def get_intensity_data(cls, group_ids, account_type, report_year, comparison_type, demand_type):
if demand_type != 'all':
report_index = "has_peak"
else:
report_index = "no_peak"
y_unit_map = "sum_btu"
final_data = []
for group_id in group_ids:
descendants = GroupRepository.get_descendants(group_id)
accounts = []
for g in descendants:
accounts += cls.get_accounts_for_group(g['id'], account_type, report_year, comparison_type,
demand_type)
if len(accounts) >= 1:
data = Db.r.db("pathian").table("compiled_energy_records").get_all(*accounts, index=report_index).group(
lambda record: {"year": record['year'], "value": record["comparison_value"]}).map(
lambda record: {y_unit_map: record[y_unit_map],
"sum_size_normalization": record["sum_size_normalization"]}).reduce(
lambda a, b: {y_unit_map: a[y_unit_map] + b[y_unit_map],
"sum_size_normalization": a["sum_size_normalization"] + b[
"sum_size_normalization"]}).ungroup().run()
data_insert = []
for entry in data:
data_insert.append(
[entry['group']['value'],
entry['reduction'][y_unit_map] / entry['reduction']['sum_size_normalization']])
final_data.append({'name': GroupRepository.get_group_by_id(group_id).name, 'data': data_insert})
return final_data
def get_total_energy_data_with_y(cls, group_id, report_year, benchmark_year, account_type, comparison_type, demand_type, y_units, y_unit_map):
if demand_type != 'all':
report_index = "has_peak"
else:
report_index = "no_peak"
same_year = int(report_year) == int(benchmark_year)
unit_factor = get_factor(y_units, y_unit_map)
data = TotalEnergyData
descendants = GroupRepository.get_descendants(group_id)
accounts = []
# Get all accounts for each group for the desired year
for g in descendants:
accounts += cls.get_accounts_for_group(g['id'], account_type, report_year, comparison_type, demand_type)
if not same_year:
accounts += cls.get_accounts_for_group(g['id'], account_type, benchmark_year, comparison_type,demand_type)
if len(accounts) < 1:
benchmark_data = []
else:
benchmark_data = Db.r.db("pathian").table("compiled_energy_records").get_all(*accounts,
index=report_index).group(
lambda record: {"year": record['year'], "value": record["comparison_value"]}).map(
lambda record: {y_unit_map: record[y_unit_map],
"sum_hours_in_record": record["sum_hours_in_record"]}).reduce(
lambda a, b: {y_unit_map: a[y_unit_map] + b[y_unit_map],
"sum_hours_in_record": a["sum_hours_in_record"] + b["sum_hours_in_record"]}).ungroup().run()
grouped = defaultdict(list)
for d in benchmark_data:
grouped[d['group']['value']].append(d)
reported_consumption = []
diff = []
benchmark_diff = []
benchmark_consumption = []
grouped_keys = list(grouped.keys())
grouped_keys.sort()
if same_year:
for value in grouped_keys:
entry = grouped[value]
reported_consumption.append(
[value, entry[0]['reduction'][y_unit_map] * unit_factor / entry[0]['reduction']['sum_hours_in_record']]
)
diff.append([value, 0])
benchmark_consumption.append(
[value, entry[0]['reduction'][y_unit_map] * unit_factor / entry[0]['reduction']['sum_hours_in_record']])
benchmark_diff.append([value, 0])
else:
for value in grouped_keys:
entry = grouped[value]
if len(entry) > 1:
report_record = 0
benchmark_record = 0
for record in entry:
if record['group']['year'] == int(report_year):
report_record = record['reduction'][y_unit_map] * unit_factor / record['reduction'][
'sum_hours_in_record']
reported_consumption.append([value, report_record])
elif record['group']['year'] == int(benchmark_year):
benchmark_record = record['reduction'][y_unit_map] * unit_factor / record['reduction'][
'sum_hours_in_record']
benchmark_consumption.append([value, benchmark_record])
benchmark_diff.append([value, 0])
diff.append([value, benchmark_record - report_record])
data.reported_consumption = reported_consumption
data.benchmark_consumption = benchmark_consumption
data.diff = diff
data.benchmark_diff = benchmark_diff
return data
