def lint_text(text):
#print(type(text))
lint_results = []
suggestions = lint(str(text))
for s in suggestions:
#print(type(s))
excerpt = " ..." + str(text[s[4]-15:s[5]+15]) + "..."
while "\n\n" in excerpt:
excerpt = excerpt.replace("\n\n", " ")
while " " in excerpt:
excerpt = excerpt.replace(" ", " ")
lint_results.append((s[0], s[1][:-1], excerpt))
return lint_results
def __init__(self, path):
"""
Create document instance for analysis.
Opens and reads document to string raw_text.
Textract interprets the document format and
opens to plain text string (docx, pdf, odt, txt)
Args:
path (str): path to file to open, anaylze, close
Public attributes:
-user: (str) optional string to set username.
-path: (str) relative path to document.
-abs_path: (str) the absolute path to the document.
-file_name: (str) the file name with extension of document (base
name).
-mime: tbd
-guessed_type: makes best guess of mimetype of document.
-file_type: returns index[0] from guessed_type.
-raw_text: (str) plain text extracted from .txt, .odt, .pdf, .docx,
and .doc.
-ptext: (str) raw text after a series of regex expressions to
eliminate special characters.
-text_no_feed: (str) ptext with most new line characters eliminated
/n/n stays intact.
-sentence_tokens: list of all sentences in a comma separated list
derived by nltk.
-sentence_count: (int) count of sentences found in list.
-passive_sentences: list of passive sentences identified by the
passive module.
-passive_sentence_count: count of the passive_sentences list.
-percent_passive: (float) ratio of passive sentences to all sentences
in percent form.
-be_verb_analysis: (int) sum number of occurrences of each to be verb
(am, is, are, was, were, be, being been).
-be_verb_count: tbd
-be_verb_analysis: tbd
-weak_sentences_all: (int) sum of be verb analysis.
-weak_sentences_set: (set) set of all sentences identified as
having to be verbs.
-weak_sentences_count: (int) count of items in weak_sentences_set.
-weak_verbs_to_sentences: (float) proportion of sentences with to
be to all sentences in percent (this might not be sound).
-word_tokens: list of discreet words in text that breaks
contractions up (default nltk tokenizer).
-word_tokens_no_punct: list of all words in text including
contractions but otherwise no punctuation.
-no_punct: (str) full text string without sentence punctuation.
-word_tokens_no_punct: uses white-space tokenizer to create a list
of all words.
-readability_flesch_re: (int) Flesch Reading Ease Score (numeric
score) made by textstat module.
-readability_smog_index: (int) grade level as determined by the
SMOG algorithum made by textstat module.
-readability_flesch_kincaid_grade: (int) Flesch-Kincaid grade level
of reader made by textstat module.
-readability_coleman_liau_index: (int) grade level of reader as made by
textstat module.
-readability_ari: (int) grade leader of reader determined by
automated readability index algorithum implemented by textstat.
-readability_linser_write: FIX SPELLING grade level as determined
by Linsear Write algorithum implemented by textstat.
-readability_dale_chall: (int) grade level based on Dale-Chall
readability as determined by textstat.
-readability_standard: composite grade level based on readability
algorithums.
-flesch_re_key: list for interpreting Flesch RE Score.
-word_count: word count of document based on white space tokener,
this word count should be used.
-page_length: (float) page length in decimal format given 250
words per page.
-paper_count: (int) number of printed pages given 250 words per
page.
-parts_of_speech: words with parts of speech tags.
-pos_counts: values in word, tag couple grouped in a list (Counter).
-pos_total: (int) sum of pos_counts values
-pos_freq: (dict) word, ratio of whole
-doc_pages: (float) page length based on 250 words per page
(warning, this is the second time this attribute is defined).
-freq_words: word frequency count not standardized based on the
correct word tokener (not ratio, just count).
modal_dist: count of auxillary verbs based on word_tokens_no_punct.
sentence_count (int): Count the sentence tokens
passive_sentences (list): List of all sentences identified as passive
passive_sentence_count (int): count of items in passive_sentences
be_verb_count (int): count "to be" verbs in text
word_tokens_no_punct (list): words separated, stripped of punctuation,
made lower case
flesch_re_key (str): reading ease score to description
freq_words (list or dict): frequency distribution of all words
modal_dist (list): frequency distribution of aux verbs
"""
self.user = ""
self.path = path
self.abs_path = os.path.abspath(self.path)
if os.path.isfile(self.path):
self.time_stamp = self.timestamp()
self.file_name = os.path.basename(path)
self.mime = MimeTypes()
self.guessed_type = self.mime.guess_type(self.path)
self.file_type = self.guessed_type[0]
self.raw_text = textract.process(self.path, encoding="ascii")
self.ptext = re.sub(u'[\u201c\u201d]', '"', self.raw_text)
self.ptext = re.sub(u"\u2014", "--", self.ptext)
self.ptext = re.sub(",", ",", self.ptext)
self.ptext = re.sub("—", "--", self.ptext)
self.ptext = re.sub("…", "...", self.ptext)
self.text_no_feed = self.clean_new_lines(self.ptext)
self.sentence_tokens = self.sentence_tokenize(self.text_no_feed)
self.sentence_count = len(self.sentence_tokens)
self.passive_sentences = passive(self.text_no_feed)
self.passive_sentence_count = len(self.passive_sentences)
self.percent_passive = (100 * (float(self.passive_sentence_count) /
float(self.sentence_count)))
self.percent_passive_round = round(self.percent_passive, 2)
self.be_verb_analysis = self.count_be_verbs(self.sentence_tokens)
self.be_verb_count = self.be_verb_analysis[0]
self.weak_sentences_all = self.be_verb_analysis[1]
self.weak_sentences_set = set(self.weak_sentences_all)
self.weak_sentences_count = len(self.weak_sentences_set)
self.weak_verbs_to_sentences = 100 * float(
self.weak_sentences_count) / float(self.sentence_count)
self.weak_verbs_to_sentences_round = round(
self.weak_verbs_to_sentences, 2)
self.word_tokens = self.word_tokenize(self.text_no_feed)
self.word_tokens_no_punct = \
self.word_tokenize_no_punct(self.text_no_feed)
self.no_punct = self.strip_punctuation(self.text_no_feed)
# use this! It make lower and strips symbols
self.word_tokens_no_punct = self.ws_tokenize(self.no_punct)
self.readability_flesch_re = \
textstat.flesch_reading_ease(self.text_no_feed)
self.readability_smog_index = \
textstat.smog_index(self.text_no_feed)
self.readability_flesch_kincaid_grade = \
textstat.flesch_kincaid_grade(self.text_no_feed)
self.readability_coleman_liau_index = \
textstat.coleman_liau_index(self.text_no_feed)
self.readability_ari = \
textstat.automated_readability_index(self.text_no_feed)
self.readability_linser_write = \
textstat.linsear_write_formula(self.text_no_feed)
self.readability_dale_chall = \
textstat.dale_chall_readability_score(self.text_no_feed)
self.readability_standard = \
textstat.text_standard(self.text_no_feed)
self.flesch_re_desc_str = self.flesch_re_desc(
int(textstat.flesch_reading_ease(self.text_no_feed)))
self.polysyllabcount = textstat.polysyllabcount(self.text_no_feed)
self.lexicon_count = textstat.lexicon_count(self.text_no_feed)
self.avg_syllables_per_word = textstat.avg_syllables_per_word(
self.text_no_feed)
self.avg_sentence_per_word = textstat.avg_sentence_per_word(
self.text_no_feed)
self.avg_sentence_length = textstat.avg_sentence_length(
self.text_no_feed)
self.avg_letter_per_word = textstat.avg_letter_per_word(
self.text_no_feed)
self.difficult_words = textstat.difficult_words(self.text_no_feed)
self.rand_passive = self.select_random(self.passive_sentence_count,
self.passive_sentences)
self.rand_weak_sentence = self.select_random(
len(self.weak_sentences), self.weak_sentences)
if self.word_tokens_no_punct:
self.word_count = len(self.word_tokens_no_punct)
self.page_length = float(self.word_count) / float(250)
self.paper_count = int(math.ceil(self.page_length))
self.parts_of_speech = pos_tag(self.word_tokens_no_punct)
self.pos_counts = Counter(
tag for word, tag in self.parts_of_speech)
self.pos_total = sum(self.pos_counts.values())
self.pos_freq = dict(
(word, float(count) / self.pos_total)
for word, count in self.pos_counts.items())
self.doc_pages = float(float(self.word_count) / float(250))
self.freq_words = \
self.word_frequency(self.word_tokens_no_punct)
self.modal_dist = self.modal_count(self.word_tokens_no_punct)
# self.ws_tokens = self.ws_tokenize(self.text_no_cr)
self.pos_count_dict = self.pos_counts.items()
# Model - use for any pos
self.modals = self.pos_isolate('MD', self.pos_count_dict)
self.preposition_count = self.pos_isolate('IN',
self.pos_count_dict)
self.adjective_count = self.pos_isolate_fuzzy(
'JJ', self.pos_count_dict)
self.adverb_count = self.pos_isolate_fuzzy('RB',
self.pos_count_dict)
self.proper_nouns = self.pos_isolate_fuzzy('NNP',
self.pos_count_dict)
self.cc_count = self.pos_isolate('CC', self.pos_count_dict)
self.commas = self.char_count(",")
self.comma_sentences = self.list_sentences(",")
self.comma_example = self.select_random(len(self.comma_sentences),
self.comma_sentences)
self.semicolons = self.char_count(";")
self.semicolon_sentences = self.list_sentences(";")
self.semicolon_example = self.select_random(
len(self.semicolon_sentences), self.semicolon_sentences)
self.lint_suggestions = lint(self.raw_text)
def test_on_no_newlines(self):
"""Test that lint works on text without a terminal newline."""
assert len(lint(self.text_with_no_newline)) == 1
def test_errors_sorted(self):
"""Test that errors are sorted by line and column number."""
lines_and_cols = [self.extract_line_col(e) for e in lint(self.text)]
assert sorted(lines_and_cols) == lines_and_cols
def test_on_no_newlines(self):
"""Test that lint works on text without a terminal newline."""
assert len(lint(self.text_with_no_newline)) == 1
def test_errors_sorted(self):
"""Test that errors are sorted by line and column number."""
lines_and_cols = [self.extract_line_col(e) for e in lint(self.text)]
assert sorted(lines_and_cols) == lines_and_cols
