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) def get_tr(m, i, target, nb, sv): """ # 计算tau_p的方式是统计周围邻居剩余的tr # 本函数计算第i轮，所有有消耗tr的player，最后剩余的tr # 由于是计算剩余tr，所以不能计算和target之间的交互 # nb是target的邻居 """ trs = {} req = m.query('action', 'request').where(lambda x: x['rno'] == i+1) app = m.query('action', 'approve').where(lambda x: x['rno'] == i+1).raw_data can = m.query('action', 'cancel').where(lambda x: x['rno'] == i+1).raw_data den = m.query('action', 'deny').where(lambda x: x['rno'] == i+1).raw_data fr = [] for r in m.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == target or x['b'] == target)).raw_data: if r['a'] == target: fr.append(r['b']) elif r['b'] == target: fr.append(r['a']) truenb = [] for r in nb: if r not in fr and r in sv: truenb.append(r) trs[r] = 1440 for r in req.raw_data: if r['from'] in truenb and r['to'] != target : if r['from'] not in trs: trs[r['from']] = 1440 trs[r['from']] -= r['tr'] for r in app: if r['from'] in truenb and r['to'] != target: if r['from'] not in trs: trs[r['from']] = 1440 trs[r['from']] -= r['tr'] for r in can: if r['from'] in trs and r['to'] != target: trs[r['from']] += req.where(lambda x: x['from'] == r['from'] and x['to'] == r['to'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr'] for r in den: if r['to'] in trs and r['from'] != target: trs[r['to']] += req.where(lambda x: x['from'] == r['to'] and x['to'] == r['from'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr'] return trs matches = Matches('wos-data-new') max_round = 15 survivals = {} with open('survivals.json', 'r') as f: survivals = json.load(f) neighbors = {} coopr = [] x = np.arange(1, max_round) mwCo = {} # Match-wise frequency of cooperation mwTau = {} # Match-wise Tau bx = [] 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): co = [] for j in range(len(matches.data)): coop = 0 rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data for row in rows: if row['act_a'] == 'C': coop += 1 if row['act_b'] == 'C': coop += 1 if rows: co.append(float(coop) / float(len(rows)*2)) mwCo["%s-%d"%(j,i)] = co[-1] bx.append(co) if co: coopr.append(np.average(co)) 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 = get_tr(matches.data[j], 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) blue = '#0984e3' red = '#d63031' # p1折线图 # fig = plt.figure(figsize=(6.4, 3.6)) # ax = fig.gca() # ax.plot(x, coopr, color=blue, linewidth=3) # ax.set_ylim(0.5, 1) # 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("Frequency of Cooperation", color=blue) # ax.tick_params(axis='y', labelcolor=blue) # 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_co_plot.eps') tau2 = [] coopr2 = [] tau_r = [] coopr_r = [] for i in range(len(tau)): if tau[i] <= 720: tau2.append(tau[i]) coopr2.append(coopr[i]) else: tau_r.append(tau[i]) coopr_r.append(coopr[i]) # 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(tau2, coopr2, 1, full=True) print("残差：",residuals) print('Model parameter:',fp1) f1 = sp.poly1d(fp1) print("error= %f" % error(f1, tau2, coopr2)) # fx = sp.linspace(0,max(tau2),1000) fx = sp.linspace(0,720,2) plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0) plt.scatter(tau2, coopr2, color=blue, linewidths=2, zorder=100) plt.scatter(tau_r, coopr_r, color='white', edgecolors=blue, linewidths=2, zorder=101) ax.set_xlabel('$\\tau_{p}$', family='sans-serif') ax.set_ylabel('Frequency of Cooperation') ax.set_xlim(0, 1440) ax.set_xticks(sp.linspace(0, 1440, 13)) ax.set_ylim(0.5, 1) plt.tight_layout() plt.show() # plt.savefig('graph/tau_p_co_sca.eps') # 皮尔逊相关系数 print("pearson: %f, p-value: %f" % pearsonr(tau2, coopr2))