170 lines
7.0 KiB
Python
170 lines
7.0 KiB
Python
import os
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import matplotlib.pyplot as plt
|
|
from mpl_toolkits.mplot3d import Axes3D
|
|
from multiprocessing import Pool, cpu_count
|
|
|
|
# Define PendulumController class
|
|
from PendulumController import PendulumController
|
|
|
|
# Constants
|
|
g = 9.81 # Gravity
|
|
R = 1.0 # Length of the pendulum
|
|
m = 10.0 # Mass
|
|
dt = 0.02 # Time step
|
|
num_steps = 500 # Simulation time steps
|
|
|
|
# ODE solver (RK4 method)
|
|
def pendulum_ode_step(state, dt, desired_theta, controller):
|
|
theta, omega, alpha = state
|
|
|
|
def compute_torque(th, om, al):
|
|
inp = torch.tensor([[th, om, al, desired_theta]], dtype=torch.float32)
|
|
with torch.no_grad():
|
|
torque = controller(inp)
|
|
torque = torch.clamp(torque, -250, 250)
|
|
return float(torque)
|
|
|
|
def derivatives(state, torque):
|
|
th, om, al = state
|
|
a = (g / R) * np.sin(th) + torque / (m * R**2)
|
|
return np.array([om, a, 0]) # dtheta, domega, dalpha
|
|
|
|
# Compute RK4 steps
|
|
torque1 = compute_torque(theta, omega, alpha)
|
|
k1 = dt * derivatives(state, torque1)
|
|
|
|
state_k2 = state + 0.5 * k1
|
|
torque2 = compute_torque(state_k2[0], state_k2[1], state_k2[2])
|
|
k2 = dt * derivatives(state_k2, torque2)
|
|
|
|
state_k3 = state + 0.5 * k2
|
|
torque3 = compute_torque(state_k3[0], state_k3[1], state_k3[2])
|
|
k3 = dt * derivatives(state_k3, torque3)
|
|
|
|
state_k4 = state + k3
|
|
torque4 = compute_torque(state_k4[0], state_k4[1], state_k4[2])
|
|
k4 = dt * derivatives(state_k4, torque4)
|
|
|
|
new_state = state + (k1 + 2*k2 + 2*k3 + k4) / 6.0
|
|
return new_state
|
|
|
|
def run_simulation(params):
|
|
controller_file, initial_condition = params
|
|
theta0, omega0, alpha0, desired_theta = initial_condition
|
|
epoch = int(controller_file.split('_')[1].split('.')[0])
|
|
|
|
# Load controller
|
|
controller = PendulumController()
|
|
controller.load_state_dict(torch.load(os.path.join(controller_dir, controller_file)))
|
|
controller.eval()
|
|
|
|
# Run simulation
|
|
state = np.array([theta0, omega0, alpha0])
|
|
theta_vals = []
|
|
|
|
for _ in range(num_steps):
|
|
theta_vals.append(state[0])
|
|
state = pendulum_ode_step(state, dt, desired_theta, controller)
|
|
|
|
return epoch, theta_vals # Return epoch with data
|
|
|
|
# Named initial conditions
|
|
initial_conditions = {
|
|
"small_perturbation": (0.1*np.pi, 0.0, 0.0, 0.0),
|
|
"large_perturbation": (-np.pi, 0.0, 0.0, 0),
|
|
"overshoot_vertical_test": (-0.1*np.pi, 2*np.pi, 0.0, 0.0),
|
|
"overshoot_angle_test": (0.2*np.pi, 2*np.pi, 0.0, 0.3*np.pi),
|
|
"extreme_perturbation": (4*np.pi, 0.0, 0.0, 0),
|
|
}
|
|
|
|
# Loss functions to iterate over
|
|
loss_functions = ["constant", "linear", "quadratic", "cubic", "inverse", "inverse_squared", "inverse_cubed"]
|
|
|
|
|
|
epoch_start = 0 # Start of the epoch range
|
|
epoch_end = 1000 # End of the epoch range
|
|
epoch_step = 10 # Interval between epochs
|
|
|
|
if __name__ == "__main__":
|
|
for condition_name, initial_condition in initial_conditions.items():
|
|
full_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/average_normalized/{condition_name}"
|
|
os.makedirs(full_path, exist_ok=True) # Create directory if it does not exist
|
|
|
|
for loss_function in loss_functions:
|
|
controller_dir = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/average_normalized/{loss_function}/controllers"
|
|
controller_files = sorted([f for f in os.listdir(controller_dir) if f.startswith("controller_") and f.endswith(".pth")])
|
|
# Extract epoch numbers and filter based on the defined range and interval
|
|
epoch_numbers = [int(f.split('_')[1].split('.')[0]) for f in controller_files]
|
|
selected_epochs = [e for e in epoch_numbers if epoch_start <= e <= epoch_end and (e - epoch_start) % epoch_step == 0]
|
|
|
|
# Filter the controller files to include only those within the selected epochs
|
|
selected_controllers = [f for f in controller_files if int(f.split('_')[1].split('.')[0]) in selected_epochs]
|
|
selected_controllers.sort(key=lambda f: int(f.split('_')[1].split('.')[0]))
|
|
|
|
# Setup parallel processing
|
|
num_workers = min(cpu_count(), 16) # Limit to 16 workers max
|
|
print(f"Using {num_workers} parallel workers for {loss_function} with initial condition {condition_name}...")
|
|
|
|
with Pool(processes=num_workers) as pool:
|
|
params = [(controller_file, initial_condition) for controller_file in selected_controllers]
|
|
results = pool.map(run_simulation, params)
|
|
|
|
results.sort(key=lambda x: x[0])
|
|
epochs, theta_over_epochs = zip(*results)
|
|
|
|
fig = plt.figure(figsize=(7, 5))
|
|
ax = fig.add_subplot(111, projection='3d')
|
|
time_steps = np.arange(num_steps) * dt
|
|
|
|
# Plot the epochs in reverse order because we view it where epoch 0 is in front
|
|
for epoch, theta_vals in reversed(list(zip(epochs, theta_over_epochs))):
|
|
ax.plot([epoch] * len(time_steps), time_steps, theta_vals)
|
|
|
|
|
|
# Add a horizontal line at desired_theta across all epochs and time steps
|
|
epochs_array = np.array([epoch for epoch, _ in zip(epochs, theta_over_epochs)])
|
|
desired_theta = initial_condition[-1]
|
|
ax.plot(
|
|
epochs_array, # X-axis spanning all epochs
|
|
[time_steps.max()] * len(epochs_array), # Y-axis at the maximum time step
|
|
[desired_theta] * len(epochs_array), # Constant Z-axis value of desired_theta
|
|
color='r', linestyle='--', linewidth=2, label='Desired Theta at End Time'
|
|
)
|
|
|
|
ax.set_xlabel("Epoch")
|
|
ax.set_ylabel("Time (s)")
|
|
ax.set_zlabel("Theta (rad)")
|
|
condition_text = f"IC_{'_'.join(map(lambda x: str(round(x, 2)), initial_condition))}"
|
|
ax.set_title(f"Pendulum Angle Evolution for {loss_function} and {condition_text}")
|
|
|
|
# Calculate the range of theta values across all epochs
|
|
theta_values = np.concatenate(theta_over_epochs)
|
|
theta_min = np.min(theta_values)
|
|
theta_max = np.max(theta_values)
|
|
|
|
# Determine the desired range around the desired_theta
|
|
desired_range_min = desired_theta - 1 * np.pi
|
|
desired_range_max = desired_theta + 1 * np.pi
|
|
|
|
# Check if current theta values fall outside the desired range
|
|
if theta_min < desired_range_min:
|
|
desired_range_min = desired_range_min
|
|
else:
|
|
desired_range_min = theta_min
|
|
|
|
if theta_max > desired_range_max:
|
|
desired_range_max = desired_range_max
|
|
else:
|
|
desired_range_max = theta_max
|
|
|
|
ax.set_zlim(desired_range_min, desired_range_max)
|
|
|
|
ax.view_init(elev=20, azim=-135) # Adjust 3D perspective
|
|
|
|
plot_filename = os.path.join(full_path, f"{loss_function}.png")
|
|
plt.savefig(plot_filename, dpi=300)
|
|
plt.close()
|
|
print(f"Saved plot as '{plot_filename}'.") |