def microcar(insts,acts):
    import numpy as np

    #defining final return lists
    x_exp_disp=[]
    y_exp_disp=[]
    x_act_disp=[]
    y_act_disp=[]
    exp_dist=[]
    act_dist=[]

    #determining expecting values for microcar
    for inst in insts:

    #variables declared at the start of the loop so when the next file is opened it will reset
        x_exp_disp=0
        y_exp_disp=0
        exp_dist=0

        imput=np.loadtxt(inst,delimiter=',',dtype=[('dir','U1'),('time',int),('speed',int)])

        for row in input:
            #N is positive virtical and E is positive horizontal with respect to displacement
            #check which direction the car is travelling
            if row[0]=='N':
                #calculating the new displacement from the origin and the distance travelled
                y_exp_disp+=round(float(row[1])*float(row[2]),10)
                exp_dist+=round(float(row[1])*float(row[2]),10)

            elif row[0]=='S':
                y_exp_dist-=round(float(row[1])*float(row[2]),10)
                exp_dist+=round(float(row[1])*float(row[2]),10)

            elif row[0]=='E':
                x_exp_dist+=round(float(row[1])*float(row[2]),10)
                exp_dist+=round(float(row[1])*float(row[2]),10)

            elif row[0]=='W':
                x_exp_dist-=round(float(row[1])*float(row[2]),10)
                exp_dist+=round(float(row[1])*float(row[2]),10)

        x_exp_disp_list.append(round(x_exp_disp,2))
        y_exp_disp_list.append(round(y_exp_disp,2))
        exp_dist_list.append(round(exp_dist,2))

    #determining the actual value of the micro cars
    for act in acts:

        x_exp_disp=0
        y_exp_disp=0
        exp_dist=0

        input=np.loadtxt(act,delimiter=',',dtype=[('dir','U1'),('time',int),('speed',int)])

       if row[0]=='N':
                #calculating the new actual displacement from the origin and the distance travelled
                y_act_disp+=round(float(row[1])*float(row[2]),10)
                act_dist+=round(float(row[1])*float(row[2]),10)

            elif row[0]=='S':
                y_act_dist-=round(float(row[1])*float(row[2]),10)
                act_dist+=round(float(row[1])*float(row[2]),10)

            elif row[0]=='E':
                x_act_dist+=round(float(row[1])*float(row[2]),10)
                act_dist+=round(float(row[1])*float(row[2]),10)

            elif row[0]=='W':
                x_act_dist-=round(float(row[1])*float(row[2]),10)
                act_dist+=round(float(row[1])*float(row[2]),10)

        x_act_disp_list.append(round(x_exp_disp,2))
        y_act_disp_list.append(round(y_exp_disp,2))
        act_dist_list.append(round(exp_dist,2))

    #convert lists to arrays
    exp_dist_list=np.array(exp_dist_list)
    act_dist_list=np.array(act_dist_list)
    x_exp_disp_list=np.array(x_exp_dist_list)
    y_exp_disp_list=np.array(y_exp_dist_list)
    x_act_disp_list=np.array(x_act_disp_list)
    y_act_disp_list=np.array(y_act_disp_list)

    return x_exp_disp_list, y_exp_disp_list, x_act_disp_list, y_act_disp_list, exp_dist_list, act_dist_list

#Part two
def plotmicrocar(insts,acts):

    #import required modules
    import numpy as np
    import matplotlib.pyplot as plot

    #retrieve calculated vales from the microcar function

    #expected values
    x_exp_disp_list=microcar(insts,acts)[0]
    y_exp_disp_list=microcar(insts,acts)[1]
    exp_dist_list=microcar(insts,acts)[4]
    #actual values
    x_act_disp_list=microcar(insts,acts)[2]
    y_act_disp_list=microcar(insts,acts)[3]
    act_dist_list=microcar(insts,acts)[5]

    #plotting the values
    plt.figure(1)

    #Graphing the bar graph
    #x-axis corresponds to the number of cars the function is considering
    x=np.arange(len(exp_dist_list))+1
    #added 1 to array because the car number starts at 0
    #the length of the list is the number of cars

    #place the bar graph at the top of the graphic
    plt.subplot(211)
    #graph of expected values
    plt.bar(x-0.125,exp_dist_list,0.125,0,align='edge',color='blue')
    #subtracted 0.125 both the expected and actual bars are plotted side by side

    #actual values
    plt.bar(x,act_dist_list,0.125,0,align='edge',color='red')

    #adding graph labels
    plt.xlabel('Microcar (number)', fontsize=9)
    plt.ylabel('Distance (m)', fontsize=9)
    plt.legend(['Expected','Actual'],loc='upper left',fontsize=8)
    plt.xticks(x) #whole number increases per mark on the x-axis
    plt.title('Microcar Distances Travelled',fontsize=10)

    #plotting displacements on a scatter graph
    #find the largest displacement to get the graph dimensions of both axis
    max_disp=np.amax([x_exp_disp_list,y_exp_disp_list,x_act_disp_list,y_act_disp_list])
    axis_lim=[-max_disp-0.25*max_disp,max_disp+0.25*max_disp]

    #the bottom left graph is the expected displacements
    plt.subplot(223)
    label=[] #which dot is which car
    n=0 #start lists at 0

    #counting the number of cars and then plotting a point for each car
    for value in x_exp_disp_list:
        plt.scatter(x_exp_disp_list[n],y_exp_disp_list[n],marker='x')
        label.append('Car '+str(n+1)) #n+1 because cars start at 1 not 0
        n+=1

    #making the scatter plot square
    plt.ylim(axis_lim)
    plt.xlim(axis_lim)

    #graph
    plt.xlabel('Horizontal Disp (m)',fontsize=9)
    plt.ylabel('Vertical Disp (m)',fontsize=9)
    plt.legend(label,fontsize=8)
    plt.title('Expected Microcar Displacement',fontsize=10)

    #actual displacements
    plt.subplot(224)
    n=0

    for value in x_act_disp_list:
        plt.scatter(x_act_disp_list[n],y_act_disp_list[n],marker='x')
        n+=1

    plt.ylim(axis_lim)
    plt.xlim(axis_lim)

    #graph
    plt.xlabel('Horizontal Disp (m)',fontsize=9)
    plt.ylabel('Vertical Disp (m)',fontsize=9)
    plt.legend(label,loc='upper left',fontsize=8)
    plt.title('Actual Microcar Displacement',fontsize=10)

    plt.tight_layout() #stops the graphs from overlapping
    plt.show()