import logging
import pandas as pd
from tabulate import tabulate
from core import container, operations
from core.random_generators import SequencialGenerator, FakerGenerator, NumpyRandomGenerator, ConstantDependentGenerator, ConstantGenerator
import core.util_functions as util_functions
util_functions.setup_logging()
example_circus = container.Container(name="example",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
id_gen = SequencialGenerator(prefix="PERSON_")
age_gen = NumpyRandomGenerator(method="normal",
loc=3,
scale=5,
seed=next(example_circus.seeder))
name_gen = FakerGenerator(method="name", seed=next(example_circus.seeder))
person = example_circus.create_population(name="person",
size=1000,
ids_gen=id_gen)
person.create_attribute("NAME", init_gen=name_gen)
person.create_attribute("AGE", init_gen=age_gen)
hello_world = example_circus.create_story(
name="hello_world",
initiating_population=example_circus.populations["person"],
return cells, cities, timer_config
if __name__ == "__main__":
# This is meant to be executed only once, to create the data on disk.
# Note: using generators and persisting the result could make sense
# if such generation is costly or for facilitating reproduceability,
# though a more common use cas might be to build such Populations and
# relationship from empirical exploration of a dataset.
# Note2: only the "static" properties of an environment are saved here,
# whereas the "dynamic parts" (e.g. stories) are stored "in code", i.e.
# in the withXYZ() class above that then need to be mixed in a Circus.
setup_logging()
cells, cities, timer_config = build_uganda_populations()
db.remove_namespace("uganda")
db.save_population(population=cells,
namespace="uganda",
population_id="cells")
db.save_population(population=cities,
namespace="uganda",
population_id="cities")
db.save_timer_gen(timer_gen=timer_config,
namespace="uganda",
timer_gen_id="cell_repair_timer_profile")