from datetime import datetime, timezone, timedelta, date

import pandas as pd
import requests
import json

from matplotlib import pyplot as plt


#登录接口认证

def loginAuth():
    # 接口地址
    global auth_token
    url = "https://xj-tb.maimaiag.com/api/auth/login"  # 请替换为实际地址

    # 请求头
    headers = {
        "Content-Type": "application/json"
    }

    # 请求体数据
    payload = {
        "username": "read@xj.com",
        "password": "maimai"
    }

    try:
        # 发送POST请求
        response = requests.post(
            url,
            headers=headers,
            data=json.dumps(payload)  # 或者直接使用 json=payload
        )

        # 检查响应状态码
        if response.status_code == 200:
            print("登录成功！")
            print("响应数据:", response.json())

            # 通常登录接口会返回token，可以这样获取
            response_data = response.json()
            auth_token = response_data.get("token")  # 根据实际返回字段调整
            if auth_token:
                print("获取到的认证Token:", auth_token)
        else:
            print(f"登录失败，状态码: {response.status_code}")
            print("错误信息:", response.text)

    except requests.exceptions.RequestException as e:
        print(f"请求发生异常: {str(e)}")
    except json.JSONDecodeError as e:
        print(f"JSON解析错误: {str(e)}")
    return auth_token
# 调用数据接口--历史数据
def getWeatherAndSoilData(auth_token,device_id,startTs,endTs,agg,interval,limit,orderBy,keys):
    # 配置信息
    BASE_URL = "https://xj-tb.maimaiag.com/"  # 请替换为实际API地址
    TOKEN = auth_token # 替换为实际的Bearer token
    DEVICE_ID = device_id  # 替换为实际的设备ID
    # 构造请求URL
    endpoint = f"/api/plugins/telemetry/DEVICE/{DEVICE_ID}/values/timeseries"
    url = BASE_URL + endpoint
    # 请求头
    headers = {
        "Content-Type": "application/json",
        "X-Authorization": f"Bearer+{TOKEN}"
    }
    # 查询参数
    params = {
        "keys": keys,  # 要查询的参数，多个用逗号分隔
        "startTs": startTs,  # 开始时间戳（毫秒）- 可选
        "endTs": endTs,  # 结束时间戳（毫秒）- 可选
        "agg": agg,  # 聚合函数 - 可选
        "interval": interval,  # 聚合间隔（毫秒）- 可选
        "limit": limit,  # 返回条数 - 可选
        "orderBy": orderBy  # 排序方式 - 可选
    }
    try:
        # 发送GET请求
        response = requests.get(
            url,
            headers=headers,
            params=params
        )
        # 检查响应状态码
        if response.status_code == 200:
            # print("请求成功！")
            data = response.json()
            # print("响应数据:", json.dumps(data, indent=2))
            converted_data = {k: convert_timestamp_to_datetime(v) for k, v in data.items()}
            return converted_data
        elif response.status_code == 401:
            print("认证失败，请检查Token是否正确")
        elif response.status_code == 404:
            print("设备不存在或路径错误")
        else:
            print(f"请求失败，状态码: {response.status_code}")
            print("错误信息:", response.text)

    except requests.exceptions.RequestException as e:
        print(f"请求发生异常: {str(e)}")
    except json.JSONDecodeError as e:
        print(f"JSON解析错误: {str(e)}")
        print("原始响应:", response.text)
def convert_timestamp_to_datetime(data_dict):
    """
    递归地将字典中的所有'timestamp'字段转换为可读的日期时间格式
    保留原始时间戳的同时添加新的'datetime'字段
    """
    if isinstance(data_dict, dict):
        if "ts" in data_dict:
            # 转换毫秒时间戳为datetime对象
            timestamp_sec = data_dict["ts"] / 1000
            dt = datetime.fromtimestamp(timestamp_sec)
            # 添加新字段，保留原始时间戳
            data_dict["datetime"] = dt.strftime("%Y-%m-%d %H:%M:%S")
        return data_dict
    elif isinstance(data_dict, list):
        return [convert_timestamp_to_datetime(item) for item in data_dict]
    else:
        return data_dict

def dateToTimestamp(date):
    # dt = datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
    if isinstance(date, datetime):
        # 如果已经是datetime对象，直接使用
        dt = date
    elif isinstance(date, str):
        # 如果是字符串，解析为datetime对象
        dt = datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f")
    else:
        raise TypeError("date参数必须是字符串或datetime对象")
    timestamp_ms = int(dt.timestamp() * 1000)
    return timestamp_ms

#调用接口获取未来气象数据
def getFutureWeather(monitor_date):
    base_url_future="https://api.open-meteo.com/"
    end_point="v1/forecast"
    current_date = datetime.now().date()
    params = {
        "latitude": "41.34252110189814",  # 纬度
        "longitude": "86.28810755462379",  # 经度
        "hourly": "temperature_2m,precipitation,precipitation_probability"
    }
    # if monitor_date.date()<current_date:
    #     # url = base_url_history + end_point
    #     params["start_date"] = (monitor_date + timedelta(days=1)).strftime("%Y-%m-%d")
    #     params["end_date"] = (monitor_date + timedelta(days=3)).strftime("%Y-%m-%d")
    #
    # else:
    url = base_url_future + end_point
    params["forecast_days"]= 4

    try:
        # 发送GET请求
        response = requests.get(
            url,
            params=params
        )
        # 检查响应状态码
        if response.status_code == 200:
            data = response.json()
            result={}
            if monitor_date.date()<current_date:
                result["qw_day1"] = get_tjvalue(data['hourly']['temperature_2m'][:24])
                result["qw_day2"] = get_tjvalue(data['hourly']['temperature_2m'][24:48])
                result["qw_day3"] = get_tjvalue(data['hourly']['temperature_2m'][48:72])
                result["js_day1"] = get_tjvalue(data['hourly']['precipitation'][:24])
                result["js_day2"] = get_tjvalue(data['hourly']['precipitation'][24:48])
                result["js_day3"] = get_tjvalue(data['hourly']['precipitation'][48:72])
            else:
                result["qw_day1"] = get_tjvalue(data['hourly']['temperature_2m'][24:48])
                result["qw_day2"] = get_tjvalue(data['hourly']['temperature_2m'][48:72])
                result["qw_day3"] = get_tjvalue(data['hourly']['temperature_2m'][72:96])
                result["js_day1"] = get_tjvalue(data['hourly']['precipitation'][24:48])
                result["js_day2"] = get_tjvalue(data['hourly']['precipitation'][48:72])
                result["js_day3"] = get_tjvalue(data['hourly']['precipitation'][72:96])
            return result
        elif response.status_code == 401:
            print("认证失败，请检查Token是否正确")
        elif response.status_code == 404:
            print("设备不存在或路径错误")
        else:
            print(f"请求失败，状态码: {response.status_code}")
            print("错误信息:", response.text)

    except requests.exceptions.RequestException as e:
        print(f"请求发生异常: {str(e)}")
    except json.JSONDecodeError as e:
        print(f"JSON解析错误: {str(e)}")
        print("原始响应:", response.text)
def get_tjvalue(data):
    result={}
    max_val = max(data)
    min_val = min(data)
    mean_val = sum(data) / len(data)
    sum_val = sum(data)
    result['max']=max_val
    result['min']=min_val
    result['mean']=mean_val
    result['sum'] = sum_val
    return result
soil_params={
    "1号传感器":{
        "device_id":"31fbb2d0-561b-11f0-a556-4f10f26fc07f"
    },
    "2号传感器": {
        "device_id": "3bf4cc90-561b-11f0-a556-4f10f26fc07f"
    },
    "3号传感器": {
        "device_id": "43a57cf0-561b-11f0-a556-4f10f26fc07f"
    },
    "4号传感器": {
        "device_id": "4ff6b780-561b-11f0-a556-4f10f26fc07f"
    },
    "5号传感器": {
        "device_id": "598c5480-561b-11f0-a556-4f10f26fc07f"
    },
    "6号传感器": {
        "device_id": "6ff07260-561b-11f0-a556-4f10f26fc07f"
    },
    "7号传感器": {
        "device_id": "8637b970-561b-11f0-a556-4f10f26fc07f"
    },
    "8号传感器": {
        "device_id": "955de5f0-561b-11f0-a556-4f10f26fc07f"
    },
    "9号传感器": {
        "device_id": "a24e94d0-561b-11f0-a556-4f10f26fc07f"
    },
    "10号传感器": {
        "device_id": "adb7e060-561b-11f0-a556-4f10f26fc07f"
    },
    "11号传感器": {
        "device_id": "b74c6bf0-561b-11f0-a556-4f10f26fc07f"
    },
    "12号传感器": {
        "device_id": "c0f36e10-561b-11f0-a556-4f10f26fc07f"
    },

}
def get_soil_data_dk1(auth_token,startTs, endTs):
    keys_soil = "SOIL_MOISTURE_SURFACE,SOIL_MOISTURE_MIDDLE,SOIL_MOISTURE_BOTTOM"
    limit = ""
    orderBy = ""
    result={}
    for key in soil_params:
        id=soil_params[key]['device_id']
        soil_data = getWeatherAndSoilData(auth_token,id, startTs, endTs, "AVG", 3600000, limit, orderBy,
                                          keys_soil)
        result[key]=soil_data
    return result
def get_soil_data_dk2(auth_token,device_id,startTs, endTs):
    keys_soil = "SOIL_MOISTURE_SURFACE,SOIL_MOISTURE_MIDDLE,SOIL_MOISTURE_BOTTOM"
    limit = ""
    orderBy = ""
    result={}
    soil_data = getWeatherAndSoilData(auth_token,device_id, startTs, endTs, "AVG", 3600000, limit, orderBy,
                                          keys_soil)
    result[device_id]=soil_data
    return result
weather_params={
    "device_id" : "18d121f0-561b-11f0-a556-4f10f26fc07f",
    "agg" :"AVG",
    "interval" : 3600000, #1小时
    "limit" :"",
    "orderBy" : "",
    "keys":"AIR_TEMPERATURE,WIND_SPEED,AIR_LUX,AIR_HUMIDITY,RAIN_FALL_REALTIME"
}
def get_plot(data_list):
    df = pd.DataFrame(data_list)
    df['value'] =  pd.to_numeric(df['value'], errors='coerce').round(3)# 转换湿度值为浮点数
    df['datetime'] = pd.to_datetime(df['datetime'])  # 转换为datetime类型

    # 设置画布大小
    plt.figure(figsize=(12, 6))

    # 绘制折线图
    plt.plot(df['datetime'], df['value'],
             color='teal',
             linestyle='-',
             linewidth=2,
             marker='o',
             markersize=4,
             markerfacecolor='orange',
             markeredgecolor='orange',
             label='Soil Moisture (%)')

    # 添加22点后的数据高亮（符合你之前获取夜间数据的需求）
    night_data = df[df['datetime'].dt.hour >= 22]
    plt.scatter(night_data['datetime'], night_data['value'],
                color='red',
                s=30,
                label='22:00-06:00 Data')

    # 设置标题和坐标轴标签
    plt.title('Soil Moisture Time Series (2025-06-22 to 2025-06-30)',
              fontsize=14,
              pad=15)
    plt.xlabel('Datetime', fontsize=12, labelpad=10)
    plt.ylabel('Soil Moisture (%)', fontsize=12, labelpad=10)

    # 设置x轴刻度旋转，避免重叠
    plt.xticks(rotation=45, ha='right', fontsize=10)

    # 添加网格线
    plt.grid(axis='y', linestyle='--', alpha=0.7)

    # 添加图例
    plt.legend(loc='upper right', fontsize=10)

    # 调整布局
    plt.tight_layout()

    # 显示图表
    plt.show()
if __name__ == "__main__":
    auth_token = loginAuth()
    device_id="18d121f0-561b-11f0-a556-4f10f26fc07f"
    agg="AVG"
    interval=21600000
    limit=""
    orderBy=""
    keys="RAIN_FALL_REALTIME"
    date_start = "2025-06-25 00:00:00.123"
    date_end = "2025-07-01 23:59:59.123"
    startTs=dateToTimestamp(date_start)
    endTs=dateToTimestamp(date_end)
    # data=get_soil_data_dk1(auth_token,startTs,endTs)
    # print(data)
    data=getWeatherAndSoilData(auth_token,device_id,startTs,endTs,"AVG",3600000,limit,orderBy,keys)
    print(data['RAIN_FALL'])
    # get_plot(data['1号传感器']['SOIL_MOISTURE_SURFACE'])
    get_plot(data['RAIN_FALL_REALTIME'])