lottery.py 6.0 KB

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  1. import matplotlib.pyplot as plt
  2. from tqdm import tqdm
  3. import time
  4. from datetime import timedelta
  5. from core.darkie import *
  6. from pid.cascade import *
  7. from tqdm import tqdm
  8. import random
  9. class DarkfiTable:
  10. def __init__(self, airdrop, running_time, controller_type=CONTROLLER_TYPE_DISCRETE, kp=0, ki=0, kd=0, dt=1, kc=0, ti=0, td=0, ts=0, debug=False, r_kp=0, r_ki=0, r_kd=0):
  11. self.Sigma=airdrop
  12. self.darkies = []
  13. self.running_time=running_time
  14. self.start_time=None
  15. self.end_time=None
  16. self.secondary_pid = SecondaryDiscretePID(kp=kp, ki=ki, kd=kd) if controller_type==CONTROLLER_TYPE_DISCRETE else SecondaryTakahashiPID(kc=kc, ti=ti, td=td, ts=ts)
  17. print('secondary min/max : {}/{}'.format(self.secondary_pid.clip_min, self.secondary_pid.clip_max))
  18. self.primary_pid = PrimaryDiscretePID(kp=r_kp, ki=r_ki, kd=r_kd) if controller_type==CONTROLLER_TYPE_DISCRETE else PrimaryTakahashiPID(kc=kc, ti=ti, td=td, ts=ts)
  19. print('primary min/max : {}/{}'.format(self.primary_pid.clip_min, self.primary_pid.clip_max))
  20. self.debug=debug
  21. self.rewards = []
  22. self.winners = []
  23. def add_darkie(self, darkie):
  24. self.darkies+=[darkie]
  25. def background(self, rand_running_time=True, debug=False, hp=True):
  26. self.debug=debug
  27. self.start_time=time.time()
  28. feedback=0 # number leads in previous slot
  29. # random running time
  30. rand_running_time = random.randint(1,self.running_time) if rand_running_time else self.running_time
  31. self.running_time = rand_running_time
  32. #if rand_running_time and debug:
  33. #print("random running time: {}".format(self.running_time))
  34. #print('running time: {}'.format(self.running_time))
  35. rt_range = tqdm(np.arange(0,self.running_time, 1))
  36. for count in rt_range:
  37. #while count < self.running_time:
  38. winners=0
  39. f = self.secondary_pid.pid_clipped(float(feedback), debug)
  40. if count%EPOCH_LENGTH == 0:
  41. acc = self.secondary_pid.acc()
  42. #staked_ratio = self.avg_stake_ratio()
  43. reward = self.primary_pid.pid_clipped(acc, debug)
  44. self.rewards += [reward]
  45. #note! thread overhead is 10X slower than sequential node execution!
  46. total_stake = 0
  47. for i in range(len(self.darkies)):
  48. self.darkies[i].set_sigma_feedback(self.Sigma, feedback, f, count, hp)
  49. self.darkies[i].update_vesting()
  50. self.darkies[i].run(hp)
  51. total_stake += self.darkies[i].stake
  52. for i in range(len(self.darkies)):
  53. winners += self.darkies[i].won_hist[-1]
  54. ###
  55. self.winners +=[winners]
  56. feedback = winners
  57. if self.winners[-1]==1:
  58. for i in range(len(self.darkies)):
  59. if self.darkies[i].won_hist[-1]:
  60. if random.random() < SLASHING_RATIO:
  61. self.darkies.remove(self.darkies[i])
  62. print('stakeholder {} slashed'.format(i))
  63. break
  64. self.darkies[i].update_stake(self.rewards[-1])
  65. break
  66. # resolve finalization
  67. self.Sigma += self.rewards[-1]
  68. # resync nodes
  69. merge_length = 0
  70. for i in reversed(self.winners[:-1]):
  71. if i !=1:
  72. merge_length+=1
  73. else:
  74. break
  75. for i in range(merge_length):
  76. resync_slot_id = count-(i+1)
  77. resync_reward_id = int((resync_slot_id)/EPOCH_LENGTH)
  78. resync_reward = self.rewards[resync_reward_id]
  79. # resyncing depends on the random branch chosen,
  80. # it's simulated by choosing first wining node
  81. darkie_winning_idx = 0
  82. random.shuffle(self.darkies)
  83. for darkie_idx in range(len(self.darkies)):
  84. if self.darkies[darkie_idx].won_hist[resync_slot_id]:
  85. darkie_winning_idx = darkie_idx
  86. break
  87. self.darkies[darkie_winning_idx].resync_stake(resync_reward)
  88. self.Sigma += resync_reward
  89. rt_range.set_description('issuance {} DRK, acc: {}, stake = {}%, sr: {}%, reward:{}'.format(round(sum(self.rewards),2), round(acc,2), round(total_stake/self.Sigma*100 if self.Sigma>0 else 0,2), self.avg_stake_ratio()*100, self.rewards[-1]))
  90. #print('[2]stake: {}, sigma: {}, reward: {}'.format(total_stake, self.Sigma, self.rewards[-1]))
  91. assert(round(total_stake,1) <= round(self.Sigma,1))
  92. count+=1
  93. self.end_time=time.time()
  94. avg_reward = sum(self.rewards)/len(self.rewards)
  95. stake_ratio = self.avg_stake_ratio()
  96. avg_apy = self.avg_apy()
  97. avg_apr = self.avg_apr()
  98. #print('apy: {}, staked_ratio: {}'.format(avg_apy, stake_ratio))
  99. return self.secondary_pid.acc_percentage(), avg_apy, avg_reward, stake_ratio, avg_apr
  100. def avg_apy(self):
  101. return Num(sum([darkie.apy_scaled_to_runningtime(self.rewards) for darkie in self.darkies])/len(self.darkies))
  102. def avg_apr(self):
  103. return Num(sum([darkie.apr_scaled_to_runningtime() for darkie in self.darkies])/len(self.darkies))
  104. def avg_stake_ratio(self):
  105. return sum([darkie.staked_tokens_ratio() for darkie in self.darkies])/len(self.darkies)
  106. def write(self):
  107. elapsed=self.end_time-self.start_time
  108. for id, darkie in enumerate(self.darkies):
  109. darkie.write(id)
  110. if self.debug:
  111. print("total time: {}, slot time: {}".format(str(timedelta(seconds=elapsed)), str(timedelta(seconds=elapsed/self.running_time))))
  112. self.secondary_pid.write()
  113. with open('log/rewards.log', 'w+') as f:
  114. buff = ','.join([str(i) for i in self.rewards])
  115. f.write(buff)