import os import pandas as pd import plotly.express as px from algorithmContract.confBusiness import * from algorithmContract.contract import Contract from behavior.analystWithGoodBadPoint import AnalystWithGoodBadPoint class MinPitchAnalyst(AnalystWithGoodBadPoint): """ 风电机组最小桨距角分析 """ def typeAnalyst(self): return "min_pitch" def selectColums(self): return [Field_DeviceCode,Field_Time,Field_WindSpeed,Field_ActiverPower,Field_PitchAngel1] def processDateTime(self, dataFrame: pd.DataFrame, fieldTime:str = None): super().processDateTime(dataFrame,Field_YearMonthDay) def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes): dictionary = self.processTurbineData(turbineCodes,conf,self.selectColums()) turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo) dataFrameMerge = self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self) return self.drawTrendGraph(dataFrameMerge,turbineInfos, outputAnalysisDir, conf) def drawTrendGraph(self, dataFrameMerge: pd.DataFrame, turbineModelInfo: pd.Series,outputAnalysisDir, conf: Contract): """ Generates pitch angle distribution scatter plots for turbines in a wind farm using plotly. Parameters: - dataFrameMerge: pd.DataFrame, DataFrame containing turbine data. - outputAnalysisDir: str, path to save the output plots. - conf: Contract, configuration object containing field names. """ # 检查所需列是否存在 required_columns = {Field_YearMonthDay, Field_PitchAngel1} if not required_columns.issubset(dataFrameMerge.columns): raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}") pitchAngleRate = 'pitch_angle_rate' fieldPitchAngleBin = 'pitch_angle_bin' # Custom color scale: Blue (high pitch_angle_rate) to Light Grey (low pitch_angle_rate) # custom_color_scale = [ # (0.0, "rgb(255, 255, 255)"), # white for the lowest values # (0.05, "rgb(240, 240, 240)"), # Light grey for the lowest values # (0.5, "rgba(55.0, 135.0, 192.33333333333334, 1.0)"), # Medium blue-grey # # (0.75, "rgba(55.0, 135.0, 192.33333333333334, 1.0)"), # Medium blue-grey # # Dark blue for the highest values # (1.0, "rgba(55.0, 135.0, 192.33333333333334, 1.0)") # ] custom_color_scale = [ (0.0, "rgb(255, 255, 255)"), # White for the lowest values (0%) # (0.05, "rgb(135, 206, 235)"),# Sky blue for low but non-zero values (0.05, "rgb(30, 144, 255)"), # Dodger blue for for low but non-zero values (0.5, "rgb(0, 0, 255)"), # Blue for higher values (1.0, "rgb(0, 0, 139)") # Dark blue (Dark slate blue) ] # Group data by turbine identifier # dataFrameMerge = dataFrameMerge[dataFrameMerge[Field_PitchAngel1] <= 5] grouped = dataFrameMerge.groupby( [Field_NameOfTurbine, Field_CodeOfTurbine]) # 创建一个列表来存储各个风电机组的数据 turbine_data_list = [] result_rows = [] for name, group in grouped: # Convert the date column to datetime type group[Field_YearMonthDay] = pd.to_datetime( group[Field_YearMonthDay]) # Creating bins of 0.2 intervals for pitch angles bins = pd.interval_range(start=group[Field_PitchAngel1].min(), end=group[Field_PitchAngel1].max( ) + 0.1, freq=0.2, closed='right') group[fieldPitchAngleBin] = pd.cut( group[Field_PitchAngel1], bins=bins) group[fieldPitchAngleBin] = group[fieldPitchAngleBin].apply( lambda x: x.left) # 提取每个区间的左端点作为值 # Calculate the pitch angle rate within each day # df = group.groupby([Field_YearMonthDay, Field_PitchAngel1] # ).size().reset_index(name='count') df = group.groupby([Field_YearMonthDay, fieldPitchAngleBin] ).size().reset_index(name='count') total_counts = group.groupby(Field_YearMonthDay ).size().reset_index(name='total_count') df = df.merge(total_counts, on=Field_YearMonthDay) # df[pitchAngleRate] = df['count'] / df['total_count'] * 100 # df[pitchAngleRate] = (df['count'] / df['total_count']).apply(lambda x: x ** 0.5)*100 df[pitchAngleRate] = (df['count'] / df['total_count']) * 100 # Plotting using plotly fig = px.scatter(df, x=Field_YearMonthDay, # y=Field_PitchAngel1, # 桨距角不分仓方式 y=fieldPitchAngleBin, # 桨距角分仓方式 size='count', color=pitchAngleRate, # color_continuous_scale='Blues', color_continuous_scale=custom_color_scale ) # Set date format on x-axis fig.update_xaxes( title='时间', tickformat='%Y-%m-%d', tickangle=-45) fig.update_yaxes(title='桨距角', dtick=self.axisStepPitchAngle, range=[self.axisLowerLimitPitchAngle, 10], ) # Customizing legend fig.update_layout( title={ "text": f' {name[0]}的最小桨距角分布', "x": 0.5 }, coloraxis_colorbar=dict(title='百分比'), margin=dict(t=50, b=10), # t为顶部（top）间距，b为底部（bottom）间距 # plot_bgcolor='rgb(240, 240, 240)' ) # Set marker size if fixed size is needed # Fixed size for all points fig.update_traces(marker=dict(size=3, opacity=0.9)) # 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": "最小桨距角分析", "engineCode": engineTypeCode, "engineTypeName": engineTypeName, "xaixs": "时间", "yaixs": "桨距角(度)", "data": [{ "engineName": name[0], "engineCode": name[1], "title":f' {name[0]}的最小桨距角分布', "xData": df[Field_YearMonthDay].dt.strftime('%Y-%m-%d').tolist(), "yData": df[fieldPitchAngleBin].tolist(), "colorbar": df[pitchAngleRate].tolist(), "colorbartitle": "百分比(%)" }] } # Save plot filePathOfImage = os.path.join(outputAnalysisDir, f"{name[0]}.png") fig.write_image(filePathOfImage, scale=3) # filePathOfHtml = os.path.join(outputAnalysisDir, f"{name[0]}.html") # fig.write_html(filePathOfHtml) # 将JSON对象保存到文件 output_json_path = os.path.join(outputAnalysisDir, f"min_pitch{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) # 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: 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: name[1], # Field_Return_FilePath: filePathOfHtml, # Field_Return_IsSaveDatabase: True # }) result_df = pd.DataFrame(result_rows) return result_df