105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
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# import libraries
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import networkx as nx
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import matplotlib.pyplot as plt
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import numpy as np
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from .supportingFunctions import *
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from .subpopulation_clusters import *
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# Assumptions:
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# Amount received in shilling when withdraw occurs
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leverage = 1
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# process time
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process_lag = 15 # timesteps
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# red cross drip amount
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drip = 10000
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# starting operatorFiatBalance
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initialOperatingFiatBalance = 100000
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redCrossDripFrequency = 90 # days
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# system actors
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system = ['external','cic']
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# chamas
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chama = ['chama_1','chama_2','chama_3','chama_4']
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# traders
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traders = ['ta','tb','tc'] #only trading on the cic. Link to external and cic not to other agents
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allAgents = clusters + system
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R0 = 40000 #xDAI
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kappa = 4 #leverage
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P0 = 1/100 #initial price
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S0 = kappa*R0/P0
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V0 = invariant(R0,S0,kappa)
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P = spot_price(R0, V0, kappa)
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# Price level
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priceLevel = 100
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fractionOfDemandInCIC = 0.5
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fractionOfActualSpendInCIC = 0.5
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def create_network():
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# Create network graph
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network = nx.DiGraph()
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# Add nodes for n participants plus the external economy and the cic network
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for i in clusters:
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network.add_node(i,type='Agent',tokens=clustersMedianSourceBalance[int(i)], native_currency = int(np.random.uniform(low=clusters1stQSourceBalance[int(i)], high=clusters3rdQSourceBalance[int(i)], size=1)[0]))
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network.add_node('external',type='Cloud',native_currency = 100000000,tokens = 0,delta_native_currency = 0, pos=(1,50))
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network.add_node('cic',type='Contract',tokens= S0, native_currency = R0,pos=(50,1))
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for i in chama:
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network.add_node(i,type='Chama')
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for i in traders:
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network.add_node(i,type='Trader',tokens=20, native_currency = 20,
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price_belief = 1, trust_level = 1)
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# Create bi-directional edges between all participants
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for i in allAgents:
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for j in allAgents:
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if i!=j:
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network.add_edge(i,j)
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# Create bi-directional edges between each trader and the external economy and the cic environment
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for i in traders:
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for j in system:
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if i!=j:
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network.add_edge(i,j)
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# Create bi-directional edges between some agent and a chama node representing membershio
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for i in chama:
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for j in clusters:
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if np.random.choice(['Member','Non_Member'],1,p=[.50,.50])[0] == 'Member':
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network.add_edge(i,j)
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# Type colors
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color_map = []
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for i in network.nodes:
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if network.nodes[i]['type'] == 'Agent':
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color_map.append('Red')
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elif network.nodes[i]['type'] == 'Cloud':
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color_map.append('Blue')
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elif network.nodes[i]['type'] == 'Contract':
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color_map.append('Green')
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elif network.nodes[i]['type'] == 'Trader':
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color_map.append('Yellow')
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elif network.nodes[i]['type'] == 'Chama':
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color_map.append('Orange')
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pos = nx.spring_layout(network,pos=nx.get_node_attributes(network,'pos'),fixed=nx.get_node_attributes(network,'pos'),seed=10)
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nx.draw(network,node_color = color_map,pos=pos,with_labels=True,alpha=0.7)
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plt.savefig('images/graph.png')
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plt.show()
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return network |