def _parse_results_set_info(self):
result_elem = extract_elem(self.parser.xml, ['SearchResults'])
self.total = extract_attrib(result_elem, ['@numberOfRecordsMatched'])
self.subtotal = extract_attrib(result_elem,
['@numberOfRecordsReturned'])
self.schema = extract_attrib(result_elem, ['@recordSchema'])
def _parse_children(self, dialect):
children = []
result_elem = extract_elem(self.parser.xml, ['SearchResults'])
for child in result_elem.iterchildren():
item = self._parse_child(child, dialect)
if item:
children.append(item)
return children
def _parse_children(self, dialect):
children = []
result_elem = extract_elem(self.parser.xml, ['SearchResults'])
for child in result_elem.iterchildren():
item = self._parse_child(child, dialect)
if item:
children.append(item)
return children
def _handle_polygon(self, polygon_elem):
elem = extract_elem(polygon_elem, ['polygon', 'Polygon'])
srs_name = elem.attrib.get('srsName', 'EPSG:4326')
geom = gml_to_geom(elem)
if srs_name != '':
geom = reproject(geom, srs_name, 'EPSG:4326')
# TODO: generate the envelope?
return {"dc:spatial": to_wkt(geom)}
def _handle_polygon(self, polygon_elem):
elem = extract_elem(polygon_elem, ['polygon', 'Polygon'])
srs_name = elem.attrib.get('srsName', 'EPSG:4326')
geom = gml_to_geom(elem)
if srs_name != '':
geom = reproject(geom, srs_name, 'EPSG:4326')
# TODO: generate the envelope?
return {"dc:spatial": to_wkt(geom)}
def _parse_child(self, child, dialect):
identifier = extract_item(child, ['header', 'identifier'])
timestamp = extract_item(child, ['header', 'datestamp'])
if dialect == 'oai_dc':
dc_elem = extract_elem(child, ['metadata', 'dc'])
dc_parser = DcItemReader(dc_elem)
return dict(
chain(
{"identifier": identifier, "timestamp": timestamp}.items(),
dc_parser.parse_item().items()
)
)
def _parse_child(self, child, dialect):
identifier = extract_item(child, ['header', 'identifier'])
timestamp = extract_item(child, ['header', 'datestamp'])
if dialect == 'oai_dc':
dc_elem = extract_elem(child, ['metadata', 'dc'])
dc_parser = DcItemReader(dc_elem)
return dict(
chain({
"identifier": identifier,
"timestamp": timestamp
}.items(),
dc_parser.parse_item().items()))
def _parse_responsibleparty(self, elem):
'''
parse any CI_ResponsibleParty
'''
individual_name = extract_item(elem,
['individualName', 'CharacterString'])
organization_name = extract_item(
elem, ['organisationName', 'CharacterString'])
position_name = extract_item(elem, ['positionName', 'CharacterString'])
e = extract_elem(elem, ['contactInfo', 'CI_Contact'])
contact = self._parse_contact(e)
return tidy_dict({
"individual": individual_name,
"organization": organization_name,
"position": position_name,
"contact": contact
})
def _parse_responsibleparty(self, elem):
'''
parse any CI_ResponsibleParty
'''
individual_name = extract_item(
elem, ['individualName', 'CharacterString'])
organization_name = extract_item(
elem, ['organisationName', 'CharacterString'])
position_name = extract_item(
elem, ['positionName', 'CharacterString'])
e = extract_elem(elem, ['contactInfo', 'CI_Contact'])
contact = self._parse_contact(e)
return tidy_dict({
"individual": individual_name,
"organization": organization_name,
"position": position_name,
"contact": contact
})
def parse(self):
# get the series
self.description = {}
md = extract_elem(self.elem, ['seriesMetadata', 'MD_Metadata'])
if md is None:
return
md_parser = MxParser(md)
md_parser.parse()
self.description = md_parser.description
self.description['children'] = []
# get the children
children = extract_elems(
self.elem, ['composedOf', 'DS_DataSet', 'has', 'MD_Metadata'])
for child in children:
child_parser = MxParser(child)
child_parser.parse()
if child_parser.description:
self.description['children'].append(child_parser.description)
self.description = tidy_dict(self.description)
def parse(self):
# get the series
self.description = {}
md = extract_elem(self.elem, ['seriesMetadata', 'MD_Metadata'])
if md is None:
return
md_parser = MxParser(md)
md_parser.parse()
self.description = md_parser.description
self.description['children'] = []
# get the children
children = extract_elems(
self.elem, ['composedOf', 'DS_DataSet', 'has', 'MD_Metadata'])
for child in children:
child_parser = MxParser(child)
child_parser.parse()
if child_parser.description:
self.description['children'].append(child_parser.description)
self.description = tidy_dict(self.description)
def _parse_extent(self, elem):
'''
handle the spatial and/or temporal extent
starting from the *:extent element
'''
extents = {}
geo_elem = extract_elem(
elem, ['extent', 'EX_Extent', 'geographicElement'])
if geo_elem is not None:
# we need to sort out what kind of thing it
# is bbox, polygon, list of points
bbox_elem = extract_elem(geo_elem, ['EX_GeographicBoundingBox'])
if bbox_elem is not None:
extents.update(self._handle_bbox(bbox_elem))
# NOTE: this will obv overwrite the above
poly_elem = extract_elem(geo_elem, ['EX_BoundingPolygon'])
if poly_elem is not None:
extents.update(self._handle_polygon(poly_elem))
time_elem = extract_elem(
elem,
[
'extent',
'EX_Extent',
'temporalElement',
'EX_TemporalExtent',
'extent',
'TimePeriod'
]
)
if time_elem is not None:
begin_position = extract_elem(time_elem, ['beginPosition'])
end_position = extract_elem(time_elem, ['endPosition'])
if begin_position is not None and 'indeterminatePosition' not in begin_position.attrib:
begin_position = self._parse_timestamp(begin_position.text)
if end_position is not None and 'indeterminatePosition' not in end_position.attrib:
end_position = self._parse_timestamp(end_position.text)
extents.update({
"esip:startDate": begin_position.isoformat(),
"esip:endDate": end_position.isoformat()
})
return extents
def _parse_extent(self, elem):
'''
handle the spatial and/or temporal extent
starting from the *:extent element
'''
extents = {}
geo_elem = extract_elem(elem,
['extent', 'EX_Extent', 'geographicElement'])
if geo_elem is not None:
# we need to sort out what kind of thing it
# is bbox, polygon, list of points
bbox_elem = extract_elem(geo_elem, ['EX_GeographicBoundingBox'])
if bbox_elem is not None:
extents.update(self._handle_bbox(bbox_elem))
# NOTE: this will obv overwrite the above
poly_elem = extract_elem(geo_elem, ['EX_BoundingPolygon'])
if poly_elem is not None:
extents.update(self._handle_polygon(poly_elem))
time_elem = extract_elem(elem, [
'extent', 'EX_Extent', 'temporalElement', 'EX_TemporalExtent',
'extent', 'TimePeriod'
])
if time_elem is not None:
begin_position = extract_elem(time_elem, ['beginPosition'])
end_position = extract_elem(time_elem, ['endPosition'])
if begin_position is not None and 'indeterminatePosition' not in begin_position.attrib:
begin_position = self._parse_timestamp(begin_position.text)
if end_position is not None and 'indeterminatePosition' not in end_position.attrib:
end_position = self._parse_timestamp(end_position.text)
extents.update({
"esip:startDate": begin_position.isoformat(),
"esip:endDate": end_position.isoformat()
})
return extents
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_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_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):
'''
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_results_set_info(self):
result_elem = extract_elem(self.parser.xml, ['SearchResults'])
self.total = extract_attrib(result_elem, ['@numberOfRecordsMatched'])
self.subtotal = extract_attrib(result_elem, ['@numberOfRecordsReturned'])
self.schema = extract_attrib(result_elem, ['@recordSchema'])