Added Flesch-Kincaid readability scores; added readme; final tidy

post_stats/post_stats.py

@@ -3,11 +3,13 @@
 Post Statistics
 ========================
 
-This plugin calculates various Statistics about a post and stores them in an article.stats disctionary:
+This plugin calculates various statistics about a post and stores them in an article.stats dictionary:
 
 wc: how many words
 read_mins: how many minutes to read this article, based on 250 wpm (http://en.wikipedia.org/wiki/Words_per_minute#Reading_and_comprehension)
 word_counts: frquency count of all the words in the article; can be used for tag/word clouds/
+fi: Flesch-kincaid Index/ Reading Ease
+fk: Flesch-kincaid Grade Level
 
 """
 
@@ -16,6 +18,8 @@ from bs4 import BeautifulSoup
 import re
 from collections import Counter
 
+from .readability import *
+
 
 def calculate_stats(instance):
 
@@ -26,18 +30,22 @@ def calculate_stats(instance):
         # How fast do average people read?
         WPM = 250
 
-        # Pre-process the text to remove entities
-        entities = r'\&\#?.+?;'
-        content = content.replace(' ', ' ')
-        content = re.sub(entities, '', content)
-
-        # Pre-process the text to remove punctuation
-        drop = u'.,?!@#$%^&*()_+-=\|/[]{}`~:;\'\"‘’—…“”'
-        content = content.translate(dict((ord(c), u'') for c in drop))
-
         # Use BeautifulSoup to get readable/visible text
         raw_text = BeautifulSoup(content).getText()
 
+        # Process the text to remove entities
+        entities = r'\&\#?.+?;'
+        raw_text = raw_text.replace(' ', ' ')
+        raw_text = re.sub(entities, '', raw_text)
+
+        # Flesch-kincaid readbility stats counts sentances,
+        # so save before removing punctuation
+        tmp = raw_text
+
+        # Process the text to remove punctuation
+        drop = u'.,?!@#$%^&*()_+-=\|/[]{}`~:;\'\"‘’—…“”'
+        raw_text = raw_text.translate(dict((ord(c), u'') for c in drop))
+
         # Count the words in the text
         words = raw_text.lower().split()
         word_count = Counter(words)
@@ -45,11 +53,17 @@ def calculate_stats(instance):
         # Return the stats
         stats['word_counts'] = word_count
         stats['wc'] = sum(word_count.values())
+
         # Calulate how long it'll take to read, rounding up
         stats['read_mins'] = (stats['wc'] + WPM - 1) // WPM
         if stats['read_mins'] == 0:
             stats['read_mins'] = 1
 
+        # Calculate Flesch-kincaid readbility stats
+        readability_stats = stcs, words, sbls = text_stats(tmp, stats['wc'])
+        stats['fi'] = "{:.2f}".format(flesch_index(readability_stats))
+        stats['fk'] = "{:.2f}".format(flesch_kincaid_level(readability_stats))
+
         instance.stats = stats
 
 

post_stats/readability.py

@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+
+# Adadpted from here: http://acdx.net/calculating-the-flesch-kincaid-level-in-python/
+# See here for details: http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_test
+
+from __future__ import division
+import re
+
+
+def mean(seq):
+    return sum(seq) / len(seq)
+
+
+def syllables(word):
+    if len(word) <= 3:
+        return 1
+
+    word = re.sub(r"(es|ed|(?<!l)e)$", "", word)
+    return len(re.findall(r"[aeiouy]+", word))
+
+
+def normalize(text):
+    terminators = ".!?:;"
+    term = re.escape(terminators)
+    text = re.sub(r"[^%s\sA-Za-z]+" % term, "", text)
+    text = re.sub(r"\s*([%s]+\s*)+" % term, ". ", text)
+    return re.sub(r"\s+", " ", text)
+
+
+def text_stats(text, wc):
+    text = normalize(text)
+    stcs = [s.split(" ") for s in text.split(". ")]
+    stcs = filter(lambda s: len(s) >= 2, stcs)
+
+    if wc:
+        words = wc
+    else:
+        words = sum(len(s) for s in stcs)
+
+    sbls = sum(syllables(w) for s in stcs for w in s)
+
+    return len(stcs), words, sbls
+
+
+def flesch_index(stats):
+    stcs, words, sbls = stats
+    if stcs == 0 or words == 0:
+        return 0
+    return 206.835 - 1.015 * (words / stcs) - 84.6 * (sbls / words)
+
+
+def flesch_kincaid_level(stats):
+    stcs, words, sbls = stats
+    if stcs == 0 or words == 0:
+        return 0
+    return 0.39 * (words / stcs) + 11.8 * (sbls / words) - 15.59

post_stats/readme.rst

@@ -0,0 +1,49 @@
+Post Statistics
+==================
+
+A Pelican plugin to calculate various statistics about a post and store them in an article.stats dictionary:
+
+- ``wc``: how many words
+- ``read_mins``: how many minutes to read this article, based on 250 wpm (http://en.wikipedia.org/wiki/Words_per_minute#Reading_and_comprehension)
+- ``word_counts``: frquency count of all the words in the article; can be used for tag/word clouds/
+- ``fi``: Flesch-kincaid Index/ Reading Ease (see: http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests)
+- ``fk``: Flesch-kincaid Grade Level
+
+Example:
+
+.. code-block:: python
+
+    {
+        'wc': 2760,
+        'fi': '65.94',
+        'fk': '7.65',
+        'word_counts': Counter({u'to': 98, u'a': 90, u'the': 83, u'of': 50, ...}),
+        'read_mins': 12
+    }
+
+This allows you to output these values in your templates, like this, for example:
+
+.. code-block:: html+jinja
+
+	<p title="~{{ article.stats['wc'] }} words">~{{ article.stats['read_mins'] }} min read</p>
+	<ul>
+	    <li>Flesch-kincaid Index/ Reading Ease: {{ article.stats['fi'] }}</li>
+	    <li>Flesch-kincaid Grade Level: {{ article.stats['fk'] }}</li>
+	</ul>
+
+The ``word_counts`` variable is a Counter dictionary and looks like this, with each unique word and it's frequency:
+
+.. code-block:: python
+
+	Counter({u'to': 98, u'a': 90, u'the': 83, u'of': 50, u'karma': 50, .....
+
+and could be used to create a tag/word cloud for a post.
+
+Requirements
+============
+
+`post_stats` requires BeautifulSoup.
+
+.. code-block:: console
+
+    $ pip install beautifulsoup4