def _parse_service(self):
output = {}
service = {
"object_id": generate_uuid_urn(),
"dcterms:title": ' '.join(extract_items(
self.parser.xml, ["Identify", "repositoryName"])),
"rdf:type": "OAI-PMH",
"relationships": [],
"urls": []
}
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": url_id,
"dc:identifier": generate_sha_urn(self.url)
})
service['urls'] = [dist]
service['relationships'].append({
"relate": "bcube:originatedFrom",
"object_id": url_id
})
# output['version'] = extract_items(self.parser.xml, ["Identify", "protocolVersion"])
# output['endpoints'] = [{'url': e} for e
# in extract_items(self.parser.xml, ["Identify", "baseURL"])]
output['services'] = [service]
return tidy_dict(output)
def _parse_keywords(self, elem):
'''
for each descriptiveKeywords block
in an identification block
'''
keywords = []
for key_elem in extract_elems(elem, ['descriptiveKeywords']):
# TODO: split these up (if *-delimited in some way)
terms = extract_items(
key_elem,
['MD_Keywords', 'keyword', 'CharacterString'])
key_type = extract_attrib(
key_elem,
['MD_Keywords', 'type', 'MD_KeywordTypeCode', '@codeListValue']
)
thesaurus = extract_item(
key_elem,
[
'MD_Keywords',
'thesaurusName',
'CI_Citation',
'title',
'CharacterString'
]
)
if terms:
keywords.append(
tidy_dict({
"object_id": generate_uuid_urn(),
"dc:partOf": thesaurus,
"bcube:hasType": key_type,
"bcube:hasValue": terms
})
)
# TODO: add the Anchor element handling
# ['descriptiveKeywords', 'MD_Keywords', 'keyword', 'Anchor']
# add a generic set for the iso topic category
isotopics = extract_items(
elem, ['topicCategory', 'MD_TopicCategoryCode'])
if isotopics:
keywords.append(
tidy_dict({
"object_id": generate_uuid_urn(),
"dc:partOf": 'IsoTopicCategories',
"bcube:hasValue": isotopics
})
)
return keywords
def _parse_keywords(self, elem):
'''
for each descriptiveKeywords block
in an identification block
'''
keywords = []
for key_elem in extract_elems(elem, ['descriptiveKeywords']):
# TODO: split these up (if *-delimited in some way)
terms = extract_items(
key_elem, ['MD_Keywords', 'keyword', 'CharacterString'])
key_type = extract_attrib(key_elem, [
'MD_Keywords', 'type', 'MD_KeywordTypeCode', '@codeListValue'
])
thesaurus = extract_item(key_elem, [
'MD_Keywords', 'thesaurusName', 'CI_Citation', 'title',
'CharacterString'
])
if terms:
keywords.append(
tidy_dict({
"object_id": generate_uuid_urn(),
"dc:partOf": thesaurus,
"bcube:hasType": key_type,
"bcube:hasValue": terms
}))
# TODO: add the Anchor element handling
# ['descriptiveKeywords', 'MD_Keywords', 'keyword', 'Anchor']
# add a generic set for the iso topic category
isotopics = extract_items(elem,
['topicCategory', 'MD_TopicCategoryCode'])
if isotopics:
keywords.append(
tidy_dict({
"object_id": generate_uuid_urn(),
"dc:partOf": 'IsoTopicCategories',
"bcube:hasValue": isotopics
}))
return keywords
def parse(self):
output = {}
urls = set()
if 'service' in self.identify:
service = {
"object_id": generate_uuid_urn(),
"dcterms:title": extract_attrib(self.parser.xml, ['@name']),
"rdf:type": "UNIDATA:THREDDS {0}".format(
extract_attrib(self.parser.xml, ['@version'])),
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', ''),
"relationships": [],
"urls": []
}
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
original_url = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": url_sha
})
service['urls'].append(original_url)
# NOTE: this is not the sha from the url
service['relationships'].append(
{
"relate": "bcube:originatedFrom",
"object_id": url_sha
}
)
# deal with the "dataset"
service_bases = self.parser.xml.xpath(
'//*[local-name()="service" and @base != ""]'
)
self.service_bases = {
s.attrib.get('name'): s.attrib.get('base') for s in service_bases
}
# if 'dataset' in self.identify:
# # TODO: this is really not right but it is not
# # a proper web service so meh
# datasets = self._parse_datasets()
# # if 'metadata' in self.identify:
# # self.description['metadata'] = self._parse_metadata()
output['services'] = [service]
self.description = tidy_dict(output)
def _parse_service(self):
output = {}
service = {
"object_id":
generate_uuid_urn(),
"dcterms:title":
' '.join(
extract_items(self.parser.xml,
["Identify", "repositoryName"])),
"rdf:type":
"OAI-PMH",
"relationships": [],
"urls": []
}
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": url_id,
"dc:identifier": generate_sha_urn(self.url)
})
service['urls'] = [dist]
service['relationships'].append({
"relate": "bcube:originatedFrom",
"object_id": url_id
})
# output['version'] = extract_items(self.parser.xml, ["Identify", "protocolVersion"])
# output['endpoints'] = [{'url': e} for e
# in extract_items(self.parser.xml, ["Identify", "baseURL"])]
output['services'] = [service]
return tidy_dict(output)
def _parse_identification_info(self, elem):
# ignoring the larger get all the identifiers above
# in favor of, hopefully, getting a better dataset id
dataset_identifier = extract_item(elem, [
'citation',
'CI_Citation',
'identifier',
'MD_Identifier',
'code',
'CharacterString'
])
dataset = {
"object_id": generate_uuid_urn(),
"dc:identifier": dataset_identifier,
"dc:description": extract_item(
elem, ['abstract', 'CharacterString']),
"dcterms:title": extract_item(elem, [
'citation', 'CI_Citation', 'title', 'CharacterString']),
"relationships": []
}
# TODO: i think the rights blob is not in the ontology prototypes
# the rights information from MD_Constraints or MD_LegalConstraints
# rights = extract_item(elem, ['resourceConstraints', '*',
# 'useLimitation', 'CharacterString'])
# deal with the extent
extents = self._parse_extent(elem)
dataset.update(extents)
keywords = self._parse_keywords(elem)
for keyword in keywords:
dataset['relationships'].append({
"relate": "dc:conformsTo",
"object_id": keyword['object_id']
})
return tidy_dict(dataset), keywords
def _parse_identification_info(self, elem):
# ignoring the larger get all the identifiers above
# in favor of, hopefully, getting a better dataset id
dataset_identifier = extract_item(elem, [
'citation', 'CI_Citation', 'identifier', 'MD_Identifier', 'code',
'CharacterString'
])
dataset = {
"object_id":
generate_uuid_urn(),
"dc:identifier":
dataset_identifier,
"dc:description":
extract_item(elem, ['abstract', 'CharacterString']),
"dcterms:title":
extract_item(
elem, ['citation', 'CI_Citation', 'title', 'CharacterString']),
"relationships": []
}
# TODO: i think the rights blob is not in the ontology prototypes
# the rights information from MD_Constraints or MD_LegalConstraints
# rights = extract_item(elem, ['resourceConstraints', '*',
# 'useLimitation', 'CharacterString'])
# deal with the extent
extents = self._parse_extent(elem)
dataset.update(extents)
keywords = self._parse_keywords(elem)
for keyword in keywords:
dataset['relationships'].append({
"relate": "dc:conformsTo",
"object_id": keyword['object_id']
})
return tidy_dict(dataset), keywords
def parse(self):
'''
from the root node, parse:
identification (title, abstract, point of contact, keywords,
extent) if identificationInfo contains SV_ServiceIdentification,
add as child distribution info
'''
# set up the url set
urls = set()
urls.add(self.output['catalog_record']['urls'][0]['object_id'])
for id_elem in extract_elems(
self.elem,
['//*', 'identificationInfo', 'MD_DataIdentification']):
dataset, keywords = self._parse_identification_info(id_elem)
dataset['relationships'].append({
"relate":
"bcube:hasMetadataRecord",
"object_id":
self.output['catalog_record']['object_id']
})
dataset.update({
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', '')
})
self.output['catalog_record']['relationships'].append({
"relate":
"foaf:primaryTopic",
"object_id":
dataset['object_id']
})
# point of contact from the root node and this might be an issue
# in things like the -1/-3 from ngdc so try for an idinfo blob
poc_elem = extract_elem(id_elem, [
'identificationInfo', 'MD_DataIdentification',
'pointOfContact', 'CI_ResponsibleParty'
])
# if poc_elem is None:
# # and if that fails try for the root-level contact
# poc_elem = extract_elem(
# self.elem,
# ['contact', 'CI_ResponsibleParty'])
# TODO: point of contact is not necessarily the publisher
if poc_elem is not None:
poc = self._parse_responsibleparty(poc_elem)
location = (' '.join([
poc['contact'].get('city', ''), poc['contact'].get(
'country', '')
])).strip() if poc.get('contact', {}) else ''
self.output['publishers'].append(
tidy_dict({
"object_id": generate_uuid_urn(),
"name": poc.get('organization', ''),
"location": location
}))
dataset['relationships'].append({
"relate":
"dcterms:publisher",
"object_id":
self.output['publisher']['object_id']
})
dataset['urls'] = []
dist_elems = extract_elems(self.elem, ['distributionInfo'])
for dist_elem in dist_elems:
for d in self._parse_distribution(dist_elem):
if not d:
continue
url_sha = generate_sha_urn(d)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": d,
"object_id": url_id,
"dc:identifier": url_sha
})
dataset['urls'].append(dist)
dataset['relationships'].append({
"relate": "dcterms:references",
"object_id": url_id
})
self.output['datasets'].append(dataset)
self.output['keywords'] += keywords
# TODO: removing this until we have a definition for SERVICE
# # check for the service elements
# service_elems = extract_elems(self.elem,
# ['identificationInfo', 'SV_ServiceIdentification'])
# self.description['services'] = []
# for service_elem in service_elems:
# sv = SrvParser(service_elem)
# self.description['services'].append(sv.parse())
# switch the catalog record to a list for conformity. eep.
self.output['catalog_records'] = [self.output['catalog_record']]
del self.output['catalog_record']
self.description = tidy_dict(self.output)
def _parse_service(self):
output = {}
urls = set()
service = {
"object_id":
generate_uuid_urn(),
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', ''),
"rdf:type":
'OpenSearch1.1:Description',
"dcterms:title":
extract_item(self.parser.xml, ["ShortName"]),
"dc:description":
' '.join(
extract_items(self.parser.xml, ["LongName"]) +
extract_items(self.parser.xml, ["Description"])),
"urls": [],
"webpages": [],
"relationships": []
}
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
original_url = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": generate_uuid_urn(),
"dc:identifier": url_sha
})
service['urls'].append(original_url)
service['relationships'].append({
"relate": "bcube:originatedFrom",
"object_id": original_url['object_id']
})
# output['source'] = extract_items(
# self.parser.xml, ["Attribution"])
# output['contact'] = extract_items(
# self.parser.xml, ["Developer"])
# output['rights'] = extract_items(
# self.parser.xml, ["SyndicationRight"])
key_id = generate_uuid_urn()
output['keywords'] = [{
"object_id":
key_id,
"bcube:hasValue":
extract_items(self.parser.xml, ["Tags"])
}]
service['relationships'].append({
"relate": "dc:conformsTo",
"object_id": key_id
})
for t in extract_elems(self.parser.xml, ['Url']):
ep = self._parse_endpoint(t)
url_sha = generate_sha_urn(ep['url'])
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Generated",
"bcube:hasConfidence": "Not Sure",
"vcard:hasURL": ep['url'],
"object_id": url_id,
"dc:identifier": url_sha
})
service['urls'].append(dist)
wb_id = generate_uuid_urn()
service['webpages'].append({
"object_id":
wb_id,
"relationships": [{
"relate": "dcterms:references",
"object_id": url_id
}]
})
service['relationships'].append({
"relate": "dcterms:references",
"object_id": wb_id
})
output['services'] = [service]
return tidy_dict(output)
def parse(self):
'''
run the routing
'''
if not self.identity:
# we're going to have to sort it out
self.identity = {}
metadata = self.identity.get('metadata', {})
if not metadata:
return {}
metadata_type = metadata.get('name', '')
if not metadata_type:
return {}
# TODO: this is unlikely to be correct, given the ds record
# but we're not going there just yet
# TODO: deal with conformsTo (multiple schemaLocations, etc)
catalog_record = {
"object_id": generate_uuid_urn(),
"rdf:type": self._version_to_urn(),
"bcube:dateCreated": self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated": self.harvest_details.get('harvest_date', ''),
# "dc:conformsTo": extract_attrib(
# self.parser.xml, ['@schemaLocation']).split(),
"relationships": [],
"urls": []
}
original_url = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": generate_uuid_urn()
})
catalog_record['urls'].append(original_url)
catalog_record['relationships'].append({
"relate": "bcube:originatedFrom",
"object_id": original_url['object_id']
})
if metadata_type == 'Data Series':
# run the set
self.reader = DsParser(self.parser.xml, catalog_record)
elif metadata_type == '19119':
# run that
for srv in extract_elems(
self.parser.xml,
['identificationInfo', 'SV_ServiceIdentification']):
reader = SrvParser(srv, catalog_record)
reader.parse()
elif metadata_type == '19115':
# it's a mi/md so run that
self.reader = MxParser(
self.parser.xml,
catalog_record,
self.harvest_details
)
self.reader.parse()
# self.reader.parse()
# # pass it back up the chain a bit
self.description = self.reader.output
def parse(self):
'''
from the root node, parse:
identification (title, abstract, point of contact, keywords,
extent) if identificationInfo contains SV_ServiceIdentification,
add as child distribution info
'''
# set up the url set
urls = set()
urls.add(self.output['catalog_record']['urls'][0]['object_id'])
for id_elem in extract_elems(
self.elem,
['//*', 'identificationInfo', 'MD_DataIdentification']):
dataset, keywords = self._parse_identification_info(id_elem)
dataset['relationships'].append({
"relate": "bcube:hasMetadataRecord",
"object_id": self.output['catalog_record']['object_id']
})
dataset.update({
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', '')
})
self.output['catalog_record']['relationships'].append({
"relate": "foaf:primaryTopic",
"object_id": dataset['object_id']
})
# point of contact from the root node and this might be an issue
# in things like the -1/-3 from ngdc so try for an idinfo blob
poc_elem = extract_elem(id_elem, [
'identificationInfo',
'MD_DataIdentification',
'pointOfContact',
'CI_ResponsibleParty'])
# if poc_elem is None:
# # and if that fails try for the root-level contact
# poc_elem = extract_elem(
# self.elem,
# ['contact', 'CI_ResponsibleParty'])
# TODO: point of contact is not necessarily the publisher
if poc_elem is not None:
poc = self._parse_responsibleparty(poc_elem)
location = (
' '.join(
[poc['contact'].get('city', ''),
poc['contact'].get('country', '')])
).strip() if poc.get('contact', {}) else ''
self.output['publishers'].append(tidy_dict({
"object_id": generate_uuid_urn(),
"name": poc.get('organization', ''),
"location": location
}))
dataset['relationships'].append({
"relate": "dcterms:publisher",
"object_id": self.output['publisher']['object_id']
})
dataset['urls'] = []
dist_elems = extract_elems(self.elem, ['distributionInfo'])
for dist_elem in dist_elems:
for d in self._parse_distribution(dist_elem):
if not d:
continue
url_sha = generate_sha_urn(d)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": d,
"object_id": url_id,
"dc:identifier": url_sha
})
dataset['urls'].append(dist)
dataset['relationships'].append({
"relate": "dcterms:references",
"object_id": url_id
})
self.output['datasets'].append(dataset)
self.output['keywords'] += keywords
# TODO: removing this until we have a definition for SERVICE
# # check for the service elements
# service_elems = extract_elems(self.elem,
# ['identificationInfo', 'SV_ServiceIdentification'])
# self.description['services'] = []
# for service_elem in service_elems:
# sv = SrvParser(service_elem)
# self.description['services'].append(sv.parse())
# switch the catalog record to a list for conformity. eep.
self.output['catalog_records'] = [self.output['catalog_record']]
del self.output['catalog_record']
self.description = tidy_dict(self.output)
def parse(self):
# for ogc, a catalog record is the getcapabilities rsp
output = {
"layers": [],
"catalog_records": []
}
urls = set()
if not self.reader:
self.description = {}
return
if 'service' in self.identify:
service_id = generate_uuid_urn()
service = {
"object_id": service_id,
"bcube:dateCreated": self.harvest_details.get(
'harvest_date', ''),
"bcube:lastUpdated": self.harvest_details.get(
'harvest_date', ''),
"relationships": [],
"urls": [],
"rdf:type": self.urn
}
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
url_id = generate_uuid_urn()
service['urls'].append(
self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": url_id,
"dc:identifier": url_sha
})
)
service['relationships'].append({
"relate": "bcube:originatedFrom",
"object_id": url_id
})
# self._get_service_config(service_name, version)
service_reader = self._parse_service(
self.reader, self.service_name, self.version)
# map to triples
service.update({
"dc:description": service_reader.get('abstract')
})
keywords = service_reader.get('subject', [])
if keywords:
output['keywords'] = [{
"object_id": generate_uuid_urn(),
"bcube:hasValue": keywords
}]
for k in output['keywords']:
service['relationships'].append(
{
"relate": "dc:conformsTo",
"object_id": k['object_id']
}
)
if self.identify['service'].get('request', '') == 'GetCapabilities':
# this is also awkward. meh. needs must.
layers = []
listed_layers = self._parse_getcap_datasets(self.reader)
for ld in listed_layers:
layer = {
"object_id": generate_uuid_urn(),
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', ''),
"dc:description": ld.get('abstract', ''),
"dc:title": ld.get('title', ''),
"relationships": []
}
service['relationships'].append({
"relate": "bcube:contains",
"object_id": layer['object_id']
})
# add the generated url for the service
generated_url = self._generate_url(
self.url,
ld.get('name'),
ld.get('bbox'),
self.service_name,
self.version
)
if generated_url:
url_sha = generate_sha_urn(generated_url)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
layer_url = self._generate_harvest_manifest(**{
"vcard:hasURL": generated_url,
"bcube:hasUrlSource": "Generated",
"bcube:hasConfidence": "Not Sure",
"object_id": url_id,
"dc:identifier": url_sha
})
service['urls'].append(layer_url)
# don't add to the larger set, but do
# include the reference within the layer
layer['relationships'].append({
"relate": "dcterms:references",
"object_id": url_id
})
# add each as a dataset with just a url for now
for mu in ld.get('metadata_urls', []):
url_link = generate_uuid_urn()
url_sha = generate_sha_urn(mu.get('url'))
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
output['catalog_records'] += [
{
"object_id": url_link,
"urls": [
self._generate_harvest_manifest(**{
"vcard:hasURL": mu.get('url'),
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"object_id": url_id,
"dc:identifier": url_sha
})
],
"relationships": [
{
"relate": "dc:describes",
"object_id": layer['object_id']
},
{
"relate": "bcube:originatedFrom",
"object_id": url_id
}
]
}
]
if 'temporal_extent' in ld:
temporal = tidy_dict(
{
"esip:startDate":
ld['temporal_extent'].get('begin', ''),
"esip:endDate":
ld['temporal_extent'].get('end', '')
}
)
if temporal:
layer.update(temporal)
if 'bbox' in ld:
layer.update(ld['bbox'])
layers.append(layer)
service['layers'] = layers
# if layers:
# service['layers'] = layers
# for layer in layers:
# service['relationships'].append({
# "relate": "bcube:contains",
# "object_id": layer['object_id']
# })
output['services'] = [service]
self.description = tidy_dict(output)
def parse_item(self):
output = {}
urls = set()
catalog_object_id = generate_uuid_urn()
output['catalog_record'] = {
"object_id": catalog_object_id,
"bcube:dateCreated": self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated": self.harvest_details.get('harvest_date', ''),
# "dc:conformsTo": extract_attrib(
# self.elem, ['@noNamespaceSchemaLocation']).split(),
"rdf:type": "FGDC:CSDGM",
"relationships": [],
"urls": []
}
output['urls'] = []
# add the harvest info
# this is not necessary as a sha just for set inclusion
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
original_url = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": generate_uuid_urn(),
"dc:identifier": url_sha
})
output['catalog_record']['urls'].append(original_url)
# NOTE: this is not the sha from the url
output['catalog_record']['relationships'].append({
"relate":
"bcube:originatedFrom",
"object_id":
original_url['object_id']
})
datsetid = extract_item(self.elem, ['idinfo', 'datsetid'])
dataset_object_id = generate_uuid_urn()
dataset = {
"object_id":
dataset_object_id,
"dcterms:identifier":
datsetid,
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', ''),
"dc:description":
extract_item(self.elem, ['idinfo', 'descript', 'abstract']),
"dcterms:title":
extract_item(self.elem,
['idinfo', 'citation', 'citeinfo', 'title']),
"urls": [],
"relationships": []
}
bbox_elem = extract_elem(self.elem, ['idinfo', 'spdom', 'bounding'])
if bbox_elem is not None:
# that's not even valid
west = extract_item(bbox_elem, ['westbc'])
east = extract_item(bbox_elem, ['eastbc'])
north = extract_item(bbox_elem, ['northbc'])
south = extract_item(bbox_elem, ['southbc'])
bbox = [west, south, east, north]
bbox = bbox_to_geom(bbox)
bbox = to_wkt(bbox)
dataset.update({
"dc:spatial": bbox,
"esip:westBound": west,
"esip:eastBound": east,
"esip:northBound": north,
"esip:southBound": south
})
time_elem = extract_elem(self.elem, ['idinfo', 'timeperd', 'timeinfo'])
if time_elem is not None:
caldate = extract_item(time_elem, ['sngdate', 'caldate'])
if caldate:
# TODO: we should see if it's at least a valid date
dataset['esip:startDate'] = self._convert_date(caldate)
rngdate = extract_elem(time_elem, ['rngdates'])
if rngdate is not None:
dataset['esip:startDate'] = self._convert_date(
extract_item(rngdate, ['begdate']))
dataset['esip:endDate'] = self._convert_date(
extract_item(rngdate, ['enddate']))
# TODO: add the min/max of the list of dates
dataset['relationships'] = [{
"relate": "bcube:hasMetadataRecord",
"object_id": catalog_object_id
}]
publisher = {
"object_id":
generate_uuid_urn(),
"name":
extract_item(
self.elem,
['idinfo', 'citation', 'citeinfo', 'pubinfo', 'publish']),
"location":
extract_item(
self.elem,
['idinfo', 'citation', 'citeinfo', 'pubinfo', 'pubplace'])
}
output['publisher'] = publisher
dataset['relationships'].append({
"relate": "dcterms:publisher",
"object_id": publisher['object_id']
})
distrib_elems = extract_elems(self.elem,
['distinfo', 'stdorder', 'digform'])
for distrib_elem in distrib_elems:
link = extract_item(
distrib_elem,
['digtopt', 'onlinopt', 'computer', 'networka', 'networkr'])
# format = extract_item(distrib_elem, ['digtinfo', 'formname'])
url_sha = generate_sha_urn(link)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": link,
"object_id": url_id,
"dc:identifier": url_sha
})
dataset['urls'].append(dist)
# this is a distribution link so
# we are assuming it is to data
dataset['relationships'].append({
"relate": "dcterms:references",
"object_id": url_id
})
webpages = []
onlink_elems = extract_elems(
self.elem, ['idinfo', 'citation', 'citeinfo', 'onlink'])
for onlink_elem in onlink_elems:
link = onlink_elem.text.strip() if onlink_elem.text else ''
if not link:
continue
url_sha = generate_sha_urn(link)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": link,
"object_id": url_id,
"dc:identifier": url_sha
})
dataset['urls'].append(dist)
webpages.append({
"object_id":
generate_uuid_urn(),
"relationships": [{
"relate": "dcterms:references",
"object_id": url_id
}]
})
output['catalog_record']['webpages'] = webpages
for webpage in webpages:
dataset['relationships'].append({
"relate": "dcterms:references",
"object_id": webpage['object_id']
})
# retain the keyword sets with type, thesaurus name and split
# the terms as best we can
keywords = []
key_elem = extract_elem(self.elem, ['idinfo', 'keywords'])
for child in key_elem.iterchildren():
key_type = extract_element_tag(child.tag)
key_tag = 'strat' if key_type == 'stratum' else key_type
key_tag = 'temp' if key_tag == 'temporal' else key_tag
thesaurus = extract_item(child, ['%skt' % key_tag])
# TODO: split these up
terms = extract_items(child, ['%skey' % key_tag])
if terms:
# if there's a parsing error (bad cdata, etc) may not have
# TODO: add something for a set without a thesaurus name
keywords.append(
tidy_dict({
"object_id": generate_uuid_urn(),
"dc:partOf": thesaurus,
"bcube:hasType": key_type,
"bcube:hasValue": terms
}))
output['keywords'] = keywords
for keyword in keywords:
dataset['relationships'].append({
"relate": "dc:conformsTo",
"object_id": keyword['object_id']
})
output['datasets'] = [dataset]
# add the metadata relate
output['catalog_record']['relationships'].append({
"relate":
"foaf:primaryTopic",
"object_id":
dataset_object_id
})
output['catalog_records'] = [output['catalog_record']]
del output['catalog_record']
self.description = tidy_dict(output)
def parse(self):
'''
run the routing
'''
if not self.identity:
# we're going to have to sort it out
self.identity = {}
metadata = self.identity.get('metadata', {})
if not metadata:
return {}
metadata_type = metadata.get('name', '')
if not metadata_type:
return {}
# TODO: this is unlikely to be correct, given the ds record
# but we're not going there just yet
# TODO: deal with conformsTo (multiple schemaLocations, etc)
catalog_record = {
"object_id": generate_uuid_urn(),
"rdf:type": self._version_to_urn(),
"bcube:dateCreated": self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated": self.harvest_details.get('harvest_date', ''),
# "dc:conformsTo": extract_attrib(
# self.parser.xml, ['@schemaLocation']).split(),
"relationships": [],
"urls": []
}
original_url = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": generate_uuid_urn()
})
catalog_record['urls'].append(original_url)
catalog_record['relationships'].append({
"relate":
"bcube:originatedFrom",
"object_id":
original_url['object_id']
})
if metadata_type == 'Data Series':
# run the set
self.reader = DsParser(self.parser.xml, catalog_record)
elif metadata_type == '19119':
# run that
for srv in extract_elems(
self.parser.xml,
['identificationInfo', 'SV_ServiceIdentification']):
reader = SrvParser(srv, catalog_record)
reader.parse()
elif metadata_type == '19115':
# it's a mi/md so run that
self.reader = MxParser(self.parser.xml, catalog_record,
self.harvest_details)
self.reader.parse()
# self.reader.parse()
# # pass it back up the chain a bit
self.description = self.reader.output
def parse(self):
# for ogc, a catalog record is the getcapabilities rsp
output = {"layers": [], "catalog_records": []}
urls = set()
if not self.reader:
self.description = {}
return
if 'service' in self.identify:
service_id = generate_uuid_urn()
service = {
"object_id": service_id,
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', ''),
"relationships": [],
"urls": [],
"rdf:type": self.urn
}
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
url_id = generate_uuid_urn()
service['urls'].append(
self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": url_id,
"dc:identifier": url_sha
}))
service['relationships'].append({
"relate": "bcube:originatedFrom",
"object_id": url_id
})
# self._get_service_config(service_name, version)
service_reader = self._parse_service(self.reader,
self.service_name,
self.version)
# map to triples
service.update({"dc:description": service_reader.get('abstract')})
keywords = service_reader.get('subject', [])
if keywords:
output['keywords'] = [{
"object_id": generate_uuid_urn(),
"bcube:hasValue": keywords
}]
for k in output['keywords']:
service['relationships'].append({
"relate": "dc:conformsTo",
"object_id": k['object_id']
})
if self.identify['service'].get('request',
'') == 'GetCapabilities':
# this is also awkward. meh. needs must.
layers = []
listed_layers = self._parse_getcap_datasets(self.reader)
for ld in listed_layers:
layer = {
"object_id":
generate_uuid_urn(),
"bcube:dateCreated":
self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated":
self.harvest_details.get('harvest_date', ''),
"dc:description":
ld.get('abstract', ''),
"dc:title":
ld.get('title', ''),
"relationships": []
}
service['relationships'].append({
"relate":
"bcube:contains",
"object_id":
layer['object_id']
})
# add the generated url for the service
generated_url = self._generate_url(self.url,
ld.get('name'),
ld.get('bbox'),
self.service_name,
self.version)
if generated_url:
url_sha = generate_sha_urn(generated_url)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
layer_url = self._generate_harvest_manifest(
**{
"vcard:hasURL": generated_url,
"bcube:hasUrlSource": "Generated",
"bcube:hasConfidence": "Not Sure",
"object_id": url_id,
"dc:identifier": url_sha
})
service['urls'].append(layer_url)
# don't add to the larger set, but do
# include the reference within the layer
layer['relationships'].append({
"relate": "dcterms:references",
"object_id": url_id
})
# add each as a dataset with just a url for now
for mu in ld.get('metadata_urls', []):
url_link = generate_uuid_urn()
url_sha = generate_sha_urn(mu.get('url'))
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
output['catalog_records'] += [{
"object_id":
url_link,
"urls": [
self._generate_harvest_manifest(
**{
"vcard:hasURL": mu.get('url'),
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"object_id": url_id,
"dc:identifier": url_sha
})
],
"relationships": [{
"relate":
"dc:describes",
"object_id":
layer['object_id']
}, {
"relate": "bcube:originatedFrom",
"object_id": url_id
}]
}]
if 'temporal_extent' in ld:
temporal = tidy_dict({
"esip:startDate":
ld['temporal_extent'].get('begin', ''),
"esip:endDate":
ld['temporal_extent'].get('end', '')
})
if temporal:
layer.update(temporal)
if 'bbox' in ld:
layer.update(ld['bbox'])
layers.append(layer)
service['layers'] = layers
# if layers:
# service['layers'] = layers
# for layer in layers:
# service['relationships'].append({
# "relate": "bcube:contains",
# "object_id": layer['object_id']
# })
output['services'] = [service]
self.description = tidy_dict(output)
def _parse_service(self):
output = {}
urls = set()
service = {
"object_id": generate_uuid_urn(),
"bcube:dateCreated": self.harvest_details.get("harvest_date", ""),
"bcube:lastUpdated": self.harvest_details.get("harvest_date", ""),
"rdf:type": "OpenSearch1.1:Description",
"dcterms:title": extract_item(self.parser.xml, ["ShortName"]),
"dc:description": " ".join(
extract_items(self.parser.xml, ["LongName"]) + extract_items(self.parser.xml, ["Description"])
),
"urls": [],
"webpages": [],
"relationships": [],
}
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
original_url = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": generate_uuid_urn(),
"dc:identifier": url_sha,
}
)
service["urls"].append(original_url)
service["relationships"].append({"relate": "bcube:originatedFrom", "object_id": original_url["object_id"]})
# output['source'] = extract_items(
# self.parser.xml, ["Attribution"])
# output['contact'] = extract_items(
# self.parser.xml, ["Developer"])
# output['rights'] = extract_items(
# self.parser.xml, ["SyndicationRight"])
key_id = generate_uuid_urn()
output["keywords"] = [{"object_id": key_id, "bcube:hasValue": extract_items(self.parser.xml, ["Tags"])}]
service["relationships"].append({"relate": "dc:conformsTo", "object_id": key_id})
for t in extract_elems(self.parser.xml, ["Url"]):
ep = self._parse_endpoint(t)
url_sha = generate_sha_urn(ep["url"])
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(
**{
"bcube:hasUrlSource": "Generated",
"bcube:hasConfidence": "Not Sure",
"vcard:hasURL": ep["url"],
"object_id": url_id,
"dc:identifier": url_sha,
}
)
service["urls"].append(dist)
wb_id = generate_uuid_urn()
service["webpages"].append(
{"object_id": wb_id, "relationships": [{"relate": "dcterms:references", "object_id": url_id}]}
)
service["relationships"].append({"relate": "dcterms:references", "object_id": wb_id})
output["services"] = [service]
return tidy_dict(output)
def parse_item(self):
output = {}
urls = set()
catalog_object_id = generate_uuid_urn()
output['catalog_record'] = {
"object_id": catalog_object_id,
"bcube:dateCreated": self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated": self.harvest_details.get('harvest_date', ''),
# "dc:conformsTo": extract_attrib(
# self.elem, ['@noNamespaceSchemaLocation']).split(),
"rdf:type": "FGDC:CSDGM",
"relationships": [],
"urls": []
}
output['urls'] = []
# add the harvest info
# this is not necessary as a sha just for set inclusion
url_sha = generate_sha_urn(self.url)
urls.add(url_sha)
original_url = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": self.url,
"object_id": generate_uuid_urn(),
"dc:identifier": url_sha
})
output['catalog_record']['urls'].append(original_url)
# NOTE: this is not the sha from the url
output['catalog_record']['relationships'].append(
{
"relate": "bcube:originatedFrom",
"object_id": original_url['object_id']
}
)
datsetid = extract_item(self.elem, ['idinfo', 'datsetid'])
dataset_object_id = generate_uuid_urn()
dataset = {
"object_id": dataset_object_id,
"dcterms:identifier": datsetid,
"bcube:dateCreated": self.harvest_details.get('harvest_date', ''),
"bcube:lastUpdated": self.harvest_details.get('harvest_date', ''),
"dc:description": extract_item(
self.elem, ['idinfo', 'descript', 'abstract']),
"dcterms:title": extract_item(
self.elem, ['idinfo', 'citation', 'citeinfo', 'title']),
"urls": [],
"relationships": []
}
bbox_elem = extract_elem(self.elem, ['idinfo', 'spdom', 'bounding'])
if bbox_elem is not None:
# that's not even valid
west = extract_item(bbox_elem, ['westbc'])
east = extract_item(bbox_elem, ['eastbc'])
north = extract_item(bbox_elem, ['northbc'])
south = extract_item(bbox_elem, ['southbc'])
bbox = [west, south, east, north]
bbox = bbox_to_geom(bbox)
bbox = to_wkt(bbox)
dataset.update({
"dc:spatial": bbox,
"esip:westBound": west,
"esip:eastBound": east,
"esip:northBound": north,
"esip:southBound": south
})
time_elem = extract_elem(self.elem, ['idinfo', 'timeperd', 'timeinfo'])
if time_elem is not None:
caldate = extract_item(time_elem, ['sngdate', 'caldate'])
if caldate:
# TODO: we should see if it's at least a valid date
dataset['esip:startDate'] = self._convert_date(caldate)
rngdate = extract_elem(time_elem, ['rngdates'])
if rngdate is not None:
dataset['esip:startDate'] = self._convert_date(
extract_item(rngdate, ['begdate']))
dataset['esip:endDate'] = self._convert_date(
extract_item(rngdate, ['enddate']))
# TODO: add the min/max of the list of dates
dataset['relationships'] = [
{
"relate": "bcube:hasMetadataRecord",
"object_id": catalog_object_id
}
]
publisher = {
"object_id": generate_uuid_urn(),
"name": extract_item(
self.elem,
['idinfo', 'citation', 'citeinfo', 'pubinfo', 'publish']),
"location": extract_item(
self.elem,
['idinfo', 'citation', 'citeinfo', 'pubinfo', 'pubplace'])
}
output['publisher'] = publisher
dataset['relationships'].append({
"relate": "dcterms:publisher",
"object_id": publisher['object_id']
})
distrib_elems = extract_elems(
self.elem, ['distinfo', 'stdorder', 'digform'])
for distrib_elem in distrib_elems:
link = extract_item(
distrib_elem,
['digtopt', 'onlinopt', 'computer', 'networka', 'networkr'])
# format = extract_item(distrib_elem, ['digtinfo', 'formname'])
url_sha = generate_sha_urn(link)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": link,
"object_id": url_id,
"dc:identifier": url_sha
})
dataset['urls'].append(dist)
# this is a distribution link so
# we are assuming it is to data
dataset['relationships'].append({
"relate": "dcterms:references",
"object_id": url_id
})
webpages = []
onlink_elems = extract_elems(
self.elem, ['idinfo', 'citation', 'citeinfo', 'onlink'])
for onlink_elem in onlink_elems:
link = onlink_elem.text.strip() if onlink_elem.text else ''
if not link:
continue
url_sha = generate_sha_urn(link)
if url_sha not in urls:
urls.add(url_sha)
url_id = generate_uuid_urn()
dist = self._generate_harvest_manifest(**{
"bcube:hasUrlSource": "Harvested",
"bcube:hasConfidence": "Good",
"vcard:hasURL": link,
"object_id": url_id,
"dc:identifier": url_sha
})
dataset['urls'].append(dist)
webpages.append({
"object_id": generate_uuid_urn(),
"relationships": [
{
"relate": "dcterms:references",
"object_id": url_id
}
]}
)
output['catalog_record']['webpages'] = webpages
for webpage in webpages:
dataset['relationships'].append({
"relate": "dcterms:references",
"object_id": webpage['object_id']
})
# retain the keyword sets with type, thesaurus name and split
# the terms as best we can
keywords = []
key_elem = extract_elem(self.elem, ['idinfo', 'keywords'])
for child in key_elem.iterchildren():
key_type = extract_element_tag(child.tag)
key_tag = 'strat' if key_type == 'stratum' else key_type
key_tag = 'temp' if key_tag == 'temporal' else key_tag
thesaurus = extract_item(child, ['%skt' % key_tag])
# TODO: split these up
terms = extract_items(child, ['%skey' % key_tag])
if terms:
# if there's a parsing error (bad cdata, etc) may not have
# TODO: add something for a set without a thesaurus name
keywords.append(
tidy_dict({
"object_id": generate_uuid_urn(),
"dc:partOf": thesaurus,
"bcube:hasType": key_type,
"bcube:hasValue": terms
})
)
output['keywords'] = keywords
for keyword in keywords:
dataset['relationships'].append(
{
"relate": "dc:conformsTo",
"object_id": keyword['object_id']
}
)
output['datasets'] = [dataset]
# add the metadata relate
output['catalog_record']['relationships'].append(
{
"relate": "foaf:primaryTopic",
"object_id": dataset_object_id
}
)
output['catalog_records'] = [output['catalog_record']]
del output['catalog_record']
self.description = tidy_dict(output)