Python WordCloud.font_path方法代码示例

# 需要导入模块: from wordcloud import WordCloud [as 别名]
# 或者: from wordcloud.WordCloud import font_path [as 别名]
def make_word_cloud(content):
    # read the mask image
    d = path.dirname(__file__)
    # alice_mask = np.array(Image.open(path.join(d, "mask/terran.jpg")))
    mask = np.array(Image.open(path.join(d, mask_img)))
    # font__dir = '/var/www/FlaskApp/FlaskApp/word_cloud_min/_fonts/lth.ttf'
    # font__dir = 'C:\Users\zjsep_000\PycharmProjects\myDrone\word_cloud_min\_fonts\lth.ttf'
    # font__dir = '_fonts/lth.ttf'

    wc = WordCloud(background_color="white", max_words=1000, mask=mask)

    # give the absolute dir for font ttf file
    # wc.font_path = 'C:\Users\JI\Documents\GitHub\PycharmProjects\myDrone\word_cloud\_fonts\lth.ttf'
    wc.font_path = abs_font_dir
    # wc.font_path = 'C:\Users\zjsep_000\PycharmProjects\myDrone\word_cloud_min\_fonts\lth.ttf'

    # wc.font_path = '_fonts/lth.ttf'
    # wc.font_path = '/var/www/FlaskApp/FlaskApp/word_cloud_min/_fonts/lth.ttf'
    # brush options: {'shoujin_brush.ttf','Japan_brush.ttf','qingke_fangzheng.ttf','KouzanBrushFont.ttf'}
    # serfi-fonts:[]

    wc.generate_from_frequencies(content)
    # generate word cloud
    # wc.generate(text)


    # store to file
    wc.to_file(path.join(d, "img/output.png"))
    # store to static foder in web server
    # wc.to_file(path.join(d, "../static/output.png"))

    # show
    plt.imshow(wc)
    plt.axis("off")
    plt.figure()
    plt.imshow(mask, cmap=plt.cm.gray)
    plt.axis("off")
    plt.show()

# 需要导入模块: from wordcloud import WordCloud [as 别名]
# 或者: from wordcloud.WordCloud import font_path [as 别名]
def unknowncoll(filename='unknownwords.p', stem=False):
    """
    Word cloud from sentiment analysis.
    
    Finds the bi-collocation of unknown words (words without sentiment) 
    and displays the 10 most common words based on frequency in a word-cloud, 
    colored green for words seen mostly in positive sentiments and red 
    for the opposite. Comparison is made on all comments concatenated
    
    -> filename: name of the file to load unknown words from
    -> stem: stem the words
    """
    bigram_measures = nltk.collocations.BigramAssocMeasures()
    subreddits = scraper.load_data('sub-reddits.txt')
    fullcomment = []
    
    print 'building comment'
    for name, data in subreddits.items():
        for sub_id, sub in data.items():
            fullcomment += [fixer(comment, True, stem).split() for comment in sub.comments]

    print 'getting unknowns'
    unknownwords = unknownsent(filename)
    
    #flatten the comment structure
    fullcomment = [word for comment in fullcomment for word in comment]
    
    basefinder = BigramCollocationFinder.from_words(fullcomment)
    count = 0
    
    for unknown, unknownscore in unknownwords:
        finder = copy.copy(basefinder)
        
        print '\n' + unknown
        #only bigrams that contain the unknown word
        finder.apply_ngram_filter(lambda w1, w2: unknown != w1 and unknown != w2)
        
        wordcloud = WordCloud()
        wordcloud.font_path = 'C:\Windows\Fonts\comic.ttf'
        #trick the wordcloud to accept custom input
        wordcloud.generate('generate')
        
        colls = finder.score_ngrams(bigram_measures.raw_freq)
        colls = colls[:10]        
        maximum = colls[1][1]
        
        #generate the tuple (word, score) for the wordcloud
        cloudwords = [(word, score) for ((word, _), score) in colls if word != unknown]
        cloudwords += [(word, score) for ((_, word), score) in colls if word != unknown]
        
        #normalize the scores
        cloudwords = [(word, score / maximum) for (word, score) in cloudwords]
        
        #tricking part 2.
        wordcloud.fit_words(cloudwords)
        wordcloud.to_image()
        if(unknownscore > 0):
            wordcloud = wordcloud.recolor(color_func=green_color_func, random_state=3)
        else:
            wordcloud = wordcloud.recolor(color_func=red_color_func, random_state=3)
        
        count += 1
        plt.figure(count)
        plt.title(unknown)
        plt.imshow(wordcloud)
        plt.axis("off")
        plt.savefig('plots/' + unknown + '.png', bbox_inches='tight')
        plt.close()