def get_context_data(self, request, **kwargs):
context = super(AttributeWordView, self).get_context_data(request, **kwargs)
dataset = context['dataset']
analysis = context['analysis']
attribute = context['attribute']
value = context['value']
word = Word.objects.get(dataset=dataset, type=kwargs['word'])
documents = word.document_set.filter(attribute=attribute,
attributevaluedocument__value=value)
words = []
for document in documents:
w = WordSummary(word.type)
set_word_context(w, document, analysis)
words.append(w)
w.url = '%s/%s/values/%s/documents/%d?kwic=%s' \
% (context['attributes_url'], attribute.name, value.value,
document.id, word.type)
w.doc_name = document.filename
w.doc_id = document.id
context['words'] = words
context['breadcrumb'].word(word)
context['attribute_post_link'] = '/words/%s' % word.type
return context
def top_words_tab(topic, topic_url, images_url):
tab = Tab("Top Words", path='topics/top_words')
word_url = '%s/words/' % topic_url
topicwords = topic.topicword_set.filter(
word__ngram=False).order_by('-count')
words = []
for topicword in topicwords[:100]:
percent = float(topicword.count) / topic.total_count
w = WordSummary(topicword.word.type, percent)
w.url = word_url + topicword.word.type
words.append(w)
tab.add(word_chart_widget(words))
# tab.add(Widget('Word Cloud',content_html=unigram_cloud(words)))
tab.add(word_cloud_widget(words, title='Word Cloud'))
ttcloud = turbo_topics_cloud_widget(topic)
if ttcloud: tab.add(ttcloud)
ngcloud = ngram_cloud_widget(topic, word_url)
if ngcloud: tab.add(ngcloud)
tab.add(words_in_context_widget(images_url, words))
return tab
def get_context_data(self, request, **kwargs):
dataset_name = kwargs['dataset']
analysis_name = kwargs['analysis']
word = Word.objects.get(dataset__name=dataset_name, type=kwargs['word'])
filter = TopicFilterByWord(Analysis.objects.get(dataset__name=dataset_name,
name=analysis_name), 0)
filter.current_word = word
context = super(TopicWordView, self).get_context_data(request, extra_filters=[filter], **kwargs)
analysis = context['analysis']
topic = context['topic']
context['word'] = word
documents = word.documenttopicword_set.filter(topic=topic).order_by(
'document__filename')
docs = []
for dtw in documents:
d = dtw.document
w = WordSummary(word.type)
set_word_context(w, d, analysis, topic.number)
docs.append(w)
w.url = "%s/%d/documents/%d?kwic=%s" % (context['topics_url'],
topic.number, d.id, word.type)
w.doc_name = d.filename
w.doc_id = d.id
context['documents'] = docs
context['breadcrumb'].word(word)
context['topic_post_link'] = '/words/%s' % word.type
word_url = '%s/%d/words/' % (context['topics_url'], topic.number)
context['tabs'] = [self._topic_word_tab(analysis, word, word_url, context['IMAGES'])]
return context
def word_in_context_widget(word, word_url, images_url):
w = Widget('Word In Context', 'words/word_in_context')
words = []
for i in range(0,5):
ws = WordSummary(word.type, number=i)
ws.url = word_url
words.append(ws)
w['IMAGES'] = images_url
w['words'] = words
return w
def get_words(attribute, value, attributes_url, token_count):
words = []
attrvalwords = get_attrvalwords(attribute, value)
for attrvalword in attrvalwords[:100]:
type = attrvalword.word.type
percent = float(attrvalword.count) / token_count
w = WordSummary(type, percent)
w.url = (attributes_url+'/'+attribute.name+'/values/'+
value.value+'/words/'+type)
words.append(w)
return words
def get_topics(analysis, attribute, value, analysis_url, token_count):
topics = []
topic_set = analysis.topic_set.all()
attrvaltopics = attribute.attributevaluetopic_set.filter(value=value,
topic__in=topic_set).order_by('-count')
for attrvaltopic in attrvaltopics[:10]:
type = attrvaltopic.topic.name
percent = float(attrvaltopic.count) / token_count
t = WordSummary(type, percent)
t.url = analysis_url + '/topics/%s' % (attrvaltopic.topic.number)
topics.append(t)
return topics
def get_ngrams(attribute, value, attributes_url, tokens):
attrvalngrams = attribute.attributevalueword_set.filter(
value=value).order_by('-count')
attrvalngrams = attrvalngrams.filter(word__ngram=True)
ngrams = []
for attrvalngram in attrvalngrams[:10]:
type = attrvalngram.word.type
percent = float(attrvalngram.count) / tokens
w = WordSummary(type, percent)
w.url = (attributes_url+'/'+attribute.name+'/values/'+
value.value+'/words/'+type)
ngrams.append(w)
return ngrams
def ngram_cloud_widget(topic, word_url):
topicngrams = topic.topicword_set.filter(
word__ngram=True).order_by('-count')
ngrams = []
for topicngram in topicngrams[:10]:
percent = float(topicngram.count) / topic.total_count
w = WordSummary(topicngram.word.type, percent)
w.url = word_url + topicngram.word.type
ngrams.append(w)
if ngrams:
# Name must not contain spaces!
return word_cloud_widget(ngrams, title='N-grams')
return None
def word_in_context(request, dataset, analysis, word, topic=None):
analysis = Analysis.objects.get(name=analysis, dataset__name=dataset)
w = Word.objects.get(dataset__name=dataset, type=word)
word_context = WordSummary(word)
if topic is None:
docset = w.documentword_set.all()
else:
topic = Topic.objects.get(analysis=analysis, number=topic)
docset = topic.documenttopicword_set.filter(word=w)
num_docs = len(docset)
d = docset[random.randint(0, num_docs - 1)]
word_context.left_context, word_context.word, word_context.right_context \
= d.document.get_context_for_word(word, analysis, topic.number if topic else None)
word_context.doc_name = d.document.filename
word_context.doc_id = d.document.id
return HttpResponse(anyjson.dumps(vars(word_context)))
