diff --git a/fail_reason.py b/fail_reason.py
index 6688687..8a579bc 100644
--- a/fail_reason.py
+++ b/fail_reason.py
@@ -52,7 +52,7 @@ percents[0] /= _all
 percents[1] /= _all
 
 
-labels = ['Insufficient Time Resource', 'Sufficient Time Resource']
+labels = ['Insufficient Temporal Resource', 'Sufficient Temporal Resource']
 
 plt.figure()
 plt.pie(percents, labels=labels, autopct="%1.2f%%", pctdistance=1.1, labeldistance=2,startangle=90, colors=['#00b894', '#fdcb6e'])
diff --git a/rewiring_rate.py b/rewiring_rate.py
new file mode 100644
index 0000000..92d4bda
--- /dev/null
+++ b/rewiring_rate.py
@@ -0,0 +1,143 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+import numpy as np
+import scipy as sp
+from scipy.stats import pearsonr
+from matplotlib import markers
+
+def error(f,x,y):
+    return sp.sum((f(x)-y)**2)
+
+if __name__ == '__main__':
+
+    matches = Matches('wos-data-new')
+    max_round = 15
+
+    survivals = {}
+    with open('survivals.json', 'r') as f:
+        survivals = json.load(f)
+
+    neighbors = {}
+    rewires = []
+    x = np.arange(1, max_round)
+    mwRe = {} # Match-wise frequency of rewiring
+    mwTau = {} # Match-wise Tau
+    tau = []
+
+    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):
+        re = []
+        for j in range(len(matches.data)):
+            rewire = 0
+            rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
+            for r in rows:
+                if matches.data[j].query('action', 'done').where(lambda x: x['rno']==i and ((x['a']==r['a'] and x['b']==r['b']) or (x['a']==r['b'] and x['b']==r['a']))).count() == 0:
+                    rewire += 1
+
+            if rows:
+                re.append(float(rewire) / float(len(rows)*2))
+                mwRe["%s-%d"%(j,i)] = re[-1]
+        if re:
+            rewires.append(np.average(re))
+
+
+    for i in range(max_round-1):
+        tp = []
+        for j in range(len(matches.data)):
+            if i == 0:
+                for r in matches.data[j].query('player', 'join').raw_data:
+                    t = 0
+                    k = r['pid']
+                    if k not in neighbors[matches.names[j]]:
+                        print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
+                    else:
+                        t = 1440 * len(neighbors[matches.names[j]][k])
+                    tp.append(t if t < 1440 else 1440)
+                    mwTau["%s-%d"%(j,i)] = tp[-1]
+            else:
+                if str(i) not in survivals[matches.names[j]]:
+                    continue
+                for k in survivals[matches.names[j]][str(i)]:
+                    t = 0
+                    if k not in neighbors[matches.names[j]]:
+                        print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
+                    else:
+                        trs = matches.data[j].get_tr(i, k, neighbors[matches.names[j]][k], survivals[matches.names[j]][str(i)])
+                        for l in neighbors[matches.names[j]][k]:
+                            if l in trs:
+                                t += trs[l]
+                    tp.append(t if t < 1440 else 1440)
+                    mwTau["%s-%d"%(j,i)] = tp[-1]
+
+        if tp:
+            tau.append(np.average(tp))
+        else:
+            tau.append(0)
+
+
+    green = '#00b894'
+    red = '#d63031'
+
+    # p1折线图
+    # fig = plt.figure(figsize=(6.4, 3.6))
+    # ax = fig.gca()
+    # ax.plot(x, rewires, color=green, linewidth=3)
+    # ax.set_ylim(0, 0.5)
+    # ax2 = ax.twinx()
+    # ax2.plot(x, tau, color=red, linewidth=3)
+    # ax2.set_ylim(0,1440)
+    # ax.set_xlim(1,14)
+    # ax.set_xlabel("Rounds")
+    # ax.set_ylabel("Rewiring Rate", color=green)
+    # ax.tick_params(axis='y', labelcolor=green)
+    # ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
+    # ax2.tick_params(axis='y', labelcolor=red)
+
+    # plt.tight_layout()
+    # # plt.show()
+    # plt.savefig('graph/tau_p_rewire_plot.eps')
+
+    # # p2散点图
+    fig = plt.figure(figsize=(6.4, 3.6))
+    ax = fig.gca()
+    # ax.set_ylim(0.5, 1)
+    fp1,residuals,rank,sv,rcond = sp.polyfit(tau, rewires, 1, full=True)
+    print("残差：",residuals)
+    print('Model parameter:',fp1)
+    f1 = sp.poly1d(fp1)
+    print("error= %f" % error(f1, tau, rewires))
+    # fx = sp.linspace(0,max(tau2),1000)
+    fx = sp.linspace(0,1440,2)
+
+    plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
+    plt.scatter(tau, rewires, color=green, linewidths=2, zorder=100)
+    # plt.scatter(tau_r, coopr_r, color='white', edgecolors=green, linewidths=2, zorder=101)
+    ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
+    ax.set_ylabel('Rewiring Rate')
+    ax.set_xlim(0, 1440)
+    ax.set_xticks(sp.linspace(0, 1440, 13))
+    ax.set_ylim(0, 0.6)
+    plt.tight_layout()
+    # plt.show()
+    plt.savefig('graph/tau_p_rewire_sca.eps')
+
+
+
+    # # 皮尔逊相关系数
+    print("pearson: %f, p-value: %f" % pearsonr(tau, rewires))
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