def exercise_9b(world, timestep, reset):
"""Exercise example"""
# Parameters
n_joints = 10
listamplitude=np.linspace(0.05,0.4, 4)
listphaselag=np.linspace(0., 0.9, 6)
listuple=[]
for i in range(len(listamplitude)):
for j in range(len(listphaselag)):
listuple.append((listamplitude[i], listphaselag[j]))
nbsimu=len(listamplitude)* len(listphaselag)
parameter_set = [SimulationParameters(
simulation_duration=15,
amplitude_body_value=amp,
phase_lag=phasel,
drive_mlr=None)
for amp, phasel in listuple]
# Grid search
for simulation_i, parameters in enumerate(parameter_set):
print(simulation_i)
reset.reset()
run_simulation(
world,
parameters,
timestep,
int(1000*parameters.simulation_duration/timestep),
logs="./logs/9b/simulation_{}.npz".format(simulation_i)
)
plot_results.main(nbsimu,listamplitude, listphaselag, plot=True)
def exercise_9g(world, timestep, reset):
"""Exercise 9g"""
# Parameters
n_joints = 10
listuple = []
Rtail = np.linspace(0.2, 0.2, 1)
Rhead = np.linspace(0.1, 0.2, 1)
for i in range(Rtail.size):
for j in range(len(Rhead)):
listuple.append((Rtail[i], Rhead[j]))
nbsimu = Rhead.size * Rtail.size
parameter_set = [
SimulationParameters(
simulation_duration=30,
phase_lag=2 * np.pi / 10,
drive_mlr=1.2,
turn=0,
) for grad in listuple
]
# Grid search
for simulation_i, parameters in enumerate(parameter_set):
print(simulation_i)
reset.reset()
run_simulation(world,
parameters,
timestep,
int(1000 * parameters.simulation_duration / timestep),
logs="./logs/9g/simulation_{}.npz".format(simulation_i))
plot_results.main(nbsimu, Rtail, Rhead, plot=True)
def exercise_9c(world, timestep, reset):
"""Exercise 9c"""
# Parameters
n_joints = 10
listuple = []
Rtail = np.linspace(0.1, 0.4, 2)
Rhead = np.linspace(0.1, 0.4, 4)
for i in range(Rtail.size):
for j in range(len(Rhead)):
listuple.append((Rtail[i], Rhead[j]))
nbsimu = Rhead.size * Rtail.size
parameter_set = [
SimulationParameters(
simulation_duration=1,
#drive=drive,
amplitude_gradient=-(grad[1] - grad[0]) / 10,
amplitude_body_value=grad[1],
phase_lag=2 * np.pi / 10) for grad in listuple
]
# Grid search
for simulation_i, parameters in enumerate(parameter_set):
print(simulation_i)
reset.reset()
run_simulation(world,
parameters,
timestep,
int(1000 * parameters.simulation_duration / timestep),
logs="./logs/9c/simulation_{}.npz".format(simulation_i))
plot_results.main(nbsimu, Rtail, Rhead, plot=True)
def exercise_9b(world, timestep, reset):
"""Exercise 9b"""
# Parameters
amplitudes = [0.2, 0.5]
phase_bias = [np.pi / 2, np.pi / 10, np.pi / 14, np.pi / 20]
parameter_set = [
SimulationParameters(
simulation_duration=4,
#drive=drive,
amplitudes=amp,
phase_bias_vertical=phase,
turn=0,
freqs=3,
) for amp in amplitudes for phase in phase_bias
]
# Grid search
for simulation_i, parameters in enumerate(parameter_set):
reset.reset()
path = "./logs/9b/simulation_{}.npz".format(simulation_i)
run_simulation(world,
parameters,
timestep,
int(1000 * parameters.simulation_duration / timestep),
logs=path)
plot_results.main("./logs/9b/simulation_{}.npz", len(parameter_set))
def run_network(duration, update=False, drive=0):
"""Run network without Webots and plot results"""
# Simulation setup
timestep = 5e-3
times = np.arange(0, duration, timestep)
n_iterations = len(times)
parameters = SimulationParameters(
drive=drive,
amplitude_gradient=None,
phase_lag=None,
turn=1.0,
use_drive_saturation=1,
shes_got_legs=1,
cR_body=[0.05, 0.16], #cR1, cR0
cR_limb=[0.131, 0.131], #cR1, cR0
amplitudes_rate=40,
)
network = SalamanderNetwork(timestep, parameters)
osc_left = np.arange(10)
osc_right = np.arange(10, 20)
osc_legs = np.arange(20, 24)
# Logs
phases_log = np.zeros([n_iterations, len(network.state.phases)])
phases_log[0, :] = network.state.phases
amplitudes_log = np.zeros([n_iterations, len(network.state.amplitudes)])
amplitudes_log[0, :] = network.state.amplitudes
freqs_log = np.zeros([n_iterations, len(network.parameters.freqs)])
freqs_log[0, :] = network.parameters.freqs
outputs_log = np.zeros(
[n_iterations, len(network.get_motor_position_output())])
outputs_log[0, :] = network.get_motor_position_output()
# Run network ODE and log data
tic = time.time()
for i, _ in enumerate(times[1:]):
if update:
network.parameters.update(
SimulationParameters(
# amplitude_gradient=None,
# phase_lag=None
))
network.step()
phases_log[i + 1, :] = network.state.phases
amplitudes_log[i + 1, :] = network.state.amplitudes
outputs_log[i + 1, :] = network.get_motor_position_output()
freqs_log[i + 1, :] = network.parameters.freqs
toc = time.time()
# Network performance
pylog.info("Time to run simulation for {} steps: {} [s]".format(
n_iterations, toc - tic))
# Implement plots of network results
pylog.warning("Implement plots")
plot_results.main()
def exercise_9d1(world, timestep, reset):
"""Exercise 9d1"""
# Parameters
n_joints = 10
listuple = []
Rtail = np.linspace(0.3, 0.4, 1)
Rhead = np.linspace(0.3, 0.4, 1)
for i in range(Rtail.size):
for j in range(len(Rhead)):
listuple.append((Rtail[i], Rhead[j]))
nbsimu = Rhead.size * Rtail.size
parameter_set = [
SimulationParameters(
simulation_duration=20,
#drive=drive,
drive_mlr=4,
amplitude_gradient=None,
amplitude_value=0.2,
phase_lag=2 * np.pi / 10,
freqs=(np.ones((n_joints * 2 + 4, 1)) * 2.5)[:, 0],
backward=False,
turn=-0.2,
# ...
) for grad in listuple
]
# Grid search
for simulation_i, parameters in enumerate(parameter_set):
print(simulation_i)
reset.reset()
run_simulation(world,
parameters,
timestep,
int(1000 * parameters.simulation_duration / timestep),
logs="./logs/9b/simulation_{}.npz".format(simulation_i))
plot_results.main(nbsimu, Rtail, Rhead, plot=True)
def exercise_9f(world, timestep, reset):
"""Exercise 9f"""
# Parameters
n_joints = 10
Phase_offset = np.linspace(-3, 3, 10)
amp = np.linspace(0.3, 0.3, 2)
nbsimu = Phase_offset.size * amp.size
listuple = []
for i in range(Phase_offset.size):
for j in range(len(amp)):
listuple.append((Phase_offset[i], amp[j]))
nbsimu = Phase_offset.size * amp.size
parameter_set = [
SimulationParameters(
simulation_duration=15,
drive_mlr=1,
phaseoffset=phaseof,
amplitude_value=ampli,
phase_lag=2 * np.pi / 10,
) for phaseof, ampli in listuple
]
# Grid search
for simulation_i, parameters in enumerate(parameter_set):
print(simulation_i)
reset.reset()
run_simulation(world,
parameters,
timestep,
int(1000 * parameters.simulation_duration / timestep),
logs="./logs/9b/simulation_{}.npz".format(simulation_i))
plot_results.main(nbsimu, Phase_offset, amp, plot=True)