def step1():
example1 = circus.Circus(name="example1",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example1.create_population(
name="person",
size=1000,
ids_gen=SequencialGenerator(prefix="PERSON_"))
hello_world = example1.create_story(
name="hello_world",
initiating_population=person,
member_id_field="PERSON_ID",
# after each story, reset the timer to 0, so that it will get
# executed again at the next clock tick (next hour)
timer_gen=ConstantDependentGenerator(value=0))
hello_world.set_operations(
ConstantGenerator(value="hello world").ops.generate(named_as="HELLO"),
FieldLogger(log_id="hello"))
example1.run(duration=pd.Timedelta("48h"),
log_output_folder="output/example1",
delete_existing_logs=True)
with open("output/example1/hello.csv") as f:
print("Logged {} lines".format(len(f.readlines()) - 1))
def step4():
"""
Woah, this got drastically slower
"""
example1 = circus.Circus(name="example1",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example1.create_population(
name="person",
size=1000,
ids_gen=SequencialGenerator(prefix="PERSON_"))
person.create_attribute("NAME",
init_gen=FakerGenerator(method="name",
seed=next(
example1.seeder)))
sites = SequencialGenerator(prefix="SITE_").generate(1000)
random_site_gen = NumpyRandomGenerator(method="choice",
a=sites,
seed=next(example1.seeder))
allowed_sites = person.create_relationship(name="sites")
for i in range(5):
allowed_sites \
.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size))
hello_world = example1.create_story(
name="hello_world",
initiating_population=person,
member_id_field="PERSON_ID",
# after each story, reset the timer to 0, so that it will get
# executed again at the next clock tick (next hour)
timer_gen=ConstantDependentGenerator(value=0))
duration_gen = NumpyRandomGenerator(method="exponential",
scale=60,
seed=next(example1.seeder))
hello_world.set_operations(
person.ops.lookup(id_field="PERSON_ID", select={"NAME": "NAME"}),
ConstantGenerator(value="hello world").ops.generate(named_as="HELLO"),
duration_gen.ops.generate(named_as="DURATION"),
allowed_sites.ops.select_one(from_field="PERSON_ID", named_as="SITE"),
example1.clock.ops.timestamp(named_as="TIME"),
FieldLogger(log_id="hello"))
example1.run(duration=pd.Timedelta("48h"),
log_output_folder="output/example1",
delete_existing_logs=True)
with open("output/example1/hello.csv") as f:
print("Logged {} lines".format(len(f.readlines()) - 1))
def create_circus_with_population():
example_circus = circus.Circus(
name="example",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example_circus.create_population(
name="person", size=1000,
ids_gen=SequencialGenerator(prefix="PERSON_"))
person.create_attribute(
"NAME",
init_gen=FakerGenerator(method="name",
seed=next(example_circus.seeder)))
person.create_attribute(
"age",
init_gen=NumpyRandomGenerator(
method="normal", loc=35, scale=5,
seed=next(example_circus.seeder)))
return example_circus
import logging
import pandas as pd
from tabulate import tabulate
from trumania.core import circus
import trumania.core.util_functions as util_functions
from trumania.core.random_generators import SequencialGenerator, FakerGenerator, NumpyRandomGenerator
util_functions.setup_logging()
logging.info("building circus")
example = circus.Circus(name="example",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example.create_population(
name="person", size=1000, ids_gen=SequencialGenerator(prefix="PERSON_"))
person.create_attribute("NAME",
init_gen=FakerGenerator(method="name",
seed=next(example.seeder)))
person.create_attribute("age",
init_gen=NumpyRandomGenerator(method="normal",
loc=35,
scale=5,
seed=next(
example.seeder)))
def build_circus():
return circus.Circus(name="example3",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
def step7():
example1 = circus.Circus(name="example1",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example1.create_population(
name="person",
size=1000,
ids_gen=SequencialGenerator(prefix="PERSON_"))
person.create_attribute("NAME",
init_gen=FakerGenerator(method="name",
seed=next(
example1.seeder)))
person.create_attribute("POPULARITY",
init_gen=NumpyRandomGenerator(
method="uniform",
low=0,
high=1,
seed=next(example1.seeder)))
sites = SequencialGenerator(prefix="SITE_").generate(1000)
random_site_gen = NumpyRandomGenerator(method="choice",
a=sites,
seed=next(example1.seeder))
allowed_sites = person.create_relationship(name="sites")
# SITES ------------------
# Add HOME sites
allowed_sites.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size),
weights=0.4)
# Add WORK sites
allowed_sites.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size),
weights=0.3)
# Add OTHER sites
for i in range(3):
allowed_sites \
.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size),
weights=0.1)
# FRIENDS ------------------
friends = person.create_relationship(name="friends")
friends_df = pd.DataFrame.from_records(
make_random_bipartite_data(
person.ids,
person.ids,
p=0.005, # probability for a node to be connected to
# another one : 5 friends on average = 5/1000
seed=next(example1.seeder)),
columns=["A", "B"])
friends.add_relations(from_ids=friends_df["A"], to_ids=friends_df["B"])
# PRICE ------------------
def price(story_data):
result = pd.DataFrame(index=story_data.index)
result["PRICE"] = story_data["DURATION"] * 0.05
result["CURRENCY"] = "EUR"
return result
# STORIES ------------------
hello_world = example1.create_story(
name="hello_world",
initiating_population=person,
member_id_field="PERSON_ID",
# after each story, reset the timer to 0, so that it will get
# executed again at the next clock tick (next hour)
timer_gen=ConstantDependentGenerator(value=0))
duration_gen = NumpyRandomGenerator(method="exponential",
scale=60,
seed=next(example1.seeder))
hello_world.set_operations(
person.ops.lookup(id_field="PERSON_ID", select={"NAME": "NAME"}),
ConstantGenerator(value="hello world").ops.generate(named_as="HELLO"),
duration_gen.ops.generate(named_as="DURATION"),
friends.ops.select_one(
from_field="PERSON_ID",
named_as="COUNTERPART_ID",
weight=person.get_attribute_values("POPULARITY"),
# For people that do not have friends, it will try to find
# the POPULARITY attribute of a None and crash miserably
# Adding this flag will discard people that do not have friends
discard_empty=True),
person.ops.lookup(id_field="COUNTERPART_ID",
select={"NAME": "COUNTER_PART_NAME"}),
allowed_sites.ops.select_one(from_field="PERSON_ID", named_as="SITE"),
allowed_sites.ops.select_one(from_field="COUNTERPART_ID",
named_as="COUNTERPART_SITE"),
Apply(source_fields=["DURATION", "SITE", "COUNTERPART_SITE"],
named_as=["PRICE", "CURRENCY"],
f=price,
f_args="dataframe"),
example1.clock.ops.timestamp(named_as="TIME"),
FieldLogger(log_id="hello"))
example1.run(duration=pd.Timedelta("48h"),
log_output_folder="output/example1",
delete_existing_logs=True)
with open("output/example1/hello.csv") as f:
print("Logged {} lines".format(len(f.readlines()) - 1))
def build_circus():
return circus.Circus(name="salestock",
master_seed=12345,
start=pd.Timestamp("1 Jun 2018 00:00"),
step_duration=pd.Timedelta("1h"))
# These are already declared in the geography
# "n_dealers_l2": 25,
# "n_dealers_l1": 4,
"n_telcos": 1,
"n_field_agents": 100,
"n_customers": 20000,
}
if __name__ == "__main__":
util_functions.setup_logging()
snd = circus.Circus(name=static_params["circus_name"],
master_seed=12345,
start=pd.Timestamp(static_params["clock_start_date"]),
step_duration=pd.Timedelta(
static_params["clock_time_step"]))
distributor_id_gen = random_generators.SequencialGenerator(prefix="DIST_")
snd_products.create_products(snd, static_params)
snd_geo.load_geo_actors(snd, static_params)
snd_customers.add_customers(snd, static_params)
snd_pos.add_pos(snd, static_params)
snd_dealer.add_telcos(snd, static_params, distributor_id_gen)
snd_dealer.prepare_dealers(snd, params=static_params)
hrs = "48h"
#4 days
#for changing num logs per user per hour
#2 logs per hr = 0.5h, 5 logs per hour = 1/5=0.2h
num_log_per_hr = 1
val = 1 / num_log_per_hr
step_dur = "{0}h".format(val)
#7am-7pm = 13 hrs
num_log = 13 * (num_log_per_hr)
setup_logging()
start_date = pd.Timestamp("09 Aug 2020 00:00:00")
example1 = circus.Circus(name="example1",
master_seed=123456,
start=start_date,
step_duration=pd.Timedelta(step_dur))
person = example1.create_population(name="person",
size=num_user,
ids_gen=SequencialGenerator(prefix=""))
person.create_attribute("NAME",
init_gen=FakerGenerator(method="name",
seed=next(example1.seeder)))
activity = (100, 100, 100, 100, 54, 54, 54, 26, 20, 22)
normed_activity = [float(i) / sum(activity) for i in activity]
"""
sites = SequencialGenerator(prefix="").generate(num_sites)
random_site_gen = NumpyRandomGenerator(method="choice", a=sites,
