New data

calc_coopr_rate.py

@@ -6,9 +6,9 @@ import json
 
 
 def calcAndSave(mode, network_type=None):
-    MAX_ROUND = 30
+    MAX_ROUND = 16 # = game_end_at
     # matches = Matches.from_profile(mode)
-    matches = Matches('wos-data-2022', network_type=network_type)
+    matches = Matches('wos-data-2022-1', network_type=network_type)
     times = np.zeros(MAX_ROUND)
     cs = np.zeros(MAX_ROUND)
     crs = []

calc_survivals.py

@@ -4,9 +4,10 @@ from island.match import Match
 
 result = {}
 
-MAX_ROUND = 30
+# = game_end_at + 1
+MAX_ROUND = 17
 
-for f in Path('wos-data-2022').iterdir():
+for f in Path('wos-data-2022-1').iterdir():
     p = Path(f)
     if p.suffix == '.json':
         name = p.stem

collision_detection.py

@@ -0,0 +1,29 @@
+from island.match import Match
+from island.matches import Matches
+import json
+
+matches = Matches('wos-data-2022-1')
+crs = []
+for m in matches.data:
+    game_end_at = int(m.query('game', 'created').first()['info']['game_end_at'])
+    games = [{} for _ in range(game_end_at)]
+    for round in range(1, game_end_at+1):
+        rows = m.query('action', 'done').where(lambda x: x['rno'] == round).raw_data
+        for row in rows:
+            players = [f"{row['a']}-{row['b']}", f"{row['b']}-{row['a']}"]
+            log_id = -1
+            for p in players:
+                if p in games[round-1]:
+                    log_id = games[round-1][p]
+            if log_id != -1:
+                print("Collision detected: match=%s, row=%s, collision_log_id=%d" %(m.name, json.dumps(row), log_id))
+                print("History of this two players:")
+                logs = m.query('action', 'done').where(lambda x: f"{x['a']}-{x['b']}" in players).raw_data + \
+                       m.query('action', 'request').where(lambda x: f"{x['from']}-{x['to']}" in players).raw_data + \
+                       m.query('action', 'approve').where(lambda x: f"{x['from']}-{x['to']}" in players).raw_data
+                for log in sorted(logs, key=lambda a: a['log_id']):
+                    print(json.dumps(log))
+
+            else:
+                for p in players:
+                    games[round-1][p] = row['log_id']

eid_calc.py

@@ -34,8 +34,8 @@ class Eid:
         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-2022-1', network_type='BA'), name='NEW_BA'),
-            dict(season=Matches('wos-data-2022', network_type='WS'), name='NEW_WS')
+            dict(season=Matches('wos-data-2022-1', network_type='WS'), name='NEW_WS')
         ]
         # self.seasonSurvive = Matches.from_profile('SURVIVE')
         # self.seasonClassic = Matches.from_profile('CLASSIC')

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
+MAX_ROUND = 16
 
 def p1(x, coopr, crawx, crawy, postfix, show=True):
     fig = plt.figure(figsize=(5, 2))
@@ -39,7 +39,7 @@ def p1(x, coopr, crawx, crawy, postfix, show=True):
     ax.tick_params(labelsize=10)
     ax.tick_params(direction='in')
     ax.set_xlim(0, MAX_ROUND+1)
-    ax.set_xticks([1, 5, 10, 15, 20, 25, 30])
+    ax.set_xticks([1, 5, 10, 15])
     # ax.set_xticklabels([''])
     ax.set_xlabel("Rounds", size=11)
     ax.set_ylabel(r"f$_{\rm c}$", size=11, fontstyle='normal')
@@ -76,6 +76,7 @@ if __name__ == '__main__':
     elif argv[1] == 'nb':
         mode = 'NEW'
         network_type = 'BA'
+        MAX_ROUND = 14
     else:
         mode = 'NEW'
         network_type = 'WS'

gen_neighborhood_new.py

@@ -3,7 +3,7 @@ from island.matches import Matches
 
 
 # matches = Matches.from_profile_expr(lambda r:True)
-matches = Matches('wos-data-2022')
+matches = Matches('wos-data-2022-1')
 
 neighbors = {}
 

outputs/CRAW_NEW_BA.json

@@ -1 +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]]
+[[0.9329268292682927, 0.926829268292683, 0.845679012345679, 0.8506493506493507, 0.8287671232876712, 0.8375, 0.7608695652173914, 0.8333333333333334, 0.7954545454545454, 0.7214285714285714, 0.7578125, 0.7622950819672131, 0.6384615384615384, 0.5819672131147541], [0.9038461538461539, 0.831081081081081, 0.8066666666666666, 0.7246376811594203, 0.7578125, 0.7236842105263158, 0.7364864864864865, 0.7121212121212122, 0.7071428571428572, 0.717948717948718, 0.6890243902439024, 0.75, 0.753968253968254]]

outputs/CRAW_NEW_WS.json

@@ -1 +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]]
+[[0.9274809160305344, 0.8943089430894309, 0.8925619834710744, 0.8878504672897196, 0.8773584905660378, 0.8387096774193549, 0.8333333333333334, 0.8411764705882353, 0.7658227848101266, 0.8035714285714286, 0.788235294117647, 0.7352941176470589, 0.689873417721519, 0.6818181818181818], [0.91015625, 0.9217391304347826, 0.9107142857142857, 0.9030612244897959, 0.8939393939393939, 0.907608695652174, 0.8764705882352941, 0.8166666666666667, 0.788235294117647, 0.7528089887640449, 0.65, 0.5602409638554217, 0.49375, 0.30246913580246915, 0.29411764705882354, 0.23026315789473684]]

outputs/CR_NEW_BA.csv

@@ -1 +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
+0.91875,0.8814102564102564,0.8269230769230769,0.791095890410959,0.7956204379562044,0.782051282051282,0.7482517482517482,0.7753623188405797,0.75,0.7195945945945946,0.7191780821917808,0.7560975609756098,0.6953125,0.5819672131147541,0.0,0.0

outputs/CR_NEW_WS.csv

@@ -1 +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
+0.918918918918919,0.907563025210084,0.9012875536480687,0.8951219512195122,0.8853658536585366,0.8729729729729729,0.8532608695652174,0.8285714285714286,0.7774390243902439,0.7774566473988439,0.7212121212121212,0.6488095238095238,0.5911949685534591,0.4873417721518987,0.29411764705882354,0.23026315789473684

outputs/EID_NEW_BA.csv

@@ -1 +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
+1221.2386363636363,1163.7443181818182,1186.4147727272727,1163.4943181818182,1170.2670454545455,1144.4943181818182,1050.6761363636363,1182.8125,1070.0568181818182,1123.6931818181818,1240.5738636363637,1161.4488636363637,1203.4941176470588,0.0,0.0,0.0

outputs/EID_NEW_WS.csv

@@ -1 +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
+1435.4651162790697,1425.843023255814,1429.9883720930231,1429.0697674418604,1435.5755813953488,1427.6744186046512,1431.9011627906978,1434.0755813953488,1433.7093023255813,1433.7209302325582,1432.9941860465117,1438.953488372093,1427.6744186046512,1436.551724137931,1431.7241379310344,0.0

theta_plot.py

@@ -5,27 +5,27 @@ from matplotlib import markers
 blue = '#0984e3'
 red = '#d63031'
 
-MAX_ROUND = 28
+MAX_ROUND = 16 # = game_end_at + 1
 x = np.arange(1, MAX_ROUND+1)
 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"BA")
+ax.plot(x[:-3], tc[:-3], 'o--', color='limegreen', linewidth=2, label=r"BA")
 ax.plot(x[:-1], ts[:-1], 'o-', color='darkorange', linewidth=2, label=r"WS")
 
 # ax.set_ylim(-1500, 500)
 # ax.set_yticks(sp.linspace(-1500, 500, 6))
 ax.tick_params(labelsize=14)
 ax.set_xlim(1, MAX_ROUND)
-ax.set_xticks(sp.linspace(1,MAX_ROUND,8, dtype=int))
+ax.set_xticks(sp.linspace(1,MAX_ROUND-1,8, 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)
 
 plt.legend(numpoints=2, fontsize=14)
 plt.tight_layout()
-# plt.show()
+plt.show()
 # plt.savefig("graph/theta_plot.eps")
-plt.savefig("graph/theta_plot_new.pdf")
+# plt.savefig("graph/theta_plot_new.pdf")

theta_r.py

@@ -22,7 +22,7 @@ import numpy as np
 from island.match import Match
 from island.matches import Matches
 
-MAX_ROUND = 28
+MAX_ROUND = 16 # game_end_at
 
 class theta_r_i:
     def __init__(self):
@@ -34,8 +34,8 @@ class theta_r_i:
         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-2022-1', network_type='BA'), name='NEW_BA'),
-            dict(season=Matches('wos-data-2022', network_type='WS'), name='NEW_WS')
+            dict(season=Matches('wos-data-2022-1', network_type='WS'), name='NEW_WS')
         ]
         # self.seasonSurvive = Matches.from_profile('SURVIVE')
         # self.seasonClassic = Matches.from_profile('CLASSIC')
@@ -82,7 +82,7 @@ class theta_r_i:
         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))))
+            e = max(0, min(1440, self.getNeighborTR(m, r, p, self.getNeighborhood(m, r, p))))
             ans[p] = e
             sigma += e
         return (sigma, ans)