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conviction
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conviction model 2 

introduced social network
introducec conflict network
introduced killed proposals
introduced failed proposals
This commit is contained in:
Michael Zargham 2019-06-01 00:48:33 -07:00
parent 0a72c514e8
commit 8f74ab75ea
6 changed files with 3067 additions and 106 deletions
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.ipynb_checkpoints/conviction_cadCAD2-checkpoint.ipynb Normal file
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conviction_helpers.py
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@ -137,6 +137,19 @@ def conflict_links(network,proposal ,rate = .25):
network.edges[(i,j)]['type'] = 'conflict'
return network
def social_affinity_booster(network, proposal, participant):
participants = get_nodes_by_type(network, 'participant')
influencers = get_edges_by_type(network, 'influence')
j=proposal
i=participant
total_inf = np.sum([network.edges[(i,node)]['influence'] for node in participants if (i, node) in influencers ])
boosts=[network.edges[(node,j)]['affinity']*network.edges[(i,node)]['influence']/total_inf for node in participants if (i, node) in influencers ]
return np.sum(boosts)
def trigger_sweep(field, trigger_func,xmax=.2,default_alpha=.5):
if field == 'token_supply':
@ -261,10 +274,10 @@ def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=Fa
cNorm = colors.Normalize(vmin=0, vmax=max_tok)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
net_cand = [j for j in net_props if net.nodes[j]['status']=='candidate']
for j in net_props:
node_size[j] = net.nodes[j]['funds_requested']*size_scale
node_size[j] = net.nodes[j]['funds_requested']*size_scale/4
if net.nodes[j]['status']=="candidate":
node_color[j] = colors.to_rgba('blue')
trigger = net.nodes[j]['trigger']
@ -277,6 +290,12 @@ def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=Fa
elif net.nodes[j]['status']=="completed":
node_color[j] = colors.to_rgba('green')
net_node_label[j] = ''
elif net.nodes[j]['status']=="failed":
node_color[j] = colors.to_rgba('gray')
net_node_label[j] = ''
elif net.nodes[j]['status']=="killed":
node_color[j] = colors.to_rgba('black')
net_node_label[j] = ''
for i in net_parts:
node_size[i] = net.nodes[i]['holdings']*size_scale
@ -287,9 +306,10 @@ def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=Fa
for ind in range(E):
e = edges[ind]
tokens = net.edges[e]['tokens']
if tokens >0:
included_edges.append(e)
edge_color[ind] = scalarMap.to_rgba(tokens)
if e[1] in net_cand:
included_edges.append(e)
iE = len(included_edges)
included_edge_color = np.empty((iE,4))
@ -306,6 +326,7 @@ def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=Fa
edgelist=included_edges,
labels = net_node_label)
plt.title('Tokens Staked by Partipants to Proposals')
if ani:
nx.draw(net,
pos=pos,