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14
func.py
14
func.py
@ -30,16 +30,20 @@ def chat_content_exec(update, context):
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chat_id = update.effective_message.chat_id
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print("\n---------------------------")
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print("内容: " + text)
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if "/" in text:
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print("这是一条指令信息")
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print("群组类型: " + str(chat_type))
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print("用户ID: " + str(user_id))
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print("chat_id: " + str(chat_id))
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r.append("{}_chat_content".format(chat_id), text)
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r.incrby("{}_user_message_amount", user_id)
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if "/" in text:
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print("这是一条指令信息,跳过")
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else:
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if text[-1] not in [",", "。", "!", ":", "?", "!", "?", ",", ":", "."]:
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r.append("{}_chat_content".format(chat_id), text + "。")
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else:
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r.append("{}_chat_content".format(chat_id), text)
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r.incrby("{}_total_message_amount".format(chat_id))
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r.hincrby("{}_user_message_amount".format(chat_id), user_id)
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print("---------------------------")
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start_handler = CommandHandler('start', start)
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chat_content_handler = MessageHandler(Filters.text, chat_content_exec)
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@ -8,3 +8,5 @@ r = redis.StrictRedis(connection_pool=pool)
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# r.hincrby('user', "b")
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print(r.hget("user", "a"))
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print(r.hget("user", "b"))
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r.delete()
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114
test/rediswr.py
114
test/rediswr.py
@ -1,56 +1,96 @@
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import redis
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# encoding=utf-8
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import re
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import redis
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import jieba
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import wordcloud
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import jieba.posseg as pseg
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import time # 引入time模块
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import wordcloud
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# 导入imageio库中的imread函数,并用这个函数读取本地图片,作为词云形状图片
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import imageio
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import time # 引入time模块
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pool = redis.ConnectionPool(host='127.0.0.1', port=6379, encoding='utf8', decode_responses=True, db=0)
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start_time = float(time.time())
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# import datetime
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# import threading
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# import telegram
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# from telegram import InlineKeyboardMarkup, InlineKeyboardButton, ForceReply
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# from telegram.ext import CommandHandler, MessageHandler, Filters, ConversationHandler, CallbackQueryHandler
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# from config import TOKEN
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# import sqlite3
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# import time
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# import os
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# import importlib
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# import requests
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#
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# bot = telegram.Bot(token=TOKEN)
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pool = redis.ConnectionPool(host='127.0.0.1', port=6379, encoding='utf8', decode_responses=True)
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r = redis.StrictRedis(connection_pool=pool)
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with open("/root/Jupyter/143751443703354.txt", "r") as file:
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i = 0
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for line in file.readlines():
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i += 1
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r.append("maozedong", line)
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if i == 10:
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break
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# content = file.read()
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# print(content)
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print(r.get("maozedong"))
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mk = imageio.imread("/root/Jupyter/circle.png")
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w = wordcloud.WordCloud(mask=mk)
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key_list = r.keys()
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group_list = []
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for i in key_list:
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if "chat_content" in i:
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group_list.append(i[:i.find("_")])
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print(group_list)
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# mk = imageio.imread("/root/Jupyter/circle.png")
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# w = wordcloud.WordCloud(mask=mk)
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# 构建并配置词云对象w,注意要加scale参数,提高清晰度
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w = wordcloud.WordCloud(width=800,
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height=800,
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background_color='white',
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font_path='/root/Jupyter/hanyiqihei.ttf',
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mask=mk,
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# mask=mk,
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scale=5)
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# 对来自外部文件的文本进行中文分词,得到string
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for group in group_list:
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start_time = float(time.time())
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# 生成词云图片
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jieba.enable_paddle() # 启动paddle模式。 0.40版之后开始支持,早期版本不支持
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words = pseg.cut(r.get("{}_chat_content".format(group)), use_paddle=True) # paddle模式
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word_list = []
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for word, flag in words:
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# print(word + "\t" + flag)
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if flag in ["n", "nr", "nz", "PER", "f", "ns", "LOC", "s", "nt", "ORG", "nw"]:
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# 判断该词是否有效,不为空格
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if re.match(r"^\s+?$", word) is None:
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word_list.append(word)
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# print(word_list)
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jieba.enable_paddle() # 启动paddle模式。 0.40版之后开始支持,早期版本不支持
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words = pseg.cut(r.get("maozedong"), use_paddle=True) # paddle模式
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word_list = []
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for word, flag in words:
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# print(word + "\t" + flag)
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if flag in ["n", "nr", "nz", "PER", "f", "ns", "LOC", "s", "nt", "ORG", "nw"]:
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word_list.append(word)
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# 分析高频词
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word_amount = {}
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print(word_amount)
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for word in word_list:
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# 判断该词是否之前已经出现
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if word_amount.get(word) is not None:
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word_amount[word] = word_amount.get(word) + 1
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else:
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word_amount[word] = 1
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print(word_amount)
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word_amount = sorted(word_amount.items(), key=lambda kv: (kv[1]), reverse=True)
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print("排序后的热词:" + str(word_amount))
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hot_word_string = ""
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for i in range(min(5, len(word_amount))):
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hot_word_string += str(word_amount[i][0]) + "\t热度: " + str(word_amount[i][1]) + "\n"
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print(hot_word_string)
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# 获取消息总数
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total_message_amount = r.get("{}_total_message_amount".format(group))
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string = " ".join(word_list)
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# 获取发言用户数
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user_amount = len(r.hkeys("{}_user_message_amount".format(group)))
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# 获取所有用户发言数字典
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user_message_amount = r.hgetall("-1001403536948_user_message_amount")
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user_message_amount = sorted(user_message_amount.items(), key=lambda kv: (kv[1]), reverse=True)
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print("排序后的用户:" + str(user_message_amount))
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top_5_user = ""
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for i in range(min(5, len(user_message_amount))):
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top_5_user += str(user_message_amount[i][0]) + "\t发言数: " + str(user_message_amount[i][1]) + "\n"
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print(top_5_user)
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string = " ".join(word_list)
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# 将string变量传入w的generate()方法,给词云输入文字
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w.generate(string)
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# 将词云图片导出到当前文件夹
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w.to_file('{}_chat_word_cloud.png'.format(group))
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# 将string变量传入w的generate()方法,给词云输入文字
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w.generate(string)
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# 将词云图片导出到当前文件夹
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w.to_file('maozedong-3.png')
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stop_time = float(time.time())
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print(stop_time - start_time)
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stop_time = float(time.time())
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print("当前群组处理耗时:" + str(stop_time - start_time))
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30
test/test1.py
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30
test/test1.py
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group_name = '-1001403536948_chat_content'
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print(group_name[:group_name.find("_")])
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word_amount = {}
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word_amount['y1111'] = 1
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word_amount['y2222'] = 2
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word_amount['y3333'] = 4
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word_amount['y4444'] = 3
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print(word_amount.get("123"))
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print(word_amount.get("y4444"))
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print(word_amount)
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word_amount = sorted(word_amount.items(), key=lambda word: (word[1]))
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print(word_amount)
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print("--------------")
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import re
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str = '''
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23
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'''
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rst = re.match(r"^\s+?$", str)
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print(rst)
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