def get_locations_list(self):
print('Getting locations list...')
world_x = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['world_x']
world_y = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['world_y']
world_z = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['world_z']
return [(x, y, z) for x in range(world_x) for y in range(world_y)
for z in range(world_z)]
def get_locations_list(self):
print('Getting locations list...')
world_x = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['world_x']
world_y = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['world_y']
world_z = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['world_z']
return [(x,y,z)
for x in range(world_x)
for y in range(world_y)
for z in range(world_z)]
def analyze_individual_status_by_generation(self, csv_output, status, status_analysis, aggregate_functions = None, generations = 'all'):
print('\n[INDIVIDUAL ' + status.upper() + ' STATUS ANALYSIS]')
print('Opening outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Getting population size...')
pop_size = database_calls.db_reconstruct_simulation_parameters(self.cur, self.starting_time)['population_size']
print('Writing outputfile header...')
header = ['Generation'] + [str(i) for i in range(1, pop_size + 1)]
if aggregate_functions != None:
header = header + [key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
print('Starting main analysis...')
if generations == 'all':
generation_list = database_calls.db_list_generations(self.cur, self.starting_time)
else:
generation_list = [str(gen) for gen in generations]
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
print()
status_list = [status_analysis(stat) for stat in self.get_individual_status_list_by_generation(status, generation)]
status_row = [str(generation)] + [str(stat_result) for stat_result in status_list]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(str(aggregate_functions[key](status_list)))
outputfile.write(','.join(status_row) + '\n')
print('\nIndividual [' + status + '] analysis complete!')
def get_fitness_range_by_percentage(self, percentage):
print('Getting fitness range...')
fitness_goal = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['goal']
return range(int(fitness_goal * percentage),
int(fitness_goal + 1))
def revive_simulation(rev_parameters, sim_functions):
print('\n[' + rev_parameters["simulation_name"].upper() + ' REVIVAL SIMULATION]')
Populations = {}
if "sim_folder" in rev_parameters:
print('Accessing simulation files directory...')
print('Excavating World entity: ' + rev_parameters['eco_file'] + '...')
World = excavate_world(rev_parameters['sim_folder'] + \
rev_parameters['eco_file'])
print('Updating parameters with World dimensions...')
rev_parameters["world_z"] = len(World.ecosystem[0][0][0])
rev_parameters["world_y"] = len(World.ecosystem[0][0])
rev_parameters["world_x"] = len(World.ecosystem[0])
for i in range(len(rev_parameters["pop_files"])):
print('\nReviving population file: ' + \
rev_parameters["pop_files"][i] + '...')
pop_file = rev_parameters["sim_folder"] + \
rev_parameters["pop_files"][i]
Populations[rev_parameters["population_names"][i]] = \
revive_population(pop_file)
print('\nUpdating revival generation start in simulation parameters...')
rev_parameters["rev_start"] = [Populations[pop_name].generation
for pop_name in Populations]
elif "database_source" in rev_parameters:
print('Constructing database directory...')
dbpath = os.sep.join([os.getcwd(),
'Simulations',
rev_parameters["database_source"]])
print('Connecting to database file: ' + \
rev_parameters["database_source"] + '...')
(con, cur) = connect_database(dbpath, None)
if rev_parameters["simulation_time"] == 'default':
print('Acquiring simulation starting time...')
rev_parameters["simulation_time"] = db_list_simulations(cur)[0][0]
print('Reconstructing old simulation parameters...')
temp_parameters = db_reconstruct_simulation_parameters(cur,
rev_parameters["simulation_time"])
print('Assimilating old simulation parameters with new simulation parameters...')
for key in temp_parameters:
if key not in rev_parameters:
rev_parameters[key] = temp_parameters[key]
print('Reconstructing World entity...')
World = db_reconstruct_world(cur, rev_parameters["simulation_time"],
rev_parameters["rev_start"][0])
print('\nUpdating population names parameter...')
for pop_name in rev_parameters["population_names"]:
print('Reconstructing population: ' + pop_name + '...')
Populations[pop_name] = db_reconstruct_population(cur,
rev_parameters["simulation_time"], pop_name,
rev_parameters["rev_start"][rev_parameters["population_names"].index(pop_name)])
print('Terminating database connection...')
con.close()
print('Updating last generation revival and population size simulation parameters...')
rev_parameters["rev_finish"] = [(Populations[pop_name].generation + \
rev_parameters["extend_gen"])
for pop_name in Populations]
rev_parameters["rev_pop_size"] = [len(Populations[pop_name].agents)
for pop_name in Populations]
print('\nStarting simulation core...')
simulation_core(sim_functions, rev_parameters, Populations, World)
def analyze_individual_status_by_generation(self,
csv_output,
status,
status_analysis,
aggregate_functions=None,
generations='all'):
print('\n[INDIVIDUAL ' + status.upper() + ' STATUS ANALYSIS]')
print('Getting population size...')
pop_size = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['population_size']
print('Constructing generations list...')
if generations == 'all':
generation_list = database_calls.db_list_generations(
self.cur, self.starting_time)
else:
generation_list = [str(gen) for gen in generations]
print('Preparing outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Writing outputfile header...')
header = ['Generation'] + [str(i) for i in range(1, pop_size + 1)]
if aggregate_functions != None:
header = header + \
[key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
outputfile.close()
print('Starting main analysis...')
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
status_list = \
[status_analysis(stat)
for stat in self.get_individual_status_list_by_generation(status, generation)]
status_row = [str(generation)] + \
[str(stat_result) for stat_result in status_list]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(
str(aggregate_functions[key](status_list)))
outputfile = open(csv_output, 'a')
outputfile.write(','.join(status_row) + '\n')
outputfile.close()
print('\nIndividual [' + status + '] analysis complete!')
def revive_simulation(rev_parameters, sim_functions, eb = event_broker()):
eb.log("[" + rev_parameters["simulation_name"].upper() + " REVIVAL SIMULATION]")
populations = {}
world = None
eb.log("Accessing simulation files directory")
if "sim_folder" in rev_parameters:
eb.log('Excavating World entity: ' + rev_parameters['eco_file'])
world = excavate_world(rev_parameters['sim_folder'] + rev_parameters['eco_file'])
eb.log('Updating parameters with World dimensions')
rev_parameters["world_z"] = len(world.ecosystem[0][0][0])
rev_parameters["world_y"] = len(world.ecosystem[0][0])
rev_parameters["world_x"] = len(world.ecosystem[0])
for i in range(len(rev_parameters["pop_files"])):
eb.log('Reviving population file: ' + rev_parameters["pop_files"][i])
pop_file = rev_parameters["sim_folder"] + rev_parameters["pop_files"][i]
populations[rev_parameters["population_names"][i]] = revive_population(pop_file)
eb.log('Updating revival generation start in simulation parameters')
rev_parameters["rev_start"] = [populations[pop_name].generation for pop_name in populations]
elif "database_source" in rev_parameters:
eb.log('Constructing database directory')
dbpath = os.sep.join([os.getcwd(), 'Simulations', rev_parameters["database_source"]])
eb.log('Connecting to database file: ' + rev_parameters["database_source"])
(con, cur) = connect_database(dbpath, None)
if rev_parameters["simulation_time"] == 'default':
eb.log('Acquiring simulation starting time')
rev_parameters["simulation_time"] = db_list_simulations(cur)[0][0]
eb.log('Reconstructing old simulation parameters')
temp_parameters = db_reconstruct_simulation_parameters(cur, rev_parameters["simulation_time"])
eb.log('Assimilating old simulation parameters with new simulation parameters')
for key in temp_parameters:
if key not in rev_parameters:
rev_parameters[key] = temp_parameters[key]
eb.log('Reconstructing World entity')
world = db_reconstruct_world(cur, rev_parameters["simulation_time"], rev_parameters["rev_start"][0])
eb.log('Updating population names parameter')
for pop_name in rev_parameters["population_names"]:
eb.log('Reconstructing population: ' + pop_name)
populations[pop_name] = db_reconstruct_population(cur, rev_parameters["simulation_time"], pop_name,
rev_parameters["rev_start"][rev_parameters
["population_names"].index(pop_name)])
eb.log('Terminating database connection')
con.close()
eb.log('Updating last generation revival and population size simulation parameters')
rev_parameters["rev_finish"] = [(populations[pop_name].generation + rev_parameters["extend_gen"])
for pop_name in populations]
rev_parameters["rev_pop_size"] = [len(populations[pop_name].agents) for pop_name in populations]
eb.log('Starting simulation core')
simulation_core(sim_functions, rev_parameters, populations, world)
def get_fitness_range_by_percentage(self, percentage):
print('Getting fitness range...')
fitness_goal = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['goal']
return range(int(fitness_goal * percentage), int(fitness_goal + 1))