distribute_points_along_curve.py

import numpy as np
import replicate_Kountouriotis_et_al_2016
import matplotlib.pyplot as plt

def interpcurve(N,pX,pY):
    #equally spaced in arclength
    N = np.transpose(np.linspace(0,1,N))

#how many points will be uniformly interpolated?
    nt=N.size

#number of points on the curve
    n=pX.size
    pxy=np.array((pX,pY)).T
    p1=pxy[0,:]
    pend=pxy[-1,:]
    last_segment= np.linalg.norm(np.subtract(p1,pend))
    epsilon= 10*np.finfo(float).eps

    #IF the two end points are not close enough lets close the curve
    if last_segment > epsilon*np.linalg.norm(np.amax(abs(pxy),axis=0)):
        pxy=np.vstack((pxy,p1))
        nt = nt + 1
    else:
        print('Contour already closed')

    pt=np.zeros((nt,2))

#Compute the chordal arclength of each segment.
    chordlen = (np.sum(np.diff(pxy,axis=0)**2,axis=1))**(1/2)
#Normalize the arclengths to a unit total
    chordlen = chordlen/np.sum(chordlen)
#cumulative arclength
    cumarc = np.append(0,np.cumsum(chordlen))

    tbins= np.digitize(N,cumarc) # bin index in which each N is in

#catch any problems at the ends
    tbins[np.where(tbins<=0 | (N<=0))]=1
    tbins[np.where(tbins >= n | (N >= 1))] = n - 1      

    s = np.divide((N - cumarc[tbins]),chordlen[tbins-1])
    pt = pxy[tbins,:] + np.multiply((pxy[tbins,:] - pxy[tbins-1,:]),(np.vstack([s]*2)).T)

    return pt 


center_X,center_Y = replicate_Kountouriotis_et_al_2016.center_x_array, replicate_Kountouriotis_et_al_2016.center_y_array
time_duration = 1
perceived_x_center, perceived_y_center = replicate_Kountouriotis_et_al_2016.waypoint_fixation(13.8, time_duration, center_X, center_Y)


pt = interpcurve(10,perceived_x_center,perceived_y_center)

new_x_array = []
new_y_array = []
for i in range(0,10):
    current_x = pt[i,0]
    current_y = pt[i,1]
    new_x_array.append(current_x)
    new_y_array.append(current_y)

# for i in range(0,len(center_X)):
#     current_center_x = center_X[i]
#     current_center_y = center_Y[i]
#     plt.plot(current_center_x,current_center_y, marker = "o",markerfacecolor="green" )


for i in range(0,len(new_x_array)):
    new_center_x = new_x_array[i]
    new_center_y = new_y_array[i]
    plt.plot(new_center_x,new_center_y, marker = "o",markerfacecolor="red" )

for i in range(0,len(perceived_x_center)):
    old_center_x = perceived_x_center[i]
    old_center_y = perceived_y_center[i]
    plt.plot(old_center_x,old_center_y, marker = "o",markerfacecolor="blue")

center_arc = plt.plot(center_X,center_Y,c='yellow',ls='--')

plt.show()


# def distribute_points(x,y):

#     pt = interpcurve(N,x,y)