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# -*- coding: utf-8 -*-
import re
import nltk
import json
from App.bin import constants
class FiguresCleaner(object):
def __init__(self, sections):
self.sections = sections
def clean_figures(self):
sections = self.sections
clean_content = []
with open(constants.ASSETS + "wordAfterNumber", 'r') as l:
after_words = l.read().splitlines()
after_words_patterns = re.compile('|'.join(after_words))
with open(constants.ASSETS + "wordBeforeNumber", 'r') as l:
before_words = l.read().splitlines()
before_words_patterns = re.compile('|'.join(before_words))
#sections = sections.splitlines()
words = nltk.word_tokenize(sections)
tagged_words = nltk.pos_tag(words)
for i in range(len(tagged_words)):
if i < len(tagged_words) - 1:
next_word = tagged_words[i + 1][0]
current_word = tagged_words[i][0]
previous_word = tagged_words[i - 1][0]
currentWordTag = tagged_words[i][1]
if currentWordTag == 'CD' and not re.match(after_words_patterns,
next_word) is not None and not re.match(
before_words_patterns, previous_word) is not None:
if re.search(r'\d', current_word) is not None:
continue
else:
clean_content.append(current_word + " ")
else:
clean_content.append("\n")
return clean_content |