def do_simulation(args):
if args.seed is not None:
random.seed(args.seed)
data, stats = perform_simulation(args)
namespace = utils.serialize_args(args)
namespace.update(stats)
output = args.output
write_json_line(output, namespace)
for i, seq in data:
output.write("%s %s\n" % (i, seq))
def do_mapper(args):
if args.seed is not None:
random.seed(args.seed)
namespace = utils.serialize_args(args)
simulation, simulation_stats = perform_simulation(args)
namespace.update(simulation_stats)
clustering, clustering_stats = perform_clustering(args, simulation)
namespace.update(clustering_stats)
analysis_stats = perform_analysis(args, clustering)
namespace.update(analysis_stats)
args.output.write("%s\n" % json.dumps(namespace))
def do_cluster(args):
namespace = {}
sim_namespace, simulation = load_simulation(args)
namespace.update(sim_namespace)
clustering_results, clustering_stats = perform_clustering(args, simulation)
clustering_namespace = utils.serialize_args(args)
namespace.update(clustering_namespace)
namespace.update(clustering_stats)
write_json_line(args.output, namespace)
for cluster in clustering_results:
write_json_line(args.output, cluster)
def do_mapper(args):
params = dict(
n=args.sim_size,
nclusters=args.nclusters,
split_join=args.split_join,
join_negatives=bool(args.join_negatives),
population_size=args.population_size,
with_warnings=args.sampling_warnings,
)
h0 = Grid.with_sim_clusters(p_err=args.h0_err, **params)
h1 = Grid.with_sim_clusters(p_err=args.h1_err, **params)
with PMTimer() as timer:
results = h0.compare(h1, args.metrics)
for result in results:
result.update(timer.to_dict())
result.update(utils.serialize_args(args))
write_json_line(args.output, result)
