def test_rule(rulename, rulepath, g):
ibfm_utility.plotPgvGraph(g, "plots/beforeRule.svg", promoteNodeLabels="function", printRelationships="flowType")
r = ibfm_utility.grammars.Rule(rulename, os.path.join(rulepath, "lhs.csv"), os.path.join(rulepath, "rhs.csv"))
r.recognize(g)
g = r.apply(g)
# g = r.apply(g,1)
print(r.recognize_mappings)
ibfm_utility.plotPgvGraph(
g, filename="plots/afterRule.svg", promoteNodeLabels="function", printRelationships="flowType"
)
def test_rule(rulename,rulepath,g):
ibfm_utility.plotPgvGraph(g,'plots/beforeRule.svg',
promoteNodeLabels='function',
printRelationships='flowType')
r = ibfm_utility.grammars.Rule(rulename,os.path.join(rulepath,'lhs.csv'),os.path.join(rulepath,'rhs.csv'))
r.recognize(g)
g = r.apply(g)
# g = r.apply(g,1)
print(r.recognize_mappings)
ibfm_utility.plotPgvGraph(g,filename='plots/afterRule.svg',
promoteNodeLabels='function',
printRelationships='flowType')
def test_rule(rulename,rulepath,g):
ibfm_utility.plotPgvGraph(g,'plots/beforeRule.svg',
promoteNodeLabels='function',
printRelationships='flowType')
r = ibfm_utility.grammars.Rule(rulename,os.path.join(rulepath,'lhs.csv'),os.path.join(rulepath,'rhs.csv'))
r.recognize(g)
g = r.apply(g)
# g = r.apply(g,1)
print(r.recognize_mappings)
ibfm_utility.plotPgvGraph(g,filename='plots/afterRule.svg',
promoteNodeLabels='function',
printRelationships='flowType')
depth = 3
popu = rs.build_population_random_stack(g,breadth,depth)
#graph each member of population
path = 'plots/graph_population/'
extension = '.svg'
for f in os.listdir(path):
if f.endswith(extension):
os.remove(os.path.join(path,f))
for label,rulename,location,graph in rs.get_population(popu):
print(label,rulename,location,graph)
path = 'plots/graph_population/'
extension = '.svg'
filename = '-'.join([str(label),rulename,str(location)])
ibfm_utility.plotPgvGraph(graph,filename=os.path.join(path,filename)+extension,
promoteNodeLabels='function',
printRelationships='flowType')
for label,rulename,location,graph in rs.get_population(popu):
if ibfm_utility.grammars.check_model(graph) == True:
popu[label]['isSimulatable'] = True
else:
popu[label]['isSimulatable'] = False
#Run every valid model in the population
results = {}
max_simultaneous_faults = 2
for label,rulename,location,graph in rs.get_population(popu):
print(label,rulename)
if popu[label]['isSimulatable'] == True:
eps = ibfm.Experiment(graph)
'''This is an example of how to import a functional model from csv
'''
import sys
sys.path.append('ibfm_utility')
import ibfm
import ibfm_utility
if __name__ == '__main__':
filename = 'FunctionalModels/small_eps.csv'
g = ibfm_utility.ImportFunctionalModel(filename, type='dsm')
ibfm_utility.plotPgvGraph(g,
promoteNodeLabels='function',
printRelationships='flowType')
print(g.nodes(data=True))
print(g.edges(data=True))
eps = ibfm.Experiment(g)
#Run with 2 then 3 simultaneous faults
eps.run(2)
eps.run(3)
# rulename = 'AddSeriesAnyElectricalEnergy'
# rulepath = 'ibfm_utility/rules/testRules/AddSeriesAnyElectricalEnergy/'
# test_rule(rulename,rulepath,g)
#create population from ruleset
ruleset_path = 'ibfm_utility/rules/ruleset/'
rs = ibfm_utility.grammars.Ruleset(ruleset_path)
breadth = 3
depth = 3
pop = rs.build_population_random_stack(g,breadth,depth)
#graph each member of population
path = 'plots/graph_population/'
extension = '.svg'
for f in os.listdir(path):
if f.endswith(extension):
os.remove(os.path.join(path,f))
for label,rulename,location,graph in rs.get_population(pop):
print(label,rulename,location,graph)
path = 'plots/graph_population/'
extension = '.svg'
filename = '-'.join([str(label),rulename,str(location)])
ibfm_utility.plotPgvGraph(graph,filename=os.path.join(path,filename)+extension,
promoteNodeLabels='function',
printRelationships='flowType')
# #Create and run experiments (broken until we resolve FunctionFlow vs Function_Flow convention)
# eps = ibfm.Experiment(g)
# #Run with 2 then 3 simultaneous faults
# eps.run(2)
# eps.run(3)
"""This is an example of how to import a functional model from csv
"""
import sys
sys.path.append("ibfm_utility")
import ibfm
import ibfm_utility
if __name__ == "__main__":
filename = "FunctionalModels/small_eps.csv"
g = ibfm_utility.ImportFunctionalModel(filename, type="dsm")
ibfm_utility.plotPgvGraph(g, promoteNodeLabels="function", printRelationships="flowType")
print(g.nodes(data=True))
print(g.edges(data=True))
eps = ibfm.Experiment(g)
# Run with 2 then 3 simultaneous faults
eps.run(2)
eps.run(3)
