词云

import numpy as np
from wordcloud import WordCloud, ImageColorGenerator#, STOPWORDS
import matplotlib.pyplot as plt
from PIL import Image
import jieba # cutting Chinese sentences into words

def plt_imshow(x, ax=None, show=True):
if ax is None:
fig, ax = plt.subplots()
ax.imshow(x)
ax.axis("off")
if show: plt.show()
return ax

def count_frequencies(word_list):
freq = dict()
for w in word_list:
if w not in freq.keys():
freq[w] = 1
else:
freq[w] += 1
return freq

if __name__ == '__main__':
# setting paths
fname_text = 'texts/article.txt'
fname_stop = 'stopwords/hit_stopwords.txt'
fname_mask = 'pictures/owl.jpeg'
fname_font = 'SourceHanSerifK-Light.otf'

# read in texts (an article)
text = open(fname_text, encoding='utf8').read()
# Chinese stop words
STOPWORDS_CH = open(fname_stop, encoding='utf8').read().split()

# processing texts: cutting words, removing stop-words and single-charactors
word_list = [
w for w in jieba.cut(text)
if w not in STOPWORDS_CH and len(w) > 1
]
freq = count_frequencies(word_list)

# processing image
im_mask = np.array(Image.open(fname_mask))
im_colors = ImageColorGenerator(im_mask)

# generate word cloud
wcd = WordCloud(font_path=fname_font, # font for Chinese charactors
background_color='white',
mode="RGBA",
mask=im_mask,
)
#wcd.generate(text) # for English words
wcd.generate_from_frequencies(freq)
wcd.recolor(color_func = im_colors)

# visualization
ax = plt_imshow(wcd,)
ax.figure.savefig(f'single_wcd.png', bbox_inches='tight', dpi=150)

fig, axs = plt.subplots(1, 2)
plt_imshow(im_mask, axs[0], show=False)
plt_imshow(wcd, axs[1])
fig.savefig(f'conbined_wcd.png', bbox_inches='tight', dpi=150)