def look_up(request, item_type):
""" Returns JSON data for entities
limited by certain criteria
"""
ent = Entity()
qstring = ''
class_uri = False
project_uuid = False
vocab_uri = False
ent_type = False
context_uuid = False
data_type = False
if len(item_type) < 2:
item_type = False
if 'q' in request.GET:
qstring = request.GET['q']
if 'class_uri' in request.GET:
class_uri = request.GET['class_uri']
if 'project_uuid' in request.GET:
project_uuid = request.GET['project_uuid']
if 'vocab_uri' in request.GET:
vocab_uri = request.GET['vocab_uri']
if 'ent_type' in request.GET:
ent_type = request.GET['ent_type']
if 'context_uuid' in request.GET:
context_uuid = request.GET['context_uuid']
if 'data_type' in request.GET:
data_type = request.GET['data_type']
entity_list = ent.search(qstring, item_type, class_uri, project_uuid,
vocab_uri, ent_type, context_uuid, data_type)
json_output = json.dumps(entity_list, indent=4, ensure_ascii=False)
return HttpResponse(json_output,
content_type='application/json; charset=utf8')
def look_up(request, item_type):
""" Returns JSON data for entities
limited by certain criteria
"""
ent = Entity()
qstring = ''
class_uri = False
project_uuid = False
vocab_uri = False
ent_type = False
context_uuid = False
data_type = False
context = False
if len(item_type) < 2:
item_type = False
if 'q' in request.GET:
qstring = request.GET['q']
if 'class_uri' in request.GET:
class_uri = request.GET['class_uri']
if 'project_uuid' in request.GET:
project_uuid = request.GET['project_uuid']
if 'vocab_uri' in request.GET:
vocab_uri = request.GET['vocab_uri']
if 'ent_type' in request.GET:
ent_type = request.GET['ent_type']
if 'context_uuid' in request.GET:
context_uuid = request.GET['context_uuid']
if 'data_type' in request.GET:
data_type = request.GET['data_type']
if 'context' in request.GET:
context = request.GET['context']
entity_list = ent.search(qstring,
item_type,
class_uri,
project_uuid,
vocab_uri,
ent_type,
context_uuid,
data_type,
context)
json_output = json.dumps(entity_list,
indent=4,
ensure_ascii=False)
return HttpResponse(json_output,
content_type='application/json; charset=utf8')
def look_up(request, item_type):
""" Returns JSON data for entities
limited by certain criteria
"""
# -----------------------------------------------------------------
# NOTE: This is mainly for "in-house" use as an API to to look up
# entities already in Open Context's database. It is mainly
# used in preparation of datasets for ingest, where we want to
# reconcile entities in an outside dataset with data already in
# Open Context.
#
# NOTE: This does not use the Solr index at all. It only uses data
# in the postgres datastore because the Solr index may not have
# all data relevant in projects that are in preparation.
# -----------------------------------------------------------------
ent = Entity()
arg_list = [
'qstring',
'item_type',
'class_uri',
'project_uuid',
'vocab_uri',
'ent_type',
'context_uuid',
'data_type',
'context',
'label',
]
# Get all the request parameters to make an argument dict.
args = {arg: request.GET.get(arg) for arg in arg_list}
args['qstring'] = request.GET.get('q', '')
# Item type is set in the URL path
if item_type and len(item_type) < 2:
item_type = None
args['item_type'] = item_type
entity_list = ent.search(**args)
json_output = json.dumps(entity_list, indent=4, ensure_ascii=False)
return HttpResponse(json_output,
content_type='application/json; charset=utf8')
def do_manifest_items_search_tests(project_uuid, item_type, class_uri, uuid):
"""Actually tests random combinations of args on manifest
entity searches
"""
man = Manifest.objects.get(uuid=uuid)
# The qstring is meant for filter on partial string matches, so
# make a list of different strings to filter on based on
# different attributes of the manifest object.
qstring_opts = [
man.slug, man.slug[:5], man.uuid, man.uuid[:5], man.label,
man.label[:5]
]
all_args = {
'qstring': random.choice(qstring_opts),
'project_uuid': project_uuid,
'item_type': item_type,
'class_uri': class_uri,
'label': man.label,
}
# The entity.search results are a list of dicts with slightly
# different keys than the arguments used in the search.
# Below is a mapping between args (keys) and result
# item keys.
key_mappings = {
'project_uuid': 'partOf_id',
'item_type': 'type',
}
# Randomly generate different argument dicts that
# use only certain keys and values of the all_args dict
# above.
keys = list(all_args.keys())
args_list = [all_args]
for num_sample in range(2, len(keys)):
sample_keys = random.sample(keys, num_sample)
new_args = {}
for key in sample_keys:
new_args[key] = all_args[key]
if key == 'qstring':
# Selects a random query string option.
new_args[key] = random.choice(qstring_opts)
if not 'qstring' in new_args:
# The qstring is required, so make sure that
# it is always at least an empty string.
new_args['qstring'] = ''
args_list.append(new_args)
for args in args_list:
ent = Entity()
# Get the search results using the args.
results = ent.search(**args)
# Now test to make sure that the search results have the
# expected values based on the search filtering criteria
# passed by args.
for key, value in args.items():
if key == 'qstring' or not value:
# Don't check qstring or an empty / missing value.
continue
check = key_mappings.get(key, key)
for result in results:
assert result[check] == value
