import numpy
import matplotlib.pyplot as plt
import os

poi_time = {}
af_time = {}
ss_time = {}

with open('./output/ReconstructionTime.txt', 'r') as timefile:
    theline = timefile.readline()
    while theline != '':
        theline = theline.split('  ')
        recon_type = theline[0]
        recon_num = theline[1]
        recon_time = float(theline[2])

        if recon_type == 'poi':
            poi_time[recon_num] = recon_time
        if recon_type == 'adf':
            af_time[recon_num] = recon_time
        if recon_type == 'sas':
            ss_time[recon_num] = recon_time
        theline = timefile.readline()
    
OriginalShape = './dataset/Planes/'
PoiOutputSurfaces = './output/surfaces/poisson/'
poi_recons_name = os.listdir(PoiOutputSurfaces)
# cause the number of successful poisson recons are the least, use it as index
poi_recons_num = []
for name in poi_recons_name:
    poi_recons_num.append(name[:-4])

poi_time_y = []
af_time_y = []
ss_time_y = []
for recon_num in poi_recons_num:
    poi_time_y.append(poi_time[recon_num])
    af_time_y.append(af_time[recon_num])
    ss_time_y.append(ss_time[recon_num])

mean_poi = numpy.mean(poi_time_y)
mean_af = numpy.mean(af_time_y)
mean_ss = numpy.mean(ss_time_y)

plt.xlabel('Date #')
plt.ylabel('Time(s)')
x = list(range(len(poi_recons_num)))
plt.plot(x, poi_time_y, label='Poisson, Mean = '+str(round(mean_poi,2)))
plt.plot(x, af_time_y, label='Advancing Front, Mean = '+str(round(mean_af,2)))
plt.plot(x, ss_time_y, label='Scale Space, Mean = '+str(round(mean_ss,2)))
plt.legend()
plt.show()