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"
]