import json
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import random


def showHist(same, cross):
    Same = np.array(same)
    Cross = np.array(cross)

    fig, axs = plt.subplots(2, 1)
    axs[0].hist(Same, bins=50, edgecolor='black')
    axs[0].set_xlim([-0.1, 1])
    axs[0].set_title('Same Barcode')

    axs[1].hist(Cross, bins=50, edgecolor='black')
    axs[1].set_xlim([-0.1, 1])
    axs[1].set_title('Cross Barcode')
    # plt.savefig('plot.png')
    plt.show()


def showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct):
    x = np.linspace(start=0, stop=1.0, num=50, endpoint=True).tolist()
    plt.figure(figsize=(10, 6))
    plt.plot(x, recall, color='red', label='recall:TP/TPFN')
    plt.plot(x, recall_TN, color='black', label='recall_TN:TN/TNFP')
    plt.plot(x, PrecisePos, color='blue', label='PrecisePos:TP/TPFN')
    plt.plot(x, PreciseNeg, color='green', label='PreciseNeg:TN/TNFP')
    plt.plot(x, Correct, color='m', label='Correct：(TN+TP)/(TPFN+TNFP)')
    plt.legend()
    plt.xlabel('threshold')
    # plt.ylabel('Similarity')
    plt.grid(True, linestyle='--', alpha=0.5)
    plt.savefig('grid.png')
    plt.show()
    plt.close()


def compute_accuracy_recall(score, labels):
    th = 0.1
    squence = np.linspace(-1, 1, num=50)
    recall, PrecisePos, PreciseNeg, recall_TN, Correct = [], [], [], [], []
    Same = score[:len(score) // 2]
    Cross = score[len(score) // 2:]
    for th in squence:
        t_score = (score > th)
        t_labels = (labels == 1)
        TP = np.sum(np.logical_and(t_score, t_labels))
        FN = np.sum(np.logical_and(np.logical_not(t_score), t_labels))
        f_score = (score < th)
        f_labels = (labels == 0)
        TN = np.sum(np.logical_and(f_score, f_labels))
        FP = np.sum(np.logical_and(np.logical_not(f_score), f_labels))
        print("Threshold:{} TP:{},FP:{},TN:{},FN:{}".format(th, TP, FP, TN, FN))

        PrecisePos.append(0 if TP / (TP + FP) == 'nan' else TP / (TP + FP))
        PreciseNeg.append(0 if TN == 0 else TN / (TN + FN))
        recall.append(0 if TP == 0 else TP / (TP + FN))
        recall_TN.append(0 if TN == 0 else TN / (TN + FP))
        Correct.append(0 if TP == 0 else (TP + TN) / (TP + FP + TN + FN))

    showHist(Same, Cross)
    showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct)


def get_similarity(features1, features2, n, m):
    features1 = np.array(features1)
    features2 = np.array(features2)
    all_similarity = []
    for feature1 in features1:
        for feature2 in features2:
            similarity = np.dot(feature1, feature2) / (np.linalg.norm(feature1) * np.linalg.norm(feature2))
            all_similarity.append(similarity)
    test_similarity = np.array(all_similarity)
    np_all_array = np.array(all_similarity).reshape(len(features1), len(features2))
    if n == 5 and m == 5:
        print(all_similarity)
    return np.mean(np_all_array), all_similarity
    # return sum(all_similarity)/len(all_similarity), all_similarity
    # return max(all_similarity), all_similarity


def deal_similarity(dicts):
    all_similarity = []
    similarity = []
    same_barcode, diff_barcode = [], []
    for n, (key1, value1) in enumerate(dicts.items()):
        print('key1 >> {}'.format(key1))
        for m, (key2, value2) in enumerate(dicts.items()):
            print('key1 >> {}  key2 >> {}  peidui {}{}'.format(key1, key2, n, m))
            max_similarity, some_similarity = get_similarity(value1, value2, n, m)
            similarity.append(max_similarity)
            if key1 == key2:
                same_barcode += some_similarity
            else:
                diff_barcode += some_similarity
        all_similarity.append(similarity)
        similarity = []
    all_similarity = np.array(all_similarity)
    random.shuffle(diff_barcode)
    same_list = [1] * len(same_barcode)
    diff_list = [0] * len(same_barcode)
    all_list = same_list + diff_list
    all_score = same_barcode + diff_barcode[:len(same_barcode)]
    compute_accuracy_recall(np.array(all_score), np.array(all_list))
    print(all_similarity.shape)


with open('../search_library/data_zhanting.json', 'r') as file:
    data = json.load(file)
dicts = {}
for dict in data['total']:
    key = dict['key']
    value = dict['value']
    dicts[key] = value
deal_similarity(dicts)