generate_plots.py

import numpy as np
import main
import replicate_Kountouriotis_et_al_2016
import matplotlib.pyplot as plt
import calculate_parallel_curves
import batch_functions

# Input variables
trial_time_in_seconds = 6
time_ploted = 3 # similar to Kountouriotis

# Get the center line, as well as the locations of the agent throughout the simulation
agent_x_array,agent_y_array = batch_functions.get_agent_location(main.magical_dictionary)
center_arc_x,center_arc_y = replicate_Kountouriotis_et_al_2016.center_curve_x_array,replicate_Kountouriotis_et_al_2016.center_curve_y_array
full_road_x, full_road_y = replicate_Kountouriotis_et_al_2016.center_x_array,replicate_Kountouriotis_et_al_2016.center_y_array
first_straight_x,first_straight_y = replicate_Kountouriotis_et_al_2016.center_initial_straight_x_array,replicate_Kountouriotis_et_al_2016.center_initial_straight_y_array
last_straight_x,last_straight_y = replicate_Kountouriotis_et_al_2016.center_last_straight_x_array,replicate_Kountouriotis_et_al_2016.center_last_straight_y_array

# Adjust arrays to isolate agent_x and y coordinates to specific road segments (Note: last straight segment isn't analyzed here because the agent doesn't travel along it, only waypoints do)
first_straight_only_agent_x,first_straight_only_agent_y = calculate_parallel_curves.slice_agentArray_wrt_roadSegment(agent_x_array,agent_y_array,"first_straight",replicate_Kountouriotis_et_al_2016.firstStraightResolution)
arc_only_agent_x,arc_only_agent_y  = calculate_parallel_curves.slice_agentArray_wrt_roadSegment(agent_x_array,agent_y_array,"middle_arc",replicate_Kountouriotis_et_al_2016.firstStraightResolution)

# Calculate error for road segments seperately
error_over_time_for_curve = batch_functions.get_bias_over_time(arc_only_agent_x,arc_only_agent_y,0,(-1*replicate_Kountouriotis_et_al_2016.centerCircleRadius), replicate_Kountouriotis_et_al_2016.centerCircleRadius)
error_over_time_for_first_straight = batch_functions.get_straight_bias_over_time(first_straight_only_agent_x, first_straight_only_agent_y,0,(-1*replicate_Kountouriotis_et_al_2016.centerCircleRadius), replicate_Kountouriotis_et_al_2016.centerCircleRadius,first_straight_x,first_straight_y)

# Bring together total error across full road
error_over_time = np.concatenate((error_over_time_for_first_straight,error_over_time_for_curve),axis=None)
time_array = np.linspace(0,trial_time_in_seconds,len(error_over_time))

# ====================================================================================================================

# Create a plot!

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


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

# for i in range(0,len(error_over_time)):
#     ax2.plot(error_over_time[i],time_array[i], marker='.', c='red')


plt.title('FP Distance:'+ str(round(main.farpoint_distance)) + ', FP Gain:' + str(main.derivative_farPoint_gain) + ', NP Gains:' + str(main.derivative_nearPoint_gain) + ',' + str(main.proportional_nearPoint_gain))
plt.xlabel('steering bias (m):')
plt.ylabel('time (s)')


# fig2 = plt.figure(2,figsize=(7,6), constrained_layout=True)
# ax2 = plt.axes(xlim=(-30,70), ylim=(-70,30))

# center_arc = ax2.plot(center_arc_x,center_arc_y,c='black',ls='--')
# inside_last_straight = ax2.plot(replicate_Kountouriotis_et_al_2016.inside_x_array,replicate_Kountouriotis_et_al_2016.inside_y_array,c='black')
# outside_last_straight = ax2.plot(replicate_Kountouriotis_et_al_2016.outside_x_array,replicate_Kountouriotis_et_al_2016.outside_y_array,c='black')

# center_arc = ax2.plot(agent_x_array[7],agent_y_array[7], marker='.', c='red')
# center_arc = ax2.plot(0,-60, marker='.', c='red')



plt.show()