def test1():
class BaseAgent:
def __init__(self, name):
self.name = name
class FooAgent(BaseAgent):
pass
class BarAgent(BaseAgent):
pass
class BahAgent(BaseAgent):
pass
d = AgentDict({
'foo1': FooAgent('foo1'),
'foo2': FooAgent('foo2'),
'bar1': BarAgent('bar1')
})
foo_d = d.filterByClass(FooAgent)
assert sorted(foo_d.keys()) == ['foo1', 'foo2']
bar_d = d.filterByClass(BarAgent)
assert sorted(bar_d.keys()) == ['bar1']
bah_d = d.filterByClass(BahAgent)
assert sorted(bah_d.keys()) == []
def test2():
class FooAgent:
def __init__(self, name):
self.name = name
def BPT(self, pool):
return 0.0
d = AgentDict({'foo1': FooAgent('foo1')})
assert not d.filterByNonzeroStake(FooAgent('foo2'))
assert not d.filterToPool()
assert not d.filterToPublisher()
assert not d.filterToStakerspeculator()
assert not d.filterToDataconsumer()
assert len(d.filterByClass(FooAgent)) == 1
def __init__(self):
self.agents = AgentDict({})
self.ss = MockSS()
def __init__(self):
self.agents = AgentDict({})
def __init__(self, ss: SimStrategy.SimStrategy):
log.debug("init:begin")
#main
self.ss = ss
self.tick = 0
#used to manage names
self._next_free_marketplace_number = 0
#used to add agents
self._marketplace_tick_previous_add = 0
#main storage of agents. Fill this below
self.agents = AgentDict() #agent_name : Agent instance
#<<Note many magic numbers below, for simplicity>>
#note: KPIs class also has some magic number
#as ecosystem improves, these parameters may change / improve
self._marketplace_percent_toll_to_ocean = 0.002 #magic number
self._percent_burn: float = 0.05 #to burning, vs to DAO #magic number
self._total_OCEAN_minted: float = 0.0
self._total_OCEAN_burned: float = 0.0
self._total_OCEAN_burned_USD: float = 0.0
self._speculation_valuation = 5e6 #in USD #magic number
self._percent_increase_speculation_valuation_per_s = 0.10 / S_PER_YEAR # ""
#Instantiate and connnect agent instances. "Wire up the circuit"
new_agents: Set[BaseAgent] = set()
#FIXME: replace MarketplacesAgent with DataecosystemAgent, when ready
new_agents.add(
MarketplacesAgent(
name="marketplaces1",
USD=0.0,
OCEAN=0.0,
toll_agent_name="opc_address",
n_marketplaces=float(ss.init_n_marketplaces),
revenue_per_marketplace_per_s=2e3 / S_PER_MONTH, #magic number
time_step=self.ss.time_step,
))
new_agents.add(
RouterAgent(name="opc_address",
USD=0.0,
OCEAN=0.0,
receiving_agents={
"ocean_dao": self.percentToOceanDao,
"opc_burner": self.percentToBurn
}))
new_agents.add(OCEANBurnerAgent(name="opc_burner", USD=0.0, OCEAN=0.0))
#func = MinterAgents.ExpFunc(H=4.0)
func = MinterAgents.RampedExpFunc(
H=4.0, #magic number
T0=0.5,
T1=1.0,
T2=1.4,
T3=3.0, #""
M1=0.10,
M2=0.25,
M3=0.50) #""
new_agents.add(
MinterAgents.OCEANFuncMinterAgent(
name="ocean_51",
receiving_agent_name="ocean_dao",
total_OCEAN_to_mint=UNMINTED_OCEAN_SUPPLY,
s_between_mints=S_PER_DAY,
func=func))
new_agents.add(
GrantGivingAgent(
name="opf_treasury_for_ocean_dao",
USD=0.0,
OCEAN=OPF_TREASURY_OCEAN_FOR_OCEAN_DAO, #magic number
receiving_agent_name="ocean_dao",
s_between_grants=S_PER_MONTH,
n_actions=12 * 3)) #""
new_agents.add(
GrantGivingAgent(
name="opf_treasury_for_opf_mgmt",
USD=OPF_TREASURY_USD,
OCEAN=OPF_TREASURY_OCEAN_FOR_OPF_MGMT, #magic number
receiving_agent_name="opf_mgmt",
s_between_grants=S_PER_MONTH,
n_actions=12 * 3)) #""
new_agents.add(
GrantGivingAgent(
name="bdb_treasury",
USD=BDB_TREASURY_USD,
OCEAN=BDB_TREASURY_OCEAN, #magic number
receiving_agent_name="bdb_mgmt",
s_between_grants=S_PER_MONTH,
n_actions=17)) #""
new_agents.add(
RouterAgent(name="ocean_dao",
receiving_agents={"opc_workers": funcOne},
USD=0.0,
OCEAN=0.0))
new_agents.add(
RouterAgent(name="opf_mgmt",
receiving_agents={"opc_workers": funcOne},
USD=0.0,
OCEAN=0.0))
new_agents.add(
RouterAgent(name="bdb_mgmt",
receiving_agents={"bdb_workers": funcOne},
USD=0.0,
OCEAN=0.0))
new_agents.add(GrantTakingAgent(name="opc_workers", USD=0.0,
OCEAN=0.0))
new_agents.add(GrantTakingAgent(name="bdb_workers", USD=0.0,
OCEAN=0.0))
for agent in new_agents:
self.agents[agent.name] = agent
#track certain metrics over time, so that we don't have to load
self.kpis = Kpis.KPIs(self.ss.time_step)
log.debug("init: end")