from multiprocessing import Pool, cpu_count
import os
import numpy as np
import matplotlib.pyplot as plt
from simulation import run_simulation
from data_processing import get_controller_files

# Constants and setup
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 = ["constant", "linear", "quadratic", "cubic", "inverse", "inverse_squared", "inverse_cubed"]
loss_functions_mirrored = ["linear", "quadratic", "cubic", "inverse", "inverse_squared", "inverse_cubed"]
loss_functions_mirrored = [i+"_mirrored" for i in loss_functions_mirrored]
loss_functions = loss_functions + loss_functions_mirrored
epoch_range = (0, 100)  # Start and end of epoch range
epoch_step = 1          # Interval between epochs
dt = 0.02                # Time step for simulation
num_steps = 500          # Number of steps in each simulation

# Plotting functions
def plot_3d_epoch_evolution(epochs, theta_over_epochs, desired_theta, save_path, title, num_steps, dt):
    fig = plt.figure(figsize=(7, 5))
    ax = fig.add_subplot(111, projection='3d')
    time_steps = np.arange(num_steps) * dt

    theta_values = np.concatenate(theta_over_epochs)
    theta_min = np.min(theta_values)
    theta_max = np.max(theta_values)

    desired_range_min = desired_theta - 1.5 * np.pi
    desired_range_max = desired_theta + 1.5 * np.pi
    desired_range_min = max(theta_min, desired_range_min)
    desired_range_max = min(theta_max, desired_range_max)

    for epoch, theta_vals in reversed(list(zip(epochs, theta_over_epochs))):
        masked_theta_vals = np.array(theta_vals)
        masked_theta_vals[(masked_theta_vals < desired_range_min) | (masked_theta_vals > desired_range_max)] = np.nan
        ax.plot([epoch] * len(time_steps), time_steps, masked_theta_vals)

    epochs_array = np.array([epoch for epoch, _ in zip(epochs, theta_over_epochs)])
    ax.plot(epochs_array, [time_steps.max()] * len(epochs_array), [desired_theta] * len(epochs_array),
            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)")
    ax.set_zscale('symlog')
    ax.set_title(title)
    ax.set_zlim(desired_range_min, desired_range_max)
    ax.view_init(elev=20, azim=-135)

    if not os.path.exists(os.path.dirname(save_path)):
        os.makedirs(os.path.dirname(save_path))
    plt.savefig(save_path, dpi=300)
    plt.close()
    print(f"Saved plot as '{save_path}'.")


def plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, title, specific_theta_index=-1):
    """
    Plots the theta values at a specific time over epochs for different loss functions for a specific condition, and adds a horizontal line at desired theta.

    :param all_results: Dictionary with structure {loss_function: {condition_name: (epochs, theta_over_epochs)}}
    :param condition_name: The key for the specific condition to plot.
    :param desired_theta: The y-value at which to draw a horizontal line across the plot.
    :param save_path: Path to save the final plot.
    :param title: Title of the plot.
    :param specific_theta_index: The index of the theta value to plot. Default is -1 for the last theta.
    """
    fig, ax = plt.subplots(figsize=(10, 7))  # Correct usage of plt.subplots for creating a figure and an axes.
    
    if condition_name not in all_results[next(iter(all_results))]:
        print(f"No data available for condition '{condition_name}'. Exiting plot function.")
        return

    for loss_function, conditions in all_results.items():
        if condition_name in conditions:
            epochs, theta_over_epochs = conditions[condition_name]
            # Extract final theta values for each epoch
            final_thetas = [thetas[specific_theta_index] for thetas in theta_over_epochs if thetas]  # Ensuring thetas is not empty
            ax.plot(epochs, final_thetas, label=f"{loss_function}")

    # Add a horizontal line at the desired_theta
    ax.axhline(y=desired_theta, color='r', linestyle='--', linewidth=2, label='Desired Theta')

    ax.set_title(title)
    ax.set_xlabel('Epoch')
    ax.set_ylabel('Final Theta (rad)')
    ax.legend()

    plt.yscale('symlog')
    
    plt.savefig(save_path)
    plt.close()
    print(f"Plot saved to {save_path}")


# Main execution
if __name__ == "__main__":
    all_results = {}  # Dictionary to store results by loss function

    for condition_name, initial_condition in initial_conditions.items():
        condition_text = f"IC_{'_'.join(map(lambda x: str(round(x, 2)), initial_condition))}"
        desired_theta = initial_condition[-1]
        condition_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/time_weighting2/{condition_name}"
        os.makedirs(condition_path, exist_ok=True)  # Create directory if it does not exist
        for loss_function in loss_functions:
            # Construct the path to the controller directory
            directory = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/time_weighting/{loss_function}/controllers"
            # Fetch the controller files according to the specified range and interval
            controllers = get_controller_files(directory, epoch_range, epoch_step)
            # Pack parameters for parallel processing
            tasks = [(c, initial_condition, directory, dt, num_steps) for c in controllers]
            
            # Execute simulations in parallel
            print("Starting worker processes")
            with Pool(min(cpu_count(), 16)) as pool:
                results = pool.map(run_simulation, tasks)

            # Sorting the results
            results.sort(key=lambda x: x[0])  # Assuming x[0] is the epoch number
            epochs, state_histories, torque_histories = zip(*results)  # Assuming results contain these

            # Convert state_histories to a more manageable form if necessary, e.g., just theta values
            theta_over_epochs = [[state[0] for state in history] for history in state_histories]
            
            # Store results for later use
            if loss_function not in all_results:
                all_results[loss_function] = {}
            all_results[loss_function][condition_name] = (epochs, theta_over_epochs)

            # continue
            # Plotting the 3D epoch evolution
            print(f"Plotting the 3d epoch evolution for {loss_function} under {condition_text}")
            title = f"Pendulum Angle Evolution for {loss_function} and {condition_text}"
            save_path = os.path.join(condition_path, f"epoch_evolution")
            save_path = os.path.join(save_path, f"{loss_function}.png")
            plot_3d_epoch_evolution(epochs, theta_over_epochs, desired_theta, save_path, title, num_steps, dt)
            print("")

        # Plot the  theta as a function of epoch for all loss functions
        continue

        specific_theta_index = num_steps // 2
        save_path = os.path.join(condition_path, f"theta_at_5sec_across_epochs.png")
        plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, f"Theta at 5 Seconds across Epochs for {condition_text}", specific_theta_index)

        specific_theta_index = -1
        save_path = os.path.join(condition_path, f"final_theta_across_epochs.png")
        plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, f"Final Theta across Epochs for {condition_text}", specific_theta_index)

        print(f"Completed plotting for all loss functions under {condition_name} condition.\n")

    # import json

    # with open("all_results.json", 'w') as file:
    #     json.dump(all_results, file)