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+# -*- coding: utf-8 -*-
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+"""
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+Post Statistics
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+========================
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+
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+This plugin calculates various Statistics about a post and stores them in an article.stats disctionary.
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+
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+wc: how many words
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+read_minutes: how many minutes to read this article, based on 250 wpm (http://en.wikipedia.org/wiki/Words_per_minute#Reading_and_comprehension)
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+word_count: frquency count of all the words in the article; can be used for tag/word clouds/
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+
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+"""
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+
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+from pelican import signals, contents
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+
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+# import nltk
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+
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+from bs4 import BeautifulSoup
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+
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+# import lxml.html
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+# from lxml.html.clean import Cleaner
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+
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+import re
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+from collections import Counter
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+
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+
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+def calculate_stats(instance):
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+
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+ WPM = 250
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+
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+ if instance._content is not None:
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+ stats = {}
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+ content = instance._content
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+
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+ # print content
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+ entities = r'\&\#?.+?;'
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+ content = content.replace(' ', ' ')
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+ content = re.sub(entities, '', content)
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+ # print content
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+
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+ # Pre-process the text to remove punctuation
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+ drop = u'.,?!@#$%^&*()_+-=\|/[]{}`~:;\'\"‘’—…“”'
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+ content = content.translate(dict((ord(c), u'') for c in drop))
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+
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+ # nltk
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+ # raw_text = nltk.clean_html(content)
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+
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+ # BeautifulSoup
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+ raw_text = BeautifulSoup(content).getText()
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+ # raw_text = ''.join(BeautifulSoup(content).findAll(text=True))
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+
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+ # lxml
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+ # cleaner = Cleaner(style=True)
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+ # html = lxml.html.fromstring(content)
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+ # raw_text = cleaner.clean_html(html).text_content()
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+
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+ # stats['wc'] = len(re.findall(r'\b', raw_text)) >> 1
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+
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+ # print raw_text
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+
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+ words = raw_text.lower().split()
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+ word_count = Counter(words)
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+ # print word_count
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+
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+ stats['word_counts'] = word_count
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+ stats['wc'] = sum(word_count.values())
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+ stats['read_minutes'] = (stats['wc'] + WPM // 2) // WPM
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+
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+ instance.stats = stats
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+ instance.raw_text = raw_text
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+
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+
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+def register():
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+ signals.content_object_init.connect(calculate_stats)
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