def p_compare_conviction_and_threshold(params, step, sL, s, **kwargs):
"""
This policy simply goes through the Proposals to see if their thresholds
are smaller than their gathered conviction
"""
network = s["network"]
funding_pool = s["funding_pool"]
token_supply = s["token_supply"]
proposals_w_enough_conviction = []
proposals = get_proposals(network, status=ProposalStatus.CANDIDATE)
for idx, proposal in proposals:
total_conviction = calc_total_conviction(network, idx)
proposal.conviction = total_conviction
res = proposal.has_enough_conviction(
funding_pool, token_supply, params["max_proposal_request"])
if params.get("debug"):
print(
"ProposalFunding: Proposal {} has {} conviction, and needs {} to pass"
.format(idx, proposal.conviction, proposal.trigger))
if res:
proposals_w_enough_conviction.append(idx)
return {
"proposal_idxs_with_enough_conviction":
proposals_w_enough_conviction
}
def test_calc_total_conviction(self):
"""
Ensure that the function reports the correct sum of conviction from all
support edges.
"""
self.network = setup_support_edges(self.network)
ans = calc_total_conviction(self.network, 1)
self.assertEqual(ans, 0)
support_edges = get_edges_by_type(self.network, "support")
# Every support edge gets a conviction value. Since there are 5
# Participants and 5 Proposals, this should result in a sum of 5
# conviction for each Proposal.
for u, v in support_edges:
self.network.edges[u, v]["conviction"] = 1
for i in [1, 3, 5, 7, 9]:
ans = calc_total_conviction(self.network, i)
self.assertEqual(ans, 5)