WTOAAM/wtoaamapi/apps/business/algorithm/generatorSpeedPowerAnalyst.py

import os
import pandas as pd
from datetime import datetime
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
import matplotlib.cm as cm
from matplotlib.ticker import MultipleLocator
from matplotlib.colors import Normalize
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import Field_NameOfTurbine, ConfBusiness


class GeneratorSpeedPowerAnalyst(Analyst):
    """
    风电机组发电机转速-有功功率分析
    """

    def typeAnalyst(self):
        return "speed_power"

    def turbinesAnalysis(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, confData: ConfBusiness):
        self.create_and_save_plots(
            dataFrameMerge, outputAnalysisDir, confData, confData.field_gen_speed, confData.field_power)

    def create_and_save_plots(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, confData: ConfBusiness, field_Generator_Speed, field_Active_Power):
        x_name = 'generator_speed'
        y_name = 'power'

        grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
        for name, group in grouped:
            # 创建图形和坐标轴
            fig, ax = plt.subplots()
            cmap = cm.get_cmap('rainbow')

            # 绘制散点图
            scatter = ax.scatter(x=group[confData.field_gen_speed]*confData.value_gen_speed_multiple if not self.common.isNone(confData.value_gen_speed_multiple) else group[confData.field_gen_speed],
                                 y=group[confData.field_power], c=group['monthIntTime'], cmap=cmap, s=5)

            # g = sns.lmplot(x=field_Generator_Speed, y=field_Active_Power, data=group,
            #                fit_reg=False, scatter_kws={"s": 5, "color": "b"}, legend=False, height=6, aspect=1.2)
            # g = sns.lmplot(x=field_Generator_Speed, y=field_Active_Power, data=group,
            #                fit_reg=False, scatter_kws={"s": 5}, legend=False, height=6, aspect=1.2)
            # for ax in g.axes.flat:
            #     # ax.xaxis.set_major_locator(MultipleLocator(100))
            #     ax.set_xlim(confData.value_gen_speed_min, confData.value_gen_speed_max)
            #     ax.set_xlabel(x_name)
            #     ax.set_ylabel(y_name)

            

            # 设置图形标题和坐标轴标签
            ax.set_title(f'turbine_name={name}')
            ax.set_xlim(confData.value_gen_speed_min,
                        confData.value_gen_speed_max)
            # 设置x轴的刻度步长  
            # 假设您想要每100个单位一个刻度  
            if not self.common.isNone(confData.value_gen_speed_step):
                loc = MultipleLocator(confData.value_gen_speed_step) # 创建每100个单位一个刻度的定位器  
                ax.xaxis.set_major_locator(loc) # 将定位器应用到x轴上 
            ax.set_xlabel(x_name)
            ax.set_ylabel(y_name)

            # 显示图例，并调整位置
            ax.legend(loc='lower right')

            # 设置颜色条
            unique_months = len(group['年月'].unique())
            ticks = np.linspace(group['monthIntTime'].min(
            ), group['monthIntTime'].max(), min(unique_months, 6))  # 减少刻度数量
            ticklabels = [datetime.fromtimestamp(
                tick).strftime('%Y-%m') for tick in ticks]
            norm = Normalize(group['monthIntTime'].min(),
                             group['monthIntTime'].max())
            sm = cm.ScalarMappable(norm=norm, cmap=cmap)

            # 添加颜色条
            cbar = fig.colorbar(sm, ax=ax)
            cbar.set_ticks(ticks)
            cbar.set_ticklabels(ticklabels)
            # 旋转x轴刻度标签
            plt.xticks(rotation=45)

            plt.tight_layout()
            plt.title(f'{Field_NameOfTurbine}={name}')
            # 保存图片到指定路径
            output_file = os.path.join(outputAnalysisDir, f"{name}.png")
            plt.savefig(output_file, bbox_inches='tight', dpi=120)
            plt.close()