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