self.cut_off_pos = [0] * (len(self.V)+1)
        for a in self.ad.number_of_v_removed:
            self.cut_off_pos[a] = self.ad.number_of_v_removed[a]


if __name__ == "__main__":
    increment = 1
    iterations = 18
    average_over = 10
    
    iter_nums = [x*increment + increment for x in xrange(iterations)]
    p_results = [0] * iterations
    for j in xrange(average_over):
        for i in xrange(iterations):
            print j, i
            a = Attack(200, 40, iter_nums[i])
            a.attack(100)
            p_vals = binom_stats(a.sorted_tally, 100)
            p_results[i] += sum(p_vals)/len(p_vals)
    p_results = [x / average_over for x in p_results]
    
    plt.plot(iter_nums, p_results);
    plt.xlabel("Threshold")
    plt.ylabel("p value")
    plt.show()    
       
    raw_input()
    
    
        self.cut_off_pos = [0] * (len(self.V)+1)
        for a in self.ad.number_of_v_removed:
            self.cut_off_pos[a] = self.ad.number_of_v_removed[a]

if __name__ == "__main__":
    iterations = 200
    increment = 25
    average_over = 10
    min_p_vals = [0] * iterations
    iterations_array = [i*increment + increment for i in xrange(iterations)]
    for j in xrange(average_over):
        a = Attack(200, 20, 3)
        for i in xrange(iterations):
            print j, i
            a.attack(increment)
            p_vals = binom_stats(a.sorted_tally, iterations_array[i])
            min_p_vals[i] += min(p_vals)
    min_p_vals = [x/average_over for x in min_p_vals]
    
    fig = plt.figure()
    ax = fig.add_subplot(121)
    ax.plot(iterations_array, min_p_vals, 'b-', label="Minimum p-value")
    ax.plot(iterations_array, [0.05] * iterations, 'r--', label="0.05 level of confidence")
    ax.plot(iterations_array, [0.01] * iterations, 'm--', label="0.01 level of confidence")
    ax.set_xlabel("Number of attacks")
    ax.set_ylabel("Level of confidence")
    ax.legend(bbox_to_anchor=(1.05, 0.5), loc=6, borderaxespad=0.)
    fig.show()