import json from matplotlib import pyplot as plt from island.match import Match from island.matches import Matches import numpy as np from scipy.stats import pearsonr matches = Matches('wos-data-new') k = np.arange(2, 11) succ = np.zeros(9) total = np.zeros(9) survivals = {} with open('survivals.json', 'r') as f: survivals = json.load(f) neighbors = {} 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 m_i in range(len(matches.data)): m = matches.data[m_i] info = m.query('game', 'created').select('info').first()['info'] conf = json.loads(info['config']) game_end_at = int(info['game_end_at']) for p in m.query('player', 'join').raw_data: pid = p['pid'] for i in range(2, game_end_at): neighborhood = [] if pid not in neighbors[matches.names[m_i]]: break for j in neighbors[matches.names[m_i]][pid]: if j in survivals[matches.names[m_i]][str(i-1)]: neighborhood.append(j) if len(neighborhood) < 2: break previous_round_partner = [] for r in m.query('action', 'done').where(lambda x: x['rno']==i-1 and (x['a']==pid or x['b']==pid)).raw_data: if r['a'] == pid: previous_round_partner.append(r['b']) else: previous_round_partner.append(r['a']) new_partner_request = 0 for r in m.query('action', 'request').where(lambda x: x['rno']==i and (x['from']==pid or x['to']==pid)).raw_data: if r['from'] == pid: if r['to'] not in previous_round_partner: new_partner_request += 1 else: if r['from'] not in previous_round_partner: new_partner_request += 1 if new_partner_request == 0: continue new_partner_succ = 0 for r in m.query('action', 'done').where(lambda x: x['rno']==i and (x['a']==pid or x['b']==pid)).raw_data: if r['a'] == pid: if r['b'] not in previous_round_partner: new_partner_succ += 1 else: if r['a'] not in previous_round_partner: new_partner_succ += 1 succ[len(neighborhood)-2] += new_partner_succ total[len(neighborhood)-2] += new_partner_request red = '#d63031' fig = plt.figure(figsize=(6.4, 4)) ax = fig.gca() bar_width = 0.35 opacity = 1 error_config = {'ecolor': '0.3', 'capsize': 4} rects1 = ax.bar(k, succ, bar_width, alpha=opacity, color='#00b894', # yerr=c_req_suc_std, error_kw=error_config, label='Success') rects3 = ax.bar(k + bar_width, total, bar_width, alpha=opacity, color='#fdcb6e', # yerr=d_req_suc_mean, error_kw=error_config, label='Requests') ax.set_xlabel('k') ax.set_ylabel('Count') # ax.set_title('Scores by group and gender') ax.set_xticks(k + bar_width / 2) ax.set_xticklabels(k) ax.legend() ax2 = ax.twinx() ax2.plot(k, succ/total,linewidth=2,color=red, ls='--') ax2.set_ylabel("Frequency of new partners", family='sans-serif', color=red) ax2.tick_params(axis='y', labelcolor=red) ax2.set_ylim(0,1) fig.tight_layout() # plt.show() # plt.savefig('graph/k_and_new_partner.eps') print("[succ vs k]pearson: %f, p-value: %f" % pearsonr(succ, k)) print("[total vs k]pearson: %f, p-value: %f" % pearsonr(total, k)) print("[rate vs k]pearson: %f, p-value: %f" % pearsonr(succ/total, k)) print(np.average(succ/total))