Ответ 1
Комментарии в коде должны объяснять все. Дайте мне знать, если что-то указано, и вам нужно больше комментариев.
Короче говоря, я использую регулярное выражение, чтобы найти строки разделителя '=', чтобы разделить весь текст на подразделы, а затем обрабатывать каждый тип разделов отдельно для ясности (так что вы можете рассказать, как я обрабатываю каждый случай).
Боковое примечание: я использую слово "посетитель" и "автор" взаимозаменяемо.
EDIT: Обновлен код для сортировки на основе шаблона [x], который находится рядом с участником/автором в разделе презентации /QA. Также изменилась красивая печатная часть, так как pprint не очень хорошо обрабатывает OrderedDict.
Чтобы удалить любые дополнительные пробелы, включая \n в любом месте строки, просто выполните str.strip(). если вам нужно снять только \n, просто сделайте str.strip('\n').
Я изменил код, чтобы удалить пробелы в разговорах.
import json
import re
from collections import OrderedDict
from pprint import pprint
# Subdivides a collection of lines based on the delimiting regular expression.
# >>> example_string =' =============================
# asdfasdfasdf
# sdfasdfdfsdfsdf
# =============================
# asdfsdfasdfasd
# =============================
# >>> subdivide(example_string, "^=+")
# >>> ['asdfasdfasdf\nsdfasdfdfsdfsdf\n', 'asdfsdfasdfasd\n']
def subdivide(lines, regex):
equ_pattern = re.compile(regex, re.MULTILINE)
sections = equ_pattern.split(lines)
sections = [section.strip('\n') for section in sections]
return sections
# for processing sections with dashes in them, returns the heading of the section along with
# a dictionary where each key is the subsection header, and each value is the text in the subsection.
def process_dashed_sections(section):
subsections = subdivide(section, "^-+")
heading = subsections[0] # header of the section.
d = {key: value for key, value in zip(subsections[1::2], subsections[2::2])}
index_pattern = re.compile("\[(.+)\]", re.MULTILINE)
# sort the dictionary by first capturing the pattern '[x]' and extracting 'x' number.
# Then this is passed as a compare function to 'sorted' to sort based on 'x'.
def cmp(d):
mat = index_pattern.findall(d[0])
if mat:
print(mat[0])
return int(mat[0])
# There are issues when dealing with subsections containing '- but not containing '[x]' pattern.
# This is just to deal with that small issue.
else:
return 0
o_d = OrderedDict(sorted(d.items(), key=cmp))
return heading, o_d
# this is to rename the keys of 'd' dictionary to the proper names present in the attendees.
# it searches for the best match for the key in the 'attendees' list, and replaces the corresponding key.
# >>> d = {'mr. man ceo of company [1]' : ' This is talk a' ,
# ... 'ms. woman ceo of company [2]' : ' This is talk b'}
# >>> l = ['mr. man', 'ms. woman']
# >>> new_d = assign_attendee(d, l)
# new_d = {'mr. man': 'This is talk a', 'ms. woman': 'This is talk b'}
def assign_attendee(d, attendees):
new_d = OrderedDict()
for key, value in d.items():
a = [a for a in attendees if a in key]
if len(a) == 1:
# to strip out any additional whitespace anywhere in the text including '\n'.
new_d[a[0]] = value.strip()
elif len(a) == 0:
# to strip out any additional whitespace anywhere in the text including '\n'.
new_d[key] = value.strip()
return new_d
if __name__ == '__main__':
with open('input.txt', 'r') as input:
lines = input.read()
# regex pattern for matching headers of each section
header_pattern = re.compile("^.*[^\n]", re.MULTILINE)
# regex pattern for matching the sections that contains
# the list of attendee (those that start with asterisks )
ppl_pattern = re.compile("^(\s+\*)(.+)(\s.*)", re.MULTILINE)
# regex pattern for matching sections with subsections in them.
dash_pattern = re.compile("^-+", re.MULTILINE)
ppl_d = dict()
talks_d = dict()
# Step1. Divide the the entire document into sections using the '=' divider
sections = subdivide(lines, "^=+")
header = []
print(sections)
# Step2. Handle each section like a switch case
for section in sections:
# Handle headers
if len(section.split('\n')) == 1: # likely to match only a header (assuming )
header = header_pattern.match(section).string
# Handle attendees/authors
elif ppl_pattern.match(section):
ppls = ppl_pattern.findall(section)
d = {key.strip(): value.strip() for (_, key, value) in ppls}
ppl_d.update(d)
# assuming that if the previous section was detected as a header, then this section will relate
# to that header
if header:
talks_d.update({header: ppl_d})
# Handle subsections
elif dash_pattern.findall(section):
heading, d = process_dashed_sections(section)
talks_d.update({heading: d})
# Else its just some random text.
else:
# assuming that if the previous section was detected as a header, then this section will relate
# to that header
if header:
talks_d.update({header: section})
#pprint(talks_d)
# To assign the talks material to the appropriate attendee/author. Still works if no match found.
for key, value in talks_d.items():
talks_d[key] = assign_attendee(value, ppl_d.keys())
# ordered dict does not pretty print using 'pprint'. So a small hack to make use of json output to pretty print.
print(json.dumps(talks_d, indent=4))