import os
from datetime import datetime
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.ticker import MultipleLocator
from matplotlib.colors import Normalize
import seaborn as sns
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from geopy.distance import geodesic
from behavior.analyst import Analyst
from utils.directoryUtil import DirectoryUtil as dir
from algorithmContract.confBusiness import *


class RatedWindSpeedAnalyst(Analyst):
    """
    风电机组额定风速分析。
    秒级scada数据运算太慢，建议使用分钟级scada数据
    """

    def typeAnalyst(self):
        return "rated_windspeed"

    def turbinesAnalysis(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        self.draw(dataFrameMerge, outputAnalysisDir, confData)

    def draw(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir,  confData: ConfBusiness):
        """  
        绘制并保存满发风速区间数据计数图。  

        参数:  
        dataFrameMerge (pd.DataFrame): 包含数据的DataFrame，需要包含设备名、风速和功率列。
        outputAnalysisDir (str): 分析输出目录。  
        confData (ConfBusiness): 配置   
        """

        # 初始化结果列表
        res = []

        # 按设备名分组并计算统计数据
        grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
        for name, group in grouped:
            group = group[group[confData.field_wind_speed] >= 11]
            res.append([name, group[confData.field_power].min(), group[confData.field_power].max(
            ), group[confData.field_power].mean(), group.shape[0]])

        # 创建结果DataFrame
        data = pd.DataFrame(res, columns=[
                            Field_NameOfTurbine, 'power-min', 'power-max', 'power-mean', 'count'])

        # 绘制风速区间数据计数图
        fig, ax = plt.subplots()
        sns.barplot(x=Field_NameOfTurbine, y='count',
                    data=data, ax=ax, color='dodgerblue')
        ax.set_title('Rated - full wind speed interval data count')
        ax.grid(True)
        # 旋转45度
        plt.xticks(rotation=45)  
        # 保存图表
        plt.savefig(os.path.join(outputAnalysisDir, "风速区间数据计数.png"),
                    bbox_inches='tight', dpi=120)
        plt.close()