WTOAAM/dataAnalysisBusiness/algorithm/pitchTSRCpAnalyst.py

import os

import pandas as pd
import plotly.graph_objects as go
from algorithmContract.confBusiness import *
from algorithmContract.contract import Contract
from behavior.analystWithGoodPoint import AnalystWithGoodPoint
from plotly.subplots import make_subplots


class PitchTSRCpAnalyst(AnalystWithGoodPoint):

    def typeAnalyst(self):
        return "pitch_tsr_cp"

    def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
        dictionary=self.processTurbineData(turbineCodes,conf,[Field_DeviceCode,Field_Time,Field_PitchAngel1,Field_WindSpeed,Field_ActiverPower,Field_RotorSpeed,Field_GeneratorSpeed])
        dataFrameMerge=self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
        return self.windRoseAnalysis(dataFrameMerge, outputAnalysisDir, conf)


    def windRoseAnalysis(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract):
        """
        绘制3D曲面图。

        参数:
        df: pandas.DataFrame, 必须包含Field_PitchAngel1, Field_TSR, 和 Field_Cp这三个字段。

        返回:
        一个Plotly图形对象。
        """
        # 检查所需列是否存在
        required_columns = {Field_PitchAngel1, Field_TSR, Field_Cp}
        if not required_columns.issubset(dataFrameMerge.columns):
            raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns},Available: {dataFrameMerge.columns}")

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

        result_rows = []


        ## 分图
        dataFrameMerge['PitchAngleBin'] = pd.cut(dataFrameMerge[Field_PitchAngel1], bins=[-1, 1, 3, 5, 7, 9], right=True, labels=['[-1,1]', '(1,3]', '(3,5]', '(5,7]', '(7,9]'])
        # turbines = dataFrameMerge[Field_NameOfTurbine].unique()
        turbines = dataFrameMerge.groupby([Field_NameOfTurbine, Field_CodeOfTurbine]).groups.keys()

        for turbine_name, turbine_code in turbines:
            fig = make_subplots(rows=1, cols=1)
            # 选择当前风力发电机的数据
            turbine_data = dataFrameMerge[dataFrameMerge[Field_NameOfTurbine] == turbine_name]
            # 按角区间分组
            grouped = turbine_data.groupby('PitchAngleBin')
            # 为每个区间添加一个数据迹
            for pitch_bin, group in grouped:
                fig.add_trace(go.Scatter(
                    x=group[Field_TSR],
                    y=group[Field_Cp],
                    mode='markers',
                    name=pitch_bin,
                    marker=dict(
                        size=3,  # Smaller size, default is usually around 10
                        opacity=0.8  # Reduced opacity, default is 1
                    )
                    # name=f'Pitch: {name}'
                ))

            # 更新布局
            fig.update_layout(
                title=f'风能利用系数 vs. 叶尖速比 : {turbine_name}',
                xaxis_title='叶尖速比',
                yaxis_title='风能利用系数',
                legend_title='桨距角',
                legend=dict(
                    # orientation="h",
                    itemsizing="constant",  # Use constant size for legend items
                    itemwidth=80  # Set the width of legend items to 50 pixels
                ),
                xaxis=dict(
                    # dtick=2,
                    range=[5,20]),
                yaxis=dict(
                    dtick=self.axisStepCp,
                    range=[self.axisLowerLimitCp,self.axisUpperLimitCp]
                )
                # legend=dict(
                #     orientation="h",
                #     xanchor="center",
                #     x=0.5,
                #     y=-0.2
                # )
            )

            # 保存html
            filePathOfPng = os.path.join(outputAnalysisDir, f"{turbine_name}.png")
            fig.write_image(filePathOfPng)
            filePathOfHtml = os.path.join(outputAnalysisDir, f"{turbine_name}.html")
            fig.write_html(filePathOfHtml)

            result_rows.append({
                Field_Return_TypeAnalyst: self.typeAnalyst(),
                Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
                Field_CodeOfTurbine: turbine_code,
                Field_Return_FilePath: filePathOfPng,
                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: turbine_code,
                Field_Return_FilePath: filePathOfHtml,
                Field_Return_IsSaveDatabase: True
            })

        result_df = pd.DataFrame(result_rows)

        return result_df