WTOAAM/dataAnalysisBusiness/algorithm/tsrCpPowerScatterAnalyst.py

import os
import pandas as pd
import math
import numpy as np
from plotly.subplots import make_subplots
import plotly.express as px
import pandas as pd
import plotly.graph_objects as go
import seaborn as sns
from matplotlib.ticker import MultipleLocator
from behavior.analystWithGoodPoint import AnalystWithGoodPoint
from utils.directoryUtil import DirectoryUtil as dir
from algorithmContract.confBusiness import *
from algorithmContract.contract import Contract


class TSRCpPowerScatterAnalyst(AnalystWithGoodPoint):
    """
    风电机组叶尖速比-Cp-功率散点分析
    """

    def typeAnalyst(self):
        return "tsr_cp_power_scatter"

    def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
        dictionary = self.processTurbineData(turbineCodes,conf,[Field_DeviceCode,Field_Time,Field_WindSpeed,Field_ActiverPower,Field_RotorSpeed,Field_GeneratorSpeed])
        turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes,
                                                   self.turbineInfo)
        dataFrameMerge = self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
        # return self.plot_tsr_distribution(self.tsr(dataFrameMerge), outputAnalysisDir, conf)
        dataFrameMerge[Field_PowerFarmName] = self.currPowerFarmInfo.loc[Field_PowerFarmName]

        # Ensure the necessary columns are of float type
        dataFrameMerge[Field_WindSpeed] = dataFrameMerge[Field_WindSpeed].astype(float)
        dataFrameMerge[Field_RotorSpeed] = dataFrameMerge[Field_RotorSpeed].astype(float)
        dataFrameMerge[Field_Cp] = dataFrameMerge[Field_Cp].astype(float)
        dataFrameMerge[Field_ActiverPower] = dataFrameMerge[Field_ActiverPower].astype(float)
        dataFrameMerge[Field_GeneratorSpeed] = dataFrameMerge[Field_GeneratorSpeed].astype(float)

        max_cutin = self.turbineModelInfo[Field_CutInWS].max()
        min_rated = self.turbineModelInfo[Field_RatedWindSpeed].min()
        dataFrameMerge = dataFrameMerge[(dataFrameMerge[Field_WindSpeed] > max_cutin) & (dataFrameMerge[Field_WindSpeed] < min_rated)]

        # Group by 'power_floor' and calculate median, max, and min of TSR
        dataFrameMerge[Field_TSRModified] = dataFrameMerge[Field_TSR] / (dataFrameMerge[Field_Cp] ** (1/3))

        return self.plot_tsrcp_distribution(dataFrameMerge, turbineInfos, outputAnalysisDir, conf)

    def plot_tsrcp_distribution(self, dataFrameMerge: pd.DataFrame, turbineModelInfo: pd.Series, outputAnalysisDir, conf: Contract, encoding=charset_unify):
        """
        Generates tsr distribution plots for turbines in a wind farm.

        Parameters:
        - outputAnalysisDir: str, path to the directory containing input CSV files.
        - farm_name: str, name of the wind farm.
        - encoding: str, encoding of the input CSV files. Defaults to 'utf-8'.
        """
        x_name = Field_ActiverPower
        y_name = Field_TSRModified

        upLimitOfTSR = 20

        result_rows = []

        # 绘制每个设备的TSR分布图
        for name, group in dataFrameMerge.groupby([Field_NameOfTurbine, Field_CodeOfTurbine]):
            fig = px.scatter(group,x=x_name, y=y_name)
            fig.update_layout(
                title={"text": '机组: {}'.format(name[0])},
                xaxis=dict(
                    title='功率',
                    dtick=200,
                    tickangle=-45,
                    range=[0, 1800]),
                yaxis=dict(
                    title= '叶尖速比/风能利用系数分析^(1/3)', # r"$\frac{TSR}{Cp^{1/3}}$"  # 仅在.png格式下正确显示
                    dtick=self.axisStepTSR,
                    range=[self.axisLowerLimitTSR,
                           self.axisUpperLimitTSR]
                )
            )
            fig.update_traces(marker=dict(size=3))
            fig.update_xaxes(tickangle=-45)

            # 确保从 Series 中提取的是具体的值
            engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
            if isinstance(engineTypeCode, pd.Series):
                engineTypeCode = engineTypeCode.iloc[0]

            engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
            if isinstance(engineTypeName, pd.Series):
                engineTypeName = engineTypeName.iloc[0]
            # 构建最终的JSON对象
            json_output = {
                "analysisTypeCode": "机组叶尖速比-Cp-功率散点分析",
                "engineCode": engineTypeCode,
                "engineTypeName": engineTypeName,
                "xaixs": "功率(kW)",
                "yaixs": "叶尖速比/风能利用系数分析^(1/3)",
                "data": [{
                    "engineName": name[0],  # Field_NameOfTurbine
                    "engineCode": name[1],  # Field_CodeOfTurbine
                    "title": f' 机组: {format(name[0])}',
                    "xData": group[x_name].tolist(),
                    "yData": group[y_name].tolist(),

                }]
            }

            # 保存图像
            pngFileName = f"{name[0]}.png"
            pngFilePath = os.path.join(outputAnalysisDir, pngFileName)
            fig.write_image(pngFilePath, scale=3)

            # 将JSON对象保存到文件
            output_json_path = os.path.join(outputAnalysisDir, f"{name[0]}.json")
            with open(output_json_path, 'w', encoding='utf-8') as f:
                import json
                json.dump(json_output, f, ensure_ascii=False, indent=4)

            # 保存HTML
            # htmlFileName = f"{name[0]}.html"
            # htmlFilePath = os.path.join(outputAnalysisDir, htmlFileName)
            # fig.write_html(htmlFilePath)

            # 如果需要返回DataFrame，可以包含文件路径
            result_rows.append({
                Field_Return_TypeAnalyst: self.typeAnalyst(),
                Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
                Field_CodeOfTurbine: name[1],
                Field_Return_FilePath: output_json_path,
                Field_Return_IsSaveDatabase: True
            })


            result_rows.append({
                Field_Return_TypeAnalyst: self.typeAnalyst(),
                Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
                Field_CodeOfTurbine: name[1],
                Field_Return_FilePath: pngFilePath,
                Field_Return_IsSaveDatabase: False
            })

            # result_rows.append({
            #     Field_Return_TypeAnalyst: self.typeAnalyst(),
            #     Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
            #    Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
            #     Field_CodeOfTurbine: name[1],
            #     Field_Return_FilePath: htmlFilePath,
            #     Field_Return_IsSaveDatabase: True
            # })

        result_df = pd.DataFrame(result_rows)
        return result_df