def tv_cloud(request):
"""Generate termvector word cloud using the termvector approach.
Returns word cloud data for a single document word cloud (based on a single
document id) and multiple document word clouds (either based on a list of
document ids (i.e., timeline burst cloud) or a query with metadata).
For multiple document word clouds, a celery task generates the cloud data.
"""
if settings.DEBUG:
print >> stderr, "termvector cloud()"
logger.info('services/cloud/ - termvector word cloud')
logger.info('services/cloud/ - user: {}'.format(request.user.username))
# Retrieve the cloud settings
query_id = request.GET.get('queryID')
min_length = int(request.GET.get('min_length', 2))
use_stopwords = request.GET.get('stopwords') == "1"
use_default_stopwords = request.GET.get('stopwords_default') == "1"
stems = request.GET.get('stems') == "1"
# Retrieve the stopwords
stopwords = []
if use_stopwords:
stopwords_user = list(StopWord.objects
.filter(user=request.user)
.filter(query=None)
.values_list('word', flat=True))
stopwords_query = []
if query_id:
stopwords_query = list(StopWord.objects
.filter(user=request.user)
.filter(query__id=query_id)
.values_list('word', flat=True))
stopwords_default = []
if use_default_stopwords:
stopwords_default = list(StopWord.objects
.filter(user=None)
.filter(query=None)
.values_list('word', flat=True))
stopwords = stopwords_user + stopwords_query + stopwords_default
record_id = request.GET.get('record_id')
logger.info('services/cloud/ - record_id: {}'.format(record_id))
idf_timeframe = request.GET.get('idf_timeframe')
if record_id:
# Cloud for a single document
t_vector = single_document_word_cloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
record_id,
min_length,
stopwords,
stems)
normalized = normalize_cloud(t_vector['result'], idf_timeframe)
return json_response_message('ok', 'Word cloud generated', {'result': normalized})
else:
# Cloud for a query
logger.info('services/cloud/ - multiple document word cloud')
query = get_object_or_404(Query, pk=query_id)
params = query.get_query_dict()
# If we're creating a timeline cloud, set the min/max dates
date_range = None
if request.GET.get('is_timeline'):
date_range = daterange2dates(request.GET.get('date_range'))
task = generate_tv_cloud.delay(params, min_length, stopwords, date_range, stems, idf_timeframe)
logger.info('services/cloud/ - Celery task id: {}'.format(task.id))
return json_response_message('ok', '', {'task': task.id})
def tv_cloud(request):
"""Generate termvector word cloud using the termvector approach.
Returns word cloud data for a single document word cloud (based on a single
document id) and multiple document word clouds (either based on a list of
document ids (i.e., timeline burst cloud) or a query with metadata).
For multiple document word clouds, a celery task generates the cloud data.
"""
if settings.DEBUG:
print >> stderr, "termvector cloud()"
logger.info('services/cloud/ - termvector word cloud')
logger.info('services/cloud/ - user: {}'.format(request.user.username))
params = get_search_parameters(request.REQUEST)
ids = request.REQUEST.get('ids')
query_id = request.GET.get('queryID')
min_length = int(request.GET.get('min_length', 2))
use_stopwords = request.GET.get('stopwords') == "1"
use_default_stopwords = request.GET.get('stopwords_default') == "1"
stems = request.GET.get('stems') == "1"
# Retrieve the stopwords
stopwords = []
if use_stopwords:
stopwords_user = list(StopWord.objects
.filter(user=request.user)
.filter(query=None)
.values_list('word', flat=True))
stopwords_query = []
if query_id:
stopwords_query = list(StopWord.objects
.filter(user=request.user)
.filter(query__id=query_id)
.values_list('word', flat=True))
stopwords_default = []
if use_default_stopwords:
stopwords_default = list(StopWord.objects
.filter(user=None)
.filter(query=None)
.values_list('word', flat=True))
stopwords = stopwords_user + stopwords_query + stopwords_default
# Cloud by ids
if ids:
ids = ids.split(',')
if len(ids) == 1:
# Word cloud for single document
logger.info('services/cloud/ - single document word cloud')
t_vector = single_document_word_cloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
ids[0],
min_length,
stopwords,
stems)
return json_response_message('ok', 'Word cloud generated', t_vector)
# Cloud by queryID or multiple ids
logger.info('services/cloud/ - multiple document word cloud')
task = generate_tv_cloud.delay(params, min_length, stopwords, ids, stems)
logger.info('services/cloud/ - Celery task id: {}'.format(task.id))
return json_response_message('ok', '', {'task': task.id})
def tv_cloud(request):
"""Generate termvector word cloud using the termvector approach.
Returns word cloud data for a single document word cloud (based on a single
document id) and multiple document word clouds (either based on a list of
document ids (i.e., timeline burst cloud) or a query with metadata).
For multiple document word clouds, a celery task generates the cloud data.
"""
if settings.DEBUG:
print >> stderr, "termvector cloud()"
logger.info('services/cloud/ - termvector word cloud')
logger.info('services/cloud/ - user: {}'.format(request.user.username))
# Retrieve the cloud settings
query_id = request.GET.get('queryID')
min_length = int(request.GET.get('min_length', 2))
use_stopwords = request.GET.get('stopwords') == "1"
use_default_stopwords = request.GET.get('stopwords_default') == "1"
stems = request.GET.get('stems') == "1"
# Retrieve the stopwords
stopwords = []
if use_stopwords:
stopwords_user = list(
StopWord.objects.filter(user=request.user).filter(
query=None).values_list('word', flat=True))
stopwords_query = []
if query_id:
stopwords_query = list(
StopWord.objects.filter(user=request.user).filter(
query__id=query_id).values_list('word', flat=True))
stopwords_default = []
if use_default_stopwords:
stopwords_default = list(
StopWord.objects.filter(user=None).filter(
query=None).values_list('word', flat=True))
stopwords = stopwords_user + stopwords_query + stopwords_default
record_id = request.GET.get('record_id')
logger.info('services/cloud/ - record_id: {}'.format(record_id))
idf_timeframe = request.GET.get('idf_timeframe')
if record_id:
# Cloud for a single document
t_vector = single_document_word_cloud(settings.ES_INDEX,
settings.ES_DOCTYPE, record_id,
min_length, stopwords, stems)
normalized = normalize_cloud(t_vector['result'], idf_timeframe)
return json_response_message('ok', 'Word cloud generated',
{'result': normalized})
else:
# Cloud for a query
logger.info('services/cloud/ - multiple document word cloud')
query = get_object_or_404(Query, pk=query_id)
params = query.get_query_dict()
# If we're creating a timeline cloud, set the min/max dates
date_range = None
if request.GET.get('is_timeline'):
date_range = daterange2dates(request.GET.get('date_range'))
task = generate_tv_cloud.delay(params, min_length, stopwords,
date_range, stems, idf_timeframe)
logger.info('services/cloud/ - Celery task id: {}'.format(task.id))
return json_response_message('ok', '', {'task': task.id})
