# -*- coding: utf-8 -*-

 import struct

 import os

 # 拼音表偏移，

 startPy = 0x1540;

 # 汉语词组表偏移

 startChinese = 0x2628;

 # 全局拼音表

 GPy_Table = {}

 # 解析结果

 # 元组(词频,拼音,中文词组)的列表

 # 原始字节码转为字符串

 def byte2str(data):

     pos = 0

     str = ''

     while pos < len(data):

         c = chr(struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0])

         if c != chr(0):

             str += c

         pos += 2

     return str

 # 获取拼音表

 def getPyTable(data):

     data = data[4:]

     pos = 0

     while pos < len(data):

         index = struct.unpack('H', bytes([data[pos],data[pos + 1]]))[0]

         pos += 2

         lenPy = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

         pos += 2

         py = byte2str(data[pos:pos + lenPy])

         GPy_Table[index] = py

         pos += lenPy

 # 获取一个词组的拼音

 def getWordPy(data):

     pos = 0

     ret = ''

     while pos < len(data):

         index = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

         ret += GPy_Table[index]

         pos += 2

     return ret

 # 读取中文表

 def getChinese(data):

     GTable = []

     pos = 0

     while pos < len(data):

         # 同音词数量

         same = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

         # 拼音索引表长度

         pos += 2

         py_table_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

         # 拼音索引表

         pos += 2

         py = getWordPy(data[pos: pos + py_table_len])

         # 中文词组

         pos += py_table_len

         for i in range(same):

             # 中文词组长度

             c_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

             # 中文词组

             pos += 2

             word = byte2str(data[pos: pos + c_len])

             # 扩展数据长度

             pos += c_len

             ext_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

             # 词频

             pos += 2

             count = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

             # 保存

             GTable.append((count, py, word))

             # 到下个词的偏移位置

             pos += ext_len

     return GTable

 def scel2txt(file_name):

     print('-' * 60)

     with open(file_name, 'rb') as f:

         data = f.read()

     print("词库名：", byte2str(data[0x130:0x338])) # .encode('GB18030')

     print("词库类型：", byte2str(data[0x338:0x540]))

     print("描述信息：", byte2str(data[0x540:0xd40]))

     print("词库示例：", byte2str(data[0xd40:startPy]))

     getPyTable(data[startPy:startChinese])

     getChinese(data[startChinese:])

     return getChinese(data[startChinese:])

 if __name__ == '__main__':

     # scel所在文件夹路径

     in_path = r"C:\Users\Administrator\Downloads"   #修改为你的词库文件存放文件夹

     # 输出词典所在文件夹路径

     out_path = r"C:\Users\Administrator\Downloads\text"  # 转换之后文件存放文件夹

     fin = [fname for fname in os.listdir(in_path) if fname[-5:] == ".scel"]

     for f in fin:

         try:

             for word in scel2txt(os.path.join(in_path, f)):

                 file_path=(os.path.join(out_path, str(f).split('.')[0] + '.txt'))

                 # 保存结果

                 with open(file_path,'a+',encoding='utf-8')as file:

                     file.write(word[2] + '\n')

             os.remove(os.path.join(in_path, f))

         except Exception as e:

             print(e)

             pass

 # 统计词频次数

 for word in tokens:

     if len(word) == 1:

         continue

     else:

         wcdict[word] = wcdict.get(word, 0) + 1

 # 词频排序

 wcls = list(wcdict.items())

 wcls.sort(key=lambda x: x[1], reverse=True)

 Filess= open(r'G:\aa\stops_chinese.txt', 'r', encoding='utf8')  # 打开中文停用词表

 stops = Filess.read().split('\n')  # 以回车键作为标识符把停用词表放到stops列表中

 tokens=[token for token in wordsls if token not in stops]

 print("过滤后中文内容对比:",len(tokens), len(wordsls))

 # 打印前25词频最高的中文

 for i in range(25):

     print(wcls[i])

 # 存储过滤后的文本

 pd.DataFrame(wcls).to_csv('three.csv', encoding='utf-8')

 # 读取csv词云

 txt = open('three.csv', 'r', encoding='utf-8').read()

 # 读取csv词云

 txt = open('three.csv', 'r', encoding='utf-8').read()

 # 用空格键隔开文本并把它弄进列表中

 cut_text = "".join(jieba.lcut(txt))

 mywc = WordCloud().generate(cut_text)

 plt.imshow(mywc)

 plt.axis("off")

 plt.show()