import os
from datetime import datetime
import pandas as pd
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from behavior.analyst import Analyst
from utils.directoryUtil import DirectoryUtil as dir
from algorithmContract.confBusiness import *
import plotly.express.colors as px_colors


class PowerScatterAnalyst(Analyst):
    """
    风电机组功率曲线散点分析。
    秒级scada数据运算太慢，建议使用分钟级scada数据
    """

    def typeAnalyst(self):
        return "power_scatter"

    def turbinesAnalysis(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        if len(dataFrameMerge) <= 0:
            print("After screening for blade pitch angle less than the configured value, plot power curve scatter points without data")
            return

        dataFrameGuaranteePowerCurve = self.dataFrameContractOfTurbine

        self.drawOfPowerCurveScatter(
            dataFrameMerge, outputAnalysisDir, confData, dataFrameGuaranteePowerCurve)

    def contractGuaranteePowerCurveData(self, csvPowerCurveFilePath):
        dataFrameGuaranteePowerCurve = pd.read_csv(
            csvPowerCurveFilePath, encoding=charset_unify)

        return dataFrameGuaranteePowerCurve

    def drawOfPowerCurveScatter(self, dataFrame: pd.DataFrame,  outputAnalysisDir, confData: ConfBusiness, dataFrameGuaranteePowerCurve: pd.DataFrame):
        """  
        绘制风速-功率分布图并保存为文件。  

        参数:  
        dataFrameMerge (pd.DataFrame): 包含数据的DataFrame，需要包含设备名、风速和功率列。
        csvPowerCurveFilePath (str): 功率曲线文件路径。   
        outputAnalysisDir (str): 分析输出目录。  
        confData (ConfBusiness): 配置   
        """
        # 按设备名分组数据
        colorsList = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
                      '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', '#aec7e8', '#ffbb78']
        grouped = dataFrame.groupby(Field_NameOfTurbine)

        # 遍历每个设备的数据
        for name, group in grouped:
            # 创建颜色映射，将每个年月映射到一个唯一的颜色
            unique_months = group[Field_YearMonth].unique()
            colors = [
                colorsList[i % 12] for i in range(len(unique_months))]
            color_map = dict(zip(unique_months, colors))

            # 使用go.Scatter3d创建3D散点图
            trace = go.Scatter3d(
                x=group[confData.field_wind_speed],
                y=group[Field_YearMonth],
                z=group[confData.field_power],
                mode='markers',
                marker=dict(
                    color=[color_map[month]
                           for month in group[Field_YearMonth]],
                    size=1.5,
                    line=dict(
                        color='rgba(0, 0, 0, 0)',  # 设置边框颜色为透明，以去掉白色边框
                        width=0  # 设置边框宽度为0，进一步确保没有边框
                    ),
                    opacity=0.8  # 调整散点的透明度，增加透视效果
                )
            )

            # 创建图形
            fig = go.Figure(data=[trace])

            # 更新图形的布局
            fig.update_layout(
                title={
                    "text": f'Monthly power 3D scatter plot {name}',
                    "x": 0.5
                },
                scene=dict(
                    xaxis=dict(title='Wind Speed'),
                    yaxis=dict(
                        title='Time',
                        tickmode='array',
                        tickvals=unique_months,
                        ticktext=unique_months,
                        categoryorder='category ascending'
                    ),
                    zaxis=dict(
                        title='Power',
                        dtick=confData.graphSets["activePower"]["step"] if not self.common.isNone(
                            confData.graphSets["activePower"]) and not self.common.isNone(
                            confData.graphSets["activePower"]["step"]) else 250,
                        range=[confData.graphSets["activePower"]["min"] if not self.common.isNone(
                            confData.graphSets["activePower"]["min"]) else 0, confData.graphSets["activePower"]["max"] if not self.common.isNone(confData.graphSets["activePower"]["max"]) else confData.rated_power*1.2],
                    )
                ),
                scene_camera=dict(
                    up=dict(x=0, y=0, z=1),  # 保持相机向上方向不变
                    center=dict(x=0, y=0, z=0),  # 保持相机中心位置不变
                    eye=dict(x=-1.8, y=-1.8, z=1.2)  # 调整eye属性以实现水平旋转180°
                ),
                margin=dict(t=50, b=10)  # t为顶部（top）间距，b为底部（bottom）间距
            )

            # 保存图像
            outputFileHtml = os.path.join(
                outputAnalysisDir, "{}.html".format(name))

            fig.write_html(outputFileHtml)