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Smarten punctuation: Convert double dashes to em dashes. Preprocessing: Various tweaks
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commit
aec2ebb9a2
@ -353,7 +353,7 @@ class HTMLPreProcessor(object):
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(re.compile(r'((?<=</a>)\s*file:////?[A-Z].*<br>|file:////?[A-Z].*<br>(?=\s*<hr>))', re.IGNORECASE), lambda match: ''),
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# Center separator lines
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(re.compile(u'<br>\s*(?P<break>([*#•]+\s*)+)\s*<br>'), lambda match: '<p>\n<p style="text-align:center">' + match.group(1) + '</p>'),
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(re.compile(u'<br>\s*(?P<break>([*#•✦]+\s*)+)\s*<br>'), lambda match: '<p>\n<p style="text-align:center">' + match.group(1) + '</p>'),
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# Remove page links
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(re.compile(r'<a name=\d+></a>', re.IGNORECASE), lambda match: ''),
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@ -363,13 +363,11 @@ class HTMLPreProcessor(object):
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# Remove gray background
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(re.compile(r'<BODY[^<>]+>'), lambda match : '<BODY>'),
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# Detect Chapters to match default XPATH in GUI
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(re.compile(r'<br>\s*(?P<chap>(<[ibu]>){0,2}\s*.?(Introduction|Chapter|Kapitel|Epilogue|Prologue|Book|Part|Dedication|Volume|Preface|Acknowledgments)\s*([\d\w-]+\s*){0,3}\s*(</[ibu]>){0,2})\s*(<br>\s*){1,3}\s*(?P<title>(<[ibu]>){0,2}(\s*\w+){1,4}\s*(</[ibu]>){0,2}\s*<br>)?', re.IGNORECASE), chap_head),
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# Cover the case where every letter in a chapter title is separated by a space
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(re.compile(r'<br>\s*(?P<chap>([A-Z]\s+){4,}\s*([\d\w-]+\s*){0,3}\s*)\s*(<br>\s*){1,3}\s*(?P<title>(<[ibu]>){0,2}(\s*\w+){1,4}\s*(</[ibu]>){0,2}\s*(<br>))?'), chap_head),
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# Convert line breaks to paragraphs
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(re.compile(r'<br[^>]*>\s*'), lambda match : '</p>\n<p>'),
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(re.compile(r'<body[^>]*>\s*'), lambda match : '<body>\n<p>'),
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(re.compile(r'\s*</body>'), lambda match : '</p>\n</body>'),
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# Have paragraphs show better
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(re.compile(r'<br.*?>'), lambda match : '<p>'),
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# Clean up spaces
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(re.compile(u'(?<=[\.,;\?!”"\'])[\s^ ]*(?=<)'), lambda match: ' '),
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# Add space before and after italics
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@ -455,9 +453,9 @@ class HTMLPreProcessor(object):
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# delete soft hyphens - moved here so it's executed after header/footer removal
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if is_pdftohtml:
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# unwrap/delete soft hyphens
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end_rules.append((re.compile(u'[](\s*<p>)+\s*(?=[[a-z\d])'), lambda match: ''))
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end_rules.append((re.compile(u'[](</p>\s*<p>\s*)+\s*(?=[[a-z\d])'), lambda match: ''))
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# unwrap/delete soft hyphens with formatting
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end_rules.append((re.compile(u'[]\s*(</(i|u|b)>)+(\s*<p>)+\s*(<(i|u|b)>)+\s*(?=[[a-z\d])'), lambda match: ''))
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end_rules.append((re.compile(u'[]\s*(</(i|u|b)>)+(</p>\s*<p>\s*)+\s*(<(i|u|b)>)+\s*(?=[[a-z\d])'), lambda match: ''))
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# Make the more aggressive chapter marking regex optional with the preprocess option to
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# reduce false positives and move after header/footer removal
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@ -475,7 +473,7 @@ class HTMLPreProcessor(object):
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end_rules.append((re.compile(u'(?<=.{%i}[–—])\s*<p>\s*(?=[[a-z\d])' % length), lambda match: ''))
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end_rules.append(
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# Un wrap using punctuation
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(re.compile(u'(?<=.{%i}([a-zäëïöüàèìòùáćéíóńśúâêîôûçąężı,:)\IA\u00DF]|(?<!\&\w{4});))\s*(?P<ital></(i|b|u)>)?\s*(<p.*?>\s*)+\s*(?=(<(i|b|u)>)?\s*[\w\d$(])' % length, re.UNICODE), wrap_lines),
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(re.compile(u'(?<=.{%i}([a-zäëïöüàèìòùáćéíóńśúâêîôûçąężıãõñæøþðß,:)\IA\u00DF]|(?<!\&\w{4});))\s*(?P<ital></(i|b|u)>)?\s*(</p>\s*<p>\s*)+\s*(?=(<(i|b|u)>)?\s*[\w\d$(])' % length, re.UNICODE), wrap_lines),
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)
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for rule in self.PREPROCESS + start_rules:
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@ -508,7 +506,15 @@ class HTMLPreProcessor(object):
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if is_pdftohtml and length > -1:
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# Dehyphenate
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dehyphenator = Dehyphenator()
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html = dehyphenator(html,'pdf', length)
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html = dehyphenator(html,'html', length)
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if is_pdftohtml:
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from calibre.ebooks.conversion.utils import PreProcessor
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pdf_markup = PreProcessor(self.extra_opts, None)
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totalwords = 0
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totalwords = pdf_markup.get_word_count(html)
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if totalwords > 7000:
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html = pdf_markup.markup_chapters(html, totalwords, True)
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#dump(html, 'post-preprocess')
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@ -554,5 +560,9 @@ class HTMLPreProcessor(object):
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html = smartyPants(html)
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html = html.replace(start, '<!--')
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html = html.replace(stop, '-->')
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# convert ellipsis to entities to prevent wrapping
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html = re.sub('(?u)(?<=\w)\s?(\.\s?){2}\.', '…', html)
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# convert double dashes to em-dash
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html = re.sub('\s--\s', u'\u2014', html)
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return substitute_entites(html)
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@ -6,8 +6,10 @@ __copyright__ = '2010, Kovid Goyal <kovid@kovidgoyal.net>'
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__docformat__ = 'restructuredtext en'
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import re
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from math import ceil
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from calibre.ebooks.conversion.preprocess import DocAnalysis, Dehyphenator
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from calibre.utils.logging import default_log
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from calibre.utils.wordcount import get_wordcount_obj
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class PreProcessor(object):
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@ -17,6 +19,9 @@ class PreProcessor(object):
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self.found_indents = 0
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self.extra_opts = extra_opts
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def is_pdftohtml(self, src):
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return '<!-- created by calibre\'s pdftohtml -->' in src[:1000]
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def chapter_head(self, match):
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chap = match.group('chap')
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title = match.group('title')
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@ -64,7 +69,7 @@ class PreProcessor(object):
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inspect. Percent is the minimum percent of line endings which should
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be marked up to return true.
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'''
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htm_end_ere = re.compile('</p>', re.DOTALL)
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htm_end_ere = re.compile('</(p|div)>', re.DOTALL)
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line_end_ere = re.compile('(\n|\r|\r\n)', re.DOTALL)
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htm_end = htm_end_ere.findall(raw)
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line_end = line_end_ere.findall(raw)
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@ -101,12 +106,101 @@ class PreProcessor(object):
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with open(os.path.join(odir, name), 'wb') as f:
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f.write(raw.encode('utf-8'))
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def get_word_count(self, html):
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word_count_text = re.sub(r'(?s)<head[^>]*>.*?</head>', '', html)
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word_count_text = re.sub(r'<[^>]*>', '', word_count_text)
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wordcount = get_wordcount_obj(word_count_text)
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return wordcount.words
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def markup_chapters(self, html, wordcount, blanks_between_paragraphs):
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# Typical chapters are between 2000 and 7000 words, use the larger number to decide the
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# minimum of chapters to search for
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self.min_chapters = 1
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if wordcount > 7000:
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self.min_chapters = int(ceil(wordcount / 7000.))
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#print "minimum chapters required are: "+str(self.min_chapters)
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heading = re.compile('<h[1-3][^>]*>', re.IGNORECASE)
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self.html_preprocess_sections = len(heading.findall(html))
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self.log("found " + unicode(self.html_preprocess_sections) + " pre-existing headings")
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# Build the Regular Expressions in pieces
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init_lookahead = "(?=<(p|div))"
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chapter_line_open = "<(?P<outer>p|div)[^>]*>\s*(<(?P<inner1>font|span|[ibu])[^>]*>)?\s*(<(?P<inner2>font|span|[ibu])[^>]*>)?\s*(<(?P<inner3>font|span|[ibu])[^>]*>)?\s*"
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title_line_open = "<(?P<outer2>p|div)[^>]*>\s*(<(?P<inner4>font|span|[ibu])[^>]*>)?\s*(<(?P<inner5>font|span|[ibu])[^>]*>)?\s*(<(?P<inner6>font|span|[ibu])[^>]*>)?\s*"
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chapter_header_open = r"(?P<chap>"
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title_header_open = r"(?P<title>"
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chapter_header_close = ")\s*"
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title_header_close = ")"
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chapter_line_close = "(</(?P=inner3)>)?\s*(</(?P=inner2)>)?\s*(</(?P=inner1)>)?\s*</(?P=outer)>"
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title_line_close = "(</(?P=inner6)>)?\s*(</(?P=inner5)>)?\s*(</(?P=inner4)>)?\s*</(?P=outer2)>"
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is_pdftohtml = self.is_pdftohtml(html)
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if is_pdftohtml:
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chapter_line_open = "<(?P<outer>p)[^>]*>(\s*<[ibu][^>]*>)?\s*"
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chapter_line_close = "\s*(</[ibu][^>]*>\s*)?</(?P=outer)>"
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title_line_open = "<(?P<outer2>p)[^>]*>\s*"
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title_line_close = "\s*</(?P=outer2)>"
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if blanks_between_paragraphs:
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blank_lines = "(\s*<p[^>]*>\s*</p>){0,2}\s*"
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else:
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blank_lines = ""
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opt_title_open = "("
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opt_title_close = ")?"
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n_lookahead_open = "\s+(?!"
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n_lookahead_close = ")"
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default_title = r"(<[ibu][^>]*>)?\s{0,3}([\w\'\"-]+\s{0,3}){1,5}?(</[ibu][^>]*>)?(?=<)"
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chapter_types = [
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[r"[^'\"]?(Introduction|Synopsis|Acknowledgements|Chapter|Kapitel|Epilogue|Volume\s|Prologue|Book\s|Part\s|Dedication|Preface)\s*([\d\w-]+\:?\s*){0,4}", True, "Searching for common Chapter Headings"],
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[r"<b[^>]*>\s*(<span[^>]*>)?\s*(?!([*#•]+\s*)+)(\s*(?=[\d.\w#\-*\s]+<)([\d.\w#-*]+\s*){1,5}\s*)(?!\.)(</span>)?\s*</b>", True, "Searching for emphasized lines"], # Emphasized lines
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[r"[^'\"]?(\d+(\.|:)|CHAPTER)\s*([\dA-Z\-\'\"#,]+\s*){0,7}\s*", True, "Searching for numeric chapter headings"], # Numeric Chapters
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[r"([A-Z]\s+){3,}\s*([\d\w-]+\s*){0,3}\s*", True, "Searching for letter spaced headings"], # Spaced Lettering
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[r"[^'\"]?(\d+\.?\s+([\d\w-]+\:?\'?-?\s?){0,5})\s*", True, "Searching for numeric chapters with titles"], # Numeric Titles
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[r"[^'\"]?(\d+|CHAPTER)\s*([\dA-Z\-\'\"\?!#,]+\s*){0,7}\s*", True, "Searching for simple numeric chapter headings"], # Numeric Chapters, no dot or colon
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[r"\s*[^'\"]?([A-Z#]+(\s|-){0,3}){1,5}\s*", False, "Searching for chapters with Uppercase Characters" ] # Uppercase Chapters
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]
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# Start with most typical chapter headings, get more aggressive until one works
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for [chapter_type, lookahead_ignorecase, log_message] in chapter_types:
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if self.html_preprocess_sections >= self.min_chapters:
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break
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full_chapter_line = chapter_line_open+chapter_header_open+chapter_type+chapter_header_close+chapter_line_close
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n_lookahead = re.sub("(ou|in|cha)", "lookahead_", full_chapter_line)
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self.log("Marked " + unicode(self.html_preprocess_sections) + " headings, " + log_message)
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if lookahead_ignorecase:
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chapter_marker = init_lookahead+full_chapter_line+blank_lines+n_lookahead_open+n_lookahead+n_lookahead_close+opt_title_open+title_line_open+title_header_open+default_title+title_header_close+title_line_close+opt_title_close
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chapdetect = re.compile(r'%s' % chapter_marker, re.IGNORECASE)
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else:
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chapter_marker = init_lookahead+full_chapter_line+blank_lines+opt_title_open+title_line_open+title_header_open+default_title+title_header_close+title_line_close+opt_title_close+n_lookahead_open+n_lookahead+n_lookahead_close
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chapdetect = re.compile(r'%s' % chapter_marker, re.UNICODE)
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html = chapdetect.sub(self.chapter_head, html)
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words_per_chptr = wordcount
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if words_per_chptr > 0 and self.html_preprocess_sections > 0:
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words_per_chptr = wordcount / self.html_preprocess_sections
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self.log("Total wordcount is: "+ str(wordcount)+", Average words per section is: "+str(words_per_chptr)+", Marked up "+str(self.html_preprocess_sections)+" chapters")
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return html
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def __call__(self, html):
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self.log("********* Preprocessing HTML *********")
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# Count the words in the document to estimate how many chapters to look for and whether
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# other types of processing are attempted
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totalwords = 0
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totalwords = self.get_word_count(html)
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if totalwords < 20:
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self.log("not enough text, not preprocessing")
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return html
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# Arrange line feeds and </p> tags so the line_length and no_markup functions work correctly
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html = re.sub(r"\s*</p>", "</p>\n", html)
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html = re.sub(r"\s*<p(?P<style>[^>]*)>\s*", "\n<p"+"\g<style>"+">", html)
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html = re.sub(r"\s*</(?P<tag>p|div)>", "</"+"\g<tag>"+">\n", html)
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html = re.sub(r"\s*<(?P<tag>p|div)(?P<style>[^>]*)>\s*", "\n<"+"\g<tag>"+"\g<style>"+">", html)
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###### Check Markup ######
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#
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@ -141,12 +235,17 @@ class PreProcessor(object):
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self.log("replaced "+unicode(self.found_indents)+ " nbsp indents with inline styles")
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# remove remaining non-breaking spaces
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html = re.sub(ur'\u00a0', ' ', html)
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# Get rid of various common microsoft specific tags which can cause issues later
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# Get rid of empty <o:p> tags to simplify other processing
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html = re.sub(ur'\s*<o:p>\s*</o:p>', ' ', html)
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# Delete microsoft 'smart' tags
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html = re.sub('(?i)</?st1:\w+>', '', html)
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# Get rid of empty span, bold, & italics tags
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html = re.sub(r"\s*<span[^>]*>\s*(<span[^>]*>\s*</span>){0,2}\s*</span>\s*", " ", html)
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html = re.sub(r"\s*<[ibu][^>]*>\s*(<[ibu][^>]*>\s*</[ibu]>\s*){0,2}\s*</[ibu]>", " ", html)
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html = re.sub(r"\s*<span[^>]*>\s*(<span[^>]>\s*</span>){0,2}\s*</span>\s*", " ", html)
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# ADE doesn't render <br />, change to empty paragraphs
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#html = re.sub('<br[^>]*>', u'<p>\u00a0</p>', html)
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# If more than 40% of the lines are empty paragraphs and the user has enabled remove
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# paragraph spacing then delete blank lines to clean up spacing
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@ -168,59 +267,12 @@ class PreProcessor(object):
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#print "blanks between paragraphs is marked True"
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else:
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blanks_between_paragraphs = False
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#self.dump(html, 'before_chapter_markup')
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# detect chapters/sections to match xpath or splitting logic
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#
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# Build the Regular Expressions in pieces
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init_lookahead = "(?=<(p|div))"
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chapter_line_open = "<(?P<outer>p|div)[^>]*>\s*(<(?P<inner1>font|span|[ibu])[^>]*>)?\s*(<(?P<inner2>font|span|[ibu])[^>]*>)?\s*(<(?P<inner3>font|span|[ibu])[^>]*>)?\s*"
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title_line_open = "<(?P<outer2>p|div)[^>]*>\s*(<(?P<inner4>font|span|[ibu])[^>]*>)?\s*(<(?P<inner5>font|span|[ibu])[^>]*>)?\s*(<(?P<inner6>font|span|[ibu])[^>]*>)?\s*"
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chapter_header_open = r"(?P<chap>"
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title_header_open = r"(?P<title>"
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chapter_header_close = ")\s*"
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title_header_close = ")"
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chapter_line_close = "(</(?P=inner3)>)?\s*(</(?P=inner2)>)?\s*(</(?P=inner1)>)?\s*</(?P=outer)>"
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title_line_close = "(</(?P=inner6)>)?\s*(</(?P=inner5)>)?\s*(</(?P=inner4)>)?\s*</(?P=outer2)>"
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if blanks_between_paragraphs:
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blank_lines = "(\s*<p[^>]*>\s*</p>){0,2}\s*"
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else:
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blank_lines = ""
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opt_title_open = "("
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opt_title_close = ")?"
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n_lookahead_open = "\s+(?!"
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n_lookahead_close = ")"
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default_title = r"\s{0,3}([\w\'\"-]+\s{0,3}){1,5}?(?=<)"
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min_chapters = 10
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heading = re.compile('<h[1-3][^>]*>', re.IGNORECASE)
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self.html_preprocess_sections = len(heading.findall(html))
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self.log("found " + unicode(self.html_preprocess_sections) + " pre-existing headings")
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chapter_types = [
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[r"[^'\"]?(Introduction|Synopsis|Acknowledgements|Chapter|Kapitel|Epilogue|Volume\s|Prologue|Book\s|Part\s|Dedication)\s*([\d\w-]+\:?\s*){0,4}", True, "Searching for common Chapter Headings"],
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[r"[^'\"]?(\d+\.?|CHAPTER)\s*([\dA-Z\-\'\"\?\.!#,]+\s*){0,7}\s*", True, "Searching for numeric chapter headings"], # Numeric Chapters
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[r"<b[^>]*>\s*(<span[^>]*>)?\s*(?!([*#•]+\s*)+)(\s*(?=[\w#\-*\s]+<)([\w#-*]+\s*){1,5}\s*)(</span>)?\s*</b>", True, "Searching for emphasized lines"], # Emphasized lines
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[r"[^'\"]?(\d+\.?\s+([\d\w-]+\:?\'?-?\s?){0,5})\s*", True, "Searching for numeric chapters with titles"], # Numeric Titles
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[r"\s*[^'\"]?([A-Z#]+(\s|-){0,3}){1,5}\s*", False, "Searching for chapters with Uppercase Characters" ] # Uppercase Chapters
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]
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# Start with most typical chapter headings, get more aggressive until one works
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for [chapter_type, lookahead_ignorecase, log_message] in chapter_types:
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if self.html_preprocess_sections >= min_chapters:
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break
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full_chapter_line = chapter_line_open+chapter_header_open+chapter_type+chapter_header_close+chapter_line_close
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n_lookahead = re.sub("(ou|in|cha)", "lookahead_", full_chapter_line)
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self.log("Marked " + unicode(self.html_preprocess_sections) + " headings, " + log_message)
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if lookahead_ignorecase:
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chapter_marker = init_lookahead+full_chapter_line+blank_lines+n_lookahead_open+n_lookahead+n_lookahead_close+opt_title_open+title_line_open+title_header_open+default_title+title_header_close+title_line_close+opt_title_close
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chapdetect = re.compile(r'%s' % chapter_marker, re.IGNORECASE)
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else:
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chapter_marker = init_lookahead+full_chapter_line+blank_lines+opt_title_open+title_line_open+title_header_open+default_title+title_header_close+title_line_close+opt_title_close+n_lookahead_open+n_lookahead+n_lookahead_close
|
||||
chapdetect = re.compile(r'%s' % chapter_marker, re.UNICODE)
|
||||
|
||||
html = chapdetect.sub(self.chapter_head, html)
|
||||
html = self.markup_chapters(html, totalwords, blanks_between_paragraphs)
|
||||
|
||||
|
||||
###### Unwrap lines ######
|
||||
@ -247,7 +299,7 @@ class PreProcessor(object):
|
||||
# Calculate Length
|
||||
unwrap_factor = getattr(self.extra_opts, 'html_unwrap_factor', 0.4)
|
||||
length = docanalysis.line_length(unwrap_factor)
|
||||
self.log("*** Median line length is " + unicode(length) + ", calculated with " + format + " format ***")
|
||||
self.log("Median line length is " + unicode(length) + ", calculated with " + format + " format")
|
||||
# only go through unwrapping code if the histogram shows unwrapping is required or if the user decreased the default unwrap_factor
|
||||
if hardbreaks or unwrap_factor < 0.4:
|
||||
self.log("Unwrapping required, unwrapping Lines")
|
||||
@ -260,7 +312,7 @@ class PreProcessor(object):
|
||||
self.log("Done dehyphenating")
|
||||
# Unwrap lines using punctation and line length
|
||||
#unwrap_quotes = re.compile(u"(?<=.{%i}\"')\s*</(span|p|div)>\s*(</(p|span|div)>)?\s*(?P<up2threeblanks><(p|span|div)[^>]*>\s*(<(p|span|div)[^>]*>\s*</(span|p|div)>\s*)</(span|p|div)>\s*){0,3}\s*<(span|div|p)[^>]*>\s*(<(span|div|p)[^>]*>)?\s*(?=[a-z])" % length, re.UNICODE)
|
||||
unwrap = re.compile(u"(?<=.{%i}([a-zäëïöüàèìòùáćéíóńśúâêîôûçąężı,:)\IA\u00DF]|(?<!\&\w{4});))\s*</(span|p|div)>\s*(</(p|span|div)>)?\s*(?P<up2threeblanks><(p|span|div)[^>]*>\s*(<(p|span|div)[^>]*>\s*</(span|p|div)>\s*)</(span|p|div)>\s*){0,3}\s*<(span|div|p)[^>]*>\s*(<(span|div|p)[^>]*>)?\s*" % length, re.UNICODE)
|
||||
unwrap = re.compile(u"(?<=.{%i}([a-zäëïöüàèìòùáćéíóńśúâêîôûçąężıãõñæøþðß,:)\IA\u00DF]|(?<!\&\w{4});))\s*</(span|p|div)>\s*(</(p|span|div)>)?\s*(?P<up2threeblanks><(p|span|div)[^>]*>\s*(<(p|span|div)[^>]*>\s*</(span|p|div)>\s*)</(span|p|div)>\s*){0,3}\s*<(span|div|p)[^>]*>\s*(<(span|div|p)[^>]*>)?\s*" % length, re.UNICODE)
|
||||
html = unwrap.sub(' ', html)
|
||||
#check any remaining hyphens, but only unwrap if there is a match
|
||||
dehyphenator = Dehyphenator()
|
||||
@ -276,7 +328,7 @@ class PreProcessor(object):
|
||||
html = re.sub(u'\xad\s*(</span>\s*(</[iubp]>\s*<[iubp][^>]*>\s*)?<span[^>]*>|</[iubp]>\s*<[iubp][^>]*>)?\s*', '', html)
|
||||
|
||||
# If still no sections after unwrapping mark split points on lines with no punctuation
|
||||
if self.html_preprocess_sections < 5:
|
||||
if self.html_preprocess_sections < self.min_chapters:
|
||||
self.log("Looking for more split points based on punctuation,"
|
||||
" currently have " + unicode(self.html_preprocess_sections))
|
||||
chapdetect3 = re.compile(r'<(?P<styles>(p|div)[^>]*)>\s*(?P<section>(<span[^>]*>)?\s*(?!([*#•]+\s*)+)(<[ibu][^>]*>){0,2}\s*(<span[^>]*>)?\s*(<[ibu][^>]*>){0,2}\s*(<span[^>]*>)?\s*.?(?=[a-z#\-*\s]+<)([a-z#-*]+\s*){1,5}\s*\s*(</span>)?(</[ibu]>){0,2}\s*(</span>)?\s*(</[ibu]>){0,2}\s*(</span>)?\s*</(p|div)>)', re.IGNORECASE)
|
||||
|
85
src/calibre/utils/wordcount.py
Normal file
85
src/calibre/utils/wordcount.py
Normal file
@ -0,0 +1,85 @@
|
||||
#!/usr/bin/python
|
||||
# vim:fileencoding=UTF-8:ts=4:sw=4:sta:et:sts=4:ai
|
||||
"""
|
||||
Get word, character, and Asian character counts
|
||||
|
||||
1. Get a word count as a dictionary:
|
||||
wc = get_wordcount(text)
|
||||
words = wc['words'] # etc.
|
||||
|
||||
2. Get a word count as an object
|
||||
wc = get_wordcount_obj(text)
|
||||
words = wc.words # etc.
|
||||
|
||||
properties counted:
|
||||
* characters
|
||||
* chars_no_spaces
|
||||
* asian_chars
|
||||
* non_asian_words
|
||||
* words
|
||||
|
||||
Sourced from:
|
||||
http://ginstrom.com/scribbles/2008/05/17/counting-words-etc-in-an-html-file-with-python/
|
||||
http://ginstrom.com/scribbles/2007/10/06/counting-words-characters-and-asian-characters-with-python/
|
||||
"""
|
||||
__version__ = 0.1
|
||||
__author__ = "Ryan Ginstrom"
|
||||
|
||||
IDEOGRAPHIC_SPACE = 0x3000
|
||||
|
||||
def is_asian(char):
|
||||
"""Is the character Asian?"""
|
||||
|
||||
# 0x3000 is ideographic space (i.e. double-byte space)
|
||||
# Anything over is an Asian character
|
||||
return ord(char) > IDEOGRAPHIC_SPACE
|
||||
|
||||
def filter_jchars(c):
|
||||
"""Filters Asian characters to spaces"""
|
||||
if is_asian(c):
|
||||
return ' '
|
||||
return c
|
||||
|
||||
def nonj_len(word):
|
||||
u"""Returns number of non-Asian words in {word}
|
||||
- 日本語AアジアンB -> 2
|
||||
- hello -> 1
|
||||
@param word: A word, possibly containing Asian characters
|
||||
"""
|
||||
# Here are the steps:
|
||||
# 本spam日eggs
|
||||
# -> [' ', 's', 'p', 'a', 'm', ' ', 'e', 'g', 'g', 's']
|
||||
# -> ' spam eggs'
|
||||
# -> ['spam', 'eggs']
|
||||
# The length of which is 2!
|
||||
chars = [filter_jchars(c) for c in word]
|
||||
return len(u''.join(chars).split())
|
||||
|
||||
def get_wordcount(text):
|
||||
"""Get the word/character count for text
|
||||
|
||||
@param text: The text of the segment
|
||||
"""
|
||||
|
||||
characters = len(text)
|
||||
chars_no_spaces = sum([not x.isspace() for x in text])
|
||||
asian_chars = sum([is_asian(x) for x in text])
|
||||
non_asian_words = nonj_len(text)
|
||||
words = non_asian_words + asian_chars
|
||||
|
||||
return dict(characters=characters,
|
||||
chars_no_spaces=chars_no_spaces,
|
||||
asian_chars=asian_chars,
|
||||
non_asian_words=non_asian_words,
|
||||
words=words)
|
||||
|
||||
def dict2obj(dictionary):
|
||||
"""Transform a dictionary into an object"""
|
||||
class Obj(object):
|
||||
def __init__(self, dictionary):
|
||||
self.__dict__.update(dictionary)
|
||||
return Obj(dictionary)
|
||||
|
||||
def get_wordcount_obj(text):
|
||||
"""Get the wordcount as an object rather than a dictionary"""
|
||||
return dict2obj(get_wordcount(text))
|
Loading…
x
Reference in New Issue
Block a user