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