#!/usr/bin/python
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import math
from sets import ImmutableSet

mpl.rcParams['font.size'] = 5
mpl.rcParams['lines.linewidth'] = 0.5
mpl.rcParams['figure.figsize'] = 2.2,2.2
mpl.rcParams['legend.fontsize'] = 5
mpl.rcParams['axes.linewidth'] = 0.5
mpl.rcParams['figure.subplot.hspace'] = 0.4
mpl.rcParams['figure.subplot.wspace'] = 0.4
legend_width = 0.2

def distance(a,b):
    return math.sqrt(np.square(a-b).sum())

def gen_pairs(var,sk_data):
    np.random.shuffle(sk_data)
    sk_data = sk_data[:,1:]
    noise1 = np.random.normal(0,var,sk_data.shape)
    noise2 = np.random.normal(0,var,sk_data.shape)
    sk1 = sk_data+noise1
    sk2 = sk_data+noise2
    randoms = np.random.randint(0,sk_data.shape[0],(2000,2))
    dict = {}
    u_pairs = []
    i = 0
    while len(u_pairs) < sk_data.shape[0]:
        pair = randoms[i]
        key = ImmutableSet(pair)
        i += 1
        if pair[0] != pair[1] and key not in dict:
            dict[key] = True
            u_pairs += [(pair[0],pair[1])]

    m_pairs = zip(range(sk_data.shape[0]),range(sk_data.shape[0]))
    result = []
    for j in range(sk_data.shape[0]):
            result += [(distance(sk1[m_pairs[j][0]],sk2[m_pairs[j][1]]),
                        distance(sk1[u_pairs[j][0]],sk2[u_pairs[j][1]]))]

    return result

if __name__ == "__main__":
    plt.figure(figsize=(3,2.2))
    ap = np.loadtxt("associatepredict.txt",delimiter=",")
    indices = [i for i in range(ap.shape[0]) if ap[i,1]<0.1]
    ap_false = ap[:,1][indices]
    ap_true = ap[:,0][indices]
    plt.plot(100*ap_false,100*ap_true,label="Face recognition")
    plt.xlabel("False positive rate [%]")
    plt.ylabel("True positive rate [%]")
    np.random.seed()
    std = map(float,sys.argv[2].split(","))
    sk_data  = np.loadtxt(sys.argv[1],comments="#",delimiter=",")
    for s in std:
        result = gen_pairs(s,sk_data)
        thresholds = np.square(np.arange(0,10,0.001))
        true_pos = []
        false_pos = []
        for threshold in thresholds:
            true = 0
            false = 0
            for j in range(len(result)):
                if result[j][0] < threshold:
                    true += 1
                if result[j][1] < threshold:
                    false += 1
            true_pos += [float(true)/len(result)]
            false_pos += [float(false)/len(result)]
        indices = [i for i in range(len(false_pos)) if false_pos[i]<0.1]
        false_pos = np.array(false_pos)
        false_pos = false_pos[indices]
        true_pos = np.array(true_pos)
        true_pos = true_pos[indices]
        plt.plot(100*false_pos,100*true_pos,label="$\sigma$ = "+str(s))
    leg = plt.legend(loc="lower right")
    leg.get_frame().set_linewidth(legend_width)
    plt.savefig("roc.pdf",bbox_inches="tight",pad_inches=0.05)