input_for_experiment.py

import replicate_Kountouriotis_et_al_2016
import calculate_parallel_curves
import numpy as np
import initialize_simulation
import wayPoint_functions
import matplotlib.pyplot as plt

def mode_selection(mode):
    if mode == "standard":
        center_x,center_y = calculate_parallel_curves.x, calculate_parallel_curves.final_sine
        init_phi = np.radians(45) # Standard
        velocity = 16.9 # Standard
        number_of_frames = 130 # Standard
        total_simulation_time = 6 # in seconds
        agent_size = 2
        ax1 = plt.axes(xlim=(0,100), ylim=(-50,50))
    elif mode == "replication":
        center_x,center_y = replicate_Kountouriotis_et_al_2016.center_x_array, replicate_Kountouriotis_et_al_2016.center_y_array
        init_phi = np.radians(90) 
        velocity = 13.8 
        number_of_frames = 120 # Standard
        total_simulation_time = 6 # in seconds
        agent_size = 1
        ax1 = plt.axes(xlim=(-30,70), ylim=(-70,30))
    return(center_x,center_y,init_phi,velocity,number_of_frames,total_simulation_time,agent_size,ax1)

# Mutable variables =======================================================================================================================================
mode = "replication" # "replication" or "standard"
wayPoint_mode_choice = "yes" # "yes" or "no"
wayPoint_time_increment = 0.9
# wayPoint time increment options: 0.06 to 0.9
look_ahead_distance = 1.5
# distance options: 1, 1.5
phi_gain,derivative_farPoint_gain,derivative_nearPoint_gain,proportional_nearPoint_gain = 1,30,13.5,36 # Salvucci & Gray's gain values for the steering model

center_x,center_y,init_phi,velocity,number_of_frames,total_simulation_time,agent_size,ax1 = mode_selection(mode)

# Immutable variables =======================================================================================================================================
# Agent, nearPoint and farPoint initial positions
color ='red'
start_position = 0
start_position_x,start_position_y = center_x[start_position],center_y[start_position] # start at the front center of the road

# Simulation parameters/variables
frames_per_second = number_of_frames/total_simulation_time
simulation_resolution = 1/frames_per_second
farpoint_distance = velocity*look_ahead_distance
waypoint_set,delta_alpha_set= wayPoint_functions.wayPoint_mode(wayPoint_mode_choice,simulation_resolution,number_of_frames,wayPoint_time_increment,1) # ignore the 1

# Values specified in literature
nearpoint_distance = 6.2 # Standard Salvucci & Gray value
delta_time = 0.05 # Based on literature (p 1237 of Salvucci & Gray,2004)
init_delta_alpha,init_alpha = 0,0
# =======================================================================================================================================

simulation1, agent1, road1 = initialize_simulation.init_simulation_objects(start_position_x,start_position_y,agent_size,color,velocity,
                                                                           phi_gain,derivative_nearPoint_gain,derivative_farPoint_gain,proportional_nearPoint_gain,
                                                                           delta_time,number_of_frames,init_delta_alpha,init_alpha,init_phi,simulation_resolution,
                                                                           center_x,center_y,nearpoint_distance,farpoint_distance,delta_alpha_set, waypoint_set)


def batch_of_experimental_conditions(number_of_conditions, time_array, manipulatable_vars,mode_vars,simulation_loop):
    velocity,number_of_frames,init_phi,center_x,center_y = mode_vars[0], mode_vars[1],mode_vars[2], mode_vars[3], mode_vars[4]
    phi_gain,derivative_nearPoint_gain,derivative_farPoint_gain,proportional_nearPoint_gain,farpoint_distance = manipulatable_vars[0],manipulatable_vars[1],manipulatable_vars[2],manipulatable_vars[3],manipulatable_vars[4]

    dictionary_array = []

    for i in range(0,number_of_conditions):
        current_phi_gain, current_derivative_np_gain, current_derivative_fp_gain,current_proportional_np_gain,current_fp_distance = phi_gain[i],derivative_nearPoint_gain[i],derivative_farPoint_gain[i],proportional_nearPoint_gain[i],farpoint_distance[i]

        # generate simulation objects for one single trial
        simulation1, agent1, road1 = initialize_simulation.init_simulation_objects(start_position_x,start_position_y,agent_size,color,
                                                                           velocity,current_phi_gain,current_derivative_np_gain,current_derivative_fp_gain,current_proportional_np_gain,
                                                                           delta_time,number_of_frames,init_delta_alpha,init_alpha,init_phi,simulation_resolution,
                                                                           center_x,center_y,nearpoint_distance,current_fp_distance,delta_alpha_set, waypoint_set)

        magical_dictionary = simulation_loop(time_array, agent1, simulation1, road1)
        dictionary_array.append(magical_dictionary)

    return(dictionary_array)