import numpy as np
from ast import literal_eval
import getopt
import json
import sys


def main(argv):
    usage = 'usage: remove_outlier.py -x <xvals> -y <yvals> -z <zvals>'
    x = []
    y = []
    z = []

    try:
        opts, args = getopt.getopt(argv, "srdhu:x:y:z:", ["help"])
    except getopt.GetoptError as err:
        print(str(err))
        print(usage)
        sys.exit(2)

    for opt, arg in opts:
        if opt in ("-h", "--help"):
            print(usage)
            sys.exit()
        elif opt == '-x':
            x = parse_array(arg)
        elif opt == '-y':
            y = parse_array(arg)
        elif opt == '-z':
            z = parse_array(arg)


    if None in (x, y, z):
        print('Error: Missing values')
        sys.exit(2)

    (inliers_x, inliers_y, inliers_z) = remove_outliers(x, y, z)

    print(inliers_x)
    print(inliers_y)
    print(inliers_z)

    #print(json.dumps({'x' : str(inliers_x), 'y' : str(inliers_y), 'z' : str(inliers_z)}))

def parse_array(arg):
    return [float(n) for n in literal_eval(arg)]

def remove_outliers(x,y,z):
    avg_x = np.mean(x)
    avg_y = np.mean(y)
    avg_z = np.mean(z)

    std_x = np.std(x)
    std_y = np.std(y)
    std_z = np.std(z)

    outlier_indices = []

    for i in range(0, len(x)):
        if(x[i] > avg_x + 1.7 * std_x or x[i] < avg_x - 1.7 * std_x):
            outlier_indices.append(i)
        elif(y[i] > avg_y + 1.7 * std_y or y[i] < avg_y - 1.7 * std_y):
            outlier_indices.append(i)
        elif(z[i] > avg_z + 1.7 * std_z or z[i] < avg_z - 1.7 * std_z):
            outlier_indices.append(i)

    inliers_x =  [x[i] for i in range(0, len(x)) if i not in outlier_indices]
    inliers_y =  [y[i] for i in range(0, len(y)) if i not in outlier_indices]
    inliers_z =  [z[i] for i in range(0, len(z)) if i not in outlier_indices]

    return (inliers_x, inliers_y, inliers_z)

def get_average(x, y, z):
    return (np.mean(x), np.mean(y), np.mean(z))

if __name__ == "__main__":
    main(sys.argv[1:])