import time
from tarfile import data_filter

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

from pandas.io.sas.sas_constants import dataset_length

# 示例数据（一组数据即可）
data = [
    {"time": 1114, "params": {"key1": 190, "key2": 328, "key3": 312}},
    {"time": 1113, "params": {"key1": 150, "key2": 318, "key3": 334}},
    {"time": 1112, "params": {"key1": 126, "key2": 299, "key3": 340}},
    {"time": 1111, "params": {"key1": 119, "key2": 270, "key3": 350}},
    {"time": 1110, "params": {"key1": 110, "key2": 200, "key3": 100}}
]


class DataVisualizer:
    def __init__(self, data_list):
        self.data_list = data_list
        self.dfs = self._process_data_list()
        self.keys = self._get_keys()  # 自动获取所有key
        # 添加中文字体配置
        self._setup_chinese_font()

    def _setup_chinese_font(self):
        """配置中文字体支持"""
        try:
            plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
            plt.rcParams['axes.unicode_minus'] = False
        except Exception as e:
            print(f"字体配置警告: {e}")

    def _process_data_list(self):
        """处理多个原始数据为DataFrame列表"""
        dfs = []
        print(self.data_list)
        for data in self.data_list:
            df_data = []
            for item in data:
                row = {"time": item["time"]}
                row.update(item["params"])
                df_data.append(row)
            df = pd.DataFrame(df_data)
            df = df.sort_values('time')  # 按时间升序排列
            dfs.append(df)
        return dfs

    def _get_keys(self):
        """自动获取所有key（即params中的键）"""
        if self.dfs:
            return list(self.dfs[0].columns)  # 假设所有数据的key一致
        return []

    def get_data_summary(self):
        """生成多个数据数组的统计摘要"""
        summaries = []
        for df in self.dfs:
            summary = {'数据点数量': len(df), '时间范围': f"{df['time'].min()} - {df['time'].max()}"}
            for key in self.keys:
                start = df[key].iloc[0]
                end = df[key].iloc[-1]
                diff = end - start
                rate = (diff / start) * 100 if start != 0 else 0
                summary[f'{key}变化'] = f"{start} → {end} (Δ{diff}, {rate:.1f}%)"
            summaries.append(summary)
        return summaries

    def convertTime(timesMs):
        local_time = time.localtime(timesMs)
        return f"{local_time.tm_hour:02d}:{local_time.tm_min:02d}"

    def create_individual_key_plots11(self):
        """为每个key单独生成一个独立图表"""
        for key in self.keys:
            if key == 'time':
                continue
            plt.figure(figsize=(10, 6))
            for i, df in enumerate(self.dfs):
                plt.plot(df['time'], df[key],
                         marker='o', linewidth=2, markersize=8,
                         label=f'数据组{i + 1} - {key}变化趋势')
                print('xx = ' , df['time'])
                print('mm = ',  df[key])
                for j, (time, value) in enumerate(zip(df['time'], df[key])):
                    plt.annotate(f'{value}', (time, value), textcoords="offset points", xytext=(0, 10), ha='center', fontsize=12)
            plt.xlabel('时间', fontsize=12)
            plt.ylabel('参数值', fontsize=12)
            plt.title(f'{key}在不同数据组随时间的变化对比', fontsize=14, fontweight='bold')
            plt.legend()
            plt.grid(True, alpha=0.3)
            plt.xticks(self.dfs[0]['time'])
            plt.tight_layout()
            plt.show()

    def create_individual_key_plots(self):
        """为每个key单独生成一个独立图表，时间戳转换为年月日时分秒格式"""
        for key in self.keys:
            if key == 'time':
                continue
            plt.figure(figsize=(10, 6))

            # 存储所有格式化后的时间，用于统一x轴刻度
            all_formatted_times = []

            for i, df in enumerate(self.dfs):
                # 将时间戳转换为年月日时分秒格式
                formatted_times = []
                for timestamp in df['time']:
                    # 假设时间戳为整数或浮点数
                    dt_obj = datetime.fromtimestamp(timestamp / 1000)
                    formatted_time = dt_obj.strftime("%H:%M:%S")
                    formatted_times.append(formatted_time)

                # 如果是第一个数据组，保存格式化后的时间用于x轴刻度
                if i == 0:
                    all_formatted_times = formatted_times

                # 使用格式化后的时间进行绘图
                plt.plot(formatted_times, df[key],
                         marker='o', linewidth=2, markersize=8,
                         label=f'数据组{i + 1} - {key}变化趋势')

                print('原始时间戳 = ', df['time'])
                print('格式化时间 = ', formatted_times)
                print('数值数据 = ', df[key])

                # 为每个数据点添加标注
                for j, (formatted_time, value) in enumerate(zip(formatted_times, df[key])):
                    plt.annotate(f'{value}',
                                 (formatted_time, value),
                                 textcoords="offset points",
                                 xytext=(0, 10),
                                 ha='center',
                                 fontsize=12)

            plt.xlabel('时间', fontsize=12)
            plt.ylabel('参数值', fontsize=12)
            plt.title(f'{key}在不同数据组随时间的变化对比', fontsize=14, fontweight='bold')
            plt.legend()
            plt.grid(True, alpha=0.3)

            # 设置x轴刻度为格式化后的时间
            plt.xticks(all_formatted_times, rotation=45)
            plt.tight_layout()
            plt.show()

def show(data1):
    # 创建数据数组（示例：1个数据数组）
    data_list = [data1]

    # 创建可视化器实例
    visualizer = DataVisualizer(data_list)

    print("=" * 50)
    print("数据表格展示")
    print("=" * 50)
    for i, df in enumerate(visualizer.dfs):
        print(f"数据组{i + 1}表格：")
        print(df)
        print()

    print("=" * 50)
    print("数据统计摘要")
    print("=" * 50)
    summaries = visualizer.get_data_summary()
    for i, summary in enumerate(summaries):
        print(f"数据组{i + 1}统计摘要：")
        for key, value in summary.items():
            print(f"  {key}: {value}")
        print()

    # 为每个key单独生成图表
    print("正在生成每个key的独立对比图表...")
    visualizer.create_individual_key_plots()

'''
if __name__ == "__main__":
    main()
'''