371 lines
15 KiB
Python
371 lines
15 KiB
Python
from api.cd import CDAPI
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from api.od import ODAPI, bind_api_readonly
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from util.eval import exec_then_eval
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from state.base import State
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from uuid import UUID
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from services.bottom.V0 import Bottom
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from services.scd import SCD
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from services import od as services_od
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from transformation.vf2 import Graph, Edge, Vertex, MatcherVF2
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from transformation import ramify
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import itertools
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import re
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import functools
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from util.timer import Timer
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class _is_edge:
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def __repr__(self):
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return "EDGE"
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def to_json(self):
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return "EDGE"
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# just a unique symbol that is only equal to itself
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IS_EDGE = _is_edge()
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class _is_modelref:
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def __repr__(self):
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return "REF"
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def to_json(self):
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return "REF"
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IS_MODELREF = _is_modelref()
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# class IS_TYPE:
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# def __init__(self, type):
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# # mvs-node of the type
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# self.type = type
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# def __repr__(self):
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# return f"TYPE({str(self.type)[-4:]})"
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class NamedNode(Vertex):
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def __init__(self, value, name):
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super().__init__(value)
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# the name of the node in the context of the model
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# the matcher by default ignores this value
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self.name = name
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# MVS-nodes become vertices
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class MVSNode(NamedNode):
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def __init__(self, value, node_id, name):
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super().__init__(value, name)
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# useful for debugging
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self.node_id = node_id
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def __repr__(self):
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if self.value == None:
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return f"N({self.name})"
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if isinstance(self.value, str):
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return f"N({self.name}=\"{self.value}\")"
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return f"N({self.name}={self.value})"
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# if isinstance(self.value, str):
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# return f"N({self.name}=\"{self.value}\",{str(self.node_id)[-4:]})"
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# return f"N({self.name}={self.value},{str(self.node_id)[-4:]})"
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# MVS-edges become vertices.
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class MVSEdge(NamedNode):
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def __init__(self, node_id, name):
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super().__init__(IS_EDGE, name)
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# useful for debugging
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self.node_id = node_id
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def __repr__(self):
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return f"E({self.name})"
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# return f"E({self.name}{str(self.node_id)[-4:]})"
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# dirty way of detecting whether a node is a ModelRef
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UUID_REGEX = re.compile(r"[0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z]-[0-9a-z][0-9a-z][0-9a-z][0-9a-z]-[0-9a-z][0-9a-z][0-9a-z][0-9a-z]-[0-9a-z][0-9a-z][0-9a-z][0-9a-z]-[0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z][0-9a-z]")
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# Converts an object diagram in MVS state to the pattern matcher graph type
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# ModelRefs are flattened
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def model_to_graph(state: State, model: UUID, metamodel: UUID,
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_filter=lambda node: True, prefix=""):
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# with Timer("model_to_graph"):
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od = services_od.OD(model, metamodel, state)
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scd = SCD(model, state)
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scd_mm = SCD(metamodel, state)
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bottom = Bottom(state)
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graph = Graph()
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mvs_edges = []
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modelrefs = {}
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# constraints = {}
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names = {}
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def to_vtx(el, name):
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# print("name:", name)
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if bottom.is_edge(el):
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# if filter_constraint:
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# try:
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# supposed_obj = bottom.read_edge_source(el)
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# slot_node = od.get_slot(supposed_obj, "constraint")
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# if el == slot_node:
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# # `el` is the constraint-slot
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# constraints[supposed_obj] = el
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# return
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# except:
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# pass
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mvs_edges.append(el)
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edge = MVSEdge(el, name)
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names[name] = edge
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return edge
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# If the value of the el is a ModelRef (only way to detect this is to match a regex - not very clean), then extract it. We'll create a link to the referred model later.
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value = bottom.read_value(el)
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if isinstance(value, str):
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if UUID_REGEX.match(value) != None:
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# side-effect
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modelrefs[el] = (UUID(value), name)
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return MVSNode(IS_MODELREF, el, name)
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node = MVSNode(value, el, name)
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names[name] = node
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return node
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# Objects and Links become vertices
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uuid_to_vtx = { node: to_vtx(node, prefix+key) for key in bottom.read_keys(model) for node in bottom.read_outgoing_elements(model, key) if _filter(node) }
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graph.vtxs = [ vtx for vtx in uuid_to_vtx.values() ]
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# For every Link, two edges are created (for src and tgt)
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for mvs_edge in mvs_edges:
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mvs_src = bottom.read_edge_source(mvs_edge)
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if mvs_src in uuid_to_vtx:
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graph.edges.append(Edge(
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src=uuid_to_vtx[mvs_src],
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tgt=uuid_to_vtx[mvs_edge],
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label="outgoing"))
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mvs_tgt = bottom.read_edge_target(mvs_edge)
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if mvs_tgt in uuid_to_vtx:
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graph.edges.append(Edge(
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src=uuid_to_vtx[mvs_edge],
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tgt=uuid_to_vtx[mvs_tgt],
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label="tgt"))
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for node, (ref_m, name) in modelrefs.items():
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vtx = uuid_to_vtx[node]
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# Get MM of ref'ed model
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ref_mm, = bottom.read_outgoing_elements(node, "Morphism")
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# print("modelref type node:", type_node)
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# Recursively convert ref'ed model to graph
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# ref_graph = model_to_graph(state, ref_m, ref_mm, prefix=name+'/')
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vtx.modelref = (ref_m, ref_mm)
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# We no longer flatten:
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# # Flatten and create link to ref'ed model
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# graph.vtxs += ref_model.vtxs
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# graph.edges += ref_model.edges
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# graph.edges.append(Edge(
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# src=uuid_to_vtx[node],
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# tgt=ref_model.vtxs[0], # which node to link to?? dirty
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# label="modelref"))
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def add_types(node):
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vtx = uuid_to_vtx[node]
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type_node, = bottom.read_outgoing_elements(node, "Morphism")
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# Put the type straight into the Vertex-object
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# The benefit is that our Vertex-matching callback can then be coded cleverly, look at the types first, resulting in better performance
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vtx.typ = type_node
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# The old approach (creating special vertices containing the types), commented out:
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# print('node', node, 'has type', type_node)
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# We create a Vertex storing the type
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# type_vertex = Vertex(value=IS_TYPE(type_node))
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# graph.vtxs.append(type_vertex)
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# type_edge = Edge(
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# src=uuid_to_vtx[node],
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# tgt=type_vertex,
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# label="type")
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# # print(type_edge)
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# graph.edges.append(type_edge)
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# Add typing information for:
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# - classes
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# - attributes
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# - associations
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for class_name, class_node in scd_mm.get_classes().items():
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objects = scd.get_typed_by(class_node)
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# print("typed by:", class_name, objects)
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for obj_name, obj_node in objects.items():
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if _filter(obj_node):
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add_types(obj_node)
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for attr_name, attr_node in scd_mm.get_attributes(class_name).items():
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attrs = scd.get_typed_by(attr_node)
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for slot_name, slot_node in attrs.items():
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if _filter(slot_node):
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add_types(slot_node)
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for assoc_name, assoc_node in scd_mm.get_associations().items():
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objects = scd.get_typed_by(assoc_node)
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# print("typed by:", assoc_name, objects)
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for link_name, link_node in objects.items():
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if _filter(link_node):
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add_types(link_node)
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return names, graph
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# This function returns a Generator of matches.
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# The idea is that the user can iterate over the match set, lazily generating it: if only interested in the first match, the entire match set doesn't have to be generated.
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def match_od(state, host_m, host_mm, pattern_m, pattern_mm, pivot={}):
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bottom = Bottom(state)
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# compute subtype relations and such:
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cdapi = CDAPI(state, host_mm)
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odapi = ODAPI(state, host_m, host_mm)
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pattern_odapi = ODAPI(state, pattern_m, pattern_mm)
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pattern_mm_odapi = ODAPI(state, pattern_mm, cdapi.mm)
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# Function object for pattern matching. Decides whether to match host and guest vertices, where guest is a RAMified instance (e.g., the attributes are all strings with Python expressions), and the host is an instance (=object diagram) of the original model (=class diagram)
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class RAMCompare:
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def __init__(self, bottom, host_od):
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self.bottom = bottom
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self.host_od = host_od
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type_model_id = bottom.state.read_dict(bottom.state.read_root(), "SCD")
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self.scd_model = UUID(bottom.state.read_value(type_model_id))
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# constraints need to be checked at the very end, after a complete match is established, because constraint code may refer to matched elements by their name
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self.conditions_to_check = {}
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def match_types(self, g_vtx_type, h_vtx_type):
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# types only match with their supertypes
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# we assume that 'RAMifies'-traceability links have been created between guest and host types
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try:
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g_vtx_unramified_type = ramify.get_original_type(self.bottom, g_vtx_type)
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except:
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return False
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try:
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host_type_name = cdapi.type_model_names[h_vtx_type]
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guest_type_name_unramified = cdapi.type_model_names[g_vtx_unramified_type]
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except KeyError:
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return False
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return cdapi.is_subtype(
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super_type_name=guest_type_name_unramified,
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sub_type_name=host_type_name)
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# Memoizing the result of comparison gives a huge performance boost!
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# Especially `is_subtype_of` is very slow, and will be performed many times over on the same pair of nodes during the matching process.
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# Assuming the model is not altered *during* matching, this is safe.
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@functools.cache
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def __call__(self, g_vtx, h_vtx):
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# First check if the types match (if we have type-information)
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if hasattr(g_vtx, 'typ'):
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if not hasattr(h_vtx, 'typ'):
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# if guest has a type, host must have a type
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return False
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return self.match_types(g_vtx.typ, h_vtx.typ)
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if hasattr(g_vtx, 'modelref'):
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if not hasattr(h_vtx, 'modelref'):
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return False
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python_code = services_od.read_primitive_value(self.bottom, g_vtx.node_id, pattern_mm)[0]
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self.conditions_to_check[g_vtx.name] = python_code
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# self.conditions_to_check.append((python_code, h_vtx.name, g_vtx.name))
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return True # do be determined later, if it's actually a match
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if g_vtx.value == None:
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return h_vtx.value == None
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# mvs-edges (which are converted to vertices) only match with mvs-edges
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if g_vtx.value == IS_EDGE:
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return h_vtx.value == IS_EDGE
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if h_vtx.value == IS_EDGE:
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return False
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if g_vtx.value == IS_MODELREF:
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return h_vtx.value == IS_MODELREF
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if h_vtx.value == IS_MODELREF:
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return False
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return True
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# Convert to format understood by matching algorithm
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h_names, host = model_to_graph(state, host_m, host_mm)
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# Only match matchable pattern elements
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# E.g., the 'condition'-attribute that is added to every class, cannot be matched with anything
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def is_matchable(pattern_el):
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pattern_el_name = pattern_odapi.get_name(pattern_el)
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if pattern_odapi.get_type_name(pattern_el) == "GlobalCondition":
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return False
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# Super-cheap and unreliable way of filtering out the 'condition'-attribute, added to every class:
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return not (pattern_el_name.endswith("condition")
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# as an extra safety measure, if the user defined her own 'condition' attribute, RAMification turned this into 'RAM_condition', and we can detect this
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# of course this breaks if the class name already ended with 'RAM', but let's hope that never happens
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# also, we are assuming the default "RAM_" prefix is used, but the user can change this...
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and not pattern_el_name.endswith("RAM_condition"))
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g_names, guest = model_to_graph(state, pattern_m, pattern_mm,
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_filter=is_matchable)
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graph_pivot = {
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g_names[guest_name] : h_names[host_name]
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for guest_name, host_name in pivot.items()
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if guest_name in g_names
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}
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obj_conditions = []
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for class_name, class_node in pattern_mm_odapi.get_all_instances("Class"):
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for obj_name, obj_node in pattern_odapi.get_all_instances(class_name):
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python_code = pattern_odapi.get_slot_value_default(obj_node, "condition", 'True')
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if class_name == "GlobalCondition":
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obj_conditions.append((python_code, None))
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else:
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obj_conditions.append((python_code, obj_name))
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def check_conditions(name_mapping):
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def check(python_code: str, loc):
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return exec_then_eval(python_code,
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_globals={
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**bind_api_readonly(odapi),
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'matched': lambda name: bottom.read_outgoing_elements(host_m, name_mapping[name])[0],
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},
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_locals=loc)
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# Attribute conditions
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for pattern_name, host_name in name_mapping.items():
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try:
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python_code = compare.conditions_to_check[pattern_name]
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except KeyError:
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continue
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host_node = odapi.get(host_name)
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if not check(python_code, {'this': host_node}):
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return False
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for python_code, pattern_el_name in obj_conditions:
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if pattern_el_name == None:
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# GlobalCondition
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if not check(python_code, {}):
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return False
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else:
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# object-lvl condition
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host_el_name = name_mapping[pattern_el_name]
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host_node = odapi.get(host_el_name)
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if not check(python_code, {'this': host_node}):
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return False
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return True
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compare = RAMCompare(bottom, services_od.OD(host_mm, host_m, state))
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matcher = MatcherVF2(host, guest, compare)
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for m in matcher.match(graph_pivot):
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# Convert mapping
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name_mapping = {}
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for guest_vtx, host_vtx in m.mapping_vtxs.items():
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if isinstance(guest_vtx, NamedNode) and isinstance(host_vtx, NamedNode):
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name_mapping[guest_vtx.name] = host_vtx.name
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if not check_conditions(name_mapping):
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continue # not a match after all...
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yield name_mapping
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