def make_cloud(channel, time, myType=None, drawLabels=True, font_path=None):
#get the log file
directory = "logs/" + channel + '/' + time
if myType == None:
file_path = os.path.relpath(directory + '/words.log')
with open(file_path, 'r') as f:
words = f.read().upper()
file_path = os.path.relpath(directory + '/emotes.log')
with open(file_path, 'r') as f:
emotes = " ".join(filter(lambda x:len(x)>3 and x != 'double' and x != 'triple', f.read().split('\n')))
directory = "images/" + channel + '/' + time
if not os.path.exists(directory):
os.makedirs(directory)
print "Generating word cloud... Hold on! (This takes a while if there are a lot of messages)"
scale = 2
w = wordcloud.process_text(words, max_features=1500)
elements = wordcloud.fit_words(w, width=w_words/scale, height=h_words/scale)
wordcloud.draw(elements, os.path.relpath(directory + '/wordcloud.png'),
width=w_words/scale, height=h_words/scale, scale=scale)
print "Word cloud created!"
print "Generating emote cloud..."
w = wordcloud.process_text(emotes, max_features=1500)
elements = wordcloud.fit_words(w, width=w_emotes, height=h_emotes)
wordcloud.draw(elements, os.path.relpath(directory + '/emotecloud.png'),
width=w_emotes, height=h_emotes)
print "Emote cloud created!"
else: #if running the program manually. this is mainly for my debugging purposes.
w_custom = 1100
h_custom = 700
file_path = os.path.relpath(directory + '/'+myType+'.log')
directory = "images/" + channel + '/' + time
if not os.path.exists(directory):
os.makedirs(directory)
with open(file_path, 'r') as f:
data = f.read()
if myType.lower() == 'authors':
data = data.upper()
print "Generating " +myType+ " cloud... Hold on!"
scale = 2
w = wordcloud.process_text(data, max_features=1000)
elements = wordcloud.fit_words(w, width=w_custom/scale,
height=h_custom/scale, font_path=font_path)
wordcloud.draw(elements, os.path.relpath(directory + '/'+myType+'cloud.png'),
width=w_custom/scale, height=h_custom/scale, scale=scale, font_path=font_path)
print myType + " cloud created!"
def make_cloud(self, text): words = wordcloud.process_text(text) elements = wordcloud.fit_words(words, width = 400, height = 400) wordcloud.draw(elements, self.out, width = 400, height = 400, scale = 2) return self.out
def make_cloud(self, text):
words = wordcloud.process_text(text)
elements = wordcloud.fit_words(words, width=400, height=400)
wordcloud.draw(elements, self.out, width=400, height=400, scale=2)
return self.out
def makeCloud(self, text, font=None):
if font is None:
font = random.choice(self.fonts)
words, counts = wordcloud.process_text(text, max_features=2000)
elements = wordcloud.fit_words(words, counts, width=self.size,
height=self.size, font_path=font)
wordcloud.draw(elements, self.outFile, width=self.size,
height=self.size, scale=self.scale, font_path=font)
def makeCloud(self, text, font=None):
if font is None:
font = random.choice(self.fonts)
words, counts = wordcloud.process_text(text, max_features=2000)
elements = wordcloud.fit_words(words, counts, width=self.size,
height=self.size, font_path=font)
wordcloud.draw(elements, self.outFile, width=self.size,
height=self.size, scale=self.scale, font_path=font)
def make_word_cloud(text, filepath):
import wordcloud #@UnresolvedImport
if isinstance(text, str):
text = wordcloud.process_text(text, max_features=20)
w, h = (400, 400)
text = remove_letters(text)
elements = wordcloud.fit_words(text, width=w, height=h)
wordcloud.draw(elements, filepath, width=w, height=h, scale=1)
return filepath
d = path.dirname(__file__)
# String to hold the text from the webpages.
text = "";
# Array of webpages which we'll loop through (from googling Deonte Burton draft).
url_list = ["http://www.draftexpress.com/profile/Deonte-Burton-6487/",
"http://blogs.rgj.com/chrismurray/2014/01/10/nba-scouts-view-nevadas-deonte-burton-as-solid-draft-pick-but-not-a-first-rounder/",
"http://www.nbadraftroom.com/2014/01/deonte-burton.html",
"http://www.nbadraftinsider.com/deonte-burton/",
"http://nbaprospects.blogspot.com/2012/08/scouting-report-deonte-burton-nevada.html",
"http://rushthecourt.net/2014/01/09/a-college-basketball-resolution-for-2014-get-to-know-nevadas-deonte-burton/",
"http://mrsportsblog.wordpress.com/2014/03/05/trust-me-on-this-dynamic-deonte-burton-of-nevada-will-be-making-a-living-in-the-nba/",
"http://www.draftexpress.com/article/NBA-Draft-Prospect-of-the-Week-Deonte-Burton-4392/",
"http://www.nevadawolfpack.com/sports/m-baskbl/spec-rel/021214aad.html"]
# Loop through url items and get the text from each.
for url in url_list:
content = urllib2.urlopen(url)
text += Document(content).summary() + " "
# Separate into a list of word, frequency).
words = wordcloud.process_text(text)
# Compute the position of the words.
elements = wordcloud.fit_words(words)
# Draw the positioned words to a PNG file.
wordcloud.draw(elements, path.join(d, 'db2.png'))
txtfeatWords5 = [lmtzr.lemmatize(word) for word in txtfeatWords4]
# stem using snowball stemmer
txtfeatWords6 = [stemmer.stem(word) for word in txtfeatWords5]
# remove punctuations
txtfeatWords7 = [
word.encode('utf-8').translate(None, string.punctuation)
for word in txtfeatWords6
]
# remove empty strings
txtfeatWords8 = [word for word in txtfeatWords7 if word <> '']
txtfeatWordList[i] = ' '.join(txtfeatWords8)
#pprint('Iteration: %d'% i)
#pprint(txtfeatWordList[i])
pprint(txtfeatWordList)
text = '\n'.join([str(txtfeatWordList[i]) for i in range(num_total)])
#tags = make_tags(get_tag_counts( '\n'.join([ str(txtfeatWordList[i]) for i in range(num_total) ]) ))
#create_tag_image(tags, 'cloud_large.png', size=(1800, 1200), fontname='Lobster')
d = os.path.dirname(__file__)
words = wordcloud.process_text(text)
elements = wordcloud.fit_words(words)
wordcloud.draw(elements, os.path.join(d, 'lemmatized_wordle.png'))
#!/usr/bin/env python2
from os import path
import sys
import wordcloud
d = path.dirname(__file__)
# Read the whole text.
text = open(path.join(d, 'presinaug-addresses.txt')).read()
# Separate into a list of (word, frequency).
words = wordcloud.process_text(text, max_features=10000)
# Compute the position of the words.
elements = wordcloud.fit_words(words, width=900, height=1600)
# Draw the positioned words to a PNG file.
wordcloud.draw(elements,
path.join(d, 'presinaug-wordcloud-1600x900.png'),
width=900,
height=1600,
scale=1)
def getTopWords(self, text):
words = wordcloud.process_text(text, self.config['maxWords'],
self.stopwords)
return words
#!/usr/bin/env python2
from os import path
import sys
import wordcloud
d = path.dirname(__file__)
# Read the whole text.
text = open(path.join(d, '4chdata/all.dat')).read()
# Separate into a list of (word, frequency).
words = wordcloud.process_text(text, max_features=1000)
# Compute the position of the words.
elements = wordcloud.fit_words(words, width=1000, height=1000)
# Draw the positioned words to a PNG file.
wordcloud.draw(elements, path.join(d, str(sys.argv[1])), width=1000, height=1000,
scale=2)
def produceWordCloud(inputText, outputPng): words = wordcloud.process_text(inputText, max_features=400) elements = wordcloud.fit_words(words, width=800, height=500) wordcloud.draw(elements, outputPng, width=800, height=500, scale=2)
def wordclouds(x):
d = path.dirname("/Users/MrG/Capstone/")
words = wordcloud.process_text(str(x), max_features = 500)
elements = wordcloud.fit_words(words)
wordcloud.draw(elements, path.join(d,"WC.png"), scale = 5)
return Image(filename='/Users/MrG/Capstone/WC.png', height= 1000, width= 618)
def generateCloud(text): dir = path.dirname(__file__) words = wordcloud.process_text(text, max_features=1000) elements = wordcloud.fit_words(words, width=1000, height=1000) wordcloud.draw(elements, path.join(dir, 'wordcloud.png'), width=1000, height=1000)
