Datasets:
jblitzar
/
github-python-corpus

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# always skip (this is for the user's username, not name!) (username is the @ one)
# Variables related to avoiding users don't need to have a value
banned_users = []
# Same but but in this case applied to the author's name
banned_name_keywords = ["bot", "spotting", "spot", "spotter"]
# Search_rate means how long it'll have to wait to loop again after checking all tweets
search_rate = 2
# How long to wait to pass to next tweet + follow_rate or dm_rate if it has to follow or dm someone
retweet_rate = 36
# How long to wait after messaging someone. Just the diff between its value and retweet_rate's is added
msg_rate = 30
# How long to wait after following someone. 1st time the diff is added, afterwards the entire rate is added to the sleep
follow_rate = 30
# Enable printing in colors
print_in_color = False
# I still want to add more features! So please have patience :D
# <FILESEP>
from multiprocessing import Process, Pipe
import numpy as np
import os
def worker(remote, parent_remote, env_fn_wrapper):
parent_remote.close()
env = env_fn_wrapper.x()
while True:
cmd, data = remote.recv()
if cmd == 'step':
ob, reward, done, info = env.step(data)
# 如果本轮游戏结束，则开启新一轮游戏
if done:
print("游戏结束")
ob, reward, _, info = env.reset()
remote.send((ob, reward, done, info))
elif cmd == 'reset':
ob, reward, done, info = env.reset()
remote.send((ob, reward, done, info))
elif cmd == 'close':
remote.close()
break
elif cmd == 'net':
remote.send(env.net)
elif cmd == 'exist_rw_allocation':
is_exist, route_index, wave_index = env.net.exist_rw_allocation(data)
remote.send((is_exist, route_index, wave_index))
elif cmd == 'k_shortest_paths':
paths = env.k_shortest_paths(data[0], data[1])
remote.send(paths)
else:
raise NotImplementedError
class SubprocEnv(object):
"""
RWA游戏的多进程实现，用于A2C，替代replay memory的作用。
"""
def __init__(self, envs: list):
"""
多进程启动RWA游戏
:param envs: list of envs
"""
self.waiting = False
self.closed = False
nenvs = len(envs)
self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
self.processes = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, envs)]
for p in self.processes:
p.daemon = True # 如果主进程挂掉，保证子进程不受影响。但这是为啥捏？
p.start() # 开启子进程
for remote in self.work_remotes:
remote.close()
def step_async(self, actions):
"""
异步在多进程中执行行为，无返回值。返回值在step_wait函数中得到
:param actions:
"""
for remote, action in zip(self.remotes, actions):
remote.send(('step', action))
self.waiting = True
def step_wait(self):
"""
异步在多进程中或许执行结果，一定要在step_async后调用
:return:
"""
results = [remote.recv() for remote in self.remotes]
self.waiting = False
obs, rews, dones, infos = zip(*results)
return np.stack(obs), np.stack(rews), np.stack(dones), np.stack(infos)
def reset(self):
for remote in self.remotes:
remote.send(('reset', None))
results = [remote.recv() for remote in self.remotes]
obs, rews, dones, infos = zip(*results)