def sample_axelrod_model(model, args, simconfig):
counts = stats.get_culture_counts_dbformat(model)
klemm = stats.klemm_normalized_L_axelrod(model, simconfig)
data.store_stats_axelrod_original(
simconfig.popsize, simconfig.sim_id, simconfig.num_features,
simconfig.num_traits, simconfig.drift_rate,
simconfig.INTERACTION_RULE_CLASS,
simconfig.POPULATION_STRUCTURE_CLASS, simconfig.script, len(counts),
model.get_time_last_interaction(), counts, klemm)
if args.diagram == True:
model.draw_network_colored_by_culture()
def sample_treestructured_model(model, args, simconfig, timestep, finalized):
log.debug("sampling tree structured model")
trait_analyzer = stats.PopulationTraitFrequencyAnalyzer(model)
trait_analyzer.calculate_trait_frequencies()
trait_spectrum = trait_analyzer.get_trait_spectrum()
culture_counts_dbformat = stats.get_culture_counts_dbformat(model)
culture_count_map = stats.get_culture_count_map(model)
(mean_traits, sd_traits) = stats.get_num_traits_per_individual_stats(model)
#log.debug("culture size - mean: %s sd: %s", mean_traits, sd_traits)
klemm = stats.klemm_normalized_L_extensible(model, simconfig)
graphml_blobs = []
trait_tree_stats = []
traitset_map = get_traitset_map(model)
for culture, traits in traitset_map.items():
g = dict(cultureid=str(culture),
content=model.trait_universe.get_graphml_for_culture(traits))
if simconfig.save_graphs == True:
graphml_blobs.append(g)
trait_tree_stats.append(
get_tree_symmetries_for_traitset(model, simconfig, culture, traits,
culture_count_map))
#log.debug("graphml: %s", pp.pformat(graphml_blobs))
sample_time = timestep
if finalized == 1:
convergence_time = model.get_time_last_interaction()
else:
convergence_time = 0
# Not recording the entropy yet, it doesn't mean anything given the way frequencies work.
data.store_stats_axelrod_treestructured(
simconfig.popsize, simconfig.sim_id, simconfig.maxtraits,
simconfig.learning_rate, simconfig.loss_rate, simconfig.innov_rate,
simconfig.num_trees, simconfig.branching_factor,
simconfig.depth_factor, simconfig.INTERACTION_RULE_CLASS,
simconfig.POPULATION_STRUCTURE_CLASS,
simconfig.NETWORK_FACTORY_CLASS, simconfig.script,
len(culture_counts_dbformat), trait_spectrum, convergence_time,
sample_time, culture_counts_dbformat, klemm, mean_traits,
sd_traits, graphml_blobs, trait_tree_stats,
trait_analyzer.get_trait_richness(), None, finalized,
simconfig.ws_rewiring)
if args.diagram == True and finalized == 1:
for culture, traits in traitset_map.items():
model.trait_universe.draw_trait_network_for_culture(
culture, traits)
def sample_extensible_model(model, args, simconfig):
counts = stats.get_culture_counts_dbformat(model)
(mean_traits, sd_traits) = stats.get_num_traits_per_individual_stats(model)
log.debug("culture size - mean: %s sd: %s", mean_traits, sd_traits)
klemm = stats.klemm_normalized_L_extensible(model, simconfig)
data.store_stats_axelrod_extensible(
simconfig.popsize, simconfig.sim_id, simconfig.maxtraits,
simconfig.add_rate, simconfig.drift_rate,
simconfig.INTERACTION_RULE_CLASS,
simconfig.POPULATION_STRUCTURE_CLASS, simconfig.script, len(counts),
model.get_time_last_interaction(), counts, klemm, mean_traits,
sd_traits)
if args.diagram == True:
model.draw_network_colored_by_culture()
def sample_axelrod_model(model,args,simconfig):
counts = stats.get_culture_counts_dbformat(model)
klemm = stats.klemm_normalized_L_axelrod(model,simconfig)
data.store_stats_axelrod_original(simconfig.popsize,
simconfig.sim_id,
simconfig.num_features,
simconfig.num_traits,
simconfig.drift_rate,
simconfig.INTERACTION_RULE_CLASS,
simconfig.POPULATION_STRUCTURE_CLASS,
simconfig.script,
len(counts),
model.get_time_last_interaction(),
counts,
klemm)
if args.diagram == True:
model.draw_network_colored_by_culture()
def main():
structure_class_name = simconfig.POPULATION_STRUCTURE_CLASS
log.debug(
"Configuring Axelrod model with structure class: %s graph factory: %s interaction rule: %s",
structure_class_name, simconfig.NETWORK_FACTORY_CLASS,
simconfig.INTERACTION_RULE_CLASS)
log.debug("Run for popsize %s maxinittraits: %s, addrate: %s",
simconfig.popsize, simconfig.maxtraits, simconfig.add_rate)
model_constructor = utils.load_class(structure_class_name)
rule_constructor = utils.load_class(simconfig.INTERACTION_RULE_CLASS)
graph_factory_constructor = utils.load_class(
simconfig.NETWORK_FACTORY_CLASS)
trait_factory_constructor = utils.load_class(simconfig.TRAIT_FACTORY_CLASS)
graph_factory = graph_factory_constructor(simconfig)
trait_factory = trait_factory_constructor(simconfig)
model = model_constructor(simconfig, graph_factory, trait_factory)
model.initialize_population()
ax = rule_constructor(model)
timestep = 0
last_interaction = 0
counts = analysis.get_culture_counts_dbformat(model)
while (1):
timestep += 1
if (timestep % 10000 == 0):
log.debug("time: %s frac active links %s", timestep,
ax.get_fraction_links_active())
ax.step(timestep)
if model.get_time_last_interaction() != timestep:
live = utils.check_liveness(ax, model, args, simconfig, timestep)
if live == False:
log.info("Finalizing statistics at time: %s",
model.get_time_last_interaction())
utils.sample_extensible_model(model, args, simconfig)
exit(0)
def sample_extensible_model(model, args, simconfig):
counts = stats.get_culture_counts_dbformat(model)
(mean_traits,sd_traits) = stats.get_num_traits_per_individual_stats(model)
log.debug("culture size - mean: %s sd: %s", mean_traits, sd_traits)
klemm = stats.klemm_normalized_L_extensible(model, simconfig)
data.store_stats_axelrod_extensible(simconfig.popsize,
simconfig.sim_id,
simconfig.maxtraits,
simconfig.add_rate,
simconfig.drift_rate,
simconfig.INTERACTION_RULE_CLASS,
simconfig.POPULATION_STRUCTURE_CLASS,
simconfig.script,
len(counts),
model.get_time_last_interaction(),
counts,
klemm,
mean_traits,
sd_traits)
if args.diagram == True:
model.draw_network_colored_by_culture()
def main():
structure_class_name = simconfig.POPULATION_STRUCTURE_CLASS
log.debug("Configuring Axelrod model with structure class: %s graph factory: %s interaction rule: %s", structure_class_name, simconfig.NETWORK_FACTORY_CLASS, simconfig.INTERACTION_RULE_CLASS)
log.debug("Run for popsize %s maxinittraits: %s, addrate: %s", simconfig.popsize,
simconfig.maxtraits, simconfig.add_rate)
model_constructor = utils.load_class(structure_class_name)
rule_constructor = utils.load_class(simconfig.INTERACTION_RULE_CLASS)
graph_factory_constructor = utils.load_class(simconfig.NETWORK_FACTORY_CLASS)
trait_factory_constructor = utils.load_class(simconfig.TRAIT_FACTORY_CLASS)
graph_factory = graph_factory_constructor(simconfig)
trait_factory = trait_factory_constructor(simconfig)
model = model_constructor(simconfig, graph_factory, trait_factory)
model.initialize_population()
ax = rule_constructor(model)
timestep = 0
last_interaction = 0
counts = analysis.get_culture_counts_dbformat(model)
while(1):
timestep += 1
if(timestep % 10000 == 0):
log.debug("time: %s frac active links %s", timestep, ax.get_fraction_links_active())
ax.step(timestep)
if model.get_time_last_interaction() != timestep:
live = utils.check_liveness(ax, model, args, simconfig, timestep)
if live == False:
log.info("Finalizing statistics at time: %s", model.get_time_last_interaction())
utils.sample_extensible_model(model, args, simconfig)
exit(0)
def test_culture_counts(self):
counts = analysis.get_culture_counts_dbformat(self.pop)
log.info("counts: %s", counts)
def test_culture_counts(self):
counts = analysis.get_culture_counts_dbformat(self.pop)
log.info("counts: %s", counts)
def sample_treestructured_model(model, args, simconfig, timestep, finalized):
log.debug("sampling tree structured model")
trait_analyzer = stats.PopulationTraitFrequencyAnalyzer(model)
trait_analyzer.calculate_trait_frequencies()
trait_spectrum = trait_analyzer.get_trait_spectrum()
culture_counts_dbformat = stats.get_culture_counts_dbformat(model)
culture_count_map = stats.get_culture_count_map(model)
(mean_traits,sd_traits) = stats.get_num_traits_per_individual_stats(model)
#log.debug("culture size - mean: %s sd: %s", mean_traits, sd_traits)
klemm = stats.klemm_normalized_L_extensible(model, simconfig)
graphml_blobs = []
trait_tree_stats = []
traitset_map = get_traitset_map(model)
for culture, traits in traitset_map.items():
g = dict(cultureid=str(culture), content=model.trait_universe.get_graphml_for_culture(traits))
if simconfig.save_graphs == True:
graphml_blobs.append(g)
trait_tree_stats.append( get_tree_symmetries_for_traitset(model, simconfig, culture, traits, culture_count_map))
#log.debug("graphml: %s", pp.pformat(graphml_blobs))
sample_time = timestep
if finalized == 1:
convergence_time = model.get_time_last_interaction()
else:
convergence_time = 0
# Not recording the entropy yet, it doesn't mean anything given the way frequencies work.
data.store_stats_axelrod_treestructured(simconfig.popsize,
simconfig.sim_id,
simconfig.maxtraits,
simconfig.learning_rate,
simconfig.loss_rate,
simconfig.innov_rate,
simconfig.num_trees,
simconfig.branching_factor,
simconfig.depth_factor,
simconfig.INTERACTION_RULE_CLASS,
simconfig.POPULATION_STRUCTURE_CLASS,
simconfig.NETWORK_FACTORY_CLASS,
simconfig.script,
len(culture_counts_dbformat),
trait_spectrum,
convergence_time,
sample_time,
culture_counts_dbformat,
klemm,
mean_traits,
sd_traits,
graphml_blobs,
trait_tree_stats,
trait_analyzer.get_trait_richness(),
None,
finalized,
simconfig.ws_rewiring)
if args.diagram == True and finalized == 1:
for culture, traits in traitset_map.items():
model.trait_universe.draw_trait_network_for_culture(culture, traits)