Update GetInfo.py

Author: WadeStack

wx/TypeInfo/GetInfo.py

@@ -1,18 +1,84 @@
 from wxpy import *
+import jieba
+import re
+from snownlp import SnowNLP
+import jieba.analyse
+import matplotlib.pyplot as plt
 
-
-# 初始化机器人，扫码登陆
-# bot = Bot()
-bot = Bot(console_qr=True, cache_path=True) # 保留缓存自动登录
+bot = Bot(console_qr=True, cache_path=True) # 登陆一次后利用缓存登陆
+# bot =Bot() # 每次都需要登陆
 friends = bot.friends()
-for friend in friends:
-    print(friend.name,friend.sex,friend.signature,friend.province,friend.city)
-    # name
-    # sex   (0:不清楚 1:男 2:女)
-    # signature
-    # province
-    # city
 
+# 总体分析
+def analyseTotal(friends):
+    result = friends.stats_text()
+    print(result)
+
+# 具体分析每个好友
+def analyseConcrete(friends):
+    text = friends.stats()
+    print('sex:',text['sex'])
+    print('province:',text['province'])
+    print('city:',text['city'])
+    for friend in friends[1:]:
+        print(friend.name,friend.sex,friend.province,friend.city,friend.signature)
+
+# 性别分析,饼状图显示
+def analyseSex(friends):
+    text = friends.stats()
+    male = text['sex'][1]
+    female = text['sex'][2]
+    unknown = text['sex'][0]
+    labels = 'male','female','unknown'
+    sizes = [male,female,unknown]
+    explode = (0, 0.1, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')
+
+    fig1, ax1 = plt.subplots()
+    ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
+            shadow=True, startangle=90)
+    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+    plt.show()
+
+# 分析个性签名
+def analyseSignature(friends):
+    signatures = ''
+    emotions = []
+    pattern = re.compile("lf\d.+")
+    for friend in friends[1:]:
+        signature = friend.signature
+        if signature != None:
+            signature = signature.strip().replace('span','').replace('class','').replace('emoji','')
+            signature = re.sub(r'lf(\d.+)','',signature)
+            # print(signature)
+            if len(signature) > 0:
+                nlp = SnowNLP(signature)
+                emotions.append(nlp.sentiments)
+                signatures += ''.join(jieba.analyse.extract_tags(signature,5))
+    # with open('signatures.txt', 'wt', encoding='utf-8') as file:
+    #     file.write(signatures)
+    # Signature Emotional Judgment
+    count_good = len(list(filter(lambda x: x > 0.66, emotions)))
+    count_normal = len(list(filter(lambda x: x >= 0.33 and x <= 0.66, emotions)))
+    count_bad = len(list(filter(lambda x: x < 0.33, emotions)))
+    labels = [u'负面消极', u'中性', u'正面积极']
+    values = (count_bad, count_normal, count_good)
+    plt.rcParams['font.sans-serif'] = ['simHei']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.xlabel(u'情感判断')
+    plt.ylabel(u'频数')
+    plt.xticks(range(3), labels)
+    plt.legend(loc='upper right', )
+    plt.bar(range(3), values, color='rgb')
+    plt.title(u'%s的微信好友签名信息情感分析' % friends[0])
+    plt.show()
+
+def main():
+    analyseTotal(friends=friends)
+    # analyseConcrete(friends=friends)
+    analyseSex(friends=friends)
+    analyseSignature(friends=friends)
+    x = input('输入任意字符退出')
 
+main()