logos-blockchain/logos-blockchain-specs
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Naming fixed

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danielSanchezQ 2025-07-10 14:21:08 +00:00
parent 189c4c8244
commit c7c5750b92
1 changed files with 8 additions and 8 deletions
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16
da/assignations/refill.py
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@ -9,7 +9,7 @@ from heapq import heappush, heappop, heapify
DeclarationId: TypeAlias = bytes DeclarationId: TypeAlias = bytes
Assignations: TypeAlias = List[Set[DeclarationId]] Assignations: TypeAlias = List[Set[DeclarationId]]
ChaCha20: TypeAlias = Any BlakeRng: TypeAlias = Any
@dataclass(order=True) @dataclass(order=True)
@ -36,11 +36,11 @@ class Subnetwork:
return len(self.participants) return len(self.participants)
def are_subnetworks_filled_up_to_replication_factor(subnetworks: Sequence[Subnetwork], replication_factor: int) -> bool: def subnetworks_filled_up_to_replication_factor(subnetworks: Sequence[Subnetwork], replication_factor: int) -> bool:
return all(len(subnetwork) >= replication_factor for subnetwork in subnetworks) return all(len(subnetwork) >= replication_factor for subnetwork in subnetworks)
def all_nodes_are_assigned(participants: Sequence[Participant], average_participation: int) -> bool: def all_nodes_assigned(participants: Sequence[Participant], average_participation: int) -> bool:
return all(participant.participation >= average_participation for participant in participants) return all(participant.participation >= average_participation for participant in participants)
@ -55,7 +55,7 @@ def heappop_next_for_subnetwork(subnetwork: Subnetwork, participants: List[Parti
return participant return participant
# sample using fisher yates shuffling, returning # sample using fisher yates shuffling, returning
def sample(elements: Sequence[Any], random: ChaCha20, k: int) -> List[Any]: def sample(elements: Sequence[Any], random: BlakeRng, k: int) -> List[Any]:
# list is sorted for reproducibility # list is sorted for reproducibility
elements = sorted(elements) elements = sorted(elements)
# pythons built-in is fisher yates shuffling # pythons built-in is fisher yates shuffling
@ -73,8 +73,8 @@ def fill_subnetworks(
heapify(subnetworks) heapify(subnetworks)
while not ( while not (
are_subnetworks_filled_up_to_replication_factor(subnetworks, replication_factor) and subnetworks_filled_up_to_replication_factor(subnetworks, replication_factor) and
all_nodes_are_assigned(available_nodes, average_participation) all_nodes_assigned(available_nodes, average_participation)
): ):
# take the fewest participants subnetwork # take the fewest participants subnetwork
subnetwork = heappop(subnetworks) subnetwork = heappop(subnetworks)
@ -92,7 +92,7 @@ def fill_subnetworks(
def balance_subnetworks_shrink( def balance_subnetworks_shrink(
subnetworks: List[Subnetwork], subnetworks: List[Subnetwork],
random: ChaCha20, random: BlakeRng,
): ):
while (len(max(subnetworks)) - len(min(subnetworks))) > 1: while (len(max(subnetworks)) - len(min(subnetworks))) > 1:
max_subnetwork = max(subnetworks) max_subnetwork = max(subnetworks)
@ -108,7 +108,7 @@ def balance_subnetworks_grow(
subnetworks: List[Subnetwork], subnetworks: List[Subnetwork],
participants: List[Participant], participants: List[Participant],
average_participation: int, average_participation: int,
random: ChaCha20, random: BlakeRng,
): ):
for participant in filter(lambda x: x.participation > average_participation, sorted(participants)): for participant in filter(lambda x: x.participation > average_participation, sorted(participants)):
for subnework in sample( for subnework in sample(