def get_peoples_id(URL):
time.sleep(2)
print('dentro-get_peoples_id')
print(URL)
try:
r = requests.get(URL)
if r.ok:
print("okey!")
d = r.json()
print(d['total_pages'])
for actores in d['results']:
try:
val = int(actores['name'])
print("val: " + val)
pass
except:
lista_peliculas = []
for x in actores['known_for']:
lista_peliculas.append(x['title'])
print(actores['name'])
lista_actores.append(
actor(
actores['name'],
"http://image.tmdb.org/t/p/w500/" +
actores['profile_path'], lista_peliculas,
actores['id']))
# return render_template('index.html', output=listita)
except:
pass
def __init__(self, id, type='basic'):
self.token = None
self.cookie_redirect = None
self.cookie = None
self.type = type
self.oauth = None
self.trust = None
# Proposed response code after checkAuthentication() or authorise() have been called
self.response = {
'code': 403, # Result code (http)
'text': "Forbidden", # Proposed response text
'headers': [], # Headers to add to response after authentication has been done
}
# Whether authentication is complete or not (depends on flow)
self.authn_done = False
# acl stores the actual verified credentials and access rights after
# authentication and authorisation have been done
self.acl = {
"authenticated": False, # Has authentication been verified and passed?
"authorised": False, # Has authorisation been done and appropriate acls set?
"rights": '', # "a", "r" (approve or reject)
"relationship": None, # E.g. creator, friend, admin, etc
"peerid": '', # Peerid if there is a relationship
"approved": False, # True if the peer is approved
}
Config = config.config()
self.config = Config
self.actor = actor.actor(id)
if not self.actor.id:
self.actor = None
self.oauth = oauth.oauth(token=None)
self.token = None
self.expiry = None
self.refresh_expiry = None
self.refresh_token = None
return
# We need to initialise oauth for use towards the external oauth service
self.property = 'oauth_token' # Property name used to set self.token
self.token = self.actor.getProperty(self.property).value
self.oauth = oauth.oauth(token=self.token)
self.expiry = self.actor.getProperty('oauth_token_expiry').value
self.refresh_expiry = self.actor.getProperty('oauth_refresh_token_expiry').value
self.refresh_token = self.actor.getProperty('oauth_refresh_token').value
if self.type == 'basic':
self.realm = Config.auth_realm
elif self.type == 'oauth':
if self.oauth.enabled():
self.cookie = 'oauth_token'
if self.actor.getProperty('cookie_redirect').value:
self.cookie_redirect = Config.root + \
self.actor.getProperty('cookie_redirect').value
else:
self.cookie_redirect = None
self.redirect = Config.root + self.actor.id + '/oauth'
else:
self.type = 'none'
def parseActor(self):
doc = xml.dom.minidom.parse(self.filename)
actors = doc.getElementsByTagName("actor")
for Actor in actors:
id = Actor.getAttribute("id")
firstName = Actor.getAttribute("firstName")
lastName = Actor.getAttribute("lastName")
fatherName = Actor.getAttribute("fatherName")
rank = Actor.getAttribute("rank")
expirience = Actor.getAttribute("expirience")
newActor = actor(firstName, lastName, fatherName, rank, expirience)
self.actorsList[id] = newActor
print(self.actorsList)
def readDb(self,inp):
a = actor("Gleb","Nazemnov","Andreevich","glavnui","15")
p = performances("12 стульев","2015","322")
lab = labor(a,p,"Остап Бендер","20000000")
conn = db.connect(inp)
curs = conn.cursor()
curs.execute('select * from actor')
data=curs.fetchall()
for r in data:print(r)
curs.execute('select * from performance')
data=curs.fetchall()
for r in data:print(r)
curs.execute('select * from labor')
data=curs.fetchall()
for r in data:print(r)
conn.close()
def get_actors(self,film_id):
r=proxy.gethtml(url='https://movie.douban.com/subject/'+str(film_id)+'/celebrities',headers=util.headers,params={})
if r is None:
return
soup=BeautifulSoup(r.content.decode(),'html.parser')
if soup is None:
return
lis=soup.find_all('li',{'class':'celebrity'})
if lis is None:
return
for li in lis:
act=actor.actor()
sf=li.find('span',{'class':'name'})
if sf:
sfa=sf.find('a')
if sfa:
act.actor_name=sfa.get_text()
ar=re.search(r'(\d+)\/?$',sfa.get('href'))
if ar:
act.actor_id=ar.group(1)
if act.actor_id=="":
continue
sf=li.find('span',{'class':'role'})
if sf:
act.actor_role=sf.get_text()
sf=li.find('span',{'class':'works'})
if sf:
sfa=sf.find_all('a')
if sfa:
act.main_works=util.listtostr(sfa,'/')
dbacts=sql.get_actor_byid(act.actor_id)
try:
if len(dbacts)==0:
self.get_actor_info(act)
sql.save_actor(act)
dbactfs=sql.get_actor_film_byid(act.actor_id,film_id)
if len(dbactfs)==0:
sql.save_actor_film(act.actor_id,film_id,act.actor_role)
except Exception as e:
log.logger.info(str(e))
print(act)
upper_quartile_alpha = scoreatpercentile(alpha,75)
at_risk = [agent for agent in graph.nodes()
if alpha[agent]>=upper_quartile_alpha and fraction_of_influencers(influence_kernel[agent],upper_quartile_influence_kernel)>upper_quartile_receiving_influence]
return at_risk
G = nx.MultiDiGraph()
tmp = nx.barabasi_albert_graph(params['n']['nodes'],params['n']['edges'])
G.add_edges_from(tmp.edges())
G.add_nodes_from(tmp)
G.add_edges_from(nx.MultiDiGraph(nx.barabasi_albert_graph(params['n']['nodes'],params['n']['edges'])).reverse().edges())
max_degree = max(nx.degree(G).values())
#Link an agent with each node
actors = [actor() for _ in xrange(params['n']['nodes'])]
for node_idx,actor in zip(G.nodes(),actors):
G.node[node_idx]['actor'] = actor
alpha=np.random.random_sample(size=(params['n']['nodes'],))
#alpha = np.zeros((params['n']['nodes'],))
influence = {node:len(G.predecessors(node))/float(len(G.successors(node))) for node in G.nodes_iter()}
influence_kernel = {node:normalize(np.array([influence[predecessor]
for predecessor in G.predecessors(node)]).astype(float))
for node in G.nodes_iter()}
fraction = int(options.fraction)*.01
timesteps = directory['timesteps']
total_duration = 3*timesteps
attitudes = np.zeros((params['n']['nodes'],total_duration))
def newActor(self, id, firsName, lastName, fatherName, rank, expirience):
a = actor(firsName, lastName, fatherName, rank, expirience)
actor.setId(id)
self.setactor(a)
def main(incomeTax = True, toTax = [True, True, True], initITP = [0, 0.25, 0.9], incomeTaxThresholds = [0.33, 0.66], long = False, name = "",
actorNum = 95, nobleNum = 5, moneyPerActor = 1000, rounds = 100000, taxMod = 0.3):
actors = [];
for i in range(actorNum):
actors.append(actor.actor(moneyPerActor));
for i in range(nobleNum):
actors.append(actor.actor(moneyPerActor, 3));
totGold = 0;
totPop = 0;
for a in actors:
totGold += a.gold;
totPop += 1;
# Init variables
lastPrices = [1,2,3];
curPrices = [1,2,3];
allPrices = [[],[],[]]
allPops = [[],[],[],[]]
allSold = [[],[],[]]
allProduced = [[],[],[]]
allWanted = [[],[],[]]
movements = [[],[],[],[],[]]
taxRevs = []
nobleSpending = []
totGoldarray = [[],[],[],[],[]]
gdp = [];
conSpend = [];
govSpend = [];
investSpend = [];
ppp = []; # gdp/(# actors * price of food). Guess it's kinda an adjusted gdp for 'inflation' (mostly adusts for actors eating money supply)
cci = []; # consumer confidence index. Measures proportion of actors willing to bid for luxury goods.
tax = [0,0,0];
jewelersCanBuyLux = False;
# If incomeTax is true, then income taxes are used. If false, then VAT tax is used.
#incomeTax = True;
# VAT tax variables
#toTax = [False, False, True];
taxes = [[],[],[]]
# Income tax proportions (Income Tax Prop. - ITP)
#initITP = [0, 0.25, 0.5];
curITP = initITP.copy();
#incomeTaxThresholds = [0.33, 0.66];
actualTaxThresholds = [];
for i in range(len(incomeTaxThresholds)):
actualTaxThresholds.append([]);
# Run loops (with fancy progress display)
for i in tqdm(range(rounds)):
for a in actors:
a.beforeTrades(i);
# Run auctions for food, tools and luxury goods, in that order
prodCosts = [];
buyerValues = [];
taxRevs.append(0);
nobleSpending.append(0);
gdp.append(0);
conSpend.append(0);
govSpend.append(0);
investSpend.append(0);
cci.append(0);
for j in range(3):
prodCosts.clear();
buyerValues.clear();
# Determine production costs and buyer values
for a in actors:
# food auction
if (j == 0):
if (a.type == 0 and a.inv[0] > 0):
# base food cost roughly on last round's food cost, plus some random variation
# (farmers should never starve)
numSelling = max(a.inv[0], 0);
prodCost = max((lastPrices[0] + r.randint(-2,2) / numSelling * (1 + tax[0])), 1)
if (a.lastUsedTool):
prodCost = max(int(lastPrices[1] / numSelling * (1 + tax[0])), 1);
prodCosts.extend([[prodCost, a]] * numSelling);
else:
buyPrice = a.getValue(j, lastPrices, incomeTax);
buyPriceRatio = int(buyPrice / lastPrices[0]);
if (buyPriceRatio > 4):
buyerValues.append([buyPrice / 2, a]);
buyerValues.append([buyPrice / 4, a] * 2);
else:
buyerValues.append([buyPrice / 2, a]);
# tool auction
elif (j == 1):
if (a.type == 1):
# Set tool production cost to be based on last round food price, plus
# some random markup/down based on previous sell price
numToSell = max(a.inv[1] - 1, 1);
if (numToSell != 0):
prodCost = int(max((lastPrices[0] + r.randint(-2,2)) / numToSell * (1 + tax[1]), 1));
prodCosts.extend([[randint(prodCost, lastPrices[1] + 5), a]] * numToSell);
else:
buyerValues.append([a.getValue(j, lastPrices, incomeTax), a]);
# luxury auction
else: # j == 2
if (a.type == 2):
# Set luxury production cost to be based on last round food and tool price,
# plus some random markup/down based on last sell price
numToSell = max(a.inv[2], 0);
if (numToSell != 0):
prodCost = int(max((lastPrices[0] + lastPrices[1] + r.randint(-2,2)) / numToSell * (1 + tax[2]), 1));
prodCosts.extend([[randint(prodCost, lastPrices[2] + 5), a]] * numToSell);
elif (a.type != 2 or jewelersCanBuyLux):
numToBuy = 1;
if (a.type == 3):
numToBuy = int(max(1, (a.gold - lastPrices[0] * 3)/lastPrices[2] - 1));
buyerValues.extend([[a.getValue(j, lastPrices, incomeTax)/(numToBuy * r.randint(1, 2)), a]] * numToBuy);
cci[-1] += 1;
else:
luxVal = a.getValue(j, lastPrices, incomeTax);
if (luxVal > 0):
buyerValues.append([luxVal, a]);
cci[-1] += 1;
cci[-1] = cci[-1]/totPop;
allProduced[j].append(len(prodCosts));
allWanted[j].append(len(buyerValues));
if (len(buyerValues) == 0 or len(prodCosts) == 0):
if (len(prodCosts) > len(buyerValues)):
allPrices[j].append(curPrices[j] - 1);
curPrices[j] -= 1;
else:
allPrices[j].append(curPrices[j] + 1)
curPrices[j] += 1;
allSold[j].append(0);
else:
# Arrange production costs from low to high
bubble_sort(prodCosts);
bubble_sort(buyerValues);
# equate costs & values list lengths by removing lowest buyer bids
# or highest seller bids
if (len(prodCosts) < len(buyerValues)):
buyerValues = buyerValues[len(buyerValues) - len(prodCosts):];
elif (len(prodCosts) > len(buyerValues)):
prodCosts = prodCosts[:len(buyerValues)];
# Arrange buyer bids from high to low
buyerValues.reverse();
# Get price for good j (use buyervalues to set price for both buyers
# and sellers, to prevent incentives from adding/removing gold from economy)
k = 0;
for k in range(len(prodCosts) - 1):
if (prodCosts[k][0] <= buyerValues[k][0] and prodCosts[k + 1][0] > buyerValues[k + 1][0]):
break;
curPrices[j] = prodCosts[k][0];
allPrices[j].append(curPrices[j]);
# Complete trades
totSold = 0;
for b in range(len(buyerValues)):
if (buyerValues[b][0] >= curPrices[j] and prodCosts[b][0] <= curPrices[j]):
buyerValues[b][1].gold -= curPrices[j];
# Add transaction to gdp
gdp[-1] += curPrices[j];
if (j != 1 and buyerValues[b][1].type != 3):
conSpend[-1] += curPrices[j];
elif (j == 1):
investSpend[-1] += curPrices[j];
elif (buyerValues[b][1].type == 3):
govSpend[-1] += curPrices[j];
nobleSpending[-1] += curPrices[j];
buyerValues[b][1].inv[j] += 1;
assert(buyerValues[b][1].gold >= 0);
prodCosts[b][1].pay(curPrices[j] / (1 + tax[j]));
taxRevs[-1] += curPrices[j] - curPrices[j] / (1 + tax[j]);
prodCosts[b][1].inv[j] -= 1;
assert(prodCosts[b][1].inv[j] >= 0);
totSold += 1;
#if (buyerValues[b][1].type != 3 and j == 2):
# print(buyerValues[b][1].type)
allSold[j].append(totSold);
#assert(conSpend[-1] + investSpend[-1] + govSpend[-1] == gdp[-1]);
for n in range(5):
movements[n].append(0);
# Adjust tax rate to support noble spending
if (not incomeTax):
mod = taxMod;
if (len(totGoldarray[0]) > 0 and totGoldarray[4][-1]/totGoldarray[0][-1] > 0.5):
mod = 1.25;
for taxType in range(len(tax)):
if (taxRevs[-1] == 0 and toTax[taxType]):
tax[taxType] = 0.1;
else:
tax[taxType] *= nobleSpending[-1]/max(mod * taxRevs[-1],1);
tax[taxType] = min(tax[taxType], 2);
taxes[taxType].append(tax[taxType]);
else:
# Calculate income thresholds
incTax = [];
for act in actors:
incTax.append(act.lastIncome);
incTax.sort();
thresh = [incTax[int(len(incTax) * kek)] for kek in incomeTaxThresholds];
for kekek in range(len(actualTaxThresholds)):
actualTaxThresholds[kekek].append(thresh[kekek]);
# Collect income tax
for act in actors:
taxBracket = 0;
for th in thresh:
if (act.lastIncome > thresh[taxBracket]):
taxBracket += 1;
else:
break;
newTax = min(int(act.lastIncome * curITP[taxBracket]), act.gold);
taxRevs[-1] += newTax;
act.gold -= newTax;
act.lastTax = newTax
act.lastIncome = 0;
mod = taxMod; # as mod increases, nobles get less money from taxes
if (len(totGoldarray[0]) > 0 and totGoldarray[4][-1]/totGoldarray[0][-1] > 0.5):
mod = 1.25;
#print("oh no")
for taxType in range(len(curITP)):
if (taxRevs[-1] < 1 and initITP[taxType] > 0): # and nobleSpending[-1] != 0):
curITP[taxType] = initITP[taxType];
else:
curITP[taxType] *= nobleSpending[-1]/max(mod * taxRevs[-1],1); #1.013 good for sales tax
curITP[taxType] = min(curITP[taxType], 4)
if (taxType != 0):
taxes[taxType-1].append(curITP[taxType]);
# Distribute tax revenue (split equally among economy if no nobles)
noblecount = 0;
for a in actors:
if (a.type == 3):
noblecount += 1;
if (noblecount == 0):
for a in actors:
a.gold += taxRevs[-1]/totPop;
else:
for a in actors:
if (a.type == 3):
a.gold += taxRevs[-1]/noblecount;
for a in actors:
a.afterTrades(lastPrices, movements, totGold, totPop);
if (a.dead):
actors.remove(a);
print("d");
del a;
lastPrices = curPrices;
tGold = 0;
fGold = 0;
sGold = 0;
jGold = 0;
nGold = 0;
pop = [0,0,0,0]
for kek in actors:
tGold += kek.gold;
pop[kek.type] += 1;
if (kek.type == 0):
fGold += kek.gold;
elif (kek.type == 1):
sGold += kek.gold;
elif (kek.type == 2):
jGold += kek.gold;
elif (kek.type == 3):
nGold += kek.gold;
totGoldarray[0].append(tGold);
totGoldarray[1].append(fGold);
totGoldarray[2].append(sGold);
totGoldarray[3].append(jGold);
totGoldarray[4].append(nGold);
for n in range(4):
allPops[n].append(pop[n])
ppp.append(gdp[-1]/(totPop * max(1, lastPrices[0])))
if (not long):
plt.figure(1);
lbs = ["Food", "Tools", "Luxury"];
for i in range(3):
plt.plot(ma(allPrices[i]), label = ("Price of " + lbs[i]));
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(2);
lbs = ["Food", "Tools", "Luxury"];
for i in range(3):
plt.plot(ma(allSold[i]), label = ("Units sold: " + lbs[i]));
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(3);
lbs = ["Farmers", "Smiths", "Jewelers", "Nobles"];
for i in range(4):
plt.plot(allPops[i],label = lbs[i]);
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(4);
plt.plot(ma(allProduced[0]), label = "Food Supply")
plt.plot(ma(allWanted[0]), label = "Food Demand")
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(5);
plt.plot(ma(allProduced[1]), label = "Tool Supply")
plt.plot(ma(allWanted[1]), label = "Tool Demand")
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(6);
plt.plot(allProduced[2], label = "Luxury Supply")
plt.plot(allWanted[2], label = "Luxury Demand")
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(8);
plt.plot(ma(taxRevs), label = "Tax Revenue")
plt.plot(ma(nobleSpending), label = "Noble Spending");
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(9);
lbs = ["Total Gold", "Farmer Gold", "Smith Gold", "Jeweler Gold", "Noble Gold"];
for i in range(5):
plt.plot(totGoldarray[i],label = lbs[i]);
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(10);
lbs = ["Avg. Farmer Gold", "Avg. Smith Gold", "Avg. Jeweler Gold", "Avg. Noble Gold"];
for i in range(4):
plt.plot(ma([totGoldarray[i+1][j]/max(1, allPops[i][j]) for j in range(len(allPops[i]))]),label = lbs[i]);
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(13);
if (not incomeTax):
lbs = ["Food Tax", "Tool Tax", "Luxury Tax"];
for i in range(3):
plt.plot(ma(taxes[i]),label = lbs[i]);
else:
lbs = [str(kek) + "+ Tax" for kek in incomeTaxThresholds];
for i in range(len(incomeTaxThresholds)):
plt.plot(ma(taxes[i]),label = lbs[i]);
plt.legend();
plt.ylim(ymin=0);
plt.show();
if (incomeTax):
plt.figure(14);
lbs = [str(kek) + "+ Tax Threshold" for kek in incomeTaxThresholds];
for i in range(len(actualTaxThresholds)):
plt.plot(ma(actualTaxThresholds[i]),label = lbs[i]);
plt.legend();
plt.ylim(ymin=0);
plt.show();
plt.figure(11);
smoothing = 250;
if (not long):
plt.plot(ma(gdp), label = "GDP US");
plt.plot(ma(investSpend), label = "Investment Spending");
plt.plot(ma(conSpend), label = "Consumer Spending");
plt.plot(ma(govSpend), label = "Government Spending");
plt.title("Nominal GDP Over Time");
plt.xlabel("Round");
plt.ylabel("Nominal GDP ($)");
plt.plot(ma(gdp, smoothing), label = (name + " GDP"))
plt.legend();
plt.ylim(ymin=0, ymax = 3000);
plt.grid(True);
plt.show();
plt.figure(12);
if (not long):
plt.plot(ma(ppp), label = "rGDP US")
plt.plot(ma(ppp, smoothing), label = (name + " rGDP"))
plt.legend();
plt.title("rGDP Over Time");
plt.xlabel("Round");
plt.ylabel("Real Per-Capita GDP (Units of Food)");
plt.ylim(ymin=0.75, ymax = 3.25);
plt.grid(True);
plt.show();
plt.figure(16);
if (not long):
plt.plot(ma(cci), label = "CCI US")
plt.plot(ma(cci, smoothing), label = (name + " CCI"));
plt.title("CCI Over Time");
plt.xlabel("Round");
plt.ylabel("Consumer Confidence Index");
plt.legend();
plt.ylim(ymin=0, ymax = 1);
plt.grid(True);
plt.show();
plt.figure(7);
#lbs = ["Starved", "NEL", "F->S", "S->J", "J->N"];
#for i in range(5):
# plt.plot(ma(movements[i]),label = lbs[i]);
plt.plot(ma([movements[0][i] / (totPop - allPops[0][i]) for kek in range(len(movements[0]))], 100),label = (name + " Starvation Index"));
plt.legend();
plt.ylim(ymin=0, ymax=0.5);
plt.title("Starvation Index Over Time");
plt.xlabel("Round");
plt.ylabel("Starvation Index");
plt.grid(True);
plt.show();
plt.figure(15);
jqi=[(allPops[1][i] + allPops[2][i] * 2) / totPop for i in range(len(allPops[1]))]
plt.plot(ma(jqi, smoothing), label = (name + " Job Quality Index"))
plt.legend();
plt.ylim(ymin=0, ymax = 1);
plt.title("Job Quality Index Over Time");
plt.xlabel("Round");
plt.ylabel("Job Quality Index");
plt.grid(True);
plt.show();
print(name + " avg gdp: " + str(sum(gdp)/len(gdp)))
print(name + " avg rgdp: " + str(sum(ppp)/len(ppp)))
print(name + " avg jqi: " + str(sum(jqi)/len(jqi)))
with open(custom) as f:
custom_args = yaml.load(f.read())
args.update(custom_args)
args['cuda'] = args['cuda'] and torch.cuda.is_available()
log_dir = f'results/{datetime.now().strftime("%Y-%m-%d-%H:%M:%S")}-{args["env"]}-{args["prefix"]}'
log_dir = MPI.COMM_WORLD.bcast(log_dir, root=0)
args['log_dir'] = log_dir
if MPI.COMM_WORLD.Get_rank() == 0:
os.makedirs(log_dir)
with open(log_dir + '/configuration.yaml', 'w') as f:
f.write(yaml.dump(args))
args['device'] = torch.device('cuda' if args['cuda'] else 'cpu')
return args
if __name__ == '__main__':
torch.set_num_threads(1)
args = parse()
set_context(args['num_units'], args['num_actors'])
if global_dict['unit_idx'] == args['num_units']:
evaluator(args)
else:
rank = global_dict['rank_local']
if rank == global_dict['rank_learner']:
learner(args)
elif rank == global_dict['rank_replay']:
replay(args)
else:
actor_id = rank
actor(args, actor_id)
def __init__(self, distance, neighbour):
self.e = en.env(distance, neighbour)
self.ac = ac.actor()
self.cr = ac.critic()
self.aco = self.ac.optimizer()
self.cro = self.cr.optimizer()
#plt.switch_backend('Agg')
from numpy.linalg import norm
from actor import actor
from optparse import OptionParser
from scipy.stats import percentileofscore, scoreatpercentile,kruskal
from progressbar import Bar, Percentage, ProgressBar
from termcolor import cprint
from awesome_print import ap
#(1/100)
n = 100
start = 1
stop = 20
actors = [actor() for _ in xrange(10)]
gain = .01
EFFECT_SIZE = -gain * n
normalize = lambda data: data/np.linalg.norm(data)
effect = np.zeros((stop,))
intent = np.zeros_like(effect)
INITIAL_ATTITUDES = 2*np.random.random_sample(size=(n,))-1
attitudes = np.tile(INITIAL_ATTITUDES,(stop,1)).T
G = nx.MultiDiGraph()
tmp = nx.barabasi_albert_graph(n,3)
G.add_edges_from(tmp.edges())
G.add_nodes_from(tmp)
G.add_edges_from(nx.MultiDiGraph(nx.barabasi_albert_graph(n,3)).reverse().edges())
max_degree = max(nx.degree(G).values())
from actor import actor
from performances import performances
from labor import labor
a = actor("Gleb","Nazemnov","Andreevich","glavnui","15")
p = performances("12 стульев","2015","322")
cr = labor(a,p,"Остап Бендер","20000000")
print(a)
print(p)
print(cr)
def networkthread(clientid):
global predict
global messages
global msgtimeout
global kicked
global player1
#print("yes")
while True:
try:
data, addr2 = sock.recvfrom(4096)
except:
break
data = data.decode('UTF-8')
split = data.split("\n")
for p in split:
split2 = p.split(";")
if split2[0] == "spawn":
if split2[1] is not clientid:
multiplays.append(
actor.actor(int(split2[2]), int(split2[3]), 25, 50, 0,
hitbox, split2[1], split2[4]))
if split2[0] == "join":
if split2[1] is not clientid:
multiplays.append(
actor.actor(int(split2[2]), int(split2[3]), 25, 50, 0,
hitbox, split2[1]))
if split2[0] == "pos":
for x in multiplays:
if x.index == split2[1]:
x.setPos(int(split2[2]), int(split2[3]))
x.setRoom(split2[4])
if split2[0] == "leave":
print("x")
if split2[1] == clientid:
os._exit(1)
for x in multiplays:
if x.index == split2[1]:
multiplays.remove(x)
if split2[0] == "msg":
messages.append(split2[1] + ": " + split2[2])
msgtimeout = 0
if split2[0] == "bspawn":
newbul = bullet.bullet(
pygame.math.Vector2(int(float(split2[1])),
int(float(split2[2]))),
pygame.math.Vector2(int(float(split2[3])),
int(float(split2[4]))))
newbul.idd = split2[5]
newbul.room = split2[6]
newbul.size = split2[7]
player1.all_bullets.append(newbul)
if split2[0] == "die":
messages.append(split2[1] + " has died")
msgtimeout = 0
if split2[0] == "killed":
names = None
print(split2)
for p in multiplays:
print(p.index)
if str(p.index) == str(split2[2]):
names = p.name
messages.append(names + " has killed " + split2[1])
msgtimeout = 0
os._exit(1)
state.state = "start"
pygame.display.flip()
print("hello?")
sock.settimeout(None)
initdata, addr = sock.recvfrom(4096)
initdata = initdata.decode('UTF-8')
split = initdata.split("\n")
for p in split:
split2 = p.split(";")
if split2[0] == "init":
clientid = split2[1]
multiplays.append(
actor.actor(int(split2[2]), int(split2[3]), 25, 50, 0, hitbox,
split2[1], split2[4]))
player1 = actor.actor(int(split2[2]), int(split2[3]), 25, 50, 0,
hitbox, split2[1], split2[4])
clock = pygame.time.Clock()
FPS = 60
fps = clock.tick(FPS)
def networkthread(clientid):
global predict
global messages
global msgtimeout
global kicked
global player1
#print("yes")
def __init__(self,params,env,state_size,action_size): self.params=params self.critic_object=critic.critic(params,env,state_size,action_size) self.actor_object=actor.actor(params,env,state_size,action_size,self.critic_object.target_network) self.buffer_object=buffer_class.buffer_class(max_length=self.params['max_buffer_size'])