127 lines
3.3 KiB
Python
127 lines
3.3 KiB
Python
import json
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from matplotlib import pyplot as plt
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from island.match import Match
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from island.matches import Matches
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from numpy import mean, std
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import numpy as np
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matches = Matches('wos-data-new')
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max_round = 15
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survivals = {}
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with open('survivals.json', 'r') as f:
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survivals = json.load(f)
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neighbors = {}
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cmean = []
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dmean = []
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cstd = []
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dstd = []
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def is_cooperator(rows, pid):
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m = 0
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mall = 0
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for r in rows:
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if pid == r['a'] and r['act_a'] == 'C':
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m += 1
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mall += 1
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elif pid == r['a'] and r['act_a'] == 'D':
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mall += 1
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elif pid == r['b'] and r['act_b'] == 'C':
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m += 1
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mall += 1
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elif pid == r['b'] and r['act_b'] == 'D':
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mall += 1
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return m*2 >= mall
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for i in range(len(matches.data)):
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m = matches.data[i]
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n = {}
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for r in m.query('neighbor', 'create').raw_data:
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if r['a'] in n:
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n[r['a']].append(r['b'])
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else:
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n[r['a']] = [r['b']]
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if r['b'] in n:
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n[r['b']].append(r['a'])
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else:
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n[r['b']] = [r['a']]
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neighbors[matches.names[i]] = n
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for i in range(max_round):
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cneigh = []
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dneigh = []
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for j in range(len(matches.data)):
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rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
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calced = set()
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for row in rows:
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if row['a'] not in calced:
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nn = 0
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for k in neighbors[matches.names[j]][row['a']]:
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if k in survivals[matches.names[j]][str(i+1)]:
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nn += 1
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if is_cooperator(rows, row['a']):
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cneigh.append(nn)
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else:
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dneigh.append(nn)
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calced.add(row['a'])
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if row['b'] not in calced:
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nn = 0
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for k in neighbors[matches.names[j]][row['b']]:
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if k in survivals[matches.names[j]][str(i+1)]:
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nn += 1
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if is_cooperator(rows, row['b']):
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cneigh.append(nn)
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else:
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dneigh.append(nn)
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calced.add(row['b'])
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if cneigh:
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cm = mean(cneigh)
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cs = std(cneigh)
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else:
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cm = 0
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cs = 0
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cmean.append(cm)
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cstd.append(cs)
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if dneigh:
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dm = mean(dneigh)
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ds = std(dneigh)
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else:
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dm = 0
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ds = 0
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dmean.append(dm)
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dstd.append(ds)
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fig = plt.figure(figsize=(6.4, 3.6))
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ax = fig.gca()
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index = np.arange(max_round)
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bar_width = 0.45
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opacity = 1
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error_config = dict(ecolor='#2d3436', capsize=0, elinewidth=1)
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rects1 = ax.bar(index, cmean, bar_width,
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alpha=opacity, color='#0984e3',
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yerr=cstd, error_kw=error_config,
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label='Cooperator')
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rects2 = ax.bar(index + bar_width, dmean, bar_width,
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alpha=opacity, color='#d63031',
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yerr=dstd, error_kw=error_config,
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label='Defector')
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ax.set_xlabel('Round')
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ax.set_ylabel('Size of the Neighborhood')
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# ax.set_title('Scores by group and gender')
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ax.set_xticks(index + bar_width / 2)
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ax.set_xticklabels(index+1)
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ax.legend()
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fig.tight_layout()
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# plt.show()
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plt.savefig('graph/neigh_per_round.eps') |