modelverse-thomas/integration/code/pn_interface.alc

include "primitives.alh"
include "constructors.alh"
include "object_operations.alh"
include "library.alh"
include "conformance_scd.alh"
include "io.alh"
include "metamodels.alh"
include "modelling.alh"

//helper function that returns the type of an attribute in string format
Element function find_attribute_type_s(model : Element, elem : String, name : String):
	Element mm_elem
	Element direct_type
	direct_type = dict_read_node(model["type_mapping"], model["model"][elem])
	mm_elem = find_attribute_definer(model["metamodel"], direct_type, name)
	if (element_eq(mm_elem, read_root())):
		// Couldn't find element, so is not allowed!
		log("Undefined attribute!")
		return read_root()
	else:
		return getName(model["metamodel"], read_edge_dst( dict_read_edge(mm_elem, name)))

//given a model and a node, it will return the name of it's type in the metamodel
String function getTypeName(model:Element, node:Element):
	return getName(model["metamodel"], dict_read_node(model["type_mapping"], node))

//given a model and a node it will return the name of it's type in the metametamodel
String function getMetaTypeName(model:Element, node:Element):
	return getTypeName(model["metamodel"], dict_read_node(model["type_mapping"], node))

Boolean function isAttributeRelated(model:Element, node:Element):
	if(getMetaTypeName(model, node) == "Type"):
		return True
	elif(getMetaTypeName(model, node) == "l5"):
		if(getMetaTypeName(model, read_edge_dst(node)) == "Type" ):
			return True
	return False


//function to print information about a node (attributes, type and name)
Element function printNode(tf_pn_model: Element, tf_pn_node: Element):
	
	//print header with name, metatype
	
	//print node_name
	String tf_pn_node_name
	tf_pn_node_name = getName(tf_pn_model, tf_pn_node)
	log(string_join("--name: ",tf_pn_node_name) )
	//print node type
	log(string_join("type: ", getTypeName(tf_pn_model, tf_pn_node) ))
	//print node metatype
	String metatype
	metatype = getMetaTypeName(tf_pn_model, tf_pn_node)
	log(string_join("metatype: ",  metatype))
	if (isAttributeRelated(tf_pn_model, tf_pn_node)):
		log(">attribute related")
	
	//print attributes
	
	log("attributes:")
	//get the list of attributes
	Element tf_pn_attributelist
	tf_pn_attributelist = dict_keys(getAttributeList(tf_pn_model, tf_pn_node_name))
	String tf_pn_attributename
	String tf_pn_attributetype
	while (integer_lt(0, read_nr_out(tf_pn_attributelist))):
		//print attribute name and its value
		tf_pn_attributename = set_pop(tf_pn_attributelist)
		tf_pn_attributetype = find_attribute_type_s(tf_pn_model, tf_pn_node_name, tf_pn_attributename)
		if( tf_pn_attributetype == "String"):
			log(string_join(string_join(string_join(" ",tf_pn_attributename), " : "),read_attribute(tf_pn_model, tf_pn_node_name, tf_pn_attributename)))
		elif (tf_pn_attributetype == "Integer"):
			log(string_join(string_join(string_join(" ",tf_pn_attributename), " : "),cast_i2s(read_attribute(tf_pn_model, tf_pn_node_name, tf_pn_attributename))))
		else:
			log(string_join(tf_pn_attributename, " : "))
			log(read_attribute(tf_pn_model, tf_pn_node_name, tf_pn_attributename))
	return 0
	
Element pivots

Boolean function node_match(transf_model : Element, transf_elem : Element, host_model : Element, host_elem : Element):

	//type "equality"
	String type1
	type1 = getTypeName(transf_model, transf_elem)
	String type2
	type2 = string_join(getTypeName(host_model, host_elem), "_LHS")
	
	if (type1 != type2):
		return False
	//semantic check
	Element func
	func = transf_model["semantic_checks"][read_attribute(transf_model, getName(transf_model,transf_elem), "function")]
	//pivot check
	String pivotInName
	pivotInName = read_attribute(transf_model, getName(transf_model, transf_elem), "pivotIn")
	if(func(host_model, host_elem)):
		if( pivotInName == ""):
			return True
		else:
			if(set_in(pivots[pivotInName], getName(host_model,host_elem))):
				return True
			else:
				return False
	return False

Integer function are_connected(model: Element, el1:Element, el2:Element):
	if ( bool_not(is_edge(el1)) ):
		if(bool_not(is_edge(el2))):
			return 0 //not connected, no edge inbetween
		elif(is_edge(el2)):
			if( element_eq(read_edge_src(el2) , el1)):
				return 1 //outgoing
			elif( element_eq(read_edge_dst(el2) , el1)):
				return 2 //incoming
			else:
				return 0 //the edge is not connected
	else:
		if( element_eq(read_edge_src(el1) , el2)):
			return 2 //incoming
		elif( element_eq(read_edge_dst(el1) , el2)):
			return 1 //outgoing
		else:
			return 0 //edge is not connected
	return 0 // if the function doesn't support it, assume disconnected

Element function read_outgoing_assoc(model:Element, el1:Element, allowed:Element):
	Element result
	result = create_node()
	if(is_edge(el1)):
		set_add(result, read_edge_dst(el1))
		return result
	else:
		Integer length
		length = read_nr_out(el1)
		log(cast_i2s(length))
		Integer counter
		counter = 0
		Element edge
		while (counter < length):
			edge = read_out(el1, counter)
			//check if edge is part of the model
			if (set_in_node(allowed, edge)): 
				//check if it is not attribute related
				 if(bool_not(isAttributeRelated(model, edge))):
					set_add(result, edge)
			counter = counter + 1
		return result
	return create_node()

Element function read_incoming_assoc(model:Element, el1:Element, allowed:Element):
	Element result
	result = create_node()
	if(is_edge(el1)):
		set_add(result, read_edge_src(el1))
		return result
	else:
		//get possible types of associations		
		Integer length
		length = read_nr_in(model["typemapping"][el1])
		Integer counter
		counter = 0
		
		Element edge
		while (counter < length):
			edge = read_in(el1, counter)
			//check if edge is part of the model
			if (set_in_node(allowed, edge)): 
				//check if it is not attribute related
				 if(bool_not(isAttributeRelated(model, edge))):
					set_add(result, edge)
			counter = counter + 1
		return result
	return create_node()

Element function new_SearchState(OldSearchState : Element, TransfModel : Element, NewTransfElem: Element, HostModel : Element, NewHostElem : Element):
	//some variables
	Element nss // new searchstate
	nss = create_node()
	Integer index // used for list looping
	Element elem // reusable loop variable
	Element ol //old list = old pattern out
	Element nl //new list = new N pattern out
	Element allowedP
	allowedP = OldSearchState["V_pattern"]
	Element allowedH
	allowedH = OldSearchState["V_host"]
	
	//new MATCHES
	dict_add(nss, "matches", OldSearchState["matches"]) //hopefully works as a reference
	
	//new CURMATCH
	Element OldCurMatchKeys // for looping trough old curmatch (for copying)
	OldCurMatchKeys = dict_keys(OldSearchState["curmatch"])
	Element new_curmatch //the new curmatch
	new_curmatch = create_node()
	Element new_curmatch_pattern //keep a set of all matches pattern nodes
	new_curmatch_pattern = create_node() 
	Element new_curmatch_host //keep a set of all matched host nodes
	new_curmatch_host = create_node()
	while (integer_lt(0, list_len(OldCurMatchKeys))): //copying the old curmatch
		index = set_pop(OldCurMatchKeys)
		dict_add(new_curmatch, index, OldSearchState["curmatch"][index]) 
		set_add(new_curmatch_pattern, OldSearchState["curmatch"][index]["pn"] )
		set_add(new_curmatch_host, OldSearchState["curmatch"][index]["hn"] )
	Element newPair // the new pair to be added
	newPair = create_node()
	dict_add(newPair, "pn", NewTransfElem) //PatternNode
	set_add(new_curmatch_pattern, NewTransfElem)
	dict_add(newPair, "hn", NewHostElem) // HostNode
	set_add(new_curmatch_host, NewHostElem)
	Integer plabel
	plabel = read_attribute(TransfModel, getName(TransfModel, NewTransfElem), "plabel")
	dict_add(new_curmatch, plabel, newPair) //add pair to new curmatch
	dict_add(nss, "curmatch", new_curmatch) //add curmatch to state
	
	//new CURMATCH_pattern and host (constructed during the previous loop)
	dict_add(nss, "curmatch_pattern", new_curmatch_pattern)
	dict_add(nss, "curmatch_host", new_curmatch_pattern)
	
	//new N_PATTERN_OUT (copy everything but the new match elements)
	ol = OldSearchState["N_pattern_out"]
	nl = create_node()
	index = 0
	while (index < list_len(ol)): //loop trough old list
		elem = list_read(ol, index)
		index = index + 1
		if(bool_not(element_eq(elem, NewTransfElem))): 
			set_add(nl, elem)
	ol = read_outgoing_assoc(TransfModel, NewTransfElem, allowedP)
	while (read_nr_out(ol)>0):
		elem = set_pop(ol)
		if (bool_not(set_in_node(new_curmatch_pattern, elem))):
			list_append(nl,elem)
	dict_add(nss, "N_pattern_out", nl)
	
	//new N_HOST_OUT (copy everything but the new match elements)
	ol = OldSearchState["N_host_out"]
	nl = create_node()
	index = 0
	while (index < list_len(ol)): //loop trough old list
		elem = list_read(ol, index)
		index = index + 1
		if(bool_not(element_eq(elem, NewHostElem))): 
			list_append(nl, elem)
	ol = read_outgoing_assoc(HostModel, NewHostElem, allowedH)
	while (read_nr_out(ol)>0):
		elem = set_pop(ol)
		if (bool_not(set_in_node(new_curmatch_host, elem))):
			list_append(nl,elem)
	dict_add(nss, "N_host_out", nl)
	
	//new N_PATTERN_IN (copy everything but the new match elements)
	ol = OldSearchState["N_pattern_in"]
	nl = create_node()
	index = 0
	while (index < list_len(ol)): //loop trough old list
		elem = list_read(ol, index)
		index = index + 1
		if(bool_not(element_eq(elem, NewTransfElem))):
			list_append(nl, elem)
	ol = read_incoming_assoc(TransfModel, NewTransfElem, allowedP)
	while (read_nr_out(ol)>0):
		elem = set_pop(ol)
		if (bool_not(set_in_node(new_curmatch_pattern, elem))):
			list_append(nl,elem)
	dict_add(nss, "N_pattern_in", nl)
	
	//new N_HOST_IN (copy everything but the new match elements
	ol = OldSearchState["N_host_in"]
	nl = create_node()
	index = 0
	while (index < list_len(ol)): //loop trough old list
		elem = list_read(ol, index)
		index = index + 1
		if(bool_not(element_eq(elem, NewHostElem))): 
			list_append(nl, elem)
	ol = read_incoming_assoc(HostModel, NewHostElem, allowedH)
	while (read_nr_out(ol) > 0):
		elem = set_pop(ol)
		if (bool_not(set_in_node(new_curmatch_host, elem))):
			list_append(nl,elem)
	dict_add(nss, "N_host_in", nl)
	
	//new V_PATTERN
	dict_add(nss, "V_pattern", allowedP)
	
	//new V_HOST
	dict_add(nss, "V_host", allowedH)
	
	// copy pattern_size
	dict_add(nss, "pattern_size", OldSearchState["pattern_size"])
	
	return nss

Element function destruct(nss:Element):
	log("destructing")
	//destruct current_match
	Element todelete
	while (read_nr_out(nss["curmatch"])>0):
		todelete = set_pop(nss["curmatch"])
		delete_element(todelete)
	
	delete_element(nss["curmatch_pattern"])
	delete_element(nss["curmatch_host"])
	delete_element(nss["N_pattern_out"])
	delete_element(nss["N_pattern_in"])
	delete_element(nss["N_host_in"])
	delete_element(nss["N_host_out"])
	
	
	
	delete_element(nss)
	log("end destructing")
	
	return 0

Element function transf_vf2(SearchState: Element, transf_m : Element, host_m : Element):
	log("transf_vf2")
	// get the current match from the search-state
	Element current_match
	current_match = dict_read(SearchState,"curmatch")

	//if match is complete: add to matches and return
	Integer pattern_size
	pattern_size = SearchState["pattern_size"]
	if pattern_size == list_len(current_match):
		Element OldCurMatchKeys // for looping trough old curmatch (for copying)
		OldCurMatchKeys = dict_keys(SearchState["curmatch"])
		Element new_curmatch //the new curmatch
		new_curmatch = create_node()
		Element deepcopy
		Integer index
		
		while (integer_lt(0, list_len(OldCurMatchKeys))): //copying the old curmatch
			index = set_pop(OldCurMatchKeys)
			deepcopy = create_node()
			dict_add(deepcopy, "pn", SearchState["curmatch"][index]["pn"])
			dict_add(deepcopy, "hn", SearchState["curmatch"][index]["hn"])
			dict_add(new_curmatch, index, deepcopy) 
		list_append(SearchState["matches"], new_curmatch)
		//log("MATCHED")
		return SearchState

	//feasability checks
	if (list_len(SearchState["N_pattern_out"]) > list_len(SearchState["N_host_out"])):
		return SearchState
	if (list_len(SearchState["N_pattern_in"]) > list_len(SearchState["N_host_in"])):
		return SearchState

	//initialise some variables
	Integer index2 //an index used for looping trough host_list
	Element host_list // the set with host nodes to be considered
	Element pattern_node // node to be tested from the patterns side
	Element host_node //node to be tested from the hosts side
	Element nss //variable for storing next searchstate
	
	//search candidate
	if ( list_len(SearchState["N_pattern_out"]) > 0 ):
		//check for candidates in (N_pattern_out, N_host_out)
		host_list = SearchState["N_host_out"]
		pattern_node = list_read(SearchState["N_pattern_out"], 0)
		index2 = 0
		while (index2 < list_len(host_list)):
			host_node = list_read(host_list, index2)
			index2= index2 + 1
			if(node_match(transf_m, pattern_node, host_m, host_node)):
				nss = new_SearchState(SearchState, transf_m, pattern_node, host_m, host_node)
				nss = transf_vf2(nss, transf_m, host_m)
				dict_delete(SearchState, "matches")
				dict_add(SearchState, "matches", nss["matches"])
				destruct(nss)
	elif ( list_len(SearchState["N_pattern_in"]) > 0 ):
		//check for candidates in (N_pattern_in, N_host_in)
		host_list = SearchState["N_host_in"]
		pattern_node = list_read(SearchState["N_pattern_in"], 0)
		index2 = 0
		while (index2 < list_len(host_list)):
			host_node = list_read(host_list, index2)
			index2= index2 + 1
			if(node_match(transf_m, pattern_node, host_m, host_node)):
				nss = new_SearchState(SearchState, transf_m, pattern_node, host_m, host_node)
				nss = transf_vf2(nss, transf_m, host_m)
				dict_delete(SearchState, "matches")
				dict_add(SearchState, "matches", nss["matches"])
				destruct(nss)
	elif ( list_len(SearchState["V_pattern"]) > 0 ):
		//check for candidates in (V_pattern, V_host))
		host_list = SearchState["V_host"]
		pattern_node = set_pop(SearchState["V_pattern"])
		//list_read(SearchState["V_pattern"], 0)
		index2 = 0
		while (index2 < list_len(host_list)):
			host_node = list_read(host_list, index2)
			index2= index2 + 1
			if(node_match(transf_m, pattern_node, host_m, host_node)):
				nss = new_SearchState(SearchState, transf_m, pattern_node, host_m, host_node)
				nss = transf_vf2(nss, transf_m, host_m)
				dict_delete(SearchState, "matches")
				dict_add(SearchState, "matches", nss["matches"])
				destruct(nss)
	else:
		log("no candidate found")
	log(" end transf_vf2")
	return SearchState


// given a pattern, this function will return all matches in a model
Element function tf_matchAll(tf_ma_transformation: Element, tf_ma_pattern : Element, tf_ma_model: Element ):
	
	//timing info
	Integer starttime
	starttime = current_time()
	
	//find all nodes in the pattern
	
	Element tf_ma_metamodelTransf //transformation metamodel
	tf_ma_metamodelTransf = tf_ma_transformation["metamodel"]["model"]
	Element tf_ma_pattern_edges //all pre2ram edges
	tf_ma_pattern_edges = allOutgoingAssociationInstances(tf_ma_transformation, tf_ma_pattern, dict_read(tf_ma_metamodelTransf, "pre2ram"))
	Element tf_ma_ramified_nodes //dict in the form plabel->node
	tf_ma_ramified_nodes = create_node()
	//get the destinations
	while (integer_lt(0, list_len(tf_ma_pattern_edges))):
		list_append(tf_ma_ramified_nodes, read_edge_dst(set_pop(tf_ma_pattern_edges)))
	
	//find all nodes in the graph that are not related to attributes: ie a userdefined type 
	
	Element hostmodel_keys //keys of all elements in the model
	hostmodel_keys = dict_keys(tf_ma_model["model"])
	Element host_nodes //all nodes in the host graph
	host_nodes = create_node()
	Element host_model_key
	while (integer_lt(0, read_nr_out(hostmodel_keys))):
		host_model_key = set_pop(hostmodel_keys)
		if bool_not(isAttributeRelated(tf_ma_model, tf_ma_model["model"][host_model_key])):
			list_append(host_nodes, tf_ma_model["model"][host_model_key])
	
	// initialise the start-state
	
	//initialise an start state
	Element SearchState 
	SearchState = create_node()
	dict_add(SearchState, "matches", create_node())
	dict_add(SearchState, "curmatch", create_node())
	dict_add(SearchState, "curmatch_pattern", create_node())
	dict_add(SearchState, "curmatch_host", create_node())
	dict_add(SearchState, "N_pattern_out", create_node())
	dict_add(SearchState, "N_host_out", create_node())
	dict_add(SearchState, "N_pattern_in", create_node())
	dict_add(SearchState, "N_host_in", create_node())
	dict_add(SearchState, "V_pattern", tf_ma_ramified_nodes)
	dict_add(SearchState, "V_host", host_nodes)
	dict_add(SearchState, "pattern_size", list_len(tf_ma_ramified_nodes))
	
	//call vf2 for the matching
	
	SearchState = transf_vf2(SearchState, tf_ma_transformation, tf_ma_model)
	
	//timing info
	log(string_join("matching took ", cast_i2s( current_time() - starttime)))
	
	delete_element(tf_ma_ramified_nodes)
	delete_element(host_nodes)
	
	Element matches 
	matches = SearchState["matches"]
	destruct(SearchState)
	return matches

Element function mergeLHSNAC(tf_er_lhs : Element, tf_er_nac: Element):
	
	//timing info
	Integer starttime
	starttime = current_time()
	
	Element final_matches
	final_matches = create_node()
	Element lhs_match
	Element nac_match
	Element lhs_match_keys
	Element lhs_match_key
	Boolean allow_match
	Integer nac_index
	Boolean plabel_match_exists
	while(list_len(tf_er_lhs) > 0):
		lhs_match = set_pop(tf_er_lhs)
		allow_match=True
		nac_index = 0
		while(bool_and(nac_index < read_nr_out(tf_er_nac), allow_match)):
			nac_match = list_read(tf_er_nac, nac_index)
			nac_index = nac_index + 1 
			lhs_match_keys = dict_keys(lhs_match)
			plabel_match_exists = False //we assume that the nac prohibits all
			while (bool_and(read_nr_out(lhs_match_keys)>0, allow_match)):
				lhs_match_key = set_pop(lhs_match_keys)
				if dict_in(nac_match, lhs_match_key):
					plabel_match_exists = True //the assumption is disproved, which means we can continue looking
					if (element_eq(nac_match[lhs_match_key]["hn"],lhs_match[lhs_match_key]["hn"])):
						allow_match = False
			if bool_not(plabel_match_exists): //if the assumption is not disproved, the nac prohibits all matches and no matches should remain after merge
				return create_node()
		if (allow_match):
			list_append(final_matches, lhs_match)
	
	//timing info
	log(string_join("merging LHS with NAC took ", cast_i2s( current_time() - starttime)))
	
	return final_matches

Element function extractPivots(final_matches : Element, tf_er_transformation : Element, tf_er_model : Element):
	Integer match_index
	match_index = 0
	Element lhs_match
	Element lhs_match_keys
	Element lhs_match_key
	String pivotOutName
	Element pivotSet
	pivotSet = create_node()
	while (match_index < read_nr_out(final_matches)):
		lhs_match = list_read(final_matches, match_index)
		match_index = match_index +1
		lhs_match_keys = dict_keys(lhs_match) 
		while (read_nr_out(lhs_match_keys) > 0):
			lhs_match_key = set_pop(lhs_match_keys)
			pivotOutName = read_attribute(tf_er_transformation, getName(tf_er_transformation, lhs_match[lhs_match_key]["pn"]), "pivotOut")
			if(pivotOutName != ""):
				if ( bool_not(set_in(pivotSet, pivotOutName))):
					set_add(pivotSet, pivotOutName)
					if (dict_in(pivots, pivotOutName)):
						dict_delete(pivots, pivotOutName)
					dict_add(pivots, pivotOutName, create_node())
				set_add(pivots[pivotOutName], getName(tf_er_model,lhs_match[lhs_match_key]["hn"]))
	return 0

Element function cast_set2list(sset:Element):
	Element rreturnlist //this name must be made weird, otherwise compilation error
	rreturnlist = create_node()
	while (read_nr_out(sset) > 0):
		list_append(rreturnlist, set_pop(sset))
	return rreturnlist
	
Element function rhs_rewrite(final_matches : Element,plabel_to_rhsnode:Element, tf_er_transformation:Element, tf_er_model:Element ):
	
	//timing info
	Integer starttime
	starttime = current_time()
	
	//loop over all matches
	Element rhs_plabels
	Element rhs_plabel
	Element rhs_action
	Element rhs_to_host //keep this mapping, so that we know wich nodes in the hostmodel new edges connect
	Element lhs_match_keys
	Element lhs_match_key
	Element host_match_node
	Element lhs_match
	Integer source_plabel
	Integer destination_plabel
	Integer index
	while (read_nr_out(final_matches)>0):
		lhs_match = set_pop(final_matches)
		//get set of plables
		rhs_plabels = dict_keys(plabel_to_rhsnode)
		//loop trough every node in the match
		lhs_match_keys = dict_keys(lhs_match) 
		rhs_to_host = create_node()
		while (read_nr_out(lhs_match_keys) > 0):
			lhs_match_key = set_pop(lhs_match_keys) 
			host_match_node = lhs_match[lhs_match_key]["hn"] 
			if set_in(rhs_plabels, lhs_match_key): //if node matches plabel with rhs
				//run function
				rhs_action = read_attribute(tf_er_transformation,getName(tf_er_transformation, plabel_to_rhsnode[lhs_match_key]), "function")
				rhs_action = tf_er_transformation["actions"][rhs_action]
				rhs_action(tf_er_model, host_match_node)
				dict_add(rhs_to_host, lhs_match_key, host_match_node)
				//remove key from rhs_plabels
				set_remove(rhs_plabels, lhs_match_key)
			else: // if node doesn't match plabel with rhs
				model_delete_element(tf_er_model, getName(tf_er_model, host_match_node))
		//for each remaining rhs-key (it is not in the match, so should be added)
		rhs_plabels = cast_set2list(rhs_plabels)
		while (read_nr_out(rhs_plabels) > 0):
			//rhs_plabel = set_pop(rhs_plabels)
			index = 0
			while(index < read_nr_out(rhs_plabels)):
				rhs_plabel = list_read(rhs_plabels, index)
				//if it is a node, than we can simply add it
				if (bool_not(is_edge(plabel_to_rhsnode[rhs_plabel]))):
					host_match_node = instantiate_node(tf_er_model, string_substr(getTypeName(tf_er_transformation, plabel_to_rhsnode[rhs_plabel]), 0,-4), "")
					//run the function
					rhs_action = read_attribute(tf_er_transformation,getName(tf_er_transformation, plabel_to_rhsnode[rhs_plabel]), "function")
					rhs_action = tf_er_transformation["actions"][rhs_action]
					rhs_action(tf_er_model, tf_er_model["model"][host_match_node])
					list_delete(rhs_plabels,index)
				else:
					//if it is an edge, we can only add it of source and edge exist, else we have to store the edge and get it later
					source_plabel = read_attribute(tf_er_transformation, getName(tf_er_transformation, read_edge_src(plabel_to_rhsnode[rhs_plabel]) ) , "plabel")
					destination_plabel = read_attribute(tf_er_transformation, getName(tf_er_transformation, read_edge_dst(plabel_to_rhsnode[rhs_plabel]) ) , "plabel")
					if(bool_and(dict_in(rhs_to_host, source_plabel), dict_in(rhs_to_host, destination_plabel))):
						rhs_action =  instantiate_link(tf_er_model, string_substr(getTypeName(tf_er_transformation, plabel_to_rhsnode[rhs_plabel]), 0,-4), "", getName(tf_er_transformation, read_edge_src(plabel_to_rhsnode[rhs_plabel])),  getName(tf_er_transformation, read_edge_dst(plabel_to_rhsnode[rhs_plabel])))
						//run function
						rhs_action = read_attribute(tf_er_transformation,getName(tf_er_transformation, plabel_to_rhsnode[rhs_plabel]), "function")
						rhs_action = tf_er_transformation["actions"][rhs_action]
						rhs_action(tf_er_model, tf_er_model["model"][host_match_node])
						list_delete(rhs_plabels,index)
					else:
						index = index + 1
	
	//timing info
	log(string_join("rewriting took ", cast_i2s( current_time() - starttime)))

	return 0
	
Element function tf_executeFRule(tf_er_transformation: Element,tf_er_rule: Element, tf_er_model: Element):
	
	//find LHS
	Element tf_er_lhs
	tf_er_lhs = allOutgoingAssociationInstances(tf_er_transformation, tf_er_rule, dict_read(dict_read(dict_read(tf_er_transformation, "metamodel"), "model"), "lhs"))
	Integer nr_of_lhs_nodes
	nr_of_lhs_nodes = read_nr_out(tf_er_lhs)
	//match lhses
	tf_er_lhs = tf_matchAll(tf_er_transformation, read_edge_dst(set_pop(tf_er_lhs)), tf_er_model)
	Integer nr_of_lhs_matches
	nr_of_lhs_matches = list_len(tf_er_lhs)
	
	//find NACs if at least one lhs is found (otherwise not needed)
	Integer nr_of_nac_matches
	nr_of_nac_matches = 0
	Element tf_er_nac
	tf_er_nac = create_node()
	if(bool_or(nr_of_lhs_matches > 0, nr_of_lhs_nodes==0)):
		Element tf_er_nac_
		tf_er_nac_ = allOutgoingAssociationInstances(tf_er_transformation, tf_er_rule, dict_read(dict_read(dict_read(tf_er_transformation, "metamodel"), "model"), "nac"))
		Element partial_nac_matches
		while (read_nr_out(tf_er_nac_) > 0):
			partial_nac_matches = tf_matchAll(tf_er_transformation, read_edge_dst(set_pop(tf_er_nac_)), tf_er_model)
			while (read_nr_out(partial_nac_matches)>0):
				list_append(tf_er_nac, set_pop(partial_nac_matches))
		nr_of_nac_matches = list_len(tf_er_nac)
	
	if( nr_of_lhs_nodes == 0):
		if (nr_of_nac_matches == 0):
			return True
		else:
			return False
	
	//remove from lhs matches that share a node with a nac match
	Element final_matches
	final_matches = mergeLHSNAC(tf_er_lhs, tf_er_nac)
	
	if( read_nr_out(final_matches) == 0 ):
		return False
		
	
	//extract pivots from the matches
	extractPivots(final_matches, tf_er_transformation, tf_er_model)

	//rhs part
	
	//create plabel_to_rhsnode
	Element RHS
	RHS = allOutgoingAssociationInstances(tf_er_transformation, tf_er_rule, tf_er_transformation["metamodel"]["model"]["rhs"])
	if(read_nr_out(RHS) > 0):
		RHS = read_edge_dst(set_pop(RHS))
		Element metamodelTransf //transformation metamodel
		metamodelTransf = tf_er_transformation["metamodel"]["model"]
		Element post2ram // type of the post2ram edge
		post2ram = dict_read(metamodelTransf, "post2ram")
		post2ram = allOutgoingAssociationInstances(tf_er_transformation, RHS, post2ram)
		Element plabel_to_rhsnode //dict in the form plabel->node
		plabel_to_rhsnode = create_node()
		Element rhs_node
		Integer rhs_plabel
		while (integer_lt(0, list_len(post2ram))):
			rhs_node = read_edge_dst(set_pop(post2ram))
			rhs_plabel = read_attribute(tf_er_transformation, getName(tf_er_transformation, rhs_node), "plabel")
			dict_add(plabel_to_rhsnode,rhs_plabel, rhs_node)
		
		rhs_rewrite(final_matches ,plabel_to_rhsnode, tf_er_transformation, tf_er_model )
	
	//clean up matches
	Element todelete
	Element todelete2
	while (read_nr_out(tf_er_nac) >0):
		todelete2 = set_pop(tf_er_nac)
		while (read_nr_out(todelete2)>0):
			todelete = set_pop(todelete2)
			delete_element(todelete)
		
	while (read_nr_out(tf_er_lhs) >0):
		todelete2 = set_pop(tf_er_nac)
		while (read_nr_out(todelete2)>0):
			todelete = set_pop(todelete2)
			delete_element(todelete)
		
	return True

Integer function modulo(a:Integer,b:Integer):
	return a - integer_division(a,b)*b

Integer RandomNumberState
Integer function getRandomNumber(min:Integer, max:Integer):
	//returns a random integer in min and max range, never max
	if(min==max):
		return min
	if( bool_not(is_physical_int(RandomNumberState))):
		RandomNumberState = 4 // xkcd.com/221/
	RandomNumberState = modulo((8121*RandomNumberState) + 28411, 134456)
	return modulo(RandomNumberState , (max-min))+min

Element function list_select_random(list:Element):
	return list_read(list, getRandomNumber(0,read_nr_out(list)))


Element function tf_executeARule(tf_er_transformation: Element,tf_er_rule: Element, tf_er_model: Element):
	
	//find LHS
	Element tf_er_lhs
	tf_er_lhs = allOutgoingAssociationInstances(tf_er_transformation, tf_er_rule, dict_read(dict_read(dict_read(tf_er_transformation, "metamodel"), "model"), "lhs"))
	Integer nr_of_lhs_nodes
	nr_of_lhs_nodes = read_nr_out(tf_er_lhs)
	tf_er_lhs = tf_matchAll(tf_er_transformation, read_edge_dst(set_pop(tf_er_lhs)), tf_er_model)
	Integer nr_of_lhs_matches
	nr_of_lhs_matches = list_len(tf_er_lhs)
	
	//find NACs if at least one lhs is found (otherwise not needed)
	Integer nr_of_nac_matches
	nr_of_nac_matches = 0
	Element tf_er_nac
	tf_er_nac = create_node()
	if(bool_or(nr_of_lhs_matches > 0, nr_of_lhs_nodes==0)):
		Element tf_er_nac_
		tf_er_nac_ = allOutgoingAssociationInstances(tf_er_transformation, tf_er_rule, dict_read(dict_read(dict_read(tf_er_transformation, "metamodel"), "model"), "nac"))
		Element partial_nac_matches
		while (read_nr_out(tf_er_nac_) > 0):
			partial_nac_matches = tf_matchAll(tf_er_transformation, read_edge_dst(set_pop(tf_er_nac_)), tf_er_model)
			while (read_nr_out(partial_nac_matches)>0):
				list_append(tf_er_nac, set_pop(partial_nac_matches))
		nr_of_nac_matches = list_len(tf_er_nac)
		
	if( nr_of_lhs_nodes == 0):
		if (nr_of_nac_matches == 0):
			return True
		else:
			return False
	
	//remove from lhs matches that share a node with a nac match
	Element final_matches
	final_matches = mergeLHSNAC(tf_er_lhs, tf_er_nac)
	
	if( read_nr_out(final_matches) == 0 ):
		return False
	
	//select only one match
	Element selected 
	selected = list_select_random(final_matches)
	final_matches = create_node()
	list_append(final_matches, selected)
	
	
	//extract pivots from the matches
	extractPivots(final_matches, tf_er_transformation, tf_er_model)

	//rhs part
	
	//create plabel_to_rhsnode
	Element RHS
	RHS = allOutgoingAssociationInstances(tf_er_transformation, tf_er_rule, tf_er_transformation["metamodel"]["model"]["rhs"])
	if(read_nr_out(RHS) > 0):
		RHS = read_edge_dst(set_pop(RHS))
		Element metamodelTransf //transformation metamodel
		metamodelTransf = tf_er_transformation["metamodel"]["model"]
		Element post2ram // type of the post2ram edge
		post2ram = dict_read(metamodelTransf, "post2ram")
		post2ram = allOutgoingAssociationInstances(tf_er_transformation, RHS, post2ram)
		Element plabel_to_rhsnode //dict in the form plabel->node
		plabel_to_rhsnode = create_node()
		Element rhs_node
		Integer rhs_plabel
		while (integer_lt(0, list_len(post2ram))):
			rhs_node = read_edge_dst(set_pop(post2ram))
			rhs_plabel = read_attribute(tf_er_transformation, getName(tf_er_transformation, rhs_node), "plabel")
			dict_add(plabel_to_rhsnode,rhs_plabel, rhs_node)
		
		rhs_rewrite(final_matches ,plabel_to_rhsnode, tf_er_transformation, tf_er_model )
		
	return True

Element function executeRuleBlock(transformation_model : Element,hostmodel:Element, ruleblock : Element):
	//execute the correct ruletype
	String typename
	typename = getTypeName(transformation_model, ruleblock)
	Boolean succes
	succes = False
	Element Rule
	Integer curtime
	curtime = current_time()
	if (typename == "RuleBlockRoot"):
		// do nothing
		succes = True
	elif (typename == "ARule"):
		//run Arule
		Rule = read_edge_dst(set_pop(allOutgoingAssociationInstances(transformation_model, ruleblock, transformation_model["metamodel"]["model"]["getrule"])))
		succes = tf_executeARule(transformation_model, Rule, hostmodel)
	elif (typename == "FRule"):
		//run Frule
		Rule = read_edge_dst(set_pop(allOutgoingAssociationInstances(transformation_model, ruleblock, transformation_model["metamodel"]["model"]["getrule"])))
		succes = tf_executeFRule(transformation_model, Rule, hostmodel)
	else:
		log("RuleType is not supported yet")
	
	log(string_join(string_join(string_join("= running RuleBlock: ",getName(transformation_model, Rule)), " took "),cast_i2s(current_time() - curtime)))
	
	//Return the next
	Element next
	if succes: 
		next = allOutgoingAssociationInstances(transformation_model, ruleblock, transformation_model["metamodel"]["model"]["next_onSucces"])
	else:
		next = allOutgoingAssociationInstances(transformation_model, ruleblock, transformation_model["metamodel"]["model"]["next_onFailure"])
	if (read_nr_out(next) == 0):
		next = transformation_model
	else:
		next = read_edge_dst(set_pop(next))
		
	return next

Element function tf_transform(tf_tf_transformation: Element, tf_tf_model: Element):
	//create a list of all rules	
	Element ruleblock
	ruleblock = allInstances(tf_tf_transformation, tf_tf_transformation["metamodel"]["model"]["RuleBlockRoot"])
	if(read_nr_out(ruleblock) > 0):
		ruleblock = set_pop(ruleblock)
		pivots = create_node()
		Boolean continue
		continue = True
		while (continue):
			ruleblock = executeRuleBlock(tf_tf_transformation,tf_tf_model, ruleblock)
			if(element_eq(ruleblock,tf_tf_transformation)):
				log("finished")
				continue = False
	else:
		log("root ruleblock not found")
	return 0

Element function test_simple_petrinet1(root:Element):
	//create a petrinet for testing purposes
	
	//	P1	->	T1	->	P2 	-> T2
	//	m:1	w:1		w:2	m:0	w:1
	//		l1		l2		l3
	
	//////////////////////////////////
	//initialise an empty petrinet
	Element my_model
	my_model = instantiate_model(root["PetriNets"])
	dict_add(root, "pnt_test1", my_model)
	
	// init t1
	instantiate_node(my_model, "Transition", "t1")
	// init p1
	instantiate_node(my_model, "Place", "p1")
	instantiate_attribute(my_model, "p1","tokens",1)
	// init link1
	instantiate_link(my_model, "P2T", "p1_to_t1", "p1", "t1")
	instantiate_attribute(my_model, "p1_to_t1", "weight", 1)
	//init p2
	instantiate_node(my_model, "Place", "p2")
	instantiate_attribute(my_model, "p2","tokens",0)
	//init link2
	instantiate_link(my_model, "T2P", "t1_to_p2", "t1", "p2")
	instantiate_attribute(my_model, "t1_to_p2", "weight", 2)
	//init t2
	instantiate_node(my_model, "Transition", "t2")
	//init link3
	instantiate_link(my_model, "P2T", "p2_to_t2", "p2", "t2")
	instantiate_attribute(my_model, "p2_to_t2", "weight", 1)
	
	
	return my_model
	
	

Element function petrinet_transformation(root: Element):
	// Create A transformation model for petrinets (useful for testing)

	//////////////////////////////////
	//initialise an empty transformation model for petrinet
	Element my_model
	my_model = instantiate_model(root["PetriNets_MTransformation"])
	define_inheritance(my_model, "l3")
	dict_add(root, "transf_petrinet_fire", my_model)

	////////////////////////////
	// root node
	instantiate_node(my_model, "Transformation", "PetrinetFire")
	instantiate_attribute(my_model, "PetrinetFire", "name", "PetriNetFire")

	////////////////////////////
	// rule: find transitions

	//rule: find_transitions
	instantiate_node(my_model, "Rule", "find_transitions")
	instantiate_attribute(my_model, "find_transitions", "name", "find_transitions")
	instantiate_attribute(my_model, "find_transitions", "precedence", 1)
	//connect to transformation
	instantiate_link(my_model, "transformation_to_rule", "", "PetrinetFire", "find_transitions")

	//precondition: lhs_precondition
	instantiate_node(my_model, "Precondition","lhs_precondition")
	//connect to rule
	instantiate_link(my_model, "lhs", "", "find_transitions", "lhs_precondition")

	//Transition_LHS: lhs_find_all
	instantiate_node(my_model, "Transition_LHS","lhs_find_all")
	instantiate_attribute(my_model, "lhs_find_all", "plabel", 1)
	instantiate_attribute(my_model, "lhs_find_all", "function", "always_true")
	instantiate_attribute(my_model, "lhs_find_all", "pivotOut", "enabled")
	instantiate_attribute(my_model, "lhs_find_all", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "lhs_precondition", "lhs_find_all")

	//precondition: nac_precondition
	instantiate_node(my_model, "Precondition","nac_precondition")
	//connect to rule
	instantiate_link(my_model, "nac", "", "find_transitions", "nac_precondition")

	//Transition_LHS: nac_transition_place_before
	instantiate_node(my_model, "Transition_LHS","nac_transition_before")
	instantiate_attribute(my_model, "nac_transition_before", "plabel", 1)
	instantiate_attribute(my_model, "nac_transition_before", "function", "always_true")
	instantiate_attribute(my_model, "nac_transition_before", "pivotOut", "")
	instantiate_attribute(my_model, "nac_transition_before", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "nac_precondition", "nac_transition_before")

	//Place_LHS: nac_place_before
	instantiate_node(my_model, "Place_LHS","nac_place_before")
	instantiate_attribute(my_model, "nac_place_before", "plabel", 2)
	instantiate_attribute(my_model, "nac_place_before", "function", "always_true")
	instantiate_attribute(my_model, "nac_place_before", "pivotOut", "")
	instantiate_attribute(my_model, "nac_place_before", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "nac_precondition", "nac_place_before")

	//P2T_LHS: nac_ft_p2t
	instantiate_link(my_model, "P2T_LHS", "nac_p2tlhs_before", "nac_place_before", "nac_transition_before")
	instantiate_attribute(my_model, "nac_p2tlhs_before", "plabel", 3)
	instantiate_attribute(my_model, "nac_p2tlhs_before", "function", "is_not_consumable")
	instantiate_attribute(my_model, "nac_p2tlhs_before", "pivotOut", "")
	instantiate_attribute(my_model, "nac_p2tlhs_before", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "nac_precondition", "nac_p2tlhs_before")
	
	////////////////////////////
	// rule: consume tokens

	//rule: consume_tokens
	instantiate_node(my_model, "Rule", "consume_tokens")
	instantiate_attribute(my_model, "consume_tokens", "name", "consume_tokens")
	instantiate_attribute(my_model, "consume_tokens", "precedence", 2)
	//connect to transformation
	instantiate_link(my_model, "transformation_to_rule", "", "PetrinetFire", "consume_tokens")

	//precondition: lhs_precondition
	instantiate_node(my_model, "Precondition","ct_lhs_precondition")
	//connect to rule
	instantiate_link(my_model, "lhs", "", "consume_tokens", "ct_lhs_precondition")
	
	//Transition_LHS: ct_lhs_transition
	instantiate_node(my_model, "Transition_LHS","ct_lhs_transition")
	instantiate_attribute(my_model, "ct_lhs_transition", "plabel", 1)
	instantiate_attribute(my_model, "ct_lhs_transition", "function", "always_true")
	instantiate_attribute(my_model, "ct_lhs_transition", "pivotOut", "")
	instantiate_attribute(my_model, "ct_lhs_transition", "pivotIn", "enabled")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "ct_lhs_precondition", "ct_lhs_transition")
	
	//Place_LHS: ct_lhs_place
	instantiate_node(my_model, "Place_LHS","ct_lhs_place")
	instantiate_attribute(my_model, "ct_lhs_place", "plabel", 2)
	instantiate_attribute(my_model, "ct_lhs_place", "function", "always_true")
	instantiate_attribute(my_model, "ct_lhs_place", "pivotOut", "")
	instantiate_attribute(my_model, "ct_lhs_place", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "ct_lhs_precondition", "ct_lhs_place")
	
	//P2T_LHS: ct_lhs_link
	instantiate_link(my_model, "P2T_LHS", "ct_lhs_link", "ct_lhs_place", "ct_lhs_transition")
	instantiate_attribute(my_model, "ct_lhs_link", "plabel", 3)
	instantiate_attribute(my_model, "ct_lhs_link", "function", "always_true")
	instantiate_attribute(my_model, "ct_lhs_link", "pivotOut", "")
	instantiate_attribute(my_model, "ct_lhs_link", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "ct_lhs_precondition", "ct_lhs_link")
	
	//precondition: rhs_postcondition
	instantiate_node(my_model, "Postcondition","ct_rhs_postcondition")
	//connect to rule
	instantiate_link(my_model, "rhs", "", "consume_tokens", "ct_rhs_postcondition")
	
	//Transition_RHS: ct_rhs_transition
	instantiate_node(my_model, "Transition_RHS","ct_rhs_transition")
	instantiate_attribute(my_model, "ct_rhs_transition", "plabel", 1)
	instantiate_attribute(my_model, "ct_rhs_transition", "function", "do_nothing")
	instantiate_attribute(my_model, "ct_rhs_transition", "pivotOut", "")
	instantiate_attribute(my_model, "ct_rhs_transition", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "post2ram", "", "ct_rhs_postcondition", "ct_rhs_transition")
	
	//Place_RHS: ct_rhs_place
	instantiate_node(my_model, "Place_RHS","ct_rhs_place")
	instantiate_attribute(my_model, "ct_rhs_place", "plabel", 2)
	instantiate_attribute(my_model, "ct_rhs_place", "function", "do_nothing")
	instantiate_attribute(my_model, "ct_rhs_place", "pivotOut", "")
	instantiate_attribute(my_model, "ct_rhs_place", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "post2ram", "", "ct_rhs_postcondition", "ct_rhs_place")
	
	//P2T_RHS: ct_rhs_link
	instantiate_link(my_model, "P2T_RHS", "ct_rhs_link", "ct_rhs_place", "ct_rhs_transition")
	instantiate_attribute(my_model, "ct_rhs_link", "plabel", 3)
	instantiate_attribute(my_model, "ct_rhs_link", "function", "remove_tokens")
	instantiate_attribute(my_model, "ct_rhs_link", "pivotOut", "")
	instantiate_attribute(my_model, "ct_rhs_link", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "post2ram", "", "ct_rhs_postcondition", "ct_rhs_link")
	
	////////////////////////////
	// rule: produce tokens

	//rule: produce_tokens
	instantiate_node(my_model, "Rule", "produce_tokens")
	instantiate_attribute(my_model, "produce_tokens", "name", "produce_tokens")
	instantiate_attribute(my_model, "produce_tokens", "precedence", 3)
	//connect to transformation
	instantiate_link(my_model, "transformation_to_rule", "", "PetrinetFire", "produce_tokens")

	//precondition: lhs_precondition
	instantiate_node(my_model, "Precondition","pt_lhs_precondition")
	//connect to rule
	instantiate_link(my_model, "lhs", "", "produce_tokens", "pt_lhs_precondition")
	
	//Transition_LHS: pt_lhs_transition
	instantiate_node(my_model, "Transition_LHS","pt_lhs_transition")
	instantiate_attribute(my_model, "pt_lhs_transition", "plabel", 1)
	instantiate_attribute(my_model, "pt_lhs_transition", "function", "always_true")
	instantiate_attribute(my_model, "pt_lhs_transition", "pivotOut", "")
	instantiate_attribute(my_model, "pt_lhs_transition", "pivotIn", "enabled")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "pt_lhs_precondition", "pt_lhs_transition")
	
	//Place_LHS: pt_lhs_place
	instantiate_node(my_model, "Place_LHS","pt_lhs_place")
	instantiate_attribute(my_model, "pt_lhs_place", "plabel", 2)
	instantiate_attribute(my_model, "pt_lhs_place", "function", "always_true")
	instantiate_attribute(my_model, "pt_lhs_place", "pivotOut", "")
	instantiate_attribute(my_model, "pt_lhs_place", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "pt_lhs_precondition", "pt_lhs_place")
	
	//P2T_LHS: pt_lhs_link
	instantiate_link(my_model, "T2P_LHS", "pt_lhs_link", "pt_lhs_transition", "pt_lhs_place")
	instantiate_attribute(my_model, "pt_lhs_link", "plabel", 3)
	instantiate_attribute(my_model, "pt_lhs_link", "function", "always_true")
	instantiate_attribute(my_model, "pt_lhs_link", "pivotOut", "")
	instantiate_attribute(my_model, "pt_lhs_link", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "pre2ram", "", "pt_lhs_precondition", "pt_lhs_link")
	
	//precondition: rhs_postcondition
	instantiate_node(my_model, "Postcondition","pt_rhs_postcondition")
	//connect to rule
	instantiate_link(my_model, "rhs", "", "produce_tokens", "pt_rhs_postcondition")
	
	//Transition_RHS: pt_rhs_transition
	instantiate_node(my_model, "Transition_RHS","pt_rhs_transition")
	instantiate_attribute(my_model, "pt_rhs_transition", "plabel", 1)
	instantiate_attribute(my_model, "pt_rhs_transition", "function", "do_nothing")
	instantiate_attribute(my_model, "pt_rhs_transition", "pivotOut", "")
	instantiate_attribute(my_model, "pt_rhs_transition", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "post2ram", "", "pt_rhs_postcondition", "pt_rhs_transition")
	
	//Place_RHS: pt_rhs_place
	instantiate_node(my_model, "Place_RHS","pt_rhs_place")
	instantiate_attribute(my_model, "pt_rhs_place", "plabel", 2)
	instantiate_attribute(my_model, "pt_rhs_place", "function", "do_nothing")
	instantiate_attribute(my_model, "pt_rhs_place", "pivotOut", "")
	instantiate_attribute(my_model, "pt_rhs_place", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "post2ram", "", "pt_rhs_postcondition", "pt_rhs_place")
	
	//P2T_RHS: ct_rhs_link
	instantiate_link(my_model, "T2P_RHS", "pt_rhs_link", "pt_rhs_transition", "pt_rhs_place")
	instantiate_attribute(my_model, "pt_rhs_link", "plabel", 3)
	instantiate_attribute(my_model, "pt_rhs_link", "function", "add_tokens")
	instantiate_attribute(my_model, "pt_rhs_link", "pivotOut", "")
	instantiate_attribute(my_model, "pt_rhs_link", "pivotIn", "")
	//connect to rule
	instantiate_link(my_model, "post2ram", "", "pt_rhs_postcondition", "pt_rhs_link")
	
	////////////////////////////
	// rule ordering part
	
	//start node
	instantiate_node(my_model, "RuleBlockRoot", "rule_root")
	
	//ARule: enabled
	instantiate_node(my_model, "ARule", "arule1")
	//connect to startnode
	instantiate_link(my_model, "next_onSucces", "", "rule_root", "arule1")
	//connect to rule
	instantiate_link(my_model, "getrule","", "arule1", "find_transitions")
	
	//FRule: consume
	instantiate_node(my_model, "FRule", "frule2")
	//connect to arule1
	instantiate_link(my_model, "next_onSucces", "", "arule1", "frule2")
	//connect to rule
	instantiate_link(my_model, "getrule","", "frule2", "consume_tokens")
	
	//Frule: produce
	instantiate_node(my_model, "FRule", "frule3")
	//connect to frule2
	instantiate_link(my_model, "next_onSucces", "",  "frule2",  "frule3")
	//connect to rule
	instantiate_link(my_model, "getrule","", "frule3", "produce_tokens")
	
	//create circularity link
	//instantiate_link(my_model, "next_onSucces", "",  "frule3",  "arule1")
	//if you cannot consume, still try to produce (for source transitions)
	//instantiate_link(my_model, "next_onFailure", "",  "frule2",  "frule3")
	//if you cannot produce, still check for other transitions (it might have consumed already)
	//instantiate_link(my_model, "next_onFailure", "",  "frule3",  "arule1")
	
	
	
	////////////////////////////
	// semantic checks
	
	Element semantic_checks
	semantic_checks = create_node()
	dict_add(semantic_checks, "always_true", always_true)
	dict_add(semantic_checks, "is_not_consumable", is_not_consumable)
	dict_add(my_model, "semantic_checks", semantic_checks)
	
	Element actions
	actions = create_node()
	dict_add(actions, "do_nothing", do_nothing)
	dict_add(actions, "add_tokens", add_tokens)
	dict_add(actions, "remove_tokens", remove_tokens)
	dict_add(my_model, "actions", actions)
	

	return my_model
	
Boolean function is_not_consumable(model : Element, matched_node : Element):
	//get place before
	Element place_before
	place_before = read_edge_src(matched_node)
	//get tokens from place
	Integer tokens
	tokens = read_attribute(model, getName(model, place_before), "tokens")
	//get weight from matched_node
	Integer weight
	weight = read_attribute(model, getName(model, matched_node), "weight")
	return weight > tokens
	
Boolean function always_true(model : Element, self : Element):
	return True

Element function do_nothing(model: Element,  self : Element):
	return 0

Element function remove_tokens(model:Element, self: Element):
	//get place before
	Element place_before
	place_before = read_edge_src(self)
	//get tokens from place
	Integer tokens
	tokens = read_attribute(model, getName(model, place_before), "tokens")
	//get weight from matched_node
	Integer weight
	weight = read_attribute(model, getName(model, self), "weight")
	//rewrite tokens
	unset_attribute(model, getName(model, place_before), "tokens")
	instantiate_attribute(model, getName(model, place_before), "tokens", tokens-weight)
	return 0

Element function add_tokens(model:Element, self: Element):
	//get place before
	Element place_after
	place_after = read_edge_dst(self)
	//get tokens from place
	Integer tokens
	tokens = read_attribute(model, getName(model, place_after), "tokens")
	//get weight from matched_node
	Integer weight
	weight = read_attribute(model, getName(model, self), "weight")
	//rewrite tokens
	unset_attribute(model, getName(model, place_after), "tokens")
	instantiate_attribute(model, getName(model, place_after), "tokens", tokens+weight)
	return 0

Element function pn_operations():
	Element ops
	ops = create_node()
	dict_add(ops, "fire", petrinet_fire)
	dict_add(ops, "enabled", petrinet_enabled)
	return ops

Element function petrinet_enabled(model : Element):
	Element set_enabled
	set_enabled = petrinet_enabled_set(model)
	output("Enabled transitions:")
	while (0 < read_nr_out(set_enabled)):
		output(getName(model, set_pop(set_enabled)))
	return model

Element function petrinet_enabled_set(model : Element):
	Element all_transitions
	all_transitions = allInstances(model, model["metamodel"]["model"]["Transition"])

	Element enabled_transitions
	enabled_transitions = create_node()

	Element under_study
	Element in_arcs
	Element arc_under_study
	Boolean enabled
	while (0 < read_nr_out(all_transitions)):
		under_study = set_pop(all_transitions)
		enabled = True

		// Find all incoming transitions
		in_arcs = allIncomingAssociationInstances(model, under_study, model["metamodel"]["model"]["P2T"])

		while (0 < read_nr_out(in_arcs)):
			arc_under_study = set_pop(in_arcs)

			Integer present_tokens
			Integer required_tokens
			required_tokens = read_attribute(model, getName(model, arc_under_study), "weight")
			present_tokens = read_attribute(model, getName(model, read_edge_src(arc_under_study)), "tokens")
			if (present_tokens < required_tokens):
				// Less tokens than required, so disable the transition completely
				enabled = False

		if (enabled):
			set_add(enabled_transitions, under_study)

	return enabled_transitions

Element function petrinet_fire(model : Element):
	output("Transition to fire?")
	Element transition
	transition = input()
	if (dict_in(model["model"], transition)):
		transition = model["model"][transition]
		if (set_in(petrinet_enabled_set(model), transition)):
			Element workset
			Element working_place
			Element working_arc
			Integer new_value

			// Consume tokens
			workset = allIncomingAssociationInstances(model, transition, model["metamodel"]["model"]["P2T"])
			while (0 < read_nr_out(workset)):
				working_arc = set_pop(workset)
				working_place = read_edge_src(working_arc)
				new_value = integer_subtraction(read_attribute(model, getName(model, working_place), "tokens"), read_attribute(model, getName(model, working_arc), "weight"))
				unset_attribute(model, getName(model, working_place), "tokens")
				instantiate_attribute(model, getName(model, working_place), "tokens", new_value)
				output((("  " + getName(model, working_place)) + ": ") + cast_i2s(read_attribute(model, getName(model, working_place), "tokens")))

			// Add tokens
			workset = allOutgoingAssociationInstances(model, transition, model["metamodel"]["model"]["T2P"])
			while (0 < read_nr_out(workset)):
				working_arc = set_pop(workset)
				working_place = read_edge_dst(working_arc)
				new_value = integer_addition(read_attribute(model, getName(model, working_place), "tokens"), read_attribute(model, getName(model, working_arc), "weight"))
				unset_attribute(model, getName(model, working_place), "tokens")
				instantiate_attribute(model, getName(model, working_place), "tokens", new_value)
				output((("  " + getName(model, working_place)) + ": ") + cast_i2s(read_attribute(model, getName(model, working_place), "tokens")))
			output("Transition fired!")
		else:
			output("Cannot fire if not enabled; aborting")
	else:
		output("Unknown transition; aborting")
	return model
	
Element function petrinet_loaded(model : Element):
	String cmd

	Element attr_list_pn
	Element attr_keys_pn
	String attr_key_pn
	Element metamodel_element_pn
	String typename
	Boolean bottom
	Element other_metamodel

	bottom = False
	other_metamodel = create_node()
	dict_add(other_metamodel, "model", model["model"])
	dict_add(other_metamodel, "type_mapping", create_node())
	dict_add(other_metamodel, "metamodel", import_node("models/LTM_bottom"))
	dict_add(other_metamodel, "inheritance", other_metamodel["metamodel"]["model"]["__Inheritance"])

	output("Model loaded, ready for commands!")
	output("Use 'help' command for a list of possible commands")

	while (True):
		output("Please give your command.")
		cmd = input()
		if (cmd == "help"):
			output("Generic model operations:")
			output("  instantiate -- Create a new model element")
			output("  delete      -- Delete an existing element")
			output("  attr_add    -- Add an attribute to an element")
			output("  attr_del    -- Delete an attribute of an element")
			output("  rename      -- Rename an existing element")
			output("  modify      -- Modify the attributes of an element")
			output("  list        -- Prints the list of elements in the model")
			output("  types       -- Prints the list of elements that can be instantiated")
			output("  read        -- Prints the current state of a model element")
			output("  verify      -- Check whether the model conforms to the metamodel")
			output("  retype      -- Change the type of an element")
			output("  switch      -- Switch between conformance bottom and the linguistic metamodel")
			output("  exit        -- Unload the model and go back to the loading prompt")
			if (bool_not(bottom)):
				output("Model-specific operations:")
				Element specific_ops
				specific_ops = dict_keys(pn_operations())
				String specific_op
				while (0 < dict_len(specific_ops)):
					specific_op = set_pop(specific_ops)
					output("  " + specific_op)
		elif (cmd == "exit"):
			return model
		elif (cmd == "instantiate"):
			String mm_type_name
			output("Type to instantiate?")
			mm_type_name = input()
			if (dict_in(model["metamodel"]["model"], mm_type_name)):
				String element_name
				output("Name of new element?")
				element_name = input()
				if (dict_in(model["model"], element_name)):
					output("Element already exists; aborting")
				else:
					if (is_edge(model["metamodel"]["model"][mm_type_name])):
						output("Source name?")
						String src_name
						src_name = input()
						if (dict_in(model["model"], src_name)):
							output("Destination name?")
							String dst_name
							dst_name = input()
							if (dict_in(model["model"], dst_name)):
								instantiate_link(model, mm_type_name, element_name, src_name, dst_name)
								if (dict_in(model["model"], element_name)):
									output("Instantiation successful!")
								else:
									output("Instantiation error!")
							else:
								output("Unknown destination; aborting")
						else:
							output("Unknown source; aborting")
					else:
						instantiate_node(model, mm_type_name, element_name)
						if (dict_in(model["model"], element_name)):
							output("Instantiation successful!")
						else:
							output("Instantiation error!")
			else:
				output("Unknown type specified; aborting")
		elif (cmd == "modify"):
			String model_name
			output("Element to modify?")
			model_name = input()
			if (dict_in(model["model"], model_name)):
				Element attrs
				attrs = getAttributeList(model, model_name)
				String attr_name
				output("Attribute to modify?")
				attr_name = input()
				if (set_in(dict_keys(attrs), attr_name)):
					output("New value?")
					unset_attribute(model, model_name, attr_name)
					instantiate_attribute(model, model_name, attr_name, input())
					output("Modified!")
				else:
					output("No such attribute!")
			else:
				output("No such model!")
		elif (cmd == "attr_add"):
			String model_name
			output("Which model do you want to assign an attribute to?")
			model_name = input()
			if (dict_in(model["model"], model_name)):
				Element attrs
				attrs = getAttributeList(model, model_name)
				String attr_name
				output("Which attribute do you wish to assign?")
				attr_name = input()
				if (set_in(dict_keys(attrs), attr_name)):
					output("Value of attribute?")
					instantiate_attribute(model, model_name, attr_name, input())
					output("Added attribute!")
				else:
					output("No such attribute!")
			else:
				output("No such model!")
		elif (cmd == "attr_del"):
			String model_name
			output("Which model do you want to remove an attribute of?")
			model_name = input()
			if (dict_in(model["model"], model_name)):
				Element attrs
				attrs = getAttributeList(model, model_name)
				output(print_dict(attrs))
				String attr_name
				output("Which attribute do you want to delete?")
				attr_name = input()
				if (set_in(dict_keys(attrs), attr_name)):
					unset_attribute(model, model_name, attr_name)
				else:
					output("No such attribute!")
			else:
				output("No such model!")
		elif (cmd == "delete"):
			output("What is the name of the element you want to delete?")
			cmd = input()
			if (dict_in(model["model"], cmd)):
				model_delete_element(model, cmd)
				output("Deleted!")
			else:
				output("No such element; aborting")
		elif (cmd == "rename"):
			output("Old name?")
			String old_name_e
			old_name_e = input()
			if (dict_in(model["model"], old_name_e)):
				output("New name?")
				String new_name_e
				new_name_e = input()
				if (dict_in(model["model"], new_name_e)):
					output("New name already used; aborting")
				else:
					dict_add(model["model"], new_name_e, model["model"][old_name_e])
					dict_delete(model["model"], old_name_e)
					output("Rename complete!")
			else:
				output("Unknown element; aborting")
		elif (cmd == "list"):
			Element keys_m
			keys_m = dict_keys(model["model"])
			output("List of all elements:")
			String v_m
			while (read_nr_out(keys_m) > 0):
				v_m = set_pop(keys_m)
				// Filter out anonymous objects
				typename = getName(model["metamodel"], dict_read_node(model["type_mapping"], model["model"][v_m]))
				if (bool_not(string_startswith(v_m, "__"))):
					output((("  " + v_m) + " : ") + typename)
		elif (cmd == "read"):
			output("Element to read?")
			cmd = input()
			if (dict_in(model["model"], cmd)):
				Element read_elem
				read_elem = model["model"][cmd]
				metamodel_element_pn = dict_read_node(model["type_mapping"], read_elem)

				output("Name: " + cmd)
				output("Type: " + getName(model["metamodel"], metamodel_element_pn))
				if (is_edge(read_elem)):
					output("Source: " + getName(model, read_edge_src(read_elem)))
					output("Destination: " + getName(model, read_edge_dst(read_elem)))
				if (cast_v2s(read_elem) != "None"):
					output("Value: " + cast_v2s(read_elem))
				output("Defines attributes:")
				attr_list_pn = getInstantiatableAttributes(model, read_elem)
				attr_keys_pn = dict_keys(attr_list_pn)
				while (0 < read_nr_out(attr_keys_pn)):
					attr_key_pn = set_pop(attr_keys_pn)
					output(((("  " + attr_key_pn) + " : ") + cast_v2s(attr_list_pn[attr_key_pn])))
				output("Attributes:")
				attr_list_pn = getAttributeList(model, cmd)
				attr_keys_pn = dict_keys(attr_list_pn)
				while (0 < read_nr_out(attr_keys_pn)):
					attr_key_pn = set_pop(attr_keys_pn)
					output((((("  " + cast_v2s(attr_key_pn)) + " : ") + cast_v2s(attr_list_pn[attr_key_pn])) + " = ") + cast_v2s(read_attribute(model, getName(model, read_elem), attr_key_pn)))
			else:
				output("Unknown element; aborting")
		elif (cmd == "verify"):
			output(conformance_scd(model))
		elif (cmd == "types"):
			Element keys_t
			keys_t = dict_keys(model["metamodel"]["model"])
			output("List of types:")
			String v_t
			while (read_nr_out(keys_t) > 0):
				v_t = set_pop(keys_t)
				if (bool_not(string_startswith(v_t, "__"))):
					output(string_join(("  " + v_t) + " : ", getName(model["metamodel"]["metamodel"], dict_read_node(model["metamodel"]["type_mapping"], model["metamodel"]["model"][v_t]))))
		elif (cmd == "retype"):
			output("Element to retype?")
			String elementname
			elementname = input()
			if (dict_in(model["model"], elementname)):
				output("New type")
				typename = input()
				if (dict_in(model["metamodel"]["model"], typename)):
					// OK, do the retyping
					// First try removing the previous type if it exists
					dict_delete(model["type_mapping"], model["model"][elementname])
					// Now add the new type
					dict_add(model["type_mapping"], model["model"][elementname], model["metamodel"]["model"][typename])
					output("Retyped!")
				else:
					output("Unknown type; aborting")
			else:
				output("Unknown element; aborting")
		elif (cmd == "switch"):
			bottom = bool_not(bottom)

			Element tmp_model
			tmp_model = model
			model = other_metamodel
			other_metamodel = tmp_model

			if (bottom):
				// The type mapping we are using is probably not complete for our model
				// so we completely recreate it from the model we have.
				output("Switching to conformance bottom mode!")
				generate_bottom_type_mapping(model)
			else:
				// We already switched the models and such, so we are already done!
				output("Switching to linguistic metamodel!")
		elif (bool_and(dict_in(pn_operations(), cmd), bool_not(bottom))):
			// A model-specific operation, so execute that one
			Element specific_op
			specific_op = dict_read(pn_operations(), cmd)
			specific_op(model)
		else:
			output("Unknown command; aborting")
			output("Use command 'help' to get a list of available commands")

Element function initial_prompt():
	output("Welcome to the Model Management Interface, running live on the Modelverse!")
	output("Use 'help' command for a list of possible commands")
	String command
	Element root
	Element metamodel
	String name
	Element my_model
	String mm_name

	root = create_metamodels()

	while (True):
		output("Please give your command.")
		command = input()

		if (command == "help"):
			output("Currently no model is loaded, so your operations are limited to:")
			output("  new    -- Create a new model and save it for future use")
			output("  load   -- Load a previously made model")
			output("  rename -- Rename a previously made model")
			output("  delete -- Delete a previously made model")
			output("  list   -- Show a list of all stored models")
			output("  help   -- Show a list of possible commands")
		elif (command == "new"):
			output("Metamodel to instantiate?")
			mm_name = input()
			if (dict_in(root, mm_name)):
				output("Name of model?")
				name = input()
				if (dict_in(root, name)):
					output("Model exists; aborting")
				else:
					my_model = instantiate_model(root[mm_name])
					dict_add(root, name, my_model)
					petrinet_loaded(my_model)
			else:
				output("Unknown metamodel; aborting")
		elif (command == "load"):
			output("Model to load?")
			name = input()
			if (dict_in(root, name)):
				my_model = root[name]
				petrinet_loaded(my_model)
			else:
				output("Model not found; aborting")
		elif (command == "list"):
			Element keys
			String m_menu_list
			keys = dict_keys(root)
			output("Found models:")
			while (read_nr_out(keys) > 0):
				m_menu_list = set_pop(keys)
				output((("  " + m_menu_list) + " : ") + reverseNameLookup(root, root[m_menu_list]["metamodel"]))
		elif (command == "delete"):
			output("Model to delete?")
			name = input()
			if (dict_in(root, name)):
				dict_delete(root, name)
				output("Deleted!")
			else:
				output("Model not found; aborting")
		elif (command == "rename"):
			output("Old name?")
			String old_name
			old_name = input()
			if (dict_in(root, old_name)):
				output("New name?")
				String new_name
				new_name = input()
				if (dict_in(root, new_name)):
					output("Model exists; aborting")
				else:
					dict_add(root, new_name, root[old_name])
					dict_delete(root, old_name)
					output("Rename complete!")
			else:
				output("Model not found; aborting")
		else:
			output("Command not recognized, use 'help' for a list of possible commands")

Void function main():
	//initial_prompt()
	log("==========================================================================")
	Integer init_time
	init_time = current_time()
	Element root
	root = create_metamodels()
	log(string_join("metamodel creation took ", cast_i2s(current_time() - init_time)))
	init_time = current_time()
	Element transf
	transf = petrinet_transformation(root)
	log(string_join("transform model creation took ", cast_i2s(current_time() - init_time)))
	init_time = current_time()
	Element test1
	test1 = test_simple_petrinet1(root)
	log(string_join("simple petrinet model creation took ", cast_i2s(current_time() - init_time)))
	init_time = current_time()
	log(conformance_scd(transf))
	log(string_join("transformation conformance checking took ", cast_i2s(current_time() - init_time)))
	init_time = current_time()
	tf_transform(transf, test1)
	log(string_join("transformation execution took ", cast_i2s(current_time() - init_time)))
	init_time = current_time()
	
	log("==========================================================================")
	printNode(test1, test1["model"]["p1"])
	printNode(test1, test1["model"]["p2"])
	log("=== done")
	return