WTOAAM/dataAnalysisBusiness/algorithm/powerScatter2DAnalyst.py

import os
from datetime import datetime

import numpy as np
import pandas as pd
import plotly.graph_objects as go
from algorithmContract.confBusiness import *
from algorithmContract.contract import Contract
from behavior.analystWithGoodBadPoint import AnalystWithGoodBadPoint
from plotly.subplots import make_subplots


class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
    """
    风电机组功率曲线散点分析。
    秒级scada数据运算太慢，建议使用分钟级scada数据
    """

    def typeAnalyst(self):
        return "power_scatter_2D"

    def selectColumns(self):
        return [Field_DeviceCode, Field_Time, Field_WindSpeed, Field_ActiverPower]

    def addPropertyToDataFrame(self,dataFrameOfTurbine : pd.DataFrame, currTurbineInfo : pd.Series, currTurbineModelInfo : pd.Series):
        dataFrameOfTurbine[Field_PowerFarmCode] = self.currPowerFarmInfo[Field_PowerFarmCode]
        dataFrameOfTurbine[Field_MillTypeCode] = currTurbineModelInfo[Field_MillTypeCode]

    def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
        dictionary = self.processTurbineData(turbineCodes, conf, self.selectColumns())
        dataFrame = self.userDataFrame(dictionary, conf.dataContract.configAnalysis, self)
        turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
        if len(dataFrame) <= 0:
            print("After screening for blade pitch angle less than the configured value, plot power curve scatter points without data")
            return
        grouped = self.dataFrameContractOfTurbine.groupby(
            [Field_PowerFarmCode, Field_MillTypeCode])

        for groupByKey, contractPowerCurveOfMillType in grouped:
            break
        return self.drawOfPowerCurveScatter(dataFrame, turbineInfos,outputAnalysisDir, conf, contractPowerCurveOfMillType)

    def drawOfPowerCurveScatter(self, dataFrame: pd.DataFrame,  turbineModelInfo: pd.Series, outputAnalysisDir, conf: Contract, dataFrameGuaranteePowerCurve: pd.DataFrame):
        """
        绘制风速-功率分布图并保存为文件。

        参数:
        dataFrameMerge (pd.DataFrame): 包含数据的DataFrame，需要包含设备名、风速和功率列。
        csvPowerCurveFilePath (str): 功率曲线文件路径。
        outputAnalysisDir (str): 分析输出目录。
        confData (ConfBusiness): 配置
        """
        x_name = '风速'
        y_name = '功率'

        #机型切入风速 series
        cutInWsField = self.turbineModelInfo[Field_CutInWS]
        cut_in_ws = cutInWsField.min() - 1 if cutInWsField.notna().any() else 2
        # if not dataFrame.empty and Field_CutInWS in dataFrame.columns and dataFrame[Field_CutInWS].notna().any():
        #     cut_in_ws = dataFrame[Field_CutInWS].min() - 1
        # else:
        #     cut_in_ws = 2

        # 按设备名分组数据
        grouped = dataFrame.groupby([Field_NameOfTurbine, Field_CodeOfTurbine])
        result_rows = []

        # 定义固定的颜色映射列表
        fixed_colors = [
                "#3E409C",
                "#476CB9",
                "#3586BF",
                "#4FA4B5",
                "#52A3AE",
                "#60C5A3",
                "#85D0AE",
                "#A8DCA2",
                "#CFEE9E",
                "#E4F39E",
                "#EEF9A7",
                "#FBFFBE",
                "#FDF1A9",
                "#FFE286",
                "#FFC475",
                "#FCB06C",
                "#F78F4F",
                "#F96F4A",
                "#E4574C",
                "#CA3756",
                "#AF254F"
        ]

        # 将 fixed_colors 转换为 Plotly 的 colorscale 格式
        fixed_colorscale = [
            [i / (len(fixed_colors) - 1), color] for i, color in enumerate(fixed_colors)
        ]
        fixed_colors_points = [
            "#F96F4A",
            "#FFC475",
            "#FBFFBE",
            "#85D0AE",
            "#3586BF",
            "#3E409C"

        ]

        # 遍历每个设备的数据
        for name, group in grouped:
            fig = make_subplots()

            # 提取月份
            group['month'] = group['monthIntTime'].apply(lambda x: datetime.fromtimestamp(x).month)
            unique_months = group['month'].unique()

            # 计算时间跨度
            time_span_months = len(unique_months)

            if time_span_months >= 6:
                # 绘制散点图（时间跨度大于等于6个月）
                scatter = go.Scatter(x=group[Field_WindSpeed],
                                     y=group[Field_ActiverPower],
                                     mode='markers',
                                     marker=dict(
                                         color=group['monthIntTime'],
                                         colorscale=fixed_colorscale,  # 使用自定义的 colorscale
                                         size=3,
                                         opacity=0.7,
                                         colorbar=dict(
                                             tickvals=np.linspace(
                                                 group['monthIntTime'].min(), group['monthIntTime'].max(), 6),
                                             ticktext=[datetime.fromtimestamp(ts).strftime(
                                                 '%Y-%m') for ts in np.linspace(group['monthIntTime'].min(), group['monthIntTime'].max(), 6)],
                                             thickness=18,
                                             len=1,  # 设置颜色条的长度，使其占据整个图的高度
                                             outlinecolor='rgba(255,255,255,0)'
                                         ),
                                         showscale=True
                                     ),
                                     showlegend=False)  # 不显示散点图的 legend，用 colorbar 代替
                fig.add_trace(scatter)
            else:
                # 绘制散点图（时间跨度小于6个月）
                for i, month in enumerate(unique_months):
                    month_data = group[group['month'] == month]
                    # 使用固定的颜色列表
                    color = fixed_colors_points[i % len(fixed_colors_points)]
                    scatter = go.Scatter(x=month_data[Field_WindSpeed],
                                         y=month_data[Field_ActiverPower],
                                         mode='markers',
                                         marker=dict(
                                             color=color,
                                             size=3,
                                             opacity=0.7
                                         ),
                                         name=f'{datetime.fromtimestamp(month_data["monthIntTime"].iloc[0]).strftime("%Y-%m")}',
                                         showlegend=True)
                    fig.add_trace(scatter)

           # 绘制合同功率曲线
            line = go.Scatter(x=dataFrameGuaranteePowerCurve[Field_WindSpeed],
                              y=dataFrameGuaranteePowerCurve[Field_ActiverPower],
                              mode='lines+markers',
                              marker=dict(color='gray', size=7),
                              name='合同功率曲线')
            fig.add_trace(line, secondary_y=False)

            # 设置图形布局
            fig.update_layout(
                title=f'机组: {name[0]}',
                xaxis=dict(title=x_name,
                           range=[cut_in_ws, 25],
                           tickmode='linear', tick0=0, dtick=1,
                           tickangle=-45),
                yaxis=dict(title=y_name,
                           dtick=self.axisStepActivePower,
                           range=[self.axisLowerLimitActivePower,
                                  self.axisUpperLimitActivePower]
                           ),
                legend=dict(yanchor="bottom", y=0, xanchor="right", x=1, font=dict(
                    size=10), bgcolor='rgba(255,255,255,0)')
            )
            # 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]
              # 使用 apply() 对每个元素调用 datetime.fromtimestamp
            group['monthIntTime'] = group['monthIntTime'].apply(lambda x: datetime.fromtimestamp(x).strftime('%Y-%m'))
            # 定义要替换的空值类型
            na_values = {pd.NA, float('nan')}
            # 构建最终的JSON对象
            json_output = {
                "analysisTypeCode": "逐月有功功率散点2D分析",
                "engineCode":  engineTypeCode,
                "engineTypeName": engineTypeName,
                "xaixs": "风速(m/s)",
                "yaixs": "有功功率(kW)",
                "data": [
                    {# 提取机组数据
                    "engineName": name[0],
                    "engineCode": name[1],
                    "title":f' 逐月有功功率散点2D分析-机组: {name[0]}',
                    "xData": group[Field_WindSpeed].replace(na_values, None).tolist(),
                    "xrange":[cut_in_ws, 25],
                    "yData": group[Field_ActiverPower].replace(na_values, None).tolist(),
                    "yrange":[self.axisLowerLimitActivePower,self.axisUpperLimitActivePower],
                    "colorbar": group['monthIntTime'].tolist(),
                    "colorbartitle": "年月",
                    "mode":"markers"

                    },
                    {# 提取合同功率曲线数据
                        "enginName": "合同功率曲线",
                        "xData":dataFrameGuaranteePowerCurve[Field_WindSpeed].replace(na_values, None).tolist(),
                        "yData":dataFrameGuaranteePowerCurve[Field_ActiverPower].replace(na_values, None).tolist(),
                        "zData": [],
                        "mode":"lines+markers"
                    }]
            }

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

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


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

            # 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