def test_creating_an_empty_population_and_adding_attributes_later_should_be_possible(
):
# empty population
a = Population(circus=None, size=0)
assert a.ids.shape[0] == 0
# empty attributes
a.create_attribute("att1")
a.create_attribute("att2")
dynamically_created = pd.DataFrame(
{
"att1": [1, 2, 3],
"att2": [11, 12, 13],
},
index=["ac1", "ac2", "ac3"])
a.update(dynamically_created)
assert a.ids.tolist() == ["ac1", "ac2", "ac3"]
assert a.get_attribute_values("att1",
["ac1", "ac2", "ac3"]).tolist() == [1, 2, 3]
assert a.get_attribute_values(
"att2", ["ac1", "ac2", "ac3"]).tolist() == [11, 12, 13]
def test_insert_poppulation_value_for_existing_populations_should_update_all_values(
):
# copy of dummy population that will be updated
tested_population = Population(circus=None,
size=10,
ids_gen=SequencialGenerator(max_length=1,
prefix="a_"))
ages = [10, 20, 40, 10, 100, 98, 12, 39, 76, 23]
tested_population.create_attribute("age", init_values=ages)
city = ["a", "b", "b", "a", "d", "e", "r", "a", "z", "c"]
tested_population.create_attribute("city", init_values=city)
current = tested_population.get_attribute_values("age",
["a_0", "a_7", "a_9"])
assert current.tolist() == [10, 39, 23]
update = pd.DataFrame({
"age": [139, 123],
"city": ["city_7", "city_9"]
},
index=["a_7", "a_9"])
tested_population.update(update)
# we should have the same number of populations
assert tested_population.ids.shape[0] == 10
updated_age = tested_population.get_attribute_values(
"age", ["a_0", "a_7", "a_9"])
updated_city = tested_population.get_attribute_values(
"city", ["a_0", "a_7", "a_9"])
assert updated_age.tolist() == [10, 139, 123]
assert updated_city.tolist() == ["a", "city_7", "city_9"]
def test_insert_op_population_value_for_existing_populations_should_update_all_values(
):
# same as test above but triggered as an Operation on story data
# copy of dummy population that will be updated
tested_population = Population(circus=None,
size=10,
ids_gen=SequencialGenerator(max_length=1,
prefix="a_"))
ages = [10, 20, 40, 10, 100, 98, 12, 39, 76, 23]
tested_population.create_attribute("age", init_values=ages)
city = ["a", "b", "b", "a", "d", "e", "r", "a", "z", "c"]
tested_population.create_attribute("city", init_values=city)
story_data = pd.DataFrame(
{
"the_new_age": [139, 123, 1, 2],
"location": ["city_7", "city_9", "city_11", "city_10"],
"updated_populations": ["a_7", "a_9", "a_11", "a_10"]
},
index=["d_1", "d_2", "d_4", "d_3"])
update_op = tested_population.ops.update(id_field="updated_populations",
copy_attributes_from_fields={
"age": "the_new_age",
"city": "location"
})
story_data_2, logs = update_op(story_data)
# there should be no impact on the story data
assert story_data_2.shape == (4, 3)
assert sorted(story_data_2.columns.tolist()) == [
"location", "the_new_age", "updated_populations"
]
# we should have 2 new populations
assert tested_population.ids.shape[0] == 12
updated_age = tested_population.get_attribute_values(
"age", ["a_0", "a_7", "a_9", "a_10", "a_11"])
updated_city = tested_population.get_attribute_values(
"city", ["a_0", "a_7", "a_9", "a_10", "a_11"])
assert updated_age.tolist() == [10, 139, 123, 2, 1]
assert updated_city.tolist() == [
"a", "city_7", "city_9", "city_10", "city_11"
]
def create_random_cells(self, n_cells):
"""
Creation of a basic population for cells, with latitude and longitude
"""
cells = Population(size=n_cells)
latitude_generator = FakerGenerator(method="latitude",
seed=next(self.seeder))
longitude_generator = FakerGenerator(method="longitude",
seed=next(self.seeder))
cells.create_attribute("latitude", init_gen=latitude_generator)
cells.create_attribute("longitude", init_gen=longitude_generator)
return cells
def test_overwrite_attribute():
population = Population(circus=tc, size=10,
ids_gen=SequencialGenerator(prefix="u_", max_length=1))
ages = [10, 20, 40, 10, 100, 98, 12, 39, 76, 23]
age_attr = population.create_attribute("age", init_values=ages)
# before modification
ages = age_attr.get_values(["u_0", "u_4", "u_9"]).tolist()
assert ages == [10, 100, 23]
story_data = pd.DataFrame({
# id of the populations to update
"A_ID": ["u_4", "u_0"],
# new values to copy
"new_ages": [34, 30]},
# index of the story data has, in general, nothing to do with the
# updated population
index=["cust_1", "cust_2"]
)
update = age_attr.ops.update(
member_id_field="A_ID",
copy_from_field="new_ages"
)
_, logs = update(story_data)
assert logs == {}
# before modification
ages = age_attr.get_values(["u_0", "u_4", "u_9"]).tolist()
assert ages == [30, 34, 23]
import path
import pandas as pd
import os
import pytest
from trumania.core.random_generators import SequencialGenerator
from trumania.core.population import Population
dummy_population = Population(circus=None,
size=10,
ids_gen=SequencialGenerator(max_length=1,
prefix="id_"))
ages = [10, 20, 40, 10, 100, 98, 12, 39, 76, 23]
dummy_population.create_attribute("age", init_values=ages)
city = ["a", "b", "b", "a", "d", "e", "r", "a", "z", "c"]
dummy_population.create_attribute("city", init_values=city)
# some fake story data with an index corresponding to another population
# => simulates an story triggered by that other population
# the column "NEIGHBOUR" contains value that point to the dummy population, with
# a duplication (id2)
story_data = pd.DataFrame(
{
"A": ["a1", "a2", "a3", "a4"],
"B": ["b1", "b2", "b3", "b4"],
"NEIGHBOUR": ["id_2", "id_4", "id_7", "id_2"],
"COUSINS": [
["id_2", "id_4", "id_7", "id_2"],
["id_3"],