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 simulate(sim_parameters, sim_functions, eb = event_broker()):
'''
Function called by simulation to run the actual simulation based on a
set of parameters and functions.
Simulation parameters dictionary contains the following keys:
- simulation_name: Short name of the simulation.
- population_names: Population name(s) in a list.
- population_locations: A list of lists containing (x, y, z)
coordinates in the world to deploy the populations. There must be
equal numbers of items in this list as population_names. However,
one population can be deployed into one or more ecological cell(s).
- deployment_code: Defines the type of deployment. Allowable values
are 0 (custom deployment scheme which users can implement by over-
riding dose.dose_functions.deployment_scheme() function), 1 (all
organisms are in one location), 2 (organisms are randomly deployed
across a list of population locations given for the population), 3
(oganisms are randomly deployed across a list of population locations
given for the population), 4 (organisms are dispersed from a specific
location where the defined location will have the maximum allocation
before dispersal happens). (see simulation_calls.deployment for more
details)
- chromosome_bases: List containing allowable bases in the
chromosome.
- background_mutation: Defines background mutation rate where 0.01
represents 1% mutation and 0.5 represents 50% mutation.
- additional_mutation: Defines mutation rate on top of background
mutation rate where 0.01 represents 1% mutation and 0.5 represents
50% mutation.
- mutation_type: Type of mutation for background mutation rate.
Allowable values are 'point' (point mutation), 'insert' (insert a
base), 'delete' (delete a base), 'invert' (invert a stretch of the
chromosome), 'duplicate' (duplicate a stretch of the chromosome),
'translocate' (translocate a stretch of chromosome to another
random position).
- chromosome_size: Number of bases for a chromosome.
- genome_size: Number of chromosome(s) in a genome.
- max_tape_length: Maximum number of cells for array used in
Ragaraja interpreter, which will be stored as
Organism.status['blood'].
- clean_cell: Toggle to define if a new tape will be used for
Ragaraja interpreter. If True, at every Ragaraja interpretation of
the source (genome), a new tape ([0] * max_tape_length) will be
provided. If False, the tape stored as Organism.status['blood']
(results from previous interpretation of genome) will be used.
- interpret_chromosome: Toggle to define whether genomes will be
interpreted by Ragaraja. If True, chromosomes of each organism will
be interpreted by Ragaraja.
- max_codon: Maximum number of Ragaraja instructions to execute per
organism, which can be used as force-break from endless loop.
- population_size: Number of organisms per population for initial
deployment.
- eco_cell_capacity: Maximum number of organisms per ecological
cell at the time of deployment prior to the start of simulation.
- world_x: Number of ecological cells in the x-axis of the
World.ecosystem.
- world_y: Number of ecological cells in the y-axis of the
World.ecosystem.
- world_z: Number of ecological cells in the z-axis of the
World.ecosystem.
- goal: Goal for population to reach. This provides a goal for use
in fitness functions.
- maximum_generations: Number of generations to simulate.
- fossilized_ratio: Proportion (less than 1) of the population to
fossilize or freeze.
- fossilized_frequency: Number of generations (intervals) for each
population fossilization or freezing event.
- print_frequency: Number of generations (intervals) for each
reporting event into files.
- ragaraja_version: Ragaraga instruction version to activate. (see
ragaraja.activate_version() for more details)
- ragaraja_instructions: A list defining a set of Ragaraga
instructions to activate. This is only useful when ragaraja_version
is 0.
- eco_buried_frequency: Number of generations (intervals) for each
ecological freezing or burial event.
- database_file: Logging database file name. This file will be
located in <current working directory>/Simulations folder
- database_logging_frequency: Number of generations (intervals) for
each database logging event.
Methods / Functions from dose.dose_functions class to be over-ridden
as simulation_functions (for more details, please look at
dose.dose_functions class):
- organism_movement: organism_movement and organism_location are
both methods / functions to execute movement of organisms within
the world. The semantic difference between organism_movement and
organism_location is that organism_movement is generally used for
short travels while organism_location is used for long travel.
- organism_location: organism_movement and organism_location are
both methods / functions to execute movement of organisms within
the world. The semantic difference between organism_movement and
organism_location is that organism_movement is generally used for
short travels while organism_location is used for long travel.
- ecoregulate: Broad spectrum management of the entire
ecosystem.
- update_ecology: Process the input and output from the activities
of the organisms in the current ecological cell into a local
ecological cell condition, and update the ecosystem.
- update_local: Update local ecological cell condition from the
ecosystem.
- report: Generate a text report of ecosystem (World.ecosystem) at
regular intervals, within the simulation, as determined by
"print_frequency" in the simulation parameters.
- fitness: Calculate the fitness score of each organism within the
population(s).
- mutation_scheme: Trigger mutational events in each chromosome
of the genome within an organism.
- prepopulation_control: Trigger population control events before
mating event in each generation.
- mating: Trigger mating events in each generation.
- postpopulation_control: Trigger population control events after
mating event in each generation.
- generation_events: Trigger other defined events in each
generation.
- population_report: Generate a text report of the population(s)
and/or each organisms within the population at regular intervals,
within the simulation, as determined by "print_frequency" in the
simulation parameters
- database_report: Implement database logging of each organism
in each population, and the ecosystem status. The frequency of
logging is determined by "database_logging_frequency" in the
simulation_parameters.
- deployment_scheme: Implement a user-specific / simulation-
specific deployment scheme used to deploy organisms into the
World. This function will only be used when "deployment_code" in
simulation parameters dictionary equals to 0.
@param sim_parameters: Dictionary of simulation parameters
@param sim_functions: A class inherited from dose.dose_functions
class to implement all the needed simulation functions.
@param eb: An instance of dose.event_broker
'''
eb.log("[" + sim_parameters["simulation_name"].upper() + " SIMULATION]")
if "initial_chromosome" not in sim_parameters:
eb.log('Adding initial chromosome to simulation parameters')
sim_parameters["initial_chromosome"] = ['0'] * sim_parameters["chromosome_size"]
eb.log('Adding deployment scheme to simulation parameters')
sim_parameters["deployment_scheme"] = sim_functions.deployment_scheme
eb.log('Constructing World entity')
world = dose_world.World(sim_parameters["world_x"], sim_parameters["world_y"], sim_parameters["world_z"])
eb.log('Spawning populations')
populations = spawn_populations(sim_parameters)
eb.log('Starting simulation core')
simulation_core(sim_functions, sim_parameters, eb, populations, world)
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)
