import numpy as np
import matplotlib.pyplot as plt


#val = input("1:WoodYield/SoilLoss \n 2:WoodYield/RiskPercentile \n 3:SoilLoss/RiskPercentile \n 4:All three

f=open("paretoWSR.csv","r")
#lines=sorted(list(map(str, f.readlines())))

f2 =open("nonParetoWSR.csv","r")
#lines2 = sorted(list(map(str, f2.readlines())))

lines=f.readlines()
lines2 = f2.readlines()


paretoPoints = []
dominatedPoints = []
x = []
y = []
z = []
lst = []
scatterx = []
scattery = []

for i in lines:
	result = i.split(',')
	x.append(int(result[0]))
	y.append(int(result[1]))
	paretoPoints.append([int(result[0]),int(result[1]),int(result[2])])
f.close()

for i in lines2:
	result = i.split(',')
	x.append(int(result[0]))
	y.append(int(result[1]))
	z.append(int(result[2]))
	if([int(result[0]),int(result[1]),int(result[2])] not in paretoPoints):	
		dominatedPoints.append([int(result[0]),int(result[1]),int(result[2])])
f2.close()

#print(dominatedPoints)

def simple_cull(inputPoints, dominates):
    paretoPoints = set()
    candidateRowNr = 0
    dominatedPoints = set()
    while True:
        candidateRow = inputPoints[candidateRowNr]
        inputPoints.remove(candidateRow)
        rowNr = 0
        nonDominated = True
        while len(inputPoints) != 0 and rowNr < len(inputPoints):
            row = inputPoints[rowNr]
            if dominates(candidateRow, row):
                # If it is worse on all features remove the row from the array
                inputPoints.remove(row)
                dominatedPoints.add(tuple(row))
            elif dominates(row, candidateRow):
                nonDominated = False
                dominatedPoints.add(tuple(candidateRow))
                rowNr += 1
            else:
                rowNr += 1

        if nonDominated:
            # add the non-dominated point to the Pareto frontier
            paretoPoints.add(tuple(candidateRow))

        if len(inputPoints) == 0:
            break
    return paretoPoints, dominatedPoints

def dominates(row, candidateRow):
    return sum([row[x] >= candidateRow[x] for x in range(len(row))]) == len(row)  

import random
inputPoints = [[random.randint(70,100) for i in range(3)] for j in range(500)]
#paretoPoints, dominatedPoints = simple_cull(inputPoints, dominates)

#print()

#print(paretoPoints)

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
dp = np.array(dominatedPoints)
pp = np.array(paretoPoints)
#print(dp)
ax.scatter(dp[:,0],dp[:,1],dp[:,2])
ax.scatter(pp[:,0],pp[:,1],pp[:,2],color='red')

import matplotlib.tri as mtri
triang = mtri.Triangulation(pp[:,0],pp[:,1])
ax.plot_trisurf(triang,pp[:,2],color='red')

ax.set_xlabel("Wood Yield",linespacing=5)
ax.set_ylabel("Soil Loss",linespacing=5)
ax.set_zlabel("Fire Risk Protection",linespacing=5)
ax.xaxis.labelpad=7
ax.yaxis.labelpad=7
ax.zaxis.labelpad=7
#plt.xlabel("Wood Yield")
#plt.ylabel("Soil Loss")
#plt.clabel("Fire Risk Protection")

plt.show()