def __init__(self,
hatchers=5,
proposals=2,
hatch_tribute=0.2,
vesting_80p_unlocked=60,
exit_tribute=0.35,
kappa=2,
days_to_80p_of_max_voting_weight=10,
max_proposal_request=0.2,
timesteps_days=730,
random_seed=None):
self.hatchers = hatchers
self.proposals = proposals
self.hatch_tribute = hatch_tribute
# 60 is 2 months until 80% of tokens are unlocked
self.vesting_80p_unlocked = vesting_80p_unlocked
self.exit_tribute = exit_tribute
self.kappa = kappa # This is the shape of the bonding curve
self.days_to_80p_of_max_voting_weight = days_to_80p_of_max_voting_weight
# Proposal may only request up to 20% of the funding pool
self.max_proposal_request = max_proposal_request
self.timesteps_days = timesteps_days # Simulate 2*365=730 days
self.random_seed = random_seed
self.probability_func = new_probability_func(random_seed)
self.exponential_func = new_exponential_func(random_seed)
self.gamma_func = new_gamma_func(random_seed)
self.random_number_func = new_random_number_func(random_seed)
self.choice_func = new_choice_func(random_seed)
def setUp(self):
self.params = {
"debug": True,
"probability_func": new_probability_func(seed=None),
"exponential_func": new_exponential_func(seed=None),
"gamma_func": new_gamma_func(seed=None),
"random_number_func": new_random_number_func(seed=None)
}
vesting_participants = [
TokenBatch(1000, 1000, vesting_options=VestingOptions(10, 30))
for _ in range(2)
]
nonvesting_participants = [
TokenBatch(0, 1000, vesting_options=VestingOptions(10, 30))
for _ in range(2)
]
self.network = bootstrap_network(
vesting_participants + nonvesting_participants, 1, 3000, 4e6, 0.2,
self.params["probability_func"], self.params["random_number_func"],
self.params["gamma_func"], self.params["exponential_func"])
self.commons = Commons(1000, 1000)
self.network, _ = add_proposal(self.network, Proposal(100, 1),
self.params["random_number_func"])
self.default_state = {
"network": self.network,
"commons": self.commons,
"funding_pool": 1000,
"token_supply": 1000
}
def get_simulation_results(c):
df = run_simulation(c)
df_final = df[df.substep.eq(2)]
random_func = new_random_number_func(None)
last_network = df_final.iloc[-1, 0]
candidates = len(
get_proposals(last_network, status=ProposalStatus.CANDIDATE))
actives = len(get_proposals(last_network, status=ProposalStatus.ACTIVE))
completed = len(
get_proposals(last_network, status=ProposalStatus.COMPLETED))
failed = len(get_proposals(last_network, status=ProposalStatus.FAILED))
participants = len(get_participants(last_network))
score = CommonsScore(params=c, df_final=df_final)
result = {
"timestep": list(df_final["timestep"]),
"funding_pool": list(df_final["funding_pool"]),
"token_price": list(df_final["token_price"]),
"sentiment": list(df_final["sentiment"]),
"score": score.eval(),
"participants": participants,
"proposals": {
"candidates": candidates,
"actives": actives,
"completed": completed,
"failed": failed,
"total": candidates + actives + completed + failed
}
}
return result, df_final
def setUp(self):
self.params = {
"debug": False,
"days_to_80p_of_max_voting_weight": 10,
"probability_func": new_probability_func(seed=None),
"exponential_func": new_exponential_func(seed=None),
"gamma_func": new_gamma_func(seed=None),
"random_number_func": new_random_number_func(seed=None)
}
self.network = bootstrap_network([
TokenBatch(1000, 0, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2, self.params["probability_func"],
self.params["random_number_func"],
self.params["gamma_func"],
self.params["exponential_func"])
self.network, _ = add_proposal(self.network, Proposal(100, 1),
self.params["random_number_func"])
"""
For proper testing, we need to make sure the Proposals are CANDIDATE and
ensure Proposal-Participant affinities are not some random value
"""
self.network.nodes[4]["item"].status = ProposalStatus.CANDIDATE
self.network.nodes[5]["item"].status = ProposalStatus.CANDIDATE
support_edges = get_edges_by_type(self.network, "support")
for u, v in support_edges:
self.network[u][v]["support"] = self.network[u][v][
"support"]._replace(affinity=0.9)
def get_simulation_results(c):
df = run_simulation(c)
df_final = df[df.substep.eq(2)]
random_func = new_random_number_func(None)
result = {
"timestep": list(df_final["timestep"]),
"funding_pool": list(df_final["funding_pool"]),
"token_price": list(df_final["token_price"]),
"sentiment": list(df_final["sentiment"]),
"score": int(random_func() * 1000)
}
return result, df_final
def setUp(self):
self.params = {
"debug": True,
"probability_func": new_probability_func(seed=None),
"exponential_func": new_exponential_func(seed=None),
"gamma_func": new_gamma_func(seed=None),
"random_number_func": new_random_number_func(seed=None)
}
self.network = bootstrap_network([
TokenBatch(1000, 0, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2, self.params["probability_func"],
self.params["random_number_func"],
self.params["gamma_func"],
self.params["exponential_func"])
def setUp(self):
self.network = nx.DiGraph()
self.params = {
"probability_func": new_probability_func(seed=None),
"exponential_func": new_exponential_func(seed=None),
"gamma_func": new_gamma_func(seed=None),
"random_number_func": new_random_number_func(seed=None)
}
for i in range(0, 10, 2):
self.network.add_node(i,
item=Participant(
TokenBatch(0, 0),
self.params["probability_func"],
self.params["random_number_func"]))
self.network.add_node(i + 1, item=Proposal(10, 5))
def setUp(self):
self.params = {
"probability_func": new_probability_func(seed=None),
"exponential_func": new_exponential_func(seed=None),
"gamma_func": new_gamma_func(seed=None),
"random_number_func": new_random_number_func(seed=None)
}
self.network = bootstrap_network([
TokenBatch(1000, 0, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2, self.params["probability_func"],
self.params["random_number_func"],
self.params["gamma_func"],
self.params["exponential_func"])
self.network, _ = add_proposal(self.network, Proposal(100, 1),
self.params["random_number_func"])
self.network.nodes[4]["item"].status = ProposalStatus.ACTIVE
self.network.nodes[5]["item"].status = ProposalStatus.ACTIVE
def setUp(self):
self.params = {
"max_proposal_request": 0.2,
"alpha_days_to_80p_of_max_voting_weight": 10,
"probability_func": new_probability_func(seed=None),
"exponential_func": new_exponential_func(seed=None),
"gamma_func": new_gamma_func(seed=None),
"random_number_func": new_random_number_func(seed=None)
}
self.commons = Commons(1000, 1000)
self.network = bootstrap_network([
TokenBatch(1000, 0, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2, self.params["probability_func"],
self.params["random_number_func"],
self.params["gamma_func"],
self.params["exponential_func"])
self.network, _ = add_proposal(self.network, Proposal(100, 1),
self.params["random_number_func"])
self.network.nodes[4]["item"].status = ProposalStatus.CANDIDATE
self.network.nodes[5]["item"].status = ProposalStatus.CANDIDATE
def test_new_random_number_func(self):
random_number_func = utils.new_random_number_func(seed=None)
result = random_number_func()
self.assertTrue(result >= 0 and result <= 1)
