def __init__(self, sim_param=SimParam(), no_seed=False):
"""
Initialize the Simulation object.
:param sim_param: is an optional SimParam object for parameter pre-configuration
:param no_seed: is an optional parameter. If it is set to True, the RNG should be initialized without a
a specific seed.
"""
self.sim_param = sim_param
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
# TODO Task 2.4.3: Uncomment the line below
self.counter_collection = CounterCollection(self)
# TODO Task 3.1.2: Uncomment the line below and replace the "None"
if no_seed:
#if the mean = 1.0, then 1/lambda_ = 1.0 -> lambda_ = 1
self.rng = RNG(ExponentialRNS(1.0),
ExponentialRNS(1. / float(self.sim_param.RHO)))
else:
self.rng = RNG(
ExponentialRNS(1.0, self.sim_param.SEED_IAT),
ExponentialRNS(1. / float(self.sim_param.RHO),
self.sim_param.SEED_ST))
def initialize_slices(sim_param, log_file):
slices = []
# SLA requirements list [RR, MCQI PF]
delay_thresholds = sim_param.delay_requirements# [50, 50, 50] # ms
rate_thresholds = sim_param.rate_requirements# [2000, 2000, 2000]#[2000, 3200, 2000] # kbps float(slc.slice_param.P_SIZE / slc.slice_param.MEAN_IAT)
packet_sizes = sim_param.packet_sizes# [10000, 10000, 10000] # [2000, 10000, 5000] # in bits
mean_iats = sim_param.mean_iats# [5, 5, 5]
# dist_arr = [[10]*10,[10]*10,[10]*10]#[[10, 10, 10, 10, 10, 100, 100, 100, 100, 100],[12]*10,[10, 10, 10, 10, 10, 100, 100, 100, 100, 100]]
seed_dist = sim_param.SEED_OFFSET # users in all slices have identical distance distributions
# rng_dist = RNG(ExponentialRNS(lambda_x=1. / float(sim_param.MEAN_Dist)), s_type='dist') # , the_seed=seed_dist
rng_dist = RNG (UniformRNS (sim_param.DIST_MIN, sim_param.DIST_MAX, the_seed=seed_dist), s_type='dist')
tmp_user_id=0
for i in range(sim_param.no_of_slices):
slice_param_tmp = SliceParam(sim_param)
slice_param_tmp.SLICE_ID = i
slice_param_tmp.P_SIZE = packet_sizes[i]
slice_param_tmp.MEAN_IAT = mean_iats[i]
# SLA requirements
slice_param_tmp.DELAY_REQ = delay_thresholds[i]
slice_param_tmp.RATE_REQ = rate_thresholds[i]
slices.append(SliceSimulation(slice_param_tmp))
# initialize all users with traffics and distances
tmp_users = []
# dist_arr_tmp = dist_arr[i] # [10, 10, 10, 10, 10, 100, 100, 100, 100, 100] # 10*(1+user_id%no_of_users_per_slice)**2
for j in range(sim_param.no_of_users_list[i]):
# user_id = i * sim_param.no_of_users_per_slice + j
tmp_users.append(User(tmp_user_id, rng_dist.get_dist(), slice_list=[slices[i]], sim_param=sim_param))
# tmp_users.append(User(user_id, dist_arr_tmp[j], slice_list=[slices[i]], sim_param=sim_param))
tmp_user_id+=1
# insert users to slice
slices[i].insert_users(tmp_users)
# Choose Slice Manager Algorithm
slices[i].slice_param.SM_ALGO = sim_param.SM_ALGO_list[i]
# # Choose Slice Manager Algorithm 'PF': prop fair, 'MCQI': Max Channel Quality Index, 'RR': round-robin
# slices[0].slice_param.SM_ALGO = 'RR'
# slices[1].slice_param.SM_ALGO = 'MCQI'
# slices[2].slice_param.SM_ALGO = 'PF'
if log_file is None:
pass
else:
# log Slice Parameters
for i in range(sim_param.no_of_slices):
attrs = vars(slices[i].slice_param)
log_file.write('\nSliceParam\n' + ''.join("%s: %s\n" % item for item in attrs.items()))
log_file.close()
return slices
def gen_sample_array(self):
rng = RNG()
s = StratifiedShuffleSplit(n_splits=self.n_splits, test_size=0.5)
X = rng.randn(self.class_vector.shape[0], 2)
y = self.class_vector
s.get_n_splits(X, y)
train_index, test_index = next(s.split(X, y))
return np.hstack([train_index, test_index])
def __init__(self, sim_param):
"""
"""
self.sim_param = sim_param
self.slices_cycle = []
self.avg_rate_pf = []
self.df = pd.DataFrame()
# random allocation
self.rng_rb_allocate = RNG(UniformIntRNS(
0, sim_param.no_of_slices - 1, the_seed=sim_param.SEED_OFFSET),
s_type='rb_allocation')
def get_shadowing(self):
t_final = self.user.sim_param.T_FINAL * 10
shadowing = np.empty((0, t_final))
seed_shadowing = self.user.user_id * len(self.RB_pool)
for i in range(len(self.RB_pool)):
self.rng_shadowing = RNG(NormalRNS(
0, self.user.sim_param.SIGMA_shadowing, seed_shadowing),
s_type='shadowing')
temp_arr = self.rng_shadowing.get_shadowing(t_final)
shadowing = np.append(shadowing, [temp_arr], axis=0)
if self.user.sim_param.freq_selective:
seed_shadowing += 1
return shadowing
def get_shadowing(self):
t_final = self.user.sim_param.T_FINAL
t_c = self.user.sim_param.T_C
t_coh = self.user.sim_param.T_COH # in ms
buffer = 3 # for the packets in simulation final
shadowing = np.empty((0, int(t_final) + t_coh * (buffer -1) ))
seed_shadowing = (self.user.user_id * len(self.RB_pool)) + self.user.sim_param.SEED_OFFSET
for i in range(len(self.RB_pool)):
self.rng_shadowing = RNG(NormalRNS(0, self.user.sim_param.SIGMA_shadowing, seed_shadowing), s_type='shadowing')
temp_shadowing = self.rng_shadowing.get_shadowing2(t_final, t_c, t_coh, buffer)
shadowing = np.append(shadowing, [temp_shadowing], axis=0)
if self.user.sim_param.freq_selective:
seed_shadowing += 1
return shadowing
def get_rng(self):
seed_vector = []
m = hashlib.md5()
for key in sorted(self.keys):
m.update(bytes(self.keys[key], encoding="utf-8"))
seed_vector.append(int(m.hexdigest(), 16))
rng = RNG(*seed_vector)
return rng
def reset(self, no_seed=False):
"""
Reset the Simulation object.
:param no_seed: is an optional parameter. If it is set to True, the RNG should be reset without a
a specific seed.
"""
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
# TODO Task 2.4.3: Uncomment the line below
self.counter_collection = CounterCollection(self)
# TODO Task 3.1.2: Uncomment the line below and replace the "None"
if no_seed:
self.rng = RNG(ExponentialRNS(1.0), ExponentialRNS(1./float(self.sim_param.RHO)))
else:
self.rng = RNG(ExponentialRNS(1.0, self.sim_param.SEED_IAT), ExponentialRNS(1./float(self.sim_param.RHO),self.sim_param.SEED_ST))
def reset(self, no_seed=False):
"""
Reset the Simulation object.
:param no_seed: is an optional parameter. If it is set to True, the RNG should be reset without a
a specific seed.
"""
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
self.counter_collection = CounterCollection(self)
if no_seed:
self.rng = RNG(ExponentialRNS(1),
ExponentialRNS(1. / float(self.sim_param.RHO)))
else:
self.rng = RNG(
ExponentialRNS(1, self.sim_param.SEED_IAT),
ExponentialRNS(1. / float(self.sim_param.RHO),
self.sim_param.SEED_ST))
def __init__(self, sim_param=SimParam(), no_seed=False):
"""
Initialize the Simulation object.
:param sim_param: is an optional SimParam object for parameter pre-configuration
:param no_seed: is an optional parameter. If it is set to True, the RNG should be initialized without a
a specific seed.
"""
self.sim_param = sim_param
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
self.counter_collection = CounterCollection(self)
if no_seed:
self.rng = RNG(ExponentialRNS(1),
ExponentialRNS(1. / float(self.sim_param.RHO)))
else:
self.rng = RNG(
ExponentialRNS(1, self.sim_param.SEED_IAT),
ExponentialRNS(1. / float(self.sim_param.RHO),
self.sim_param.SEED_ST))
def poisson_arrivals(self, slicesim):
"""
"""
iat = slicesim.slice_param.MEAN_IAT
self.rng = RNG(ExponentialRNS(1. / float(iat), self.seed_iat),
s_type='iat')
#iat = self.sim_param.MEAN_IAT
t_start = slicesim.sim_state.now
t_end = self.sim_param.T_FINAL
# Poisson
t_arr = []
iat_arr = []
t_temp = t_start + self.rng.get_iat()
while t_temp < t_end:
t_arr.append(t_temp)
t_temp += self.rng.get_iat()
if len(t_arr) > 2:
iat_arr.append(t_arr[-1] - t_arr[-2])
# # Fixed IAT
# t_arr = np.arange(t_start, t_end, iat)
# # Plot histogram of interarrivals for poisson dist
# count, bins, ignored = plt.hist(iat_arr, 30, normed=True)
# lmda = 1/iat
# plt.plot(bins, lmda * np.exp(- lmda*bins), linewidth=2, color='r')
# plt.show()
arrivals = []
for i in t_arr:
arrivals.append(PacketArrival(slicesim, i))
return arrivals
def __init__(self, game, seed):
logger.info('Making dungeon')
self.game = game
self.rng = RNG(seed=seed)
self.levels = []
for i in range(NUM_LEVELS):
self.levels.append(self.rng.get_int(0,1000000))
self.rng.delete()
self.tcod_map = libtcod.map_new(LEVEL_W,LEVEL_H)
#does the fov need to be refreshed (ie new level, player moved...)
self.refresh_fov = True
#does the tcod map need to be refreshed (ie terrain change)
self.refresh_tcod = True
#positions of entities that block movement or sight
self.creature_positions = []
def main():
r = remote('eof.ais3.org', 39091)
r.sendlineafter('> ', '0')
r.sendlineafter('> ', '0')
r.recvuntil('Lucky Number: ')
_R000 = int(r.recvline().strip())
r.sendlineafter('> ', '226')
r.sendlineafter('> ', '0')
r.recvuntil('Lucky Number: ')
_R227 = int(r.recvline().strip())
S000 = undo_php_mt_rand(_R000)
S227 = undo_php_mt_rand(_R227)
seed = undo_php_mt_reload(S000, S227, 0, 1)
if seed:
print seed
else:
exit(-1)
rng = RNG()
rng.srand(seed)
if rng.rand() != _R000:
exit(-1)
for i in range(226):
rng.rand()
if rng.rand() != _R227:
exit(-1)
r.sendlineafter('> ', '0')
r.sendlineafter('> ', str(rng.rand()))
print r.recvall()
r.close()
class ShamirTest(unittest.TestCase):
rng = RNG()
debug = 0
def _testShamirRecover(self, minimum, shares, prime, secret, keys):
if self.debug >= 100:
print(
"testShamirRecover(minimum=%d,shares=%d,prime=%d,secret=%s,keys=%s)"
% (minimum, shares, prime, secret, keys))
shamir = Shamir(minimum, prime)
choices = list(range(shares))
for k in range(minimum):
i = self.rng.next(len(choices))
shamir.setKey(choices[i] + 1, keys[choices[i]])
del choices[i]
assert shamir.getSecret() == secret
for i in range(shares):
assert shamir.getKey(i + 1) == keys[i]
def _testShamir(self, minimum, shares, prime, secret):
if self.debug >= 10:
print("testShamir(minimum=%d,shares=%d,prime=%d,secret=%d)" %
(minimum, shares, prime, secret))
shamir = Shamir(minimum, prime)
shamir.setSecret(secret)
shamir.randomizeKeys(shares, self.rng)
keys = [shamir.getKey(i) for i in range(1, shares + 1)]
for t in range(100):
self._testShamirRecover(minimum, shares, prime, secret, keys)
def testShamir(self):
if self.debug >= 1:
print("testShamir()")
millerrabin = MillerRabin()
for bits in range(32, 256, 4):
prime = millerrabin.prevPrime(2**bits)
for shares in range(1, 9):
for minimum in range(1, shares + 1):
for secret in [
0, 1, 2,
self.rng.next(prime), prime - 2, prime - 1
]:
self._testShamir(minimum, shares, prime, secret)
def plot_cifar10(X,
y,
samples_per_class=7,
title='CIFAR-10 dataset',
title_params=None,
imshow_params=None):
# check params
title_params = title_params or {}
title_params.setdefault('fontsize', 20)
title_params.setdefault('y', 0.95)
imshow_params = imshow_params or {}
imshow_params.setdefault('interpolation', 'none')
num_classes = 10
classes = range(num_classes)
for c in classes:
idxs = np.flatnonzero(y == c)
idxs = RNG(seed=1337).choice(idxs, samples_per_class, replace=False)
for i, idx in enumerate(idxs):
plt_idx = i * num_classes + c + 1
ax = plt.subplot(samples_per_class, num_classes, plt_idx)
ax.spines['bottom'].set_linewidth(2.)
ax.spines['top'].set_linewidth(2.)
ax.spines['left'].set_linewidth(2.)
ax.spines['right'].set_linewidth(2.)
plt.tick_params(axis='both',
which='both',
bottom='off',
top='off',
left='off',
right='off',
labelbottom='off',
labelleft='off',
labelright='off')
plt.imshow(X[idx].astype('uint8'), **imshow_params)
if i == 0:
plt.title(get_cifar10_label(c))
plt.suptitle(title, **title_params)
plt.subplots_adjust(wspace=0, hspace=0)
def roll():
try:
min = int(session.get('min', None))
max = int(session.get('max', None))
nNum = int(session.get('nNum', None))
seenList = session.get('seen', None)
except TypeError as err:
return redirect(url_for('index'))
try:
newNumbers = RNG(min, max, nNum, seenList)
except NegativeNNum as err:
flash('Only positive numbers are allowed')
return redirect(url_for('preRoll'))
except InvalidRange as err:
flash('Please input a valid min-max range')
return redirect(url_for('index'))
session['newNumbers'] = newNumbers
seenList.append(newNumbers)
session['seen'] = seenList
return redirect(url_for('display'))
class ChannelModal(object):
"""
"""
def __init__(self, user):
"""
R
"""
self.user = user
self.RB_pool = user.sim_param.RB_pool
#self.rng = RNG(ExponentialRNS(1. / float(user.sim_param.MEAN_CG), user.sim_param.SEED_CG), s_type='cg')
#self.rng = RNG(UniformRNS(2*float(user.sim_param.MEAN_CG), 0., user.sim_param.SEED_CG), s_type='cg')
#self.rng = RNG(ExponentialRNS(1. / float(user.sim_param.MEAN_CG), user.user_id), s_type='cg')
#self.rng = RNG(UniformRNS(2. , 0., 21), s_type='cg')
# self.channel_gains = (user.user_id%2+0.1)*self.rng.get_cg(len(self.RB_pool), user.sim_param.T_FINAL + user.sim_param.T_C)
#self.channel_gains = np.random.rand(len(self.RB_pool), user.sim_param.T_FINAL+user.sim_param.T_C)
#self.channel_gains = np.ones((len(self.RB_pool), user.sim_param.T_FINAL + user.sim_param.T_C))
# Rayleigh Fading:
# self.rayleigh_envelope = GenerateRayleighEnvelope(self.RB_pool, user.sim_param.T_FINAL, user.sim_param.T_S, fm=10)
# New channel model rate = BW * log2(1+SINR)
# SNR(dB) = Pt-PL-P_noise,
#self.pathloss_dB = self.get_pathloss()
self.noise_per_rb_dBm = self.get_noise_per_rb()
#self.SINR_dB = self.user.sim_param.P_TX_dBm - self.pathloss_dB - self.noise_per_rb_dBm
#seed_shadowing = self.user.user_id # % self.user.sim_param.no_of_users_per_slice
#self.rng_shadowing = RNG(NormalRNS(0, self.user.sim_param.SIGMA_shadowing, seed_shadowing), s_type='shadowing')
#self.shadowing = self.rng_shadowing.get_shadowing(user.sim_param.T_FINAL)
self.shadowing = self.get_shadowing()
def get_shadowing(self):
t_final = self.user.sim_param.T_FINAL * 10
shadowing = np.empty((0, t_final))
seed_shadowing = self.user.user_id * len(self.RB_pool)
for i in range(len(self.RB_pool)):
self.rng_shadowing = RNG(NormalRNS(
0, self.user.sim_param.SIGMA_shadowing, seed_shadowing),
s_type='shadowing')
temp_arr = self.rng_shadowing.get_shadowing(t_final)
shadowing = np.append(shadowing, [temp_arr], axis=0)
if self.user.sim_param.freq_selective:
seed_shadowing += 1
return shadowing
def get_noise_per_rb(self):
"""
returns noise in dB
P_noise_dBm = 10*log10(k_boltzmann * temperature * bandwidth / 1mW)
"""
try:
k_boltzmann = constants.Boltzmann
temperature = self.user.sim_param.TEMPERATURE
rb_bw = self.user.sim_param.RB_BANDWIDTH
P_noise_dBm = 10 * np.log10(
k_boltzmann * temperature * rb_bw / 1e-3)
except:
raise RuntimeError("Noise calculation error")
return
return P_noise_dBm
def get_pathloss(self, rb, time):
"""
returns pathloss in dB
PL= FSPL(f,1m)+10n log_10(d/1m)+ shadowing
FSPL(f,1m)=20 log_10(4πf/c)
shadowing =
"""
try:
f = self.user.sim_param.FREQ
n = self.user.sim_param.PL_Exponent
d = self.user.distance # in meter
c = constants.c
pi = constants.pi
shadowing_dB = self.shadowing[rb, time]
fs_pl_dB = 20 * np.log10(4 * pi * f / c)
pl_dB = fs_pl_dB + 10 * n * np.log10(d) + shadowing_dB
except:
raise RuntimeError("Path loss calculation error")
return
return pl_dB
def get_data_rate(self, rb_arr, time):
"""
add effect of time in deterministic manner!!!!
rate = BW * log2(1 + SINR) in bits per sec
:return: summation of data rate of given user for given resource blocks
"""
try:
rate = 0
rb_bw = self.user.sim_param.RB_BANDWIDTH
if time - int(time) > 0.0001:
raise RuntimeError("Time variable is not integer!!")
if rb_arr == [-1]:
return 0
for rb in rb_arr:
pathloss_dB = self.get_pathloss(rb, time)
SINR_dB = self.user.sim_param.P_TX_dBm - pathloss_dB - self.noise_per_rb_dBm
SINR = np.power(10, SINR_dB / 10)
rate += rb_bw * np.log2(1 + SINR)
#rate += 1000 # for testing each ms 1 bit is served
except:
raise RuntimeError("Data Rate Calculation error")
return rate
def get_load_change(self, rb_arr, time_arr):
"""
:return: data rate * duration of given user for given resource blocks at given time array
data rate is calculated separately for each ms and summed.
load_change in bits
"""
try:
if len(time_arr) == 0:
return 0
load_change = 0
for t in time_arr:
if isinstance(t, float):
raise RuntimeError("time must be integer")
load_change += np.nansum(self.get_data_rate(rb_arr, t) *
1e-3) # rate * 1ms
except:
raise RuntimeError("Error during transmitted load calculation")
return load_change # np.sum(self.channel_gains[rb_arr][time]) # user_id-1 for indexing
def get_serving_duration(self, packet):
"""
Change the status of the packet once the serving process starts.
"""
t_s = self.user.sim_param.T_S
#t_temp = packet.slicesim.sim_state.now # - packet.slicesim.sim_state.t_round_start)
t_temp = packet.t_last_start
t_arr = np.arange(t_temp, t_temp + t_s)
RB_arr = packet.server.RB_list
r_temp = self.get_load_change(RB_arr, t_arr)
try:
while r_temp < packet.remaining_load:
t_temp += t_s
t_arr = np.arange(t_temp, t_temp + t_s)
r_temp += self.get_load_change(RB_arr, t_arr)
except:
raise RuntimeError("Error during get_serving_duration")
r_last = self.get_load_change(RB_arr, t_arr)
t_last = (packet.remaining_load - (r_temp - r_last)) / (r_last / t_s)
t_est = t_temp + t_last
#packet.estimated_duration = t_est - packet.slicesim.sim_state.now
#packet.occupation_duration = t_temp+t_s-packet.slicesim.sim_state.now
packet.t_finish_real = t_est #+packet.slicesim.sim_state.t_round_start
packet.t_finish = t_temp + t_s #+packet.slicesim.sim_state.t_round_start
def update_remaining_load(self, packet):
"""
substract load change since the latest serving from remaining load
return tp = load_change / duration
"""
t_start = int(
round(packet.t_arrival +
(packet.d_wait +
packet.d_served))) # starting time of latest serve
if packet.t_arrival + (packet.d_wait +
packet.d_served) - t_start > 0.0001:
raise RuntimeError("time calculation error in packet")
t_arr = np.arange(t_start, packet.slicesim.sim_state.now)
RB_arr = packet.server.RB_list # function is called before RB_list is changed
r_temp = self.get_load_change(RB_arr, t_arr)
packet.remaining_load -= r_temp
if packet.remaining_load < 0:
raise RuntimeError("Remaining load can't be negative!")
if packet.slicesim.sim_state.now - t_start != 0:
tp = float(r_temp) / float(packet.slicesim.sim_state.now - t_start)
return tp # throughput in kilobits per second
else:
return float(0)
def get_remaining_load(self, packet):
"""
returns the remaining load without changing anything
initially same as update_remaining_load
"""
t_start = int(
round(packet.t_arrival +
(packet.d_wait +
packet.d_served))) # starting time of latest serve
if packet.t_arrival + (packet.d_wait +
packet.d_served) - t_start > 0.0001:
raise RuntimeError("time calculation error in packet")
t_arr = np.arange(t_start, packet.slicesim.sim_state.now)
RB_arr = packet.server.RB_list
r_temp = self.get_load_change(RB_arr, t_arr)
return packet.remaining_load - r_temp
def get_throughput_rt(self, packet):
"""
NOT USED
returns throughput for round termination event without changing anything
"""
t_start = int(
round(packet.t_arrival +
(packet.d_wait +
packet.d_served))) # starting time of latest serve
if packet.t_arrival + (packet.d_wait +
packet.d_served) - t_start > 0.0001:
raise RuntimeError("time calculation error in packet")
t_arr = np.arange(t_start, packet.slicesim.sim_state.now)
RB_arr = packet.server.RB_list
r_temp = self.get_load_change(RB_arr, t_arr)
if packet.slicesim.sim_state.now - t_start != 0:
tp = float(r_temp) / float(packet.slicesim.sim_state.now -
t_start) # total served size over time
return tp #throughput
else:
return float(0)
def get_throughput_sc(self, packet):
"""
Only when service is completed, calculates the latest serve
"""
t_start = int(
round(packet.t_arrival +
(packet.d_wait +
packet.d_served))) # starting time of latest serve
if packet.t_arrival + (packet.d_wait +
packet.d_served) - t_start > 0.0001:
raise RuntimeError("time calculation error in packet")
t_arr = np.arange(t_start, packet.slicesim.sim_state.now)
#RB_arr = packet.server.RB_list # function is called after RB_list is changed
#r_temp = self.get_output_rate(RB_arr, t_arr)
if packet.slicesim.sim_state.now - t_start != 0:
tp = float(packet.remaining_load) / float(
packet.slicesim.sim_state.now -
t_start) # total served size over time
return tp #throughput
else:
return float(0)
def get_throughput_sc2(self, packet):
"""
Only when service is completed, counts just service completions
"""
t_start = packet.t_last_start # int(round(packet.t_arrival + (packet.d_wait + packet.d_served))) # starting time of latest serve
t_arr = np.arange(t_start, packet.slicesim.sim_state.now)
if packet.slicesim.sim_state.now - t_start != 0:
tp = float(
packet.size) / float(packet.slicesim.sim_state.now -
t_start) # total served size over time
return tp #throughput
else:
return float(0)
def get_CQI_list(self, server, RB_mapping_sm):
"""
returns data_rate values for each rb at each timeslot in T_sm
only get the rate at the beginning of new Ts period.
"""
CQI_list = []
t_s = self.user.sim_param.T_S
t_temp = int(server.slicesim.sim_state.now
) # - packet.slicesim.sim_state.t_round_start)
if server.slicesim.sim_state.now - t_temp > 0.0001:
raise RuntimeError("time calculation error in get_CQI_list")
#t_arr = np.arange(t_temp, t_temp + t_s)
# fill RB_matching_sm
for i in range(RB_mapping_sm.shape[1]): # loop over time
RB_arr = []
tmp_arr = []
for j in range(RB_mapping_sm.shape[0]): # loop over RBs
if RB_mapping_sm[j][i]:
#RB_arr.append(j)
#tmp_arr.append(self.get_load_change([j], t_arr))
tmp_arr.append(self.get_data_rate([j], t_temp))
else:
tmp_arr.append(-1.)
#CQI_list.append(self.get_output_rate(RB_arr, t_arr))
CQI_list.append(tmp_arr)
t_temp += t_s # increase time by t_s for the next slots rate
#t_arr = np.arange(t_temp, t_temp + t_s)
return CQI_list # indexing: [time], [RB]
MAXBOSSHP = 50
DEBUG = 'DEBUG' in os.environ
if 'DVORAK' in os.environ:
commands = ",oae.u'" # Mapping from char to action ID
else:
commands = "wsadefq" # Mapping from char to action ID
# Offset of each direction
# Dir: ^ v < >
moveOff = [-16, 16, -1, 1]
# Variable initialization
p = 198543992929796982831856294122484542605661595500963697094465382211596515703899
rng = RNG(p, pow(0xdeadbeef, 31337, p) // 2)
maze = [list(line) for line in maze.split('\n')]
player, monster = rng.extract(8), rng.extract(8)
items = {i: 0 for i in range(256)}
HP, BossHP, score = MAXHP, MAXBOSSHP, 0
history = "" # For debug env
def draw(pos, c):
"""Draw a character on the dungeons map"""
x, y = pos % 16 * 4 + 2, pos // 16 * 2 + 1
maze[y][x] = c
def renderNormal():
"""Render normal stage"""
class PhrasesTest(unittest.TestCase):
rng = RNG()
def mkPhrase(self, language, length):
phrases = Phrases.forLanguage(language)
words = phrases.words
phrase = [words[self.rng.next(len(words))] for i in range(length)]
return phrases.space().join(phrase)
def testAmbiguity(self):
languages = Phrases.getLanguages()
allWords = dict()
for language in languages:
phrases = Phrases.forLanguage(language)
for word in phrases.words:
if not word in allWords:
allWords[word] = dict()
index = phrases.invWords[word]
if not index in allWords[word]:
allWords[word][index] = set()
allWords[word][index].add(language)
ambiguous = False
for word in allWords:
indexs = allWords[word]
if len(indexs) <= 1: continue
ambiguous = True
for index in indexs:
for lang in indexs[index]:
print("word %s in %s index %d" % (word, index, lang))
assert not ambiguous
def _testPhrase(self, language, phrase):
assert Phrases.forLanguage(language).isPhrase(
phrase), "phrase='%s' language=%s" % (phrase, language)
number = Phrases.forLanguage(language).toNumber(phrase)
detects = Phrases.detectLanguages(phrase)
for lang2 in detects:
number2 = Phrases.forLanguage(lang2).toNumber(phrase)
phrase2 = Phrases.forLanguage(lang2).toPhrase(number)
assert number == number2
assert phrase == phrase2
def _testPhrases(self, language):
for number in range(100000):
phrase = Phrases.forLanguage(language).toPhrase(number)
self._testPhrase(language, phrase)
for length in range(1, 20):
phrase = self.mkPhrase(language, length)
self._testPhrase(language, phrase)
def testPhrase(self):
languages = Phrases.getLanguages()
for language in [
"chinese_simplified", "chinese_traditional", "english",
"french", "italian", "japanese", "korean", "spanish"
]:
assert language in languages
for language in languages:
self._testPhrases(language)
class TrafficGenerator(object):
"""
"""
def __init__(self, user):
"""
Generate TrafficGenerator objects and initialize variables.
"""
self.sim_param = user.sim_param
# self.seed_iat = (user.user_id % user.sim_param.no_of_users_per_slice) + user.sim_param.SEED_IAT
self.seed_iat = user.user_id + user.sim_param.SEED_OFFSET
def poisson_arrivals(self, slicesim):
"""
"""
iat = slicesim.slice_param.MEAN_IAT
self.rng = RNG(ExponentialRNS(1. / float(iat), self.seed_iat),
s_type='iat')
#iat = self.sim_param.MEAN_IAT
t_start = slicesim.sim_state.now
t_end = self.sim_param.T_FINAL
# Poisson
t_arr = []
iat_arr = []
t_temp = t_start + self.rng.get_iat()
while t_temp < t_end:
t_arr.append(t_temp)
t_temp += self.rng.get_iat()
if len(t_arr) > 2:
iat_arr.append(t_arr[-1] - t_arr[-2])
# # Fixed IAT
# t_arr = np.arange(t_start, t_end, iat)
# # Plot histogram of interarrivals for poisson dist
# count, bins, ignored = plt.hist(iat_arr, 30, normed=True)
# lmda = 1/iat
# plt.plot(bins, lmda * np.exp(- lmda*bins), linewidth=2, color='r')
# plt.show()
arrivals = []
for i in t_arr:
arrivals.append(PacketArrival(slicesim, i))
return arrivals
def periodic_arrivals(self, slicesim):
"""
"""
iat = slicesim.slice_param.MEAN_IAT
t_start = slicesim.sim_state.now
t_end = self.sim_param.T_FINAL
# Fixed IAT
t_arr = np.arange(t_start, t_end, iat)
arrivals = []
for i in t_arr:
arrivals.append(PacketArrival(slicesim, i))
return arrivals
def ran_simulation():
"""
Main ran_simulation
"""
# define sim_param and inside RB pool: all available Resources list
sim_param = SimParam()
no_of_slices = sim_param.no_of_slices
no_of_users_per_slice = sim_param.no_of_users_per_slice
# create result directories
create_dir(sim_param)
# create logfile and write SimParameters
results_dir = "results/" + sim_param.timestamp
log_file = open(results_dir + "/logfile.txt", "wt")
log_file.write('no_of_slices: %d\nno_of_users_per_slice: %d\n\n' % (no_of_slices, no_of_users_per_slice))
attrs = vars(sim_param)
log_file.write('SimParam\n'+''.join("%s: %s\n" % item for item in attrs.items()))
# log_file.close()
# initialize SD_RAN_Controller
SD_RAN_Controller = Controller(sim_param)
# Each slice has different users
slices = []
slice_results = []
# initialize all slices
for i in range(no_of_slices):
slice_param_tmp = SliceParam(sim_param)
slice_param_tmp.SLICE_ID = i
slices.append(SliceSimulation(slice_param_tmp))
slice_results.append([])
# initialize all users with traffics and distances
tmp_users = []
seed_dist = 0 # users in all slices have identical distance distributions
#rng_dist = RNG(ExponentialRNS(lambda_x=1. / float(sim_param.MEAN_Dist)), s_type='dist') # , the_seed=seed_dist
rng_dist = RNG(UniformRNS(sim_param.DIST_MIN,sim_param.DIST_MAX, the_seed=seed_dist), s_type='dist') #
dist_arr = [10, 100 ]#[30, 30, 100, 100, 100, 100, 100, 100, 100, 100] # 10*(1+user_id%no_of_users_per_slice)**2
for j in range(no_of_users_per_slice):
user_id = i*no_of_users_per_slice + j
#tmp_users.append(User(user_id, rng_dist.get_dist(), slice_list=[slices[i]], sim_param=sim_param))
tmp_users.append(User(user_id, dist_arr[j], slice_list=[slices[i]], sim_param=sim_param))
# insert user to slices
slices[i].insert_users(tmp_users)
# Choose Slice Manager Algorithm, 'PF': prop fair, 'MCQI': Max Channel Quality Index, 'RR': round-robin
slices[0].slice_param.SM_ALGO = 'RR'
slices[1].slice_param.SM_ALGO = 'MCQI'
slices[2].slice_param.SM_ALGO = 'PF'
# log Slice Parameters
for i in range(no_of_slices):
attrs = vars(slices[i].slice_param)
log_file.write('\nSliceParam\n' + ''.join("%s: %s\n" % item for item in attrs.items()))
#log_file.close()
# loop rounds for each slice
for i in range(int(sim_param.T_FINAL/sim_param.T_C)):
RB_mapping = SD_RAN_Controller.RB_allocate_to_slices(slices[0].sim_state.now, slices)
for j in range(len(slices)):
slices[j].prep_next_round(RB_mapping[j,:,:])
slice_results[j].append(slices[j].simulate_one_round())
# Store Simulation Results
# user results
parent_dir = "results/" + sim_param.timestamp + "/user_results"
path = parent_dir + "/tp"
for i in range(len(slice_results)):
user_count = len(slice_results[i][-1].server_results) # choose latest result for data
for k in range(user_count):
common_name = "/slice%d_user%d_" % (i, slice_results[i][-1].server_results[k].server.user.user_id)
cc_temp = slice_results[i][-1].server_results[k].server.counter_collection
# tp
filename = parent_dir + "/tp" + common_name + "sum_power_two.csv"
savetxt(filename, cc_temp.cnt_tp.sum_power_two, delimiter=',')
filename = parent_dir + "/tp" + common_name + "values.csv"
savetxt(filename, cc_temp.cnt_tp.values, delimiter=',')
filename = parent_dir + "/tp" + common_name + "timestamps.csv"
savetxt(filename, cc_temp.cnt_tp.timestamps, delimiter=',')
filename = parent_dir + "/tp" + common_name + "all_data.csv"
#savetxt(filename, np.transpose(np.array([cc_temp.cnt_tp.values,cc_temp.cnt_tp.timestamps])), delimiter=',')
df = DataFrame(np.transpose(np.array([cc_temp.cnt_tp.values,cc_temp.cnt_tp.timestamps])), columns=['Values', 'Timestamps'])
export_csv = df.to_csv(filename, index=None, header=True)
# tp2
filename = parent_dir + "/tp2" + common_name + "sum_power_two.csv"
savetxt(filename, cc_temp.cnt_tp2.sum_power_two, delimiter=',')
filename = parent_dir + "/tp2" + common_name + "values.csv"
savetxt(filename, cc_temp.cnt_tp2.values, delimiter=',')
filename = parent_dir + "/tp2" + common_name + "timestamps.csv"
savetxt(filename, cc_temp.cnt_tp2.timestamps, delimiter=',')
# ql
filename = parent_dir + "/ql" + common_name + "sum_power_two.csv"
savetxt(filename, cc_temp.cnt_ql.sum_power_two, delimiter=',')
filename = parent_dir + "/ql" + common_name + "values.csv"
savetxt(filename, cc_temp.cnt_ql.values, delimiter=',')
filename = parent_dir + "/ql" + common_name + "timestamps.csv"
savetxt(filename, cc_temp.cnt_ql.timestamps, delimiter=',')
# system time (delay)
filename = parent_dir + "/delay" + common_name + "values.csv"
savetxt(filename, cc_temp.cnt_syst.values, delimiter=',')
filename = parent_dir + "/delay" + common_name + "timestamps.csv"
savetxt(filename, cc_temp.cnt_syst.timestamps, delimiter=',')
# Find how to insert histograms
# plot results
parent_dir = "results/" + sim_param.timestamp
plot_results(parent_dir, no_of_slices, no_of_users_per_slice, sim_param, slices)
# rb dist printing
filename = "results/" + sim_param.timestamp + "/summary"
rb_total = 0
rb_dist = []
for s in slices:
rb_dist_slice = []
for u in s.server_list:
rb_dist_slice.append(u.RB_counter)
slicesum = np.nansum(rb_dist_slice)
print("Slice %d dist: " % s.slice_param.SLICE_ID, *np.round(np.divide(rb_dist_slice,slicesum/100), 1))
# write these to file savetxt(filename, cc_temp.cnt_ql.sum_power_two, delimiter=',')
rb_dist.append(slicesum)
totalsum = np.nansum(rb_dist)
print("rb dist (RR MCQI PF): ", *np.round(np.divide(rb_dist, totalsum / 100), 1))
import os
from rng import RNG
from pwn import *
from arts import maze, mazeDir, bossBG, gameOver, boom, win
import time
# # Variable initialization
p = 198543992929796982831856294122484542605661595500963697094465382211596515703899
rng = RNG(p, pow(0xdeadbeef, 31337, p) // 2)
print(bin(rng.M)[2:])
print(bin(rng.max)[2:])
print(bin(rng.entropy_pool)[2:])
print(len(bin(rng.entropy_pool)[2:]))
M_list = [int(i) for i in bin(rng.M)[2:]]
# now_status = [ int(i) for i in bin(rng.entropy_pool)[2:]]+ \
# [ 0 for i in range(256)]
# for i in range(256):
# if now_status[i] == 1:
# for j,m in zip(range(i,i+256),M_list):
# now_status[j+1] = (now_status[j+1] + m) % 2
# print(now_status[256:])
x = 0b1000000000011011011100101001000000011111101101101010001001111110100101011011001110100000110101100011110111100101000111001010000100110111010111101100101100010101100101101100111000010100111110100111111000111010000100110110111010110101010001101010000111111101
M3 = rng.M ^ (rng.M * 2) ^ (rng.M * 4)
print(bin(M3))
print(bin(x))
# exit()
r = remote('edu-ctf.zoolab.org', 8400)
with open('path31337', 'r') as fin:
for row in fin:
class Controller(object):
"""
"""
def __init__(self, sim_param):
"""
"""
self.sim_param = sim_param
self.slices_cycle = []
self.avg_rate_pf = []
self.df = pd.DataFrame()
# random allocation
self.rng_rb_allocate = RNG(UniformIntRNS(
0, sim_param.no_of_slices - 1, the_seed=sim_param.SEED_OFFSET),
s_type='rb_allocation')
def RB_allocate_from_action(self, t_start, slice_list, action):
'''""" method 0: 1slice-1agent: discrete action (0, no_of_rb),
mapped to no_of_rb by bidding
"""
# mapping actions to rbs
no_of_rb = len(self.sim_param.RB_pool)
if np.sum(action,axis=0)==0:
bidding = np.sum(action,axis=1)/1.
else:
bidding = np.sum(action,axis=1)/np.sum(action,axis=0)
bidding *= no_of_rb
rb_allocation = np.floor(bidding).astype(int)
rb_remaining = no_of_rb - np.sum(rb_allocation)
while rb_remaining>0:
bidding-=rb_allocation
rb_allocation[np.argmax(bidding)]+=1 # only first occurance of max argument is returned
rb_remaining-=1
t_s = self.sim_param.T_S
t_c = self.sim_param.T_C
t_arr = np.arange(t_start, t_start+t_c, t_s)
RB_mapping = np.zeros([len(slice_list), len(self.sim_param.RB_pool), len(t_arr)], dtype=bool)
rb_idx = 0
for i in range(len(slice_list)):
count = rb_allocation[i]
while count>0:
RB_mapping[i, rb_idx, 0]= True
rb_idx+=1
count -=1'''
## -------------------------------------------------------
'''"""
method 1: 1agent: discrete action (0, no_of_slices ** no_of_rb),
mapped to no_of_rb by convertion
"""
no_of_rb = len(self.sim_param.RB_pool)
def dec_to_base( num, base, size): # Maximum base - 36
base_num = ""
while num > 0:
dig = int(num % base)
if dig < 10: base_num += str(dig)
else: base_num += chr(ord('A') + dig - 10) # Using uppercase letters
num //= base
while len(base_num)<size: base_num += str(0)
base_num = base_num[::-1] # To reverse the string
return base_num
rb_dist = dec_to_base(action,len(slice_list), no_of_rb)
t_s = self.sim_param.T_S
t_c = self.sim_param.T_C
t_arr = np.arange(t_start, t_start + t_c, t_s)
RB_mapping = np.zeros([len(slice_list), len(self.sim_param.RB_pool), len(t_arr)], dtype=bool)
for i in range(no_of_rb):
slice_idx = int(rb_dist[i])
RB_mapping[slice_idx, i, 0] = True'''
## -------------------------------------------------------
"""
method 2: 1agent: Multi discrete action ( no_of_rb * [(0,no_of_slices)] )
maps no_of_rb to slices
"""
no_of_rb = action.size
rb_dist = action
t_s = self.sim_param.T_S
t_c = self.sim_param.T_C
t_arr = np.arange(t_start, t_start + t_c, t_s)
RB_mapping = np.zeros(
[len(slice_list),
len(self.sim_param.RB_pool),
len(t_arr)],
dtype=bool)
for i in range(no_of_rb):
slice_idx = int(rb_dist[i])
RB_mapping[slice_idx, i] = True
# ## -------------------------------------------------------
# """
# method 3: PF with trimming alpha
# """
# t_s = self.sim_param.T_S
# t_c = self.sim_param.T_C
# t_arr = np.arange(t_start, t_start + t_c, t_s)
# RB_mapping = np.zeros([len(slice_list), len(self.sim_param.RB_pool), len(t_arr)], dtype=bool)
#
# # Prop Fair per RB, each user has avg_ratio( only works if each slice has same amount of users)
# if len(self.avg_rate_pf) == 0: # avg_rate initialization
# no_of_users = len(slice_list[0].user_list)
# self.avg_rate_pf = np.ones([RB_mapping.shape[0], no_of_users]) # indexing: slice,user
#
# avg_rate = self.avg_rate_pf
# #alpha = (action + 1)/4 # -1,1 mapped to 0,1
# if action==0: alpha = 0
# elif action==1: alpha = 0.1
# elif action==2: alpha = 0.5
# else: RuntimeError("ERROR: wrong alpha.")
# for j in range(RB_mapping.shape[1]): # loop over RBs
# token_slice = -1
# token_user = -1
# max_ratio = -1
# for k in range(RB_mapping.shape[0]): # loop over slices
# for u in range(len(slice_list[k].user_list)): # loop over users
# tmp_user = slice_list[k].user_list[u]
# # tmp_CQI = tmp_user.channel.channel_gains[j, t_arr[i]] # indexing: RB,time
# tmp_CQI = tmp_user.channel.get_data_rate([j], t_start) # insert index of rb as list and time
# tmp_ratio = tmp_CQI / avg_rate[k, u] # current rate average rate ratio, indexing: slice,user
# if max_ratio < tmp_ratio:
# max_ratio = tmp_ratio
# token_slice = k
# token_user = u
# RB_mapping[token_slice, j] = True # indexing: slice,RB
#
# # updating average rates for next rb-time slot (alpha = 0.1)
# for k2 in range(RB_mapping.shape[0]): # loop over slices
# for u2 in range(len(slice_list[k2].user_list)): # loop over users
# if k2 == token_slice and u2 == token_user:
# tmp_user = slice_list[k2].user_list[u2]
# # tmp_CQI = tmp_user.channel.channel_gains[j, t_arr[i]] # indexing: RB,time
# tmp_CQI = tmp_user.channel.get_data_rate([j], t_start) # insert index of rb as list and time
# avg_rate[k2, u2] = (1 - alpha) * avg_rate[k2, u2] + alpha * tmp_CQI
# else:
# avg_rate[k2, u2] = (1 - alpha) * avg_rate[k2, u2]
#
# ## -------------------------------------------------------
# Storing data with dataframe
RB_allocation = np.full(RB_mapping.shape[1:], np.nan, dtype=int)
for i in range(len(slice_list)):
RB_allocation[RB_mapping[i] == True] = i
t_arr = np.arange(t_start, t_start + t_c, t_s)
idx = 0
for t in t_arr:
self.df[t] = RB_allocation[:, idx]
idx += 1
return RB_mapping
def get_CQI_data(self, slice_list):
'''
return CQI values for each slice* user* rb* time combination within next round(T_C)
:param slice_list:
:return:
'''
t_s = self.sim_param.T_S
t_c = self.sim_param.T_C
t_start = slice_list[0].sim_state.now
t_arr = np.arange(t_start, t_start + t_c, t_s)
# CQI_matrix = np.zeros([len(slice_list),len(slice_list[0].server_list), len(self.sim_param.RB_pool), len(t_arr)])
CQI_data = []
tmp_CQI_slice = []
for s in slice_list: # loop over slices
tmp_CQI_user = []
for u in s.user_list: # loop over users
tmp_CQI_time = []
for t in t_arr: # loop over time
tmp_CQI_rb = []
for r in self.sim_param.RB_pool: # loop over RBs
data_rate = u.channel.get_data_rate(
[r], t) # insert index of rb as list, time
tmp_CQI = data_rate # / s.slice_param.P_SIZE
tmp_CQI_rb.append(tmp_CQI)
tmp_CQI_time.append(tmp_CQI_rb)
tmp_CQI_user.append(tmp_CQI_time)
tmp_CQI_slice.append(tmp_CQI_user)
CQI_data.append(tmp_CQI_slice)
return CQI_data
def RB_allocate_to_slices(self, t_start, slice_list):
"""
"""
t_s = self.sim_param.T_S
t_c = self.sim_param.T_C
t_arr = np.arange(t_start, t_start + t_c, t_s)
RB_mapping = np.zeros(
[len(slice_list),
len(self.sim_param.RB_pool),
len(t_arr)],
dtype=bool)
# Hard slicing
if self.sim_param.C_ALGO == 'H':
no_of_RB_per_slice = int(RB_mapping.shape[1] / RB_mapping.shape[0])
for i in range(RB_mapping.shape[0]):
for j in range(no_of_RB_per_slice):
rb_idx = i * no_of_RB_per_slice + j
RB_mapping[i][rb_idx] = True
# random allocation
elif self.sim_param.C_ALGO == 'Random':
for j in range(RB_mapping.shape[1]): # loop over RBs
tmp_slice_idx = self.rng_rb_allocate.get_rb()
RB_mapping[tmp_slice_idx][j] = True
# round robin
elif self.sim_param.C_ALGO == 'RR':
if not isinstance(self.slices_cycle, cycle):
# region : weighted cycle
#self.slices_cycle = cycle(np.arange(0, RB_mapping.shape[0]))
cycle_list = []
# weight : user no
# weight_list = list(self.sim_param.no_of_users_list)
# weight : user no * tp_req
# a0 = np.array ([s.slice_param.RATE_REQ for s in slice_list])
a0 = np.array(self.sim_param.rate_requirements)
a1 = np.divide(a0, np.gcd.reduce(a0))
b0 = np.array(self.sim_param.no_of_users_list)
b1 = np.divide(b0, np.gcd.reduce(b0))
weight_list = list(b1 * a1)
while max(weight_list) > 0:
for i in range(len(weight_list)):
if weight_list[i] > 0:
cycle_list.append(i)
weight_list[i] -= 1
self.slices_cycle = cycle(cycle_list)
# endregion : weighted cycle
for j in range(RB_mapping.shape[1]): # loop over RBs
tmp_slice_idx = next(self.slices_cycle)
RB_mapping[tmp_slice_idx][j] = True
# Max CQI
elif self.sim_param.C_ALGO == 'MCQI':
# CQI is calculated considering user with max CQI in a slice
CQI_arr = np.zeros([len(slice_list), len(self.sim_param.RB_pool)])
for i in range(len(slice_list)): # loop over slices
for k in range(RB_mapping.shape[1]): # loop over RBs
max_CQI = -1
for u in slice_list[i].user_list: # loop over users
# tmp_CQI2 = u.channel.channel_gains[k, t_arr[j]] # indexing: RB,time
tmp_CQI = u.channel.get_data_rate(
[k], t_start) # insert index of rb as list, time
if max_CQI < tmp_CQI:
max_CQI = tmp_CQI
CQI_arr[i, k] = max_CQI # indexing: slice,RB
# For each rb and time, map rb to max valued CQI_arr values owner slice
for j in range(RB_mapping.shape[1]): # loop over RBs
max_CQI = -1
for k in range(RB_mapping.shape[0]): # loop over slices
tmp_CQI = CQI_arr[k, j] # indexing: slice,RB
if max_CQI < tmp_CQI:
max_CQI = tmp_CQI
token = k
RB_mapping[token, j] = True # indexing: slice,RB
# Prop Fair per RB, each user has avg_ratio( only works if each slice has same amount of users)
elif self.sim_param.C_ALGO == 'PF':
if len(self.avg_rate_pf) == 0: # avg_rate initialization
no_of_users = len(slice_list[0].user_list)
self.avg_rate_pf = np.ones([RB_mapping.shape[0], no_of_users
]) # indexing: slice,user
avg_rate = self.avg_rate_pf
alpha = self.sim_param.ALPHA_C
for j in range(RB_mapping.shape[1]): # loop over RBs
token_slice = -1
token_user = -1
max_ratio = -1
for k in range(RB_mapping.shape[0]): # loop over slices
for u in range(len(
slice_list[k].user_list)): # loop over users
tmp_user = slice_list[k].user_list[u]
# tmp_CQI = tmp_user.channel.channel_gains[j, t_arr[i]] # indexing: RB,time
tmp_CQI = tmp_user.channel.get_data_rate(
[j],
t_start) # insert index of rb as list and time
tmp_ratio = tmp_CQI / avg_rate[
k,
u] # current rate average rate ratio, indexing: slice,user
if max_ratio < tmp_ratio:
max_ratio = tmp_ratio
token_slice = k
token_user = u
RB_mapping[token_slice, j] = True # indexing: slice,RB
# updating average rates for next rb-time slot (alpha = 0.1)
for k2 in range(RB_mapping.shape[0]): # loop over slices
for u2 in range(len(
slice_list[k2].user_list)): # loop over users
if k2 == token_slice and u2 == token_user:
tmp_user = slice_list[k2].user_list[u2]
# tmp_CQI = tmp_user.channel.channel_gains[j, t_arr[i]] # indexing: RB,time
tmp_CQI = tmp_user.channel.get_data_rate(
[j],
t_start) # insert index of rb as list and time
avg_rate[k2, u2] = (
1 - alpha) * avg_rate[k2, u2] + alpha * tmp_CQI
else:
avg_rate[k2, u2] = (1 - alpha) * avg_rate[k2, u2]
# Storing data with dataframe
RB_allocation = np.full(RB_mapping.shape[1:], np.nan, dtype=int)
for i in range(len(slice_list)):
RB_allocation[RB_mapping[i] == True] = i
t_arr = np.arange(t_start, t_start + t_c, t_s)
idx = 0
for t in t_arr:
self.df[t] = RB_allocation[:, idx]
idx += 1
return RB_mapping
Available bit lengths:
{', '.join(str(k) for k in BITS)}
'''
).strip())
exit(1)
try:
executions = int(argv[3])
except IndexError:
executions = 10
try:
seed = 88172645463325252
for i in range(bits):
seed += 2 ** bits
avg = 0
rng = iter(RNG(
ALGORITHMS[algorithm],
seed=seed,
limit = 2 ** bits
))
for i in range(executions):
start = now()
n = next(rng)
avg += into_milliseconds(now() - start) / executions
print(f'Average of {executions} executions of {algorithm} ({bits} bits): {avg}')
except KeyError:
print(f'Valid bitlengths: {[*BITS.keys()]}')
def __generate_angles(self, theta, phi):
if theta == None:
theta = RNG.theta()
if phi == None:
phi = RNG.phi()
return theta, phi
if SHOW_ANIMATION:
bMap = str_map(landscape, showSugar=SHOW_SUGAR)
print(f"t = {t:4.32}, alive = {len(agentList.agentList)}\n{bMap}")
if PAUSE:
# Print nice statistics and await input
# Empty input == continue to next event
print(nice_statistics(agentList, t))
repeatInput = True
while repeatInput:
uInput = input(f"Input t={t}> ")
repeatInput = interpret(uInput, agentList, calendar, landscape,
calendar.now())
rng = RNG(SEED)
eventList = EventCalendar()
landscape = Landscape(ROWS, COLUMNS, rng=rng)
agentList = AgentList(AGENTS, landscape, eventList, rng=rng)
eventList.set_preevent(preoperation, landscape, eventList)
eventList.set_postevent(postoperation, landscape, eventList, agentList)
## Pre simulation ##
print(f"Seed: {SEED}")
print(nice_statistics(agentList, 0))
init_map = str_map(landscape, showSugar=SHOW_SUGAR)
## Simulation ##
eventList.run(MAX_T)
def __init__(self, trials=64, rng=RNG()):
self.trials = trials
self.rng = rng
import sys
import argparse
parser = argparse.ArgumentParser(description="Small python based path tracer")
parser.add_argument("--samples", type=int, default=4)
parser.add_argument(
"--passtype",
type=str,
choices=['albedo', 'normal', 'indirect', 'direct', 'depth'],
default="direct")
args = parser.parse_args()
if __name__ == "__main__":
rng = RNG()
nb_samples = args.samples
w = 1024
h = 768
#setup orthographic camera
eye = Vector3(50, 52, 295.6)
gaze = Vector3(0, -0.042612, -1).normalize()
fov = 0.5135
cam_x = Vector3(w * fov / h, 0.0, 0.0)
cam_y = cam_x.cross(gaze).normalize() * fov
Ls = [None] * w * h
for i in range(w * h):
Ls[i] = Vector3()
class ChannelModalCts(object):
"""
"""
def __init__(self, user):
"""
"""
self.user = user
self.RB_pool = user.sim_param.RB_pool
'''self.rng = RNG(ExponentialRNS(1. / float(user.sim_param.MEAN_CG), user.sim_param.SEED_CG), s_type='cg')
# self.rng = RNG(UniformRNS(2*float(user.sim_param.MEAN_CG), 0., user.sim_param.SEED_CG), s_type='cg')
# self.rng = RNG(ExponentialRNS(1. / float(user.sim_param.MEAN_CG), user.user_id), s_type='cg')
# self.rng = RNG(UniformRNS(2. , 0., 21), s_type='cg')
# self.channel_gains = (user.user_id%2+0.1)*self.rng.get_cg(len(self.RB_pool), user.sim_param.T_FINAL + user.sim_param.T_C)
#self.channel_gains = np.random.rand(len(self.RB_pool), user.sim_param.T_FINAL+user.sim_param.T_C)
#self.channel_gains = np.ones((len(self.RB_pool), user.sim_param.T_FINAL + user.sim_param.T_C))'''
self.noise_per_rb_dBm = self.get_noise_per_rb()
# seed_shadowing = self.user.user_id # % self.user.sim_param.no_of_users_per_slice
# self.rng_shadowing = RNG(NormalRNS(0, self.user.sim_param.SIGMA_shadowing, seed_shadowing), s_type='shadowing')
# self.shadowing = self.rng_shadowing.get_shadowing(user.sim_param.T_FINAL)
self.shadowing = self.get_shadowing()
def get_shadowing(self):
t_final = self.user.sim_param.T_FINAL
t_c = self.user.sim_param.T_C
t_coh = self.user.sim_param.T_COH # in ms
buffer = 3 # for the packets in simulation final
shadowing = np.empty((0, int(t_final) + t_coh * (buffer -1) ))
seed_shadowing = (self.user.user_id * len(self.RB_pool)) + self.user.sim_param.SEED_OFFSET
for i in range(len(self.RB_pool)):
self.rng_shadowing = RNG(NormalRNS(0, self.user.sim_param.SIGMA_shadowing, seed_shadowing), s_type='shadowing')
temp_shadowing = self.rng_shadowing.get_shadowing2(t_final, t_c, t_coh, buffer)
shadowing = np.append(shadowing, [temp_shadowing], axis=0)
if self.user.sim_param.freq_selective:
seed_shadowing += 1
return shadowing
def get_noise_per_rb(self):
"""
returns noise in dB
P_noise_dBm = 10*log10(k_boltzmann * temperature * bandwidth / 1mW)
"""
try:
k_boltzmann = constants.Boltzmann
temperature = self.user.sim_param.TEMPERATURE
rb_bw = self.user.sim_param.RB_BANDWIDTH
P_noise_dBm = 10 * np.log10(k_boltzmann * temperature * rb_bw / 1e-3)
except:
raise RuntimeError("Noise calculation error")
return
return P_noise_dBm
def get_pathloss(self, rb, time):
"""
returns pathloss in dB
PL= FSPL(f,1m)+10n log_10(d/1m)+ shadowing
FSPL(f,1m)=20 log_10(4πf/c)
shadowing =
"""
try:
f = self.user.sim_param.FREQ
n = self.user.sim_param.PL_Exponent
d = self.user.distance # in meter
c = constants.c
pi = constants.pi
shadowing_dB = self.shadowing[rb, time]
fs_pl_dB = 20*np.log10(4*pi*f/c)
pl_dB = fs_pl_dB + 10*n*np.log10(d) + shadowing_dB
except:
raise RuntimeError("Path loss calculation error")
return pl_dB
def get_data_rate(self, rb_arr, time):
"""
add effect of time in deterministic manner!!!!
rate = BW * log2(1 + SINR) in bits per sec
:return: summation of data rate of given user for given resource blocks
"""
try:
rate = 0
rb_bw = self.user.sim_param.RB_BANDWIDTH
if time-int(time) > 0.0001:
raise RuntimeError("Time variable is not integer!!")
if rb_arr == [-1]:
return 0
for rb in rb_arr:
pathloss_dB = self.get_pathloss(rb, time)
SINR_dB = self.user.sim_param.P_TX_dBm - pathloss_dB - self.noise_per_rb_dBm # SNR(dB) = Pt-PL-P_noise
SINR = np.power(10, SINR_dB / 10)
rate += rb_bw * np.log2(1 + SINR)
#rate += 1000 # for testing each ms 1 bit is served
except:
raise RuntimeError("Data Rate Calculation error")
return rate
def get_load_change2(self, rb_arr, t_start, t_end):
"""
:return: data rate * duration of given user for given resource blocks at given time interval(float)
data rate is calculated separately for each ms and summed.
load_change in bits
"""
try:
if t_start == t_end:
return 0
t0_f = int(np.floor(t_start))
t0_c = int(np.ceil(t_start))
t1_f = int(np.floor(t_end))
if t0_c > t1_f: # interval within the same ms
assert(t0_f==t1_f)
data_rate = self.get_data_rate(rb_arr, t0_f)
duration = (t_end - t_start) * 1e-3
load_change = duration * data_rate
else: # interval on different ms's
data_rate_0 = self.get_data_rate(rb_arr, t0_f)
duration_0 = (t0_c - t_start) * 1e-3
load_change_0 = duration_0 * data_rate_0
data_rate_1 = self.get_data_rate(rb_arr, t1_f)
duration_1 = (t_end - t1_f) * 1e-3
load_change_1 = duration_1 * data_rate_1
load_change_btw = 0
t_arr = np.arange(t0_c, t1_f, dtype=int)
for t in t_arr:
load_change_btw += self.get_data_rate(rb_arr, t) * 1e-3
load_change = load_change_0 + load_change_1 + load_change_btw
except:
raise RuntimeError("Error during transmitted load calculation")
return load_change
def get_load_change(self, rb_arr, time_arr):
"""
:return: data rate * duration of given user for given resource blocks at given time array
data rate is calculated separately for each ms and summed.
load_change in bits
"""
try:
if len(time_arr) == 0:
return 0
load_change = 0
for t in time_arr:
if isinstance(t,float):
raise RuntimeError("time must be integer")
load_change += np.nansum(self.get_data_rate(rb_arr, t)*1e-3) # rate * 1ms
except:
raise RuntimeError("Error during transmitted load calculation")
return load_change # np.sum(self.channel_gains[rb_arr][time]) # user_id-1 for indexing
def get_serving_duration2(self, packet):
"""
Change the status of the packet once the serving process starts.
"""
RB_arr = packet.server.RB_list
try:
t_end_temp = np.ceil (packet.t_last_start)
load_change = self.get_load_change2 (RB_arr, packet.t_last_start, t_end_temp)
while load_change < packet.remaining_load:
t_end_temp += 1
load_change = self.get_load_change2(RB_arr, packet.t_last_start, t_end_temp)
r_last = self.get_load_change2(RB_arr, t_end_temp-1, t_end_temp)
t_delta = (load_change - packet.remaining_load) / r_last
t_end = t_end_temp - t_delta
assert(np.isclose(packet.remaining_load , self.get_load_change2(RB_arr, packet.t_last_start, t_end)))
packet.t_finish_real = t_end
packet.t_finish = packet.t_finish_real
except:
raise RuntimeError("Error during get_serving_duration")
def get_serving_duration(self, packet):
"""
Change the status of the packet once the serving process starts.
"""
RB_arr = packet.server.RB_list
#t_s = self.user.sim_param.T_S
t_temp = packet.t_last_start
if (packet.t_last_start % 1.) == 0:
t_temp = packet.t_last_start
t_arr = np.arange(t_temp, t_temp + 1)
r_temp = self.get_load_change(RB_arr, t_arr)
else:
t_1 = packet.t_last_start % 1.
t_temp = int(packet.t_last_start - t_1)
t_arr = np.arange(t_temp, t_temp + 1)
r_temp0 = self.get_load_change(RB_arr, t_arr)
r_temp = r_temp0 * (1.-t_1)/1.
try:
while r_temp < packet.remaining_load:
t_temp += 1
t_arr = np.arange(t_temp, t_temp + 1)
r_temp += self.get_load_change(RB_arr, t_arr)
except:
raise RuntimeError("Error during get_serving_duration")
r_last = self.get_load_change(RB_arr, t_arr)
t_last = (packet.remaining_load-(r_temp-r_last))/(r_last/1.)
t_est = t_temp+t_last
#packet.estimated_duration = t_est - packet.slicesim.sim_state.now
#packet.occupation_duration = t_temp+t_s-packet.slicesim.sim_state.now
packet.t_finish_real = t_est#+packet.slicesim.sim_state.t_round_start
packet.t_finish = packet.t_finish_real
def update_remaining_load2(self, packet):
"""
substract load change since the latest serving from remaining load
return tp = load_change / duration
"""
RB_arr = packet.server.RB_list # function is called before RB_list is changed
t_start = (packet.t_arrival + (packet.d_wait + packet.d_served)) # starting time of latest serve
load_change = self.get_load_change2(RB_arr, t_start, packet.slicesim.sim_state.now)
packet.remaining_load -= load_change
if packet.remaining_load < 0:
raise RuntimeError("Remaining load can't be negative!")
if packet.slicesim.sim_state.now-t_start != 0:
tp = float(load_change) / float(packet.slicesim.sim_state.now-t_start)
return tp # throughput in kilobits per second: bits / ms
else:
return float(0)
def update_remaining_load(self, packet):
"""
substract load change since the latest serving from remaining load
return tp = load_change / duration
"""
#t_s = self.user.sim_param.T_S
RB_arr = packet.server.RB_list # function is called before RB_list is changed
t_start = (packet.t_arrival + (packet.d_wait + packet.d_served)) # starting time of latest serve
if (t_start % 1) > 0.0000001:
t_0 = (t_start % 1)
t_1 = int(t_start - t_0)
t_arr = np.arange(t_1, t_1 + 1, dtype=int)
r_temp0 = self.get_load_change(RB_arr, t_arr) * t_0/1.
else:
t_1 = int(t_start)
r_temp0 = 0
t_arr = np.arange(t_1, packet.slicesim.sim_state.now, dtype=int)
r_temp = self.get_load_change(RB_arr, t_arr)
packet.remaining_load -= (r_temp - r_temp0) # rtemp0 is not received part
if packet.remaining_load < 0:
raise RuntimeError("Remaining load can't be negative!")
if packet.slicesim.sim_state.now-t_start != 0:
tp = float(r_temp - r_temp0) / float(packet.slicesim.sim_state.now-t_start)
return tp # throughput in kilobits per second
else:
return float(0)
def get_remaining_load(self, packet):
"""
returns the remaining load without changing anything
initially same as update_remaining_load
"""
RB_arr = packet.server.RB_list # function is called before RB_list is changed
t_start = (packet.t_arrival + (packet.d_wait + packet.d_served)) # starting time of latest serve
if (t_start % 1) > 0.0001:
t_0 = (t_start % 1)
t_1 = int(t_start - t_0)
t_arr = np.arange(t_1, t_1 + 1)
r_temp0 = self.get_load_change(RB_arr, t_arr) * t_0/1.
else:
t_1 = int(t_start)
r_temp0 = 0
t_arr = np.arange(t_1, packet.slicesim.sim_state.now)
r_temp = self.get_load_change(RB_arr, t_arr)
return packet.remaining_load - (r_temp - r_temp0) # rtemp0 is not received part
def get_throughput_rt(self, packet):
"""
NOT USED
returns throughput for round termination event without changing anything
"""
RB_arr = packet.server.RB_list # function is called before RB_list is changed
t_start = (packet.t_arrival + (packet.d_wait + packet.d_served)) # starting time of latest serve
if (t_start % 1) > 0.0001:
t_0 = (t_start % 1)
t_1 = int(t_start - t_0)
t_arr = np.arange(t_1, t_1 + 1)
r_temp0 = self.get_load_change(RB_arr, t_arr) * t_0/1.
else:
t_1 = int(t_start)
r_temp0 = 0
t_arr = np.arange(t_1, packet.slicesim.sim_state.now)
r_temp = self.get_load_change(RB_arr, t_arr)
if packet.slicesim.sim_state.now-t_start != 0:
tp = float(r_temp - r_temp0)/float(packet.slicesim.sim_state.now-t_start) # total served size over time
return tp #throughput
else:
return float(0)
def get_throughput_sc(self, packet):
"""
Only when service is completed,
tp in Kbps
"""
t_start = (packet.t_arrival + (packet.d_wait + packet.d_served)) # starting time of latest serve
if packet.slicesim.sim_state.now-t_start != 0:
tp = float(packet.remaining_load)/float(packet.slicesim.sim_state.now-t_start) # total served size over time
return tp #throughput
else:
return float(0)
def get_throughput_sc_pauseless(self, packet):
"""
Only when service is completed, counts the datarate when packet is served.
tp in Kbps
"""
#t_start = packet.t_last_start # int(round(packet.t_arrival + (packet.d_wait + packet.d_served))) # starting time of latest serve
t_start = packet.t_start
if packet.slicesim.sim_state.now-t_start != 0:
tp = float(packet.size)/float(packet.slicesim.sim_state.now-t_start) # total served size over time
return tp #throughput
else:
return float(0)
def get_CQI_list(self, server, RB_mapping_sm):
"""
returns data_rate values for each rb at each timeslot in T_sm
only get the rate at the beginning of new Ts period.
"""
CQI_list = []
t_s = self.user.sim_param.T_S
t_temp = int(server.slicesim.sim_state.now)# - packet.slicesim.sim_state.t_round_start)
if server.slicesim.sim_state.now - t_temp > 0.0001:
raise RuntimeError("time calculation error in get_CQI_list")
# fill RB_matching_sm
for i in range(RB_mapping_sm.shape[1]): # loop over time
RB_arr = []
tmp_arr = []
for j in range(RB_mapping_sm.shape[0]): # loop over RBs
if RB_mapping_sm[j][i]:
#RB_arr.append(j)
#tmp_arr.append(self.get_load_change([j], t_arr))
tmp_arr.append(self.get_data_rate([j], t_temp))
else:
tmp_arr.append(-1.)
#CQI_list.append(self.get_output_rate(RB_arr, t_arr))
CQI_list.append(tmp_arr)
t_temp += t_s # increase time by t_s for the next slots rate
#t_arr = np.arange(t_temp, t_temp + t_s)
return CQI_list # indexing: [time], [RB]
class Dungeon(Observer):
"""Dungeon now is mostly a container for levels, and handles
rendering"""
def __init__(self, game, seed):
logger.info('Making dungeon')
self.game = game
self.rng = RNG(seed=seed)
self.levels = []
for i in range(NUM_LEVELS):
self.levels.append(self.rng.get_int(0,1000000))
self.rng.delete()
self.tcod_map = libtcod.map_new(LEVEL_W,LEVEL_H)
#does the fov need to be refreshed (ie new level, player moved...)
self.refresh_fov = True
#does the tcod map need to be refreshed (ie terrain change)
self.refresh_tcod = True
#positions of entities that block movement or sight
self.creature_positions = []
def generate_level(self,depth):
seed = self.levels[depth]
approved = False
while not approved:
start = t = time.time()
self.levels[depth] = Level(self.game, seed, LEVEL_W, LEVEL_H)
print "Created level: \t%s"%str(time.time()-t)
'''t = time.time()
self.levels[depth].rect_automata_cave_gen()
print "Generated caves: \t%s"%str(time.time()-t)
t = time.time()
self.levels[depth].smooth_caves()
print "Smoothed caves: \t%s"%str(time.time()-t)
t = time.time()
self.levels[depth].find_caves()
print "Found caves: \t%s"%str(time.time()-t)
t = time.time()
self.levels[depth].remove_caves_by_size()
print "Removed caves (1):\t%s"%str(time.time()-t)
t = time.time()
self.levels[depth].connect_caves()
print "Connected caves: \t%s"%str(time.time()-t)
t = time.time()
self.levels[depth].remove_isolated_caves()
print "Removed caves (2):\t%s"%str(time.time()-t)
print "Total time: \t%s"%str(time.time()-start)
'''
self.levels[depth].experimental_cave_gen()
if self.levels[depth].evaluate() or True:
approved = True
print "Approved"
else:
seed += 1
print "Rejected"
print ""
self.compute_tcod_map()
self.levels[depth].tag_rooms()
self.levels[depth].populate_rooms()
self.compute_tcod_map()
return self.levels[depth].get_start_pos()
def compute_tcod_map(self):
for x in range(LEVEL_W):
for y in range(LEVEL_H):
tile = self.game.cur_level.get_tile(x,y)
libtcod.map_set_properties(self.tcod_map,x,y,
tile.see_through,
tile.move_through)
for entity in self.game.entities:
if entity != self.game.player:
x,y = entity.pos
libtcod.map_set_properties(self.tcod_map,x,y,
True,
False)
def refresh_creature_positions(self):
for pos in self.creature_positions:
tile = self.game.cur_level.get_tile(*pos)
libtcod.map_set_properties(self.tcod_map,
pos[0],pos[1],
tile.see_through,
tile.move_through)
self.creature_positions = []
for entity in self.game.living_entities:
self.creature_positions.append(entity.pos)
if entity != self.game.player:
libtcod.map_set_properties(self.tcod_map,
entity.pos[0],entity.pos[1],
entity.see_through,
entity.move_through)
def on_notify(self,event):
#WARNING: assumes items don't affect tcod map
if event.event_type == EVENT_MOVE:
entity = event.actor
from_tile = self.levels[entity.depth].get_tile(*event.from_pos)
to_tile = self.levels[entity.depth].get_tile(*event.to_pos)
if entity.creature and entity.creature.alive:
from_tile.creature = None
to_tile.creature = entity
if entity != self.game.player:
libtcod.map_set_properties(self.tcod_map,
event.from_pos[0],
event.from_pos[1],
from_tile.see_through,
from_tile.move_through)
libtcod.map_set_properties(self.tcod_map,
event.to_pos[0],
event.to_pos[1],
entity.see_through,
entity.move_through)
elif entity.item:
from_tile.item = None
to_tile.item = entity
if event.actor == self.game.player:
self.refresh_fov = True
elif event.event_type == EVENT_DIE:
entity = event.actor
tile = self.levels[entity.depth].get_tile(entity.x,entity.y)
tile.creature = None
libtcod.map_set_properties(self.tcod_map,
entity.x, entity.y,
tile.see_through,
tile.move_through)
if not tile.item:
tile.item = entity
else:
placed_corpse = False
x,y = entity.pos
r = 1
while not placed_corpse:
positions = []
for try_x in range(entity.x-r,entity.x+r+1):
positions.append((try_x,entity.y+r))
positions.append((try_x,entity.y-r))
for try_y in range(entity.y-r,entity.y+r+1):
positions.append((entity.x+r,try_y))
positions.append((entity.x-r,try_y))
i = 0
while i<len(positions) and not placed_corpse:
try_tile = self.levels[entity.depth].get_tile(*positions[i])
if try_tile.move_through and not try_tile.item:
try_tile.item = entity
placed_corpse = True
entity.x = positions[i][0]
entity.y = positions[i][1]
i += 1
r += 1
elif event.event_type == EVENT_HARVEST:
entity = event.corpse
tile = self.levels[entity.depth].get_tile(entity.x,entity.y)
tile.item = None
elif event.event_type == EVENT_CREATE:
entity = event.actor
tile = self.levels[entity.depth].get_tile(entity.x,entity.y)
if entity.creature and entity.creature.alive:
tile.creature = entity
elif entity.item:
tile.item = entity
libtcod.map_set_properties(self.tcod_map,
entity.x, entity.y,
entity.see_through,
entity.move_through)
def update(self):
if self.refresh_tcod:
self.compute_tcod_map()
self.refresh_tcod = False
if self.refresh_fov:
#libtcod.map_compute_fov(self.tcod_map,
# self.game.player.x,
# self.game.player.y)
self.game.player.fov.refresh()
self.game.cur_level.explore()
self.refresh_fov = False
def render(self, focus_x, focus_y, con, overlay=None):
level = self.game.cur_level
x_offset = 0 + focus_x - MAP_W//2
y_offset = 0 + focus_y - MAP_H//2
for x in range(MAP_W):
for y in range(MAP_H):
map_x = x+x_offset
map_y = y+y_offset
char = ' '
color = libtcod.black
background = libtcod.black
if map_x in range(level.w) and map_y in range(level.h):
tile = level.get_tile(map_x,map_y)
explored = tile.explored
if explored:
char = tile.char
visible = self.game.player.fov(map_x,map_y)
if visible:
color = tile.color
if overlay == OVERLAY_FOV:
fov_num = 0
for entity in self.game.active_entities:
if self.game.player.fov(entity) and (not entity is self.game.player) and entity.creature.ai.state!=AI_SLEEPING and entity.creature.alive:
fov_num = max(fov_num,entity.fov(map_x,map_y))
if fov_num==1:
background = libtcod.darkest_blue
elif fov_num==2:
background = libtcod.darker_blue
elif fov_num==3:
background = libtcod.dark_blue
else:
color = tile.color_not_visible
con.put_char_ex(x,y,char,color,background)
u = (Vector3(0.0, 1.0, 0.0) if abs(w[0]) > 0.1 else Vector3(
1.0, 0.0, 0.0)).cross(w).normalize()
v = w.cross(u)
sample_d = cosine_weighted_sample_on_hemisphere(
rng.uniform_float(), rng.uniform_float())
d = (sample_d[0] * u + sample_d[1] * v +
sample_d[2] * w).normalize()
r = Ray(p, d, tmin=Sphere.EPSILON_SPHERE, depth=r.depth + 1)
continue
import sys
if __name__ == "__main__":
rng = RNG()
nb_samples = int(sys.argv[1]) // 4 if len(sys.argv) > 1 else 1
w = 1024
h = 768
eye = Vector3(50, 52, 295.6)
gaze = Vector3(0, -0.042612, -1).normalize()
fov = 0.5135
cx = Vector3(w * fov / h, 0.0, 0.0)
cy = cx.cross(gaze).normalize() * fov
Ls = [None] * w * h
for i in range(w * h):
Ls[i] = Vector3()
