new

calc_coopr_rate.py

@@ -5,10 +5,12 @@ import csv
 import json
 
 
-def calcAndSave(mode):
+def calcAndSave(mode, network_type=None):
-    matches = Matches.from_profile(mode)
+    MAX_ROUND = 30
-    times = np.zeros(15)
+    # matches = Matches.from_profile(mode)
-    cs = np.zeros(15)
+    matches = Matches('wos-data-2022', network_type=network_type)
+    times = np.zeros(MAX_ROUND)
+    cs = np.zeros(MAX_ROUND)
     crs = []
     for m in matches.data:
         maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
@@ -26,17 +28,19 @@ def calcAndSave(mode):
                 cs[r-1] += coop
                 c[r - 1] = coop / (len(rows) * 2)
         crs.append(c)
-    for i in range(15):
+    for i in range(MAX_ROUND):
         if(times[i] == 0):
             times[i] = 1
     cs /= times
-    with open("outputs/CR_%s.csv"%mode, 'w') as f:
+    with open("outputs/CR_%s_%s.csv"%(mode, network_type), 'w') as f:
         csv.writer(f).writerow(cs)    
-    with open("outputs/CRAW_%s.json"%mode, 'w') as f:
+    with open("outputs/CRAW_%s_%s.json"%(mode, network_type), 'w') as f:
         json.dump(crs, f)
 
 if __name__ == '__main__':
-    calcAndSave('CLASSIC')
+    # calcAndSave('CLASSIC')
-    calcAndSave('SURVIVE')
+    # calcAndSave('SURVIVE')
+    calcAndSave('NEW', 'BA')
+    calcAndSave('NEW', 'WS')
 
 

calc_survivals.py

@@ -4,7 +4,9 @@ from island.match import Match
 
 result = {}
 
-for f in Path('wos-data-compete').iterdir():
+MAX_ROUND = 30
+
+for f in Path('wos-data-2022').iterdir():
     p = Path(f)
     if p.suffix == '.json':
         name = p.stem
@@ -18,7 +20,7 @@ for f in Path('wos-data-compete').iterdir():
         for p in m.query('player', 'join').select('pid').raw_data:
             foods[p['pid']] = conf['start_resource']
             survivals[0].append(p['pid'])
-        
+
         for i in range(1, game_end_at+1):
             survivals[i] = []
             for a in m.query('action', 'done').where(lambda x: x['rno'] == i).raw_data:
@@ -28,22 +30,25 @@ for f in Path('wos-data-compete').iterdir():
                 foods[j] -= conf['rounds']['consumption']
                 if foods[j] > 0:
                     survivals[i].append(j)
-        
+
         result[name] = survivals
 
 # print(json.dumps(result))
 
-average = [0]*20
+with open("outputs/survivals_new.json", 'w') as f:
+    json.dump(result, f)
+
+average = [0]*MAX_ROUND
 for k, v in result.items():
     for r, l in v.items():
         average[int(r)] += len(l)
 
-for i in range(20):
+for i in range(MAX_ROUND):
     average[i] /= len(result)
 
 idx=[]
 s=[]
-for i in range(1,16):
+for i in range(1,MAX_ROUND):
     idx.append(str(i))
     s.append("%.1f"%average[i])
 

eid_calc.py

@@ -16,21 +16,29 @@ E_{i,D}=所有邻居的剩余时间 - (背叛对象的剩余时间 + 与背叛
 import csv
 import json
 from functools import reduce
+from sre_constants import MAX_REPEAT
+from tkinter.tix import MAX
 import numpy as np
 from island.match import Match
 from island.matches import Matches
 
+MAX_ROUND = 28
 
 class Eid:
     def __init__(self):
+        self.details = {}
         self.survivals = {}
-        with open('outputs/survivals.json','r') as f:
+        with open('outputs/survivals_new.json','r') as f:
             self.survivals = json.load(f)
         self.neighbors = {}
-        with open('outputs/neighborhood.json', 'r') as f:
+        with open('outputs/neighborhood_new.json', 'r') as f:
             self.neighbors = json.load(f)
-        self.seasonSurvive = Matches.from_profile('SURVIVE')
+        self.seasons = [
-        self.seasonClassic = Matches.from_profile('CLASSIC')
+            dict(season=Matches('wos-data-2022', network_type='BA'), name='NEW_BA'),
+            dict(season=Matches('wos-data-2022', network_type='WS'), name='NEW_WS')
+        ]
+        # self.seasonSurvive = Matches.from_profile('SURVIVE')
+        # self.seasonClassic = Matches.from_profile('CLASSIC')
 
 
     def getNeighborTR(self, m, r, p, s, d):
@@ -135,46 +143,35 @@ class Eid:
             sigma += e
         return (sigma, ans)
 
+    def calc_season(self, season, name):
+        """
+        calc E_i,D
+        """
+        avg = np.zeros(MAX_ROUND)
+        cnt = np.zeros(MAX_ROUND)
+        for m in season.data:
+            d = {}
+            maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
+            for r in range(1, maxr):
+                sigma, ans = self.calcRoundData(m, r)
+                d[r] = ans
+                avg[r-1] += sigma
+                cnt[r-1] += len(ans)
+            self.details[m.name] = d
+        print(cnt)
+        for i in range(MAX_ROUND): 
+            if cnt[i] == 0: 
+                cnt[i] = 1
+        avg /= cnt
+        with open(f'outputs/EID_{name}.csv', 'w') as f:
+            csv.writer(f).writerow(avg)
+        return avg
+
     def calc(self):
-        detail = {}
+        for s in self.seasons:
-        avgC = np.zeros(15)
+            self.calc_season(s['season'], s['name'])
-        avgS = np.zeros(15)
+        with open('outputs/EID_new_detail.json', 'w') as f:
-        cnt = np.zeros(15)
+            json.dump(self.details, f)
-        for m in self.seasonClassic.data:
-            d = {}
-            maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
-            for r in range(1, maxr):
-                sigma, ans = self.calcRoundData(m, r)
-                d[r] = ans
-                avgC[r-1] += sigma
-                cnt[r-1] += len(ans)
-            detail[m.name] = d
-        print(cnt)
-        for i in range(15): 
-            if cnt[i] == 0: 
-                cnt[i] = 1
-        avgC /= cnt
-        cnt = np.zeros(15)
-        for m in self.seasonSurvive.data:
-            d = {}
-            maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
-            for r in range(1, maxr):
-                sigma, ans = self.calcRoundData(m, r)
-                d[r] = ans
-                avgS[r-1] += sigma
-                cnt[r-1] += len(ans)
-            detail[m.name] = d
-        print(cnt)
-        for i in range(15): 
-            if cnt[i] == 0: 
-                cnt[i] = 1
-        avgS /= cnt
-        with open('outputs/EID_CLASSIC.csv', 'w') as f:
-            csv.writer(f).writerow(avgC)
-        with open('outputs/EID_SURVIVE.csv', 'w') as f:
-            csv.writer(f).writerow(avgS)
-        with open('outputs/EID_detail.json', 'w') as f:
-            json.dump(detail, f)
         
 if __name__ == '__main__':
     e = Eid()

eid_plot.py

@@ -9,7 +9,7 @@ red = '#d63031'
 
 def error(f,x,y):
     return sp.sum((f(x)-y)**2)
-def p1(x, coopr, e, postfix, show=True):
+def p1(x, coopr, e, postfix, show=True, max_round=15):
     fig = plt.figure(figsize=(3, 2))
     ax = fig.gca()
     ax.plot(x, coopr, color=blue, linewidth=2, label=r"f$_{\rm c}$")
@@ -21,7 +21,7 @@ def p1(x, coopr, e, postfix, show=True):
     ax2.set_yticks(sp.linspace(-1500, 200, 5))
     ax2.tick_params(labelsize=10)
     ax.tick_params(labelsize=10)
-    ax.set_xlim(1, 15)
+    ax.set_xlim(1, max_round)
     ax.set_xlabel("Rounds", size=11)
     ax.set_ylabel(r"f$_{\rm c}$", color=blue, size=11)
     ax.tick_params(axis='y', labelcolor=blue)
@@ -71,25 +71,40 @@ def p2(e, coopr, postfix, show=True, showline=True):
 def p2c(e,c,m,s):
     p2(e,c,m,s,False)
 
-def plot(mode, show, isp1):
+def plot(mode, show, network_type, isp1):
-    coopr = np.loadtxt("outputs/CR_%s.csv" % mode, delimiter=',')
+    coopr = np.loadtxt("outputs/CR_%s_%s.csv" % (mode, network_type), delimiter=',')
-    x = np.arange(1, 16)
+    e = np.loadtxt("outputs/EID_%s_%s.csv" % (mode, network_type), delimiter=',')
-    e = np.loadtxt("outputs/EID_%s.csv" % mode, delimiter=',')
 
     if isp1:
-        p1(x, coopr, e, mode, show)
+        if mode == 'NEW':
+            x = np.arange(1, 29)
+        else:
+            x = np.arange(1, 16)
+        p1(x, coopr[:28], e, mode, show, max_round=28)
     else:
         if mode == 'SURVIVE':
             p2(e[:-1], coopr[:-1], mode, show)
-        else:
+        elif mode == 'CLASSIC':
             p2c(e[:-1], coopr[:-1], mode, show)
+        else:
+            p2(e[:-1], coopr[:27], f'{mode}_{network_type}', show)
 
 if __name__ == '__main__':
-    # eid_plot c/s 1/2 t/f
+    # eid_plot c/s/nw/nb 1/2 t/f
-    mode = 'CLASSIC' if argv[1] == 'c' else 'SURVIVE'
+    network_type = None
+    if argv[1] == 'c':
+        mode = 'CLASSIC'
+    elif argv[1] == 's':
+        mode = 'SURVIVE'
+    elif argv[1] == 'nb':
+        mode = 'NEW'
+        network_type = 'BA'
+    else:
+        mode = 'NEW'
+        network_type = 'WS'
     isp1 = argv[2] == '1'
     show = argv[3] == 't'
-    plot(mode, show, isp1)
+    plot(mode, show, network_type, isp1)
 
     """
 SURVIVE

fc_plot.py

@@ -19,7 +19,7 @@ nowak_survive = [0.50017833, 0.26068193, 0.88939394, 0.9595738, 0.99796403, 0.99
 santos_survive = [0.50071587, 0.27929701, 0.9098483, 0.938, 0.99437628, 0.99861352, 0.99877322, 1, 1, 1, 1, 1, 1, None, None]
 santos_classic = [0.49821874, 0.2757036, 0.26718917, 0.26270039, 0.25895974, 0.2562166, 0.25439971, 0.25290346, 0.25187032, 0.25097969, 0.25037406, 0.24983969, 0.24919843, 0.24884218, 0.24862843]
 
-
+MAX_ROUND = 28
 
 def p1(x, coopr, crawx, crawy, postfix, show=True):
     fig = plt.figure(figsize=(5, 2))
@@ -29,17 +29,17 @@ def p1(x, coopr, crawx, crawy, postfix, show=True):
         ax.plot(x, ana, marker='^', linestyle='dotted', markerfacecolor='none', color=green, linewidth=2, label="Analytical")
         ax.plot(x, santos_survive, marker='x', linestyle='dotted', markerfacecolor='none', color=kikyo, linewidth=2, label="Baseline#1")
         ax.plot(x, nowak_survive, marker='*', linestyle='dotted', markerfacecolor='none', color=hanaba, linewidth=2, label="Baseline#2")
-    else:
+    elif postfix == 'CLASSIC':
         ax.plot(x, santos_classic, marker='x', linestyle='dotted', markerfacecolor='none', color=kikyo, linewidth=2, label="Baseline#1")
         ax.plot(x, nowak_classic, marker='*', linestyle='dotted', markerfacecolor='none', color=hanaba, linewidth=2, label="Baseline#2")
     ax.plot(x, coopr, marker='o', linestyle='dotted', markerfacecolor='none', color=blue, linewidth=2, label="Human")
-    ax.scatter(crawx, crawy, s=15, c='dimgray', marker='x', linewidth=0.5, alpha=0.4)
+    ax.scatter(crawx, crawy, s=MAX_ROUND, c='dimgray', marker='x', linewidth=0.5, alpha=0.4)
     ax.set_ylim(-0.05, 1.05)
-    ax.set_yticks(sp.linspace(0, 1, 5))
+    ax.set_yticks(np.linspace(0, 1, 5))
     ax.tick_params(labelsize=10)
     ax.tick_params(direction='in')
-    ax.set_xlim(0, 16)
+    ax.set_xlim(0, MAX_ROUND+1)
-    ax.set_xticks([1, 5, 10, 15])
+    ax.set_xticks([1, 5, 10, 15, 20, 25, 30])
     # ax.set_xticklabels([''])
     ax.set_xlabel("Rounds", size=11)
     ax.set_ylabel(r"f$_{\rm c}$", size=11, fontstyle='normal')
@@ -54,20 +54,31 @@ def p1(x, coopr, crawx, crawy, postfix, show=True):
         plt.savefig("graph/fc_plot_%s.pdf" % postfix)
 
 
-def plot(mode, show):
+def plot(mode, show, network_type=None):
-    coopr = np.loadtxt("outputs/CR_%s.csv" % mode, delimiter=',')
+    coopr = np.loadtxt("outputs/CR_%s_%s.csv" % (mode, network_type), delimiter=',')
     crawx, crawy = [],[]
-    with open("outputs/CRAW_%s.json" % mode) as f:
+    with open("outputs/CRAW_%s_%s.json" % (mode, network_type)) as f:
         data = json.load(f)
         for l in data:
             for i in range(len(l)):
                 crawx.append((i + 1) + random.uniform(-0.25, 0.25))
                 crawy.append(l[i])
-    x = np.arange(1, 16)
+    x = np.arange(1, MAX_ROUND + 1)
-    p1(x, coopr, crawx, crawy, mode, show)
+    p1(x, coopr[:MAX_ROUND], crawx, crawy, f'{mode}_{network_type}', show)
 
 if __name__ == '__main__':
-    # fc_plot c/s t/f
+    # fc_plot c/s/nb/nw t/f
-    mode = 'CLASSIC' if argv[1] == 'c' else 'SURVIVE'
+    network_type = None
+    if argv[1] == 'c':
+        mode = 'CLASSIC'
+    elif argv[1] == 's':
+        mode = 'SURVIVE'
+    elif argv[1] == 'nb':
+        mode = 'NEW'
+        network_type = 'BA'
+    else:
+        mode = 'NEW'
+        network_type = 'WS'
+
     show = argv[2] == 't'
-    plot(mode, show)
+    plot(mode, show, network_type)

gen_neighborhood_new.py

@@ -0,0 +1,18 @@
+import json
+from island.matches import Matches
+
+
+# matches = Matches.from_profile_expr(lambda r:True)
+matches = Matches('wos-data-2022')
+
+neighbors = {}
+
+for i in range(len(matches.data)):
+    m = matches.data[i]
+    n = {}
+    for r in m.query('player', 'sight').raw_data:
+        n[r['player']] = r['sight']
+    neighbors[matches.names[i]] = n
+
+with open('outputs/neighborhood_new.json', 'w') as f:
+    json.dump(neighbors, f)

island/match.py

@@ -149,6 +149,17 @@ class Match:
 
         return trs
 
+
+    def network_type(self):
+        """
+        返回网络类型，如果找不到，则返回None
+        """
+        try:
+            return json.loads(self.query('game', 'created').first()['info']['config'])['network']['type']
+        except:
+            print("Can't get network: %s" % json.loads(self.query('game', 'created').first()))
+            return None
+
     @staticmethod
     def read_from_json(json_path):
         """

island/matches.py

@@ -5,20 +5,24 @@ import csv
 
 
 class Matches:
-    def __init__(self, source, from_list=False):
+    def __init__(self, source, from_list=False, network_type=None):
         self.data = []
         self.names = []
         if from_list:
             for l in source:
-                self.data.append(Match.read_from_json(l))
+                m = Match.read_from_json(l)
-                self.data[-1].name = Path(l).stem
+                if network_type == None or m.network_type() == network_type:
-                self.names.append(Path(l).stem)
+                    self.data.append(m)
+                    self.data[-1].name = Path(l).stem
+                    self.names.append(Path(l).stem)
         else:
             for f in Path(source).iterdir():
                 if Path(f).suffix == '.json':
-                    self.data.append(Match.read_from_json(str(f)))
+                    m = Match.read_from_json(str(f))
-                    self.data[-1].name = Path(f).stem
+                    if network_type == None or m.network_type() == network_type:
-                    self.names.append(Path(f).stem)
+                        self.data.append(m)
+                        self.data[-1].name = Path(f).stem
+                        self.names.append(Path(f).stem)
 
     @staticmethod
     def from_profile(pname, ppos='profile.csv'):

outputs/CRAW_NEW.json

@@ -0,0 +1 @@
+[]

outputs/CRAW_NEW_BA.json

@@ -0,0 +1 @@
+[[0.7837837837837838, 0.6785714285714286, 0.76, 0.7727272727272727, 0.7843137254901961, 0.7363636363636363, 0.7653061224489796, 0.7608695652173914, 0.7325581395348837, 0.6794871794871795, 0.7307692307692307], [0.8166666666666667, 0.73, 0.7941176470588235, 0.8292682926829268, 0.75, 0.7159090909090909, 0.7674418604651163, 0.7631578947368421, 0.7, 0.7051282051282052, 0.6219512195121951, 0.6309523809523809, 0.6351351351351351, 0.5697674418604651, 0.48484848484848486, 0.625, 0.6447368421052632, 0.6621621621621622, 0.6125, 0.578125, 0.5714285714285714, 0.5285714285714286, 0.5925925925925926, 0.5833333333333334, 0.5833333333333334, 0.5645161290322581, 0.6212121212121212, 0.65625], [0.9433962264150944, 0.8974358974358975, 0.8461538461538461, 0.8103448275862069, 0.8260869565217391, 0.8636363636363636, 0.8333333333333334, 0.8529411764705882, 0.9047619047619048, 0.8809523809523809, 0.8235294117647058, 0.7777777777777778, 0.75, 0.825, 0.8095238095238095, 0.6666666666666666, 0.7105263157894737, 0.78125], [1.0, 0.9210526315789473, 0.8333333333333334, 0.9166666666666666, 0.875, 0.9166666666666666, 0.8636363636363636, 0.8846153846153846, 0.8571428571428571, 0.8333333333333334, 0.7727272727272727, 0.8181818181818182, 0.8846153846153846, 0.7692307692307693, 0.8333333333333334, 0.8125, 0.78125, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.6666666666666666, 0.8333333333333334, 0.7083333333333334, 0.75, 0.7, 0.8125]]

outputs/CRAW_NEW_WS.json

@@ -0,0 +1 @@
+[[0.984375, 0.9705882352941176, 0.96875, 0.9230769230769231, 0.90625, 0.9333333333333333, 0.9, 0.75, 0.9117647058823529, 0.9117647058823529, 0.90625, 0.96875, 0.8666666666666667, 0.8888888888888888, 0.8888888888888888, 0.8125, 0.7666666666666667, 0.8214285714285714, 0.7142857142857143, 0.7058823529411765, 0.5769230769230769, 0.6666666666666666, 0.5625, 0.5769230769230769, 0.5769230769230769, 0.59375, 0.7142857142857143, 0.47058823529411764], [0.8809523809523809, 0.8055555555555556, 0.8333333333333334, 0.85, 0.8823529411764706, 0.8823529411764706, 0.8125, 0.8333333333333334, 0.8076923076923077, 0.9, 0.8846153846153846, 0.7916666666666666, 0.75, 0.75, 0.85, 0.8076923076923077, 0.75, 0.6923076923076923, 0.8, 0.7307692307692307, 0.75, 0.5416666666666666, 0.5, 0.5909090909090909], [0.8, 0.625, 0.7777777777777778, 0.8888888888888888, 0.7857142857142857, 0.8888888888888888, 0.875, 0.9090909090909091, 0.9, 0.8888888888888888], [0.7222222222222222, 0.7777777777777778, 0.8333333333333334, 0.8333333333333334, 0.8571428571428571, 0.9285714285714286, 0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 0.8], [0.9, 0.7, 0.75, 0.9, 0.8571428571428571, 0.75, 0.8571428571428571, 0.7857142857142857, 0.6875, 0.5, 0.5, 0.6666666666666666, 0.8333333333333334, 0.8, 0.75, 0.9, 0.6666666666666666, 0.75, 0.75, 0.75, 0.8, 0.75, 0.8333333333333334, 0.6666666666666666, 0.75]]

outputs/CR_NEW.csv

@@ -0,0 +1 @@
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

outputs/CR_NEW_BA.csv

@@ -0,0 +1 @@
+0.8757062146892656,0.774390243902439,0.795774647887324,0.8102189781021898,0.7884615384615384,0.7669172932330827,0.7862903225806451,0.7894736842105263,0.7669491525423728,0.7432432432432432,0.7083333333333334,0.6971830985915493,0.7142857142857143,0.6710526315789473,0.6515151515151515,0.6753246753246753,0.6917808219178082,0.7279411764705882,0.6634615384615384,0.6477272727272727,0.5957446808510638,0.5909090909090909,0.6282051282051282,0.625,0.6086956521739131,0.6153846153846154,0.6212121212121212,0.65625,0.0,0.0

outputs/CR_NEW_WS.csv

@@ -0,0 +1 @@
+0.8902439024390244,0.8032786885245902,0.853448275862069,0.8773584905660378,0.8703703703703703,0.8888888888888888,0.8596491228070176,0.8240740740740741,0.8454545454545455,0.8529411764705882,0.8285714285714286,0.8529411764705882,0.8181818181818182,0.8333333333333334,0.859375,0.8235294117647058,0.7413793103448276,0.7575757575757576,0.75,0.7222222222222222,0.6833333333333333,0.6363636363636364,0.5882352941176471,0.6,0.631578947368421,0.59375,0.7142857142857143,0.47058823529411764,0.0,0.0

outputs/EID_$NEW_BA.csv

@@ -0,0 +1 @@
+-203.46853146853147,-364.35664335664336,-303.83916083916085,-418.8041958041958,-408.3356643356643,-433.4195804195804,-387.8811188811189,-321.7832167832168,-362.73426573426576,-397.8811188811189,-351.02150537634407,-386.505376344086,-331.752688172043,-470.494623655914,-377.01075268817203,-421.752688172043,-356.9569892473118,-251.73015873015873,-170.1904761904762,-200.57142857142858,-274.2857142857143,-303.9047619047619,-246.14285714285714,-211.04761904761904,-42.41269841269841,33.90909090909091,124.6590909090909,0.0

outputs/EID_$NEW_WS.csv

@@ -0,0 +1 @@
+221.58620689655172,220.13793103448276,227.80459770114942,115.86206896551724,-107.86206896551724,-119.77011494252874,-114.94252873563218,22.080459770114942,59.770114942528735,-115.47169811320755,-23.39622641509434,-94.33962264150944,-109.81132075471699,42.264150943396224,70.16981132075472,-2.2641509433962264,-65.64150943396227,34.716981132075475,0.0,-140.0,-23.39622641509434,-163.0188679245283,-142.26415094339623,-174.19354838709677,-43.63636363636363,-5.545454545454546,169.0,0.0

outputs/EID_NEW_BA.csv

@@ -0,0 +1 @@
+2602.671328671329,2738.048951048951,2240.4055944055945,2148.902097902098,2126.125874125874,2413.006993006993,2532.8811188811187,2209.006993006993,2499.230769230769,2418.3636363636365,2289.569892473118,2191.989247311828,2254.559139784946,2381.8387096774195,2083.7096774193546,2352.8172043010754,2691.8064516129034,2543.84126984127,2766.031746031746,2317.777777777778,2422.15873015873,2524.222222222222,2464.095238095238,2659.6666666666665,2575.84126984127,3127.7954545454545,2913.7727272727275,0.0

outputs/EID_NEW_WS.csv

@@ -0,0 +1 @@
+4158.735632183908,4271.0344827586205,4125.241379310345,3635.862068965517,3409.655172413793,3261.1494252873563,3451.0344827586205,3672.183908045977,3746.896551724138,3076.9811320754716,3836.2264150943397,3801.8867924528304,3747.5471698113206,3430.188679245283,3855.8490566037735,3593.396226415094,3608.301886792453,3701.8867924528304,3258.1132075471696,3515.4716981132074,3428.6792452830186,3148.6792452830186,3881.8867924528304,3901.935483870968,3000.0,3730.5,3583.6363636363635,0.0

theta_plot.py

@@ -5,20 +5,21 @@ from matplotlib import markers
 blue = '#0984e3'
 red = '#d63031'
 
-x = np.arange(1, 16)
+MAX_ROUND = 28
-tc = np.loadtxt("outputs/EID_CLASSIC.csv", delimiter=',')
+x = np.arange(1, MAX_ROUND+1)
-ts = np.loadtxt("outputs/EID_SURVIVE.csv", delimiter=',')
+tc = np.loadtxt("outputs/EID_NEW_BA.csv", delimiter=',')
+ts = np.loadtxt("outputs/EID_NEW_WS.csv", delimiter=',')
 
 fig = plt.figure(figsize=(6,4.5))
 ax = fig.gca()
-ax.plot(x[:-1], tc[:-1], 'o--', color='limegreen', linewidth=2, label=r"Classic")
+ax.plot(x[:-1], tc[:-1], 'o--', color='limegreen', linewidth=2, label=r"BA")
-ax.plot(x[:-1], ts[:-1], 'o-', color='darkorange', linewidth=2, label=r"Dissipative")
+ax.plot(x[:-1], ts[:-1], 'o-', color='darkorange', linewidth=2, label=r"WS")
 
-ax.set_ylim(-1500, 500)
+# ax.set_ylim(-1500, 500)
-ax.set_yticks(sp.linspace(-1500, 500, 6))
+# ax.set_yticks(sp.linspace(-1500, 500, 6))
 ax.tick_params(labelsize=14)
-ax.set_xlim(1, 15)
+ax.set_xlim(1, MAX_ROUND)
-ax.set_xticks(sp.linspace(1,15,8, dtype=int))
+ax.set_xticks(sp.linspace(1,MAX_ROUND,8, dtype=int))
 ax.set_xlabel("Rounds", size=22)
 ax.set_ylabel(r"$\theta$", family='sans-serif', size=22)
 # ax.tick_params(axis='y', labelcolor=red)
@@ -26,4 +27,5 @@ ax.set_ylabel(r"$\theta$", family='sans-serif', size=22)
 plt.legend(numpoints=2, fontsize=14)
 plt.tight_layout()
 # plt.show()
-plt.savefig("graph/theta_plot.eps")
+# plt.savefig("graph/theta_plot.eps")
+plt.savefig("graph/theta_plot_new.pdf")

theta_r.py

@@ -0,0 +1,122 @@
+"""
+计算theta_r
+theta_r_i=所有邻居的剩余时间
+
+输出：
+1. json格式(详细)
+{
+    "GID": {
+        "RID": {
+            "PID": theta_r_i,
+        }
+    }
+}
+2. CSV格式(按轮平均值, CLASSIC/SURVIVE分别对应一个文件)
+"""
+import csv
+import json
+from functools import reduce
+from sre_constants import MAX_REPEAT
+from tkinter.tix import MAX
+import numpy as np
+from island.match import Match
+from island.matches import Matches
+
+MAX_ROUND = 28
+
+class theta_r_i:
+    def __init__(self):
+        self.details = {}
+        self.survivals = {}
+        with open('outputs/survivals_new.json','r') as f:
+            self.survivals = json.load(f)
+        self.neighbors = {}
+        with open('outputs/neighborhood_new.json', 'r') as f:
+            self.neighbors = json.load(f)
+        self.seasons = [
+            dict(season=Matches('wos-data-2022', network_type='BA'), name='NEW_BA'),
+            dict(season=Matches('wos-data-2022', network_type='WS'), name='NEW_WS')
+        ]
+        # self.seasonSurvive = Matches.from_profile('SURVIVE')
+        # self.seasonClassic = Matches.from_profile('CLASSIC')
+
+
+    def getNeighborTR(self, m, r, p, s):
+        """
+        获取该玩家所有邻居的剩余时间
+        
+        :param m: Match
+        :param r: Round ID
+        :param p: PID
+        :param s: Survivals list(neighborhood)
+        :returns: theta_{r}_{i}
+        """
+        return 1440*len(s) - reduce(lambda a, b: a + b['tr'], m.query('action', 'done').where(lambda x: x['rno'] == r and ((x['a'] in s)or(x['b']in s))).raw_data, 0)
+
+
+    def getNeighborhood(self, m, r, p):
+        """
+        获取该玩家当轮存活邻居
+        
+        :param m: Match
+        :param r: Round ID
+        :param p: PID
+        :returns: Survivals list(neighborhood)
+        """
+        if str(p) not in self.neighbors[m.name]:
+            print("Alone(%d)!" % p)
+            return []
+        return [i for i in self.survivals[m.name][str(r)] if i in self.neighbors[m.name][str(p)]]
+
+
+    def calcRoundData(self, m, r):
+        """
+        计算某场比赛，某一轮的theta值
+        
+        :param m: Match
+        :param r: Round ID
+        :param p: PID
+        :returns: an average value and a detail dict
+        """
+        r += 1
+        ans = {}
+        sigma = 0.0
+        for p in self.survivals[m.name][str(r)]:
+            e = max(0, min(144000000, self.getNeighborTR(m, r, p, self.getNeighborhood(m, r, p))))
+            ans[p] = e
+            sigma += e
+        return (sigma, ans)
+
+    def calc_season(self, season, name):
+        """
+        calc E_i,D
+        """
+        avg = np.zeros(MAX_ROUND)
+        cnt = np.zeros(MAX_ROUND)
+        for m in season.data:
+            d = {}
+            maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
+            for r in range(1, maxr):
+                sigma, ans = self.calcRoundData(m, r)
+                d[r] = ans
+                avg[r-1] += sigma
+                cnt[r-1] += len(ans)
+            self.details[m.name] = d
+        print(cnt)
+        for i in range(MAX_ROUND): 
+            if cnt[i] == 0: 
+                cnt[i] = 1
+        avg /= cnt
+        with open(f'outputs/EID_{name}.csv', 'w') as f:
+            csv.writer(f).writerow(avg)
+        return avg
+
+    def calc(self):
+        for s in self.seasons:
+            self.calc_season(s['season'], s['name'])
+        with open('outputs/EID_new_detail.json', 'w') as f:
+            json.dump(self.details, f)
+        
+if __name__ == '__main__':
+    e = theta_r_i()
+    e.calc()