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Class diagram can be rendered as object diagram textual syntax, and parsed back, without information loss

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This commit is contained in:
Joeri Exelmans 2024-10-03 17:01:13 +02:00
parent f45872d3f7
commit 175edb64d9
14 changed files with 505 additions and 249 deletions
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85
pattern_matching/mvs_adapter.py
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@ -28,12 +28,12 @@ class _is_modelref:
return "REF"
IS_MODELREF = _is_modelref()
class IS_TYPE:
def __init__(self, type):
# mvs-node of the type
self.type = type
def __repr__(self):
return f"TYPE({str(self.type)[-4:]})"
# class IS_TYPE:
# def __init__(self, type):
# # mvs-node of the type
# self.type = type
# def __repr__(self):
# return f"TYPE({str(self.type)[-4:]})"
class NamedNode(Vertex):
def __init__(self, value, name):
@ -74,7 +74,7 @@ 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-
# Converts an object diagram in MVS state to the pattern matcher graph type
# ModelRefs are flattened
def model_to_graph(state: State, model: UUID, metamodel: UUID, prefix=""):
with Timer("model_to_graph"):
# with Timer("model_to_graph"):
od = OD(model, metamodel, state)
scd = SCD(model, state)
scd_mm = SCD(metamodel, state)
@ -107,7 +107,7 @@ def model_to_graph(state: State, model: UUID, metamodel: UUID, prefix=""):
if isinstance(value, str):
if UUID_REGEX.match(value) != None:
# side-effect
modelrefs[el] = (UUID(value),name)
modelrefs[el] = (UUID(value), name)
return MVSNode(IS_MODELREF, el, name)
return MVSNode(value, el, name)
@ -131,29 +131,37 @@ def model_to_graph(state: State, model: UUID, metamodel: UUID, prefix=""):
label="tgt"))
for node, (ref, name) in modelrefs.items():
for node, (ref_m, name) in modelrefs.items():
vtx = uuid_to_vtx[node]
# Get MM of ref'ed model
type_node, = bottom.read_outgoing_elements(node, "Morphism")
print("modelref type node:", type_node)
ref_mm, = bottom.read_outgoing_elements(node, "Morphism")
# print("modelref type node:", type_node)
# Recursively convert ref'ed model to graph
ref_model = model_to_graph(state, ref, type_node, prefix=name+'/')
# ref_graph = model_to_graph(state, ref_m, ref_mm, prefix=name+'/')
# Flatten and create link to ref'ed model
graph.vtxs += ref_model.vtxs
graph.edges += ref_model.edges
graph.edges.append(Edge(
src=uuid_to_vtx[node],
tgt=ref_model.vtxs[0], # which node to link to?? dirty
label="modelref"))
vtx.modelref = (ref_m, ref_mm)
# We no longer flatten:
# # Flatten and create link to ref'ed model
# graph.vtxs += ref_model.vtxs
# graph.edges += ref_model.edges
# graph.edges.append(Edge(
# src=uuid_to_vtx[node],
# tgt=ref_model.vtxs[0], # which node to link to?? dirty
# label="modelref"))
def add_types(node):
vtx = uuid_to_vtx[node]
type_node, = bottom.read_outgoing_elements(node, "Morphism")
# Put the type straigt into the Vertex-object
uuid_to_vtx[node].typ = type_node
# Put the type straight into the Vertex-object
# The benefit is that our Vertex-matching callback can then be coded cleverly, look at the types first, resulting in better performance
vtx.typ = type_node
# We used to put the types in separate nodes, but we no longer do this:
# The old approach (creating special vertices containing the types), commented out:
# print('node', node, 'has type', type_node)
# We create a Vertex storing the type
@ -190,6 +198,7 @@ def model_to_graph(state: State, model: UUID, metamodel: UUID, prefix=""):
def match_od(state, host_m, host_mm, pattern_m, pattern_mm):
# 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)
class RAMCompare:
def __init__(self, bottom, host_od):
@ -239,6 +248,20 @@ def match_od(state, host_m, host_mm, pattern_m, pattern_mm):
return False
return self.match_types(g_vtx.typ, h_vtx.typ)
if hasattr(g_vtx, 'modelref'):
if not hasattr(h_vtx, 'modelref'):
return False
g_ref_m, g_ref_mm = g_vtx.modelref
h_ref_m, h_ref_mm = h_vtx.modelref
nested_matches = [m for m in match_od(state, h_ref_m, h_ref_mm, g_ref_m, g_ref_mm)]
# print('nested_matches:', nested_matches)
if len(nested_matches) == 0:
return False
elif len(nested_matches) == 1:
return True
else:
raise Exception("We have a problem: there is more than 1 match in the nested models.")
# Then, match by value
if g_vtx.value == None:
@ -257,20 +280,20 @@ def match_od(state, host_m, host_mm, pattern_m, pattern_mm):
if h_vtx.value == IS_MODELREF:
return False
# print(g_vtx.value, h_vtx.value)
def get_slot(h_vtx, slot_name: str):
slot_node = self.host_od.get_slot(h_vtx.node_id, slot_name)
return slot_node
# # print(g_vtx.value, h_vtx.value)
# def get_slot(h_vtx, slot_name: str):
# slot_node = self.host_od.get_slot(h_vtx.node_id, slot_name)
# return slot_node
def read_int(slot: UUID):
i = Integer(slot, self.bottom.state)
return i.read()
# def read_int(slot: UUID):
# i = Integer(slot, self.bottom.state)
# return i.read()
try:
return eval(g_vtx.value, {}, {
'v': h_vtx.value,
'get_slot': functools.partial(get_slot, h_vtx),
'read_int': read_int,
# 'get_slot': functools.partial(get_slot, h_vtx),
# 'read_int': read_int,
})
except Exception as e:
return False