def __init__(self, model, name, data):
self.model = model
self.name = name
self.data = data
self._label = data.get('label', name)
self._plural = data.get('plural', self.label)
self._description = data.get('description')
self.featured = ensure_list(data.get('featured'))
self.uri = URIRef(data.get('rdf', NS[name]))
# Do not show in listings:
self.abstract = as_bool(data.get('abstract'), False)
# Try to perform fuzzy matching. Fuzzy similarity search does not
# make sense for entities which have a lot of similar names, such
# as land plots, assets etc.
self.matchable = as_bool(data.get('matchable'), True)
self.extends = set()
self.schemata = set([self])
self.names = set([self.name])
self.descendants = set()
self.properties = {}
for name, prop in data.get('properties', {}).items():
self.properties[name] = Property(self, name, prop)
def __init__(self, model, name, data):
self.model = model
self.name = name
self.data = data
self.icon = data.get('icon')
self._label = data.get('label', name)
self._plural = data.get('plural', self.label)
self._description = data.get('description')
self._extends = ensure_list(data.get('extends'))
self.featured = ensure_list(data.get('featured'))
self.uri = NAMESPACE[name]
if 'rdf' in data:
self.uri = URIRef(data.get('rdf'))
# Do not show in listings:
self.abstract = as_bool(data.get('abstract'), False)
# Try to perform fuzzy matching. Fuzzy similarity search does not
# make sense for entities which have a lot of similar names, such
# as land plots, assets etc.
self.matchable = as_bool(data.get('matchable'), True)
self._own_properties = []
for name, prop in data.get('properties', {}).items():
self._own_properties.append(Property(self, name, prop))
def __init__(self, model, name, data):
self.model = model
self.name = name
self.data = data
self._label = data.get("label", name)
self._plural = data.get("plural", self.label)
self._description = data.get("description")
self.uri = URIRef(data.get("rdf", NS[name]))
# Do not show in listings:
self.abstract = as_bool(data.get("abstract"), False)
self.hidden = as_bool(data.get("hidden"), False)
self.hidden = self.hidden and not self.abstract
# Generated by the system, not user-managed
self.generated = as_bool(data.get("generated"), False)
# Try to perform fuzzy matching. Fuzzy similarity search does not
# make sense for entities which have a lot of similar names, such
# as land plots, assets etc.
self.matchable = as_bool(data.get("matchable"), True)
# Mark a set of properties as important, i.e. they should be shown
# first, or in an abridged view of the entity.
self.featured = ensure_list(data.get("featured"))
# Mark a set of properties as required. This is applied only when
# an entity is created by the user - bulk created entities will
# slip through even if it is technically invalid.
self.required = ensure_list(data.get("required"))
# Mark a set of properties to be used for the entity's caption.
# They will be checked in order and the first existant value will
# be used.
self.caption = ensure_list(data.get("caption"))
# A transform of the entity into an edge for its representation in
# the context of a property graph representation like Neo4J/Gephi.
edge = data.get("edge", {})
self.edge_source = edge.get("source")
self.edge_target = edge.get("target")
self.edge = self.edge_source and self.edge_target
self.edge_caption = ensure_list(edge.get("caption"))
self._edge_label = edge.get("label", self._label)
self.edge_directed = edge.get("directed", True)
self.extends = set()
self.schemata = set([self])
self.names = set([self.name])
self.descendants = set()
self.properties = {}
for name, prop in data.get("properties", {}).items():
self.properties[name] = Property(self, name, prop)
def __init__(self, model, name, data):
self.model = model
self.name = name
self.data = data
self._label = data.get('label', name)
self._plural = data.get('plural', self.label)
self._description = data.get('description')
self.uri = URIRef(data.get('rdf', NS[name]))
# Do not show in listings:
self.abstract = as_bool(data.get('abstract'), False)
# Generated by the system, not user-managed
self.generated = as_bool(data.get('generated'), False)
# Try to perform fuzzy matching. Fuzzy similarity search does not
# make sense for entities which have a lot of similar names, such
# as land plots, assets etc.
self.matchable = as_bool(data.get('matchable'), True)
# Mark a set of properties as important, i.e. they should be shown
# first, or in an abridged view of the entity.
self.featured = ensure_list(data.get('featured'))
# Mark a set of properties to be used for the entity's caption.
# They will be checked in order and the first existant value will
# be used.
self.caption = ensure_list(data.get('caption'))
# A transform of the entity into an edge for its representation in
# the context of a property graph representation like Neo4J/Gephi.
edge = data.get('edge', {})
self.edge_source = edge.get('source')
self.edge_target = edge.get('target')
self.edge = self.edge_source and self.edge_target
self.edge_caption = ensure_list(edge.get('caption'))
self._edge_label = edge.get('label', self._label)
self.edge_directed = edge.get('directed', True)
self.extends = set()
self.schemata = set([self])
self.names = set([self.name])
self.descendants = set()
self.properties = {}
for name, prop in data.get('properties', {}).items():
self.properties[name] = Property(self, name, prop)
def _add_reverse(self, data, other):
name = data.get('name', None)
if name is None:
raise InvalidModel("Unnamed reverse: %s" % other)
prop = self.get(name)
if prop is None:
data.update({
'type': registry.entity.name,
'reverse': {
'name': other.name
},
'schema': other.schema.name,
'stub': True
})
prop = Property(self, name, data)
prop.generate()
return prop
def _add_reverse(self, data, other):
name = data.pop('name', None)
if name is None:
raise InvalidModel("Unnamed reverse: %s" % other)
prop = self.get(name)
if prop is None:
data.update({
'type': 'entity',
'reverse': {'name': other.name},
'schema': other.schema.name
})
prop = Property(self, name, data, stub=True)
prop.generate()
self._own_properties.append(prop)
self._flush_properties()
assert prop.type == registry.entity, prop.type
return prop
def _add_reverse(self, data: ReverseSpec, other: Property) -> Property:
name = data.get("name")
if name is None:
raise InvalidModel("Unnamed reverse: %s" % other)
prop = self.get(name)
if prop is None:
spec: PropertySpec = {
"label": data.get("label"),
"type": registry.entity.name,
"reverse": {"name": other.name},
"range": other.schema.name,
"hidden": data.get("hidden", other.hidden),
}
prop = Property(self, name, spec)
prop.stub = True
prop.generate()
self.properties[name] = prop
return prop
def _add_reverse(self, data, other):
name = data.get("name", None)
if name is None:
raise InvalidModel("Unnamed reverse: %s" % other)
prop = self.get(name)
if prop is None:
data.update({
"type": registry.entity.name,
"reverse": {
"name": other.name
},
"range": other.schema.name,
"stub": True,
})
data["hidden"] = data.get("hidden", other.hidden)
prop = Property(self, name, data)
prop.generate()
self.properties[name] = prop
return prop
def __init__(self, model, name, data):
self._model = model
self.name = name
self.data = data
self.label = data.get('label', name)
self.plural = data.get('plural', self.label)
self.icon = data.get('icon')
# Do not show in listings:
self.hidden = data.get('hidden', False)
# Try to perform fuzzy matching. Fuzzy similarity search does not
# make sense for entities which have a lot of similar names, such
# as land plots, assets etc.
self.fuzzy = data.get('fuzzy', True)
self._extends = ensure_list(data.get('extends'))
self._own_properties = []
for name, prop in data.get('properties', {}).items():
self._own_properties.append(Property(self, name, prop))
def __init__(self, model, name, data):
#: Machine-readable name of the schema, used for identification.
self.name = name
self.model = model
self.data = data
self._label = data.get("label", name)
self._plural = data.get("plural", self.label)
self._description = data.get("description")
self._hash = hash("<Schema(%r)>" % name)
#: RDF identifier for this schema when it is transformed to a triple term.
self.uri = URIRef(data.get("rdf", NS[name]))
#: Do not store or emit entities of this type, it is used only for
#: inheritance.
self.abstract = as_bool(data.get("abstract"), False)
#: Hide this schema in listings.
self.hidden = as_bool(data.get("hidden"), False)
self.hidden = self.hidden and not self.abstract
#: Entities with this type are generated by the system - for example, via
#: `ingest-file`. The user should not be offered an option to create them
#: in the interface.
self.generated = as_bool(data.get("generated"), False)
#: Try to perform fuzzy matching. Fuzzy similarity search does not
#: make sense for entities which have a lot of similar names, such
#: as land plots, assets etc.
self.matchable = as_bool(data.get("matchable"), True)
#: Mark a set of properties as important, i.e. they should be shown
#: first, or in an abridged view of the entity. In Aleph, these properties
#: are included in tabular entity listings.
self.featured = ensure_list(data.get("featured"))
#: Mark a set of properties as required. This is applied only when
#: an entity is created by the user - bulk created entities will
#: slip through even if it is technically invalid.
self.required = ensure_list(data.get("required"))
#: Mark a set of properties to be used for the entity's caption.
#: They will be checked in order and the first existant value will
#: be used.
self.caption = ensure_list(data.get("caption"))
# A transform of the entity into an edge for its representation in
# the context of a property graph representation like Neo4J/Gephi.
edge = data.get("edge", {})
self.edge_source = edge.get("source")
self.edge_target = edge.get("target")
#: Flag to indicate if this schema should be represented by an edge (rather than
#: a node) when the data is converted into a property graph.
self.edge = self.edge_source and self.edge_target
self.edge_caption = ensure_list(edge.get("caption"))
self._edge_label = edge.get("label", self._label)
#: Flag to indicate if the edge should be presented as directed to the user, e.g.
#: by showing an error at the target end of the edge.
self.edge_directed = edge.get("directed", True)
#: Direct parent schemata of this schema.
self.extends = set()
#: All parents of this schema (including indirect parents and the schema itself).
self.schemata = set([self])
#: All names of :attr:`~schemata`.
self.names = set([self.name])
#: Inverse of :attr:`~schemata`, all derived child types of this schema
#: and their children.
self.descendants = set()
#: The full list of properties defined for the entity, including those
#: inherited from parent schemata.
self.properties = {}
for name, prop in data.get("properties", {}).items():
self.properties[name] = Property(self, name, prop)