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
    )

    # 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()