def handle(self, *args, **options):
network = options['network']
clr_pk = options['clr_pk']
if clr_pk == "all":
active_clr_rounds = GrantCLR.objects.filter(is_active=True)
else:
active_clr_rounds = GrantCLR.objects.filter(pk=clr_pk)
if active_clr_rounds:
for clr_round in active_clr_rounds:
print(f"CALCULATING CLR estimates for ROUND: {clr_round.round_num}")
predict_clr(
save_to_db=True,
from_date=timezone.now(),
clr_round=clr_round,
network=network
)
print(f"finished CLR estimates for {clr_round.round_num}")
# TOTAL GRANT
# grants = Grant.objects.filter(network=network, hidden=False, active=True, link_to_new_grant=None)
# grants = grants.filter(**clr_round.grant_filters)
# total_clr_distributed = 0
# for grant in grants:
# total_clr_distributed += grant.clr_prediction_curve[0][1]
# print(f'Total CLR allocated for {clr_round.round_num} - {total_clr_distributed}')
else:
print("No active CLRs found")
def handle(self, *args, **options):
network = options['network']
clr_pk = options['clr_pk']
what = options['what']
sync = options['sync']
print(network, clr_pk, what, sync)
if clr_pk and clr_pk.isdigit():
active_clr_rounds = GrantCLR.objects.filter(pk=clr_pk)
else:
active_clr_rounds = GrantCLR.objects.filter(is_active=True)
if active_clr_rounds:
for clr_round in active_clr_rounds:
if sync == 'true':
# run it sync -> useful for payout / debugging
predict_clr(
save_to_db=True,
from_date=timezone.now(),
clr_round=clr_round,
network=network,
what=what,
)
else:
# runs it as celery task.
process_predict_clr(
save_to_db=True,
from_date=timezone.now(),
clr_round=clr_round,
network=network,
what=what,
)
else:
print("No active CLRs found")
def handle(self, *args, **options):
clr_type = options['clr_type']
network = options['network']
predict_clr(save_to_db=True,
from_date=timezone.now(),
clr_type=clr_type,
network=network)
print("finished CLR estimates")
def recalc_clr(self, grant_id, retry: bool = True) -> None:
obj = Grant.objects.get(pk=grant_id)
from grants.clr import predict_clr
from django.utils import timezone
for clr_round in obj.in_active_clrs.all():
network = 'mainnet'
predict_clr(save_to_db=True,
from_date=timezone.now(),
clr_round=clr_round,
network=network,
only_grant_pk=obj.pk)
def process_predict_clr(self, save_to_db, from_date, clr_round,
network) -> None:
from grants.clr import predict_clr
print(
f"CALCULATING CLR estimates for ROUND: {clr_round.round_num} {clr_round.sub_round_slug}"
)
predict_clr(save_to_db, from_date, clr_round, network)
print(
f"finished CLR estimates for {clr_round.round_num} {clr_round.sub_round_slug}"
)
def handle(self, *args, **options):
clr_type = options['clr_type']
network = options['network']
clr_prediction_curves = predict_clr(save_to_db=True,
from_date=timezone.now(),
clr_type=clr_type,
network=network)
# Uncomment these for debugging and sanity checking
# for grant in clr_prediction_curves:
#print("CLR predictions for grant {}".format(grant['grant']))
#print("All grants: {}".clr_typeformat(grant['grants_clr']))
#print("prediction curve: {}\n\n".format(grant['clr_prediction_curve']))
# sanity check: sum all the estimated clr distributions - should be close to CLR_DISTRIBUTION_AMOUNT
clr_data = [g['grants_clr'] for g in clr_prediction_curves]
# print(clr_data)
if clr_data and clr_data[0]:
total_clr_funds = sum(
[each_grant['clr_amount'] for each_grant in clr_data[0]])
print("allocated CLR funds:{}".format(total_clr_funds))
print("finished CLR estimates")
def handle(self, *args, **options):
if not clr_active and not options['force']:
print(
'CLR round is not active according to grants.views.clr_active, so cowardly refusing to spend the CPU cycles + exiting instead'
)
return
clr_type = options['clr_type']
network = options['network']
# identity mechanism is profiles for traditional rounds. for experimental rounds, where we saw collusion
# make the identity mechanism into funds originated addr
# this is a stopgap until a "one identity mechanism to rule them all is round", probably in round 6.
predict_clr(save_to_db=True,
from_date=timezone.now(),
clr_type=clr_type,
network=network)
print("finished CLR estimates")
def handle(self, *args, **options):
clr_prediction_curves = predict_clr(random_data=False, save_to_db=True)
# Uncomment these for debugging and sanity checking
# for grant in clr_prediction_curves:
#print("CLR predictions for grant {}".format(grant['grant']))
#print("All grants: {}".format(grant['grants_clr']))
#print("prediction curve: {}\n\n".format(grant['clr_prediction_curve']))
# sanity check: sum all the estimated clr distributions - should be close to CLR_DISTRIBUTION_AMOUNT
clr_data = [g['grants_clr'] for g in clr_prediction_curves]
# print(clr_data)
total_clr_funds = sum([each_grant['clr_amount'] for each_grant in clr_data[0]])
print("allocated CLR funds:{}".format(total_clr_funds))
print("finished CLR estimates")
