nodes = map(int, edge_id.split(','))
pickcount = int(losscount) + int(wincount)
for i in nodes:
heropickcount[i] += int(pickcount)
herowincount[i] += int(wincount)
f = open('output.txt', 'r')
popularitygraph = nx.Graph()
winrategraph = nx.Graph()
while True:
line1 = f.readline()
line2 = f.readline()
if not line2: break
line1 = line1.rstrip()
line2 = line2.rstrip()
edge_id, losscount = line1.split('\t')
wincount = line2.split('\t')[1]
edge_id = edge_id.split(':')[0]
nodes = map(int, edge_id.split(','))
pickcount = int(losscount) + int(wincount)
popularitygraph.add_edge(*hf.getHero(nodes), weight=pickcount)
winrate = float(wincount)/pickcount
winratetarget = max(herowincount[nodes[0]]/heropickcount[nodes[0]],
herowincount[nodes[1]]/heropickcount[nodes[1]])
winrategraph.add_edge(*hf.getHero(nodes), weight=(winrate - winratetarget))
d = json_graph.node_link_data(popularitygraph)
json.dump(d, open('popularitygraph.json','w'))
d = json_graph.node_link_data(winrategraph)
json.dump(d, open('winrategraph.json', 'w'))
import matplotlib.pyplot as pp
from matplotlib import colors
import herofilter as hf
import mapreduceoutputparse as mp
# simple computation: grab N most popular heroes
N = 5
totals = mp.picksbans.sum(axis=0)
totals.sort(ascending=False)
Nmostpopular = list(totals[:N].index)
# graphing code
selected = list()
for i in range(N):
selected.append(Nmostpopular[i])
names = hf.getHero(selected)
ind = np.arange(N)
width = 1
pp.figure()
for i in range(10):
pp.bar(ind, mp.picksbans.iloc[9-i][selected], width, color=(1,i*.1,i*.1), bottom=mp.picksbans.iloc[10-i:10].sum(axis=0)[selected])
pp.bar(ind, -mp.picksbans.iloc[10+i][selected], width, color=(i*.1,i*.1,1), bottom=-mp.picksbans.iloc[10:10+i].sum(axis=0)[selected])
pp.ylabel('Ban & pick frequency')
pp.title('Top ' + str(N) + ' banned/picked heroes')
pp.xticks(ind+width/2., names)
pp.yticks(np.arange(-40000, 40000, 10000))
pp.show()
nodes = map(int, edge_id.split(','))
pickcount = int(losscount) + int(wincount)
for i in nodes:
heropickcount[i] += int(pickcount)
herowincount[i] += int(wincount)
f = open('output.txt', 'r')
popularitygraph = nx.Graph()
winrategraph = nx.Graph()
while True:
line1 = f.readline()
line2 = f.readline()
if not line2: break
line1 = line1.rstrip()
line2 = line2.rstrip()
edge_id, losscount = line1.split('\t')
wincount = line2.split('\t')[1]
edge_id = edge_id.split(':')[0]
nodes = map(int, edge_id.split(','))
pickcount = int(losscount) + int(wincount)
popularitygraph.add_edge(*hf.getHero(nodes), weight=pickcount)
winrate = float(wincount) / pickcount
winratetarget = max(herowincount[nodes[0]] / heropickcount[nodes[0]],
herowincount[nodes[1]] / heropickcount[nodes[1]])
winrategraph.add_edge(*hf.getHero(nodes), weight=(winrate - winratetarget))
d = json_graph.node_link_data(popularitygraph)
json.dump(d, open('popularitygraph.json', 'w'))
d = json_graph.node_link_data(winrategraph)
json.dump(d, open('winrategraph.json', 'w'))
import matplotlib.pyplot as pp
from matplotlib import colors
import herofilter as hf
import mapreduceoutputparse as mp
# simple computation: grab N most popular heroes
N = 5
totals = mp.picksbans.sum(axis=0)
totals.sort(ascending=False)
Nmostpopular = list(totals[:N].index)
# graphing code
selected = list()
for i in range(N):
selected.append(Nmostpopular[i])
names = hf.getHero(selected)
ind = np.arange(N)
width = 1
pp.figure()
for i in range(10):
pp.bar(ind, mp.picksbans.iloc[9-i][selected], width, color=(1,i*.1,i*.1), bottom=mp.picksbans.iloc[10-i:10].sum(axis=0)[selected])
pp.bar(ind, -mp.picksbans.iloc[10+i][selected], width, color=(i*.1,i*.1,1), bottom=-mp.picksbans.iloc[10:10+i].sum(axis=0)[selected])
pp.ylabel('Ban & pick frequency')
pp.title('Top ' + str(N) + ' banned/picked heroes')
pp.xticks(ind+width/2., names)
pp.yticks(np.arange(-40000, 40000, 10000))
pp.show()
