Python WordCloud.background_color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 background_color [as 别名]
def output():
  # Load the dictionary
  database_dict = pickle.load(open( data_folder_path + 'updated_database_dict.p', 'rb'))

  # pull 'ID' (url_string) from input field and store it
  url_string = request.args.get('ID')

  # pull the number of clusters from the user form
  if request.args.get('Nclusters'): 
    Nclusters = int(request.args.get('Nclusters'))
  else: 
    Nclusters = 3
  
  if len(url_string) < 5: 
    url_string = 'http://www.nytimes.com/2015/09/20/opinion/sunday/a-toxic-work-world.html'

  if url_string not in database_dict.keys(): 
    database_dict = update_database(url_string, database_dict)
  
  headline = database_dict[url_string]['title']
  abstract = database_dict[url_string]['abstract']
  article_summary = dict(headline = headline, abstract = abstract)

  # Generate a wordcloud plot for the article 
  Nkeywords = len(database_dict[url_string]['keyword_dict'])
  word_freq_list = [(entry['value'], Nkeywords - float(entry['rank'])) for entry in database_dict[url_string]['keyword_dict']]
  # clean up wordcloud styles
  title_wordcloud = WordCloud().generate_from_frequencies(word_freq_list)
  title_wordcloud.background_color = 'white'
  title_wordcloud.recolor(color_func=custom_color_func)
  # Prepare figure for output.html
  fig = Figure() 
  fig.set_facecolor('None')
  ax = fig.add_subplot(111)
  ax.imshow(title_wordcloud)
  ax.set_axis_off()
  canvas = FigureCanvas(fig)
  title_cloud_png_output = StringIO.StringIO()
  canvas.print_png(title_cloud_png_output)
  title_cloud_png_output = title_cloud_png_output.getvalue().encode('base64')



  # Get the three representative comments
  rep_comments = get_representative_comments(database_dict[url_string]['comments_df'], Nclusters) 
  senti_pos = database_dict[url_string]['comments_df']['senti_pos'] 
 
  # Get the pie chart 
  fig = Figure() 
  fig.set_facecolor('None')
  ax = fig.add_subplot(111)
  color_repo = ['#4D4D4D', '#5DA5DA', '#FAA43A', '#60BD68', '#F17CB0', '#B2912F', '#B276B2', '#DECF3F', '#F15854']
  sizes = [rep_comments[i]['count'] for i in range(Nclusters)]
  colors = [color_repo[i] for i in range(Nclusters)]
  sorted_sizes_args = np.argsort(sizes)[::-1]
  labels = ['Cluster ' + str(i+1) for i in range(Nclusters)]
  sorted_sizes = sorted(sizes)[::-1]
  ax.pie(sorted_sizes, labels=labels, colors=colors,
          autopct='%1.1f%%', shadow=True, startangle=90)
  ax.set_axis_off()
  canvas = FigureCanvas(fig)
  pie_png_output = StringIO.StringIO()
  canvas.print_png(pie_png_output)
  pie_png_output = pie_png_output.getvalue().encode("base64")


  # Sentiment plot
  fig = Figure()
  fig.set_facecolor('None')
  ax = fig.add_subplot(111)
  ax.hist(senti_pos, bins = 20)
  ax.set_xlim(0,1)
  ax.set_xlabel('Sentiment Scale')
  ax.set_ylabel('Number of Comments')
  ax.set_xticks(np.linspace(0, 1, 5))
  senti_labels = [item.get_text() for item in ax.get_xticklabels()]
  senti_labels[0] = 'Neg'
  senti_labels[2] = '0'
  senti_labels[-1] = 'Pos'
  ax.set_xticklabels(senti_labels)
  canvas=FigureCanvas(fig)
  png_output = StringIO.StringIO()
  canvas.print_png(png_output)
  png_output = png_output.getvalue().encode("base64")

  # Return word clouds for different comments 
  word_cloud_comments = {} # key: cluster_label, val: fig_data 
  for lab in range(Nclusters):
    comment_keywords = get_keywords(rep_comments[lab]['comment'])
    # comment_keywords = rep_comments[lab]['cluster_keywords']
    keyword_wordfreq_list = [ (word, len(comment_keywords) - i) for i,word in enumerate(comment_keywords)]
    wordcloud = WordCloud().generate_from_frequencies(keyword_wordfreq_list)
    wordcloud.background_color = 'white'
    wordcloud.recolor(color_func=custom_color_func)
    fig = Figure() 
    fig.set_facecolor('None')
    ax = fig.add_subplot(111)
    ax.imshow(wordcloud)
    ax.set_axis_off()
    canvas=FigureCanvas(fig)
#.........这里部分代码省略.........