def learn_descriptions_from(desc_g):
virtual_eco_node = BNode()
desc_g.add((virtual_eco_node, RDF.type, CORE.Ecosystem))
td_nodes = list(desc_g.subjects(RDF.type, CORE.ThingDescription))
for td_node in td_nodes:
th_node = list(desc_g.objects(td_node, CORE.describes)).pop()
desc_g.add((virtual_eco_node, CORE.hasComponent, th_node))
eco = Ecosystem.from_graph(desc_g, loader=get_context())
g = eco.to_graph()
node_map = {}
sub_eco = Ecosystem()
td_nodes = g.subjects(RDF.type, CORE.ThingDescription)
for td_node in td_nodes:
try:
skolem_id = list(g.objects(td_node, CORE.identifier)).pop()
except IndexError:
skolem_id = None
g = canonize_node(g, td_node, id='descriptions/{}'.format(skolem_id))
tdh_nodes = g.subject_objects(predicate=CORE.describes)
for td_node, th_node in tdh_nodes:
try:
skolem_id = list(g.objects(td_node, CORE.identifier)).pop()
except IndexError:
skolem_id = None
g = canonize_node(g, th_node, id='things/{}'.format(skolem_id))
td_nodes = g.subjects(RDF.type, CORE.ThingDescription)
for node in td_nodes:
td = TD.from_graph(g, node, node_map)
sub_eco.add_td(td)
network = sub_eco.network()
root_ids = filter(lambda x: network.in_degree(x) == 0, network.nodes())
root_tds = filter(lambda td: td.id in root_ids, sub_eco.tds)
for td in root_tds:
sub_eco.add_root_from_td(td)
all_types = R.agora.fountain.types
ns = R.ns()
non_td_resources = defaultdict(set)
for elm, _, cl in desc_g.triples((None, RDF.type, None)):
if isinstance(elm, URIRef) and (None, None, elm) not in g:
if cl.n3(ns) in all_types:
non_td_resources[elm].add(cl)
for r_uri, types in non_td_resources.items():
sub_eco.add_root(Resource(uri=r_uri, types=types))
ted = TED()
ted.ecosystem = sub_eco
return ted
def get_ted():
try:
local_node = URIRef(url_for('get_ted', _external=True))
fountain = R.fountain
known_types = fountain.types
ns = R.ns()
ted = TED()
g = ted.to_graph(node=local_node, abstract=True)
for root_uri, td_uri in VTED.roots:
root_uri = URIRef(root_uri)
types = get_th_types(root_uri, infer=True)
valid_types = filter(lambda t: t.n3(ns) in known_types, types)
if valid_types:
r = Resource(root_uri, types=valid_types)
if td_uri is None:
g.__iadd__(r.to_graph(abstract=True))
g.add((ted.ecosystem.node, CORE.hasComponent, root_uri))
format = TURTLE if request_wants_turtle() else JSONLD
# g = ted.to_graph(node=local_node, abstract=True)
for prefix, ns in fountain.prefixes.items():
g.bind(prefix, ns)
ted_str = serialize_graph(g,
format,
frame=CORE.ThingEcosystemDescription)
own_base = unicode(request.url_root)
ted_str = ted_str.decode('utf-8')
ted_str = ted_str.replace(REPOSITORY_BASE + u'/', own_base)
response = make_response(ted_str)
response.headers['Content-Type'] = format
return response
except (EnvironmentError, IndexError):
pass
response = make_response()
response.status_code = 404
return response
def generate_dict(res):
ns = R.ns()
d = defaultdict(set)
for k, v in map(lambda x: tuple_from_result_row(x), res):
d[k].add(R.n3(v, ns))
return d
