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])
        dataFrameMerge=self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
        return self.drawWindSpeedAnalysis(dataFrameMerge, outputAnalysisDir, conf)

    def drawWindSpeedAnalysis(self, dataFrameMerge: pd.DataFrame, 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-{self.turbineModelInfo[Field_MachineTypeCode].iloc[0]}')

        # 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

        # 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)

        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_Return_FilePath: filePathOfHtml,
            Field_Return_IsSaveDatabase: True
        })

        result_df = pd.DataFrame(result_rows)

        return result_df