added associated equipment data to combinedequipmentload report in API
Merge branch 'develop'pull/35/head^2
13621160019@163.com
commit
eb276b2123
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@ -1,6 +1,8 @@
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import base64
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import uuid
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import os
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from decimal import Decimal
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from openpyxl.chart import (
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PieChart,
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LineChart,
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@ -276,7 +278,7 @@ def generate_excel(report,
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current_row_number += 1
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ca_len = len(reporting_period_data['names'])
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wssum = 0
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wssum = Decimal(0.0)
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for i in range(0, ca_len):
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wssum = round(reporting_period_data['subtotals'][i], 2) + wssum
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for i in range(0, ca_len):
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@ -293,7 +295,9 @@ def generate_excel(report,
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ws['D' + str(current_row_number)].font = title_font
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ws['D' + str(current_row_number)].alignment = c_c_alignment
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ws['D' + str(current_row_number)].border = f_border
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ws['D' + str(current_row_number)] = '{:.2%}'.format(round(reporting_period_data['subtotals'][i], 2) / wssum)
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ws['D' + str(current_row_number)] = '{:.2%}'.format(round(
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reporting_period_data['subtotals'][i], 2) / wssum) if wssum is not None and wssum != Decimal(0.0) \
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else " "
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current_row_number += 1
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@ -449,6 +453,63 @@ def generate_excel(report,
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for i in range(37, 69 + 1):
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ws.row_dimensions[i].height = 0.1
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#####################################
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has_associated_equipment_flag = True
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current_row_number += 2
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if "associated_equipment" not in report.keys() or \
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"energy_category_names" not in report['associated_equipment'].keys() or \
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len(report['associated_equipment']["energy_category_names"]) == 0 \
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or 'associated_equipment_names_array' not in report['associated_equipment'].keys() \
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or report['associated_equipment']['associated_equipment_names_array'] is None \
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or len(report['associated_equipment']['associated_equipment_names_array']) == 0 \
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or len(report['associated_equipment']['associated_equipment_names_array'][0]) == 0:
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has_associated_equipment_flag = False
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if has_associated_equipment_flag:
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associated_equipment = report['associated_equipment']
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ws['B' + str(current_row_number)].font = title_font
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ws['B' + str(current_row_number)] = name + ' 相关设备数据'
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current_row_number += 1
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ws.row_dimensions[current_row_number].height = 60
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ws['B' + str(current_row_number)].fill = table_fill
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ws['B' + str(current_row_number)].font = name_font
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ws['B' + str(current_row_number)].alignment = c_c_alignment
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ws['B' + str(current_row_number)].border = f_border
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ws['B' + str(current_row_number)] = '相关设备'
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ca_len = len(associated_equipment['energy_category_names'])
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for i in range(0, ca_len):
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row = chr(ord('C') + i)
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ws[row + str(current_row_number)].fill = table_fill
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ws[row + str(current_row_number)].font = name_font
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ws[row + str(current_row_number)].alignment = c_c_alignment
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ws[row + str(current_row_number)].border = f_border
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ws[row + str(current_row_number)] = \
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reporting_period_data['names'][i] + " (" + reporting_period_data['units'][i] + ")"
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associated_equipment_len = len(associated_equipment['associated_equipment_names_array'][0])
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for i in range(0, associated_equipment_len):
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current_row_number += 1
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row = str(current_row_number)
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ws['B' + row].font = title_font
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ws['B' + row].alignment = c_c_alignment
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ws['B' + row] = associated_equipment['associated_equipment_names_array'][0][i]
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ws['B' + row].border = f_border
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for j in range(0, ca_len):
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col = chr(ord('C') + j)
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ws[col + row].font = title_font
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ws[col + row].alignment = c_c_alignment
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ws[col + row] = round(associated_equipment['subtotals_array'][j][i], 2)
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ws[col + row].border = f_border
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filename = str(uuid.uuid4()) + '.xlsx'
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wb.save(filename)
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@ -154,7 +154,7 @@ def generate_excel(report,
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return filename
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#################################################
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# First: 统计分析
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# First: 负荷分析
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# 6: title
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# 7: table title
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# 8~2*ca_len table_data
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@ -175,7 +175,7 @@ def generate_excel(report,
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if has_energy_data_flag:
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ws['B6'].font = title_font
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ws['B6'] = name + ' 统计分析'
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ws['B6'] = name + ' 负荷分析'
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category = reporting_period_data['names']
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@ -357,6 +357,84 @@ def generate_excel(report,
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chart_cell = str(analysis_end_row_number + 6 * i)
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ws.add_chart(line, chart_col + chart_cell)
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#####################################
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has_associated_equipment_flag = True
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current_row_number = detailed_start_row_number + 3 + time_len
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if "associated_equipment" not in report.keys() or \
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"energy_category_names" not in report['associated_equipment'].keys() or \
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len(report['associated_equipment']["energy_category_names"]) == 0 \
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or 'associated_equipment_names_array' not in report['associated_equipment'].keys() \
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or report['associated_equipment']['associated_equipment_names_array'] is None \
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or len(report['associated_equipment']['associated_equipment_names_array']) == 0 \
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or len(report['associated_equipment']['associated_equipment_names_array'][0]) == 0:
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has_associated_equipment_flag = False
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if has_associated_equipment_flag:
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associated_equipment = report['associated_equipment']
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ws['B' + str(current_row_number)].font = title_font
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ws['B' + str(current_row_number)] = name + ' 相关设备数据'
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current_row_number += 1
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table_start_row_number = current_row_number
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ws.row_dimensions[current_row_number].height = 60
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ws['B' + str(current_row_number)].fill = table_fill
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ws['B' + str(current_row_number)].font = name_font
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ws['B' + str(current_row_number)].alignment = c_c_alignment
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ws['B' + str(current_row_number)].border = f_border
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ws['B' + str(current_row_number)] = '相关设备'
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ca_len = len(associated_equipment['energy_category_names'])
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for i in range(0, ca_len):
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col_average = chr(ord('C') + 2 * i)
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col_maximum = chr(ord('D') + 2 * i)
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ws[col_average + str(current_row_number)].font = name_font
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ws[col_average + str(current_row_number)].alignment = c_c_alignment
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ws[col_average + str(current_row_number)] = names[i] + " 平均负荷(" + \
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reporting_period_data['units'][i] + "/H)"
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ws[col_average + str(current_row_number)].border = f_border
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ws[col_maximum + str(current_row_number)].font = name_font
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ws[col_maximum + str(current_row_number)].alignment = c_c_alignment
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ws[col_maximum + str(current_row_number)] = names[i] + " 最大负荷(" + \
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reporting_period_data['units'][i] + "/H)"
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ws[col_maximum + str(current_row_number)].border = f_border
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associated_equipment_len = len(associated_equipment['associated_equipment_names_array'][0])
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# table_date
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for j in range(0, associated_equipment_len):
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current_row_number += 1
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rows = str(current_row_number)
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ws['B' + rows].font = title_font
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ws['B' + rows].alignment = c_c_alignment
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ws['B' + rows] = associated_equipment['associated_equipment_names_array'][0][j]
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ws['B' + rows].border = f_border
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for index in range(0, ca_len):
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col_average = chr(ord('C') + 2 * index)
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col_maximum = chr(ord('D') + 2 * index)
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ws[col_average + str(rows)].font = name_font
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ws[col_average + str(rows)].alignment = c_c_alignment
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ws[col_average + str(rows)] = associated_equipment['sub_averages_array'][index][j] \
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if associated_equipment['sub_averages_array'][index][j] is not None else ''
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ws[col_average + str(rows)].number_format = '0.00'
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ws[col_average + str(rows)].border = f_border
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ws[col_maximum + str(rows)].font = name_font
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ws[col_maximum + str(rows)].alignment = c_c_alignment
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ws[col_maximum + str(rows)] = associated_equipment['sub_maximums_array'][index][j] \
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if associated_equipment['sub_maximums_array'][index][j] is not None else ''
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ws[col_maximum + str(rows)].number_format = '0.00'
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ws[col_maximum + str(rows)].border = f_border
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filename = str(uuid.uuid4()) + '.xlsx'
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wb.save(filename)
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@ -23,11 +23,13 @@ class Reporting:
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# Step 2: query the combined equipment
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# Step 3: query energy categories
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# Step 4: query associated points
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# Step 5: query base period energy income
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# Step 6: query reporting period energy income
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# Step 7: query tariff data
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# Step 8: query associated points data
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# Step 9: construct the report
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# Step 5: query associated equipments
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# Step 6: query base period energy income
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# Step 7: query reporting period energy income
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# Step 8: query tariff data
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# Step 9: query associated points data
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# Step 10: query associated equipments energy income
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# Step 11: construct the report
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####################################################################################################################
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@staticmethod
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def on_get(req, resp):
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@ -70,7 +72,7 @@ class Reporting:
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try:
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base_start_datetime_utc = datetime.strptime(base_start_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_BASE_PERIOD_START_DATETIME")
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@ -81,7 +83,7 @@ class Reporting:
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try:
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base_end_datetime_utc = datetime.strptime(base_end_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_BASE_PERIOD_END_DATETIME")
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@ -99,7 +101,7 @@ class Reporting:
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try:
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reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_REPORTING_PERIOD_START_DATETIME")
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@ -112,7 +114,7 @@ class Reporting:
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try:
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reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local,
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'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \
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timedelta(minutes=timezone_offset)
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timedelta(minutes=timezone_offset)
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except ValueError:
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raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST',
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description="API.INVALID_REPORTING_PERIOD_END_DATETIME")
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@ -235,7 +237,20 @@ class Reporting:
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point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
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################################################################################################################
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# Step 5: query base period energy income
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# Step 5: query associated equipments
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################################################################################################################
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associated_equipment_list = list()
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cursor_system.execute(" SELECT e.id, e.name "
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" FROM tbl_equipments e,tbl_combined_equipments_equipments ee"
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" WHERE ee.combined_equipment_id = %s AND e.id = ee.equipment_id"
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" ORDER BY id ", (combined_equipment['id'],))
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rows_associated_equipments = cursor_system.fetchall()
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if rows_associated_equipments is not None and len(rows_associated_equipments) > 0:
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for row in rows_associated_equipments:
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associated_equipment_list.append({"id": row[0], "name": row[1]})
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################################################################################################################
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# Step 6: query base period energy income
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################################################################################################################
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base = dict()
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if energy_category_set is not None and len(energy_category_set) > 0:
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@ -282,7 +297,7 @@ class Reporting:
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base[energy_category_id]['subtotal'] += actual_value
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################################################################################################################
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# Step 8: query reporting period energy income
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# Step 7: query reporting period energy income
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################################################################################################################
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reporting = dict()
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if energy_category_set is not None and len(energy_category_set) > 0:
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@ -329,7 +344,7 @@ class Reporting:
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reporting[energy_category_id]['subtotal'] += actual_value
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################################################################################################################
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# Step 9: query tariff data
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# Step 8: query tariff data
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################################################################################################################
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parameters_data = dict()
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parameters_data['names'] = list()
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@ -355,7 +370,7 @@ class Reporting:
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parameters_data['values'].append(tariff_value_list)
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################################################################################################################
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# Step 10: query associated points data
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# Step 9: query associated points data
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################################################################################################################
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for point in point_list:
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point_values = []
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@ -420,7 +435,37 @@ class Reporting:
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parameters_data['values'].append(point_values)
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################################################################################################################
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# Step 12: construct the report
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# Step 10: query associated equipments energy income
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################################################################################################################
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associated_equipment_data = dict()
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if energy_category_set is not None and len(energy_category_set) > 0:
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for energy_category_id in energy_category_set:
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associated_equipment_data[energy_category_id] = dict()
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associated_equipment_data[energy_category_id]['associated_equipment_names'] = list()
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associated_equipment_data[energy_category_id]['subtotals'] = list()
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for associated_equipment in associated_equipment_list:
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associated_equipment_data[energy_category_id]['associated_equipment_names'].append(
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associated_equipment['name'])
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cursor_billing.execute(" SELECT SUM(actual_value) "
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" FROM tbl_equipment_output_category_hourly "
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" WHERE equipment_id = %s "
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" AND energy_category_id = %s "
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" AND start_datetime_utc >= %s "
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" AND start_datetime_utc < %s "
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" ORDER BY start_datetime_utc ",
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(associated_equipment['id'],
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energy_category_id,
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reporting_start_datetime_utc,
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reporting_end_datetime_utc))
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row_subtotal = cursor_billing.fetchone()
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subtotal = Decimal(0.0) if (row_subtotal is None or row_subtotal[0] is None) else row_subtotal[0]
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associated_equipment_data[energy_category_id]['subtotals'].append(subtotal)
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################################################################################################################
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# Step 11: construct the report
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################################################################################################################
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if cursor_system:
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cursor_system.close()
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@ -497,6 +542,22 @@ class Reporting:
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"values": parameters_data['values']
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}
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result['associated_equipment'] = dict()
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result['associated_equipment']['energy_category_names'] = list()
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result['associated_equipment']['units'] = list()
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result['associated_equipment']['associated_equipment_names_array'] = list()
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result['associated_equipment']['subtotals_array'] = list()
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result['associated_equipment']['total_unit'] = config.currency_unit
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if energy_category_set is not None and len(energy_category_set) > 0:
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for energy_category_id in energy_category_set:
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result['associated_equipment']['energy_category_names'].append(
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energy_category_dict[energy_category_id]['name'])
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result['associated_equipment']['units'].append(config.currency_unit)
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result['associated_equipment']['associated_equipment_names_array'].append(
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associated_equipment_data[energy_category_id]['associated_equipment_names'])
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result['associated_equipment']['subtotals_array'].append(
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associated_equipment_data[energy_category_id]['subtotals'])
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# export result to Excel file and then encode the file to base64 string
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result['excel_bytes_base64'] = excelexporters.combinedequipmentincome.export(result,
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combined_equipment['name'],
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@ -23,11 +23,13 @@ class Reporting:
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# Step 2: query the combined equipment
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# Step 3: query energy categories
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# Step 4: query associated points
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# Step 5: query base period energy input
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# Step 6: query reporting period energy input
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# Step 7: query tariff data
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# Step 8: query associated points data
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# Step 9: construct the report
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# Step 5: query associated equipments
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# Step 6: query base period energy input
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# Step 7: query reporting period energy input
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# Step 8: query tariff data
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# Step 9: query associated points data
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# Step 10: query associated equipments energy input
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# Step 11: construct the report
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####################################################################################################################
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@staticmethod
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def on_get(req, resp):
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@ -234,6 +236,19 @@ class Reporting:
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for row in rows_points:
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point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]})
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################################################################################################################
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# Step 5: query associated equipments
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################################################################################################################
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associated_equipment_list = list()
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cursor_system.execute(" SELECT e.id, e.name "
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" FROM tbl_equipments e,tbl_combined_equipments_equipments ee"
|
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" 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'],
|
||||
|
|
|
|||
|
|
@ -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);
|
||||
associated_equipment_value['a' + 2 * energyCategoryIndex + 1] = json['associated_equipment']['sub_maximums'][energyCategoryIndex][equipmentIndex].toFixed(2);
|
||||
if (json['associated_equipment']['sub_averages_array'][energyCategoryIndex][equipmentIndex] != null) {
|
||||
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);
|
||||
});
|
||||
|
|
|
|||
Loading…
Reference in New Issue