mirror of
https://github.com/BigBodyCobain/Shadowbroker.git
synced 2026-06-01 03:51:46 +02:00
BigBodyCobain
122 lines
3.5 KiB
Python
122 lines
3.5 KiB
Python
"""Test-only helpers for synthesizing chain events.
|
|
|
|
Mirrors the dict shape that ``InfonetHashchainAdapter.dry_run_append``
|
|
emits, which in turn mirrors the legacy ``mesh_hashchain.Infonet.append``
|
|
output. Tests call these helpers to build synthetic chains; production
|
|
code is unaffected.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any
|
|
|
|
|
|
def make_event(
|
|
event_type: str,
|
|
node_id: str,
|
|
payload: dict[str, Any],
|
|
*,
|
|
timestamp: float,
|
|
sequence: int = 1,
|
|
) -> dict[str, Any]:
|
|
return {
|
|
"event_type": event_type,
|
|
"node_id": node_id,
|
|
"timestamp": float(timestamp),
|
|
"sequence": int(sequence),
|
|
"payload": dict(payload),
|
|
}
|
|
|
|
|
|
def make_market_chain(
|
|
market_id: str,
|
|
creator_id: str,
|
|
*,
|
|
market_type: str = "objective",
|
|
bootstrap_index: int | None = None,
|
|
base_ts: float = 1_700_000_000.0,
|
|
participants: int = 5,
|
|
total_stake: float = 10.0,
|
|
outcome: str | None = "yes",
|
|
is_provisional: bool = False,
|
|
predictions: list[dict[str, Any]] | None = None,
|
|
) -> list[dict[str, Any]]:
|
|
"""Build a coherent set of events for one market.
|
|
|
|
Returns events in chain order: prediction_create → prediction_place
|
|
(per ``predictions``) → market_snapshot → resolution_finalize (if
|
|
``outcome`` is not None). Use this to set up "did the mint rule
|
|
fire correctly" tests.
|
|
"""
|
|
chain: list[dict[str, Any]] = []
|
|
seq = 0
|
|
|
|
def _next_seq() -> int:
|
|
nonlocal seq
|
|
seq += 1
|
|
return seq
|
|
|
|
chain.append(make_event(
|
|
"prediction_create",
|
|
creator_id,
|
|
{
|
|
"market_id": market_id,
|
|
"market_type": market_type,
|
|
"question": f"Q for {market_id}",
|
|
"trigger_date": base_ts + 86400.0,
|
|
"creation_bond": 3,
|
|
**({"bootstrap_index": bootstrap_index} if bootstrap_index is not None else {}),
|
|
},
|
|
timestamp=base_ts,
|
|
sequence=_next_seq(),
|
|
))
|
|
|
|
predictor_ids: list[str] = []
|
|
for i, pred in enumerate(predictions or []):
|
|
chain.append(make_event(
|
|
"prediction_place",
|
|
pred["node_id"],
|
|
{
|
|
"market_id": market_id,
|
|
"side": pred["side"],
|
|
"probability_at_bet": pred.get("probability_at_bet", 50.0),
|
|
**({"stake_amount": pred["stake_amount"]} if pred.get("stake_amount") is not None else {}),
|
|
},
|
|
timestamp=base_ts + 60.0 + i,
|
|
sequence=_next_seq(),
|
|
))
|
|
predictor_ids.append(pred["node_id"])
|
|
|
|
snapshot_ts = base_ts + 3600.0
|
|
chain.append(make_event(
|
|
"market_snapshot",
|
|
creator_id,
|
|
{
|
|
"market_id": market_id,
|
|
"frozen_participant_count": participants,
|
|
"frozen_total_stake": float(total_stake),
|
|
"frozen_predictor_ids": list(dict.fromkeys(predictor_ids)),
|
|
"frozen_probability_state": {"yes": 0.5, "no": 0.5},
|
|
"frozen_at": snapshot_ts,
|
|
},
|
|
timestamp=snapshot_ts,
|
|
sequence=_next_seq(),
|
|
))
|
|
|
|
if outcome is not None:
|
|
finalize_ts = base_ts + 7200.0
|
|
chain.append(make_event(
|
|
"resolution_finalize",
|
|
creator_id,
|
|
{
|
|
"market_id": market_id,
|
|
"outcome": outcome,
|
|
"is_provisional": bool(is_provisional),
|
|
"snapshot_event_hash": f"snap-{market_id}",
|
|
},
|
|
timestamp=finalize_ts,
|
|
sequence=_next_seq(),
|
|
))
|
|
|
|
return chain
|