migrate scripts on macOS

calc_survivals.py

@@ -0,0 +1,34 @@
+import json
+from pathlib import Path
+from island.match import Match
+
+result = {}
+
+for file in Path('wos-data-new').iterdir():
+    p = Path(file)
+    if p.suffix == '.json':
+        name = p.stem
+        m = Match.read_from_json(str(file))
+        info = m.query('game', 'created').select('info').first()['info']
+        conf = json.loads(info['config'])
+        game_end_at = int(info['game_end_at'])
+        survivals = {}
+        foods = {}
+        survivals[0] = []
+        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:
+                foods[a['a']] += conf['payoffs']["%s%s"%(a['act_a'], a['act_b'])][0] * a['tr'] / 1440.0
+                foods[a['b']] += conf['payoffs']["%s%s"%(a['act_a'], a['act_b'])][1] * a['tr'] / 1440.0
+            for j in foods.keys():
+                foods[j] -= conf['rounds']['consumption']
+                if foods[j] > 0:
+                    survivals[i].append(j)
+        
+        result[name] = survivals
+
+print(json.dumps(result))

comp_co_per_match.py

@@ -0,0 +1,52 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+import numpy as np
+
+def calc_co(matches):
+    x = [0,0,0,0,0,0]
+    for j in range(len(matches.data)):
+        if len(matches.data[j].query('action', 'done').raw_data) < 5:
+            continue
+        coop = 0
+        rows = matches.data[j].query('action', 'done').raw_data
+        for row in rows:
+            if row['act_a'] == 'C' and row['act_b'] == 'C':
+                coop += 1
+
+        if rows:
+            per = float(coop) / len(rows)
+            x[int(per*100)//20] += 1
+    x[4] += x[5]
+    x.pop()
+    s = sum(x)
+    for i in range(5):
+        x[i] /= s
+    return x
+
+casual = Matches('wos-data-casual')
+compete = Matches('wos-data-new')
+
+ca = calc_co(casual)
+co = calc_co(compete)
+
+fig = plt.figure(figsize=(5,4))
+ax = fig.gca()
+index = np.arange(5)
+bar_width = 0.35
+rects1 = ax.bar(index, ca, bar_width, color='#00b894', label='Casual games')
+# rects2 = ax.bar(index + bar_width, co, bar_width, color='#005CAF', label='Competition')
+rects2 = ax.bar(index + bar_width, co, bar_width, color='#6c5ce7', label='Competition')
+
+ax.set_xticks(index + bar_width / 2)
+ax.set_xticklabels(['0~0.2','0.2~0.4','0.4~0.6','0.6~0.8','0.8~1'])
+ax.legend()
+# fig.autofmt_xdate()
+fig.set_size_inches(5, 4)
+plt.xlabel('Frequency of Cooperation Actions per Match')
+plt.ylabel('Fraction of Matches')
+fig.tight_layout()
+
+# plt.show()
+plt.savefig('graph/comp_co_per_match.eps')

fail_reason.py

@@ -0,0 +1,58 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+
+matches = Matches('wos-data-casual')
+
+labels = ['not enough tr', 'other reason']
+percents = [0.0, 0.0]
+
+op = 'D'
+
+def get_reason(m, i, target):
+    r = m.query('action', 'request').where(lambda x: x['rno'] == i+1 and x['from'] == target).raw_data
+    rs = m.query('action', 'approve').where(lambda x: x['rno'] == i+1 and x['to'] == target).raw_data
+    rsn = [n['from'] for n in rs]
+    for j in r:
+        if j['to'] not in rsn:
+            tr = 1440
+            for k in m.query('action', 'request').where(lambda x: x['rno'] == i+1 and x['from'] == j['to']).raw_data:
+                tr -= k['tr']
+            for k in m.query('action', 'cancel').where(lambda x: x['rno'] == i+1 and x['from'] == j['to']).raw_data:
+                tr += m.query('action', 'request').where(lambda x: x['rno'] == i+1 and x['from'] == j['to'] and x['to'] == k['to'] and x['log_id'] < k['log_id']).orderby('log_id').raw_data[-1]['tr']
+            for k in m.query('action', 'deny').where(lambda x: x['rno'] == i+1 and x['to'] == j['to']).raw_data:
+                tr += m.query('action', 'request').where(lambda x: x['rno'] == i+1 and x['from'] == j['to'] and x['to'] == k['from'] and x['log_id'] < k['log_id']).orderby('log_id').raw_data[-1]['tr']
+            for k in m.query('action', 'approve').where(lambda x: x['rno'] == i+1 and x['from'] == j['to']).raw_data:
+                tr -= k['tr']
+            print(tr)
+            if tr >= j['tr']:
+                percents[1] += 1
+            else:
+                percents[0] += 1
+
+
+for m in matches.data:
+    info = m.query('game', 'created').select('info').first()['info']
+    conf = json.loads(info['config'])
+    game_end_at = int(info['game_end_at'])
+
+    for i in range(1, game_end_at):
+        calced = set()
+        for row in m.query('action', 'done').where(lambda x: (x['act_a'] == op or x['act_b'] == op) and x['rno'] == i).raw_data:
+            if row['act_a'] == op and row['a'] not in calced:
+                get_reason(m, i, row['a'])
+                calced.add(row['a'])
+            if row['act_b'] == op and row['b'] not in calced:
+                get_reason(m, i, row['b'])
+                calced.add(row['b'])
+
+_all = sum(percents) / 100
+percents[0] /= _all
+percents[1] /= _all
+
+plt.figure()
+plt.pie(percents, labels=labels, autopct='%1.2f%%', startangle=90)
+plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+plt.show()
+# plt.savefig('graph/unlink_has_neighbor.png')

neighbor_per_round.py

@@ -0,0 +1,110 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+from numpy import mean, std
+import numpy as np
+
+
+matches = Matches('wos-data-new')
+max_round = 17
+
+survivals = {}
+with open('survivals.json', 'r') as f:
+    survivals = json.load(f)
+
+neighbors = {}
+
+cmean = []
+dmean = []
+cstd = []
+dstd = []
+
+
+for i in range(len(matches.data)):
+    m = matches.data[i]
+    n = {}
+    for r in m.query('neighbor', 'create').raw_data:
+        if r['a'] in n:
+            n[r['a']].append(r['b'])
+        else:
+            n[r['a']] = [r['b']]
+
+        if r['b'] in n:
+            n[r['b']].append(r['a'])
+        else:
+            n[r['b']] = [r['a']]    
+    neighbors[matches.names[i]] = n
+
+
+for i in range(max_round):
+    cneigh = []
+    dneigh = []
+    for j in range(len(matches.data)):
+        rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
+        calced = set()
+        for row in rows:
+            if row['a'] not in calced:
+                nn = 0
+                for k in neighbors[matches.names[j]][row['a']]:
+                    if k in survivals[matches.names[j]][str(i+1)]:
+                        nn += 1
+                
+                if row['act_a'] == 'C':
+                    cneigh.append(nn)
+                else:
+                    dneigh.append(nn)
+
+                calced.add(row['a'])
+            if row['b'] not in calced:
+                nn = 0
+                for k in neighbors[matches.names[j]][row['b']]:
+                    if k in survivals[matches.names[j]][str(i+1)]:
+                        nn += 1
+                
+                if row['act_b'] == 'C':
+                    cneigh.append(nn)
+                else:
+                    dneigh.append(nn)
+
+                calced.add(row['b'])
+
+    if cneigh:
+        cm = mean(cneigh)
+        cs = std(cneigh)
+    else:
+        cm = 0
+        cs = 0
+    cmean.append(cm)
+    cstd.append(cs)
+    if dneigh:
+        dm = mean(dneigh)
+        ds = std(dneigh)
+    else:
+        dm = 0
+        ds = 0
+    dmean.append(dm)
+    dstd.append(ds)
+
+
+fig, ax = plt.subplots()
+index = np.arange(17)
+bar_width = 0.35
+opacity = 0.4
+error_config = {'ecolor': '0.3', 'capsize': 4}
+rects1 = ax.bar(index, cmean, bar_width,
+                alpha=opacity, color='b',
+                yerr=cstd, error_kw=error_config,
+                label='C')
+rects2 = ax.bar(index + bar_width, dmean, bar_width,
+                alpha=opacity, color='r',
+                yerr=dstd, error_kw=error_config,
+                label='D')
+# ax.set_xlabel('Group')
+# ax.set_ylabel('Scores')
+# ax.set_title('Scores by group and gender')
+ax.set_xticks(index + bar_width / 2)
+ax.set_xticklabels(index+1)
+ax.legend()
+fig.tight_layout()
+plt.show()

request_success.py

@@ -0,0 +1,121 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+from numpy import mean, std
+import numpy as np
+
+
+matches = Matches('wos-data-casual')
+max_round = 17
+
+c_req_suc_mean = []
+d_req_suc_mean = []
+c_req_suc_std = []
+d_req_suc_std = []
+
+c_req_fail_mean = []
+d_req_fail_mean = []
+c_req_fail_std = []
+d_req_fail_std = []
+
+for i in range(max_round):
+    cReSu = []
+    cReFa = []
+    dReSu = []
+    dReFa = []
+    for j in range(len(matches.data)):
+        rows = matches.data[j].query('action', 'done').where(lambda x: x['rno'] == i+1).raw_data
+        calced = set()
+        for row in rows:
+            if row['a'] not in calced:
+                r = matches.data[j].query('action', 'request').where(lambda x: x['rno'] == i+2 and x['from'] == row['a']).raw_data
+                rs = matches.data[j].query('action', 'approve').where(lambda x: x['rno'] == i+2 and x['to'] == row['a']).raw_data
+                if row['act_a'] == 'C':
+                    cReSu.append(len(rs))
+                    cReFa.append(len(r) - len(rs))
+                else:
+                    dReSu.append(len(rs))
+                    dReFa.append(len(r) - len(rs))
+                calced.add(row['a'])
+
+            if row['b'] not in calced:
+                r = matches.data[j].query('action', 'request').where(lambda x: x['rno'] == i+2 and x['from'] == row['b']).raw_data
+                rs = matches.data[j].query('action', 'approve').where(lambda x: x['rno'] == i+2 and x['to'] == row['b']).raw_data
+                if row['act_b'] == 'C':
+                    cReSu.append(len(rs))
+                    cReFa.append(len(r) - len(rs))
+                else:
+                    dReSu.append(len(rs))
+                    dReFa.append(len(r) - len(rs))
+                calced.add(row['b'])
+
+    if cReSu:
+        cm = mean(cReSu)
+        cs = std(cReSu)
+    else:
+        cm = 0
+        cs = 0
+    c_req_suc_mean.append(cm)
+    c_req_suc_std.append(cs)
+
+    if cReFa:
+        cm = mean(cReFa)
+        cs = std(cReFa)
+    else:
+        cm = 0
+        cs = 0
+    c_req_fail_mean.append(cm)
+    c_req_fail_std.append(cs)
+
+    if dReSu:
+        dm = mean(dReSu)
+        ds = std(dReSu)
+    else:
+        dm = 0
+        ds = 0
+    d_req_suc_mean.append(dm)
+    d_req_suc_std.append(ds)
+
+    if dReFa:
+        dm = mean(dReFa)
+        ds = std(dReFa)
+    else:
+        dm = 0
+        ds = 0
+    d_req_fail_mean.append(dm)
+    d_req_fail_std.append(ds)    
+
+fig, ax = plt.subplots()
+index = np.arange(17)
+bar_width = 0.35
+opacity = 0.6
+error_config = {'ecolor': '0.3', 'capsize': 4}
+rects1 = ax.bar(index, c_req_suc_mean, bar_width,
+                alpha=opacity, color='g',
+                # yerr=c_req_suc_std, error_kw=error_config,
+                label='C-Req-Success')
+rects2 = ax.bar(index, c_req_fail_mean, bar_width,
+                alpha=opacity, color='b',
+                # yerr=c_req_fail_std, error_kw=error_config, 
+                bottom=c_req_suc_mean,
+                label='C-Req-Fail')
+
+rects3 = ax.bar(index + bar_width, d_req_suc_mean, bar_width,
+                alpha=opacity, color='y',
+                # yerr=d_req_suc_mean, error_kw=error_config,
+                label='D-Req-Success')
+
+rects4 = ax.bar(index + bar_width, d_req_fail_mean, bar_width,
+                alpha=opacity, color='r',
+                # yerr=d_req_fail_mean, error_kw=error_config, 
+                bottom=d_req_suc_mean,
+                label='D-Req-Fail')
+# ax.set_xlabel('Group')
+# ax.set_ylabel('Scores')
+# ax.set_title('Scores by group and gender')
+ax.set_xticks(index + bar_width / 2)
+ax.set_xticklabels(index+1)
+ax.legend()
+fig.tight_layout()
+plt.show()