experiment.py

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import animation
import matplotlib as mpl 
import simulation_loop
import input_for_experiment
import initialize_simulation
import plot_road_and_patches
import batch_functions
import replicate_Kountouriotis_et_al_2016
import calculate_parallel_curves

# Mutable variables ==============================================================================================================================
num_of_conditions = 5
mode = "replication"
center_x,center_y,init_phi,velocity,number_of_frames,total_simulation_time,agent_size,ax1 = input_for_experiment.mode_selection(mode)
time_array = np.arange(0,number_of_frames,1) 

wayPoint_time_increment = 0.5
look_ahead_distance = 1.5 # determines farPoint distance in terms of time
phi_gain =1

derivative_farPoint_gain = 30
derivative_nearPoint_gain = 13.5
proportional_nearPoint_gain = 36
farpoint_distance = velocity*look_ahead_distance

#look_ahead_distance_array = batch_functions.assign_arrays_to_manipulated_vars("consistent",look_ahead_distance,num_of_conditions)
look_ahead_distance_array = batch_functions.assign_arrays_to_manipulated_vars("changes",look_ahead_distance,num_of_conditions,min=0.5,max=2.5)
farpoint_distance_array = batch_functions.get_farpoint_distance_array(velocity,look_ahead_distance_array)
phi_gain_array = batch_functions.assign_arrays_to_manipulated_vars("consistent",phi_gain,num_of_conditions)

derivative_farPoint_gain_array = batch_functions.assign_arrays_to_manipulated_vars("consistent",derivative_farPoint_gain,num_of_conditions)
#derivative_farPoint_gain_array = batch_functions.assign_arrays_to_manipulated_vars("changes",derivative_farPoint_gain,num_of_conditions,min=derivative_farPoint_gain,max=50)
derivative_nearPoint_gain_array = batch_functions.assign_arrays_to_manipulated_vars("consistent",derivative_nearPoint_gain,num_of_conditions)
proportional_nearPoint_gain_array = batch_functions.assign_arrays_to_manipulated_vars("consistent",proportional_nearPoint_gain,num_of_conditions)

# Seperate manipulated variables vs mode variables
manipulatable_vars = [phi_gain_array,derivative_nearPoint_gain_array,derivative_farPoint_gain_array,proportional_nearPoint_gain_array,farpoint_distance_array]
mode_vars = [velocity,number_of_frames,init_phi,center_x,center_y]

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

# #magical_dictoinary = simulation_loop.simulation(time_array, agent1, simulation1, road1)

dictionary_array = input_for_experiment.batch_of_experimental_conditions(num_of_conditions,time_array,manipulatable_vars,mode_vars,simulation_loop.simulation)


# Plot figure from dictionary information =========================================================================================================

# Standard Road vs Kountouriotus replication
# Window to observe agent on to entire road
#ax1 = plt.axes(xlim=(0,100), ylim=(-50,50)) # Standard road
ax1 = plt.axes(xlim=(-30,70), ylim=(-70,30)) # Kountouriotus replication

# initialize road
#center_line,center_X,center_Y,left,right = plot_road_and_patches.instantiate_standard_road(ax1)
center_X,center_Y,first_road_segment,middle_road_segment,last_road_segment,full_road = plot_road_and_patches.instantiate_Kountouriotis2016_rep_road(ax1)

# Define figure, patches (Agent, nearPoint, farPoint), and vectors (headingVector,nearPointAngle_Vector,farPointAngle_Vector)
fig = plt.figure(1,figsize=(7,6), constrained_layout=True)

agent_x_array_trial1,agent_y_array_trial1 = batch_functions.get_agent_trace(dictionary_array[0],time_array)
agent_x_array_trial2,agent_y_array_trial2 = batch_functions.get_agent_trace(dictionary_array[1],time_array)
agent_x_array_trial3,agent_y_array_trial3 = batch_functions.get_agent_trace(dictionary_array[2],time_array)
agent_x_array_trial4,agent_y_array_trial4 = batch_functions.get_agent_trace(dictionary_array[3],time_array)
agent_x_array_trial5,agent_y_array_trial5 = batch_functions.get_agent_trace(dictionary_array[4],time_array)
#agent_x_array_trial6,agent_y_array_trial6 = batch_functions.get_agent_trace(dictionary_array[5],time_array)


trial1_color = "lightsteelblue"
trial2_color = "cornflowerblue"
trial3_color = "royalblue"
trial4_color = "blue"
trial5_color = "black"

plt.plot(agent_x_array_trial1,agent_y_array_trial1,c = trial1_color)
plt.plot(agent_x_array_trial2,agent_y_array_trial2,c= trial2_color)
plt.plot(agent_x_array_trial3,agent_y_array_trial3,c = trial3_color)
plt.plot(agent_x_array_trial4,agent_y_array_trial4,c= trial4_color)
plt.plot(agent_x_array_trial5,agent_y_array_trial3,c = trial5_color)

# Plot RMSE over Time across Three Conditions =========================================================================================================
trial_time_in_seconds = 6
time_ploted = 3 # similar to Kountouriotis

fig2 = plt.figure(2,figsize=(4.5,7), constrained_layout=True)
ax2 = plt.axes(xlim=(-1,1), ylim=(0,time_ploted))

# Function in Batch mode
error_over_time_t1 = batch_functions.get_error_across_trials(agent_x_array_trial1,agent_y_array_trial1)
error_over_time_t2 = batch_functions.get_error_across_trials(agent_x_array_trial2,agent_y_array_trial2)
error_over_time_t3 = batch_functions.get_error_across_trials(agent_x_array_trial3,agent_y_array_trial3)
error_over_time_t4 = batch_functions.get_error_across_trials(agent_x_array_trial4,agent_y_array_trial4)
error_over_time_t5 = batch_functions.get_error_across_trials(agent_x_array_trial5,agent_y_array_trial5)

time_array = np.linspace(0,trial_time_in_seconds,len(error_over_time_t1))

trajectory_1 = ax2.plot(error_over_time_t1,time_array,c=trial1_color)
trajectory_2 = ax2.plot(error_over_time_t2,time_array,c=trial2_color)
trajectory_3 = ax2.plot(error_over_time_t3,time_array,c=trial3_color)
trajectory_4 = ax2.plot(error_over_time_t4,time_array,c=trial4_color)
trajectory_5 = ax2.plot(error_over_time_t5,time_array,c=trial5_color)

#trajectory = ax2.plot(error_over_time_test,time_array,c='green')
center_line = plt.axvline(x = 0, color = 'black', linestyle = '--')

plt.show()