# -*- coding: utf-8 -*-
"""
"""
from os import path
from scipy.misc import imread
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
def wordCount(filename):
'''
简单计算词频的函数
:param filename: 文件名
:return: 词频
'''
wordCount = {}
file = open(filename,'r')
while True:
line = file.readline()
if line:
wordlist = line.split(',[')
if len(wordlist) == 1: continue
wordlist = wordlist[1].split('],')[0].split(',')
for word in wordlist:
word = word.replace(' ', '').replace("'", '')
if word in wordCount.keys():
wordCount[word] = wordCount[word]+1
else:
wordCount[word] = 1
else:
break
return [(k, wordCount[k]) for k in wordCount.keys()]
def generateCloud(filename,imagename,cloudname,fontname):
'''
生成标签云的函数
'''
coloring = imread(imagename) # 读取背景图片
wc = WordCloud(background_color="white", # 背景颜色max_words=2000,# 词云显示的最大词数
mask=coloring, # 设置背景图片
stopwords=STOPWORDS, # 停止词
font_path=fontname, # 兼容中文字体
max_font_size=150) # 字体最大值
#计算好词频后使用generate_from_frequencies函数生成词云
#txtFreq例子为[('词a', 100),('词b', 90),('词c', 80)]
txtFreq = wordCount(filename)
wc.generate_from_frequencies(txtFreq)
# 生成图片
plt.imshow(wc)
plt.axis("off")
# 绘制词云
plt.figure()
# 保存词云
wc.to_file(cloudname)
if __name__ == '__main__':
d = path.dirname(__file__) # 获取当前文件路径
fontname = path.join(d, 'msyh.ttf') # 中文字体路径
filename = path.join(d, '广州.txt') # txt文件路径
imagename = path.join(d, "circle.jpg") # 背景图片路径
cloudname = path.join(d, "cloud.png") # 标签云路径
generateCloud(filename, imagename, cloudname, fontname)
