def metadata(self, datasetid):
query = {'UID': datasetid}
ds = dataset.query(self.context, **query)
if ds:
# we have a generator, let's pick the first result
# metadata is extracted by query
ds = next(ds, None)
if ds is None:
raise NotFound(self.context, datasetid, self.request)
return ds
def query(self):
# TODO: remove some parameters from request?
# e.g only matching with catalog indices
# only certain indices
# remove batching and internal parameters like brains
query = self.request.form
# TODO: should optimise this. e.g. return generator,
objs = []
for item in dataset.query(self.context, **query):
objs.append(item)
return objs
def queryDataset(self, genre=None, layers=None):
params = {'object_provides': IDataset.__identifier__}
if genre:
if not isinstance(genre, (tuple, list)):
genre = (genre, )
params['BCCDataGenre'] = {'query': genre,
'operator': 'or'}
if layers:
if not isinstance(layers, (tuple, list)):
layers = (layers, )
params['BCCEnviroLayer'] = {'query': layers,
'operator': 'and'}
result = []
for brain in dataset.query(self.context, True, **params):
result.append({'uuid': brain.UID,
'title': brain.Title})
return result
def getRAT(self, datasetid, layer=None):
query = {'UID': datasetid}
dsbrain = dataset.query(self.context, brains=True, **query)
if dsbrain:
# get first brain from list
dsbrain = next(dsbrain, None)
if not dsbrain:
raise NotFound(self.context, datasetid, self.request)
md = IBCCVLMetadata(dsbrain.getObject())
rat = md.get('layers', {}).get(layer, {}).get('rat')
# if we have a rat, let's try and parse it
if rat:
try:
rat = json.loads(unicode(rat))
except Exception as e:
LOG.warning("Couldn't decode Raster Attribute Table from metadata. %s: %s", self.context, repr(e))
rat = None
return rat
