def _prepare_and_render(self,
save_type='',
data=None,
errors=None,
error_summary=None):
save_type = save_type if save_type else ''
analytics_dict = analytics.generate_analytics_data(data)
template_data = {
'data': data,
'analytics': analytics_dict,
'errors': errors,
'error_summary': error_summary,
'aborted': False
}
if '-json' in save_type:
response.headers['Content-Type'] = CONTENT_TYPES['json']
return json.dumps(template_data)
else:
return base.render('contribute_flow/create_edit.html',
extra_vars=template_data)
def _performing_search(self,
q,
fq,
facet_keys,
limit,
page,
sort_by,
search_extras,
pager_url,
context,
fq_list=None,
expand='false',
enable_update_status_facet=False):
data_dict = {
'q': q,
'fq_list': fq_list if fq_list else [],
'expand': expand,
'expand.rows':
1, # we anyway don't show the expanded datasets, but doesn't work with 0
'fq': fq.strip(),
'facet.field': facet_keys,
# added for https://github.com/OCHA-DAP/hdx-ckan/issues/3340
'facet.limit': 2000,
'rows': limit,
'start': (page - 1) * limit,
'sort': sort_by,
'extras': search_extras,
'ext_compute_freshness': 'true'
}
self._add_additional_faceting_queries(data_dict)
include_private = context.pop('ignore_capacity_check', None)
if include_private:
data_dict['include_private'] = include_private
query = get_action('package_search')(context, data_dict)
if not query.get('results', None):
log.warn(
'No query results found for data_dict: {}. Query dict is: {}. Query time {}'
.format(str(data_dict), str(query), datetime.datetime.now()))
self._process_found_package_list(query['results'])
c.facets = query['facets']
c.search_facets = query['search_facets']
# if we're using collapse/expand/batch then take total count from facet site_id
if expand:
site_id_items = query['search_facets'].get('site_id',
{}).get('items', [])
c.batch_total_items = sum(
(item.get('count', 0) for item in site_id_items))
# get_action('populate_related_items_count')(
# context, {'pkg_dict_list': query['results']})
get_action('populate_showcase_items_count')(
context, {
'pkg_dict_list': query['results']
})
c.page = h.Page(collection=query['results'],
page=page,
url=pager_url,
item_count=query['count'],
items_per_page=limit)
for dataset in query['results']:
downloads_list = (res['tracking_summary']['total']
for res in dataset.get('resources', [])
if res.get('tracking_summary', {}).get('total'))
download_sum = sum(downloads_list)
dataset['approx_total_downloads'] = find_approx_download(
dataset.get('total_res_downloads', 0))
dataset['batch_length'] = query['expanded'].get(
dataset.get('batch', ''), {}).get('numFound', 0)
if dataset.get('organization'):
dataset['batch_url'] = h.url_for(
'organization_read',
id=dataset['organization'].get('name'),
ext_batch=dataset.get('batch'))
for dataset in query['results']:
dataset['hdx_analytics'] = json.dumps(
generate_analytics_data(dataset))
c.page.items = query['results']
c.sort_by_selected = query['sort']
c.count = c.item_count = query['count']
return query