修改日志打印与散点2D图合同功率曲线取值错误bug

README.md

@@ -132,14 +132,14 @@ pip download -r requirements.txt -d download
   地址（mysql）：106.120.102.238:10336
   业务数据库（测试）名：energy_data
   业务数据库（生产）名：energy_data_prod
-  账户名：admin
+  账户名：root
   密码：admin123456
 
   地址（mysql）：106.120.102.238:16306
   基础数据库（测试）名：energy
   基础数据库（生产）名：energy_prod
   基础数据库（演示）名：energy_show
-  账户名：root
+  账户名：admin
   密码：admin123456
 
   地址（TIDB）：(无外网)

dataAnalysisBehavior/behavior/baseAnalyst.py

@@ -630,8 +630,7 @@ class BaseAnalyst(ABC):
                     dictionary[timeGranularity] = dataFrameOfTurbines
             return dictionary
         except Exception as e:
-            self.logger.error(f"Error processing turbine data:{e.message}")
-            traceback.format_exc()
+            self.logger.error(f"Error processing turbine data:{traceback.format_exc()}")
             raise
 
     def addPropertyToDataFrame(self,dataFrameOfTurbine : pd.DataFrame, currTurbineInfo : pd.Series, currTurbineModelInfo: pd.Series):

dataAnalysisBusiness/algorithm/dataProcessor.py

@@ -99,8 +99,7 @@ class DataProcessor:
             self.logger.info(f"analysis type execute result : dataFrame = {dataFram}")
             return dataFram
         except Exception as exception:
-            self.logger.info(f"analysis type execute error : {exception.message}")
-            traceback.format_exc()
+            self.logger.info(f"analysis type execute error : {traceback.format_exc()}")
             raise exception
 
     def notifyOfTurbines(self, conf: Contract,turbineCodes) -> pd.DataFrame:
@@ -118,8 +117,7 @@ class DataProcessor:
                     result = future.result()
                     results.append(result)
                 except Exception as exc:
-                    self.logger.info(f"future deal error. future : {future} , error is {exc.message}")
-                    traceback.format_exc()
+                    self.logger.info(f"future deal error. future : {future} , error is {traceback.format_exc()}")
                     raise exc
 
         returnResult = pd.concat(results, ignore_index=True)
@@ -211,12 +209,9 @@ class DataProcessor:
                 f"批次：{conf.dataContract.dataFilter.dataBatchNum} 完成机组运行数据加载及再处理，执行请求发电性能分析...")
 
             for turbine in turbines:
-                self.logger.info(
-                    f"Power Farm: {conf.dataContract.dataFilter.powerFarmID} Batch: {conf.dataContract.dataFilter.dataBatchNum}  Turbine: {turbine} .")
-                dataFrameOfReturn = self.notifyOfTurbine(
-                    conf, turbine)
-                DataFrameOutput = pd.concat(
-                    [DataFrameOutput, dataFrameOfReturn], ignore_index=True)
+                self.logger.info(f"Power Farm: {conf.dataContract.dataFilter.powerFarmID} Batch: {conf.dataContract.dataFilter.dataBatchNum}  Turbine: {turbine} .")
+                dataFrameOfReturn = self.notifyOfTurbine(conf, turbine)
+                DataFrameOutput = pd.concat([DataFrameOutput, dataFrameOfReturn], ignore_index=True)
 
             # Notify processing for all turbines
             Outputs = self.notifyOfTurbines( conf,turbines)
@@ -233,8 +228,7 @@ class DataProcessor:
             self.logger.info(
                 f"批次：{conf.dataContract.dataFilter.dataBatchNum} 完成请求发电性能分析、输出及更新状态")
         except (CustomError, Exception) as e:
-            self.logger.info(f" executeAnalysis error: {e.message}")
-            traceback.format_exc()
+            self.logger.info(f"executeAnalysis error: {traceback.format_exc()}")
             try:
                 dbUtil = GetBusinessFoundationDbUtil()
                 ex = e if isinstance(e, CustomError) else CustomError(-1)
@@ -244,9 +238,7 @@ class DataProcessor:
                     outputProcessor.analysisState(session, conf.dataContract.dataFilter.dataBatchNum,
                                                   AnalysisState.Analyzed.value, ErrorState.Err.value, code, message)
             except Exception as e1:
-                # 使用 traceback 模块获取完整的异常信息
-                error_message = ''.join(traceback.format_exception(e1))
                 # 记录异常信息
-                self.logger.error(f"捕获到异常: {error_message}")
+                self.logger.info(f"捕获到异常: {traceback.format_exc()}")
 
         return DataFrameOutput

dataAnalysisBusiness/algorithm/powerScatter2DAnalyst.py

@@ -31,14 +31,9 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
         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")
+            self.logger.info("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)
+        return self.drawOfPowerCurveScatter(dataFrame, turbineInfos,outputAnalysisDir, conf, self.dataFrameContractOfTurbine)
 
     def drawOfPowerCurveScatter(self, dataFrame: pd.DataFrame,  turbineModelInfo: pd.Series, outputAnalysisDir, conf: Contract, dataFrameGuaranteePowerCurve: pd.DataFrame):
         """
@@ -50,9 +45,6 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
         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
@@ -64,137 +56,24 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
         # 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
+            #获取当前风机信息dataFrame
+            currentEngineDataFrame = turbineModelInfo[turbineModelInfo[Field_CodeOfTurbine]==name[1]]
+            #获取当前机型
+            millTypeCode = currentEngineDataFrame.get(Field_MillTypeCode, "").iloc[0]
+            #当前机型合同功率曲线dataFrame
+            currentMillTypePowerDataFrame = dataFrameGuaranteePowerCurve[dataFrameGuaranteePowerCurve[Field_MillTypeCode] == millTypeCode]
+            # 获取机型的名字（machine_type_code）
+            engineTypeName = self.common.getTurbineModelByCode(millTypeCode, self.turbineModelInfo)[Field_MachineTypeCode]
+            # 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,
+                "engineCode":  millTypeCode,
                 "engineTypeName": engineTypeName,
                 "xaixs": "风速(m/s)",
                 "yaixs": "有功功率(kW)",
@@ -214,24 +93,12 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
                     },
                     {# 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(),
+                        "xData":currentMillTypePowerDataFrame[Field_WindSpeed].replace(na_values, None).tolist(),
+                        "yData":currentMillTypePowerDataFrame[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:
@@ -248,24 +115,6 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
                 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

wtoaamapi/apps/viewDemo/viewDataAnalysis.py

@@ -54,7 +54,7 @@ class DataAnalysis(ViewSet):
                 dataAnalysisService = ServiceOfDataAnalysis(logger, databasesUtil, minioClient, conf)
             except (CustomError, Exception) as exception:
                 # 使用 traceback 模块获取完整的异常信息
-                error_message =''.join(traceback.format_exception(exception))
+                error_message =''.join(traceback.format_exc())
                 ex = exception if isinstance(exception, CustomError) else CustomError(-1)
                 code = ex.code
                 message = ex.message
@@ -91,7 +91,7 @@ class DataAnalysis(ViewSet):
                                                   AnalysisState.Analyzed.value, ErrorState.Err.value, code, message)
             except Exception as e1:
                 # 使用 traceback 模块获取完整的异常信息
-                error_message = ''.join(traceback.format_exception(e1))
+                error_message = ''.join(traceback.format_exc())
                 # 记录异常信息
                 logger.error(f"捕获到异常: {error_message}")