Я думаю, вам следует переместить my_count часть представления другим методом, а затем переопределить его в дочернем представлении. Как это:
class ShowChart(View):
def get_my_count(self):
my_count = Electrical.objects.values('allocated_time')\
.annotate(complete=Count('allocated_time', filter=Q(batch_18=True)),
not_complete=Count('allocated_time',
filter=Q(batch_18=False)),
complete_1=Count('allocated_time',
filter=Q(batch_19=True)),
not_complete_1=Count('allocated_time',
filter=Q(batch_19=False)),
complete_2=Count('allocated_time',
filter=Q(batch_20=True)),
not_complete_2=Count('allocated_time',
filter=Q(batch_20=False)),
complete_3=Count('allocated_time',
filter=Q(batch_21=True)),
not_complete_3=Count('allocated_time',
filter=Q(batch_21=False)))
return my_count
def get(self, request):
c_batch_18 = list()
n_batch_18 = list()
c_batch_19 = list()
n_batch_19 = list()
c_batch_20 = list()
n_batch_20 = list()
c_batch_21 = list()
n_batch_21 = list()
for entry in self.get_my_count():
c_batch_18.append(entry['complete'] * entry['allocated_time'])
n_batch_18.append(entry['not_complete'] * entry['allocated_time'])
c_batch_19.append(entry['complete_1'] * entry['allocated_time'])
n_batch_19.append(entry['not_complete_1'] * entry['allocated_time'])
c_batch_20.append(entry['complete_2'] * entry['allocated_time'])
n_batch_20.append(entry['not_complete_2'] * entry['allocated_time'])
c_batch_21.append(entry['complete_3'] * entry['allocated_time'])
n_batch_21.append(entry['not_complete_3'] * entry['allocated_time'])
survived_series = [sum(c_batch_18), sum(c_batch_19), sum(c_batch_20), sum(c_batch_21), 0]
not_survived_series = [sum(n_batch_18), sum(n_batch_19), sum(n_batch_20), sum(n_batch_21), 0]
return render(request, 'chart.html', {'survived_series': json.dumps(survived_series),
'not_survived_series': json.dumps(not_survived_series)})
class ChartElectrical(ShowChart):
def get_my_count(self):
# overriding get_my_count function
my_count = Electrical.objects.values(...)
return my_count
Возможные оптимизации
Также вот несколько идей по оптимизации:
1. Вы можете сделать c_batch_18, n_batch_18 и др. Расчеты через аннотацию. Например:
from django.db.models import ExpressionWrapper, IntegerField
mycount = mycount.annotate(c_batch_18=ExpressionWrapper(
F('complete') * F('allocated_time'), output_field=IntegerField()))
.annotate(n_batch_18=ExpressionWrapper(
F('not_complete') * F('allocated_time'), output_field=IntegerField())) # and so on
2. Вы также можете рассчитать Сумма путем агрегирования:
from django.db.models import Sum
my_count_sums = my_count.aggregate(c_batch_18_sum=Sum('c_batch_18'), n_batch_18=Sum('n_batch_18')) # and so on
survived = list(filter(lambda x: x.key().startswith('c_batch'), my_count_sums))
not_survived = list(filter(lambda x: x.key().startswith('n_batch'), my_count_sums))
Используя эти оптимизации, выбудет делать большинство вычислений через БД, а не полагаться на ресурсы Python.