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introvoyz041
/

FAS-Simulator

#!/usr/bin/python

# This utility reads the given file and generates a DFA graph of all the possible state transitions.
# This interprets the signals as states.
import json
from pprint import pprint
from samba.dcerpc.dfs import DFS_GLOBAL_HIGH_PRIORITY_CLASS

outputJsonFile = open('output.json', 'r')
outputJson = json.load(outputJsonFile)
outputJsonFile.close()

lastItem = None
transitions = dict()
for item in outputJson:
    if (lastItem):
        # A transition from lastItem to item.type
        if (not transitions.has_key(lastItem)):
            transitions[lastItem] = dict()
        transitions[lastItem][item['type']] = 1
    lastItem = item['type']

# Moore reduction here
itemToKPartition = dict()
kPartitionToItems = dict()

f = open('dfa.dot', 'w')
f.write("digraph G {\n")
for fromItem in transitions.keys():
    # We will try to do minimization here also.
    toItems = transitions[fromItem].keys()
    for toItem in toItems:
        f.write("  \"" + fromItem + "\"->\"" + toItem + "\";\n")
    k = ":".join(sorted(toItems))
    # All fromItems in the same kPartition are kEquivalent, because their outputs are the same.
    itemToKPartition[fromItem] = k
    if (not kPartitionToItems.has_key(k)):
        kPartitionToItems[k] = []
    kPartitionToItems[k].append(fromItem)
print kPartitionToItems
f.write("}\n")
f.close()

f_reduced = open('dfa_reduced.dot', 'w')
f_reduced.write("digraph G {\n")

transitions_reduced = dict()

# Reducing. One iteration is enough.
for fromItemRaw in transitions.keys():
    fromItemK = itemToKPartition[fromItemRaw]
    fromItem = ":".join(sorted(kPartitionToItems[fromItemK]))
    print fromItem

    toItemsRaw = transitions[fromItemRaw].keys()
    toItems = dict()
    for toItemRaw in toItemsRaw:
        toItemK = itemToKPartition[toItemRaw]
        toItemKey = ":".join(sorted(kPartitionToItems[toItemK]))
        toItems[toItemKey] = 1
    transitions_reduced[fromItem] = toItems.keys()

for fromItem in transitions_reduced.keys():
    # We will try to do minimization here also.
    toItems = transitions_reduced[fromItem]
    for toItem in toItems:
        f_reduced.write("  \"" + fromItem + "\"->\"" + toItem + "\";\n")
f_reduced.write("}\n")
f_reduced.close()

	#!/usr/bin/python

	# This utility reads the given file and generates a DFA graph of all the possible state transitions.
	# This interprets the signals as states.
	import json
	from pprint import pprint
	from samba.dcerpc.dfs import DFS_GLOBAL_HIGH_PRIORITY_CLASS

	outputJsonFile = open('output.json', 'r')
	outputJson = json.load(outputJsonFile)
	outputJsonFile.close()

	lastItem = None
	transitions = dict()
	for item in outputJson:
	if (lastItem):
	# A transition from lastItem to item.type
	if (not transitions.has_key(lastItem)):
	transitions[lastItem] = dict()
	transitions[lastItem][item['type']] = 1
	lastItem = item['type']

	# Moore reduction here
	itemToKPartition = dict()
	kPartitionToItems = dict()

	f = open('dfa.dot', 'w')
	f.write("digraph G {\n")
	for fromItem in transitions.keys():
	# We will try to do minimization here also.
	toItems = transitions[fromItem].keys()
	for toItem in toItems:
	f.write(" \"" + fromItem + "\"->\"" + toItem + "\";\n")
	k = ":".join(sorted(toItems))
	# All fromItems in the same kPartition are kEquivalent, because their outputs are the same.
	itemToKPartition[fromItem] = k
	if (not kPartitionToItems.has_key(k)):
	kPartitionToItems[k] = []
	kPartitionToItems[k].append(fromItem)
	print kPartitionToItems
	f.write("}\n")
	f.close()

	f_reduced = open('dfa_reduced.dot', 'w')
	f_reduced.write("digraph G {\n")

	transitions_reduced = dict()

	# Reducing. One iteration is enough.
	for fromItemRaw in transitions.keys():
	fromItemK = itemToKPartition[fromItemRaw]
	fromItem = ":".join(sorted(kPartitionToItems[fromItemK]))
	print fromItem

	toItemsRaw = transitions[fromItemRaw].keys()
	toItems = dict()
	for toItemRaw in toItemsRaw:
	toItemK = itemToKPartition[toItemRaw]
	toItemKey = ":".join(sorted(kPartitionToItems[toItemK]))
	toItems[toItemKey] = 1
	transitions_reduced[fromItem] = toItems.keys()

	for fromItem in transitions_reduced.keys():
	# We will try to do minimization here also.
	toItems = transitions_reduced[fromItem]
	for toItem in toItems:
	f_reduced.write(" \"" + fromItem + "\"->\"" + toItem + "\";\n")
	f_reduced.write("}\n")
	f_reduced.close()