added associated equipment data to combinedequipmentload report in API

myems-api/excelexporters/combinedequipmentload.py

@@ -154,7 +154,7 @@ def generate_excel(report,
 
         return filename
     #################################################
-    # First: 统计分析
+    # First: 负荷分析
     # 6: title
     # 7: table title
     # 8~2*ca_len table_data
@@ -175,7 +175,7 @@ def generate_excel(report,
 
     if has_energy_data_flag:
         ws['B6'].font = title_font
-        ws['B6'] = name + ' 统计分析'
+        ws['B6'] = name + ' 负荷分析'
 
         category = reporting_period_data['names']
 
@@ -357,6 +357,84 @@ def generate_excel(report,
             chart_cell = str(analysis_end_row_number + 6 * i)
             ws.add_chart(line, chart_col + chart_cell)
 
+        #####################################
+
+        has_associated_equipment_flag = True
+
+        current_row_number = detailed_start_row_number + 3 + time_len
+
+        if "associated_equipment" not in report.keys() or \
+                "energy_category_names" not in report['associated_equipment'].keys() or \
+                len(report['associated_equipment']["energy_category_names"]) == 0 \
+                or 'associated_equipment_names_array' not in report['associated_equipment'].keys() \
+                or report['associated_equipment']['associated_equipment_names_array'] is None \
+                or len(report['associated_equipment']['associated_equipment_names_array']) == 0 \
+                or len(report['associated_equipment']['associated_equipment_names_array'][0]) == 0:
+            has_associated_equipment_flag = False
+
+        if has_associated_equipment_flag:
+            associated_equipment = report['associated_equipment']
+
+            ws['B' + str(current_row_number)].font = title_font
+            ws['B' + str(current_row_number)] = name + ' 相关设备数据'
+
+            current_row_number += 1
+            table_start_row_number = current_row_number
+
+            ws.row_dimensions[current_row_number].height = 60
+            ws['B' + str(current_row_number)].fill = table_fill
+            ws['B' + str(current_row_number)].font = name_font
+            ws['B' + str(current_row_number)].alignment = c_c_alignment
+            ws['B' + str(current_row_number)].border = f_border
+            ws['B' + str(current_row_number)] = '相关设备'
+            ca_len = len(associated_equipment['energy_category_names'])
+
+            for i in range(0, ca_len):
+                col_average = chr(ord('C') + 2 * i)
+                col_maximum = chr(ord('D') + 2 * i)
+
+                ws[col_average + str(current_row_number)].font = name_font
+                ws[col_average + str(current_row_number)].alignment = c_c_alignment
+                ws[col_average + str(current_row_number)] = names[i] + " 平均负荷(" + \
+                                                                       reporting_period_data['units'][i] + "/H)"
+                ws[col_average + str(current_row_number)].border = f_border
+
+                ws[col_maximum + str(current_row_number)].font = name_font
+                ws[col_maximum + str(current_row_number)].alignment = c_c_alignment
+                ws[col_maximum + str(current_row_number)] = names[i] + " 最大负荷(" + \
+                                                                       reporting_period_data['units'][i] + "/H)"
+                ws[col_maximum + str(current_row_number)].border = f_border
+
+                associated_equipment_len = len(associated_equipment['associated_equipment_names_array'][0])
+                # table_date
+
+            for j in range(0, associated_equipment_len):
+                current_row_number += 1
+                rows = str(current_row_number)
+
+                ws['B' + rows].font = title_font
+                ws['B' + rows].alignment = c_c_alignment
+                ws['B' + rows] = associated_equipment['associated_equipment_names_array'][0][j]
+                ws['B' + rows].border = f_border
+
+                for index in range(0, ca_len):
+                    col_average = chr(ord('C') + 2 * index)
+                    col_maximum = chr(ord('D') + 2 * index)
+
+                    ws[col_average + str(rows)].font = name_font
+                    ws[col_average + str(rows)].alignment = c_c_alignment
+                    ws[col_average + str(rows)] = associated_equipment['sub_averages_array'][index][j] \
+                        if associated_equipment['sub_averages_array'][index][j] is not None else ''
+                    ws[col_average + str(rows)].number_format = '0.00'
+                    ws[col_average + str(rows)].border = f_border
+
+                    ws[col_maximum + str(rows)].font = name_font
+                    ws[col_maximum + str(rows)].alignment = c_c_alignment
+                    ws[col_maximum + str(rows)] = associated_equipment['sub_maximums_array'][index][j] \
+                        if associated_equipment['sub_maximums_array'][index][j] is not None else ''
+                    ws[col_maximum + str(rows)].number_format = '0.00'
+                    ws[col_maximum + str(rows)].border = f_border
+
     filename = str(uuid.uuid4()) + '.xlsx'
     wb.save(filename)
 

myems-api/reports/combinedequipmentload.py

@@ -23,11 +23,13 @@ class Reporting:
     # Step 2: query the combined equipment
     # Step 3: query energy categories
     # Step 4: query associated points
-    # Step 5: query base period energy input
+    # Step 5: query associated equipments
-    # Step 6: query reporting period energy input
+    # Step 6: query base period energy input
-    # Step 7: query tariff data
+    # Step 7: query reporting period energy input
-    # Step 8: query associated points data
+    # Step 8: query tariff data
-    # Step 9: construct the report
+    # Step 9: query associated points data
+    # Step 10: query associated equipments energy input
+    # Step 11: construct the report
     ####################################################################################################################
     @staticmethod
     def on_get(req, resp):
@@ -234,6 +236,19 @@ class Reporting:
             for row in rows_points:
                 point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
 
+        ################################################################################################################
+        # Step 5: query associated equipments
+        ################################################################################################################
+        associated_equipment_list = list()
+        cursor_system.execute(" SELECT e.id, e.name "
+                              " FROM tbl_equipments e,tbl_combined_equipments_equipments ee"
+                              " WHERE ee.combined_equipment_id = %s AND e.id = ee.equipment_id"
+                              " ORDER BY id ", (combined_equipment['id'],))
+        rows_associated_equipments = cursor_system.fetchall()
+        if rows_associated_equipments is not None and len(rows_associated_equipments) > 0:
+            for row in rows_associated_equipments:
+                associated_equipment_list.append({"id": row[0], "name": row[1]})
+
         ################################################################################################################
         # Step 6: query base period energy input
         ################################################################################################################
@@ -440,7 +455,50 @@ class Reporting:
             parameters_data['values'].append(point_values)
 
         ################################################################################################################
-        # Step 10: construct the report
+        # Step 10: query associated equipments energy input
+        ################################################################################################################
+        associated_equipment_data = dict()
+
+        if energy_category_set is not None and len(energy_category_set) > 0:
+            for energy_category_id in energy_category_set:
+                associated_equipment_data[energy_category_id] = dict()
+                associated_equipment_data[energy_category_id]['associated_equipment_names'] = list()
+                associated_equipment_data[energy_category_id]['average'] = list()
+                associated_equipment_data[energy_category_id]['maximum'] = list()
+                associated_equipment_data[energy_category_id]['sub_averages'] = list()
+                associated_equipment_data[energy_category_id]['sub_maximums'] = list()
+                for associated_equipment in associated_equipment_list:
+                    associated_equipment_data[energy_category_id]['associated_equipment_names'].append(
+                        associated_equipment['name'])
+
+                    cursor_energy.execute(" SELECT start_datetime_utc, actual_value "
+                                          " FROM tbl_equipment_input_category_hourly "
+                                          " WHERE equipment_id = %s "
+                                          "     AND energy_category_id = %s "
+                                          "     AND start_datetime_utc >= %s "
+                                          "     AND start_datetime_utc < %s "
+                                          " ORDER BY start_datetime_utc ",
+                                          (associated_equipment['id'],
+                                           energy_category_id,
+                                           reporting_start_datetime_utc,
+                                           reporting_end_datetime_utc))
+                    rows_associated_equipments_hourly = cursor_energy.fetchall()
+
+                    rows_associated_equipment_periodically, \
+                        associated_equipment_data[energy_category_id]['average'], \
+                        associated_equipment_data[energy_category_id]['maximum'] = \
+                        utilities.averaging_hourly_data_by_period(rows_associated_equipments_hourly,
+                                                                  reporting_start_datetime_utc,
+                                                                  reporting_end_datetime_utc,
+                                                                  period_type)
+
+                    associated_equipment_data[energy_category_id]['sub_averages'].append(
+                        associated_equipment_data[energy_category_id]['average'])
+                    associated_equipment_data[energy_category_id]['sub_maximums'].append(
+                        associated_equipment_data[energy_category_id]['maximum'])
+
+        ################################################################################################################
+        # Step 11: construct the report
         ################################################################################################################
         if cursor_system:
             cursor_system.close()
@@ -532,6 +590,25 @@ class Reporting:
             "values": parameters_data['values']
         }
 
+        result['associated_equipment'] = dict()
+        result['associated_equipment']['energy_category_names'] = list()
+        result['associated_equipment']['units'] = list()
+        result['associated_equipment']['associated_equipment_names_array'] = list()
+        result['associated_equipment']['sub_averages_array'] = list()
+        result['associated_equipment']['sub_maximums_array'] = list()
+        if energy_category_set is not None and len(energy_category_set) > 0:
+            for energy_category_id in energy_category_set:
+                result['associated_equipment']['energy_category_names'].append(
+                    energy_category_dict[energy_category_id]['name'])
+                result['associated_equipment']['units'].append(
+                    energy_category_dict[energy_category_id]['unit_of_measure'])
+                result['associated_equipment']['associated_equipment_names_array'].append(
+                    associated_equipment_data[energy_category_id]['associated_equipment_names'])
+                result['associated_equipment']['sub_averages_array'].append(
+                    associated_equipment_data[energy_category_id]['sub_averages'])
+                result['associated_equipment']['sub_maximums_array'].append(
+                    associated_equipment_data[energy_category_id]['sub_maximums'])
+
         # export result to Excel file and then encode the file to base64 string
         result['excel_bytes_base64'] = excelexporters.combinedequipmentload.export(result,
                                                                                    combined_equipment['name'],

web/src/components/MyEMS/CombinedEquipment/CombinedEquipmentLoad.js

@@ -424,6 +424,7 @@ const CombinedEquipmentLoad = ({ setRedirect, setRedirectUrl, t }) => {
           });
         });
         setDetailedDataTableColumns(detailed_column_list);
+        
         let associated_equipment_value_list = [];
         if (json['associated_equipment']['associated_equipment_names_array'].length > 0) {
           json['associated_equipment']['associated_equipment_names_array'][0].forEach((currentEquipmentName, equipmentIndex) => {
@@ -431,8 +432,17 @@ const CombinedEquipmentLoad = ({ setRedirect, setRedirectUrl, t }) => {
             associated_equipment_value['id'] = equipmentIndex;
             associated_equipment_value['name'] = currentEquipmentName;
             json['associated_equipment']['energy_category_names'].forEach((currentValue, energyCategoryIndex) => {
-              associated_equipment_value['a' + 2 * energyCategoryIndex] = json['associated_equipment']['sub_averages'][energyCategoryIndex][equipmentIndex].toFixed(2);
+              if (json['associated_equipment']['sub_averages_array'][energyCategoryIndex][equipmentIndex] != null) {
-              associated_equipment_value['a' + 2 * energyCategoryIndex + 1] = json['associated_equipment']['sub_maximums'][energyCategoryIndex][equipmentIndex].toFixed(2);
+                associated_equipment_value['a' + 2 * energyCategoryIndex] = json['associated_equipment']['sub_averages_array'][energyCategoryIndex][equipmentIndex].toFixed(2);
+              } else {
+                associated_equipment_value['a' + 2 * energyCategoryIndex] = '';
+              };  
+              if (json['associated_equipment']['sub_maximums_array'][energyCategoryIndex][equipmentIndex] != null) {
+                associated_equipment_value['a' + (2 * energyCategoryIndex + 1)] = json['associated_equipment']['sub_maximums_array'][energyCategoryIndex][equipmentIndex].toFixed(2);
+              } else {
+                associated_equipment_value['a' + (2 * energyCategoryIndex + 1)] = '';
+              };       
+              
             });
             associated_equipment_value_list.push(associated_equipment_value);
           });