def main_menu():
set_name()
check_if_name_in_data_and_replace()
print(
f"Hello {new_player.name}\nYour starting balance is {new_player.balance}.\nGood Luck!\n"
)
game_picked_by_player = game_pick()
playing_deck = Deck()
while game_picked_by_player != 9:
while game_picked_by_player in [1, 2, 3, 4, 7, 8]:
if game_picked_by_player == 1:
run_war()
game_picked_by_player = game_pick()
if game_picked_by_player == 2:
roulette()
game_picked_by_player = game_pick()
if game_picked_by_player == 3:
play_bj()
game_picked_by_player = game_pick()
if game_picked_by_player == 4:
run_tictactoe_game()
game_picked_by_player = game_pick()
if game_picked_by_player == 7:
show_high_score()
game_picked_by_player = game_pick()
if game_picked_by_player == 8:
saving_loading_menu()
game_picked_by_player = game_pick()
else:
if game_picked_by_player != 9:
game_picked_by_player = wrong_input()
else:
if input(
"Would you like to save your progress? y or n") == 'y':
save_progress()
break
if game_picked_by_player == 9:
print(
f"Your end balance: {new_player.get_balance()}\nGoodbye {new_player.name}"
)
def __init__(self, x, y, btime, parent=None):
self.btime = btime
if parent == None:
self.size = np.random.uniform(low=5.0, high=50)
self.size_norm = self.size / 50.0
self.size_cap = 50
self.speed = np.random.uniform(low=0.0, high=10.0)
self.speed_norm = self.speed / 10.0
self.attack = np.random.uniform(low=0.0, high=1.0)
self.stamina_cap = np.random.uniform(low=0.0, high=1.0)
self.aggressiveness = np.random.uniform(low=0.0, high=1.0)
self.metabolism = np.random.uniform(low=0.0, high=1.0)
self.sight = np.random.uniform(low=0.0, high=200.0)
self.sight_norm = self.sight / 200.0
self.grate = np.random.uniform(low=0.0, high=1.0)
self.hunger = np.random.uniform(low=0.0, high=1.0)
self.brate = np.random.uniform(low=0.0, high=1.0)
self.drate = np.random.uniform(low=0.0, high=1.0)
else:
self.size = parent.size + np.random.normal(0.0, 0.1)
self.size_norm = self.size / 50.0
self.size_cap = 50
self.speed = parent.speed + np.random.normal(0.0, 0.1)
self.speed_norm = self.speed / 10.0
self.attack = parent.attack + np.random.normal(0.0, 0.1)
self.stamina_cap = parent.stamina_cap + np.random.normal(0.0, 0.1)
self.aggressiveness = parent.aggressiveness + np.random.normal(
0.0, 0.1)
self.metabolism = parent.metabolism + np.random.normal(0.0, 0.1)
self.sight = parent.sight + np.random.normal(0.0, 0.1)
self.sight_norm = self.sight / 200.0
self.grate = parent.grate + np.random.normal(0.0, 0.1)
self.hunger = parent.hunger + np.random.normal(0.0, 0.1)
self.brate = parent.brate + np.random.normal(0.0, 0.1)
self.drate = parent.drate + np.random.normal(0.0, 0.1)
self.erate = self.size / 200
self.stamina = self.stamina_cap * 5
self.dtime = int(self.drate / (self.metabolism) * 1000)
self.action_time = int(50 * self.stamina / self.metabolism)
self.fat = 0.5
if self.action_time < 50:
self.action_time = 50
a = t.timing(self.dtime, 0, 0)
self.dtime = self.btime + a
self.counter = 0
#print(self.dtime.eons,self.dtime.days,self.dtime.ticks)
self.normalize()
self.px = x
self.py = y
self.velx = 0
self.vely = 0
self.ax = 0
self.ay = 0
self.wheel = r.roulette(self)
async def on_reaction_add(reaction, user):
global ddb_list
if str(reaction.message.guild.id) != '322379168048349185':
return
channel_name = reaction.message.channel.name
if channel_name not in ['blablabla', 'archive']:
return
if 'ddb' not in str(reaction).lower():
return
if reaction.message.id not in ddb_list['messages']:
ddb_list['messages'][reaction.message.id] = []
if user.name not in [
'tama'
] and user.name in ddb_list['messages'][reaction.message.id]:
return
if user.name in ddb_list['timeouts'] and time.time(
) < ddb_list['timeouts'][user.name]:
return
ddb_list['messages'][reaction.message.id].append(user.name)
ddb_list['timeouts'][user.name] = time.time() + 15
muted_users = load("muted")
if user.name in muted_users:
return
who = roulette.roulette(user.name)
if who is not None:
muted_time = 90
if who is 'Fako':
muted_time = muted_time * 2
if user.name is 'Fako':
muted_time = muted_time * 2
maxTimeout = 0
await reaction.message.channel.send(
'[**Roulette DDB**] {0} ne peut plus poster pendant {1} secondes. ({2})'
.format(who, muted_time, user.name))
if who in muted_users:
maxTimeout = muted_users[who]
muted_users[who] = max(maxTimeout, time.time() + muted_time)
save("muted", muted_users)
ddb_list = {'messages': {}, 'timeouts': {}}
user_input = input(">>> ")
if user_input == 'Dice' or user_input == 'dice':
clear()
dice()
sleep(5)
clear()
elif user_input == 'Slots' or user_input == 'slots':
clear()
slots()
sleep(5)
clear()
elif user_input == 'Roulette' or user_input == 'roulette':
clear()
roulette()
sleep(5)
clear()
elif user_input == "21 points":
game21()
sleep(5)
clear()
elif user_input.lower() == "nvuti":
nvuti()
sleep(5)
clear()
elif user_input.lower() == "rgby":
rgby()
sleep(5)
clear()
elif user_input == 'Help' or user_input == 'help':
import roulette
import os
import psutil
import gc
process = psutil.Process(os.getpid())
print(f'pid = {os.getpid()}')
start_rec = str(process.memory_info())
print(dir(roulette))
to_remove = 'tick'
to_update = 'boy'
initial_list = [(to_update, 1), ('hell', 2.5)]
additional_insert = [('fuckton', 2), (to_remove, 1), ('smelly', 1)]
randomizer = roulette.roulette(initial_list)
print(f'---before additional insert of {additional_insert}---')
for val, chance in randomizer:
print(f'{val} has a chance of {chance}')
randomizer.insert_list(additional_insert)
print(
f'---after additional insert of {additional_insert} before removing {to_remove}---'
)
for val, chance in randomizer:
print(f'{val} has a chance of {chance}')
# randomizer.remove(to_remove)
del randomizer[to_remove]
def probes(Targets, nEpochs, procNum, TargetsConc, ProbeConc, interval, Temp,
popSize, nElite, pointmutProb, shiftmutProb, TargetCell):
start_time = time.time()
Probes = np.random.choice(4, (popSize, np.shape(Targets)[1])) + 1
theprobes = np.zeros((0, np.shape(Targets)[1]))
#Target들의 self secondary structure 에너지 계산.
Ghp_target = np.zeros((1, np.shape(Targets)[0]))
for i in range(np.shape(Targets)[0]):
Seq1 = translate(Targets[i, :])
rna_seqs = [Seq1[0]]
G = float(
nupack.mfe(rna_seqs,
ordering=None,
material='rna',
dangles='some',
T=37,
multi=0,
pseudo=False,
sodium=1.0,
magnesium=0.0,
degenerate=False)[0][1])
Ghp_target[0, i] = G
Khpt = Keq.Keq(Ghp_target, Temp)
Khp_target = Khpt.keq()
#Genetic algorithm 시작.
iter = 0
while iter < nEpochs:
#Probe들의 self secondary structure 에너지 계산.
procs = []
pipe_list = []
Ghp_probe = np.zeros((0, 1))
for i in range(procNum):
recv_end, send_end = multiprocessing.Pipe(False)
Probe_part = Probes[int(i * (popSize / procNum)):int((i + 1) *
(popSize /
procNum)), :]
proc = multiprocessing.Process(target=gibbs_single.Gibbs,
args=(Probe_part, send_end))
procs.append(proc)
pipe_list.append(recv_end)
proc.start()
for proc in procs:
proc.join()
for x in pipe_list:
result = np.array(x.recv())
Ghp_probe = np.concatenate((Ghp_probe, result), axis=0)
Khp = Keq.Keq(Ghp_probe, Temp)
Khp_probe = Khp.keq()
#Target과 Probe 사이의 결합 에너지 계산.
procs = []
pipe_list = []
G_TargetProbe = np.zeros((0, np.shape(Targets)[0]))
for i in range(procNum):
recv_end, send_end = multiprocessing.Pipe(False)
Probe_part = Probes[int(i * (popSize / procNum)):int((i + 1) *
(popSize /
procNum)), :]
proc = multiprocessing.Process(target=gibbs_multi.Gibbs,
args=(Probe_part, Targets,
send_end))
procs.append(proc)
pipe_list.append(recv_end)
proc.start()
for proc in procs:
proc.join()
for x in pipe_list:
result = np.array(x.recv())
G_TargetProbe = np.concatenate((G_TargetProbe, result), axis=0)
K = Keq.Keq(G_TargetProbe, Temp)
Keqs = K.keq()
#print(Gibbs_list)
#각 세포들의 hybridization yield 계산.
Cp_list, Ct_list, Ct_hp, Cp_hp = cpt.Conc(Keqs, Khp_target, Khp_probe,
TargetsConc, ProbeConc)
Cpts = np.zeros((np.shape(Probes)[0], np.shape(TargetsConc)[0]))
for cell in range(np.shape(TargetsConc)[0]):
for p in range(np.shape(Probes)[0]):
Cpts[p, cell] = ProbeConc - Cp_list[p, cell] - Cp_hp[p, cell]
#TargetCell의 Cpt 값의 상대 우위 계산.
fitness = np.zeros((np.shape(Probes)[0], 1))
for p in range(np.shape(Probes)[0]):
TargetCpt = Cpts[p, int(TargetCell)]
RestCpt = np.delete(Cpts[p, :], TargetCell).max()
#print Cpts[p, :], np.delete(Cpts[p, :], TargetCell), TargetCpt, RestCpt
fitness[p, 0] = TargetCpt - RestCpt
#print(fitness[p,0], Cpts[p,int(TargetCell)].max(), TargetCpt, RestCpt)
#print('f', fitness)
theprobe = Probes[np.where(fitness == fitness.max())[0], :]
# print(theprobe[0,:])
elites = elite.elitism(nElite, Probes, fitness)
#print(elites)
#print np.shape(elites), np.shape(Probes)
leftovers = roulette.roulette(elites, Probes, fitness)
#print np.shape(Probes), np.shape(leftovers)
newPopulation = crossover.cross(leftovers,
popSize - np.shape(elites)[0])
newPopulation = pointmut.pointmut(newPopulation, pointmutProb)
newPopulation = shiftmut.shiftmut(newPopulation, shiftmutProb)
Probes = np.concatenate((elites, newPopulation), axis=0)
#print(Ghp_probe)
if iter % interval == 0:
print 'Target =', str(TargetCell), '/ Iter =', iter
print "End Time =", time.time() - start_time, 'Fitness =', round(
fitness.max() / ProbeConc * 100, 3), "%"
for t in range(np.shape(Targets)[0]):
Seq1, Seq2 = translate2(theprobe[0], Targets[t, :])
rna_seqs = [Seq1[0], Seq2[0]]
print(
"MFE =", str(t),
nupack.mfe(rna_seqs,
ordering=None,
material='rna',
dangles='some',
T=37,
multi=0,
pseudo=False,
sodium=1.0,
magnesium=0.0,
degenerate=False),
Cpts[np.where(fitness == fitness.max())[0], t][0])
theprobes = np.concatenate((theprobes, [theprobe[0]]), axis=0)
#print(theprobes)
#print(Cpts[np.where(fitness == fitness.max())[0], :])
iter += 1
return theprobes, fitness.max() / ProbeConc * 100