WTOAAM/dataAnalysisBusiness/algorithm/windSpeedAnalyst.py

import os

import pandas as pd
import plotly.express as px
from algorithmContract.confBusiness import *
from algorithmContract.contract import Contract
from behavior.analystNotFilter import AnalystNotFilter


class WindSpeedAnalyst(AnalystNotFilter):

    def typeAnalyst(self):
        return "wind_speed"

    def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
        dictionary=self.processTurbineData(turbineCodes,conf,[Field_DeviceCode,Field_Time,Field_WindSpeed,Field_ActiverPower])
        turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes,
                                                   self.turbineInfo)
        dataFrameMerge=self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
        return self.drawWindSpeedAnalysis(dataFrameMerge, turbineInfos,outputAnalysisDir, conf)

    def drawWindSpeedAnalysis(self, dataFrameMerge: pd.DataFrame, turbineModelInfo: pd.Series, outputAnalysisDir, conf: Contract):
        # 检查所需列是否存在
        required_columns = {Field_NameOfTurbine, Field_WindSpeed}
        if not required_columns.issubset(dataFrameMerge.columns):
            raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}")

        # 确保'风速'列是数值类型
        dataFrameMerge[Field_WindSpeed] = pd.to_numeric(
            dataFrameMerge[Field_WindSpeed], errors='coerce')

        # 计算每个turbine_name的平均风速
        average_wind_speed = dataFrameMerge.groupby(Field_NameOfTurbine)[
            Field_WindSpeed].mean().reset_index()

        # 使用plotly绘制柱状图
        fig = px.bar(average_wind_speed, x=Field_NameOfTurbine,
                     y=Field_WindSpeed, title=f'机组平均风速')

        # 更新x轴和y轴的标签
        fig.update_xaxes(title_text='机组', tickangle=-45)
        fig.update_yaxes(title_text='平均风速 (m/s)')
        # 如果需要进一步调整标题样式或位置（尽管默认情况下标题是居中的）
        # 可以使用 update_layout 来设置标题的 x 坐标（xanchor）为 'center' 来确保居中
        fig.update_layout(title_x=0.5)  # 设置标题的x位置为图的中心

        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": "风速均值分析",
            "engineCode": engineTypeCode,
            "engineTypeName": engineTypeName,
            "xaixs": "机组",
            "yaixs": "平均风速(m/s)",
            "data": [{
                "engineName":"",
                "engineCode": "",
                "title": f'机组平均风速',
                "xData": average_wind_speed[Field_NameOfTurbine].tolist(),
                "yData": average_wind_speed[Field_WindSpeed].tolist(),

            }]
        }

        # Save plot
        # filePathOfImage = os.path.join(
        #     outputAnalysisDir, f"WindSpeedAvg_Turbines.png")
        # fig.write_image(filePathOfImage, scale=3)
        # filePathOfHtml = os.path.join(
            # outputAnalysisDir, f"WindSpeedAvg_Turbines.html")
        # fig.write_html(filePathOfHtml)

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

        result_rows = []
        # result_rows.append({
        #     Field_Return_TypeAnalyst: self.typeAnalyst(),
        #     Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
        #     Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
        #     Field_CodeOfTurbine: "total",
        #     Field_Return_FilePath: filePathOfImage,
        #     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: "total",
            Field_MillTypeCode: 'total',
            Field_Return_FilePath: output_json_path,
            Field_Return_IsSaveDatabase: True
        })

        result_df = pd.DataFrame(result_rows)

        return result_df