-            last_is_null=1
+            last_is_null = 1
-    print(standardized_time_diff)
-    standardized_time_diffs.append(standardized_time_diff)
-    print('-------------------')
 
+    standardized_time_diffs.append(standardized_time_diff)
+
+# -------- 新增：填充 0 的函数 --------
+def fill_zeros_with_nearest(values):
+    """
+    将列表中的 0 用最近的非 0 值进行填充
+    （先前向填充，再后向填充）
+    """
+    # 前向填充
+    for i in range(1, len(values)):
+        if values[i] == 0 and values[i-1] != 0:
+            values[i] = values[i-1]
+    # 后向填充
+    for i in range(len(values) - 2, -1, -1):
+        if values[i] == 0 and values[i+1] != 0:
+            values[i] = values[i+1]
+    return values
 
 # 创建子图的行列数（自动计算）
 num_plots = len(all_x_labels)
@@ -92,22 +103,34 @@ axes = axes.flatten()  # 将子图数组展平，方便迭代
 # 在每个子图中绘制折线
 for idx, label in enumerate(all_x_labels):
     # 获取当前 "位置-方向" 对应的 y 值
-    y_values = [time_diff[idx] if idx < len(time_diff) else 0 for time_diff in standardized_time_diffs]
+    y_values = []
+    for time_diff in standardized_time_diffs:
+        # 如果下标越界就补 0
+        y_values.append(time_diff[idx] if idx < len(time_diff) else 0)
+
+    # -------- 在绘图前，对 y_values 做一次非 0 值填充 --------
+    y_values_filled = fill_zeros_with_nearest(y_values)
 
     # 绘制当前子图
     ax = axes[idx]
     ax.plot(
-        range(len(standardized_time_diffs)),  # x轴为不同的时间差数据集序号
+        range(len(y_values_filled)),
-        y_values,  # y轴为对应的时间差
+        y_values_filled,
-        marker='o',  # 标记点样式
+        marker='o',
     )
+    ax.set_ylim(0, 10000)
+
 
     # 设置标题和轴标签
     ax.set_title(f'{label} 的时间差折线图', fontsize=10)
     ax.set_xlabel('标准化时间差数据集', fontsize=8)
     ax.set_ylabel('时间差 (ms)', fontsize=8)
-    ax.set_xticks(range(len(standardized_time_diffs)))
+    ax.set_xticks(range(len(y_values_filled)))
-    ax.set_xticklabels([f'TimeDiff {i+1}' for i in range(len(standardized_time_diffs))], fontsize=6, rotation=45)
+    ax.set_xticklabels(
+        [f'TimeDiff {i+1}' for i in range(len(y_values_filled))],
+        fontsize=6,
+        rotation=45
+    )
     ax.grid(axis='y', linestyle='--', alpha=0.7)
 
 # 删除多余的子图（如果子图数量多于折线图数量）

日志处理文件_真.py

@@ -0,0 +1,120 @@
+import json
+import math
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+
+# 设置字体为 SimHei（黑体）或其他支持中文的字体
+rcParams['font.sans-serif'] = ['SimHei']  # 或者 ['Microsoft YaHei']
+rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+
+# 指定 JSON 文件的路径
+file_path = r"C:\Users\10561\Desktop\2025-01-06_应用日志.json"
+
+# 打开并读取 JSON 文件
+with open(file_path, 'r', encoding='utf-8') as file:
+    data = json.load(file)
+
+# 提取指定设备和类型的日志片段
+log_ones = []
+start_idx = -1
+for idx, one in enumerate(data):
+    if 'Acar' == one['设备'] and '2' == one['类型']:
+        # print(one)
+        if 1 == one['位置'] and 1 == one['方向']:
+            start_idx = idx
+            continue
+        if 8 == one['位置'] and 2 == one['方向']:
+            if start_idx == -1:
+                continue
+            log_ones.append(data[start_idx:idx+1])
+            start_idx = -1
+            print(idx)
+# 筛选出只有 "Acar" 的日志片段
+log_ones_new = []
+for one in log_ones:
+    one_only_car = [one_one for one_one in one if 'Acar' == one_one['设备']]
+    log_ones_new.append(one_only_car)
+[print(o) for o in log_ones_new]
+
+# 计算时间差
+time_diffs = []
+for one_one in log_ones_new:
+    df = pd.DataFrame(one_one)
+    df['时间戳'] = pd.to_numeric(df['时间戳'])
+    df['时间差'] = df['时间戳'].diff()
+    time_diff = df[['位置', '方向', '时间差']].dropna().reset_index(drop=True)
+    time_diffs.append(time_diff)
+# print(time_diffs)
+# 找出行数最多的时间差数据集
+max_len_dataset = max(time_diffs, key=len)  # 找到行数最多的 DataFrame
+# print(max_len_dataset)
+all_x_labels = max_len_dataset['位置'].astype(str) + '-' + max_len_dataset['方向'].astype(str)  # 提取 X 轴标签
+# all_x_labels=pd.DataFrame({'位置-方向': all_x_labels})
+# 标准化所有数据集，缺少的补 0
+standardized_time_diffs = []
+time_diffs_new = []
+for time_diff in time_diffs:
+    time_diff['位置-方向'] = time_diff['位置'].astype(str) + '-' + time_diff['方向'].astype(str)
+    # print(time_diff)
+    standardized_time_diff = []
+
+    # 手动补零
+    data_idx=0
+    last_is_null=0
+    for idx,label in enumerate(all_x_labels):
+        first_row = time_diff.iloc[data_idx]
+        if first_row['位置-方向']==label:
+            if last_is_null==1:
+                last_is_null=0
+                standardized_time_diff.append(0)
+            else:
+                standardized_time_diff.append(first_row['时间差'])
+            data_idx+=1
+        else:
+            standardized_time_diff.append(0)
+            last_is_null=1
+    print(standardized_time_diff)
+    standardized_time_diffs.append(standardized_time_diff)
+    print('-------------------')
+
+
+# 创建子图的行列数（自动计算）
+num_plots = len(all_x_labels)
+rows = math.ceil(math.sqrt(num_plots))  # 行数
+cols = math.ceil(num_plots / rows)  # 列数
+
+# 创建大画布
+fig, axes = plt.subplots(rows, cols, figsize=(16, 12))
+axes = axes.flatten()  # 将子图数组展平，方便迭代
+
+# 在每个子图中绘制折线
+for idx, label in enumerate(all_x_labels):
+    # 获取当前 "位置-方向" 对应的 y 值
+    y_values = [time_diff[idx] if idx < len(time_diff) else 0 for time_diff in standardized_time_diffs]
+
+    # 绘制当前子图
+    ax = axes[idx]
+    ax.plot(
+        range(len(standardized_time_diffs)),  # x轴为不同的时间差数据集序号
+        y_values,  # y轴为对应的时间差
+        marker='o',  # 标记点样式
+    )
+    ax.set_ylim(0, 10000)
+
+# 设置标题和轴标签
+    ax.set_title(f'{label} 的时间差折线图', fontsize=10)
+    ax.set_xlabel('标准化时间差数据集', fontsize=8)
+    ax.set_ylabel('时间差 (ms)', fontsize=8)
+    ax.set_xticks(range(len(standardized_time_diffs)))
+    ax.set_xticklabels([f'TimeDiff {i+1}' for i in range(len(standardized_time_diffs))], fontsize=6, rotation=45)
+    ax.grid(axis='y', linestyle='--', alpha=0.7)
+
+# 删除多余的子图（如果子图数量多于折线图数量）
+for ax in axes[num_plots:]:
+    fig.delaxes(ax)
+
+# 调整布局
+plt.tight_layout()
+plt.show()