风向玫瑰分析输出json

dataAnalysisBusiness/algorithm/windDirectionFrequencyAnalyst.py

@@ -8,18 +8,36 @@ from utils.directoryUtil import DirectoryUtil as dir
 import matplotlib.pyplot as plt
 from algorithmContract.confBusiness import *
 from algorithmContract.contract import Contract
-
+from utils.jsonUtil import JsonUtil
 
 class WindDirectionFrequencyAnalyst(AnalystNotFilter):
 
     def typeAnalyst(self):
         return "wind_direction_frequency"
-
+    '''
     def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
         dictionary=self.processTurbineData(turbineCodes,conf,[Field_DeviceCode,Field_Time,Field_WindDirection,Field_WindSpeed,Field_ActiverPower])
+        # 获取机型信息
+        turbrineInfos = self.common.getTurbineInfos(
+            conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
         dataFrameMerge=self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
-        return self.windRoseAnalysis(dataFrameMerge, outputAnalysisDir, conf)
-
+        frequency_data=self.windRoseAnalysis(dataFrameMerge, outputAnalysisDir, conf)
+        returnDatas=[]
+        returnDatas.append(frequency_data)
+        returnJsonData= self.outputJsonData(conf,outputAnalysisDir,turbrineInfos,dataFrameMerge)
+        returnDatas.append(returnJsonData)
+        return returnDatas
+    '''
+    def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
+        dictionary = self.processTurbineData(turbineCodes, conf, [Field_DeviceCode, Field_Time, Field_WindDirection, Field_WindSpeed, Field_ActiverPower])
+        turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
+        dataFrameMerge = self.userDataFrame(dictionary, conf.dataContract.configAnalysis, self)
+        results=self.windRoseAnalysis(dataFrameMerge, outputAnalysisDir, conf)
+        returnJsonData = self.outputJsonData(conf, outputAnalysisDir, turbineInfos, dataFrameMerge)
+    
+        returnDatas=pd.concat([results,returnJsonData], axis=0, ignore_index=True)
+        return returnDatas
+    '''
     def windRoseAnalysis(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract):
         # 检查所需列是否存在
         required_columns = {Field_WindDirection, Field_WindSpeed}
@@ -127,3 +145,191 @@ class WindDirectionFrequencyAnalyst(AnalystNotFilter):
         result_df = pd.DataFrame(result_rows)
 
         return result_df
+    '''
+    def windRoseAnalysis(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract):
+        # 检查所需列是否存在
+        required_columns = {Field_WindDirection, Field_WindSpeed}
+        if not required_columns.issubset(dataFrameMerge.columns) or dataFrameMerge[Field_WindDirection].isna().all() or dataFrameMerge[Field_WindSpeed].isna().all():
+            msg = f"DataFrame缺少必要的列及值。涉及的列有: {required_columns}"
+            self.logger.warning(msg)
+            return pd.DataFrame()
+
+        # 定义类属性 self.frequency_data
+        self.frequency_data = {}
+
+        # 风速区间
+        bins = [0, 3, 6, 9, np.inf]
+        speed_labels = ['[0,3)', '[3,6)', '[6,9)', '>=9']
+        wind_directions = np.arange(0, 360, 22.5)
+        # 设置颜色
+        colorscale = {
+            '[0,3)': 'rgba(247.0, 251.0, 255.0, 1.0)',
+            '[3,6)': 'rgba(171.33333333333334, 207.66666666666666, 229.66666666666669, 1.0)',
+            '[6,9)': 'rgba(55.0, 135.0, 192.33333333333334, 1.0)',
+            '>=9': 'rgba(8.0, 48.0, 107.0, 1.0)'
+        }
+        # 按设备名分组数据
+        grouped = dataFrameMerge.groupby([Field_NameOfTurbine, Field_CodeOfTurbine])
+        result_rows = []
+        for name, group in grouped:
+            turbine_code = name[1]
+            self.frequency_data[turbine_code] = {}
+            self.frequency_data[turbine_code]['title'] = f"机组: {name[0]}"
+
+            # 调整风向数据以使北方为0度
+            # adjusted_wind_dir = (group[Field_WindDirection] - 90) % 360
+            group['风向分组'] = pd.cut( group[Field_WindDirection], bins=wind_directions, labels=wind_directions[:-1])
+            # 初始化子图，设定为极坐标
+            fig = make_subplots(rows=1, cols=1, specs=[[{'type': 'polar'}]])
+            # 计算每个风向分组在各个风速区间内的频率
+            for label in speed_labels:
+                subset = group[pd.cut(group[Field_WindSpeed], bins=bins, labels=speed_labels) == label]
+                counts = subset['风向分组'].value_counts().reindex(wind_directions[:-1], fill_value=0)
+                percentage = (counts / counts.sum()) * 100
+                self.frequency_data[turbine_code][label] = percentage.to_dict()
+
+                # 创建 Barpolar 跟踪，并应用单色渐变
+                trace = go.Barpolar(
+                    r=percentage.values,
+                    theta=counts.index,  # 这里的角度已经适配上北下南左西右东的布局
+                    name=label,
+                    marker_color=colorscale[label],  # 应用颜色尺度
+                    marker_showscale=False,  # 不显示颜色条
+                    marker_line_color='white',  # 设置线条颜色，增加扇区之间的分隔
+                    marker_line_width=1  # 设置线条宽度
+                )
+
+                fig.add_trace(trace)
+
+            # 设置图表的一些基本属性
+            fig.update_layout(
+                title={
+                    "text": f"机组: {name[0]}",
+                    #"x": 0.5
+                },
+                polar=dict(
+                    radialaxis=dict(visible=True),
+                    angularaxis=dict(
+                        tickmode="array",
+                        tickvals=wind_directions,
+                        # 明确标注北、东、南、西等方向，以适应以北为0度的布局
+                        ticktext=['N', 'NNE', 'NE', 'ENE', 'E', 'ESE', 'SE',
+                                  'SSE', 'S', 'SSW', 'SW', 'WSW', 'W', 'WNW', 'NW', 'NNW']
+
+                        # 更新角度标签，以适应以东为0度的布局
+                        # ticktext=['E', 'ESE', 'SE', 'SSE', 'S', 'SSW', 'SW', 'WSW', 'W', 'WNW', 'NW', 'NNW', 'N', 'NNE', 'NE', 'ENE']
+                    )
+                ),
+                legend_title="风速",
+                margin=dict(t=50, b=10)  # t为顶部（top）间距，b为底部（bottom）间距
+            )
+
+            # 保存图像
+            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)
+
+            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)
+
+        # 返回结果数据框
+        return result_df
+    
+    def outputJsonData(self, conf: Contract, outputAnalysisDir: str, turbineModelInfo: pd.Series, dataFrameMerge: pd.DataFrame) -> pd.DataFrame:
+        turbineCodes = dataFrameMerge[Field_CodeOfTurbine].unique()
+        jsonDictionary = self.convert2Json(turbineModelInfo, turbineCodes=turbineCodes, dataFrameOfTurbines=dataFrameMerge)
+        jsonFileName = f"风向玫瑰图.json"
+        jsonFilePath = os.path.join(outputAnalysisDir, jsonFileName)
+    
+        JsonUtil.write_json(jsonDictionary, file_path=jsonFilePath)
+        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: Const_Output_Total,
+        #     Field_Return_FilePath: jsonFilePath,
+        #     Field_Return_IsSaveDatabase: True
+        # })
+    
+        for turbineCode in turbineCodes:
+            result_rows.append({
+                Field_Return_TypeAnalyst: self.typeAnalyst(),
+                Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
+                Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
+                Field_CodeOfTurbine: turbineCode,
+                Field_Return_FilePath: jsonFilePath,
+                Field_Return_IsSaveDatabase: True
+            })
+    
+        returnDatas = pd.DataFrame(result_rows)
+        return returnDatas
+
+    def convert2Json(self, turbineModelInfo: pd.Series, turbineCodes, dataFrameOfTurbines: pd.DataFrame):
+        # 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]
+
+        result = {
+            "analysisTypeCode": "风向玫瑰分析",
+            "engineTypeCode": engineTypeCode,
+            "engineTypeName": engineTypeName,
+            "axes": {
+                "radial": "频率百分比",
+                "angular": "风向",
+                "levelname":"风速"
+            },
+            "data": []
+        }
+
+        for turbineCode in turbineCodes:
+            if turbineCode in self.frequency_data:
+                # 确保 enginName 是具体的值
+                enginName = dataFrameOfTurbines[dataFrameOfTurbines[Field_CodeOfTurbine]==turbineCode][Field_NameOfTurbine].iloc[0]
+                if isinstance(enginName, pd.Series):
+                    enginName = enginName.iloc[0]
+
+                turbine_info = {
+                    "enginName": enginName,
+                    "enginCode": turbineCode,
+                    "title": self.frequency_data[turbineCode]['title'],
+                    "windRoseData": []
+                }
+
+                for speed_label, direction_data in self.frequency_data[turbineCode].items():
+                    if speed_label == 'title':
+                        continue
+                    for direction, freq in direction_data.items():
+                        turbine_info["windRoseData"].append({
+                            "windDirection": float(direction),
+                            "windSpeedRange": speed_label,
+                            "frequency": float(freq)
+                        })
+
+                result["data"].append(turbine_info)
+
+        return result