import spacy

#Imports the modules needed to open all files and count the adverbs, and sets spacy language to English
import codecs, glob
from collections import Counter
nlp = spacy.load('en')

nlp.max_length = 2400000

#This cell creates the folders 'chicago_adverbs' and 'chicago_adverb_counts' where results are saved
import os
try:
    os.mkdir('chicago_adverbs')
    print 'Folder created "chicago_adverbs"'
except OSError:
    print 'Folder "chicago_adverbs" exists'
    pass

try:
    os.mkdir('chicago_adverb_counts')
    print 'Folder created "chicago_adverb_counts"'
except OSError:
    print 'Folder "chicago_adverb_counts" exists'
    pass

Folder "chicago_adverbs" exists
Folder "chicago_adverb_counts" exists

num_files = len(glob.glob('/data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/*.txt'))

for file_name in sorted(glob.glob('/data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/*.txt')):
    #transl_name = file_name.split('/')[-1].split('_')[1]
    
    if file_name.split('/')[-1] in ['00010378.txt', '00010764.txt', '00010903.txt', '00010482.txt',
                    '00010497.txt', '00010879.txt']:
        print 'TOO LARGE?', file_name
        continue

    #Opens, reads and tokenizes one file
    text_name = file_name.split('/')[-1].split('.')[0]
    text = codecs.open(file_name, 'r', encoding='utf-8')
    read_text = text.read()
    doc = nlp(read_text)

    #Iterates through the list of tokens to assemble a list of all adverbs
    adverb_list = []
    for token in doc:
        if token.pos_ == 'ADV':
            adverb_list.append(token.text)

    #Saves the list of adverbs to file, one word per line
    out_file = './chicago_adverbs/' + text_name + '_adverbs.txt'
    write_file = codecs.open(out_file, 'w', encoding='utf-8')
    for adverb in adverb_list:
        write_file.write(adverb + '\n')
    write_file.close()
    
    #Saves the most common adverbs to file, highest>lowest frequency
    adverb_counts = Counter(adverb_list)
    count_out = './chicago_adverb_counts/' + text_name + '_adverb_counts.txt'
    count_write = codecs.open(count_out, 'w', encoding='utf-8')
    for key, value in adverb_counts.most_common():
        count_write.write(key + '\t' + str(value) + '\n')
    count_write.close()

    num_files -= 1
    print text_name + ' is done! ' + str(num_files) + ' files remaining'
    
print 'Ding! Fries are done!'

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TOO LARGE? /data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/00010378.txt
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TOO LARGE? /data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/00010482.txt
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TOO LARGE? /data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/00010764.txt
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TOO LARGE? /data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/00010879.txt
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TOO LARGE? /data/1/kafka/from_box/Master_Files_Fall_2018/chicago_corpus/00010903.txt
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Ding! Fries are done!

spacy.explain('VBG')

u'verb, gerund or present participle'