def create_sim(cwd, simtool, gui, defines):
sim = Simulator(name=simtool, gui=gui, cwd=cwd)
sim.incdirs += ["../src/tb", "../src/rtl", cwd]
sim.sources += ["../src/rtl/sqrt.v", "../src/tb/tb_sqrt.sv"]
sim.defines += defines
sim.top = "tb_sqrt"
return sim
def play():
print('Move ball with WASD. Add mines with scroll wheel.')
print('Press P to pause/unpause or ESC to exit.')
random.seed(314159)
world = get_simple_world()
window = Window(int(world.width), int(world.height))
mine_input = get_mine_input(world, window)
ball_input = BallPlayer(window)
sim = Simulator(world, window, True, False, 60, 1.0 / 200.0, 1.0 / 4.0)
sim.run(mine_input, (ball_input, ))
class TestAssurance:
def __init__(self, owner=ALICE):
self.owner = owner
self.sim = Simulator(founders)
# The eth_ent.
code = compile_serpent('assurance_ent.se')
self.eth_ent = self.sim.load_contract(owner, code) # Alice is recipient.
assert self.storage(0) == int(owner.address, 16)
assert self.storage(1) == int(owner.address, 16)
assert self.storage(2) == 10
assert self.storage(3) == 20000
assert self.storage(4) == 5
def tx(self, fr, v=0, d=[]):
return self.sim.tx(fr, self.eth_ent, v, d)
def storage(self, index):
return self.sim.get_storage_data(self.eth_ent, index)
def status(self):
return "open"
def expect_funded(self):
print("NOTE: think it funded.")
def expect_refunded(self):
for v in founders: # Check if money back modulo gas.
assert INITIAL - founders[v] < max_gas_cost #WRONG
print("NOTE: think it refunded.")
def contribute(self, who, v): # Contribute.
i = self.storage(4)
assert self.tx(who, v) == []
if self.status() == "open":
assert self.storage(4) == i + 2
assert self.storage(i) == int(who.address, 16)
assert self.storage(i + 1) == v
elif self.status() == "end_funded":
self.expect_funded()
else:
self.expect_refunded()
# TODO check increase in balance
def refund(self):
assert self.tx(self.owner, 0, ["refund"]) == []
if self.status() == "open" or self.status() == "refunded":
self.expect_refunded()
else:
self.expect_funded()
def test_evolve_sim():
particles = [Particle( 0.3, 0.5, +1),
Particle( 0.0, -0.5, -1),
Particle(-0.1, -0.4, +3)]
sim = Simulator(particles)
sim.evolve(0.1)
p0, p1, p2 = particles
def fequal(a, b, eps=1e-5):
return abs(a - b) < eps
assert fequal(p0.x, 0.210269)
assert fequal(p0.y, 0.543863)
assert fequal(p1.x, -0.099334)
assert fequal(p1.y, -0.490034)
assert fequal(p2.x, 0.191358)
assert fequal(p2.y, -0.365227)
def test(tmpdir,
bridge,
reset,
simtool,
defines=[],
gui=False,
pytest_run=True):
# create sim
tb_dir = path_join(TEST_DIR, 'test_lb_bridge')
beh_dir = path_join(TEST_DIR, 'beh')
sim = Simulator(name=simtool, gui=gui, cwd=tmpdir)
sim.incdirs += [tmpdir, tb_dir, beh_dir]
sim.sources += [path_join(tb_dir, 'tb.sv')]
sim.sources += beh_dir.glob('*.sv')
sim.defines += defines
sim.top = 'tb'
sim.setup()
# prepare test
dut_src, dut_config = gen_bridge(tmpdir, bridge, reset)
sim.sources += [dut_src]
sim.defines += [
'DUT_DATA_W=%d' % dut_config['data_width'].value,
'DUT_ADDR_W=%d' % dut_config['address_width'].value,
'DUT_%s' % bridge.upper(),
'RESET_ACTIVE=%d' % ('pos' in reset),
]
# run sim
sim.run()
if pytest_run:
assert sim.is_passed
def setup_class(cls):
logging.disable(
logging.INFO
) # disable overzealous DEBUG logging of pyethereum.processblock
cls.code = compile_serpent('examples/mutuala.se')
cls.sim = Simulator({
cls.ALICE.address: 10**18,
cls.BOB.address: 10**18
})
def __init__(self, owner=ALICE):
self.owner = owner
self.sim = Simulator(founders)
# The eth_ent.
code = compile_serpent('assurance_ent.se')
self.eth_ent = self.sim.load_contract(owner, code) # Alice is recipient.
assert self.storage(0) == int(owner.address, 16)
assert self.storage(1) == int(owner.address, 16)
assert self.storage(2) == 10
assert self.storage(3) == 20000
assert self.storage(4) == 5
import time
from sim import Simulator
from input import parse_raw_itch_file
# parse_raw_itch_file('AAPL', 'data/S020118-v50.txt', 'data')
start = time.time()
simulator = Simulator('AAPL', '20180201')
simulator.run_simulation()
print(f'{time.time() - start}')
def setup_class(cls):
cls.code = load_serpent('examples/namecoin.se')
cls.sim = Simulator({cls.ALICE.address: 10**18})
def __usage_iosim():
print("""\
Usage: iosim.py [<options>] <trace path>
<options>
-h: help(this message)
-c <size in blks>
-t <storage type>: all, default(no prefetch, lru), prefetch, ml, rule
-T <timestamp range>
<options for rule>
-p: enable per-process reference history (default: disabled)
-b <count>: reference history count, (default: 1)
<options for ml>
-p: enable per-process reference history (default: enabled)
-G <width(time) x height(lba)>: grid dimension (default: 5x10)
-u <sec>: width unit for time (default: 0.005)
-L <lba max>
-m <model path>: for storage ml only
-M <model type>: for storage ml only
""")
if __name__ == "__main__":
from sim import Simulator
logger.init("iosim")
sim = Simulator()
conf.parse(__usage_iosim)
sim.run()
# constants.LEADER_REPORT,
# constants.BOUNDED_LEADER_REPORT,
]
utility_types = [
constants.LINEAR_UTILITY,
]
mutation_rule = constants.CYCLE_ONE_AGENT
# logging_mode = constants.DEBUG_LOGGING
logging_mode = None
# logging_mode = constants.LOG_DROPOUT
sim = Simulator({
"agent_options": agent_options,
"num_rounds": num_rounds,
"num_times": num_times,
"initial_param": initial_param,
"decision_types": decision_types,
"utility_types": utility_types,
"logging_mode": logging_mode,
"bounded_percent": bounded_perc,
"suppress_percent": suppress_perc,
"mutation_rule": mutation_rule,
})
start_time = time.time()
sim.start()
print("--- %s seconds ---" % (time.time() - start_time))
grafica.communicate("Ventana plot, creada desde el Emulador PAE!", 0.001)
except subprocess.TimeoutExpired:
pass
# AX12 motors, left and right
AX12 = {
MOTOR_ID_L: AX(MOTOR_ID_L),
MOTOR_ID_R: AX(MOTOR_ID_R),
}
# AXS1 sensor modules
AXS1 = AX(SENSOR_ID)
# Load file with obstacles
habitacion = World(fichero_habitacion)
# Create simulator
simulador = Simulator(habitacion, AX12, AXS1)
logging.info("Robots en (%d, %d) y angulo %d" %
(simulador.x, simulador.y, simulador.theta))
# Initial demo values
AX12[MOTOR_ID_L][AX_registers.GOAL_SPEED_L] = V_inicial_demo_L & 0xFF
AX12[MOTOR_ID_L][AX_registers.GOAL_SPEED_H] = (V_inicial_demo_L >> 8) & 0x07
AX12[MOTOR_ID_R][AX_registers.GOAL_SPEED_L] = V_inicial_demo_R & 0xFF
AX12[MOTOR_ID_R][AX_registers.GOAL_SPEED_H] = (V_inicial_demo_R >> 8) & 0x07
# Create graphical application
root = tk.Tk()
root.title("EMULADOR ROBOT PAE")
app = TkApplication(simulador, AX12, AXS1, root=root)
root.lift()
player = RLPlayer(600, 600, 5)
if mode == "train":
player.train(2000)
torch.save(player.policy_net.state_dict(), "policy_net.model")
torch.save(player.target_net.state_dict(), "target_net.model")
elif mode == "inference":
player.policy_net.load_state_dict(torch.load("./policy_net.model"))
player.policy_net.eval()
player.target_net.load_state_dict(torch.load("./target_net.model"))
player.target_net.eval()
sim = Simulator()
sim.initialize()
last_screen = sim.get_screen()
pygame.key.set_repeat(100, 0)
while True:
for event in pygame.event.get():
if event.type == QUIT:
pygame.quit()
sys.exit()
elif event.type == KEYDOWN:
current_screen = sim.get_screen()
state = current_screen - last_screen
from sim import Simulator # Conversion Constants TORAD = math.pi / 180 TODEG = 180 / math.pi TOMPS = 0.514444 TOKNTS = 1 / TOMPS IMAX = 0.479970000000000 - 0.1001 IMIN = 0 + 0.1001 # Test of the simulator object # Initalisation : delta_t = 0.1 t_history = 10 simulation = Simulator.Simulator(20, delta_t) # Note that we create one single speed calculator that we use to generate different experiences because it # saves the state of the flow and the previous incidences # Be carefull to generate experiences in a chronological way !!! speedCalculator = Hysteresis() taustep = 2 # be carefull with initialisation : due to delay we must initialize the taustep+1 first angles tau = taustep * simulation.time_step simulation.hdg[0:taustep + 1] = 5 * TORAD # Wind Generation WS = 7 mean = 0 std = 0.1 * TORAD # 2.5*TORAD
if __name__ == "__main__":
#data = dl.get_norm_data('https://poloniex.com/public?command=returnChartData¤cyPair=BTC_ETH&start=1435699200&end=9999999999&period=14400')
#orig_data = dl.get_data('https://poloniex.com/public?command=returnChartData¤cyPair=BTC_ETH&start=1435699200&end=9999999999&period=14400')
'''
data = dl.get_norm_data('btc_eth_lowtrend.npy')[1000:2000]
orig_data = dl.get_data('btc_eth_lowtrend.npy')[1000:2000]
'''
orig_data, data = dl.test_data_sin(250)
state_size = len(data[0]) + 2 # last 2 are current assets (usd, crypt)
action_size = 4 # [Buy, Sell, Hold, % to buy/sell]
agent = DQNAgent(state_size, action_size)
scores, episodes = [], []
sim = Simulator(orig_data, data)
#viz = Visualizer(
for e in range(EPISODES):
score = 0
while not sim.sim_done():
state = sim.state # Get state
action = agent.get_action(state)
# Simulate trading
#-----------
max_idx = np.argmax(action[:3]) # Choose buy/sell/hold
reward, done = sim.step(max_idx, action[3])
next_state = sim.state # Get new state
#-----------
def setup_class(cls):
cls.code = compile_serpent('examples/datafeed.se')
cls.sim = Simulator({cls.ALICE.address: 10**18})
#!/usr/bin/python3
import math
import Box2D
import pyglet
import pyglet.graphics as g
from config import *
from sim import Simulator
# TODO add toggle for fast forward
simulator = Simulator()
class Renderer(pyglet.window.Window):
def __init__(self):
super().__init__(*WINDOW_SIZE)
self.running = True
self.current_generation = None
self.generation_label = pyglet.text.Label()
self.ticker = None
self.toggle_fast_forward() # sets ticker
# background colour
pyglet.graphics.glClearColor(0.05, 0.05, 0.07, 1)
if RENDER_TEMPERATURE:
self.world_temp_backdrop = prerender_world_temp()
def simulator(): #refrences the simulator class where the AI play against eachother
s_gui = Simulator()
s_gui.intialise_dynamic()
sim = s_gui
def setup_class(cls):
cls.code = compile_lll('examples/keyval_publisher.lll')
cls.sim = Simulator({cls.ALICE.address: 10**18})
t_sprung = 5
#ufnc = stepfnc(t_sprung, 1) # Eingangsfunktion mit Zeitpunkt, Sprunghöhe
f0 = 5
Lambda = 1 / f0 # Taktzeit des PRBS Signals
dt = 5e-3 # Schrittweite des Ergebnisvektors
A = 3
ufnc, u_, N = prbsfnc(
A, Lambda) # PRBS Signal mit Amplitude, Periodendauer, Bitintervall
PID = [3, 1, 1, 5] # Parameter des PID Reglers - T_i, T_d, T_n, K
Fa = 1 / dt # Abtastfrequenz
t_max = Lambda * N * 4
t, b_out, S, IN, S_noise = Simulator(dt, t_max, ufnc, PT1**o, True, *PID,
False)
y = b_out[S]
u = np.array(b_out[IN])
# SYSTEMIDENTIFIKATION --------------------------------------------------------
System = 'PT1'
# PARAMETERIDENTIFIKATION -----------------------------------------------------
if System == '':
K_e, T_e = area_method(t, t_sprung, b_out[S])
print("K_e = " + str(K_e))
print("T_e = " + str(T_e))
K_e, T_e = area_method(t, t_sprung, b_out[S_noise])
#!/usr/bin/env python
from imem import Instr_Mem
from dmem import Data_Mem
from sim import Simulator
imem = Instr_Mem("imem.txt")
dmem = Data_Mem("dmem.txt")
sim = Simulator(imem.output_array(), dmem.output_array())
print "Simulation for Small16 Processor has begun.\n" + \
"Data memory is set in dmem.txt\n" + \
"Instruction memory is set in imem.txt\n" + \
"\nInstructions to use Simulator object:\n" + \
"sim.step(N) Steps program N instructions, default is 1.\n" + \
"sim.run() Steps program until finished.\n" + \
"sim.restart(N) Restarts the program and steps N instructions.\n" + \
"sim.output_reg() Outputs register values.\n" + \
"sim.output_dmem(M, N) Outputs data memory starting at index M with range N.\n" + \
"sim.output_imem(M, N) Outputs instruction memory starting at index M with range N.\n" + \
"sim.output_instr_cnt() Outputs the number of times each instruction has been executed."
if check_firmware(args.p): raise
cpu = construct.CPU(platform, fw=args.p, asm_file=args.f)
io_map = cpu.b.io_map
load_ware = firmware.available[args.p](io_map)
#print(load_ware.code())
fw = load_ware.assemble()
print(fw)
print("Length ",len(fw))
data = char_convert(fw)
#print(data)
load(data)
if args.action == "simulate":
print("Simulate")
if check_firmware(args.p): raise
s = Simulator(fw=args.p,asm_file=args.f)
s.run()
if args.action == "gatesim":
print("Gateware Simulation")
if check_firmware(args.p): raise
design = construct.simCPU(platform, fw=args.p, asm_file=args.f)
fragment = Fragment.get(design, platform)
f = open("test.vcd", "w")
dut = design.b.serial_port
data = []
if args.l is None:
st = "the quick brown fox jumps over the lazy dog"
data = sim_data.str_data(st)
else:
# clean the meta sub
def setup_class(cls):
cls.code = load_serpent('../voting.se')
cls.sim = Simulator({
cls.ALICE.address: 10**18,
cls.BOB.address: 10**18
})
def setup_class(cls):
cls.code = "610070515b525b61000c37f26000601f5561001a515b525b6100143961002e5861000e515b525b61000c37f26002600035025b525b54602052f2600060645c03f06000545b60005b54516020526020602060205b525b016001520360005255516005525460200103600060005360645c03f150535b525b54602052f2".decode(
'hex')
cls.sim = Simulator({cls.ALICE.address: 10**18})
def setup_class(cls):
cls.code = compile_serpent('examples/subcurrency.se')
cls.sim = Simulator({
cls.ALICE.address: 10**18,
cls.BOB.address: 10**18
})
windowsize = 20
#load_agent = False
agent = DQN(windowsize, state_size, action_size)
'''
agent.load_state()
perturb = torch.from_numpy(np.random.rand(10,10) / 1)
agent.model.state_dict()['linear2.weight'] += perturb.float()
perturb2 = torch.from_numpy(np.random.rand(4,10) / 1)
agent.model.state_dict()['linear3.weight'] += perturb2.float()
print(agent.model.state_dict())
'''
losses, scores, episodes = [], [], []
sim = Simulator(orig_data, data, windowsize=windowsize)
for e in range(EPISODES):
# Write actions to log file
score = 0
state = Tensor(sim.reset())
while not sim.sim_done():
#state = Tensor(sim.state) # Get state
action = agent.get_action(state)
# Simulate trading
#-----------
max_idx = np.argmax(action[:3]) # Choose buy/sell/hold
next_state, reward, done = sim.step(max_idx, action[3])
next_state = Tensor(next_state)
