import os
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import *


class PitchPowerAnalyst(Analyst):
    """
    风电机组变桨-功率分析
    """

    def typeAnalyst(self):
        return "pitch_power"

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

    def plot_power_pitch_angle(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        x_name = 'power'
        y_name = 'pitch_angle'
        # 按设备名分组数据
        grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
        print("self.ratedPower {}".format(confData.rated_power))
        # 遍历每个设备并绘制散点图
        for name, group in grouped:
            # sns.lmplot函数参数scatter_kws： 设置为{"s": 5}时，会出现颜色丢失问题；
            g = sns.lmplot(x=confData.field_power, y=confData.field_pitch_angle1, data=group,
                           fit_reg=False, scatter_kws={"s": 5, "color": "b"}, legend=False, height=6, aspect=1.2)
            # g = sns.lmplot(x=confData.field_power, y=confData.field_pitch_angle1, data=group,
            #                fit_reg=False, scatter_kws={"s": 5}, legend=False, height=6, aspect=1.2)

            # 设置x轴和y轴的刻度
            for ax in g.axes.flat:
                ax.xaxis.set_major_locator(MultipleLocator(150))
                ax.set_xlim(-20, confData.rated_power * (1+0.2))

                ax.yaxis.set_major_locator(MultipleLocator(2))
                ax.set_ylim(-1, 18)

                ax.set_xlabel(x_name)
                ax.set_ylabel(y_name)

            # 设置x轴刻度值旋转角度为45度  
            plt.tick_params(axis='x', rotation=45)
            # 调整布局和设置标题
            plt.tight_layout()
            plt.title(f'{Field_NameOfTurbine}={name}')

            # 保存图像并关闭绘图窗口
            output_file = os.path.join(outputAnalysisDir, f"{name}.png")
            plt.savefig(output_file, bbox_inches='tight', dpi=120)
            plt.close()