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 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 su_add_to_network(params, step, sL, s, _input, **kwargs):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
scale_factor = 0.01
if _input["new_proposal"]:
# Create the Proposal and add it to the network.
rescale = funding_pool * scale_factor
r_rv = gamma.rvs(3, loc=0.001, scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=trigger_threshold(
r_rv, funding_pool, token_supply,
params["max_proposal_request"]))
network, proposal_idx = add_proposal(network, proposal)
# add_proposal() has created support edges from other Participants
# to this Proposal. If the Participant is the one who created this
# Proposal, change his affinity for the Proposal to 1 (maximum).
network.edges[_input["proposed_by_participant"],
proposal_idx]["affinity"] = 1
if params.get("debug"):
print(
"GenerateNewProposal: Participant {} created Proposal {}".
format(_input["proposed_by_participant"], proposal_idx))
return "network", network
def su_add_to_network(params, step, sL, s, _input, **kwargs):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
gamma_func = params["gamma_func"]
random_number_func = params["random_number_func"]
if _input["new_proposal"]:
proposed_by = _input["proposed_by_participant"]
# Create the Proposal and add it to the network.
rescale = funding_pool * config.scale_factor
r_rv = gamma_func(config.funds_requested_alpha,
loc=config.funds_requested_min,
scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=trigger_threshold(
r_rv, funding_pool, token_supply,
params["max_proposal_request"]))
network, proposal_idx = add_proposal(network, proposal,
proposed_by,
random_number_func)
if params.get("debug"):
print(
"GenerateNewProposal: Participant {} created Proposal {}".
format(_input["proposed_by_participant"], proposal_idx))
return "network", network
def su_add_to_network(params, step, sL, s, _input, **kwargs):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
gamma_func = params["gamma_func"]
random_number_func = params["random_number_func"]
if _input["new_proposal"]:
# Create the Proposal and add it to the network.
rescale = funding_pool * config.scale_factor
r_rv = gamma_func(config.funds_requested_alpha,
loc=config.funds_requested_min,
scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=trigger_threshold(
r_rv, funding_pool, token_supply,
params["max_proposal_request"]))
network, proposal_idx = add_proposal(network, proposal,
random_number_func)
# add_proposal() has created support edges from other Participants
# to this Proposal. If the Participant is the one who created this
# Proposal, set the participant's role as author and change his affinity for the Proposal to 1 (maximum).
network.edges[_input["proposed_by_participant"],
proposal_idx]["support"] = \
network.edges[_input["proposed_by_participant"], proposal_idx]["support"]._replace(affinity=1, is_author=True)
if params.get("debug"):
print(
"GenerateNewProposal: Participant {} created Proposal {}".
format(_input["proposed_by_participant"], proposal_idx))
return "network", network
def setUp(self):
self.network = bootstrap_network(
[TokenBatch(1000, VestingOptions(10, 30)) for _ in range(4)], 1,
3000, 4e6)
self.network, _ = add_proposal(self.network, Proposal(100, 1))
self.network.nodes[4]["item"].status = ProposalStatus.ACTIVE
self.network.nodes[5]["item"].status = ProposalStatus.ACTIVE
def setUp(self):
self.network = bootstrap_network([
TokenBatch(1000, 0, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2)
self.network, _ = add_proposal(self.network, Proposal(100, 1))
self.network.nodes[4]["item"].status = ProposalStatus.CANDIDATE
self.network.nodes[5]["item"].status = ProposalStatus.CANDIDATE
self.params = {
"max_proposal_request": 0.2,
"alpha_days_to_80p_of_max_voting_weight": 10
}
def test_add_proposal(self):
"""
This test ensures that the Proposal was added and that
setup_conflict_edges() was executed for that particular node.
"""
n1, j = add_proposal(self.network, Proposal(23, 111))
self.assertEqual(n1.nodes[j]["item"].funds_requested, 23)
self.assertEqual(n1.nodes[j]["item"].trigger, 111)
self.assertEqual(len(n1.edges), 5)
for u, v, t in n1.edges(data="type"):
self.assertEqual(v, 10)
self.assertEqual(t, "support")
self.assertIn(u, [0, 2, 4, 6, 8])
def setUp(self):
self.network = bootstrap_network([
TokenBatch(1000, 1000, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2)
self.commons = Commons(1000, 1000)
self.network, _ = add_proposal(self.network, Proposal(100, 1))
self.params = {"debug": True}
self.default_state = {
"network": self.network,
"commons": self.commons,
"funding_pool": 1000,
"token_supply": 1000
}
def setUp(self):
self.network = bootstrap_network([
TokenBatch(1000, 0, vesting_options=VestingOptions(10, 30))
for _ in range(4)
], 1, 3000, 4e6, 0.2)
self.network, _ = add_proposal(self.network, Proposal(100, 1))
self.params = {"debug": False, "days_to_80p_of_max_voting_weight": 10}
"""
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]["affinity"] = 0.9
def su_add_to_network(params, step, sL, s, _input):
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
scale_factor = 0.01
if _input["new_proposal"]:
# Create the Proposal and add it to the network.
rescale = funding_pool * scale_factor
r_rv = gamma.rvs(3, loc=0.001, scale=rescale)
proposal = Proposal(funds_requested=r_rv,
trigger=convictionvoting.trigger_threshold(r_rv, funding_pool, token_supply))
network, j = add_proposal(network, proposal)
# add_proposal() has created support edges from other Participants
# to this Proposal. If the Participant is the one who created this
# Proposal, change his affinity for the Proposal to 1 (maximum).
network.edges[_input["proposed_by_participant"], j]["affinity"] = 1
return "network", network
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
