def test_should_return_how_many_time_first_booking(self):
# Given
create_user(id=1)
create_deposit(amount=500)
create_offerer(id=10)
create_product(id=1,
product_type="ThingType.MUSEES_PATRIMOINE_ABO")
create_venue(id=15, offerer_id=10)
create_offer(
id=30,
venue_id=15,
product_type="ThingType.MUSEES_PATRIMOINE_ABO",
product_id=1,
)
create_stock(id=20, offer_id=30)
create_booking(id=1,
user_id=1,
amount=10,
quantity=1,
stock_id=20,
is_used=True)
create_booking(
id=2,
user_id=1,
amount=10,
quantity=1,
stock_id=20,
token="ABC321",
is_used=True,
)
create_booking(
id=3,
user_id=1,
amount=10,
quantity=3,
stock_id=20,
token="FAM321",
is_cancelled=True,
)
expected_first_booking_number = pandas.Series(
index=pandas.Index(data=[30], name="offer_id"),
data=[1],
name="Nombre de premières réservations",
)
# When
query = _get_count_first_booking_query()
# Then
with ENGINE.connect() as connection:
count_first_booking = pandas.read_sql(query,
connection,
index_col="offer_id")
pandas.testing.assert_series_equal(
count_first_booking["Nombre de premières réservations"],
expected_first_booking_number,
)
def test_should_return_exact_booking_amount(self, app):
# Given
create_user(id=1)
create_deposit(user_id=1)
create_offerer(id=1)
create_venue(offerer_id=1, id=1)
create_product(id=1, product_type="ThingType.ACTIVATION")
create_offer(venue_id=1,
product_id=1,
id=1,
product_type="ThingType.ACTIVATION")
create_stock(offer_id=1, id=1)
create_product(id=2, product_type="ThingType.MUSIQUE")
create_offer(venue_id=1,
product_id=2,
id=2,
product_type="ThingType.MUSIQUE")
create_stock(offer_id=2, id=2)
create_booking(id=1,
user_id=1,
quantity=1,
stock_id=2,
is_used=True)
create_booking(
id=2,
user_id=1,
quantity=1,
amount=10,
stock_id=2,
token="ABC321",
is_used=True,
)
create_booking(
id=3,
user_id=1,
quantity=3,
amount=5,
stock_id=2,
token="FAM321",
is_cancelled=True,
)
expected_amount_per_booking = pandas.Series(
data=[0.0, 10.0, 15.0],
name="Montant de la réservation",
index=Int64Index([1, 2, 3], name="booking_id"),
)
# When
query = get_booking_amount()
# Then
with ENGINE.connect() as connection:
booking_amount = pandas.read_sql(query,
connection,
index_col="booking_id")
pandas.testing.assert_series_equal(
booking_amount["Montant de la réservation"],
expected_amount_per_booking)
def test_should_not_count_bookings_appearing_on_payment_if_payment_s_current_status_is_banned(
self, ):
# Given
create_user(id=1)
create_user(id=2, email="*****@*****.**")
create_product(id=1)
create_product(id=2)
create_offerer(id=3)
create_venue(
offerer_id=3,
id=1,
siret=None,
postal_code=None,
city=None,
departement_code=None,
is_virtual=True,
)
create_offer(venue_id=1, product_id=1, id=3)
create_stock(offer_id=3, id=1)
create_offer(venue_id=1, product_id=2, id=2)
create_stock(offer_id=2, id=2)
create_booking(user_id=1, stock_id=1, id=4, quantity=2)
create_payment(booking_id=4, id=1)
create_payment_status(payment_id=1,
id=1,
date="2019-01-01",
status="PENDING")
create_payment_status(payment_id=1,
id=2,
date="2019-01-02",
status="BANNED")
expected_stocks_booking_information = pandas.Series(
index=pandas.Index(data=[1, 2], name="stock_id"),
data=[0, 0],
name="Nombre de réservations ayant un paiement",
)
# When
query = _get_stocks_booking_information_query()
# Then
with ENGINE.connect() as connection:
stocks_booking_information = pandas.read_sql(
query, connection, index_col="stock_id")
pandas.testing.assert_series_equal(
stocks_booking_information[
"Nombre de réservations ayant un paiement"],
expected_stocks_booking_information,
)
# Then
pandas.testing.assert_series_equal(
stocks_booking_information[
"Nombre de réservations ayant un paiement"],
expected_stocks_booking_information,
)
def test_should_return_None_if_the_offerer_has_no_booking(self):
# Given
create_offerer()
# When
query = _get_first_booking_creation_dates_query()
# Then
with ENGINE.connect() as connection:
first_booking_dates = pandas.read_sql(query,
connection,
index_col="offerer_id")
assert first_booking_dates.loc[
1, "Date de première réservation"] is None
def test_should_return_column_with_total_number_of_bookings_cancelled_and_not(
self, ):
# Given
create_user(id=1)
create_user(id=2, email="*****@*****.**")
create_product(id=1)
create_product(id=2)
create_offerer(id=3)
create_venue(
offerer_id=3,
id=1,
siret=None,
postal_code=None,
city=None,
departement_code=None,
is_virtual=True,
)
create_offer(venue_id=1, product_id=1, id=3)
create_stock(offer_id=3, id=1)
create_offer(venue_id=1, product_id=2, id=2)
create_stock(offer_id=2, id=2)
create_booking(user_id=1,
stock_id=1,
id=4,
quantity=2,
is_cancelled=True)
create_booking(user_id=2,
stock_id=2,
id=5,
quantity=1,
token="IS02JE")
expected_stocks_booking_information = pandas.Series(
index=pandas.Index(data=[1, 2], name="stock_id"),
data=[2, 1],
name="Nombre total de réservations",
)
# When
query = _get_stocks_booking_information_query()
# Then
with ENGINE.connect() as connection:
stocks_booking_information = pandas.read_sql(
query, connection, index_col="stock_id")
pandas.testing.assert_series_equal(
stocks_booking_information["Nombre total de réservations"],
expected_stocks_booking_information,
)
def test_should_return_postal_code_related_to_existing_offerers(
self, app):
# Given
create_offerer(id=1, postal_code="75003")
create_offerer(id=2, postal_code="97459", siren="123456788")
expected_postal_code_dataframe = pandas.DataFrame(data={
"id": [1, 2],
"postalCode": ["75003", "97459"]
})
# When
postal_code_dataframe = get_postal_code_dataframe(ENGINE)
# Then
pandas.testing.assert_frame_equal(postal_code_dataframe,
expected_postal_code_dataframe)
def test_should_return_the_number_of_offers(self):
# Given
create_offerer(id=1)
create_venue(offerer_id=1, id=1)
create_product(id=1)
create_product(id=2)
create_offer(venue_id=1, product_id=1, id=1)
create_offer(venue_id=1, product_id=2, id=2)
# When
query = _get_number_of_offers_query()
# Then
with ENGINE.connect() as connection:
number_of_offers = pandas.read_sql(query,
connection,
index_col="offerer_id")
assert number_of_offers.loc[1, "Nombre d’offres"] == 2
def test_should_return_how_many_stocks(self):
# Given
create_offerer(id=10)
create_venue(id=15, offerer_id=10)
create_product(id=1)
create_offer(id=30, venue_id=15, product_id=1)
create_stock(id=20, offer_id=30)
create_stock(id=21, offer_id=30)
expected_offer_stock = pandas.Series(index=pandas.Index(
data=[30], name="offer_id"),
data=[20],
name="Stock")
# When
query = _get_offer_info_with_quantity()
# Then
with ENGINE.connect() as connection:
offer_stock = pandas.read_sql(query,
connection,
index_col="offer_id")
pandas.testing.assert_series_equal(offer_stock["Stock"],
expected_offer_stock)
def test_should_return_how_many_time_in_favorite_columns(self):
# Given
create_user(id=1)
create_product(id=1)
create_offerer(id=1)
create_venue(
offerer_id=1,
id=1,
siret=None,
postal_code=None,
city=None,
departement_code=None,
is_virtual=True,
)
create_offer(venue_id=1,
product_id=1,
id=1,
product_type="ThingType.LIVRE_EDITION")
create_favorite(id=1, offer_id=1, user_id=1)
expected_favorites_number = pandas.Series(
index=pandas.Index(data=[1], name="offer_id"),
data=[1],
name="Nombre de fois où l'offre a été mise en favoris",
)
# When
query = _get_count_favorites_query()
# Then
with ENGINE.connect() as connection:
count_favorites = pandas.read_sql(query,
connection,
index_col="offer_id")
pandas.testing.assert_series_equal(
count_favorites[
"Nombre de fois où l'offre a été mise en favoris"],
expected_favorites_number,
)
def test_should_return_physical_product_column_with_false_if_not_relevant_product_type(
self, ):
# Given
create_user(id=1)
create_product(id=1)
create_offerer(id=1)
create_venue(
offerer_id=1,
id=1,
siret=None,
postal_code=None,
city=None,
departement_code=None,
is_virtual=True,
)
create_offer(venue_id=1,
product_id=1,
id=1,
product_type="ThingType.CINEMA_CARD")
expected_is_physical_information = pandas.Series(
index=pandas.Index(data=[1], name="offer_id"),
data=[False],
name="Bien physique",
)
# When
query = _get_is_physical_information_query()
# Then
with ENGINE.connect() as connection:
is_physical_information = pandas.read_sql(query,
connection,
index_col="offer_id")
pandas.testing.assert_series_equal(
is_physical_information["Bien physique"],
expected_is_physical_information,
)
def test_should_return_column_with_0_cancelled_bookings_if_no_booking(
self):
# Given
create_user(id=1)
create_user(id=2, email="*****@*****.**")
create_product(id=1)
create_product(id=2)
create_offerer(id=3)
create_venue(
offerer_id=3,
id=1,
siret=None,
postal_code=None,
city=None,
departement_code=None,
is_virtual=True,
)
create_offer(venue_id=1, product_id=1, id=3)
create_stock(offer_id=3, id=1)
expected_stocks_booking_information = pandas.Series(
index=pandas.Index(data=[1], name="stock_id"),
data=[0],
name="Nombre de réservations annulées",
)
# When
query = _get_stocks_booking_information_query()
# Then
with ENGINE.connect() as connection:
stocks_booking_information = pandas.read_sql(
query, connection, index_col="stock_id")
pandas.testing.assert_series_equal(
stocks_booking_information["Nombre de réservations annulées"],
expected_stocks_booking_information,
)
def test_should_return_outings_column_with_true_if_relevant_product_type(
self, offer_type):
# Given
create_user(id=1)
create_product(id=1)
create_offerer(id=1)
create_venue(
offerer_id=1,
id=1,
siret=None,
postal_code=None,
city=None,
departement_code=None,
is_virtual=True,
)
create_offer(venue_id=1,
product_id=1,
id=1,
product_type=offer_type)
expected_is_outing_information = pandas.Series(
index=pandas.Index(data=[1], name="offer_id"),
data=[True],
name="Sortie",
)
# When
query = _get_is_outing_information_query()
# Then
with ENGINE.connect() as connection:
is_outing_information = pandas.read_sql(query,
connection,
index_col="offer_id")
pandas.testing.assert_series_equal(is_outing_information["Sortie"],
expected_is_outing_information)
def test_should_return_siren_related_to_existing_offerers(self, app):
# Given
create_offerer(id=1, siren="345678123")
create_offerer(id=2, siren=None)
create_offerer(id=3, siren="123456789")
expected_siren_dataframe = pandas.DataFrame(data={
"id": [1, 3],
"siren": ["345678123", "123456789"]
})
# When
siren_dataframe = get_siren_dataframe(ENGINE)
# Then
pandas.testing.assert_frame_equal(siren_dataframe,
expected_siren_dataframe)