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})
def cloud(request):
"""Return word cloud data using the terms aggregation approach
This view is currently not used, because it uses the terms aggregation
approach to generate word clouds, and this is not feasible in ES.
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).
"""
if settings.DEBUG:
print >> stderr, "cloud()"
result = None
params = get_search_parameters(request.REQUEST)
ids = request.REQUEST.get('ids')
# Cloud by ids
if ids:
ids = ids.split(',')
if len(ids) == 1:
# Word cloud for single document
t_vector = single_document_word_cloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
ids[0])
return json_response_message('ok', 'Word cloud generated', t_vector)
else:
# Word cloud for multiple ids
result = multiple_document_word_cloud(params.get('collection'),
settings.ES_DOCTYPE,
params.get('query'),
params.get('dates'),
params.get('distributions'),
params.get('article_types'),
params.get('pillars'),
ids)
# Cloud by queryID
query_id = request.REQUEST.get('queryID')
if query_id:
query, response = get_query_object(query_id)
if not query:
return response
# for some reason, the collection to be searched is stored in parameter
# 'collections' (with s added) instead of 'collection' as expected by
# get_search_parameters.
coll = request.REQUEST.get('collections', settings.ES_INDEX)
result = multiple_document_word_cloud(coll,
settings.ES_DOCTYPE,
query.query,
params.get('dates'),
params.get('distributions'),
params.get('article_types'),
params.get('pillars'))
if not result:
return json_response_message('error', 'No word cloud generated.')
return json_response_message('success', 'Word cloud generated', result)
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})
