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import numpy as np |
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import pandas as pd |
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import textstat |
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import spacy |
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from collections import Counter |
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from itertools import groupby |
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nlp = spacy.load("en_core_web_sm") |
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PRESENT_TENSE_VERB_LIST = ["VB", "VBP", "VBZ", "VBG"] |
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VERB_LIST = ["VB", "VBP", "VBZ", "VBG", "VBN", "VBD"] |
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NOUN_LIST = ["NNP", "NNPS"] |
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SECTIONS_MAPS = { |
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"Authors": "Authors", |
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"AUTHORS": "AUTHORS", |
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"Abstract": "Abstract", |
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"ABSTRACT": "Abstract", |
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"Date": "Date", |
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"DATE": "DATE", |
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"INTRODUCTION": "Introduction", |
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"MATERIALS AND METHODS": "Methods", |
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"Materials and methods": "Methods", |
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"METHODS": "Methods", |
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"RESULTS": "Results", |
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"CONCLUSIONS": "Conclusions", |
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"CONCLUSIONS AND FUTURE APPLICATIONS": "Conclusions", |
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"DISCUSSION": "Discussion", |
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"ACKNOWLEDGMENTS": "Acknowledgement", |
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"TABLES": "Tables", |
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"Tabnles": "Tables", |
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"DISCLOSURE": "Disclosure", |
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"CONFLICT OF INTEREST": "Disclosure", |
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"Acknowledgement": "Acknowledgements", |
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} |
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def compute_readability_stats(text): |
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""" |
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Compute reading statistics of the given text |
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Reference: https://github.com/shivam5992/textstat |
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Parameters |
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========== |
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text: str, input section or abstract text |
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""" |
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try: |
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readability_dict = { |
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"flesch_reading_ease": textstat.flesch_reading_ease(text), |
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"smog": textstat.smog_index(text), |
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"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text), |
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"coleman_liau_index": textstat.coleman_liau_index(text), |
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"automated_readability_index": textstat.automated_readability_index(text), |
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"dale_chall": textstat.dale_chall_readability_score(text), |
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"difficult_words": textstat.difficult_words(text), |
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"linsear_write": textstat.linsear_write_formula(text), |
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"gunning_fog": textstat.gunning_fog(text), |
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"text_standard": textstat.text_standard(text), |
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"n_syllable": textstat.syllable_count(text), |
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"avg_letter_per_word": textstat.avg_letter_per_word(text), |
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"avg_sentence_length": textstat.avg_sentence_length(text), |
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} |
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except: |
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readability_dict = { |
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"flesch_reading_ease": None, |
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"smog": None, |
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"flesch_kincaid_grade": None, |
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"coleman_liau_index": None, |
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"automated_readability_index": None, |
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"dale_chall": None, |
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"difficult_words": None, |
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"linsear_write": None, |
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"gunning_fog": None, |
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"text_standard": None, |
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"n_syllable": None, |
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"avg_letter_per_word": None, |
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"avg_sentence_length": None, |
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} |
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return readability_dict |
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def compute_text_stats(text): |
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""" |
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Compute part of speech features from a given spacy wrapper of text |
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Parameters |
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========== |
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text: spacy.tokens.doc.Doc, spacy wrapper of the section or abstract text |
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Output |
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====== |
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text_stat: dict, part of speech and text features extracted from the given text |
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""" |
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try: |
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pos = dict(Counter([token.pos_ for token in text])) |
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pos_tag = dict( |
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Counter([token.tag_ for token in text]) |
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) |
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n_present_verb = sum( |
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[v for k, v in pos_tag.items() if k in PRESENT_TENSE_VERB_LIST] |
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) |
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n_verb = sum([v for k, v in pos_tag.items() if k in VERB_LIST]) |
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word_shape = dict(Counter([token.shape_ for token in text])) |
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n_word_per_sents = [len([token for token in sent]) for sent in text.sents] |
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n_digits = sum([token.is_digit or token.like_num for token in text]) |
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n_word = sum(n_word_per_sents) |
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n_sents = len(n_word_per_sents) |
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text_stats_dict = { |
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"pos": pos, |
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"pos_tag": pos_tag, |
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"word_shape": word_shape, |
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"n_word": n_word, |
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"n_sents": n_sents, |
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"n_present_verb": n_present_verb, |
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"n_verb": n_verb, |
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"n_digits": n_digits, |
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"percent_digits": n_digits / n_word, |
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"n_word_per_sents": n_word_per_sents, |
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"avg_word_per_sents": np.mean(n_word_per_sents), |
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} |
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except: |
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text_stats_dict = { |
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"pos": None, |
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"pos_tag": None, |
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"word_shape": None, |
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"n_word": None, |
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"n_sents": None, |
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"n_present_verb": None, |
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"n_verb": None, |
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"n_digits": None, |
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"percent_digits": None, |
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"n_word_per_sents": None, |
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"avg_word_per_sents": None, |
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} |
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return text_stats_dict |
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def compute_journal_features(article): |
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""" |
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Parse features about journal references from a given dictionary of parsed article e.g. |
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number of reference made, number of unique journal refered, minimum year of references, |
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maximum year of references, ... |
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Parameters |
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========== |
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article: dict, article dictionary parsed from GROBID and converted to dictionary |
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see ``pdf/parse_pdf.py`` for the detail of the output dictionary |
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Output |
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====== |
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reference_dict: dict, dictionary of |
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""" |
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try: |
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n_reference = len(article["references"]) |
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n_unique_journals = len( |
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pd.unique([a["journal"] for a in article["references"]]) |
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) |
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reference_years = [] |
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for reference in article["references"]: |
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year = reference["year"] |
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if year.isdigit(): |
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if int(year) in range(1800, 2100): |
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reference_years.append(int(year)) |
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avg_ref_year = np.mean(reference_years) |
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median_ref_year = np.median(reference_years) |
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min_ref_year = np.min(reference_years) |
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max_ref_year = np.max(reference_years) |
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journal_features_dict = { |
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"n_reference": n_reference, |
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"n_unique_journals": n_unique_journals, |
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"avg_ref_year": avg_ref_year, |
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"median_ref_year": median_ref_year, |
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"min_ref_year": min_ref_year, |
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"max_ref_year": max_ref_year, |
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} |
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except: |
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journal_features_dict = { |
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"n_reference": None, |
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"n_unique_journals": None, |
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"avg_ref_year": None, |
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"median_ref_year": None, |
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"min_ref_year": None, |
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"max_ref_year": None, |
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} |
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return journal_features_dict |
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def merge_section_list(section_list, section_maps=SECTIONS_MAPS, section_start=""): |
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""" |
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Merge a list of sections into a normalized list of sections, |
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you can get the list of sections from parsed article JSON in ``parse_pdf.py`` e.g. |
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>> section_list = [s['heading'] for s in article_json['sections']] |
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>> section_list_merged = merge_section_list(section_list) |
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Parameters |
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========== |
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section_list: list, list of sections |
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Output |
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====== |
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section_list_merged: list, sections |
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""" |
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sect_map = section_start |
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section_list_merged = [] |
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for section in section_list: |
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if any([(s.lower() in section.lower()) for s in section_maps.keys()]): |
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sect = [s for s in section_maps.keys() if s.lower() in section.lower()][0] |
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sect_map = section_maps.get(sect, "") |
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section_list_merged.append(sect_map) |
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else: |
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section_list_merged.append(sect_map) |
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return section_list_merged |
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