def create(self, text=''):
#rfi = readabilityFilesInstaller()
# NewDaleChallWordsFile = open(rfi.getPath("Dale-Chall wordlist")[0]).read()
# NewDaleChallWordsList = NewDaleChallWordsFile.split(';')
#Array mit Werten erstellen
ta = textanalyzer("eng")
raw_sentences = ta.getSentences(text)
values = []
sentence_values = []
max_words = 0
print "\n\n\nhalllo\n\n\n"
for sentence in raw_sentences:
raw_words = ta.getWords(sentence)
if len(raw_words) > max_words:
max_words = len(raw_words)
for word in raw_words:
value = 0.0
# if word.lower() in NewDaleChallWordsList:
# value = 0.25
# else:
# value = 0.5
if word.isdigit():
value = 0.0
sentence_values.append(value)
values.append(sentence_values)
sentence_values = []
#mit Nullen auffuellen
for value in values:
while len(value) < max_words:
value.append(1.0)
values.reverse()
a = array(values)
fig = dale_plt.figure()
#Achsenbeschriftungen erstellen
i = len(values)
ylabels = []
while i > 0:
ylabels.append(i)
i = i - 1
yticks(arange(len(values)) + 0.5, ylabels)
#pcolor-Graph erzeugen
pcolor(a, cmap=self.my_cmap, norm=normalize(vmin=0.0, vmax=1.0))
def create(self, text=""):
# rfi = readabilityFilesInstaller()
# NewDaleChallWordsFile = open(rfi.getPath("Dale-Chall wordlist")[0]).read()
# NewDaleChallWordsList = NewDaleChallWordsFile.split(';')
# Array mit Werten erstellen
ta = textanalyzer("eng")
raw_sentences = ta.getSentences(text)
values = []
sentence_values = []
max_words = 0
print "\n\n\nhalllo\n\n\n"
for sentence in raw_sentences:
raw_words = ta.getWords(sentence)
if len(raw_words) > max_words:
max_words = len(raw_words)
for word in raw_words:
value = 0.0
# if word.lower() in NewDaleChallWordsList:
# value = 0.25
# else:
# value = 0.5
if word.isdigit():
value = 0.0
sentence_values.append(value)
values.append(sentence_values)
sentence_values = []
# mit Nullen auffuellen
for value in values:
while len(value) < max_words:
value.append(1.0)
values.reverse()
a = array(values)
fig = dale_plt.figure()
# Achsenbeschriftungen erstellen
i = len(values)
ylabels = []
while i > 0:
ylabels.append(i)
i = i - 1
yticks(arange(len(values)) + 0.5, ylabels)
# pcolor-Graph erzeugen
pcolor(a, cmap=self.my_cmap, norm=normalize(vmin=0.0, vmax=1.0))
def create_enhanced_dale_chall_list(self):
#list of sites used to create list of most frequent words
alexa_list = [
'Google', 'Facebook', 'YouTube', 'Yahoo!', 'Wikipedia',
'Microsoft', 'Amazon', 'Twitter', 'LinkedIn', 'Wordpress', 'Ebay',
'Apple', 'Paypal', 'Imdb', 'Tumblr', 'Disney', 'BBC', 'Livejasmin',
'Craigslist', 'Ask'
]
#bring all privacy texts into one list
corpus = []
data = get_all_policies()
for site in data:
if site in alexa_list:
corpus.append(data[site]["text"])
#get the words of this list into a list of words
t = textanalyzer("eng")
words = t.getWords("".join(corpus))
#open the dale chall wordlist
dale_chall_list = open(
'../nltk_contrib/dale_chall_wordlist.txt').read().split(';')
#create a text that consists of the words of the 20 privacy policies and delete all words that are on the dale-chall list of easy words
new_corpus = []
for word in words:
if word.lower() not in dale_chall_list and word not in alexa_list:
new_corpus.append(word.lower())
#create a frequency distribution of the words of this list of words
fdist = FreqDist(new_corpus)
#plot this
fdist.plot(80, cumulative=True)
#make a list of the words that make up 33% percent of the words that are not in the dale chall list (cummulative)
most_frequ = []
cum_percentage = 0.0
for sample in fdist:
cum_percentage += fdist.freq(sample)
most_frequ.append(sample)
if cum_percentage > 0.33:
break
#write those into a file
privacy_file = open("privacy_wordlist.txt", "w")
privacy_file.write(";".join(most_frequ))
def create_enhanced_dale_chall_list(self):
#list of sites used to create list of most frequent words
alexa_list = ['Google', 'Facebook', 'YouTube', 'Yahoo!', 'Wikipedia', 'Microsoft', 'Amazon', 'Twitter', 'LinkedIn', 'Wordpress', 'Ebay', 'Apple', 'Paypal', 'Imdb', 'Tumblr', 'Disney', 'BBC', 'Livejasmin', 'Craigslist', 'Ask']
#bring all privacy texts into one list
corpus = []
data = get_all_policies()
for site in data:
if site in alexa_list:
corpus.append(data[site]["text"])
#get the words of this list into a list of words
t = textanalyzer("eng")
words = t.getWords("".join(corpus))
#open the dale chall wordlist
dale_chall_list = open('../nltk_contrib/dale_chall_wordlist.txt').read().split(';')
#create a text that consists of the words of the 20 privacy policies and delete all words that are on the dale-chall list of easy words
new_corpus = []
for word in words:
if word.lower() not in dale_chall_list and word not in alexa_list:
new_corpus.append(word.lower())
#create a frequency distribution of the words of this list of words
fdist = FreqDist(new_corpus)
#plot this
fdist.plot(80, cumulative=True)
#make a list of the words that make up 33% percent of the words that are not in the dale chall list (cummulative)
most_frequ = []
cum_percentage = 0.0
for sample in fdist:
cum_percentage += fdist.freq(sample)
most_frequ.append(sample)
if cum_percentage > 0.33:
break
#write those into a file
privacy_file = open("privacy_wordlist.txt", "w")
privacy_file.write(";".join(most_frequ))
def create(self, data = {}):
sites = data.keys()
######################################
# To calculate a grade level score:
# 1. Randomly select three separate 100 word passages.
# 2. Count the number of sentences in each 100 word sample (estimate to nearest tenth).
# 3. Count the number of syllables in each 100 word sample. (Each numeral is a syllable. For example, 2007 is 5 syllables -- two-thou-sand-se-ven -- and one word.)
# 4. Plot the average sentence length and the average number of syllables on the graph.
# The area in which it falls is the approximate grade
######################################
for site in sites:
site_sentences = []
site_words = []
sentence_lengths = []
sentences_count = []
syllables_count = []
ta = textanalyzer("eng")
site_sentences = ta.getSentences(data[site]['text'])
words = ta.getWords(data[site]['text'])
word_count = len(words)
for sentence in site_sentences:
site_words.append(ta.getWords(sentence))
sentence_lengths.append(len(ta.getWords(sentence)))
print word_count
sample_size = ""
if word_count < 100:
sample_size = word_count
number_of_iterations = 1
else:
sample_size = 100
if word_count < 200:
number_of_iterations = 1
elif word_count < 300:
number_of_iterations = 2
else:
number_of_iterations = 3
j = 1
while j <= number_of_iterations:
print j
count_index = j - 1
if word_count < 100:
start = 0
else:
start = randint(0, word_count - (sample_size * number_of_iterations))
#Silben zählen
sample_words = words[start:start + sample_size]
#Sätze zählen
#Beginn des Samples finden
i = 0
start_value = start
while (start_value - sentence_lengths[i]) > 0:
start_value = start_value - sentence_lengths[i]
i += 1
sentneces_count_rest = sentence_lengths[i] - start_value
sentences_count.append(0.0)
words_to_count_for = sample_size - sentneces_count_rest
rest = sentneces_count_rest / sentence_lengths[i]
#100 Wörter abzählen (abzüglich Restwörter aus Vorsatz)
i += 1
while (words_to_count_for - sentence_lengths[i]) > 0:
words_to_count_for = words_to_count_for - sentence_lengths[i]
sentences_count[count_index] = sentences_count[count_index] + 1
i += 1
#Anzahl der Sätze zählen und Reste vorher und nachher aufaddieren
sentences_count[count_index] = sentences_count[count_index]
rest = rest + (words_to_count_for / sentence_lengths[i])
#Werte vom aktuellen Sample
sentences_count[count_index] = sentences_count[count_index] + rest
syllables_count.append(ta.countSyllables(sample_words))
#Wenn kleiner 100, dann auf 100 hochrechnen
if word_count < 100:
sentences_count[count_index] = sentences_count[count_index] * 100 / word_count
syllables_count[count_index] = syllables_count[count_index] * 100 / word_count
#das nächste sample
j += 1
data[site]['Syllables'] = float(sum(syllables_count)) / len(syllables_count)
data[site]['Sentences'] = float(sum(sentences_count)) / len(sentences_count)
fig = fry_plt.figure(figsize=(8.15,5.03))
ax = fig.add_subplot(111)
#Achse ausblenden
Axes.set_frame_on(ax, False)
for site in sites:
ax.plot(self.get_x_value(data[site]['Syllables']), self.get_y_value(data[site]['Sentences']), 'bo', ms=5)
ax.annotate(site, (self.get_x_value(data[site]['Syllables']) - 6, self.get_y_value(data[site]['Sentences'])))
fig.figimage(self.im, 82, 40)
fry_plt.xlim([108, 174])
fry_plt.xlabel("Average Number of Syllables per 100 words")
fry_plt.xticks(arange(108, 174, 4))
fry_plt.ylim([0,29])
fry_plt.ylabel("Average Number of Sentences per 100 words")
#Beschriftung gemäà Fry-Graph
y_ticks = ['2.0','2.5','3.0','3.3','3.5','3.6','3.7','3.8','4.0','4.2','4.3','4.5','4.8','5.0','5.2','5.6','5.9','6.3','6.7','7.1','7.7','8.3','9.1','10.0','11.1','12.5','14.3','16.7','20','25+']
fry_plt.yticks(arange(30), y_ticks)
labels = sites
def create(self, data={}):
sites = data.keys()
######################################
# To calculate a grade level score:
# 1. Randomly select three separate 100 word passages.
# 2. Count the number of sentences in each 100 word sample (estimate to nearest tenth).
# 3. Count the number of syllables in each 100 word sample. (Each numeral is a syllable. For example, 2007 is 5 syllables -- two-thou-sand-se-ven -- and one word.)
# 4. Plot the average sentence length and the average number of syllables on the graph.
# The area in which it falls is the approximate grade
######################################
for site in sites:
site_sentences = []
site_words = []
sentence_lengths = []
sentences_count = []
syllables_count = []
ta = textanalyzer("eng")
site_sentences = ta.getSentences(data[site]['text'])
words = ta.getWords(data[site]['text'])
word_count = len(words)
for sentence in site_sentences:
site_words.append(ta.getWords(sentence))
sentence_lengths.append(len(ta.getWords(sentence)))
print word_count
sample_size = ""
if word_count < 100:
sample_size = word_count
number_of_iterations = 1
else:
sample_size = 100
if word_count < 200:
number_of_iterations = 1
elif word_count < 300:
number_of_iterations = 2
else:
number_of_iterations = 3
j = 1
while j <= number_of_iterations:
print j
count_index = j - 1
if word_count < 100:
start = 0
else:
start = randint(
0, word_count - (sample_size * number_of_iterations))
#Silben zählen
sample_words = words[start:start + sample_size]
#Sätze zählen
#Beginn des Samples finden
i = 0
start_value = start
while (start_value - sentence_lengths[i]) > 0:
start_value = start_value - sentence_lengths[i]
i += 1
sentneces_count_rest = sentence_lengths[i] - start_value
sentences_count.append(0.0)
words_to_count_for = sample_size - sentneces_count_rest
rest = sentneces_count_rest / sentence_lengths[i]
#100 Wörter abzählen (abzüglich Restwörter aus Vorsatz)
i += 1
while (words_to_count_for - sentence_lengths[i]) > 0:
words_to_count_for = words_to_count_for - sentence_lengths[
i]
sentences_count[
count_index] = sentences_count[count_index] + 1
i += 1
#Anzahl der Sätze zählen und Reste vorher und nachher aufaddieren
sentences_count[count_index] = sentences_count[count_index]
rest = rest + (words_to_count_for / sentence_lengths[i])
#Werte vom aktuellen Sample
sentences_count[
count_index] = sentences_count[count_index] + rest
syllables_count.append(ta.countSyllables(sample_words))
#Wenn kleiner 100, dann auf 100 hochrechnen
if word_count < 100:
sentences_count[count_index] = sentences_count[
count_index] * 100 / word_count
syllables_count[count_index] = syllables_count[
count_index] * 100 / word_count
#das nächste sample
j += 1
data[site]['Syllables'] = float(
sum(syllables_count)) / len(syllables_count)
data[site]['Sentences'] = float(
sum(sentences_count)) / len(sentences_count)
fig = fry_plt.figure(figsize=(8.15, 5.03))
ax = fig.add_subplot(111)
#Achse ausblenden
Axes.set_frame_on(ax, False)
for site in sites:
ax.plot(self.get_x_value(data[site]['Syllables']),
self.get_y_value(data[site]['Sentences']),
'bo',
ms=5)
ax.annotate(site, (self.get_x_value(data[site]['Syllables']) - 6,
self.get_y_value(data[site]['Sentences'])))
fig.figimage(self.im, 82, 40)
fry_plt.xlim([108, 174])
fry_plt.xlabel("Average Number of Syllables per 100 words")
fry_plt.xticks(arange(108, 174, 4))
fry_plt.ylim([0, 29])
fry_plt.ylabel("Average Number of Sentences per 100 words")
#Beschriftung gemäà Fry-Graph
y_ticks = [
'2.0', '2.5', '3.0', '3.3', '3.5', '3.6', '3.7', '3.8', '4.0',
'4.2', '4.3', '4.5', '4.8', '5.0', '5.2', '5.6', '5.9', '6.3',
'6.7', '7.1', '7.7', '8.3', '9.1', '10.0', '11.1', '12.5', '14.3',
'16.7', '20', '25+'
]
fry_plt.yticks(arange(30), y_ticks)
labels = sites