def TEST_crowfail():
from simulation_batch import SimulationBatch
text = """# NetworkProbability data file
# bus bus_id fail_rate repair_rate
bus 101 0.0 10.0
bus 102 0.0 10.0
bus 103 0.0 10.0
# line name fbus tbus fail_rate repair_rate trans_fail
line a1 101 102 100000000000.0 10.0 0.0
line a2 102 103 0.0 10.0 0.0
# crow line1 line2 probability
crow a1 a2 0.5
"""
count = 100000
prob = NetworkProbability()
prob.read(StringIO(text))
batch = SimulationBatch()
for x in range(count):
batch.add(prob.outages(str(x)))
assert count == len(batch)
batch.write_stats(sys.stdout)
def make_outages(prob, count):
"""func make_outages :: NetworkProbability, Int -> SimulationBatch
----
Monte Carlo sample the network for it's expected condition.
e.g. existing outages, weather & load forcast, etc.
"""
batch = SimulationBatch()
for x in range(count):
batch.add(prob.outages(str(x)))
EnsureEqual(count, batch.size())
return batch
def make_failures(prob, count):
"""func make_failures :: NetworkProbability, Int -> SimulationBatch
----
Monte Carlo sample the network for unexpected changes.
e.g. new outages (failures), actual weather, actual load level, etc.
"""
batch = SimulationBatch()
for x in range(count):
batch.add(prob.failures(str(x)))
EnsureEqual(count, batch.size())
return batch
def make_failures(prob, count):
"""func make_failures :: NetworkProbability, Int -> SimulationBatch
----
Monte Carlo sample the network for unexpected changes.
e.g. new outages (failures), actual weather, actual load level, etc.
"""
batch = SimulationBatch()
for x in range(count):
batch.add(prob.failures(str(x)))
EnsureEqual(count, batch.size())
return batch
def make_outages(prob, count):
"""func make_outages :: NetworkProbability, Int -> SimulationBatch
----
Monte Carlo sample the network for it's expected condition.
e.g. existing outages, weather & load forcast, etc.
"""
batch = SimulationBatch()
for x in range(count):
batch.add(prob.outages(str(x)))
EnsureEqual(count, batch.size())
return batch
def make_outage_cases(prob, count):
"""func make_outage_cases :: NetworkProbability, Int -> SimulationBatch
----
like `make_outages` but makes `count` *after* reducing.
Monte Carlo sample the network for it's expected condition.
e.g. existing outages, weather & load forcast, etc.
"""
batch = SimulationBatch()
current = 0
while len(batch) < count:
batch.add(prob.outages(str(current)))
current += 1
EnsureEqual(count, len(batch))
return batch
def make_outage_cases(prob, count):
"""func make_outage_cases :: NetworkProbability, Int -> SimulationBatch
----
like `make_outages` but makes `count` *after* reducing.
Monte Carlo sample the network for it's expected condition.
e.g. existing outages, weather & load forcast, etc.
"""
batch = SimulationBatch()
current = 0
while len(batch) < count:
batch.add(prob.outages(str(current)))
current += 1
EnsureEqual(count, len(batch))
return batch
def make_failure_cases(prob, count):
"""func make_failure_cases :: NetworkProbability, Int -> SimulationBatch
----
like `make_failures` but makes `count` *after* reducing.
Monte Carlo sample the network for unexpected changes.
e.g. new outages (failures), actual weather, actual load level, etc.
"""
batch = SimulationBatch()
current = 0
while len(batch) < count:
batch.add(prob.failures(str(current)))
current += 1
EnsureEqual(count, len(batch))
return batch
def make_failure_cases(prob, count):
"""func make_failure_cases :: NetworkProbability, Int -> SimulationBatch
----
like `make_failures` but makes `count` *after* reducing.
Monte Carlo sample the network for unexpected changes.
e.g. new outages (failures), actual weather, actual load level, etc.
"""
batch = SimulationBatch()
current = 0
while len(batch) < count:
batch.add(prob.failures(str(current)))
current += 1
EnsureEqual(count, len(batch))
return batch