337 lines
12 KiB
Python
337 lines
12 KiB
Python
import networkx as nx
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from scipy.stats import expon, gamma
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.colors as colors
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import matplotlib.cm as cmx
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#helper functions
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def get_nodes_by_type(g, node_type_selection):
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return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection ]
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def get_edges_by_type(g, edge_type_selection):
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return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection ]
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def total_funds_given_total_supply(total_supply):
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#can put any bonding curve invariant here for initializatio!
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total_funds = total_supply**2/1000
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return total_funds
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#maximum share of funds a proposal can take
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default_beta = .2 #later we should set this to be param so we can sweep it
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# tuning param for the trigger function
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default_rho = .001
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def trigger_threshold(requested, funds, supply, beta = default_beta, rho = default_rho):
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share = requested/funds
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if share < beta:
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return rho*supply/(beta-share)**2
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else:
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return np.inf
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def initialize_network(n,m, funds_func=total_funds_given_total_supply, trigger_func =trigger_threshold ):
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network = nx.DiGraph()
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for i in range(n):
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network.add_node(i)
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network.nodes[i]['type']="participant"
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h_rv = expon.rvs(loc=0.0, scale=1000)
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network.nodes[i]['holdings'] = h_rv
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s_rv = np.random.rand()
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network.nodes[i]['sentiment'] = s_rv
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participants = get_nodes_by_type(network, 'participant')
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initial_supply = np.sum([ network.nodes[i]['holdings'] for i in participants])
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initial_funds = funds_func(initial_supply)
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#generate initial proposals
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for ind in range(m):
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j = n+ind
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network.add_node(j)
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network.nodes[j]['type']="proposal"
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network.nodes[j]['conviction']=0
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network.nodes[j]['status']='candidate'
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network.nodes[j]['age']=0
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r_rv = gamma.rvs(3,loc=0.001, scale=10000)
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network.node[j]['funds_requested'] = r_rv
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network.nodes[j]['trigger']= trigger_threshold(r_rv, initial_funds, initial_supply)
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for i in range(n):
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network.add_edge(i, j)
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rv = np.random.rand()
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a_rv = 1-4*(1-rv)*rv #polarized distribution
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network.edges[(i, j)]['affinity'] = a_rv
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network.edges[(i, j)]['tokens'] = 0
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network.edges[(i, j)]['conviction'] = 0
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network.edges[(i, j)]['type'] = 'support'
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proposals = get_nodes_by_type(network, 'proposal')
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total_requested = np.sum([ network.nodes[i]['funds_requested'] for i in proposals])
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network = initial_conflict_network(network, rate = .25)
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network = initial_social_network(network, scale = 1)
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return network, initial_funds, initial_supply, total_requested
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def initial_social_network(network, scale = 1, sigmas=3):
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participants = get_nodes_by_type(network, 'participant')
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for i in participants:
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for j in participants:
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if not(j==i):
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influence_rv = expon.rvs(loc=0.0, scale=scale)
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if influence_rv > scale+sigmas*scale**2:
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network.add_edge(i,j)
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network.edges[(i,j)]['influence'] = influence_rv
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network.edges[(i,j)]['type'] = 'influence'
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return network
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def initial_conflict_network(network, rate = .25):
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proposals = get_nodes_by_type(network, 'proposal')
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for i in proposals:
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for j in proposals:
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if not(j==i):
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conflict_rv = np.random.rand()
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if conflict_rv < rate :
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network.add_edge(i,j)
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network.edges[(i,j)]['conflict'] = 1-conflict_rv
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network.edges[(i,j)]['type'] = 'conflict'
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return network
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def social_links(network, participant, scale = 1):
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participants = get_nodes_by_type(network, 'participant')
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i = participant
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for j in participants:
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if not(j==i):
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influence_rv = expon.rvs(loc=0.0, scale=scale)
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if influence_rv > scale+scale**2:
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network.add_edge(i,j)
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network.edges[(i,j)]['influence'] = influence_rv
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network.edges[(i,j)]['type'] = 'influence'
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return network
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def conflict_links(network,proposal ,rate = .25):
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proposals = get_nodes_by_type(network, 'proposal')
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i = proposal
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for j in proposals:
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if not(j==i):
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conflict_rv = np.random.rand()
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if conflict_rv < rate :
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network.add_edge(i,j)
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network.edges[(i,j)]['conflict'] = 1-conflict_rv
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network.edges[(i,j)]['type'] = 'conflict'
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return network
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def trigger_sweep(field, trigger_func,xmax=.2,default_alpha=.5):
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if field == 'token_supply':
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alpha = default_alpha
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share_of_funds = np.arange(.001,xmax,.001)
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total_supply = np.arange(0,10**9, 10**6)
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demo_data_XY = np.outer(share_of_funds,total_supply)
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demo_data_Z0=np.empty(demo_data_XY.shape)
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demo_data_Z1=np.empty(demo_data_XY.shape)
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demo_data_Z2=np.empty(demo_data_XY.shape)
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demo_data_Z3=np.empty(demo_data_XY.shape)
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for sof_ind in range(len(share_of_funds)):
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sof = share_of_funds[sof_ind]
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for ts_ind in range(len(total_supply)):
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ts = total_supply[ts_ind]
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tc = ts /(1-alpha)
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trigger = trigger_func(sof, 1, ts)
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demo_data_Z0[sof_ind,ts_ind] = np.log10(trigger)
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demo_data_Z1[sof_ind,ts_ind] = trigger
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demo_data_Z2[sof_ind,ts_ind] = trigger/tc #share of maximum possible conviction
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demo_data_Z3[sof_ind,ts_ind] = np.log10(trigger/tc)
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return {'log10_trigger':demo_data_Z0,
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'trigger':demo_data_Z1,
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'share_of_max_conv': demo_data_Z2,
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'log10_share_of_max_conv':demo_data_Z3,
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'total_supply':total_supply,
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'share_of_funds':share_of_funds}
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elif field == 'alpha':
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alpha = np.arange(.5,1,.01)
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share_of_funds = np.arange(.001,xmax,.001)
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total_supply = 10**9
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demo_data_XY = np.outer(share_of_funds,alpha)
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demo_data_Z4=np.empty(demo_data_XY.shape)
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demo_data_Z5=np.empty(demo_data_XY.shape)
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demo_data_Z6=np.empty(demo_data_XY.shape)
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demo_data_Z7=np.empty(demo_data_XY.shape)
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for sof_ind in range(len(share_of_funds)):
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sof = share_of_funds[sof_ind]
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for a_ind in range(len(alpha)):
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ts = total_supply
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a = alpha[a_ind]
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tc = ts /(1-a)
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trigger = trigger_func(sof, 1, ts)
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demo_data_Z4[sof_ind,a_ind] = np.log10(trigger)
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demo_data_Z5[sof_ind,a_ind] = trigger
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demo_data_Z6[sof_ind,a_ind] = trigger/tc #share of maximum possible conviction
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demo_data_Z7[sof_ind,a_ind] = np.log10(trigger/tc)
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return {'log10_trigger':demo_data_Z4,
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'trigger':demo_data_Z5,
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'share_of_max_conv': demo_data_Z6,
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'log10_share_of_max_conv':demo_data_Z7,
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'alpha':alpha,
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'share_of_funds':share_of_funds}
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else:
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return "invalid field"
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def trigger_plotter(share_of_funds,Z, color_label,y, ylabel,cmap='jet'):
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dims = (10, 5)
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fig, ax = plt.subplots(figsize=dims)
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cf = plt.contourf(share_of_funds, y, Z.T, 100, cmap=cmap)
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cbar=plt.colorbar(cf)
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plt.axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
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#ax.set_xscale('log')
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plt.ylabel(ylabel)
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plt.xlabel('Share of Funds Requested')
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plt.title('Trigger Function Map')
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cbar.ax.set_ylabel(color_label)
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def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=False):
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last_net = nets[-1]
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last_props=get_nodes_by_type(last_net, 'proposal')
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M = len(last_props)
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last_parts=get_nodes_by_type(last_net, 'participant')
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N = len(last_parts)
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pos = {}
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for ind in range(N):
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i = last_parts[ind]
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pos[i] = np.array([0, 2*ind-N])
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for ind in range(M):
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j = last_props[ind]
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pos[j] = np.array([1, 2*N/M *ind-N])
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if ani:
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figs = []
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fig, ax = plt.subplots(figsize=dims)
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if savefigs:
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counter = 0
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length = 10
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import string
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unique_id = ''.join([np.random.choice(list(string.ascii_letters + string.digits)) for _ in range(length)])
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for net in nets:
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edges = get_edges_by_type(net, 'support')
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max_tok = np.max([net.edges[e]['tokens'] for e in edges])
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E = len(edges)
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net_props = get_nodes_by_type(net, 'proposal')
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net_parts = get_nodes_by_type(net, 'participant')
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net_node_label ={}
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num_nodes = len([node for node in net.nodes])
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node_color = np.empty((num_nodes,4))
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node_size = np.empty(num_nodes)
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edge_color = np.empty((E,4))
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cm = plt.get_cmap('Reds')
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cNorm = colors.Normalize(vmin=0, vmax=max_tok)
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scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
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for j in net_props:
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node_size[j] = net.nodes[j]['funds_requested']*size_scale
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if net.nodes[j]['status']=="candidate":
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node_color[j] = colors.to_rgba('blue')
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trigger = net.nodes[j]['trigger']
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conviction = net.nodes[j]['conviction']
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percent_of_trigger = " "+str(int(100*conviction/trigger))+'%'
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net_node_label[j] = str(percent_of_trigger)
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elif net.nodes[j]['status']=="active":
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node_color[j] = colors.to_rgba('orange')
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net_node_label[j] = ''
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elif net.nodes[j]['status']=="completed":
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node_color[j] = colors.to_rgba('green')
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net_node_label[j] = ''
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for i in net_parts:
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node_size[i] = net.nodes[i]['holdings']*size_scale
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node_color[i] = colors.to_rgba('red')
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net_node_label[i] = ''
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included_edges = []
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for ind in range(E):
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e = edges[ind]
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tokens = net.edges[e]['tokens']
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if tokens >0:
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included_edges.append(e)
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edge_color[ind] = scalarMap.to_rgba(tokens)
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iE = len(included_edges)
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included_edge_color = np.empty((iE,4))
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for ind in range(iE):
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e = included_edges[ind]
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tokens = net.edges[e]['tokens']
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included_edge_color[ind] = scalarMap.to_rgba(tokens)
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nx.draw(net,
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pos=pos,
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node_size = node_size,
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node_color = node_color,
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edge_color = included_edge_color,
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edgelist=included_edges,
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labels = net_node_label)
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plt.title('Tokens Staked by Partipants to Proposals')
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if ani:
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nx.draw(net,
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pos=pos,
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node_size = node_size,
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node_color = node_color,
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edge_color = included_edge_color,
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edgelist=included_edges,
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labels = net_node_label, ax=ax)
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figs.append(fig)
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else:
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nx.draw(net,
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pos=pos,
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node_size = node_size,
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node_color = node_color,
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edge_color = included_edge_color,
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edgelist=included_edges,
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labels = net_node_label)
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plt.title('Tokens Staked by Partipants to Proposals')
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if savefigs:
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plt.savefig(unique_id+'_fig'+str(counter)+'.png')
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counter = counter+1
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plt.show()
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if ani:
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False
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#anim = animation.ArtistAnimation(fig, , interval=50, blit=True, repeat_delay=1000)
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#plt.show()
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