76 lines
2.5 KiB
Python
76 lines
2.5 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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import numpy as np
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from scipy.stats import pearsonr
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class Solution:
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def __init__(self, mode):
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self.mode = mode
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self.matches = Matches.from_profile_expr(lambda r: mode in r)
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def food_loss(self):
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fl = [[],[]]
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payoff = {
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'C': {
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'C': 2.6,
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'D': 0
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},
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'D': {
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'C': 4.5,
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'D': 0.5
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}
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}
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for m in self.matches.data:
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info = m.query('game', 'created').select('info').first()['info']
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game_end_at = int(info['game_end_at'])
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for p in m.query('player', 'join').raw_data:
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pid = p['pid']
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for i in range(2, game_end_at):
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c, t = 0, 0
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for r in m.query('action', 'done').where(lambda x: x['rno'] == i - 1 and (x['a'] == pid or x['b'] == pid)).raw_data:
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if r['act_a' if r['a'] == pid else 'act_b'] == 'C':
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c += 1
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t += 1
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is_coop = 0 if c * 2 >= t else 1
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f = -3.0
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for r in m.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == pid or x['b'] == pid)).raw_data:
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if r['a'] == pid:
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f += payoff[r['act_a']][r['act_b']] * r['tr'] / 1440.0
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else:
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f += payoff[r['act_b']][r['act_a']] * r['tr'] / 1440.0
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fl[is_coop].append(f)
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return fl
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if __name__ == '__main__':
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mode = 'SURVIVE'
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s = Solution(mode)
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f = s.food_loss()
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blue = '#0984e3'
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red = '#d63031'
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c = [blue, red]
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labels = ['C', 'D']
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fig = plt.figure(figsize=(3, 3))
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ax = fig.gca()
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error_config = {'ecolor': '0.3', 'capsize': 4}
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bplot = ax.boxplot(f,
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vert=True, # vertical box alignment
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patch_artist=True, # fill with color
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notch=True, # notch shape
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labels=labels) # will be used to label x-ticks
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for patch, color in zip(bplot['boxes'], c):
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patch.set_facecolor(color)
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ax.set_ylabel('Frequency')
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# ax.set_title('Scores by group and gender')
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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/new_partner_bar_%s.eps" % mode)
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