<jupyter_start><jupyter_text>source https://en.wikipedia.org/wiki/List_of_optimization_software<jupyter_code>with open('software-list.txt','r') as f:
    data = f.read()
match = re.compile(r'\n([^\t^\n.]+)(\t|\s\–|\s\—)')
words = [ w[0].replace(' ','_') for w in match.findall(data)[1:] ]
words = dict([ (w[0],1) for w in match.findall(data)[1:] ])
words['GLPK'] = 50
words['CPLEX'] = 30
words['Gurobi'] =20
words['']
wordcloud = WordCloud(max_font_size=80, height=400, width=800, max_words=100).generate_from_frequencies(words)
figure(figsize=(16,9))
imshow(wordcloud, interpolation="bilinear")
axis("off")
show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2236024844720497, 'fraction_numerical': 0.03881987577639751, 'mean_word_length': 7.376623376623376, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '259529', 'license': 'MIT', 'n_tokens_mistral': 278, 'n_tokens_neox': 256, 'n_words': 39, 'path': '01-introduction/history.ipynb', 'repo_name': 'cochoa0x1/intro-mathematical-programming', 'size': '644'}