|
1
|
|
|
import falcon |
|
2
|
|
|
import simplejson as json |
|
3
|
|
|
import mysql.connector |
|
4
|
|
|
import config |
|
5
|
|
|
from datetime import datetime, timedelta, timezone |
|
6
|
|
|
from core import utilities |
|
7
|
|
|
from decimal import Decimal |
|
8
|
|
|
|
|
9
|
|
|
|
|
10
|
|
View Code Duplication |
class Reporting: |
|
|
|
|
|
|
11
|
|
|
@staticmethod |
|
12
|
|
|
def __init__(): |
|
13
|
|
|
pass |
|
14
|
|
|
|
|
15
|
|
|
@staticmethod |
|
16
|
|
|
def on_options(req, resp): |
|
17
|
|
|
resp.status = falcon.HTTP_200 |
|
18
|
|
|
|
|
19
|
|
|
#################################################################################################################### |
|
20
|
|
|
# PROCEDURES |
|
21
|
|
|
# Step 1: valid parameters |
|
22
|
|
|
# Step 2: query the store |
|
23
|
|
|
# Step 3: query energy categories |
|
24
|
|
|
# Step 4: query associated sensors |
|
25
|
|
|
# Step 5: query associated points |
|
26
|
|
|
# Step 6: query base period energy input |
|
27
|
|
|
# Step 7: query reporting period energy input |
|
28
|
|
|
# Step 8: query tariff data |
|
29
|
|
|
# Step 9: query associated sensors and points data |
|
30
|
|
|
# Step 10: construct the report |
|
31
|
|
|
#################################################################################################################### |
|
32
|
|
|
@staticmethod |
|
33
|
|
|
def on_get(req, resp): |
|
34
|
|
|
print(req.params) |
|
35
|
|
|
store_id = req.params.get('storeid') |
|
36
|
|
|
period_type = req.params.get('periodtype') |
|
37
|
|
|
base_start_datetime_local = req.params.get('baseperiodstartdatetime') |
|
38
|
|
|
base_end_datetime_local = req.params.get('baseperiodenddatetime') |
|
39
|
|
|
reporting_start_datetime_local = req.params.get('reportingperiodstartdatetime') |
|
40
|
|
|
reporting_end_datetime_local = req.params.get('reportingperiodenddatetime') |
|
41
|
|
|
|
|
42
|
|
|
################################################################################################################ |
|
43
|
|
|
# Step 1: valid parameters |
|
44
|
|
|
################################################################################################################ |
|
45
|
|
|
if store_id is None: |
|
46
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_ID') |
|
47
|
|
|
else: |
|
48
|
|
|
store_id = str.strip(store_id) |
|
49
|
|
|
if not store_id.isdigit() or int(store_id) <= 0: |
|
50
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_STORE_ID') |
|
51
|
|
|
|
|
52
|
|
|
if period_type is None: |
|
53
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
54
|
|
|
else: |
|
55
|
|
|
period_type = str.strip(period_type) |
|
56
|
|
|
if period_type not in ['hourly', 'daily', 'monthly', 'yearly']: |
|
57
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', description='API.INVALID_PERIOD_TYPE') |
|
58
|
|
|
|
|
59
|
|
|
timezone_offset = int(config.utc_offset[1:3]) * 60 + int(config.utc_offset[4:6]) |
|
60
|
|
|
if config.utc_offset[0] == '-': |
|
61
|
|
|
timezone_offset = -timezone_offset |
|
62
|
|
|
|
|
63
|
|
|
base_start_datetime_utc = None |
|
64
|
|
|
if base_start_datetime_local is not None and len(str.strip(base_start_datetime_local)) > 0: |
|
65
|
|
|
base_start_datetime_local = str.strip(base_start_datetime_local) |
|
66
|
|
|
try: |
|
67
|
|
|
base_start_datetime_utc = datetime.strptime(base_start_datetime_local, |
|
68
|
|
|
'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
69
|
|
|
timedelta(minutes=timezone_offset) |
|
70
|
|
|
except ValueError: |
|
71
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
72
|
|
|
description="API.INVALID_BASE_PERIOD_START_DATETIME") |
|
73
|
|
|
|
|
74
|
|
|
base_end_datetime_utc = None |
|
75
|
|
|
if base_end_datetime_local is not None and len(str.strip(base_end_datetime_local)) > 0: |
|
76
|
|
|
base_end_datetime_local = str.strip(base_end_datetime_local) |
|
77
|
|
|
try: |
|
78
|
|
|
base_end_datetime_utc = datetime.strptime(base_end_datetime_local, |
|
79
|
|
|
'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
80
|
|
|
timedelta(minutes=timezone_offset) |
|
81
|
|
|
except ValueError: |
|
82
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
83
|
|
|
description="API.INVALID_BASE_PERIOD_END_DATETIME") |
|
84
|
|
|
|
|
85
|
|
|
if base_start_datetime_utc is not None and base_end_datetime_utc is not None and \ |
|
86
|
|
|
base_start_datetime_utc >= base_end_datetime_utc: |
|
87
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
88
|
|
|
description='API.INVALID_BASE_PERIOD_END_DATETIME') |
|
89
|
|
|
|
|
90
|
|
|
if reporting_start_datetime_local is None: |
|
91
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
92
|
|
|
description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
93
|
|
|
else: |
|
94
|
|
|
reporting_start_datetime_local = str.strip(reporting_start_datetime_local) |
|
95
|
|
|
try: |
|
96
|
|
|
reporting_start_datetime_utc = datetime.strptime(reporting_start_datetime_local, |
|
97
|
|
|
'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
98
|
|
|
timedelta(minutes=timezone_offset) |
|
99
|
|
|
except ValueError: |
|
100
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
101
|
|
|
description="API.INVALID_REPORTING_PERIOD_START_DATETIME") |
|
102
|
|
|
|
|
103
|
|
|
if reporting_end_datetime_local is None: |
|
104
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
105
|
|
|
description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
106
|
|
|
else: |
|
107
|
|
|
reporting_end_datetime_local = str.strip(reporting_end_datetime_local) |
|
108
|
|
|
try: |
|
109
|
|
|
reporting_end_datetime_utc = datetime.strptime(reporting_end_datetime_local, |
|
110
|
|
|
'%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc) - \ |
|
111
|
|
|
timedelta(minutes=timezone_offset) |
|
112
|
|
|
except ValueError: |
|
113
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
114
|
|
|
description="API.INVALID_REPORTING_PERIOD_END_DATETIME") |
|
115
|
|
|
|
|
116
|
|
|
if reporting_start_datetime_utc >= reporting_end_datetime_utc: |
|
117
|
|
|
raise falcon.HTTPError(falcon.HTTP_400, title='API.BAD_REQUEST', |
|
118
|
|
|
description='API.INVALID_REPORTING_PERIOD_END_DATETIME') |
|
119
|
|
|
|
|
120
|
|
|
################################################################################################################ |
|
121
|
|
|
# Step 2: query the store |
|
122
|
|
|
################################################################################################################ |
|
123
|
|
|
cnx_system = mysql.connector.connect(**config.myems_system_db) |
|
124
|
|
|
cursor_system = cnx_system.cursor() |
|
125
|
|
|
|
|
126
|
|
|
cnx_energy = mysql.connector.connect(**config.myems_energy_db) |
|
127
|
|
|
cursor_energy = cnx_energy.cursor() |
|
128
|
|
|
|
|
129
|
|
|
cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
|
130
|
|
|
cursor_historical = cnx_historical.cursor() |
|
131
|
|
|
|
|
132
|
|
|
cursor_system.execute(" SELECT id, name, area, cost_center_id " |
|
133
|
|
|
" FROM tbl_stores " |
|
134
|
|
|
" WHERE id = %s ", (store_id,)) |
|
135
|
|
|
row_store = cursor_system.fetchone() |
|
136
|
|
|
if row_store is None: |
|
137
|
|
|
if cursor_system: |
|
138
|
|
|
cursor_system.close() |
|
139
|
|
|
if cnx_system: |
|
140
|
|
|
cnx_system.disconnect() |
|
141
|
|
|
|
|
142
|
|
|
if cursor_energy: |
|
143
|
|
|
cursor_energy.close() |
|
144
|
|
|
if cnx_energy: |
|
145
|
|
|
cnx_energy.disconnect() |
|
146
|
|
|
|
|
147
|
|
|
if cnx_historical: |
|
148
|
|
|
cnx_historical.close() |
|
149
|
|
|
if cursor_historical: |
|
150
|
|
|
cursor_historical.disconnect() |
|
151
|
|
|
raise falcon.HTTPError(falcon.HTTP_404, title='API.NOT_FOUND', description='API.STORE_NOT_FOUND') |
|
152
|
|
|
|
|
153
|
|
|
store = dict() |
|
154
|
|
|
store['id'] = row_store[0] |
|
155
|
|
|
store['name'] = row_store[1] |
|
156
|
|
|
store['area'] = row_store[2] |
|
157
|
|
|
store['cost_center_id'] = row_store[3] |
|
158
|
|
|
|
|
159
|
|
|
################################################################################################################ |
|
160
|
|
|
# Step 3: query energy categories |
|
161
|
|
|
################################################################################################################ |
|
162
|
|
|
energy_category_set = set() |
|
163
|
|
|
# query energy categories in base period |
|
164
|
|
|
cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
165
|
|
|
" FROM tbl_store_input_category_hourly " |
|
166
|
|
|
" WHERE store_id = %s " |
|
167
|
|
|
" AND start_datetime_utc >= %s " |
|
168
|
|
|
" AND start_datetime_utc < %s ", |
|
169
|
|
|
(store['id'], base_start_datetime_utc, base_end_datetime_utc)) |
|
170
|
|
|
rows_energy_categories = cursor_energy.fetchall() |
|
171
|
|
|
if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
172
|
|
|
for row_energy_category in rows_energy_categories: |
|
173
|
|
|
energy_category_set.add(row_energy_category[0]) |
|
174
|
|
|
|
|
175
|
|
|
# query energy categories in reporting period |
|
176
|
|
|
cursor_energy.execute(" SELECT DISTINCT(energy_category_id) " |
|
177
|
|
|
" FROM tbl_store_input_category_hourly " |
|
178
|
|
|
" WHERE store_id = %s " |
|
179
|
|
|
" AND start_datetime_utc >= %s " |
|
180
|
|
|
" AND start_datetime_utc < %s ", |
|
181
|
|
|
(store['id'], reporting_start_datetime_utc, reporting_end_datetime_utc)) |
|
182
|
|
|
rows_energy_categories = cursor_energy.fetchall() |
|
183
|
|
|
if rows_energy_categories is not None or len(rows_energy_categories) > 0: |
|
184
|
|
|
for row_energy_category in rows_energy_categories: |
|
185
|
|
|
energy_category_set.add(row_energy_category[0]) |
|
186
|
|
|
|
|
187
|
|
|
# query all energy categories in base period and reporting period |
|
188
|
|
|
cursor_system.execute(" SELECT id, name, unit_of_measure, kgce, kgco2e " |
|
189
|
|
|
" FROM tbl_energy_categories " |
|
190
|
|
|
" ORDER BY id ", ) |
|
191
|
|
|
rows_energy_categories = cursor_system.fetchall() |
|
192
|
|
|
if rows_energy_categories is None or len(rows_energy_categories) == 0: |
|
193
|
|
|
if cursor_system: |
|
194
|
|
|
cursor_system.close() |
|
195
|
|
|
if cnx_system: |
|
196
|
|
|
cnx_system.disconnect() |
|
197
|
|
|
|
|
198
|
|
|
if cursor_energy: |
|
199
|
|
|
cursor_energy.close() |
|
200
|
|
|
if cnx_energy: |
|
201
|
|
|
cnx_energy.disconnect() |
|
202
|
|
|
|
|
203
|
|
|
if cnx_historical: |
|
204
|
|
|
cnx_historical.close() |
|
205
|
|
|
if cursor_historical: |
|
206
|
|
|
cursor_historical.disconnect() |
|
207
|
|
|
raise falcon.HTTPError(falcon.HTTP_404, |
|
208
|
|
|
title='API.NOT_FOUND', |
|
209
|
|
|
description='API.ENERGY_CATEGORY_NOT_FOUND') |
|
210
|
|
|
energy_category_dict = dict() |
|
211
|
|
|
for row_energy_category in rows_energy_categories: |
|
212
|
|
|
if row_energy_category[0] in energy_category_set: |
|
213
|
|
|
energy_category_dict[row_energy_category[0]] = {"name": row_energy_category[1], |
|
214
|
|
|
"unit_of_measure": row_energy_category[2], |
|
215
|
|
|
"kgce": row_energy_category[3], |
|
216
|
|
|
"kgco2e": row_energy_category[4]} |
|
217
|
|
|
|
|
218
|
|
|
################################################################################################################ |
|
219
|
|
|
# Step 4: query associated sensors |
|
220
|
|
|
################################################################################################################ |
|
221
|
|
|
point_list = list() |
|
222
|
|
|
cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
223
|
|
|
" FROM tbl_stores st, tbl_sensors se, tbl_stores_sensors ss, " |
|
224
|
|
|
" tbl_points p, tbl_sensors_points sp " |
|
225
|
|
|
" WHERE st.id = %s AND st.id = ss.store_id AND ss.sensor_id = se.id " |
|
226
|
|
|
" AND se.id = sp.sensor_id AND sp.point_id = p.id " |
|
227
|
|
|
" ORDER BY p.id ", (store['id'],)) |
|
228
|
|
|
rows_points = cursor_system.fetchall() |
|
229
|
|
|
if rows_points is not None and len(rows_points) > 0: |
|
230
|
|
|
for row in rows_points: |
|
231
|
|
|
point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
232
|
|
|
|
|
233
|
|
|
################################################################################################################ |
|
234
|
|
|
# Step 5: query associated points |
|
235
|
|
|
################################################################################################################ |
|
236
|
|
|
cursor_system.execute(" SELECT p.id, p.name, p.units, p.object_type " |
|
237
|
|
|
" FROM tbl_stores s, tbl_stores_points sp, tbl_points p " |
|
238
|
|
|
" WHERE s.id = %s AND s.id = sp.store_id AND sp.point_id = p.id " |
|
239
|
|
|
" ORDER BY p.id ", (store['id'],)) |
|
240
|
|
|
rows_points = cursor_system.fetchall() |
|
241
|
|
|
if rows_points is not None and len(rows_points) > 0: |
|
242
|
|
|
for row in rows_points: |
|
243
|
|
|
point_list.append({"id": row[0], "name": row[1], "units": row[2], "object_type": row[3]}) |
|
244
|
|
|
|
|
245
|
|
|
################################################################################################################ |
|
246
|
|
|
# Step 6: query base period energy input |
|
247
|
|
|
################################################################################################################ |
|
248
|
|
|
base = dict() |
|
249
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
250
|
|
|
for energy_category_id in energy_category_set: |
|
251
|
|
|
base[energy_category_id] = dict() |
|
252
|
|
|
base[energy_category_id]['timestamps'] = list() |
|
253
|
|
|
base[energy_category_id]['sub_averages'] = list() |
|
254
|
|
|
base[energy_category_id]['sub_maximums'] = list() |
|
255
|
|
|
base[energy_category_id]['average'] = None |
|
256
|
|
|
base[energy_category_id]['maximum'] = None |
|
257
|
|
|
base[energy_category_id]['factor'] = None |
|
258
|
|
|
|
|
259
|
|
|
cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
260
|
|
|
" FROM tbl_store_input_category_hourly " |
|
261
|
|
|
" WHERE store_id = %s " |
|
262
|
|
|
" AND energy_category_id = %s " |
|
263
|
|
|
" AND start_datetime_utc >= %s " |
|
264
|
|
|
" AND start_datetime_utc < %s " |
|
265
|
|
|
" ORDER BY start_datetime_utc ", |
|
266
|
|
|
(store['id'], |
|
267
|
|
|
energy_category_id, |
|
268
|
|
|
base_start_datetime_utc, |
|
269
|
|
|
base_end_datetime_utc)) |
|
270
|
|
|
rows_store_hourly = cursor_energy.fetchall() |
|
271
|
|
|
|
|
272
|
|
|
rows_store_periodically, \ |
|
273
|
|
|
base[energy_category_id]['average'], \ |
|
274
|
|
|
base[energy_category_id]['maximum'] = \ |
|
275
|
|
|
utilities.averaging_hourly_data_by_period(rows_store_hourly, |
|
276
|
|
|
base_start_datetime_utc, |
|
277
|
|
|
base_end_datetime_utc, |
|
278
|
|
|
period_type) |
|
279
|
|
|
base[energy_category_id]['factor'] = \ |
|
280
|
|
|
(base[energy_category_id]['average'] / base[energy_category_id]['maximum'] |
|
281
|
|
|
if (base[energy_category_id]['average'] is not None and |
|
282
|
|
|
base[energy_category_id]['maximum'] is not None and |
|
283
|
|
|
base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
284
|
|
|
else None) |
|
285
|
|
|
|
|
286
|
|
|
for row_store_periodically in rows_store_periodically: |
|
287
|
|
|
current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
288
|
|
|
timedelta(minutes=timezone_offset) |
|
289
|
|
|
if period_type == 'hourly': |
|
290
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
291
|
|
|
elif period_type == 'daily': |
|
292
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
293
|
|
|
elif period_type == 'monthly': |
|
294
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m') |
|
295
|
|
|
elif period_type == 'yearly': |
|
296
|
|
|
current_datetime = current_datetime_local.strftime('%Y') |
|
297
|
|
|
|
|
298
|
|
|
base[energy_category_id]['timestamps'].append(current_datetime) |
|
|
|
|
|
|
299
|
|
|
base[energy_category_id]['sub_averages'].append(row_store_periodically[1]) |
|
300
|
|
|
base[energy_category_id]['sub_maximums'].append(row_store_periodically[2]) |
|
301
|
|
|
|
|
302
|
|
|
################################################################################################################ |
|
303
|
|
|
# Step 7: query reporting period energy input |
|
304
|
|
|
################################################################################################################ |
|
305
|
|
|
reporting = dict() |
|
306
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
307
|
|
|
for energy_category_id in energy_category_set: |
|
308
|
|
|
reporting[energy_category_id] = dict() |
|
309
|
|
|
reporting[energy_category_id]['timestamps'] = list() |
|
310
|
|
|
reporting[energy_category_id]['sub_averages'] = list() |
|
311
|
|
|
reporting[energy_category_id]['sub_maximums'] = list() |
|
312
|
|
|
reporting[energy_category_id]['average'] = None |
|
313
|
|
|
reporting[energy_category_id]['maximum'] = None |
|
314
|
|
|
reporting[energy_category_id]['factor'] = None |
|
315
|
|
|
|
|
316
|
|
|
cursor_energy.execute(" SELECT start_datetime_utc, actual_value " |
|
317
|
|
|
" FROM tbl_store_input_category_hourly " |
|
318
|
|
|
" WHERE store_id = %s " |
|
319
|
|
|
" AND energy_category_id = %s " |
|
320
|
|
|
" AND start_datetime_utc >= %s " |
|
321
|
|
|
" AND start_datetime_utc < %s " |
|
322
|
|
|
" ORDER BY start_datetime_utc ", |
|
323
|
|
|
(store['id'], |
|
324
|
|
|
energy_category_id, |
|
325
|
|
|
reporting_start_datetime_utc, |
|
326
|
|
|
reporting_end_datetime_utc)) |
|
327
|
|
|
rows_store_hourly = cursor_energy.fetchall() |
|
328
|
|
|
|
|
329
|
|
|
rows_store_periodically, \ |
|
330
|
|
|
reporting[energy_category_id]['average'], \ |
|
331
|
|
|
reporting[energy_category_id]['maximum'] = \ |
|
332
|
|
|
utilities.averaging_hourly_data_by_period(rows_store_hourly, |
|
333
|
|
|
reporting_start_datetime_utc, |
|
334
|
|
|
reporting_end_datetime_utc, |
|
335
|
|
|
period_type) |
|
336
|
|
|
reporting[energy_category_id]['factor'] = \ |
|
337
|
|
|
(reporting[energy_category_id]['average'] / reporting[energy_category_id]['maximum'] |
|
338
|
|
|
if (reporting[energy_category_id]['average'] is not None and |
|
339
|
|
|
reporting[energy_category_id]['maximum'] is not None and |
|
340
|
|
|
reporting[energy_category_id]['maximum'] > Decimal(0.0)) |
|
341
|
|
|
else None) |
|
342
|
|
|
|
|
343
|
|
|
for row_store_periodically in rows_store_periodically: |
|
344
|
|
|
current_datetime_local = row_store_periodically[0].replace(tzinfo=timezone.utc) + \ |
|
345
|
|
|
timedelta(minutes=timezone_offset) |
|
346
|
|
|
if period_type == 'hourly': |
|
347
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
348
|
|
|
elif period_type == 'daily': |
|
349
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%d') |
|
350
|
|
|
elif period_type == 'monthly': |
|
351
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m') |
|
352
|
|
|
elif period_type == 'yearly': |
|
353
|
|
|
current_datetime = current_datetime_local.strftime('%Y') |
|
354
|
|
|
|
|
355
|
|
|
reporting[energy_category_id]['timestamps'].append(current_datetime) |
|
356
|
|
|
reporting[energy_category_id]['sub_averages'].append(row_store_periodically[1]) |
|
357
|
|
|
reporting[energy_category_id]['sub_maximums'].append(row_store_periodically[2]) |
|
358
|
|
|
|
|
359
|
|
|
################################################################################################################ |
|
360
|
|
|
# Step 8: query tariff data |
|
361
|
|
|
################################################################################################################ |
|
362
|
|
|
parameters_data = dict() |
|
363
|
|
|
parameters_data['names'] = list() |
|
364
|
|
|
parameters_data['timestamps'] = list() |
|
365
|
|
|
parameters_data['values'] = list() |
|
366
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
367
|
|
|
for energy_category_id in energy_category_set: |
|
368
|
|
|
energy_category_tariff_dict = utilities.get_energy_category_tariffs(store['cost_center_id'], |
|
369
|
|
|
energy_category_id, |
|
370
|
|
|
reporting_start_datetime_utc, |
|
371
|
|
|
reporting_end_datetime_utc) |
|
372
|
|
|
tariff_timestamp_list = list() |
|
373
|
|
|
tariff_value_list = list() |
|
374
|
|
|
for k, v in energy_category_tariff_dict.items(): |
|
375
|
|
|
# convert k from utc to local |
|
376
|
|
|
k = k + timedelta(minutes=timezone_offset) |
|
377
|
|
|
tariff_timestamp_list.append(k.isoformat()[0:19][0:19]) |
|
378
|
|
|
tariff_value_list.append(v) |
|
379
|
|
|
|
|
380
|
|
|
parameters_data['names'].append('TARIFF-' + energy_category_dict[energy_category_id]['name']) |
|
381
|
|
|
parameters_data['timestamps'].append(tariff_timestamp_list) |
|
382
|
|
|
parameters_data['values'].append(tariff_value_list) |
|
383
|
|
|
|
|
384
|
|
|
################################################################################################################ |
|
385
|
|
|
# Step 9: query associated sensors and points data |
|
386
|
|
|
################################################################################################################ |
|
387
|
|
|
for point in point_list: |
|
388
|
|
|
point_values = [] |
|
389
|
|
|
point_timestamps = [] |
|
390
|
|
|
if point['object_type'] == 'ANALOG_VALUE': |
|
391
|
|
|
query = (" SELECT utc_date_time, actual_value " |
|
392
|
|
|
" FROM tbl_analog_value " |
|
393
|
|
|
" WHERE point_id = %s " |
|
394
|
|
|
" AND utc_date_time BETWEEN %s AND %s " |
|
395
|
|
|
" ORDER BY utc_date_time ") |
|
396
|
|
|
cursor_historical.execute(query, (point['id'], |
|
397
|
|
|
reporting_start_datetime_utc, |
|
398
|
|
|
reporting_end_datetime_utc)) |
|
399
|
|
|
rows = cursor_historical.fetchall() |
|
400
|
|
|
|
|
401
|
|
|
if rows is not None and len(rows) > 0: |
|
402
|
|
|
for row in rows: |
|
403
|
|
|
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
404
|
|
|
timedelta(minutes=timezone_offset) |
|
405
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
406
|
|
|
point_timestamps.append(current_datetime) |
|
407
|
|
|
point_values.append(row[1]) |
|
408
|
|
|
|
|
409
|
|
|
elif point['object_type'] == 'ENERGY_VALUE': |
|
410
|
|
|
query = (" SELECT utc_date_time, actual_value " |
|
411
|
|
|
" FROM tbl_energy_value " |
|
412
|
|
|
" WHERE point_id = %s " |
|
413
|
|
|
" AND utc_date_time BETWEEN %s AND %s " |
|
414
|
|
|
" ORDER BY utc_date_time ") |
|
415
|
|
|
cursor_historical.execute(query, (point['id'], |
|
416
|
|
|
reporting_start_datetime_utc, |
|
417
|
|
|
reporting_end_datetime_utc)) |
|
418
|
|
|
rows = cursor_historical.fetchall() |
|
419
|
|
|
|
|
420
|
|
|
if rows is not None and len(rows) > 0: |
|
421
|
|
|
for row in rows: |
|
422
|
|
|
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
423
|
|
|
timedelta(minutes=timezone_offset) |
|
424
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
425
|
|
|
point_timestamps.append(current_datetime) |
|
426
|
|
|
point_values.append(row[1]) |
|
427
|
|
|
elif point['object_type'] == 'DIGITAL_VALUE': |
|
428
|
|
|
query = (" SELECT utc_date_time, actual_value " |
|
429
|
|
|
" FROM tbl_digital_value " |
|
430
|
|
|
" WHERE point_id = %s " |
|
431
|
|
|
" AND utc_date_time BETWEEN %s AND %s ") |
|
432
|
|
|
cursor_historical.execute(query, (point['id'], |
|
433
|
|
|
reporting_start_datetime_utc, |
|
434
|
|
|
reporting_end_datetime_utc)) |
|
435
|
|
|
rows = cursor_historical.fetchall() |
|
436
|
|
|
|
|
437
|
|
|
if rows is not None and len(rows) > 0: |
|
438
|
|
|
for row in rows: |
|
439
|
|
|
current_datetime_local = row[0].replace(tzinfo=timezone.utc) + \ |
|
440
|
|
|
timedelta(minutes=timezone_offset) |
|
441
|
|
|
current_datetime = current_datetime_local.strftime('%Y-%m-%dT%H:%M:%S') |
|
442
|
|
|
point_timestamps.append(current_datetime) |
|
443
|
|
|
point_values.append(row[1]) |
|
444
|
|
|
|
|
445
|
|
|
parameters_data['names'].append(point['name'] + ' (' + point['units'] + ')') |
|
446
|
|
|
parameters_data['timestamps'].append(point_timestamps) |
|
447
|
|
|
parameters_data['values'].append(point_values) |
|
448
|
|
|
|
|
449
|
|
|
################################################################################################################ |
|
450
|
|
|
# Step 10: construct the report |
|
451
|
|
|
################################################################################################################ |
|
452
|
|
|
if cursor_system: |
|
453
|
|
|
cursor_system.close() |
|
454
|
|
|
if cnx_system: |
|
455
|
|
|
cnx_system.disconnect() |
|
456
|
|
|
|
|
457
|
|
|
if cursor_energy: |
|
458
|
|
|
cursor_energy.close() |
|
459
|
|
|
if cnx_energy: |
|
460
|
|
|
cnx_energy.disconnect() |
|
461
|
|
|
|
|
462
|
|
|
result = dict() |
|
463
|
|
|
|
|
464
|
|
|
result['store'] = dict() |
|
465
|
|
|
result['store']['name'] = store['name'] |
|
466
|
|
|
result['store']['area'] = store['area'] |
|
467
|
|
|
|
|
468
|
|
|
result['base_period'] = dict() |
|
469
|
|
|
result['base_period']['names'] = list() |
|
470
|
|
|
result['base_period']['units'] = list() |
|
471
|
|
|
result['base_period']['timestamps'] = list() |
|
472
|
|
|
result['base_period']['sub_averages'] = list() |
|
473
|
|
|
result['base_period']['sub_maximums'] = list() |
|
474
|
|
|
result['base_period']['averages'] = list() |
|
475
|
|
|
result['base_period']['maximums'] = list() |
|
476
|
|
|
result['base_period']['factors'] = list() |
|
477
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
478
|
|
|
for energy_category_id in energy_category_set: |
|
479
|
|
|
result['base_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
480
|
|
|
result['base_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
481
|
|
|
result['base_period']['timestamps'].append(base[energy_category_id]['timestamps']) |
|
482
|
|
|
result['base_period']['sub_averages'].append(base[energy_category_id]['sub_averages']) |
|
483
|
|
|
result['base_period']['sub_maximums'].append(base[energy_category_id]['sub_maximums']) |
|
484
|
|
|
result['base_period']['averages'].append(base[energy_category_id]['average']) |
|
485
|
|
|
result['base_period']['maximums'].append(base[energy_category_id]['maximum']) |
|
486
|
|
|
result['base_period']['factors'].append(base[energy_category_id]['factor']) |
|
487
|
|
|
|
|
488
|
|
|
result['reporting_period'] = dict() |
|
489
|
|
|
result['reporting_period']['names'] = list() |
|
490
|
|
|
result['reporting_period']['energy_category_ids'] = list() |
|
491
|
|
|
result['reporting_period']['units'] = list() |
|
492
|
|
|
result['reporting_period']['timestamps'] = list() |
|
493
|
|
|
result['reporting_period']['sub_averages'] = list() |
|
494
|
|
|
result['reporting_period']['sub_maximums'] = list() |
|
495
|
|
|
result['reporting_period']['averages'] = list() |
|
496
|
|
|
result['reporting_period']['averages_per_unit_area'] = list() |
|
497
|
|
|
result['reporting_period']['averages_increment_rate'] = list() |
|
498
|
|
|
result['reporting_period']['maximums'] = list() |
|
499
|
|
|
result['reporting_period']['maximums_per_unit_area'] = list() |
|
500
|
|
|
result['reporting_period']['maximums_increment_rate'] = list() |
|
501
|
|
|
result['reporting_period']['factors'] = list() |
|
502
|
|
|
result['reporting_period']['factors_increment_rate'] = list() |
|
503
|
|
|
|
|
504
|
|
|
if energy_category_set is not None and len(energy_category_set) > 0: |
|
505
|
|
|
for energy_category_id in energy_category_set: |
|
506
|
|
|
result['reporting_period']['names'].append(energy_category_dict[energy_category_id]['name']) |
|
507
|
|
|
result['reporting_period']['energy_category_ids'].append(energy_category_id) |
|
508
|
|
|
result['reporting_period']['units'].append(energy_category_dict[energy_category_id]['unit_of_measure']) |
|
509
|
|
|
result['reporting_period']['timestamps'].append(reporting[energy_category_id]['timestamps']) |
|
510
|
|
|
result['reporting_period']['sub_averages'].append(reporting[energy_category_id]['sub_averages']) |
|
511
|
|
|
result['reporting_period']['sub_maximums'].append(reporting[energy_category_id]['sub_maximums']) |
|
512
|
|
|
result['reporting_period']['averages'].append(reporting[energy_category_id]['average']) |
|
513
|
|
|
result['reporting_period']['averages_per_unit_area'].append( |
|
514
|
|
|
reporting[energy_category_id]['average'] / store['area'] |
|
515
|
|
|
if reporting[energy_category_id]['average'] is not None and |
|
516
|
|
|
store['area'] is not None and |
|
517
|
|
|
store['area'] > Decimal(0.0) |
|
518
|
|
|
else None) |
|
519
|
|
|
result['reporting_period']['averages_increment_rate'].append( |
|
520
|
|
|
(reporting[energy_category_id]['average'] - base[energy_category_id]['average']) / |
|
521
|
|
|
base[energy_category_id]['average'] if (base[energy_category_id]['average'] is not None and |
|
522
|
|
|
base[energy_category_id]['average'] > Decimal(0.0)) |
|
523
|
|
|
else None) |
|
524
|
|
|
result['reporting_period']['maximums'].append(reporting[energy_category_id]['maximum']) |
|
525
|
|
|
result['reporting_period']['maximums_increment_rate'].append( |
|
526
|
|
|
(reporting[energy_category_id]['maximum'] - base[energy_category_id]['maximum']) / |
|
527
|
|
|
base[energy_category_id]['maximum'] if (base[energy_category_id]['maximum'] is not None and |
|
528
|
|
|
base[energy_category_id]['maximum'] > Decimal(0.0)) |
|
529
|
|
|
else None) |
|
530
|
|
|
result['reporting_period']['maximums_per_unit_area'].append( |
|
531
|
|
|
reporting[energy_category_id]['maximum'] / store['area'] |
|
532
|
|
|
if reporting[energy_category_id]['maximum'] is not None and |
|
533
|
|
|
store['area'] is not None and |
|
534
|
|
|
store['area'] > Decimal(0.0) |
|
535
|
|
|
else None) |
|
536
|
|
|
result['reporting_period']['factors'].append(reporting[energy_category_id]['factor']) |
|
537
|
|
|
result['reporting_period']['factors_increment_rate'].append( |
|
538
|
|
|
(reporting[energy_category_id]['factor'] - base[energy_category_id]['factor']) / |
|
539
|
|
|
base[energy_category_id]['factor'] if (base[energy_category_id]['factor'] is not None and |
|
540
|
|
|
base[energy_category_id]['factor'] > Decimal(0.0)) |
|
541
|
|
|
else None) |
|
542
|
|
|
|
|
543
|
|
|
result['parameters'] = { |
|
544
|
|
|
"names": parameters_data['names'], |
|
545
|
|
|
"timestamps": parameters_data['timestamps'], |
|
546
|
|
|
"values": parameters_data['values'] |
|
547
|
|
|
} |
|
548
|
|
|
|
|
549
|
|
|
resp.body = json.dumps(result) |
|
550
|
|
|
|
myems /
myems-api
Loading history...