def main(processes, rounds):
simulation = abcEconomics.Simulation(name='logging_test_friendly_names',
processes=processes)
agents = simulation.build_agents(Agent,
'friendly_agent',
agent_parameters=[{
'name': 'A'
}, {
'name': 'Davoud'
}, {
'name': "fred"
}, {
'name': "F12"
}])
for rnd in range(10):
for r in range(10):
simulation.advance_round((r, rnd))
agents.go()
agents.agg_log(variables=['i', 'r'], goods=['money'])
agents.panel_log(variables=['i', 'r'], goods=['money'])
simulation.finalize()
compare('aggregated_friendly_agent.csv', simulation.path,
'aggregated logging test\t\t', processes)
compare('aggregate_friendly_agent.csv', simulation.path,
'aggregate logging test\t\t', processes)
compare('panel_friendly_agent.csv', simulation.path,
'aggregate logging test mean\t', processes)
def main(processes, rounds):
simulation = abcEconomics.Simulation(processes=processes)
print('build Killer')
killer = simulation.build_agents(Killer, 'killer', 1)
print('build Victim')
victims = simulation.build_agents(Victim, 'victim', rounds)
print('build Victim loudvictim')
loudvictims = simulation.build_agents(Victim, 'loudvictim', rounds)
print('build AddAgent')
balls = simulation.build_agents(Ball, 'ball', 0)
for time in range(rounds):
simulation.advance_round(time)
assert len(balls) == time, len(balls)
deads = killer.kill_silent()
victims.delete_agents(deads)
deads = killer.kill_loud()
loudvictims.delete_agents(deads)
victims.am_I_dead()
loudvictims.am_I_dead()
balls.create_agents(Ball, 1)
simulation.finalize()
del simulation
def main(processes, rounds):
sim = abcEconomics.Simulation()
myagent = sim.build_agents(MyAgent, 'myagent',
agent_parameters=[{'agent_parameters': 0},
{'agent_parameters': 1},
{'agent_parameters': 2},
{'agent_parameters': 3},
{'agent_parameters': 4},
{'agent_parameters': 5},
{'agent_parameters': 6}])
differentagent = sim.build_agents(DifferentAgent, 'differentagent',
agent_parameters=[{'agent_parameters': 0},
{'agent_parameters': 1},
{'agent_parameters': 2},
{'agent_parameters': 3},
{'agent_parameters': 4},
{'agent_parameters': 5},
{'agent_parameters': 6}])
print('len', len(myagent))
sim.advance_round(0)
myagent.call_me_maybe()
print('simple')
for r in range(1, 14):
sim.advance_round(r)
myagent[r % 7].call_me_maybe()
print('--')
print('two individuals')
for r in range(14, 28):
sim.advance_round(r)
(myagent[r % 7] + myagent[3]).call_me_maybe()
print('--')
print('one class')
for r in range(28, 42):
sim.advance_round(r)
myagent[r % 7, 3].call_me_maybe()
print('--')
print('whole class plus individual')
for r in range(42, 56):
sim.advance_round(r)
(myagent + myagent[r % 7]).call_me_maybe()
print('--')
print('two classes')
for r in range(56, 70):
sim.advance_round(r)
(myagent + differentagent).call_me_maybe()
print('--')
sim.finalize()
def main(processes, rounds):
sim = abcEconomics.Simulation()
sim.advance_round(0)
myagents = sim.build_agents(MyAgent, 'myagent', number=5)
for id in range(5):
assert list(myagents[id].say()) == [id], (list(myagents[id].say()),
[id])
sim.finalize()
def main(processes, rounds):
s = abcEconomics.Simulation(processes=processes, name='unittest')
endowment = s.build_agents(Endowment, 'endowment', 2, rounds=rounds)
for r in range(rounds):
s.time = r
endowment.refresh_services('labor', derived_from='labor_endowment', units=5)
endowment.Iconsume()
endowment.all_tests_completed()
s.finalize()
def main(processes, rounds):
sim = abcEconomics.Simulation(processes=processes)
myagents = sim.build_agents(MyAgent, 'myagent', number=2)
for time in range(rounds):
sim.time = time
myagents.produce()
myagents.all_tests_completed()
sim.finalize()
def main(processes, rounds):
sim = abcEconomics.Simulation(processes=processes)
returners = sim.build_agents(Returner, 'returner', number=3)
getters = sim.build_agents(Getter, 'getter', number=1)
for y in range(rounds):
sim.advance_round(y)
ret = returners.returnit()
getters.getit(ret)
getters.method('a', 'b', 'c', d='d', f='f', e='e')
sim.finalize()
print('Returning tested \t\t\t\t\t\tOK')
print('Calling with parameters tested \t\t\t\t\tOK')
def main(processes, rounds):
sim = abcEconomics.Simulation(
name='mysim',
processes=processes,
dbplugin=CustomLogging,
dbpluginargs=['sqlite:///:memory:', 'sometable'])
myagents = sim.build_agents(MyAgent, 'myagent', number=5)
for i in range(rounds):
sim.advance_round(i)
myagents.write()
sim.finalize()
def main(processes, rounds):
s = abcEconomics.Simulation(processes=processes, name='unittest')
print('build MessageA')
messagea = s.build_agents(MessageA, 'messagea', 20)
print('build MessageB')
messageb = s.build_agents(MessageB, 'messageb', 20)
for r in range(rounds):
s.time = r
(messagea + messageb).sendmsg()
(messageb + messagea).recvmsg()
print("Send_envelope and receive test:\t OK")
s.finalize()
def main(processes, rounds):
sim = abcEconomics.Simulation()
sim.advance_round(0)
myagents = sim.build_agents(MyAgent, 'myagent', agent_parameters=[{'name': 'me'},
{'name': 'you'},
{'name': 'him'},
{'name': ('firm', 0)}])
names = ['me', 'you', 'him', ('firm', 0)]
for name in names:
assert list(myagents.by_name(name).say()) == [name], (
list(myagents.by_name(name).say()), [name])
assert list(myagents.by_name(name).say()) == [name], (
list(myagents.by_names(names).say()), names)
sim.finalize()
def main(processes, rounds):
simulation = abcEconomics.Simulation(name='logging_test',
processes=processes)
agents = simulation.build_agents(Agent, 'agent', 10)
for rnd in range(100):
simulation.advance_round(rnd)
agents.go()
agents.agg_log(variables=['i', 'r'], goods=['money'])
agents.panel_log(variables=['i', 'r'], goods=['money'])
simulation.finalize()
compare('aggregated_agent.csv', simulation.path,
'aggregated logging test\t\t', processes)
compare('aggregate_agent.csv', simulation.path,
'aggregate logging test\t\t', processes)
compare('panel_agent.csv', simulation.path,
'aggregate logging test mean\t', processes)
def main(processes, rounds):
sim = abcEconomics.Simulation(processes=processes)
aagents = sim.build_agents(Agent, 'aagent', number=5)
bagents = sim.build_agents(Agent, 'bagent', number=5)
actionA = aagents.actionA
actionB = bagents.actionB
for r in range(rounds):
sim.advance_round(r)
(aagents.actionA + aagents.actionB)()
(aagents.actionA + bagents.actionB)()
(aagents.actionB + bagents.actionB)()
aagents.actionA()
bagents.actionB()
(actionA + actionB)()
sim.finalize()
def main(processes, rounds=5):
s = abcEconomics.Simulation(processes=processes,
name='production_consumption_test')
print('build ProductionMultifirm')
productionmultifirm = s.build_agents(ProductionMultifirm,
'productionmultifirm',
1,
rounds=rounds)
print('build UtilityHousehold')
utilityhousehold = s.build_agents(UtilityHousehold,
'utilityhousehold',
5,
rounds=rounds)
all_agents = utilityhousehold + productionmultifirm
for r in range(rounds):
s.advance_round(r)
productionmultifirm.production()
utilityhousehold.consumption()
all_agents.all_tests_completed()
s.finalize()
import abcEconomics as abce
from ball import Ball
from killer import Killer
from victim import Victim
rounds = num_victims = 30
simulation = abce.Simulation(processes=1)
print('build Killer')
killer = simulation.build_agents(Killer, 'killer', 1)
print('build Victim')
victims = simulation.build_agents(Victim, 'victim', num_victims)
print('build Victim loudvictim')
loudvictims = simulation.build_agents(Victim, 'loudvictim', num_victims)
print('build AddAgent')
balls = simulation.build_agents(Ball, 'ball', 0)
for time in range(rounds):
simulation.advance_round(time)
deads = killer.kill_silent()
victims.delete_agents(deads)
deads = killer.kill_loud()
loudvictims.delete_agents(deads)
victims.am_I_dead()
loudvictims.am_I_dead()
balls.create_agents(Ball, 1)
simulation.finalize()
def main(simulation_parameters):
test_sim = abce.Simulation(name='quipu_sim', processes=1)
num_agents = 5
names = list(range(0, num_agents))
money = random.sample(range(1000, 5000), num_agents)
good2 = random.sample(range(100, 500), num_agents)
good3 = random.sample(range(100, 500), num_agents)
params = agent.build_agent_parameters(num_agents,names,'money',
money,'food', good2, 'other', good3)
print(params)
agents = test_sim.build_agents(Agent, 'agent', agent_parameters=params)
num_edges = 2*num_agents
G = graph.build_graph(num_agents,names,money)
node_sizes = money
r = 0
while r < 10:
test_sim.advance_round(r)
r += 1
#Select 2 random agents
buyer = random.randint(0,num_agents-1)
seller = random.randint(0,num_agents-1)
#Transact
action = random.randint(0,1)
good = random.randint(0,10)
agents[buyer].buy_goods(seller,good)
cost = agents[seller].sell_goods(good)[0]
node_size_1 = (agents[buyer].return_curr_money()[0][0])
node_size_2 = (agents[seller].return_curr_money()[0][0])
#Buyer gets smaller, seller gets bigger
node_sizes[buyer] = node_size_1/2
node_sizes[seller] = node_size_2*2
plt.clf()
#Add edge between them
G.add_edge(buyer,seller)
print(G.nodes())
#Print
nx.draw_networkx(G, pos=nx.get_node_attributes(G,'Position'), node_size=(node_sizes))
pylab.draw()
plt.pause(1)
test_sim.finalize()
def main(simulation_parameters):
test_sim = abce.Simulation(name='tony_test',
processes=1,
trade_logging='individual')
total_cost = 0
num_agents = 2
names = list(range(0, num_agents))
money = random.sample(range(1, 50), num_agents)
good2 = random.sample(range(1, 50), num_agents)
good3 = random.sample(range(1, 50), num_agents)
#print(names.get_first_name())
params = agent.build_agent_parameters(num_agents, names, 'money', money,
'food', good2, 'other', good3)
#agents = test_sim.build_agents(Agent, 'agent', agent_parameters=params)
agents = test_sim.build_agents(Agent,
'agent',
agent_parameters=[{
'family_name': 'buyer',
'money': 100000,
'food': 0
}, {
'family_name': 'seller',
'money': 0,
'food': 100000
}])
agents[0].print_possessions()
buyer = agents[0].get_name()[0][0]
seller = agents[1].get_name()[0][0]
#Graph inital sizes, names
node_sizes = [
agents[0].return_quantity_of_good()[0][0],
agents[1].return_quantity_of_good()[0][0]
]
G = nx.DiGraph()
G.add_node(buyer,
Position=(random.randrange(0, 100), random.randrange(0, 100)))
G.add_node(seller,
Position=(random.randrange(0, 50), random.randrange(0, 50)))
G.add_edge(buyer, seller, weight=total_cost)
end = False
r = 0
#Run simulation
while (r < 10):
test_sim.advance_round(r)
r += 1
agents[0].buy_goods(1, 0)
cost = agents[1].sell_goods(0)[0]
#Ugly but ends the loop when the transactions are no longer possible
if cost[0] == -1:
end = True
total_cost += cost[0]
#Update node size to reflect good quantity
node_size_1 = int((agents[0].return_quantity_of_good()[0][0]) * 0.1)
node_size_2 = int((agents[1].return_quantity_of_good()[0][0]) * 0.1)
node_sizes = [node_size_1, node_size_2]
print(node_sizes)
#Update weight size to reflect money transferred
G.add_edge(buyer, seller, weight=(total_cost * 100))
#label the edges
labels = {(buyer, seller): total_cost}
plt.clf()
#plot the graphs
plt.title("100,000 products sold by seller")
nx.draw_networkx(G,
pos=nx.get_node_attributes(G, 'Position'),
node_list=[buyer, seller],
node_size=node_sizes,
with_labels=True)
nx.draw_networkx_edge_labels(G,
pos=nx.get_node_attributes(G, 'Position'),
edge_labels=labels,
font_size=30)
pylab.draw()
plt.pause(.5)
test_sim.finalize()
def main():
# Read in simulation parameters
f = open('sim_params_final.csv', 'r')
data = np.array(list(csv.reader(f)))
curr_sim = data[1, :]
num_agents = int(curr_sim[1])
chance_food_inside = float(curr_sim[2])
chance_raw_inside = float(curr_sim[3])
quipu = int(curr_sim[5])
print(quipu)
f.close()
test_sim = abce.Simulation(name='quipu_sim',
processes=1,
trade_logging='off')
walmart_size = 1000
names = list(range(0, num_agents + 1))
money = random.sample(range(5, 100), num_agents)
money.append(walmart_size)
food = random.sample(range(10, 50), num_agents)
quipus = random.sample(range(5, 45), num_agents)
other = random.sample(range(1, 20), num_agents)
params = agent.build_agent_parameters(num_agents, names, 'money', money,
'food', food, 'quipu', quipus,
'other', other)
agents = test_sim.build_agents(Quipu_Agent,
'agent',
agent_parameters=params)
walmart = test_sim.build_agents(Walmart,
'corporate',
agent_parameters=[{
'family_name': 'walmart',
'money': 100,
'food': 100,
'other': 100
}])
if quipu == 1:
ofile = open('data_with_quipus.csv', 'w')
print("here")
else:
ofile = open('data_just_money.csv', 'w')
print("here1")
writer = csv.writer(ofile, delimiter=',')
writer.writerow([
'Transaction ID', 'Buyer ID', 'Seller ID', 'Type of transaction',
'Currency', 'Failure from Buyer', 'Failure from Seller'
])
r = 0
num_rounds = 40
transaction_id = 0
while (r < num_rounds):
test_sim.advance_round(r)
r += 1
#Each round, select an agent and a random interaction (walmart or not)
#Each round, every agent eats some food
for x in range(num_agents):
action = random.randint(0, 100)
agents[x].eat_food()
if action > (chance_food_inside * 100):
buy = agents[x].buy_goods_from_walmart(0, 1)[0][0]
sell = walmart[0].walmart_sell_goods(1)[0][0]
writer.writerow(
[transaction_id, x, -1, 'food', 'money', buy, sell])
else:
#Buy from an agent that is not yourself
numbers = list(range(0, x)) + list(range(x + 1, num_agents))
curr = random.choice(numbers)
buy = agents[x].buy_goods(curr, 0, quipu)[0][0]
sell = agents[curr].sell_goods(0, quipu)[0][0]
if quipu == 1:
writer.writerow(
[transaction_id, x, curr, 'food', 'quipu', buy, sell])
else:
writer.writerow(
[transaction_id, x, curr, 'food', 'money', buy, sell])
# Buy raw materials 1x per week
if r % 7 == 0:
transaction_id += 1
action = random.randint(0, 100)
if action > (chance_raw_inside * 100):
buy = agents[x].buy_goods_from_walmart(0, 0)[0][0]
sell = walmart[0].walmart_sell_goods(0)[0][0]
writer.writerow(
[transaction_id, x, -1, 'raw', 'money', buy, sell])
else:
#Buy from an agent that is not yourself
numbers = list(range(0, x)) + list(range(
x + 1, num_agents))
curr = random.choice(numbers)
buy = agents[x].buy_goods(curr, 1, quipu)[0][0]
sell = agents[curr].sell_goods(1, quipu)[0][0]
if quipu == 1:
writer.writerow([
transaction_id, x, curr, 'food', 'quipu', buy, sell
])
else:
writer.writerow([
transaction_id, x, curr, 'food', 'money', buy, sell
])
transaction_id += 1
#end of loop
ofile.close()
test_sim.finalize()
def main():
chance_raw_outside = 0.88
chance_raw_inside = 1 - chance_raw_outside
chance_food_inside = 0.83
chance_food_outside = 1 - chance_food_inside
test_sim = abce.Simulation(name='quipu_sim',
processes=1,
trade_logging='off')
num_agents = 5
walmart_size = 1000
names = list(range(0, num_agents + 1))
money = random.sample(range(100, 200), num_agents)
money.append(walmart_size)
food = random.sample(range(10, 50), num_agents)
quipu = random.sample(range(10, 50), num_agents)
params = agent.build_agent_parameters(num_agents, names, 'money', money,
'food', food, 'quipu', quipu)
agents = test_sim.build_agents(Quipu_Agent,
'agent',
agent_parameters=params)
walmart = test_sim.build_agents(Walmart,
'corporate',
agent_parameters=[{
'family_name': 'walmart',
'money': 100,
'food': 100
}])
color_map = ['red'] * (num_agents + 1)
color_map[num_agents] = 'blue'
G = graph.build_graph(num_agents + 1, names, money, 1, num_agents)
edges = G.edges()
nx.draw_networkx(G,
pos=nx.get_node_attributes(G, 'Position'),
node_size=money,
node_color=color_map,
width=3,
with_labels=True)
plt.pause(1)
money_over_time = []
food_over_time = []
quipus_over_time = []
r = 0
num_rounds = 10
while (r < num_rounds):
test_sim.advance_round(r)
r += 1
curr_money = 0
curr_quipus = 0
curr_food = 0
#Each round, select an agent and a random interaction (walmart or not)
#Each round, every agent eats some food
for x in range(num_agents):
curr_money += agents[x].return_money()[0][0]
curr_food += agents[x].return_food()[0][0]
curr_quipus += agents[x].return_quipu()[0][0]
colors = []
action = random.randint(0, 1)
agents[x].eat_food()
if action == 0:
agents[x].buy_goods(0)
walmart[0].sell_goods()
for u, v in edges:
if (u == x and v == num_agents) or (u == num_agents
and v == x):
colors.append('g')
else:
colors.append('#000000')
else:
numbers = list(range(0, x)) + list(range(x + 1, num_agents))
curr = random.choice(numbers)
agents[x].buy_goods_with_quipu(curr)
agents[curr].sell_goods_with_quipu()
for u, v in edges:
if (u == x and v == curr) or (u == curr and v == x):
colors.append('g')
else:
colors.append('#000000')
nx.draw_networkx(G,
pos=nx.get_node_attributes(G, 'Position'),
node_size=money,
node_color=color_map,
edge_color=colors,
with_labels=True,
width=3)
plt.pause(0.2)
money_over_time.append(curr_money)
food_over_time.append(curr_food)
quipus_over_time.append(curr_quipus)
plt.close()
#end of loop
test_sim.finalize()
test_sim.graphs()
r_list = list(range(0, num_rounds))
# f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, figsize=(15,5))
# ax1.plot(r_list,money_over_time,'r',label='money over time')
# ax2.plot(r_list,food_over_time,'b',label='food over time')
# ax3.plot(r_list,quipus_over_time,'g',label='quipus over time')
# ax1.legend(loc='best')
# ax2.legend(loc='best')
# ax3.legend(loc='best')
# plt.show()
# plt.pause(5)
# plt.close('all')
sys.exit()