import numpy as np
import pandas as pd
from py2neo import Graph, Node, Relationship
import time
import scipy.spatial
from fuzzywuzzy import fuzz
import  hashlib
import json
import multiprocessing as mp
import heapq
import os

from functools import partial
from multiprocessing.pool import Pool

dir_project = os.getcwd()+"\\data1\\100000\\zh_en\\"

dir_align_project = os.getcwd()+"\\data1\\Align\\zh_en\\"

dir_Neo4j_File = "C:\\Users\\admin\\Desktop\\Neo4j.csv"
dir_Neo4j_Align_File = "C:\\Users\\admin\\Desktop\\Neo4j_aligned.csv"

dir_ent_ids_1_real = dir_project + "ent_ids_1_real"
dir_ent_ids_2_real = dir_project + "ent_ids_2_real"
dir_triples_1_real = dir_project + "triples_1_real"
dir_triples_2_real = dir_project + "triples_2_real"
dir_ref_ent_ids_real = dir_project + "ref_ent_ids_real"
dir_ref_ent_ids_real_neg = dir_project + "ref_ent_ids_real_neg"

dir_training_attrs_1 = dir_project + "training_attrs_1"
dir_training_attrs_2 = dir_project + "training_attrs_2"
dir_ModelId2RealId = dir_project + "ModelId2RealId"
dir_ent_ids_1 = dir_project + "ent_ids_1"
dir_triples_1 = dir_project + "triples_1"
dir_ref_ent_ids = dir_project + "ref_ent_ids"
dir_ref_ent_ids_neg = dir_project + "ref_ent_ids_neg"



def getDataFromNeo4j3(skip_num, limit_num):
    """
    取包括已对齐的公司ID在内的一共20w个公司的ID，每个公司ID取最多10条关联
    输入多进程需要的开始结束下标
    :return:
    """
    graph = Graph('http://118.31.10.60:7474', auth=('bxkc_web', 'bxkc_web'))

    lines = file2Data(dir_ref_ent_ids_real)
    id_list = []
    # for index in range(len(lines)):
    #     ids = lines[index][0:-1].split("\t")
    #     id_list.append(int(ids[0]))
    #     id_list.append(int(ids[1]))
    # id_list = list(set(id_list))

    print("Match Neo4j, getting ID...")
    result = graph.run("match (o:Organization)  "
                       "return id(o) order by id(o) "
                       " skip " + str(skip_num) +
                       " limit " + str(limit_num)
                       ).to_ndarray()
    result = result.tolist()
    for index in range(len(result)):
        id_list.append(int(result[index][0]))
    id_list = list(set(id_list))
    id_list.sort()
    print(len(id_list))
    # print(id_list)


    print("Match Neo4j...")
    start_time = time.time()
    results = []
    for index in range(len(id_list)):
        result = graph.run("MATCH (o:Organization)-[r]->(p:Project) "
                           "where p.project_name is not null and id(o)=" + str(id_list[index]) +
                           " RETURN id(o), o.name, o.nicknames, o.area, o.city, o.district, "
                           "id(r), type(r), r.price, "
                           "id(p), p.project_name, p.area, p.city, p.district "
                           "LIMIT 10"
                           ).to_ndarray()
        results.append(result.tolist())
        # print(index, len(id_list))
        if index % 500 == 0:
            end_time = time.time()
            print(index, end_time-start_time)

    print("write into csv...")
    start_time = time.time()
    df_list = []
    for i in range(len(results)):
        for j in range(len(results[i])):
            df_list.append(results[i][j])
    df = pd.DataFrame(df_list)
    df.columns = ["id(o)", "o.name", "o.nicknames", "o.area", "o.city", "o.district",
                  "id(r)", "type(r)", "r.price",
                  "id(p)", "p.project_name", "p.area", "p.city", "p.district"]
    df.to_csv("C:\\Users\\admin\\Desktop\\Neo4j"+"_"+str(skip_num)+"_"+str(limit_num)+".csv")
    end_time = time.time()
    print(end_time-start_time)


def getDataFromNeo4j_align():
    """
    取已对齐的公司ID的所有关联
    :return:
    """
    graph = Graph('http://118.31.10.60:7474', auth=('bxkc_web', 'bxkc_web'))

    print("Match Neo4j, getting ID...")
    lines = file2Data(dir_align_project+"ref_ent_ids_real")
    id_list = []
    for index in range(len(lines)):
        ids = lines[index][0:-1].split("\t")
        id_list.append(ids[0])
        id_list.append(ids[1])
    id_list = list(set(id_list))
    id_list.sort()
    # print(len(id_list))

    print("Match Neo4j...")
    start_time = time.time()
    results = []
    for index in range(len(id_list)):
        result = graph.run("MATCH (o:Organization)-[r]->(p:Project) "
                           "where p.project_name is not null and id(o)=" + id_list[index] +
                           " RETURN id(o), o.name, o.nicknames, o.area, o.city, o.district, "
                           "id(r), type(r), r.price, "
                           "id(p), p.project_name, p.area, p.city, p.district "
                           "LIMIT 10"
                           ).to_ndarray()
        results.append(result.tolist())
        # print(index, len(id_list))
    end_time = time.time()
    print(end_time-start_time)

    print("write into csv...")
    start_time = time.time()
    df_list = []
    for i in range(len(results)):
        for j in range(len(results[i])):
            df_list.append(results[i][j])
    df = pd.DataFrame(df_list)
    df.columns = ["id(o)", "o.name", "o.nicknames", "o.area", "o.city", "o.district",
                  "id(r)", "type(r)", "r.price",
                  "id(p)", "p.project_name", "p.area", "p.city", "p.district"]
    df.to_csv("C:\\Users\\admin\\Desktop\\Neo4j_aligned.csv")
    end_time = time.time()
    print(end_time-start_time)


def getDataFromNeo4j():
    """
    随机取前200w条数据
    :return:
    """
    graph = Graph('http://118.31.10.60:7474', auth=('bxkc_web', 'bxkc_web'))

    print("Match Neo4j...")
    start_time = time.time()
    results = []
    result = graph.run("MATCH (o:Organization)-[r]->(p:Project) "
                  "where p.project_name is not null "
                  "RETURN id(o), o.name, o.nicknames, o.area, o.city, o.district, "
                  "id(r), type(r), r.price, "
                  "id(p), p.project_name, p.area, p.city, p.district "
                  "skip 2000000 LIMIT 2000000"
                  ).to_ndarray()
    results.append(result.tolist())
    end_time = time.time()
    print(end_time-start_time)

    print("write into csv...")
    start_time = time.time()
    df_list = []
    for i in range(len(results)):
        for j in range(len(results[i])):
            df_list.append(results[i][j])
    df = pd.DataFrame(df_list)
    df.columns = ["id(o)", "o.name", "o.nicknames", "o.area", "o.city", "o.district",
                  "id(r)", "type(r)", "r.price",
                  "id(p)", "p.project_name", "p.area", "p.city", "p.district"]
    df.to_csv("C:\\Users\\admin\\Desktop\\Neo4j.csv")
    end_time = time.time()
    print(end_time-start_time)


def testNeo4j():
    graph = Graph('http://118.31.10.60:7474', auth=('bxkc_web', 'bxkc_web'))

    print("Match Neo4j...")
    start_time = time.time()
    a = graph.run("MATCH (o:Organization)-[r]->(p:Project) "
                  "where p.project_name is not null "
                  "RETURN id(o), o.name, o.nicknames, o.area, o.city, o.district, "
                  "id(r), type(r), r.price, "
                  "id(p), p.project_name, p.area, p.city, p.district "
                  "skip 2000000 LIMIT 2000000").to_ndarray()
    end_time = time.time()
    print(end_time-start_time)

    print("write into csv...")
    start_time = time.time()
    df = pd.DataFrame(a)
    df.columns = ["id(o)", "o.name", "o.nicknames", "o.area", "o.city", "o.district",
                  "id(r)", "type(r)", "r.price",
                  "id(p)", "p.project_name", "p.area", "p.city", "p.district"]
    df.to_csv("C:\\Users\\admin\\Desktop\\Neo4j1.csv")
    end_time = time.time()
    print(end_time-start_time)


def Neo4j2Data(filename):
    dir = dir_project
    df = pd.read_csv(filename)

    # files
    triples = []
    ent_ids = []
    training_attrs = []
    # df = df[0:100000]

    for column in df.columns:
        df[column] = df[column].apply(lambda x: "" if x == "未知" else x)

    for index, row in df.iterrows():
        triples.append(str(row["id(o)"]) + "\t" + str(row["id(r)"]) + "\t" + str(row["id(p)"]) + "\n")
        ent_ids.append(str(row["id(o)"]) + "\t" + "O" + str(row["o.name"]) + "\n")
        ent_ids.append(str(row["id(p)"]) + "\t" + "P" + str(row["p.project_name"]) + "\n")
        training_attrs.append("O" + str(row["o.name"]) + "\t" + str(row["o.nicknames"]) + "\t" + str(row["o.area"])
                              + "\t" + str(row["o.city"]) + "\t" + str(row["o.district"]) + "\n")
        training_attrs.append("P" + str(row["p.project_name"]) + "\t" + str(row["p.area"])
                              + "\t" + str(row["p.city"]) + "\t" + str(row["p.district"]) + "\n")

    triples1 = triples
    ent_ids1 = ent_ids
    training_attrs1 = training_attrs

    data2File(triples1, dir+"triples_1_real")
    # data2File(triples2, dir+"triples_2_real")
    data2File(ent_ids1, dir+"ent_ids_1_real")
    # data2File(ent_ids2, dir+"ent_ids_2_real")
    data2File(training_attrs1, dir+"training_attrs_1")
    # data2File(training_attrs1, dir+"training_attrs_2_a")


def data2File(_list, filename):
    with open(filename, 'w', encoding='UTF-8') as f:
        f.writelines(_list)


def file2Data(filename):
    with open(filename, 'r', encoding='UTF-8') as f:
        _list = f.readlines()
    return _list


def data2FileAppend(_list, filename):
    with open(filename, 'a+', encoding='UTF-8') as f:
        f.writelines(_list)


def similarity():
    df = pd.read_csv(dir_Neo4j_File)
    # df1 = df[10000:20000]
    # 59000:1000000     1037000:1500000
    df = df[:100000]

    df = df[["id(o)", "o.name", "o.area", "o.city"]]
    # df1 = df1[["id(o)", "o.name", "o.area", "o.city"]]

    org_list = []
    for index, row in df.iterrows():
        org_list.append(str(row["id(o)"]) + " " + str(row["o.name"]) + " " + str(row["o.area"])
                        + " " + str(row["o.city"]))
    # org_list1 = []
    # for index, row in df1.iterrows():
    #     org_list1.append(str(row["id(o)"]) + " " + str(row["o.name"]) + " " + str(row["o.area"])
    #                     + " " + str(row["o.city"]))

    # 去重
    org_list = list(set(org_list))
    print("去重后org_list：", len(org_list))
    # org_list1 = list(set(org_list1))
    # print("去重后org_list1：", len(org_list1))

    # 分离id和其他字段
    id_list = []
    for i in range(len(org_list)):
        ss = org_list[i].split(" ")
        id_list.append(ss[0])
        org_list[i] = ss[1] + " " + ss[2] + " " + ss[3]

    # id_list1 = []
    # for i in range(len(org_list1)):
    #     ss = org_list1[i].split(" ")
    #     id_list1.append(ss[0])
    #     org_list1[i] = ss[1] + " " + ss[2] + " " + ss[3]


    # 循环计算相似度
    # 阈值
    alpha = 95
    beta = 98
    alpha_dict ={}
    beta_dict = {}
    alpha_list = []
    beta_list = []
    for i in range(len(org_list)):
        if i % 1000 == 0:
            print("Loop:", i)

        i2 = i+1
        for j in range(i2):
            org1 = org_list[i]
            org2 = org_list[j]

            # 初步匹配两个字符串中的字符，避免每条都计算相似度，效率低
            org_name1 = org1.split(" ")[0]
            org_name2 = org2.split(" ")[0]
            cnt = 0
            if len(org_name1) <= len(org_name2):
                for c in org_name1:
                    if c in org_name2:
                        cnt += 1
            else:
                for c in org_name2:
                    if c in org_name1:
                        cnt += 1
            if cnt / (len(org_name1) if len(org_name1) <= len(org_name2) else len(org_name2)) <= 0.7:
                continue

            if org1 == org2:
                continue
            if org1 in org2:
                org1 = org2
            if org2 in org1:
                org2 = org1

            sim = fuzz.ratio(org1, org2)

            negative_flag = 1
            if alpha <= sim:
                alpha_dict[(org_list[i], org_list[j])] = sim

                # 判断IDs是否在已对齐中
                aligned_list = file2Data(dir_ref_ent_ids_real)
                for ids in aligned_list:
                    if str(id_list[i])+"\t"+str(id_list[j]) == ids \
                    or str(id_list[j])+"\t"+str(id_list[i]) == ids:
                        negative_flag = 0
                        break

                if negative_flag:
                    alpha_list.append(org_list[i]+"#"+org_list[j]+"#"+str(sim)+"\t"
                                      +str(id_list[i])+"\t"+str(id_list[j])+"\n")
                print(org_list[i], "#", org_list[j], "#", sim, id_list[i], id_list[j])
            # if sim >= beta:
            #     beta_dict[(org_list[i], org_list[j])] = sim
            #     beta_list.append([org_list[i], id_list[i], org_list[j], id_list[j], sim])
            #     print(org_list[i], "#", org_list[j], "#", sim, id_list[i], id_list[j])

    # df = pd.DataFrame.from_dict(alpha_dict, orient='index')
    # df1 = pd.DataFrame.from_dict(beta_dict, orient='index')
    # df = pd.DataFrame(alpha_list)
    # df1 = pd.DataFrame(beta_list)

    data2File(alpha_list, "C:\\Users\\admin\\Desktop\\ref_ent_ids_real_neg")
    # data2File(alpha_list, "C:\\Users\\admin\\Desktop\\Similar_10w")

    # df.to_csv("C:\\Users\\admin\\Desktop\\Similar0.9.csv")
    # df1.to_csv("C:\\Users\\admin\\Desktop\\Similar0.95.csv")


def resetID4KeyValue(filename, start_index):
    _list = file2Data(filename)
    map_dict = loadDict(dir_ModelId2RealId)

    for i in range(len(_list)):
        ss = _list[i].split("\t")
        real_id = ss[0]
        if len(ss) <= 1:
            _list[i].split(" ")

            continue
        value = ss[1]

        if str(real_id) in map_dict.keys():
            model_id = map_dict[str(real_id)]
        else:
            map_dict[str(real_id)] = start_index
            model_id = start_index

        _list[i] = str(model_id) + "\t" + value
        # print(_list[i])
        start_index += 1

    data2File(_list, dir_ent_ids_1)
    saveDict(dir_ModelId2RealId, map_dict)
    return


def resetID4Tuple(filename):
    _list = file2Data(filename)
    map_dict = loadDict(dir_ModelId2RealId)

    for i in range(len(_list)):
        ss = _list[i][:-1].split("\t")
        real_id_1 = ss[0]
        real_id_2 = ss[1]

        if str(real_id_1) in map_dict.keys():
            model_id_1 = map_dict[str(real_id_1)]
        else:
            print("not exists ID1 mapping：", real_id_1)

        if str(real_id_2) in map_dict.keys():
            model_id_2 = map_dict[str(real_id_2)]
        else:
            print("not exists ID2 mapping：", real_id_2)

        _list[i] = str(model_id_1) + "\t" + str(model_id_2) + "\n"
    data2File(_list, dir_ref_ent_ids_neg)


def resetID4Triple(filename):
    _list = file2Data(filename)
    map_dict = loadDict(dir_ModelId2RealId)

    for i in range(len(_list)):
        ss = _list[i][:-1].split("\t")
        real_id_1 = ss[0]
        ref = ss[1]
        real_id_2 = ss[2]

        if str(real_id_1) in map_dict.keys():
            model_id_1 = map_dict[str(real_id_1)]
        else:
            print("not exists ID1 mapping：", real_id_1)

        if str(real_id_2) in map_dict.keys():
            model_id_2 = map_dict[str(real_id_2)]
        else:
            print("not exists ID2 mapping：", real_id_2)

        _list[i] = str(model_id_1) + "\t" + str(ref) + "\t" + str(model_id_2) + "\n"
    data2File(_list, dir_triples_1)
    return


def loadDict(filename):
    with open(filename, "r") as json_file:
        dic = json.load(json_file)
    return dic


def saveDict(filename, dic):
    with open(filename, 'w') as json_file:
        json.dump(dic, json_file)


def deleteDuplicateId(filename):
    ## 去重
    _dir = filename
    lines = file2Data(_dir)
    print(len(lines))
    lines = list(set(lines))
    lines.sort(key=lambda x: x.split("\t")[0])
    print(len(lines))
    data2File(lines, _dir)


def deleteDuplicateId2(filename):
    ## 去重
    _dir = filename
    lines = file2Data(_dir)
    print(len(lines))
    new_lines = []
    for i in range(len(lines)):
        ss = lines[i][:-1].split("\t")
        if int(ss[0]) > int(ss[1]):
            new_lines.append(ss[1] + "\t" + ss[0] + "\n")
        else:
            new_lines.append(ss[0] + "\t" + ss[1] + "\n")
    new_lines = list(set(new_lines))
    new_lines.sort(key=lambda x: x.split("\t")[0])
    print(len(new_lines))
    data2File(new_lines, _dir)


def initMapDict():
    d = {"-1": -1}
    saveDict(dir_ModelId2RealId, d)
    d = loadDict(dir_ModelId2RealId)
    # print(d, type(d))


def multiProcess():
    process_num = 15
    skip_num = 300000
    limit_num = 50000
    for i in range(process_num):
        print(skip_num, limit_num)
        p = mp.Process(target=getDataFromNeo4j3, args=(skip_num, limit_num))
        skip_num += limit_num
        p.start()


def multiThread():

    return


def clean_ID_Entity(filename):
    lines = file2Data(filename)
    print(len(lines))
    new_lines = []
    for i in range(len(lines)):
        ss = lines[i].split("\t")
        if len(ss) == 2:
            new_lines.append(ss[0] + "\t" + ss[1])
    print(len(new_lines))
    data2File(new_lines, filename)


def mergeFile(file_name_prefix, target_file, file_num, limit_num):
    skip_num = 0
    df_all = pd.DataFrame()
    for i in range(file_num):
        df = pd.read_csv(file_name_prefix + "_" + str(skip_num) + "_" + str(limit_num) + ".csv")
        df = df[["id(o)", "o.name", "o.nicknames", "o.area", "o.city", "o.district",
                 "id(r)", "type(r)", "r.price",
                 "id(p)", "p.project_name", "p.area", "p.city", "p.district"]]
        df_all = df_all.append(df)
        # print(df_all)
        skip_num += limit_num
    df_all.columns = ["id(o)", "o.name", "o.nicknames", "o.area", "o.city", "o.district",
                  "id(r)", "type(r)", "r.price",
                  "id(p)", "p.project_name", "p.area", "p.city", "p.district"]
    df_all.to_csv(target_file)


def alignAppend2Data(filename=dir_Neo4j_Align_File):
    dir = dir_align_project
    dir2dir = dir_project
    df = pd.read_csv(filename)

    # files
    triples = []
    ent_ids = []
    training_attrs = []

    for column in df.columns:
        df[column] = df[column].apply(lambda x: "" if x == "未知" else x)

    for index, row in df.iterrows():
        triples.append(str(row["id(o)"]) + "\t" + str(row["id(r)"]) + "\t" + str(row["id(p)"]) + "\n")
        ent_ids.append(str(row["id(o)"]) + "\t" + "O" + str(row["o.name"]) + "\n")
        ent_ids.append(str(row["id(p)"]) + "\t" + "P" + str(row["p.project_name"]) + "\n")
        training_attrs.append("O" + str(row["o.name"]) + "\t" + str(row["o.nicknames"]) + "\t" + str(row["o.area"])
                              + "\t" + str(row["o.city"]) + "\t" + str(row["o.district"]) + "\n")
        training_attrs.append("P" + str(row["p.project_name"]) + "\t" + str(row["p.area"])
                              + "\t" + str(row["p.city"]) + "\t" + str(row["p.district"]) + "\n")

    data2File(triples, dir+"triples_1_real")
    data2File(ent_ids, dir+"ent_ids_1_real")
    data2File(training_attrs, dir+"training_attrs_1")

    data2FileAppend(triples, dir2dir+"triples_1_real")
    data2FileAppend(ent_ids, dir2dir+"ent_ids_1_real")
    data2FileAppend(training_attrs, dir2dir+"training_attrs_1")
    return


def getAlignIDOrg():
    list1 = file2Data(dir_align_project+"ref_ent_ids_real")
    list2 = file2Data(dir_align_project+"ent_ids_1_real")

    id_org_dict = {}
    id_align = []

    for i in range(len(list2)):
        ss = list2[i][:-1].split("\t")
        if ss[1][0] == "O":
            id_org_dict[str(ss[0])] = ss[1]

    for i in range(len(list1)):
        ss = list1[i][:-1].split("\t")
        if str(ss[0]) in id_org_dict.keys():
            id_align.append(str(ss[0])+"\t"+id_org_dict[str(ss[0])]+"\n")
        if str(ss[1]) in id_org_dict.keys():
            id_align.append(str(ss[1])+"\t"+id_org_dict[str(ss[1])]+"\n")
    id_align = list(set(id_align))

    data2File(id_align, dir_align_project+"ent_ids_aligned")



def test():
    print(os.getcwd())
    # id_vec = [[100, 1], [60, 2], [4, 200], [33, 300]]
    # id_ndarray = np.array(id_vec)
    # print(id_vec)
    # sim = scipy.spatial.distance.cdist(id_vec, id_vec[:2], metric='cityblock')
    # print(sim)
    # print(sim.shape)
    # print(heapq.nlargest(2, range(len(id_ndarray)), id_ndarray.take))


if __name__ == '__main__':
    # testNeo4j()
    # Neo4j2Data()
    # similarity()

    # initMapDict()
    # deleteDuplicateId()
    # resetID4KeyValue(dir_ent_ids_1_real, 0)
    # resetID4Tuple(dir_ref_ent_ids_real)
    # resetID4Triple(dir_triples_1_real)


    # getDataFromNeo4j2()

    #####################################
    # Neo4j2Data(dir_Neo4j_File)
    # alignAppend2Data(dir_Neo4j_Align_File)
    # deleteDuplicateId(dir_ent_ids_1_real)
    # initMapDict()
    # clean_ID_Entity(dir_ent_ids_1_real)
    # resetID4KeyValue(dir_ent_ids_1_real, 0)
    # resetID4Tuple(dir_ref_ent_ids_real_neg)
    # resetID4Triple(dir_triples_1_real)
    # deleteDuplicateId2(dir_ref_ent_ids_real)
    #######################################

    # deleteDuplicateId2()

    # getDataFromNeo4j3()
    # multiProcess()

    # dir = "C:\\Users\\admin\\Desktop\\Neo4j"
    # mergeFile(dir, dir+"4.csv", 8, 30000)

    # ll = [0, 1, 2, 3]
    # for i in range(0, len(ll), 2):
    #     i2 = i+1
    #     for j in range(0, len(ll), 2):
    #         print(ll[i], "-", ll[j])

    test()
    # getAlignIDOrg()
    # resetID4KeyValue(dir_project+"ent_ids_aligned", 0)


    # for i in range(0, 22, 5):
    #     print(i)
    # _dict = loadDict(dir_ModelId2RealId)
    #
    # for i in _dict.keys():
    #     if i == "":
    #         print(i, _dict[i])
    #         j = i
    # _dict.pop(j)
    # saveDict(dir_ModelId2RealId, _dict)

    # getDataFromNeo4j_align()
    # alignAppend2Data()