def word_cloud(self, model: LdaModel, stopwords_path, save_path):
with open(stopwords_path, 'r', encoding='utf8') as f:
words = f.readlines()
stopwords = add_stop_words(words)
print('stop words added')
word_cloud = PersianWordCloud(only_persian=True,
max_words=10,
stopwords=stopwords,
width=800,
height=800,
background_color='black',
min_font_size=1,
max_font_size=300)
topics = model.show_topics(formatted=False)
for i, topic in enumerate(topics):
topic_words = dict(topic[1])
print(topic_words)
new = {}
for word in topic_words.keys():
reshaped = get_display(arabic_reshaper.reshape(word))
new[reshaped] = topic_words[word]
print(new)
word_cloud.generate_from_frequencies(new)
image = word_cloud.to_image()
image.show()
s = save_path + '_topic_' + str(i) + '.png'
print(s)
image.save(s)
def word_cloud_generator(text):
d = path.dirname(__file__)
twitter_mask = np.array(Image.open(path.join(d, "twitter-logo.jpg")))
stopwords = add_stop_words(['کاسپین'])
stopwords |= EN_STOPWORDS
# Generate a word cloud image
wordcloud = PersianWordCloud(only_persian=False,
max_words=200,
stopwords=stopwords,
margin=0,
width=800,
height=800,
min_font_size=1,
max_font_size=500,
random_state=True,
background_color="white",
mask=twitter_mask).generate(text)
image = wordcloud.to_image()
# image.show()
# image.save('en-fa-result.png')
from io import BytesIO
bio = BytesIO()
bio.name = 'image.jpeg'
image.save(bio, 'JPEG')
bio.seek(0)
return bio
def generate(self,
from_date=None,
to_date="Today",
from_time=None,
to_time="Now",
max_words=1000):
self.from_time = abs(from_time)
if from_date and to_date:
if from_date == to_date and from_date == "Today":
# Read the whole text.
self.from_date = datetime.date.today() - datetime.timedelta(1)
self.to_date = datetime.date.today()
elif isinstance(from_date, float) and to_date == "Today":
self.from_date = datetime.date.today() + datetime.timedelta(
from_date)
self.to_date = datetime.date.today()
if from_time and to_time:
if isinstance(from_time, float) and to_time == "Now":
self.from_date = datetime.datetime.now() + datetime.timedelta(
hours=from_time)
self.to_date = datetime.datetime.now()
all_tweets = Analysis.objects(
Q(create_date__lt=self.to_date.replace(tzinfo=tz.tzlocal()))
& Q(create_date__gte=self.from_date.replace(tzinfo=tz.tzlocal()))
& Q(user_mentions=[])).all()
self.all_tweets_count = all_tweets.count()
all_words = []
for item in all_tweets:
tw_text = item.clean_text
for sentese in tw_text:
for item, key in sentese:
if key in ['Ne', 'N', 'AJ', 'AJe']:
word = ''
for w in item:
if u'\u0600' <= w <= u'\u06FF':
word += w
all_words.append(word)
text = ' '.join(all_words)
twitter_mask = np.array(
Image.open(path.join(self.d, "image/twitter-logo.jpg")))
# Generate a word cloud image
stopwords = add_stop_words(['توییت', 'رو', 'توییتر'])
self.tweet_cloud = PersianWordCloud(only_persian=True,
max_words=max_words,
stopwords=stopwords,
margin=0,
min_font_size=12,
max_font_size=100,
random_state=1,
background_color="white",
mask=twitter_mask).generate(text)
def wc_with_removing_stopWords(text, number):
stopWordsList= getStopWordsList()
stopwords = add_stop_words(stopWordsList)
wordcloud = PersianWordCloud(
only_persian=True,
max_words=100,
stopwords=stopwords,
margin=0,
width=800,
height=800,
min_font_size=1,
max_font_size=500,
background_color="black"
).generate(text)
image = wordcloud.to_image()
image.show()
image.save('../out/%s.jpg'%number)
def create_word_cloud(x="result.png"):
f = open("word_repeat_word_cloud", encoding="utf8")
text = f.read()
stopwords = add_stop_words(['نیست'])
stopwords = add_stop_words(['هست'])
stopwords = add_stop_words(['میکنیم'])
stopwords = add_stop_words(['کردند'])
stopwords = add_stop_words(['کنید'])
stopwords = add_stop_words(['میکنند'])
stopwords = add_stop_words(['کردم'])
stopwords = add_stop_words(['کردیم'])
stopwords = add_stop_words(['داریم'])
stopwords = add_stop_words(['کرده'])
stopwords = add_stop_words(['کرد'])
stopwords = add_stop_words(['میکند'])
stopwords = add_stop_words(['میکنم'])
stopwords = add_stop_words(['هستیم'])
stopwords = add_stop_words(['کردید'])
stopwords = add_stop_words(['کنیم'])
stopwords = add_stop_words(['کنند'])
stopwords = add_stop_words(['باشیم'])
stopwords = add_stop_words(['کند'])
stopwords = add_stop_words(['کند'])
stopwords = add_stop_words(['میشود'])
stopwords = add_stop_words(['میشویم'])
stopwords = add_stop_words(['میشوید'])
stopwords = add_stop_words(['اینها'])
# Generate a word cloud image
wordcloud = PersianWordCloud(only_persian=True,
max_words=300,
margin=0,
width=1000,
height=1000,
min_font_size=1,
collocations=False,
max_font_size=500,
stopwords=stopwords,
background_color="black").generate(text)
# Display the generated image:
image = wordcloud.to_image()
image.show()
image.save(x)
f.close()
def difference(data1, data2):
dict = {}
for key in data1.keys():
if key in data2:
print(data1[key], data2[key])
value = data1[key] - data2[key]
dict.update({key: value})
return dict
text = open(path.join(d, 'sohrab.txt'), encoding='utf-8').read()
# Add another stopword
stopwords = add_stop_words(['شاسوسا'])
# add_stop_words
data_s = pickle.load(open("sohrab_data.pkl", "rb"))
data_m = pickle.load(open("moshiri_data.pkl", "rb"))
frequency_data = difference(data_s, data_m)
# Generate a word cloud image
wordcloud = PersianWordCloud(
only_persian=True,
max_words=100,
stopwords=stopwords,
margin=0,
width=800,
height=800,
cloud = wordcloud.PersianWordCloud(background_color="white").generate(f1)
plt.imshow(cloud)
plt.axis('off')
plt.savefig("../out/1.jpg")
f2 = open("../../ProcessedData/label2.txt", 'r', encoding='utf-8')
f2 = f2.read()
cloud = wordcloud.PersianWordCloud(background_color="white").generate(f2)
plt.imshow(cloud)
plt.axis('off')
plt.savefig("../out/2.jpg")
f1_2 = open("../../ProcessedData/label1.txt", 'r', encoding='utf-8')
f1_2 = f1_2.read()
sub_list = list(f2)
wordcloud.add_stop_words(sub_list)
cloud = wordcloud.PersianWordCloud(background_color="white").generate(f1_2)
plt.imshow(cloud)
plt.axis('off')
plt.savefig("../out/3.jpg")
f2_1 = open("../../ProcessedData/label2.txt", 'r', encoding='utf-8')
f2_1 = f2_1.read()
sub_list = list(f1)
wordcloud.add_stop_words(sub_list)
cloud = wordcloud.PersianWordCloud(background_color="white").generate(f2_1)
plt.imshow(cloud)
plt.axis('off')
plt.savefig("../out/4.jpg")
swf = open("../stopwords.txt", "r", encoding='utf-8')
"""
Minimal Example
===============
Generating a square wordcloud from the US constitution using default arguments.
"""
from os import path
from persian_wordcloud.wordcloud import PersianWordCloud, add_stop_words
d = path.dirname(__file__)
text = open(path.join(d, 'persian.txt'), encoding='utf-8').read()
# Add another stopword
stopwords = add_stop_words(['کاسپین'])
# add_stop_words
# Generate a word cloud image
wordcloud = PersianWordCloud(
only_persian=True,
max_words=100,
stopwords=stopwords,
margin=0,
width=800,
height=800,
min_font_size=1,
max_font_size=500,
background_color="black"
).generate(text)
Generating a square wordcloud from the US constitution using default arguments.
"""
from os import path
import operator
from persian_wordcloud.wordcloud import PersianWordCloud, add_stop_words
d = path.dirname(__file__)
emam_text = open(path.join(d, 'emam.txt'), encoding='utf-8').read()
shah_text = open(path.join(d, 'shah.txt'), encoding='utf-8').read()
difference_file = open('difference.txt', 'w')
similarity_file = open('similarity.txt', 'w')
# diff = open(path.join(d, 'difference.txt'), encoding='utf-8').read()
# Add another stopword
stopwords = add_stop_words(
["که", "از", "با", "برای", "با", "به", "را", "هم", "و", "در", "تا", "یا"])
# add_stop_words
# Generate a word cloud image
wordcloud_emam = PersianWordCloud(only_persian=True,
max_words=100,
stopwords=stopwords,
margin=0,
width=800,
height=800,
min_font_size=1,
max_font_size=500,
background_color="Black").generate(emam_text)
wordcloud_shah = PersianWordCloud(only_persian=True,
max_words=100,
