def run_simulation(self, clock_type):
self.__register_strategy()
try:
data_portal = self._prepare_data_portal()
except:
return 0
clock = FactoryClock.get_clock(clock_type, data_portal)
self.api_strategy.set_internal_variables(clock, data_portal)
data_api = DataAPI(data_portal=data_portal,
traded_pairs=self.traded_pairs)
self.api_strategy.data_api = data_api
scheduler = self.api_strategy.get_scheduler()
simulation_manager = SimulationManager(clock, self.api_strategy,
scheduler)
simulation_manager.simulate()
def create_eventstream_from_simulator(simulator_file_name, number_events,
limit, model_file):
# simulator generates ~950 events per day
simulated_days = (1.25 * number_events / 950)
sm = SimulationManager(start=datetime.datetime.now(),
end=datetime.datetime.now() +
timedelta(days=simulated_days),
model_file=model_file)
# different resource_limit values produce different amounts of overlap between process instances
# limits = 5 => overlap ~40+
# limits = 10 => overlap ~20
# limits = 50 => overlap ~10
#
# writes results into <filename>.txt
sm.simulate(name=simulator_file_name,
resource_limit={
'support': limit,
'trust': limit
})
# Get file path for output of simulation
# using sys.platform to distinguish between Mac OS / Windows
if sys.platform == "darwin":
simulator_output_path = "output/" + simulator_file_name + '.txt'
else:
simulator_output_path = "output\\" + simulator_file_name + '.txt'
#Opens file to read simulator output
f = open(simulator_output_path)
average_overlap = calculate_concurrency(simulator_output_path)
print("average_overlap in simulation:", str(average_overlap))
cleaned_file_path = ""
# Open file to write ordered output into
if sys.platform == "darwin":
cleaned_file_path = "output/" + simulator_file_name + "_cleaned.txt"
else:
cleaned_file_path = "output\\" + simulator_file_name + "_cleaned.txt"
outfile = open(cleaned_file_path, "w")
data_elements = ["Name", "City", "University", "Gender", "Income"]
# list of data elements to pull from
names = ['Alice', 'Bob', 'Charlie', 'David', 'Emily', 'Frank']
accounts = ['acct' + str(x) for x in range(len(names))]
data_element_lists = [names, accounts]
sampled_data = {}
activity_data = {}
addToLine = ''
splitLine = []
number_events_in_output = 0
for line in f.readlines():
if number_events_in_output < number_events:
number_events_in_output += 1
else:
break
# remove '#' and everything beyond
line = line[:line.index("#") - 1]
splitLine = line.split()
activity_instance_id = splitLine[1]
if activity_instance_id not in activity_data.keys():
activity_data[activity_instance_id] = ' Name=' + random.choice(
data_element_lists[0])
if ('START' in splitLine):
outfile.write(line.rstrip() + '\n')
elif ('END' in splitLine):
outfile.write(line.rstrip() + '\n')
else:
outfile.write(line.rstrip() + activity_data[activity_instance_id] +
'\n')
print("generated clean log: " + cleaned_file_path)
# close files
f.close()
outfile.close()
os.rename(
cleaned_file_path, cleaned_file_path[:-4] + "_act=" +
str(number_events_in_output) + '.txt')
return number_events_in_output, cleaned_file_path