Merge branch 'release-0.3.0' into develop

.github/workflows/tests.yml

@@ -1,7 +1,8 @@
 name: CI
 on: push
 jobs:
-  tests:
+  fast-tests:
+    name: fast (without R)
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
@@ -10,5 +11,22 @@ jobs:
         python-version: 3.8
         architecture: x64
     - run: pip install nox==2020.5.24
-    - run: pip install poetry==1.0.10
+    - run: pip install poetry==1.1.4
-    - run: nox
+    - run: nox -s format lint ci-tests-fast safety docs
+  slow-tests:
+    name: slow (with R)
+    runs-on: ubuntu-latest
+    env:
+      R_LIBS: .r_libs
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v1
+      with:
+        python-version: 3.8
+        architecture: x64
+    - run: mkdir .r_libs
+    - run: sudo apt-get install r-base r-base-dev libcurl4-openssl-dev libxml2-dev patchelf
+    - run: R -e "install.packages('forecast')"
+    - run: pip install nox==2020.5.24
+    - run: pip install poetry==1.1.4
+    - run: nox -s ci-tests-slow

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "research/papers/demand-forecasting"]
+	path = research/papers/demand-forecasting
+	url = git@github.com:webartifex/urban-meal-delivery-demand-forecasting.git

README.md

@@ -16,7 +16,7 @@ that iteratively build on each other.
 ### Data Cleaning
 
 The UDP provided its raw data as a PostgreSQL dump.
-This [notebook](https://nbviewer.jupyter.org/github/webartifex/urban-meal-delivery/blob/develop/notebooks/00_clean_data.ipynb)
+This [notebook](https://nbviewer.jupyter.org/github/webartifex/urban-meal-delivery/blob/develop/research/clean_data.ipynb)
 cleans the data extensively
 and maps them onto the [ORM models](https://github.com/webartifex/urban-meal-delivery/tree/develop/src/urban_meal_delivery/db)
 defined in the `urban-meal-delivery` package
@@ -25,7 +25,7 @@ and contains all source code to drive the analyses.
 
 Due to a non-disclosure agreement with the UDP,
 neither the raw nor the cleaned data are published as of now.
-However, previews of the data can be seen throughout the [notebooks/](https://github.com/webartifex/urban-meal-delivery/tree/develop/notebooks) folders.
+However, previews of the data can be seen throughout the [research/](https://github.com/webartifex/urban-meal-delivery/tree/develop/research) folder.
 
 
 ### Real-time Demand Forecasting

docs/conf.py

@@ -5,7 +5,7 @@ import urban_meal_delivery as umd
 
 project = umd.__pkg_name__
 author = umd.__author__
-copyright = f'2020, {author}'  # pylint:disable=redefined-builtin
+copyright = f'2020, {author}'
 version = release = umd.__version__
 
 extensions = [

migrations/env.py

@@ -21,7 +21,11 @@ log_config.fileConfig(context.config.config_file_name)
 
 def include_object(obj, _name, type_, _reflected, _compare_to):
     """Only include the clean schema into --autogenerate migrations."""
-    if type_ in {'table', 'column'} and obj.schema != umd_config.DATABASE_SCHEMA:
+    if (  # noqa:WPS337
+        type_ in {'table', 'column'}
+        and hasattr(obj, 'schema')  # noqa:WPS421 => fix for rare edge case
+        and obj.schema != umd_config.CLEAN_SCHEMA
+    ):
         return False
 
     return True

migrations/versions/rev_20200806_23_f11cd76d2f45_create_the_database.py

@@ -107,13 +107,13 @@ def upgrade():
         sa.Column('id', sa.Integer(), autoincrement=False, nullable=False),
         sa.Column('primary_id', sa.Integer(), nullable=False),
         sa.Column('created_at', sa.DateTime(), nullable=False),
-        sa.Column('place_id', sa.Unicode(length=120), nullable=False),  # noqa:WPS432
+        sa.Column('place_id', sa.Unicode(length=120), nullable=False),
         sa.Column('latitude', postgresql.DOUBLE_PRECISION(), nullable=False),
         sa.Column('longitude', postgresql.DOUBLE_PRECISION(), nullable=False),
         sa.Column('city_id', sa.SmallInteger(), nullable=False),
-        sa.Column('city', sa.Unicode(length=25), nullable=False),  # noqa:WPS432
+        sa.Column('city', sa.Unicode(length=25), nullable=False),
         sa.Column('zip_code', sa.Integer(), nullable=False),
-        sa.Column('street', sa.Unicode(length=80), nullable=False),  # noqa:WPS432
+        sa.Column('street', sa.Unicode(length=80), nullable=False),
         sa.Column('floor', sa.SmallInteger(), nullable=True),
         sa.CheckConstraint(
             '-180 <= longitude AND longitude <= 180',
@@ -192,7 +192,7 @@ def upgrade():
         'restaurants',
         sa.Column('id', sa.SmallInteger(), autoincrement=False, nullable=False),
         sa.Column('created_at', sa.DateTime(), nullable=False),
-        sa.Column('name', sa.Unicode(length=45), nullable=False),  # noqa:WPS432
+        sa.Column('name', sa.Unicode(length=45), nullable=False),
         sa.Column('address_id', sa.Integer(), nullable=False),
         sa.Column('estimated_prep_duration', sa.SmallInteger(), nullable=False),
         sa.CheckConstraint(

migrations/versions/rev_20210102_18_888e352d7526_add_pixel_grid.py

@@ -0,0 +1,167 @@
+"""Add pixel grid.
+
+Revision: #888e352d7526 at 2021-01-02 18:11:02
+Revises: #f11cd76d2f45
+"""
+
+import os
+
+import sqlalchemy as sa
+from alembic import op
+
+from urban_meal_delivery import configuration
+
+
+revision = '888e352d7526'
+down_revision = 'f11cd76d2f45'
+branch_labels = None
+depends_on = None
+
+
+config = configuration.make_config('testing' if os.getenv('TESTING') else 'production')
+
+
+def upgrade():
+    """Upgrade to revision 888e352d7526."""
+    op.create_table(
+        'grids',
+        sa.Column('id', sa.SmallInteger(), autoincrement=True, nullable=False),
+        sa.Column('city_id', sa.SmallInteger(), nullable=False),
+        sa.Column('side_length', sa.SmallInteger(), nullable=True),
+        sa.PrimaryKeyConstraint('id', name=op.f('pk_grids')),
+        sa.ForeignKeyConstraint(
+            ['city_id'],
+            [f'{config.CLEAN_SCHEMA}.cities.id'],
+            name=op.f('fk_grids_to_cities_via_city_id'),
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        sa.UniqueConstraint(
+            'city_id', 'side_length', name=op.f('uq_grids_on_city_id_side_length'),
+        ),
+        # This `UniqueConstraint` is needed by the `addresses_pixels` table below.
+        sa.UniqueConstraint('id', 'city_id', name=op.f('uq_grids_on_id_city_id')),
+        schema=config.CLEAN_SCHEMA,
+    )
+
+    op.create_table(
+        'pixels',
+        sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
+        sa.Column('grid_id', sa.SmallInteger(), nullable=False),
+        sa.Column('n_x', sa.SmallInteger(), nullable=False),
+        sa.Column('n_y', sa.SmallInteger(), nullable=False),
+        sa.CheckConstraint('0 <= n_x', name=op.f('ck_pixels_on_n_x_is_positive')),
+        sa.CheckConstraint('0 <= n_y', name=op.f('ck_pixels_on_n_y_is_positive')),
+        sa.ForeignKeyConstraint(
+            ['grid_id'],
+            [f'{config.CLEAN_SCHEMA}.grids.id'],
+            name=op.f('fk_pixels_to_grids_via_grid_id'),
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        sa.PrimaryKeyConstraint('id', name=op.f('pk_pixels')),
+        sa.UniqueConstraint(
+            'grid_id', 'n_x', 'n_y', name=op.f('uq_pixels_on_grid_id_n_x_n_y'),
+        ),
+        sa.UniqueConstraint('id', 'grid_id', name=op.f('uq_pixels_on_id_grid_id')),
+        schema=config.CLEAN_SCHEMA,
+    )
+
+    op.create_index(
+        op.f('ix_pixels_on_grid_id'),
+        'pixels',
+        ['grid_id'],
+        unique=False,
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_index(
+        op.f('ix_pixels_on_n_x'),
+        'pixels',
+        ['n_x'],
+        unique=False,
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_index(
+        op.f('ix_pixels_on_n_y'),
+        'pixels',
+        ['n_y'],
+        unique=False,
+        schema=config.CLEAN_SCHEMA,
+    )
+
+    # This `UniqueConstraint` is needed by the `addresses_pixels` table below.
+    op.create_unique_constraint(
+        'uq_addresses_on_id_city_id',
+        'addresses',
+        ['id', 'city_id'],
+        schema=config.CLEAN_SCHEMA,
+    )
+
+    op.create_table(
+        'addresses_pixels',
+        sa.Column('address_id', sa.Integer(), nullable=False),
+        sa.Column('city_id', sa.SmallInteger(), nullable=False),
+        sa.Column('grid_id', sa.SmallInteger(), nullable=False),
+        sa.Column('pixel_id', sa.Integer(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ['address_id', 'city_id'],
+            [
+                f'{config.CLEAN_SCHEMA}.addresses.id',
+                f'{config.CLEAN_SCHEMA}.addresses.city_id',
+            ],
+            name=op.f('fk_addresses_pixels_to_addresses_via_address_id_city_id'),
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        sa.ForeignKeyConstraint(
+            ['grid_id', 'city_id'],
+            [
+                f'{config.CLEAN_SCHEMA}.grids.id',
+                f'{config.CLEAN_SCHEMA}.grids.city_id',
+            ],
+            name=op.f('fk_addresses_pixels_to_grids_via_grid_id_city_id'),
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        sa.ForeignKeyConstraint(
+            ['pixel_id', 'grid_id'],
+            [
+                f'{config.CLEAN_SCHEMA}.pixels.id',
+                f'{config.CLEAN_SCHEMA}.pixels.grid_id',
+            ],
+            name=op.f('fk_addresses_pixels_to_pixels_via_pixel_id_grid_id'),
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        sa.PrimaryKeyConstraint(
+            'address_id', 'pixel_id', name=op.f('pk_addresses_pixels'),
+        ),
+        sa.UniqueConstraint(
+            'address_id',
+            'grid_id',
+            name=op.f('uq_addresses_pixels_on_address_id_grid_id'),
+        ),
+        schema=config.CLEAN_SCHEMA,
+    )
+
+
+def downgrade():
+    """Downgrade to revision f11cd76d2f45."""
+    op.drop_table('addresses_pixels', schema=config.CLEAN_SCHEMA)
+    op.drop_constraint(
+        'uq_addresses_on_id_city_id',
+        'addresses',
+        type_=None,
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_index(
+        op.f('ix_pixels_on_n_y'), table_name='pixels', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_index(
+        op.f('ix_pixels_on_n_x'), table_name='pixels', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_index(
+        op.f('ix_pixels_on_grid_id'), table_name='pixels', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_table('pixels', schema=config.CLEAN_SCHEMA)
+    op.drop_table('grids', schema=config.CLEAN_SCHEMA)

migrations/versions/rev_20210106_19_e40623e10405_add_demand_forecasting.py

@@ -0,0 +1,96 @@
+"""Add demand forecasting.
+
+Revision: #e40623e10405 at 2021-01-06 19:55:56
+Revises: #888e352d7526
+"""
+
+import os
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+from urban_meal_delivery import configuration
+
+
+revision = 'e40623e10405'
+down_revision = '888e352d7526'
+branch_labels = None
+depends_on = None
+
+
+config = configuration.make_config('testing' if os.getenv('TESTING') else 'production')
+
+
+def upgrade():
+    """Upgrade to revision e40623e10405."""
+    op.create_table(
+        'forecasts',
+        sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
+        sa.Column('pixel_id', sa.Integer(), nullable=False),
+        sa.Column('start_at', sa.DateTime(), nullable=False),
+        sa.Column('time_step', sa.SmallInteger(), nullable=False),
+        sa.Column('training_horizon', sa.SmallInteger(), nullable=False),
+        sa.Column('method', sa.Unicode(length=20), nullable=False),
+        sa.Column('prediction', postgresql.DOUBLE_PRECISION(), nullable=False),
+        sa.PrimaryKeyConstraint('id', name=op.f('pk_forecasts')),
+        sa.ForeignKeyConstraint(
+            ['pixel_id'],
+            [f'{config.CLEAN_SCHEMA}.pixels.id'],
+            name=op.f('fk_forecasts_to_pixels_via_pixel_id'),
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        sa.CheckConstraint(
+            """
+                NOT (
+                    EXTRACT(HOUR FROM start_at) < 11
+                    OR
+                    EXTRACT(HOUR FROM start_at) > 22
+                )
+            """,
+            name=op.f('ck_forecasts_on_start_at_must_be_within_operating_hours'),
+        ),
+        sa.CheckConstraint(
+            'CAST(EXTRACT(MINUTES FROM start_at) AS INTEGER) % 15 = 0',
+            name=op.f('ck_forecasts_on_start_at_minutes_must_be_quarters_of_the_hour'),
+        ),
+        sa.CheckConstraint(
+            'CAST(EXTRACT(MICROSECONDS FROM start_at) AS INTEGER) % 1000000 = 0',
+            name=op.f('ck_forecasts_on_start_at_allows_no_microseconds'),
+        ),
+        sa.CheckConstraint(
+            'EXTRACT(SECONDS FROM start_at) = 0',
+            name=op.f('ck_forecasts_on_start_at_allows_no_seconds'),
+        ),
+        sa.CheckConstraint(
+            'time_step > 0', name=op.f('ck_forecasts_on_time_step_must_be_positive'),
+        ),
+        sa.CheckConstraint(
+            'training_horizon > 0',
+            name=op.f('ck_forecasts_on_training_horizon_must_be_positive'),
+        ),
+        sa.UniqueConstraint(
+            'pixel_id',
+            'start_at',
+            'time_step',
+            'training_horizon',
+            'method',
+            name=op.f(
+                'uq_forecasts_on_pixel_id_start_at_time_step_training_horizon_method',
+            ),
+        ),
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_index(
+        op.f('ix_forecasts_on_pixel_id'),
+        'forecasts',
+        ['pixel_id'],
+        unique=False,
+        schema=config.CLEAN_SCHEMA,
+    )
+
+
+def downgrade():
+    """Downgrade to revision 888e352d7526."""
+    op.drop_table('forecasts', schema=config.CLEAN_SCHEMA)

migrations/versions/rev_20210120_16_26711cd3f9b9_add_confidence_intervals_to_forecasts.py

@@ -0,0 +1,124 @@
+"""Add confidence intervals to forecasts.
+
+Revision: #26711cd3f9b9 at 2021-01-20 16:08:21
+Revises: #e40623e10405
+"""
+
+import os
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+from urban_meal_delivery import configuration
+
+
+revision = '26711cd3f9b9'
+down_revision = 'e40623e10405'
+branch_labels = None
+depends_on = None
+
+
+config = configuration.make_config('testing' if os.getenv('TESTING') else 'production')
+
+
+def upgrade():
+    """Upgrade to revision 26711cd3f9b9."""
+    op.alter_column(
+        'forecasts', 'method', new_column_name='model', schema=config.CLEAN_SCHEMA,
+    )
+    op.add_column(
+        'forecasts',
+        sa.Column('low80', postgresql.DOUBLE_PRECISION(), nullable=True),
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.add_column(
+        'forecasts',
+        sa.Column('high80', postgresql.DOUBLE_PRECISION(), nullable=True),
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.add_column(
+        'forecasts',
+        sa.Column('low95', postgresql.DOUBLE_PRECISION(), nullable=True),
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.add_column(
+        'forecasts',
+        sa.Column('high95', postgresql.DOUBLE_PRECISION(), nullable=True),
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_check_constraint(
+        op.f('ck_forecasts_on_ci_upper_and_lower_bounds'),
+        'forecasts',
+        """
+        NOT (
+            low80 IS NULL AND high80 IS NOT NULL
+            OR
+            low80 IS NOT NULL AND high80 IS NULL
+            OR
+            low95 IS NULL AND high95 IS NOT NULL
+            OR
+            low95 IS NOT NULL AND high95 IS NULL
+        )
+        """,
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_check_constraint(
+        op.f('prediction_must_be_within_ci'),
+        'forecasts',
+        """
+        NOT (
+            prediction < low80
+            OR
+            prediction < low95
+            OR
+            prediction > high80
+            OR
+            prediction > high95
+        )
+        """,
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_check_constraint(
+        op.f('ci_upper_bound_greater_than_lower_bound'),
+        'forecasts',
+        """
+        NOT (
+            low80 > high80
+            OR
+            low95 > high95
+        )
+        """,
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_check_constraint(
+        op.f('ci95_must_be_wider_than_ci80'),
+        'forecasts',
+        """
+        NOT (
+            low80 < low95
+            OR
+            high80 > high95
+        )
+        """,
+        schema=config.CLEAN_SCHEMA,
+    )
+
+
+def downgrade():
+    """Downgrade to revision e40623e10405."""
+    op.alter_column(
+        'forecasts', 'model', new_column_name='method', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_column(
+        'forecasts', 'low80', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_column(
+        'forecasts', 'high80', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_column(
+        'forecasts', 'low95', schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_column(
+        'forecasts', 'high95', schema=config.CLEAN_SCHEMA,
+    )

migrations/versions/rev_20210123_15_e86290e7305e_remove_orders_from_restaurants_with.py

@@ -0,0 +1,398 @@
+"""Remove orders from restaurants with invalid location ...
+
+... and also de-duplicate a couple of redundant addresses.
+
+Revision: #e86290e7305e at 2021-01-23 15:56:59
+Revises: #26711cd3f9b9
+
+1) Remove orders
+
+Some restaurants have orders to be picked up at an address that
+not their primary address. That is ok if that address is the location
+of a second franchise. However, for a small number of restaurants
+there is only exactly one order at that other address that often is
+located far away from the restaurant's primary location. It looks
+like a restaurant signed up with some invalid location that was then
+corrected into the primary one.
+
+Use the following SQL statement to obtain a list of these locations
+before this migration is run:
+
+SELECT
+    orders.pickup_address_id,
+    COUNT(*) AS n_orders,
+    MIN(placed_at) as first_order_at,
+    MAX(placed_at) as last_order_at
+FROM
+    {config.CLEAN_SCHEMA}.orders
+LEFT OUTER JOIN
+    {config.CLEAN_SCHEMA}.restaurants
+    ON orders.restaurant_id = restaurants.id
+WHERE
+    orders.pickup_address_id <> restaurants.address_id
+GROUP BY
+    pickup_address_id;
+
+50 orders with such weird pickup addresses are removed with this migration.
+
+
+2) De-duplicate addresses
+
+Five restaurants have two pickup addresses that are actually the same location.
+
+The following SQL statement shows them before this migration is run:
+
+SELECT
+    orders.restaurant_id,
+    restaurants.name,
+    restaurants.address_id AS primary_address_id,
+    addresses.id AS address_id,
+    addresses.street,
+    COUNT(*) AS n_orders
+FROM
+    {config.CLEAN_SCHEMA}.orders
+LEFT OUTER JOIN
+    {config.CLEAN_SCHEMA}.addresses ON orders.pickup_address_id = addresses.id
+LEFT OUTER JOIN
+    {config.CLEAN_SCHEMA}.restaurants ON orders.restaurant_id = restaurants.id
+WHERE
+    orders.restaurant_id IN (
+    SELECT
+        restaurant_id
+    FROM (
+        SELECT DISTINCT
+            restaurant_id,
+            pickup_address_id
+        FROM
+            {config.CLEAN_SCHEMA}.orders
+    ) AS restaurant_locations
+    GROUP BY
+        restaurant_id
+    HAVING
+        COUNT(pickup_address_id) > 1
+)
+GROUP BY
+    orders.restaurant_id,
+    restaurants.name,
+    restaurants.address_id,
+    addresses.id,
+    addresses.street
+ORDER BY
+    orders.restaurant_id,
+    restaurants.name,
+    restaurants.address_id,
+    addresses.id,
+    addresses.street;
+
+
+3) Remove addresses without any association
+
+After steps 1) and 2) some addresses are not associated with a restaurant any more.
+
+The following SQL statement lists them before this migration is run:
+
+SELECT
+    id,
+    street,
+    zip_code,
+    city
+FROM
+    {config.CLEAN_SCHEMA}.addresses
+WHERE
+    id NOT IN (
+    SELECT DISTINCT
+        pickup_address_id AS id
+    FROM
+        {config.CLEAN_SCHEMA}.orders
+    UNION
+    SELECT DISTINCT
+        delivery_address_id AS id
+    FROM
+        {config.CLEAN_SCHEMA}.orders
+    UNION
+    SELECT DISTINCT
+        address_id AS id
+    FROM
+        {config.CLEAN_SCHEMA}.restaurants
+);
+
+4) Ensure every `Restaurant` has exactly one `Address`.
+
+Replace the current `ForeignKeyConstraint` to from `Order` to `Restaurant`
+with one that also includes the `Order.pickup_address_id`.
+"""
+
+import os
+
+from alembic import op
+
+from urban_meal_delivery import configuration
+
+
+revision = 'e86290e7305e'
+down_revision = '26711cd3f9b9'
+branch_labels = None
+depends_on = None
+
+
+config = configuration.make_config('testing' if os.getenv('TESTING') else 'production')
+
+
+def upgrade():
+    """Upgrade to revision e86290e7305e."""
+    # 1) Remove orders
+    op.execute(
+        f"""
+        DELETE
+        FROM
+            {config.CLEAN_SCHEMA}.orders
+        WHERE pickup_address_id IN (
+            SELECT
+                orders.pickup_address_id
+            FROM
+                {config.CLEAN_SCHEMA}.orders
+            LEFT OUTER JOIN
+                {config.CLEAN_SCHEMA}.restaurants
+                ON orders.restaurant_id = restaurants.id
+            WHERE
+                orders.pickup_address_id <> restaurants.address_id
+            GROUP BY
+                orders.pickup_address_id
+            HAVING
+                COUNT(*) = 1
+        );
+        """,
+    )
+
+    # 2) De-duplicate addresses
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.orders
+        SET
+            pickup_address_id = 353
+        WHERE
+            pickup_address_id = 548916;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.orders
+        SET
+            pickup_address_id = 4850
+        WHERE
+            pickup_address_id = 6415;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.orders
+        SET
+            pickup_address_id = 16227
+        WHERE
+            pickup_address_id = 44627;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.orders
+        SET
+            pickup_address_id = 44458
+        WHERE
+            pickup_address_id = 534543;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.orders
+        SET
+            pickup_address_id = 289997
+        WHERE
+            pickup_address_id = 309525;
+        """,
+    )
+
+    # 3) Remove addresses
+    op.execute(
+        f"""
+        DELETE
+        FROM
+            {config.CLEAN_SCHEMA}.addresses_pixels
+        WHERE
+            address_id NOT IN (
+            SELECT DISTINCT
+                pickup_address_id AS id
+            FROM
+                {config.CLEAN_SCHEMA}.orders
+            UNION
+            SELECT DISTINCT
+                delivery_address_id AS id
+            FROM
+                {config.CLEAN_SCHEMA}.orders
+            UNION
+            SELECT DISTINCT
+                address_id AS id
+            FROM
+                {config.CLEAN_SCHEMA}.restaurants
+        );
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.addresses
+        SET
+            primary_id = 302883
+        WHERE
+            primary_id = 43526;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.addresses
+        SET
+            primary_id = 47597
+        WHERE
+            primary_id = 43728;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.addresses
+        SET
+            primary_id = 159631
+        WHERE
+            primary_id = 43942;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.addresses
+        SET
+            primary_id = 275651
+        WHERE
+            primary_id = 44759;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.addresses
+        SET
+            primary_id = 156685
+        WHERE
+            primary_id = 50599;
+        """,
+    )
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.addresses
+        SET
+            primary_id = 480206
+        WHERE
+            primary_id = 51774;
+        """,
+    )
+    op.execute(
+        f"""
+        DELETE
+        FROM
+            {config.CLEAN_SCHEMA}.addresses
+        WHERE
+            id NOT IN (
+            SELECT DISTINCT
+                pickup_address_id AS id
+            FROM
+                {config.CLEAN_SCHEMA}.orders
+            UNION
+            SELECT DISTINCT
+                delivery_address_id AS id
+            FROM
+                {config.CLEAN_SCHEMA}.orders
+            UNION
+            SELECT DISTINCT
+                address_id AS id
+            FROM
+                {config.CLEAN_SCHEMA}.restaurants
+        );
+        """,
+    )
+
+    # 4) Ensure every `Restaurant` has only one `Order.pickup_address`.
+    op.execute(
+        f"""
+        UPDATE
+            {config.CLEAN_SCHEMA}.orders
+        SET
+            pickup_address_id = 53733
+        WHERE
+            pickup_address_id = 54892;
+        """,
+    )
+    op.execute(
+        f"""
+        DELETE
+        FROM
+            {config.CLEAN_SCHEMA}.addresses
+        WHERE
+            id = 54892;
+        """,
+    )
+    op.create_unique_constraint(
+        'uq_restaurants_on_id_address_id',
+        'restaurants',
+        ['id', 'address_id'],
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_foreign_key(
+        op.f('fk_orders_to_restaurants_via_restaurant_id_pickup_address_id'),
+        'orders',
+        'restaurants',
+        ['restaurant_id', 'pickup_address_id'],
+        ['id', 'address_id'],
+        source_schema=config.CLEAN_SCHEMA,
+        referent_schema=config.CLEAN_SCHEMA,
+        onupdate='RESTRICT',
+        ondelete='RESTRICT',
+    )
+    op.drop_constraint(
+        'fk_orders_to_restaurants_via_restaurant_id',
+        'orders',
+        type_='foreignkey',
+        schema=config.CLEAN_SCHEMA,
+    )
+
+
+def downgrade():
+    """Downgrade to revision 26711cd3f9b9."""
+    op.create_foreign_key(
+        op.f('fk_orders_to_restaurants_via_restaurant_id'),
+        'orders',
+        'restaurants',
+        ['restaurant_id'],
+        ['id'],
+        source_schema=config.CLEAN_SCHEMA,
+        referent_schema=config.CLEAN_SCHEMA,
+        onupdate='RESTRICT',
+        ondelete='RESTRICT',
+    )
+    op.drop_constraint(
+        'fk_orders_to_restaurants_via_restaurant_id_pickup_address_id',
+        'orders',
+        type_='foreignkey',
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.drop_constraint(
+        'uq_restaurants_on_id_address_id',
+        'restaurants',
+        type_='unique',
+        schema=config.CLEAN_SCHEMA,
+    )

migrations/versions/rev_20210129_11_c2af85bada01_store_actuals_with_forecast.py

@@ -0,0 +1,41 @@
+"""Store actuals with forecast.
+
+Revision: #c2af85bada01 at 2021-01-29 11:13:15
+Revises: #e86290e7305e
+"""
+
+import os
+
+import sqlalchemy as sa
+from alembic import op
+
+from urban_meal_delivery import configuration
+
+
+revision = 'c2af85bada01'
+down_revision = 'e86290e7305e'
+branch_labels = None
+depends_on = None
+
+
+config = configuration.make_config('testing' if os.getenv('TESTING') else 'production')
+
+
+def upgrade():
+    """Upgrade to revision c2af85bada01."""
+    op.add_column(
+        'forecasts',
+        sa.Column('actual', sa.SmallInteger(), nullable=False),
+        schema=config.CLEAN_SCHEMA,
+    )
+    op.create_check_constraint(
+        op.f('ck_forecasts_on_actuals_must_be_non_negative'),
+        'forecasts',
+        'actual >= 0',
+        schema=config.CLEAN_SCHEMA,
+    )
+
+
+def downgrade():
+    """Downgrade to revision e86290e7305e."""
+    op.drop_column('forecasts', 'actual', schema=config.CLEAN_SCHEMA)

migrations/versions/rev_20210202_12_8bfb928a31f8_rename_training_horizon_into_train_horizon.py

@@ -0,0 +1,48 @@
+"""Rename `Forecast.training_horizon` into `.train_horizon`.
+
+Revision: #8bfb928a31f8 at 2021-02-02 12:55:09
+Revises: #c2af85bada01
+"""
+
+import os
+
+from alembic import op
+
+from urban_meal_delivery import configuration
+
+
+revision = '8bfb928a31f8'
+down_revision = 'c2af85bada01'
+branch_labels = None
+depends_on = None
+
+
+config = configuration.make_config('testing' if os.getenv('TESTING') else 'production')
+
+
+def upgrade():
+    """Upgrade to revision 8bfb928a31f8."""
+    op.execute(
+        f"""
+        ALTER TABLE
+            {config.CLEAN_SCHEMA}.forecasts
+        RENAME COLUMN
+            training_horizon
+            TO
+            train_horizon;
+        """,
+    )  # noqa:WPS355
+
+
+def downgrade():
+    """Downgrade to revision c2af85bada01."""
+    op.execute(
+        f"""
+        ALTER TABLE
+            {config.CLEAN_SCHEMA}.forecasts
+        RENAME COLUMN
+            train_horizon
+            TO
+            training_horizon;
+        """,
+    )  # noqa:WPS355

noxfile.py

@@ -17,7 +17,7 @@ as unified tasks to assure the quality of the source code:
     that are then interpreted as the paths the formatters and linters work
     on recursively
 
-- "lint" (flake8, mypy, pylint): same as "format"
+- "lint" (flake8, mypy): same as "format"
 
 - "test" (pytest, xdoctest):
 
@@ -25,26 +25,6 @@ as unified tasks to assure the quality of the source code:
   + accepts extra arguments, e.g., `poetry run nox -s test -- --no-cov`,
     that are passed on to `pytest` and `xdoctest` with no changes
     => may be paths or options
-
-
-GitHub Actions implements the following CI workflow:
-
-- "format", "lint", and "test" as above
-
-- "safety": check if dependencies contain known security vulnerabilites
-
-- "docs": build the documentation with sphinx
-
-
-The pre-commit framework invokes the following tasks:
-
-- before any commit:
-
-  + "format" and "lint" as above
-  + "fix-branch-references": replace branch references with the current one
-
-- before merges: run the entire "test-suite" independent of the file changes
-
 """
 
 import contextlib
@@ -92,7 +72,7 @@ nox.options.envdir = '.cache/nox'
 # Avoid accidental successes if the environment is not set up properly.
 nox.options.error_on_external_run = True
 
-# Run only CI related checks by default.
+# Run only local checks by default.
 nox.options.sessions = (
     'format',
     'lint',
@@ -141,7 +121,7 @@ def format_(session):
 
 @nox.session(python=PYTHON)
 def lint(session):
-    """Lint source files with flake8, mypy, and pylint.
+    """Lint source files with flake8 and mypy.
 
     If no extra arguments are provided, all source files are linted.
     Otherwise, they are interpreted as paths the linters work on recursively.
@@ -158,7 +138,6 @@ def lint(session):
         'flake8-expression-complexity',
         'flake8-pytest-style',
         'mypy',
-        'pylint',
         'wemake-python-styleguide',
     )
 
@@ -182,18 +161,6 @@ def lint(session):
     else:
         session.log('No paths to be checked with mypy')
 
-    # Ignore errors where pylint cannot import a third-party package due its
-    # being run in an isolated environment. For the same reason, pylint is
-    # also not able to determine the correct order of imports.
-    # One way to fix this is to install all develop dependencies in this nox
-    # session, which we do not do. The whole point of static linting tools is
-    # to not rely on any package be importable at runtime. Instead, these
-    # imports are validated implicitly when the test suite is run.
-    session.run('pylint', '--version')
-    session.run(
-        'pylint', '--disable=import-error', '--disable=wrong-import-order', *locations,
-    )
-
 
 @nox.session(python=PYTHON)
 def test(session):
@@ -222,33 +189,71 @@ def test(session):
     session.run('poetry', 'install', '--no-dev', external=True)
     _install_packages(
         session,
+        'Faker',
+        'factory-boy',
+        'geopy',
         'packaging',
         'pytest',
         'pytest-cov',
         'pytest-env',
+        'pytest-mock',
+        'pytest-randomly',
         'xdoctest[optional]',
     )
 
+    session.run('pytest', '--version')
+
+    # When the CI server runs the slow tests, we only execute the R related
+    # test cases that require the slow installation of R and some packages.
+    if session.env.get('_slow_ci_tests'):
+        session.run(
+            'pytest', '--randomly-seed=4287', '-m', 'r and not db', PYTEST_LOCATION,
+        )
+
+        # In the "ci-tests-slow" session, we do not run any test tool
+        # other than pytest. So, xdoctest, for example, is only run
+        # locally or in the "ci-tests-fast" session.
+        return
+
+    # When the CI server executes pytest, no database is available.
+    # Therefore, the CI server does not measure coverage.
+    elif session.env.get('_fast_ci_tests'):
+        pytest_args = (
+            '--randomly-seed=4287',
+            '-m',
+            'not (db or r)',
+            PYTEST_LOCATION,
+        )
+
+    # When pytest is executed in the local develop environment,
+    # both R and a database are available.
+    # Therefore, we require 100% coverage.
+    else:
+        pytest_args = (
+            '--cov',
+            '--no-cov-on-fail',
+            '--cov-branch',
+            '--cov-fail-under=100',
+            '--cov-report=term-missing:skip-covered',
+            '--randomly-seed=4287',
+            PYTEST_LOCATION,
+        )
+
     # Interpret extra arguments as options for pytest.
-    # They are "dropped" by the hack in the pre_merge() function
+    # They are "dropped" by the hack in the test_suite() function
-    # if this function is run within the "pre-merge" session.
+    # if this function is run within the "test-suite" session.
     posargs = () if session.env.get('_drop_posargs') else session.posargs
 
-    args = posargs or (
+    session.run('pytest', *(posargs or pytest_args))
-        '--cov',
-        '--no-cov-on-fail',
-        '--cov-branch',
-        '--cov-fail-under=100',
-        '--cov-report=term-missing:skip-covered',
-        '-k',
-        'not e2e',
-        PYTEST_LOCATION,
-    )
-    session.run('pytest', '--version')
-    session.run('pytest', *args)
 
     # For xdoctest, the default arguments are different from pytest.
     args = posargs or [PACKAGE_IMPORT_NAME]
+
+    # The "TESTING" environment variable forces the global `engine`, `connection`,
+    # and `session` objects to be set to `None` and avoid any database connection.
+    # For pytest above this is not necessary as pytest sets this variable itself.
+    session.env['TESTING'] = 'true'
+
     session.run('xdoctest', '--version')
     session.run('xdoctest', '--quiet', *args)  # --quiet => less verbose output
 
@@ -292,6 +297,10 @@ def docs(session):
     session.run('poetry', 'install', '--no-dev', external=True)
     _install_packages(session, 'sphinx', 'sphinx-autodoc-typehints')
 
+    # The "TESTING" environment variable forces the global `engine`, `connection`,
+    # and `session` objects to be set to `None` and avoid any database connection.
+    session.env['TESTING'] = 'true'
+
     session.run('sphinx-build', DOCS_SRC, DOCS_BUILD)
     # Verify all external links return 200 OK.
     session.run('sphinx-build', '-b', 'linkcheck', DOCS_SRC, DOCS_BUILD)
@@ -299,11 +308,63 @@ def docs(session):
     print(f'Docs are available at {os.getcwd()}/{DOCS_BUILD}index.html')  # noqa:WPS421
 
 
+@nox.session(name='ci-tests-fast', python=PYTHON)
+def fast_ci_tests(session):
+    """Fast tests run by the GitHub Actions CI server.
+
+    These regards all test cases NOT involving R via `rpy2`.
+
+    Also, coverage is not measured as full coverage can only be
+    achieved by running the tests in the local develop environment
+    that has access to a database.
+    """
+    # Re-using an old environment is not so easy here as the "test" session
+    # runs `poetry install --no-dev`, which removes previously installed packages.
+    if session.virtualenv.reuse_existing:
+        raise RuntimeError(
+            'The "ci-tests-fast" session must be run without the "-r" option',
+        )
+
+    # Little hack to pass arguments to the "test" session.
+    session.env['_fast_ci_tests'] = 'true'
+
+    # Cannot use session.notify() to trigger the "test" session
+    # as that would create a new Session object without the flag
+    # in the env(ironment).
+    test(session)
+
+
+@nox.session(name='ci-tests-slow', python=PYTHON)
+def slow_ci_tests(session):
+    """Slow tests run by the GitHub Actions CI server.
+
+    These regards all test cases involving R via `rpy2`.
+    They are slow as the CI server needs to install R and some packages
+    first, which takes a couple of minutes.
+
+    Also, coverage is not measured as full coverage can only be
+    achieved by running the tests in the local develop environment
+    that has access to a database.
+    """
+    # Re-using an old environment is not so easy here as the "test" session
+    # runs `poetry install --no-dev`, which removes previously installed packages.
+    if session.virtualenv.reuse_existing:
+        raise RuntimeError(
+            'The "ci-tests-slow" session must be run without the "-r" option',
+        )
+
+    # Little hack to pass arguments to the "test" session.
+    session.env['_slow_ci_tests'] = 'true'
+
+    # Cannot use session.notify() to trigger the "test" session
+    # as that would create a new Session object without the flag
+    # in the env(ironment).
+    test(session)
+
+
 @nox.session(name='test-suite', python=PYTHON)
 def test_suite(session):
-    """Run the entire test suite.
+    """Run the entire test suite as a pre-commit hook.
-
-    Intended to be run as a pre-commit hook.
 
     Ignores the paths passed in by the pre-commit framework
     and runs the entire test suite.
@@ -322,13 +383,12 @@ def test_suite(session):
 
     # Cannot use session.notify() to trigger the "test" session
     # as that would create a new Session object without the flag
-    # in the env(ironment). Instead, run the test() function within
+    # in the env(ironment).
-    # the "pre-merge" session.
     test(session)
 
 
 @nox.session(name='fix-branch-references', python=PYTHON, venv_backend='none')
-def fix_branch_references(session):  # noqa:WPS210
+def fix_branch_references(session):  # noqa:WPS210,WPS231
     """Replace branch references with the current branch.
 
     Intended to be run as a pre-commit hook.
@@ -336,9 +396,15 @@ def fix_branch_references(session):  # noqa:WPS210
     Many files in the project (e.g., README.md) contain links to resources
     on github.com or nbviewer.jupyter.org that contain branch labels.
 
-    This task rewrites these links such that they contain the branch reference
+    This task rewrites these links such that they contain branch references
-    of the current branch. If the branch is only a temporary one that is to be
+    that make sense given the context:
-    merged into the 'main' branch, all references are adjusted to 'main' as well.
+
+    - If the branch is only a temporary one that is to be merged into
+      the 'main' branch, all references are adjusted to 'main' as well.
+
+    - If the branch is not named after a default branch in the GitFlow
+      model, it is interpreted as a feature branch and the references
+      are adjusted into 'develop'.
 
     This task may be called with one positional argument that is interpreted
     as the branch to which all references are changed into.
@@ -362,6 +428,10 @@ def fix_branch_references(session):  # noqa:WPS210
     # into 'main', we adjust all branch references to 'main' as well.
     if branch.startswith('release') or branch.startswith('research'):
         branch = 'main'
+    # If the current branch appears to be a feature branch, we adjust
+    # all branch references to 'develop'.
+    elif branch != 'main':
+        branch = 'develop'
     # If a "--branch=BRANCH_NAME" argument is passed in
     # as the only positional argument, we use BRANCH_NAME.
     # Note: The --branch is required as session.posargs contains
@@ -445,7 +515,7 @@ def init_project(session):
 
 
 @nox.session(name='clean-pwd', python=PYTHON, venv_backend='none')
-def clean_pwd(session):  # noqa:WPS210,WPS231
+def clean_pwd(session):  # noqa:WPS231
     """Remove (almost) all glob patterns listed in .gitignore.
 
     The difference compared to `git clean -X` is that this task
@@ -519,6 +589,7 @@ def _install_packages(session: Session, *packages_or_pip_args: str, **kwargs) ->
             '--dev',
             '--format=requirements.txt',
             f'--output={requirements_txt.name}',
+            '--without-hashes',
             external=True,
         )
         session.install(

pyproject.toml

@@ -9,7 +9,7 @@ target-version = ["py38"]
 
 [tool.poetry]
 name = "urban-meal-delivery"
-version = "0.3.0.dev0"
+version = "0.4.0.dev0"
 
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 description = "Optimizing an urban meal delivery platform"
@@ -28,18 +28,23 @@ repository = "https://github.com/webartifex/urban-meal-delivery"
 python = "^3.8"
 
 # Package => code developed in *.py files and packaged under src/urban_meal_delivery
+Shapely = "^1.7.1"
 alembic = "^1.4.2"
 click = "^7.1.2"
+folium = "^0.12.1"
+matplotlib = "^3.3.3"
+pandas = "^1.1.0"
 psycopg2 = "^2.8.5"  # adapter for PostgreSQL
-python-dotenv = "^0.14.0"
+rpy2 = "^3.4.1"
 sqlalchemy = "^1.3.18"
+statsmodels = "^0.12.1"
+utm = "^0.7.0"
 
 # Jupyter Lab => notebooks with analyses using the developed package
 # IMPORTANT: must be kept in sync with the "research" extra below
 jupyterlab = { version="^2.2.2", optional=true }
 nb_black = { version="^1.0.7", optional=true }
 numpy = { version="^1.19.1", optional=true }
-pandas = { version="^1.1.0", optional=true }
 pytz = { version="^2020.1", optional=true }
 
 [tool.poetry.extras]
@@ -47,7 +52,6 @@ research = [
     "jupyterlab",
     "nb_black",
     "numpy",
-    "pandas",
     "pytz",
 ]
 
@@ -68,14 +72,18 @@ flake8-black = "^0.2.1"
 flake8-expression-complexity = "^0.0.8"
 flake8-pytest-style = "^1.2.2"
 mypy = "^0.782"
-pylint = "^2.5.3"
 wemake-python-styleguide = "^0.14.1"  # flake8 plug-in
 
 # Test Suite
+Faker = "^5.0.1"
+factory-boy = "^3.1.0"
+geopy = "^2.1.0"
 packaging = "^20.4"  # used to test the packaged version
 pytest = "^6.0.1"
 pytest-cov = "^2.10.0"
 pytest-env = "^0.6.2"
+pytest-mock = "^3.5.1"
+pytest-randomly = "^3.5.0"
 xdoctest = { version="^0.13.0", extras=["optional"] }
 
 # Documentation
@@ -83,4 +91,4 @@ sphinx = "^3.1.2"
 sphinx-autodoc-typehints = "^1.11.0"
 
 [tool.poetry.scripts]
-umd = "urban_meal_delivery.console:main"
+umd = "urban_meal_delivery.console:cli"

research/clean_data.ipynb

@@ -103,8 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_engine = db.make_engine()\n",
+    "connection = db.connection"
-    "connection = _engine.connect()"
    ]
   },
   {

research/papers/demand-forecasting

@@ -0,0 +1 @@
+Subproject commit 9ee3396a24ce20c9886b4cde5cfe2665fd5a8102

setup.cfg

@@ -89,16 +89,33 @@ extend-ignore =
     # Comply with black's style.
     # Source: https://github.com/psf/black/blob/master/docs/compatible_configs.md#flake8
     E203, W503, WPS348,
+    # Google's Python Style Guide is not reStructuredText
+    # until after being processed by Sphinx Napoleon.
+    # Source: https://github.com/peterjc/flake8-rst-docstrings/issues/17
+    RST201,RST203,RST210,RST213,RST301,
+    # String constant over-use is checked visually by the programmer.
+    WPS226,
+    # Allow underscores in numbers.
+    WPS303,
     # f-strings are ok.
     WPS305,
     # Classes should not have to specify a base class.
     WPS306,
+    # Let's be modern: The Walrus is ok.
+    WPS332,
+    # Let's not worry about the number of noqa's.
+    WPS402,
     # Putting logic into __init__.py files may be justified.
     WPS412,
     # Allow multiple assignment, e.g., x = y = 123
     WPS429,
+    # There are no magic numbers.
+    WPS432,
 
 per-file-ignores =
+    # Top-levels of a sub-packages are intended to import a lot.
+    **/__init__.py:
+        F401,WPS201,
     docs/conf.py:
         # Allow shadowing built-ins and reading __*__ variables.
         WPS125,WPS609,
@@ -108,14 +125,12 @@ per-file-ignores =
     migrations/versions/*.py:
         # Type annotations are not strictly enforced.
         ANN0, ANN2,
+        # Do not worry about SQL injection here.
+        S608,
         # File names of revisions are ok.
         WPS114,WPS118,
         # Revisions may have too many expressions.
         WPS204,WPS213,
-        # No overuse of string constants (e.g., 'RESTRICT').
-        WPS226,
-        # Too many noqa's are ok.
-        WPS402,
     noxfile.py:
         # Type annotations are not strictly enforced.
         ANN0, ANN2,
@@ -123,38 +138,70 @@ per-file-ignores =
         WPS202,
         # TODO (isort): Remove after simplifying the nox session "lint".
         WPS213,
-        # No overuse of string constants (e.g., '--version').
-        WPS226,
-        # The noxfile is rather long => allow many noqa's.
-        WPS402,
     src/urban_meal_delivery/configuration.py:
         # Allow upper case class variables within classes.
         WPS115,
-        # Numbers are normal in config files.
+    src/urban_meal_delivery/console/forecasts.py:
-        WPS432,
+        # The module is not too complex.
-    src/urban_meal_delivery/db/addresses.py:
+        WPS232,
-        WPS226,
+    src/urban_meal_delivery/db/customers.py:
-    src/urban_meal_delivery/db/orders.py:
+        # The module is not too complex.
-        WPS226,
+        WPS232,
+    src/urban_meal_delivery/db/restaurants.py:
+        # The module is not too complex.
+        WPS232,
+    src/urban_meal_delivery/forecasts/methods/decomposition.py:
+        # The module is not too complex.
+        WPS232,
+    src/urban_meal_delivery/forecasts/methods/extrapolate_season.py:
+        # The module is not too complex.
+        WPS232,
+    src/urban_meal_delivery/forecasts/models/tactical/horizontal.py:
+        # The many noqa's are ok.
+        WPS403,
+    src/urban_meal_delivery/forecasts/timify.py:
+        # No SQL injection as the inputs come from a safe source.
+        S608,
+        # The many noqa's are ok.
+        WPS403,
     tests/*.py:
         # Type annotations are not strictly enforced.
         ANN0, ANN2,
+        # The `Meta` class inside the factory_boy models do not need a docstring.
+        D106,
         # `assert` statements are ok in the test suite.
         S101,
+        # The `random` module is not used for cryptography.
+        S311,
         # Shadowing outer scopes occurs naturally with mocks.
         WPS442,
+        # Test names may be longer than 40 characters.
+        WPS118,
         # Modules may have many test cases.
         WPS202,WPS204,WPS214,
-        # No overuse of string constants (e.g., '__version__').
+        # Do not check for Jones complexity in the test suite.
-        WPS226,
+        WPS221,
-        # Numbers are normal in test cases as expected results.
+        # "Private" methods are really just a convention for
-        WPS432,
+        # fixtures without a return value.
+        WPS338,
+        # We do not care about the number of "# noqa"s in the test suite.
+        WPS402,
+        # Allow closures.
+        WPS430,
+        # When testing, it is normal to use implementation details.
+        WPS437,
 
 # Explicitly set mccabe's maximum complexity to 10 as recommended by
 # Thomas McCabe, the inventor of the McCabe complexity, and the NIST.
 # Source: https://en.wikipedia.org/wiki/Cyclomatic_complexity#Limiting_complexity_during_development
 max-complexity = 10
 
+# Allow more than wemake-python-styleguide's 5 local variables per function.
+max-local-variables = 8
+
+# Allow more than wemake-python-styleguide's 7 methods per class.
+max-methods = 12
+
 # Comply with black's style.
 # Source: https://github.com/psf/black/blob/master/docs/the_black_code_style.md#line-length
 max-line-length = 88
@@ -166,6 +213,7 @@ show-source = true
 # wemake-python-styleguide's settings
 # ===================================
 allowed-domain-names =
+    data,
     obj,
     param,
     result,
@@ -217,53 +265,28 @@ single_line_exclusions = typing
 [mypy]
 cache_dir = .cache/mypy
 
-[mypy-dotenv]
+[mypy-folium.*]
+ignore_missing_imports = true
+[mypy-matplotlib.*]
 ignore_missing_imports = true
 [mypy-nox.*]
 ignore_missing_imports = true
+[mypy-numpy.*]
+ignore_missing_imports = true
 [mypy-packaging]
 ignore_missing_imports = true
+[mypy-pandas]
+ignore_missing_imports = true
 [mypy-pytest]
 ignore_missing_imports = true
+[mypy-rpy2.*]
+ignore_missing_imports = true
 [mypy-sqlalchemy.*]
 ignore_missing_imports = true
-
+[mypy-statsmodels.*]
-
+ignore_missing_imports = true
-[pylint.FORMAT]
+[mypy-utm.*]
-# Comply with black's style.
+ignore_missing_imports = true
-max-line-length = 88
-
-[pylint.MESSAGES CONTROL]
-disable =
-    # We use TODO's to indicate locations in the source base
-    # that must be worked on in the near future.
-    fixme,
-    # Too many false positives and cannot be disabled within a file.
-    # Source: https://github.com/PyCQA/pylint/issues/214
-    duplicate-code,
-    # Comply with black's style.
-    bad-continuation, bad-whitespace,
-    # =====================
-    # flake8 de-duplication
-    # Source: https://pylint.pycqa.org/en/latest/faq.html#i-am-using-another-popular-linter-alongside-pylint-which-messages-should-i-disable-to-avoid-duplicates
-    # =====================
-    # mccabe
-    too-many-branches,
-    # pep8-naming
-    bad-classmethod-argument, bad-mcs-classmethod-argument,
-    invalid-name, no-self-argument,
-    # pycodestyle
-    bad-indentation, bare-except, line-too-long, missing-final-newline,
-    multiple-statements, trailing-whitespace, unnecessary-semicolon, unneeded-not,
-    # pydocstyle
-    missing-class-docstring, missing-function-docstring, missing-module-docstring,
-    # pyflakes
-    undefined-variable, unused-import, unused-variable,
-    # wemake-python-styleguide
-    redefined-outer-name,
-
-[pylint.REPORTS]
-score = no
 
 
 [tool:pytest]
@@ -273,5 +296,9 @@ cache_dir = .cache/pytest
 console_output_style = count
 env =
     TESTING=true
+filterwarnings =
+    ignore:::patsy.*
 markers =
-    e2e: integration tests, inlc., for example, tests touching a database
+    db: (integration) tests touching the database
+    e2e: non-db and non-r integration tests
+    r: (integration) tests using rpy2

src/urban_meal_delivery/__init__.py

@@ -5,11 +5,13 @@ Example:
     >>> umd.__version__ != '0.0.0'
     True
 """
+# The config object must come before all other project-internal imports.
+from urban_meal_delivery.configuration import config  # isort:skip
 
-import os as _os
 from importlib import metadata as _metadata
 
-from urban_meal_delivery import configuration as _configuration
+from urban_meal_delivery import db
+from urban_meal_delivery import forecasts
 
 
 try:
@@ -24,14 +26,3 @@ else:
     __author__ = _pkg_info['author']
     __pkg_name__ = _pkg_info['name']
     __version__ = _pkg_info['version']
-
-
-# Global `config` object to be used in the package.
-config: _configuration.Config = _configuration.make_config(
-    'testing' if _os.getenv('TESTING') else 'production',
-)
-
-
-# Import `db` down here as it depends on `config`.
-# pylint:disable=wrong-import-position
-from urban_meal_delivery import db  # noqa:E402,F401 isort:skip

src/urban_meal_delivery/configuration.py

@@ -13,11 +13,6 @@ import random
 import string
 import warnings
 
-import dotenv
-
-
-dotenv.load_dotenv()
-
 
 def random_schema_name() -> str:
     """Generate a random PostgreSQL schema name for testing."""
@@ -31,14 +26,43 @@ def random_schema_name() -> str:
 class Config:
     """Configuration that applies in all situations."""
 
-    # pylint:disable=too-few-public-methods
+    # Application-specific settings
+    # -----------------------------
 
+    # Date after which the real-life data is discarded.
     CUTOFF_DAY = datetime.datetime(2017, 2, 1)
 
     # If a scheduled pre-order is made within this
     # time horizon, we treat it as an ad-hoc order.
     QUASI_AD_HOC_LIMIT = datetime.timedelta(minutes=45)
 
+    # Operating hours of the platform.
+    SERVICE_START = 11
+    SERVICE_END = 23
+
+    # Side lengths (in meters) for which pixel grids are created.
+    # They are the basis for the aggregated demand forecasts.
+    GRID_SIDE_LENGTHS = [707, 1000, 1414]
+
+    # Time steps (in minutes) used to aggregate the
+    # individual orders into time series.
+    TIME_STEPS = [60]
+
+    # Training horizons (in full weeks) used to train the forecasting models.
+    # For now, we only use 8 weeks as that was the best performing in
+    # a previous study (note:4f79e8fa).
+    TRAIN_HORIZONS = [8]
+
+    # The demand forecasting methods used in the simulations.
+    FORECASTING_METHODS = ['hets', 'rtarima']
+
+    # Colors for the visualizations ins `folium`.
+    RESTAURANT_COLOR = 'red'
+    CUSTOMER_COLOR = 'blue'
+
+    # Implementation-specific settings
+    # --------------------------------
+
     DATABASE_URI = os.getenv('DATABASE_URI')
 
     # The PostgreSQL schema that holds the tables with the original data.
@@ -50,6 +74,8 @@ class Config:
     ALEMBIC_TABLE = 'alembic_version'
     ALEMBIC_TABLE_SCHEMA = 'public'
 
+    R_LIBS_PATH = os.getenv('R_LIBS')
+
     def __repr__(self) -> str:
         """Non-literal text representation."""
         return '<configuration>'
@@ -58,16 +84,12 @@ class Config:
 class ProductionConfig(Config):
     """Configuration for the real dataset."""
 
-    # pylint:disable=too-few-public-methods
-
     TESTING = False
 
 
 class TestingConfig(Config):
     """Configuration for the test suite."""
 
-    # pylint:disable=too-few-public-methods
-
     TESTING = True
 
     DATABASE_URI = os.getenv('DATABASE_URI_TESTING') or Config.DATABASE_URI
@@ -78,7 +100,7 @@ def make_config(env: str = 'production') -> Config:
     """Create a new `Config` object.
 
     Args:
-        env: either 'production' or 'testing'; defaults to the first
+        env: either 'production' or 'testing'
 
     Returns:
         config: a namespace with all configurations
@@ -86,7 +108,8 @@ def make_config(env: str = 'production') -> Config:
     Raises:
         ValueError: if `env` is not as specified
     """  # noqa:DAR203
-    config: Config
+    config: Config  # otherwise mypy is confused
+
     if env.strip().lower() == 'production':
         config = ProductionConfig()
     elif env.strip().lower() == 'testing':
@@ -95,7 +118,19 @@ def make_config(env: str = 'production') -> Config:
         raise ValueError("Must be either 'production' or 'testing'")
 
     # Without a PostgreSQL database the package cannot work.
-    if config.DATABASE_URI is None:
+    # As pytest sets the "TESTING" environment variable explicitly,
+    # the warning is only emitted if the code is not run by pytest.
+    # We see the bad configuration immediately as all "db" tests fail.
+    if config.DATABASE_URI is None and not os.getenv('TESTING'):
         warnings.warn('Bad configurartion: no DATABASE_URI set in the environment')
 
+    # Some functionalities require R and some packages installed.
+    # To ensure isolation and reproducibility, the projects keeps the R dependencies
+    # in a project-local folder that must be set in the environment.
+    if config.R_LIBS_PATH is None and not os.getenv('TESTING'):
+        warnings.warn('Bad configuration: no R_LIBS set in the environment')
+
     return config
+
+
+config = make_config('testing' if os.getenv('TESTING') else 'production')

src/urban_meal_delivery/console/__init__.py

@@ -0,0 +1,11 @@
+"""Provide CLI scripts for the project."""
+
+from urban_meal_delivery.console import forecasts
+from urban_meal_delivery.console import gridify
+from urban_meal_delivery.console import main
+
+
+cli = main.entry_point
+
+cli.add_command(forecasts.tactical_heuristic, name='tactical-forecasts')
+cli.add_command(gridify.gridify)

src/urban_meal_delivery/console/decorators.py

@@ -0,0 +1,37 @@
+"""Utils for the CLI scripts."""
+
+import functools
+import os
+import subprocess  # noqa:S404
+import sys
+from typing import Any, Callable
+
+import click
+
+
+def db_revision(rev: str) -> Callable:  # pragma: no cover -> easy to check visually
+    """A decorator ensuring the database is at a given revision."""
+
+    def decorator(func: Callable) -> Callable:
+        @functools.wraps(func)
+        def ensure(*args: Any, **kwargs: Any) -> Any:  # noqa:WPS430
+            """Do not execute the `func` if the revision does not match."""
+            if not os.getenv('TESTING'):
+                result = subprocess.run(  # noqa:S603,S607
+                    ['alembic', 'current'],
+                    capture_output=True,
+                    check=False,
+                    encoding='utf8',
+                )
+
+                if not result.stdout.startswith(rev):
+                    click.echo(
+                        click.style(f'Database is not at revision {rev}', fg='red'),
+                    )
+                    sys.exit(1)
+
+            return func(*args, **kwargs)
+
+        return ensure
+
+    return decorator

src/urban_meal_delivery/console/forecasts.py

@@ -0,0 +1,144 @@
+"""CLI script to forecast demand.
+
+The main purpose of this script is to pre-populate the `db.Forecast` table
+with demand predictions such that they can readily be used by the
+predictive routing algorithms.
+"""
+
+import datetime as dt
+import sys
+
+import click
+from sqlalchemy import func
+from sqlalchemy.orm import exc as orm_exc
+
+from urban_meal_delivery import config
+from urban_meal_delivery import db
+from urban_meal_delivery.console import decorators
+from urban_meal_delivery.forecasts import timify
+
+
+@click.command()
+@click.argument('city', default='Paris', type=str)
+@click.argument('side_length', default=1000, type=int)
+@click.argument('time_step', default=60, type=int)
+@click.argument('train_horizon', default=8, type=int)
+@decorators.db_revision('8bfb928a31f8')
+def tactical_heuristic(  # noqa:C901,WPS213,WPS216,WPS231
+    city: str, side_length: int, time_step: int, train_horizon: int,
+) -> None:  # pragma: no cover
+    """Predict demand for all pixels and days in a city.
+
+    This command makes demand `Forecast`s for all `Pixel`s and days
+    for tactical purposes with the heuristic specified in
+    `urban_meal_delivery.forecasts.timify.OrderHistory.choose_tactical_model()`.
+
+    According to this heuristic, there is exactly one `Forecast` per
+    `Pixel` and time step (e.g., hour of the day with 60-minute time steps)
+    given the lengths of the training horizon and a time step. That is so
+    as the heuristic chooses the most promising forecasting `*Model`.
+
+    All `Forecast`s are persisted to the database so that they can be readily
+    used by the predictive routing algorithms.
+
+    This command first checks, which `Forecast`s still need to be made
+    and then does its work. So, it can be interrupted at any point in
+    time and then simply continues where it left off the next time it
+    is executed.
+
+    Important: In a future revision, this command may need to be adapted such
+    that is does not simply obtain the last time step for which a `Forecast`
+    was made and continues from there. The reason is that another future command
+    may make predictions using all available forecasting `*Model`s per `Pixel`
+    and time step.
+
+    Arguments:
+
+    CITY: one of "Bordeaux", "Lyon", or "Paris" (=default)
+
+    SIDE_LENGTH: of a pixel in the grid; defaults to `1000`
+
+    TIME_STEP: length of one time step in minutes; defaults to `60`
+
+    TRAIN_HORIZON: length of the training horizon; defaults to `8`
+    """  # noqa:D412,D417,RST215
+    # Input validation.
+
+    try:
+        city_obj = (
+            db.session.query(db.City).filter_by(name=city.title()).one()  # noqa:WPS221
+        )
+    except orm_exc.NoResultFound:
+        click.echo('NAME must be one of "Paris", "Lyon", or "Bordeaux"')
+        sys.exit(1)
+
+    for grid in city_obj.grids:
+        if grid.side_length == side_length:
+            break
+    else:
+        click.echo(f'SIDE_LENGTH must be in {config.GRID_SIDE_LENGTHS}')
+        sys.exit(1)
+
+    if time_step not in config.TIME_STEPS:
+        click.echo(f'TIME_STEP must be in {config.TIME_STEPS}')
+        sys.exit(1)
+
+    if train_horizon not in config.TRAIN_HORIZONS:
+        click.echo(f'TRAIN_HORIZON must be in {config.TRAIN_HORIZONS}')
+        sys.exit(1)
+
+    click.echo(
+        'Parameters: '
+        + f'city="{city}", grid.side_length={side_length}, '
+        + f'time_step={time_step}, train_horizon={train_horizon}',
+    )
+
+    # Load the historic order data.
+    order_history = timify.OrderHistory(grid=grid, time_step=time_step)  # noqa:WPS441
+    order_history.aggregate_orders()
+
+    # Run the tactical heuristic.
+
+    for pixel in grid.pixels:  # noqa:WPS441
+        # Important: this check may need to be adapted once further
+        # commands are added the make `Forecast`s without the heuristic!
+        # Continue with forecasting on the day the last prediction was made ...
+        last_predict_at = (  # noqa:ECE001
+            db.session.query(func.max(db.Forecast.start_at))
+            .filter(db.Forecast.pixel == pixel)
+            .first()
+        )[0]
+        # ... or start `train_horizon` weeks after the first `Order`
+        # if no `Forecast`s are in the database yet.
+        if last_predict_at is None:
+            predict_day = order_history.first_order_at(pixel_id=pixel.id).date()
+            predict_day += dt.timedelta(weeks=train_horizon)
+        else:
+            predict_day = last_predict_at.date()
+
+        # Go over all days in chronological order ...
+        while predict_day <= order_history.last_order_at(pixel_id=pixel.id).date():
+            # ... and choose the most promising `*Model` for that day.
+            model = order_history.choose_tactical_model(
+                pixel_id=pixel.id, predict_day=predict_day, train_horizon=train_horizon,
+            )
+            click.echo(
+                f'Predicting pixel #{pixel.id} in {city} '
+                + f'for {predict_day} with {model.name}',
+            )
+
+            # Only loop over the time steps corresponding to working hours.
+            predict_at = dt.datetime(
+                predict_day.year,
+                predict_day.month,
+                predict_day.day,
+                config.SERVICE_START,
+            )
+            while predict_at.hour < config.SERVICE_END:
+                model.make_forecast(
+                    pixel=pixel, predict_at=predict_at, train_horizon=train_horizon,
+                )
+
+                predict_at += dt.timedelta(minutes=time_step)
+
+            predict_day += dt.timedelta(days=1)

src/urban_meal_delivery/console/gridify.py

@@ -0,0 +1,48 @@
+"""CLI script to create pixel grids."""
+
+import click
+
+from urban_meal_delivery import config
+from urban_meal_delivery import db
+from urban_meal_delivery.console import decorators
+
+
+@click.command()
+@decorators.db_revision('e86290e7305e')
+def gridify() -> None:  # pragma: no cover  note:b1f68d24
+    """Create grids for all cities.
+
+    This command creates grids with pixels of various
+    side lengths (specified in `urban_meal_delivery.config`).
+
+    Pixels are only generated if they contain at least one
+    (pickup or delivery) address.
+
+    All data are persisted to the database.
+    """
+    cities = db.session.query(db.City).all()
+    click.echo(f'{len(cities)} cities retrieved from the database')
+
+    for city in cities:
+        click.echo(f'\nCreating grids for {city.name}')
+
+        for side_length in config.GRID_SIDE_LENGTHS:
+            click.echo(f'Creating grid with a side length of {side_length} meters')
+
+            grid = db.Grid.gridify(city=city, side_length=side_length)
+            db.session.add(grid)
+
+            click.echo(f' -> created {len(grid.pixels)} pixels')
+
+        # The number of assigned addresses is the same across different `side_length`s.
+        db.session.flush()  # necessary for the query to work
+        n_assigned = (
+            db.session.query(db.AddressPixelAssociation)
+            .filter(db.AddressPixelAssociation.grid_id == grid.id)
+            .count()
+        )
+        click.echo(
+            f'=> assigned {n_assigned} out of {len(city.addresses)} addresses in {city.name}',  # noqa:E501
+        )
+
+    db.session.commit()

src/urban_meal_delivery/console/main.py

@@ -1,14 +1,14 @@
-"""Provide CLI scripts for the project."""
+"""The entry point for all CLI scripts in the project."""
 
 from typing import Any
 
 import click
-from click.core import Context
+from click import core as cli_core
 
 import urban_meal_delivery
 
 
-def show_version(ctx: Context, _param: Any, value: bool) -> None:
+def show_version(ctx: cli_core.Context, _param: Any, value: bool) -> None:
     """Show the package's version."""
     # If --version / -V is NOT passed in,
     # continue with the command.
@@ -24,7 +24,7 @@ def show_version(ctx: Context, _param: Any, value: bool) -> None:
     ctx.exit()
 
 
-@click.command()
+@click.group()
 @click.option(
     '--version',
     '-V',
@@ -33,5 +33,5 @@ def show_version(ctx: Context, _param: Any, value: bool) -> None:
     is_eager=True,
     expose_value=False,
 )
-def main() -> None:
+def entry_point() -> None:
     """The urban-meal-delivery research project."""

src/urban_meal_delivery/db/__init__.py

@@ -1,11 +1,16 @@
 """Provide the ORM models and a connection to the database."""
 
-from urban_meal_delivery.db.addresses import Address  # noqa:F401
+from urban_meal_delivery.db.addresses import Address
-from urban_meal_delivery.db.cities import City  # noqa:F401
+from urban_meal_delivery.db.addresses_pixels import AddressPixelAssociation
-from urban_meal_delivery.db.connection import make_engine  # noqa:F401
+from urban_meal_delivery.db.cities import City
-from urban_meal_delivery.db.connection import make_session_factory  # noqa:F401
+from urban_meal_delivery.db.connection import connection
-from urban_meal_delivery.db.couriers import Courier  # noqa:F401
+from urban_meal_delivery.db.connection import engine
-from urban_meal_delivery.db.customers import Customer  # noqa:F401
+from urban_meal_delivery.db.connection import session
-from urban_meal_delivery.db.meta import Base  # noqa:F401
+from urban_meal_delivery.db.couriers import Courier
-from urban_meal_delivery.db.orders import Order  # noqa:F401
+from urban_meal_delivery.db.customers import Customer
-from urban_meal_delivery.db.restaurants import Restaurant  # noqa:F401
+from urban_meal_delivery.db.forecasts import Forecast
+from urban_meal_delivery.db.grids import Grid
+from urban_meal_delivery.db.meta import Base
+from urban_meal_delivery.db.orders import Order
+from urban_meal_delivery.db.pixels import Pixel
+from urban_meal_delivery.db.restaurants import Restaurant

src/urban_meal_delivery/db/addresses.py

@@ -1,31 +1,35 @@
-"""Provide the ORM's Address model."""
+"""Provide the ORM's `Address` model."""
 
+from __future__ import annotations
+
+from typing import Any
+
+import folium
 import sqlalchemy as sa
 from sqlalchemy import orm
 from sqlalchemy.dialects import postgresql
 from sqlalchemy.ext import hybrid
 
 from urban_meal_delivery.db import meta
+from urban_meal_delivery.db import utils
 
 
 class Address(meta.Base):
-    """An Address of a Customer or a Restaurant on the UDP."""
+    """An address of a `Customer` or a `Restaurant` on the UDP."""
 
     __tablename__ = 'addresses'
 
     # Columns
     id = sa.Column(sa.Integer, primary_key=True, autoincrement=False)  # noqa:WPS125
-    _primary_id = sa.Column('primary_id', sa.Integer, nullable=False, index=True)
+    primary_id = sa.Column(sa.Integer, nullable=False, index=True)
     created_at = sa.Column(sa.DateTime, nullable=False)
-    place_id = sa.Column(
+    place_id = sa.Column(sa.Unicode(length=120), nullable=False, index=True)
-        sa.Unicode(length=120), nullable=False, index=True,  # noqa:WPS432
-    )
     latitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
     longitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-    _city_id = sa.Column('city_id', sa.SmallInteger, nullable=False, index=True)
+    city_id = sa.Column(sa.SmallInteger, nullable=False, index=True)
-    city_name = sa.Column('city', sa.Unicode(length=25), nullable=False)  # noqa:WPS432
+    city_name = sa.Column('city', sa.Unicode(length=25), nullable=False)
     zip_code = sa.Column(sa.Integer, nullable=False, index=True)
-    street = sa.Column(sa.Unicode(length=80), nullable=False)  # noqa:WPS432
+    street = sa.Column(sa.Unicode(length=80), nullable=False)
     floor = sa.Column(sa.SmallInteger)
 
     # Constraints
@@ -43,6 +47,8 @@ class Address(meta.Base):
             '-180 <= longitude AND longitude <= 180',
             name='longitude_between_180_degrees',
         ),
+        # Needed by a `ForeignKeyConstraint` in `AddressPixelAssociation`.
+        sa.UniqueConstraint('id', 'city_id'),
         sa.CheckConstraint(
             '30000 <= zip_code AND zip_code <= 99999', name='valid_zip_code',
         ),
@@ -51,18 +57,21 @@ class Address(meta.Base):
 
     # Relationships
     city = orm.relationship('City', back_populates='addresses')
-    restaurant = orm.relationship('Restaurant', back_populates='address', uselist=False)
+    restaurants = orm.relationship('Restaurant', back_populates='address')
     orders_picked_up = orm.relationship(
         'Order',
         back_populates='pickup_address',
-        foreign_keys='[Order._pickup_address_id]',
+        foreign_keys='[Order.pickup_address_id]',
     )
-
     orders_delivered = orm.relationship(
         'Order',
         back_populates='delivery_address',
-        foreign_keys='[Order._delivery_address_id]',
+        foreign_keys='[Order.delivery_address_id]',
     )
+    pixels = orm.relationship('AddressPixelAssociation', back_populates='address')
+
+    # We do not implement a `.__init__()` method and leave that to SQLAlchemy.
+    # Instead, we use `hasattr()` to check for uninitialized attributes.  grep:b1f68d24
 
     def __repr__(self) -> str:
         """Non-literal text representation."""
@@ -72,11 +81,85 @@ class Address(meta.Base):
 
     @hybrid.hybrid_property
     def is_primary(self) -> bool:
-        """If an Address object is the earliest one entered at its location.
+        """If an `Address` object is the earliest one entered at its location.
 
         Street addresses may have been entered several times with different
         versions/spellings of the street name and/or different floors.
 
-        `is_primary` indicates the first in a group of addresses.
+        `.is_primary` indicates the first in a group of `Address` objects.
         """
-        return self.id == self._primary_id
+        return self.id == self.primary_id
+
+    @property
+    def location(self) -> utils.Location:
+        """The location of the address.
+
+        The returned `Location` object relates to `.city.southwest`.
+
+        See also the `.x` and `.y` properties that are shortcuts for
+        `.location.x` and `.location.y`.
+
+        Implementation detail: This property is cached as none of the
+        underlying attributes to calculate the value are to be changed.
+        """
+        if not hasattr(self, '_location'):  # noqa:WPS421  note:b1f68d24
+            self._location = utils.Location(self.latitude, self.longitude)
+            self._location.relate_to(self.city.southwest)
+        return self._location
+
+    @property
+    def x(self) -> int:  # noqa=WPS111
+        """The relative x-coordinate within the `.city` in meters.
+
+        On the implied x-y plane, the `.city`'s southwest corner is the origin.
+
+        Shortcut for `.location.x`.
+        """
+        return self.location.x
+
+    @property
+    def y(self) -> int:  # noqa=WPS111
+        """The relative y-coordinate within the `.city` in meters.
+
+        On the implied x-y plane, the `.city`'s southwest corner is the origin.
+
+        Shortcut for `.location.y`.
+        """
+        return self.location.y
+
+    def clear_map(self) -> Address:  # pragma: no cover
+        """Shortcut to the `.city.clear_map()` method.
+
+        Returns:
+            self: enabling method chaining
+        """  # noqa:D402,DAR203
+        self.city.clear_map()
+        return self
+
+    @property  # pragma: no cover
+    def map(self) -> folium.Map:  # noqa:WPS125
+        """Shortcut to the `.city.map` object."""
+        return self.city.map
+
+    def draw(self, **kwargs: Any) -> folium.Map:  # pragma: no cover
+        """Draw the address on the `.city.map`.
+
+        By default, addresses are shown as black dots.
+        Use `**kwargs` to overwrite that.
+
+        Args:
+            **kwargs: passed on to `folium.Circle()`; overwrite default settings
+
+        Returns:
+            `.city.map` for convenience in interactive usage
+        """
+        defaults = {
+            'color': 'black',
+            'popup': f'{self.street}, {self.zip_code} {self.city_name}',
+        }
+        defaults.update(kwargs)
+
+        marker = folium.Circle((self.latitude, self.longitude), **defaults)
+        marker.add_to(self.city.map)
+
+        return self.map

src/urban_meal_delivery/db/addresses_pixels.py

@@ -0,0 +1,56 @@
+"""Model for the many-to-many relationship between `Address` and `Pixel` objects."""
+
+import sqlalchemy as sa
+from sqlalchemy import orm
+
+from urban_meal_delivery.db import meta
+
+
+class AddressPixelAssociation(meta.Base):
+    """Association pattern between `Address` and `Pixel`.
+
+    This approach is needed here mainly because it implicitly
+    updates the `_city_id` and `_grid_id` columns.
+
+    Further info:
+        https://docs.sqlalchemy.org/en/stable/orm/basic_relationships.html#association-object  # noqa:E501
+    """
+
+    __tablename__ = 'addresses_pixels'
+
+    # Columns
+    address_id = sa.Column(sa.Integer, primary_key=True)
+    city_id = sa.Column(sa.SmallInteger, nullable=False)
+    grid_id = sa.Column(sa.SmallInteger, nullable=False)
+    pixel_id = sa.Column(sa.Integer, primary_key=True)
+
+    # Constraints
+    __table_args__ = (
+        # An `Address` can only be on a `Grid` ...
+        sa.ForeignKeyConstraint(
+            ['address_id', 'city_id'],
+            ['addresses.id', 'addresses.city_id'],
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        # ... if their `.city` attributes match.
+        sa.ForeignKeyConstraint(
+            ['grid_id', 'city_id'],
+            ['grids.id', 'grids.city_id'],
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+        # Each `Address` can only be on a `Grid` once.
+        sa.UniqueConstraint('address_id', 'grid_id'),
+        # An association must reference an existing `Grid`-`Pixel` pair.
+        sa.ForeignKeyConstraint(
+            ['pixel_id', 'grid_id'],
+            ['pixels.id', 'pixels.grid_id'],
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
+    )
+
+    # Relationships
+    address = orm.relationship('Address', back_populates='pixels')
+    pixel = orm.relationship('Pixel', back_populates='addresses')

src/urban_meal_delivery/db/cities.py

@@ -1,16 +1,20 @@
-"""Provide the ORM's City model."""
+"""Provide the ORM's `City` model."""
 
-from typing import Dict
+from __future__ import annotations
 
+import folium
 import sqlalchemy as sa
 from sqlalchemy import orm
 from sqlalchemy.dialects import postgresql
 
+from urban_meal_delivery import config
+from urban_meal_delivery import db
 from urban_meal_delivery.db import meta
+from urban_meal_delivery.db import utils
 
 
 class City(meta.Base):
-    """A City where the UDP operates in."""
+    """A city where the UDP operates in."""
 
     __tablename__ = 'cities'
 
@@ -22,62 +26,227 @@ class City(meta.Base):
     kml = sa.Column(sa.UnicodeText, nullable=False)
 
     # Google Maps related columns
-    _center_latitude = sa.Column(
+    center_latitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-        'center_latitude', postgresql.DOUBLE_PRECISION, nullable=False,
+    center_longitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-    )
+    northeast_latitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-    _center_longitude = sa.Column(
+    northeast_longitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-        'center_longitude', postgresql.DOUBLE_PRECISION, nullable=False,
+    southwest_latitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-    )
+    southwest_longitude = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
-    _northeast_latitude = sa.Column(
-        'northeast_latitude', postgresql.DOUBLE_PRECISION, nullable=False,
-    )
-    _northeast_longitude = sa.Column(
-        'northeast_longitude', postgresql.DOUBLE_PRECISION, nullable=False,
-    )
-    _southwest_latitude = sa.Column(
-        'southwest_latitude', postgresql.DOUBLE_PRECISION, nullable=False,
-    )
-    _southwest_longitude = sa.Column(
-        'southwest_longitude', postgresql.DOUBLE_PRECISION, nullable=False,
-    )
     initial_zoom = sa.Column(sa.SmallInteger, nullable=False)
 
     # Relationships
     addresses = orm.relationship('Address', back_populates='city')
+    grids = orm.relationship('Grid', back_populates='city')
+
+    # We do not implement a `.__init__()` method and leave that to SQLAlchemy.
+    # Instead, we use `hasattr()` to check for uninitialized attributes.  grep:d334120e
 
     def __repr__(self) -> str:
         """Non-literal text representation."""
         return '<{cls}({name})>'.format(cls=self.__class__.__name__, name=self.name)
 
     @property
-    def location(self) -> Dict[str, float]:
+    def center(self) -> utils.Location:
-        """GPS location of the city's center.
+        """Location of the city's center.
 
-        Example:
+        Implementation detail: This property is cached as none of the
-            {"latitude": 48.856614, "longitude": 2.3522219}
+        underlying attributes to calculate the value are to be changed.
         """
-        return {
+        if not hasattr(self, '_center'):  # noqa:WPS421  note:d334120e
-            'latitude': self._center_latitude,
+            self._center = utils.Location(self.center_latitude, self.center_longitude)
-            'longitude': self._center_longitude,
+        return self._center
-        }
 
     @property
-    def viewport(self) -> Dict[str, Dict[str, float]]:
+    def northeast(self) -> utils.Location:
-        """Google Maps viewport of the city.
+        """The city's northeast corner of the Google Maps viewport.
 
-        Example:
+        Implementation detail: This property is cached as none of the
-            {
+        underlying attributes to calculate the value are to be changed.
-                'northeast': {'latitude': 48.9021449, 'longitude': 2.4699208},
+        """
-                'southwest': {'latitude': 48.815573, 'longitude': 2.225193},
+        if not hasattr(self, '_northeast'):  # noqa:WPS421  note:d334120e
-            }
+            self._northeast = utils.Location(
-        """  # noqa:RST203
+                self.northeast_latitude, self.northeast_longitude,
-        return {
+            )
-            'northeast': {
+
-                'latitude': self._northeast_latitude,
+        return self._northeast
-                'longitude': self._northeast_longitude,
+
-            },
+    @property
-            'southwest': {
+    def southwest(self) -> utils.Location:
-                'latitude': self._southwest_latitude,
+        """The city's southwest corner of the Google Maps viewport.
-                'longitude': self._southwest_longitude,
+
-            },
+        Implementation detail: This property is cached as none of the
+        underlying attributes to calculate the value are to be changed.
+        """
+        if not hasattr(self, '_southwest'):  # noqa:WPS421  note:d334120e
+            self._southwest = utils.Location(
+                self.southwest_latitude, self.southwest_longitude,
+            )
+
+        return self._southwest
+
+    @property
+    def total_x(self) -> int:
+        """The horizontal distance from the city's west to east end in meters.
+
+        The city borders refer to the Google Maps viewport.
+        """
+        return self.northeast.easting - self.southwest.easting
+
+    @property
+    def total_y(self) -> int:
+        """The vertical distance from the city's south to north end in meters.
+
+        The city borders refer to the Google Maps viewport.
+        """
+        return self.northeast.northing - self.southwest.northing
+
+    def clear_map(self) -> City:  # pragma: no cover
+        """Create a new `folium.Map` object aligned with the city's viewport.
+
+        The map is available via the `.map` property. Note that it is a
+        mutable objects that is changed from various locations in the code base.
+
+        Returns:
+            self: enabling method chaining
+        """  # noqa:DAR203
+        self._map = folium.Map(
+            location=[self.center_latitude, self.center_longitude],
+            zoom_start=self.initial_zoom,
+        )
+        return self
+
+    @property  # pragma: no cover
+    def map(self) -> folium.Map:  # noqa:WPS125
+        """A `folium.Map` object aligned with the city's viewport.
+
+        See docstring for `.clear_map()` for further info.
+        """
+        if not hasattr(self, '_map'):  # noqa:WPS421  note:d334120e
+            self.clear_map()
+
+        return self._map
+
+    def draw_restaurants(  # noqa:WPS231
+        self, order_counts: bool = False,  # pragma: no cover
+    ) -> folium.Map:
+        """Draw all restaurants on the`.map`.
+
+        Args:
+            order_counts: show the number of orders
+
+        Returns:
+            `.map` for convenience in interactive usage
+        """
+        # Obtain all primary `Address`es in the city that host `Restaurant`s.
+        addresses = (  # noqa:ECE001
+            db.session.query(db.Address)
+            .filter(
+                db.Address.id.in_(
+                    db.session.query(db.Address.primary_id)  # noqa:WPS221
+                    .join(db.Restaurant, db.Address.id == db.Restaurant.address_id)
+                    .filter(db.Address.city == self)
+                    .distinct()
+                    .all(),
+                ),
+            )
+            .all()
+        )
+
+        for address in addresses:
+            # Show the restaurant's name if there is only one.
+            # Otherwise, list all the restaurants' ID's.
+            restaurants = (  # noqa:ECE001
+                db.session.query(db.Restaurant)
+                .join(db.Address, db.Restaurant.address_id == db.Address.id)
+                .filter(db.Address.primary_id == address.id)
+                .all()
+            )
+            if len(restaurants) == 1:
+                tooltip = f'{restaurants[0].name} (#{restaurants[0].id})'  # noqa:WPS221
+            else:
+                tooltip = 'Restaurants ' + ', '.join(  # noqa:WPS336
+                    f'#{restaurant.id}' for restaurant in restaurants
+                )
+
+            if order_counts:
+                # Calculate the number of orders for ALL restaurants ...
+                n_orders = (  # noqa:ECE001
+                    db.session.query(db.Order.id)
+                    .join(db.Address, db.Order.pickup_address_id == db.Address.id)
+                    .filter(db.Address.primary_id == address.id)
+                    .count()
+                )
+                # ... and adjust the size of the red dot on the `.map`.
+                if n_orders >= 1000:
+                    radius = 20  # noqa:WPS220
+                elif n_orders >= 500:
+                    radius = 15  # noqa:WPS220
+                elif n_orders >= 100:
+                    radius = 10  # noqa:WPS220
+                elif n_orders >= 10:
+                    radius = 5  # noqa:WPS220
+                else:
+                    radius = 1  # noqa:WPS220
+
+                tooltip += f' | n_orders={n_orders}'  # noqa:WPS336
+
+                address.draw(
+                    radius=radius,
+                    color=config.RESTAURANT_COLOR,
+                    fill_color=config.RESTAURANT_COLOR,
+                    fill_opacity=0.3,
+                    tooltip=tooltip,
+                )
+
+            else:
+                address.draw(
+                    radius=1, color=config.RESTAURANT_COLOR, tooltip=tooltip,
+                )
+
+        return self.map
+
+    def draw_zip_codes(self) -> folium.Map:  # pragma: no cover
+        """Draw all addresses on the `.map`, colorized by their `.zip_code`.
+
+        This does not make a distinction between restaurant and customer addresses.
+        Also, due to the high memory usage, the number of orders is not calculated.
+
+        Returns:
+            `.map` for convenience in interactive usage
+        """
+        # First, create a color map with distinct colors for each zip code.
+        all_zip_codes = sorted(
+            row[0]
+            for row in db.session.execute(
+                sa.text(
+                    f"""  -- # noqa:S608
+                    SELECT DISTINCT
+                        zip_code
+                    FROM
+                        {config.CLEAN_SCHEMA}.addresses
+                    WHERE
+                        city_id = {self.id};
+                    """,
+                ),
+            )
+        )
+        cmap = utils.make_random_cmap(len(all_zip_codes), bright=False)
+        colors = {
+            code: utils.rgb_to_hex(*cmap(index))
+            for index, code in enumerate(all_zip_codes)
         }
+
+        # Second, draw every address on the `.map.
+        for address in self.addresses:
+            # Non-primary addresses are covered by primary ones anyway.
+            if not address.is_primary:
+                continue
+
+            marker = folium.Circle(  # noqa:WPS317
+                (address.latitude, address.longitude),
+                color=colors[address.zip_code],
+                radius=1,
+            )
+            marker.add_to(self.map)
+
+        return self.map

src/urban_meal_delivery/db/connection.py

@@ -1,17 +1,28 @@
-"""Provide connection utils for the ORM layer."""
+"""Provide connection utils for the ORM layer.
+
+This module defines fully configured `engine`, `connection`, and `session`
+objects to be used as globals within the `urban_meal_delivery` package.
+
+If a database is not guaranteed to be available, they are set to `None`.
+That is the case on the CI server.
+"""
+
+import os
 
 import sqlalchemy as sa
-from sqlalchemy import engine
+from sqlalchemy import engine as engine_mod
 from sqlalchemy import orm
 
 import urban_meal_delivery
 
 
-def make_engine() -> engine.Engine:  # pragma: no cover
+if os.getenv('TESTING'):
-    """Provide a configured Engine object."""
+    # Specify the types explicitly to make mypy happy.
-    return sa.create_engine(urban_meal_delivery.config.DATABASE_URI)
+    engine: engine_mod.Engine = None
+    connection: engine_mod.Connection = None
+    session: orm.Session = None
 
-
+else:  # pragma: no cover
-def make_session_factory() -> orm.Session:  # pragma: no cover
+    engine = sa.create_engine(urban_meal_delivery.config.DATABASE_URI)
-    """Provide a configured Session factory."""
+    connection = engine.connect()
-    return orm.sessionmaker(bind=make_engine())
+    session = orm.sessionmaker(bind=connection)()

src/urban_meal_delivery/db/couriers.py

@@ -1,4 +1,4 @@
-"""Provide the ORM's Courier model."""
+"""Provide the ORM's `Courier` model."""
 
 import sqlalchemy as sa
 from sqlalchemy import orm
@@ -8,9 +8,7 @@ from urban_meal_delivery.db import meta
 
 
 class Courier(meta.Base):
-    """A Courier working for the UDP."""
+    """A courier working for the UDP."""
-
-    # pylint:disable=too-few-public-methods
 
     __tablename__ = 'couriers'
 

src/urban_meal_delivery/db/customers.py

@@ -1,15 +1,18 @@
-"""Provide the ORM's Customer model."""
+"""Provide the ORM's `Customer` model."""
 
+from __future__ import annotations
+
+import folium
 import sqlalchemy as sa
 from sqlalchemy import orm
 
+from urban_meal_delivery import config
+from urban_meal_delivery import db
 from urban_meal_delivery.db import meta
 
 
 class Customer(meta.Base):
-    """A Customer of the UDP."""
+    """A customer of the UDP."""
-
-    # pylint:disable=too-few-public-methods
 
     __tablename__ = 'customers'
 
@@ -24,3 +27,155 @@ class Customer(meta.Base):
 
     # Relationships
     orders = orm.relationship('Order', back_populates='customer')
+
+    def clear_map(self) -> Customer:  # pragma: no cover
+        """Shortcut to the `...city.clear_map()` method.
+
+        Returns:
+            self: enabling method chaining
+        """  # noqa:D402,DAR203
+        self.orders[0].pickup_address.city.clear_map()  # noqa:WPS219
+        return self
+
+    @property  # pragma: no cover
+    def map(self) -> folium.Map:  # noqa:WPS125
+        """Shortcut to the `...city.map` object."""
+        return self.orders[0].pickup_address.city.map  # noqa:WPS219
+
+    def draw(  # noqa:C901,WPS210,WPS231
+        self, restaurants: bool = True, order_counts: bool = False,  # pragma: no cover
+    ) -> folium.Map:
+        """Draw all the customer's delivery addresses on the `...city.map`.
+
+        By default, the pickup locations (= restaurants) are also shown.
+
+        Args:
+            restaurants: show the pickup locations
+            order_counts: show both the number of pickups at the restaurants
+                and the number of deliveries at the customer's delivery addresses;
+                the former is only shown if `restaurants=True`
+
+        Returns:
+            `...city.map` for convenience in interactive usage
+        """
+        # Note: a `Customer` may have more than one delivery `Address`es.
+        # That is not true for `Restaurant`s after the data cleaning.
+
+        # Obtain all primary `Address`es where
+        # at least one delivery was made to `self`.
+        delivery_addresses = (  # noqa:ECE001
+            db.session.query(db.Address)
+            .filter(
+                db.Address.id.in_(
+                    db.session.query(db.Address.primary_id)  # noqa:WPS221
+                    .join(db.Order, db.Address.id == db.Order.delivery_address_id)
+                    .filter(db.Order.customer_id == self.id)
+                    .distinct()
+                    .all(),
+                ),
+            )
+            .all()
+        )
+
+        for address in delivery_addresses:
+            if order_counts:
+                n_orders = (  # noqa:ECE001
+                    db.session.query(db.Order)
+                    .join(db.Address, db.Order.delivery_address_id == db.Address.id)
+                    .filter(db.Order.customer_id == self.id)
+                    .filter(db.Address.primary_id == address.id)
+                    .count()
+                )
+                if n_orders >= 25:
+                    radius = 20  # noqa:WPS220
+                elif n_orders >= 10:
+                    radius = 15  # noqa:WPS220
+                elif n_orders >= 5:
+                    radius = 10  # noqa:WPS220
+                elif n_orders > 1:
+                    radius = 5  # noqa:WPS220
+                else:
+                    radius = 1  # noqa:WPS220
+
+                address.draw(
+                    radius=radius,
+                    color=config.CUSTOMER_COLOR,
+                    fill_color=config.CUSTOMER_COLOR,
+                    fill_opacity=0.3,
+                    tooltip=f'n_orders={n_orders}',
+                )
+
+            else:
+                address.draw(
+                    radius=1, color=config.CUSTOMER_COLOR,
+                )
+
+        if restaurants:
+            pickup_addresses = (  # noqa:ECE001
+                db.session.query(db.Address)
+                .filter(
+                    db.Address.id.in_(
+                        db.session.query(db.Address.primary_id)  # noqa:WPS221
+                        .join(db.Order, db.Address.id == db.Order.pickup_address_id)
+                        .filter(db.Order.customer_id == self.id)
+                        .distinct()
+                        .all(),
+                    ),
+                )
+                .all()
+            )
+
+            for address in pickup_addresses:  # noqa:WPS440
+                # Show the restaurant's name if there is only one.
+                # Otherwise, list all the restaurants' ID's.
+                # We cannot show the `Order.restaurant.name` due to the aggregation.
+                restaurants = (  # noqa:ECE001
+                    db.session.query(db.Restaurant)
+                    .join(db.Address, db.Restaurant.address_id == db.Address.id)
+                    .filter(db.Address.primary_id == address.id)  # noqa:WPS441
+                    .all()
+                )
+                if len(restaurants) == 1:  # type:ignore
+                    tooltip = (
+                        f'{restaurants[0].name} (#{restaurants[0].id})'  # type:ignore
+                    )
+                else:
+                    tooltip = 'Restaurants ' + ', '.join(  # noqa:WPS336
+                        f'#{restaurant.id}' for restaurant in restaurants  # type:ignore
+                    )
+
+                if order_counts:
+                    n_orders = (  # noqa:ECE001
+                        db.session.query(db.Order)
+                        .join(db.Address, db.Order.pickup_address_id == db.Address.id)
+                        .filter(db.Order.customer_id == self.id)
+                        .filter(db.Address.primary_id == address.id)  # noqa:WPS441
+                        .count()
+                    )
+                    if n_orders >= 25:
+                        radius = 20  # noqa:WPS220
+                    elif n_orders >= 10:
+                        radius = 15  # noqa:WPS220
+                    elif n_orders >= 5:
+                        radius = 10  # noqa:WPS220
+                    elif n_orders > 1:
+                        radius = 5  # noqa:WPS220
+                    else:
+                        radius = 1  # noqa:WPS220
+
+                    tooltip += f' | n_orders={n_orders}'  # noqa:WPS336
+
+                    address.draw(  # noqa:WPS441
+                        radius=radius,
+                        color=config.RESTAURANT_COLOR,
+                        fill_color=config.RESTAURANT_COLOR,
+                        fill_opacity=0.3,
+                        tooltip=tooltip,
+                    )
+
+                else:
+                    address.draw(  # noqa:WPS441
+                        radius=1, color=config.RESTAURANT_COLOR, tooltip=tooltip,
+                    )
+
+        return self.map

src/urban_meal_delivery/db/forecasts.py

@@ -0,0 +1,231 @@
+"""Provide the ORM's `Forecast` model."""
+
+from __future__ import annotations
+
+import math
+from typing import List
+
+import pandas as pd
+import sqlalchemy as sa
+from sqlalchemy import orm
+from sqlalchemy.dialects import postgresql
+
+from urban_meal_delivery.db import meta
+
+
+class Forecast(meta.Base):
+    """A demand forecast for a `.pixel` and `.time_step` pair.
+
+    This table is denormalized on purpose to keep things simple. In particular,
+    the `.model` and `.actual` hold redundant values.
+    """
+
+    __tablename__ = 'forecasts'
+
+    # Columns
+    id = sa.Column(sa.Integer, primary_key=True, autoincrement=True)  # noqa:WPS125
+    pixel_id = sa.Column(sa.Integer, nullable=False, index=True)
+    start_at = sa.Column(sa.DateTime, nullable=False)
+    time_step = sa.Column(sa.SmallInteger, nullable=False)
+    train_horizon = sa.Column(sa.SmallInteger, nullable=False)
+    model = sa.Column(sa.Unicode(length=20), nullable=False)
+    # We also store the actual order counts for convenient retrieval.
+    # A `UniqueConstraint` below ensures that redundant values that
+    # are to be expected are consistend across rows.
+    actual = sa.Column(sa.SmallInteger, nullable=False)
+    # Raw `.prediction`s are stored as `float`s (possibly negative).
+    # The rounding is then done on the fly if required.
+    prediction = sa.Column(postgresql.DOUBLE_PRECISION, nullable=False)
+    # The confidence intervals are treated like the `.prediction`s
+    # but they may be nullable as some methods do not calculate them.
+    low80 = sa.Column(postgresql.DOUBLE_PRECISION, nullable=True)
+    high80 = sa.Column(postgresql.DOUBLE_PRECISION, nullable=True)
+    low95 = sa.Column(postgresql.DOUBLE_PRECISION, nullable=True)
+    high95 = sa.Column(postgresql.DOUBLE_PRECISION, nullable=True)
+
+    # Constraints
+    __table_args__ = (
+        sa.ForeignKeyConstraint(
+            ['pixel_id'], ['pixels.id'], onupdate='RESTRICT', ondelete='RESTRICT',
+        ),
+        sa.CheckConstraint(
+            """
+                NOT (
+                    EXTRACT(HOUR FROM start_at) < 11
+                    OR
+                    EXTRACT(HOUR FROM start_at) > 22
+                )
+            """,
+            name='start_at_must_be_within_operating_hours',
+        ),
+        sa.CheckConstraint(
+            'CAST(EXTRACT(MINUTES FROM start_at) AS INTEGER) % 15 = 0',
+            name='start_at_minutes_must_be_quarters_of_the_hour',
+        ),
+        sa.CheckConstraint(
+            'EXTRACT(SECONDS FROM start_at) = 0', name='start_at_allows_no_seconds',
+        ),
+        sa.CheckConstraint(
+            'CAST(EXTRACT(MICROSECONDS FROM start_at) AS INTEGER) % 1000000 = 0',
+            name='start_at_allows_no_microseconds',
+        ),
+        sa.CheckConstraint('time_step > 0', name='time_step_must_be_positive'),
+        sa.CheckConstraint(
+            'train_horizon > 0', name='training_horizon_must_be_positive',
+        ),
+        sa.CheckConstraint('actual >= 0', name='actuals_must_be_non_negative'),
+        sa.CheckConstraint(
+            """
+                NOT (
+                    low80 IS NULL AND high80 IS NOT NULL
+                    OR
+                    low80 IS NOT NULL AND high80 IS NULL
+                    OR
+                    low95 IS NULL AND high95 IS NOT NULL
+                    OR
+                    low95 IS NOT NULL AND high95 IS NULL
+               )
+            """,
+            name='ci_upper_and_lower_bounds',
+        ),
+        sa.CheckConstraint(
+            """
+                NOT (
+                    prediction < low80
+                    OR
+                    prediction < low95
+                    OR
+                    prediction > high80
+                    OR
+                    prediction > high95
+                )
+            """,
+            name='prediction_must_be_within_ci',
+        ),
+        sa.CheckConstraint(
+            """
+                NOT (
+                    low80 > high80
+                    OR
+                    low95 > high95
+                )
+            """,
+            name='ci_upper_bound_greater_than_lower_bound',
+        ),
+        sa.CheckConstraint(
+            """
+                NOT (
+                    low80 < low95
+                    OR
+                    high80 > high95
+                )
+            """,
+            name='ci95_must_be_wider_than_ci80',
+        ),
+        # There can be only one prediction per forecasting setting.
+        sa.UniqueConstraint(
+            'pixel_id', 'start_at', 'time_step', 'train_horizon', 'model',
+        ),
+    )
+
+    # Relationships
+    pixel = orm.relationship('Pixel', back_populates='forecasts')
+
+    def __repr__(self) -> str:
+        """Non-literal text representation."""
+        return '<{cls}: {prediction} for pixel ({n_x}|{n_y}) at {start_at}>'.format(
+            cls=self.__class__.__name__,
+            prediction=self.prediction,
+            n_x=self.pixel.n_x,
+            n_y=self.pixel.n_y,
+            start_at=self.start_at,
+        )
+
+    @classmethod
+    def from_dataframe(  # noqa:WPS210,WPS211
+        cls,
+        pixel: db.Pixel,
+        time_step: int,
+        train_horizon: int,
+        model: str,
+        data: pd.Dataframe,
+    ) -> List[db.Forecast]:
+        """Convert results from the forecasting `*Model`s into `Forecast` objects.
+
+        This is an alternative constructor method.
+
+        Background: The functions in `urban_meal_delivery.forecasts.methods`
+        return `pd.Dataframe`s with "start_at" (i.e., `pd.Timestamp` objects)
+        values in the index and five columns "prediction", "low80", "high80",
+        "low95", and "high95" with `np.float` values. The `*Model.predic()`
+        methods in `urban_meal_delivery.forecasts.models` then add an "actual"
+        column. This constructor converts these results into ORM models.
+        Also, the `np.float` values are cast as plain `float` ones as
+        otherwise SQLAlchemy and the database would complain.
+
+        Args:
+            pixel: in which the forecast is made
+            time_step: length of one time step in minutes
+            train_horizon: length of the training horizon in weeks
+            model: name of the forecasting model
+            data: a `pd.Dataframe` as described above (i.e.,
+                with the six columns holding `float`s)
+
+        Returns:
+            forecasts: the `data` as `Forecast` objects
+        """  # noqa:RST215
+        forecasts = []
+
+        for timestamp_idx in data.index:
+            start_at = timestamp_idx.to_pydatetime()
+            actual = int(data.loc[timestamp_idx, 'actual'])
+            prediction = round(data.loc[timestamp_idx, 'prediction'], 5)
+
+            # Explicit type casting. SQLAlchemy does not convert
+            # `float('NaN')`s into plain `None`s.
+
+            low80 = data.loc[timestamp_idx, 'low80']
+            high80 = data.loc[timestamp_idx, 'high80']
+            low95 = data.loc[timestamp_idx, 'low95']
+            high95 = data.loc[timestamp_idx, 'high95']
+
+            if math.isnan(low80):
+                low80 = None
+            else:
+                low80 = round(low80, 5)
+
+            if math.isnan(high80):
+                high80 = None
+            else:
+                high80 = round(high80, 5)
+
+            if math.isnan(low95):
+                low95 = None
+            else:
+                low95 = round(low95, 5)
+
+            if math.isnan(high95):
+                high95 = None
+            else:
+                high95 = round(high95, 5)
+
+            forecasts.append(
+                cls(
+                    pixel=pixel,
+                    start_at=start_at,
+                    time_step=time_step,
+                    train_horizon=train_horizon,
+                    model=model,
+                    actual=actual,
+                    prediction=prediction,
+                    low80=low80,
+                    high80=high80,
+                    low95=low95,
+                    high95=high95,
+                ),
+            )
+
+        return forecasts
+
+
+from urban_meal_delivery import db  # noqa:E402  isort:skip

src/urban_meal_delivery/db/grids.py

@@ -0,0 +1,137 @@
+"""Provide the ORM's `Grid` model."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import folium
+import sqlalchemy as sa
+from sqlalchemy import orm
+
+from urban_meal_delivery import db
+from urban_meal_delivery.db import meta
+
+
+class Grid(meta.Base):
+    """A grid of `Pixel`s to partition a `City`.
+
+    A grid is characterized by the uniform size of the `Pixel`s it contains.
+    That is configures via the `Grid.side_length` attribute.
+    """
+
+    __tablename__ = 'grids'
+
+    # Columns
+    id = sa.Column(  # noqa:WPS125
+        sa.SmallInteger, primary_key=True, autoincrement=True,
+    )
+    city_id = sa.Column(sa.SmallInteger, nullable=False)
+    side_length = sa.Column(sa.SmallInteger, nullable=False, unique=True)
+
+    # Constraints
+    __table_args__ = (
+        sa.ForeignKeyConstraint(
+            ['city_id'], ['cities.id'], onupdate='RESTRICT', ondelete='RESTRICT',
+        ),
+        # Each `Grid`, characterized by its `.side_length`,
+        # may only exists once for a given `.city`.
+        sa.UniqueConstraint('city_id', 'side_length'),
+        # Needed by a `ForeignKeyConstraint` in `address_pixel_association`.
+        sa.UniqueConstraint('id', 'city_id'),
+    )
+
+    # Relationships
+    city = orm.relationship('City', back_populates='grids')
+    pixels = orm.relationship('Pixel', back_populates='grid')
+
+    def __repr__(self) -> str:
+        """Non-literal text representation."""
+        return '<{cls}: {area} sqr. km>'.format(
+            cls=self.__class__.__name__, area=self.pixel_area,
+        )
+
+    # Convenience properties
+    @property
+    def pixel_area(self) -> float:
+        """The area of a `Pixel` on the grid in square kilometers."""
+        return round((self.side_length ** 2) / 1_000_000, 1)
+
+    @classmethod
+    def gridify(cls, city: db.City, side_length: int) -> db.Grid:  # noqa:WPS210
+        """Create a fully populated `Grid` for a `city`.
+
+        The `Grid` contains only `Pixel`s that have at least one
+        `Order.pickup_address`. `Address` objects outside the `.city`'s
+        viewport are discarded.
+
+        Args:
+            city: city for which the grid is created
+            side_length: the length of a square `Pixel`'s side
+
+        Returns:
+            grid: including `grid.pixels` with the associated `city.addresses`
+        """
+        grid = cls(city=city, side_length=side_length)
+
+        # `Pixel`s grouped by `.n_x`-`.n_y` coordinates.
+        pixels = {}
+
+        pickup_addresses = (  # noqa:ECE:001
+            db.session.query(db.Address)
+            .join(db.Order, db.Address.id == db.Order.pickup_address_id)
+            .filter(db.Address.city == city)
+            .all()
+        )
+
+        for address in pickup_addresses:
+            # Check if an `address` is not within the `city`'s viewport, ...
+            not_within_city_viewport = (
+                address.x < 0
+                or address.x > city.total_x
+                or address.y < 0
+                or address.y > city.total_y
+            )
+            # ... and, if so, the `address` does not belong to any `Pixel`.
+            if not_within_city_viewport:
+                continue
+
+            # Determine which `pixel` the `address` belongs to ...
+            n_x, n_y = address.x // side_length, address.y // side_length
+            # ... and create a new `Pixel` object if necessary.
+            if (n_x, n_y) not in pixels:
+                pixels[(n_x, n_y)] = db.Pixel(grid=grid, n_x=n_x, n_y=n_y)
+            pixel = pixels[(n_x, n_y)]
+
+            # Create an association between the `address` and `pixel`.
+            assoc = db.AddressPixelAssociation(address=address, pixel=pixel)
+            pixel.addresses.append(assoc)
+
+        return grid
+
+    def clear_map(self) -> Grid:  # pragma: no cover
+        """Shortcut to the `.city.clear_map()` method.
+
+        Returns:
+            self: enabling method chaining
+        """  # noqa:D402,DAR203
+        self.city.clear_map()
+        return self
+
+    @property  # pragma: no cover
+    def map(self) -> folium.Map:  # noqa:WPS125
+        """Shortcut to the `.city.map` object."""
+        return self.city.map
+
+    def draw(self, **kwargs: Any) -> folium.Map:  # pragma: no cover
+        """Draw all pixels in the grid.
+
+        Args:
+            **kwargs: passed on to `Pixel.draw()`
+
+        Returns:
+            `.city.map` for convenience in interactive usage
+        """
+        for pixel in self.pixels:
+            pixel.draw(**kwargs)
+
+        return self.map

src/urban_meal_delivery/db/orders.py

@@ -1,4 +1,4 @@
-"""Provide the ORM's Order model."""
+"""Provide the ORM's `Order` model."""
 
 import datetime
 
@@ -10,14 +10,14 @@ from urban_meal_delivery.db import meta
 
 
 class Order(meta.Base):  # noqa:WPS214
-    """An Order by a Customer of the UDP."""
+    """An order by a `Customer` of the UDP."""
 
     __tablename__ = 'orders'
 
     # Generic columns
     id = sa.Column(sa.Integer, primary_key=True, autoincrement=False)  # noqa:WPS125
     _delivery_id = sa.Column('delivery_id', sa.Integer, index=True, unique=True)
-    _customer_id = sa.Column('customer_id', sa.Integer, nullable=False, index=True)
+    customer_id = sa.Column(sa.Integer, nullable=False, index=True)
     placed_at = sa.Column(sa.DateTime, nullable=False, index=True)
     ad_hoc = sa.Column(sa.Boolean, nullable=False)
     scheduled_delivery_at = sa.Column(sa.DateTime, index=True)
@@ -33,9 +33,7 @@ class Order(meta.Base):  # noqa:WPS214
     total = sa.Column(sa.Integer, nullable=False)
 
     # Restaurant-related columns
-    _restaurant_id = sa.Column(
+    restaurant_id = sa.Column(sa.SmallInteger, nullable=False, index=True)
-        'restaurant_id', sa.SmallInteger, nullable=False, index=True,
-    )
     restaurant_notified_at = sa.Column(sa.DateTime)
     restaurant_notified_at_corrected = sa.Column(sa.Boolean, index=True)
     restaurant_confirmed_at = sa.Column(sa.DateTime)
@@ -45,7 +43,7 @@ class Order(meta.Base):  # noqa:WPS214
     estimated_prep_buffer = sa.Column(sa.Integer, nullable=False, index=True)
 
     # Dispatch-related columns
-    _courier_id = sa.Column('courier_id', sa.Integer, index=True)
+    courier_id = sa.Column(sa.Integer, index=True)
     dispatch_at = sa.Column(sa.DateTime)
     dispatch_at_corrected = sa.Column(sa.Boolean, index=True)
     courier_notified_at = sa.Column(sa.DateTime)
@@ -55,9 +53,7 @@ class Order(meta.Base):  # noqa:WPS214
     utilization = sa.Column(sa.SmallInteger, nullable=False)
 
     # Pickup-related columns
-    _pickup_address_id = sa.Column(
+    pickup_address_id = sa.Column(sa.Integer, nullable=False, index=True)
-        'pickup_address_id', sa.Integer, nullable=False, index=True,
-    )
     reached_pickup_at = sa.Column(sa.DateTime)
     pickup_at = sa.Column(sa.DateTime)
     pickup_at_corrected = sa.Column(sa.Boolean, index=True)
@@ -66,9 +62,7 @@ class Order(meta.Base):  # noqa:WPS214
     left_pickup_at_corrected = sa.Column(sa.Boolean, index=True)
 
     # Delivery-related columns
-    _delivery_address_id = sa.Column(
+    delivery_address_id = sa.Column(sa.Integer, nullable=False, index=True)
-        'delivery_address_id', sa.Integer, nullable=False, index=True,
-    )
     reached_delivery_at = sa.Column(sa.DateTime)
     delivery_at = sa.Column(sa.DateTime)
     delivery_at_corrected = sa.Column(sa.Boolean, index=True)
@@ -85,12 +79,6 @@ class Order(meta.Base):  # noqa:WPS214
         sa.ForeignKeyConstraint(
             ['customer_id'], ['customers.id'], onupdate='RESTRICT', ondelete='RESTRICT',
         ),
-        sa.ForeignKeyConstraint(
-            ['restaurant_id'],
-            ['restaurants.id'],
-            onupdate='RESTRICT',
-            ondelete='RESTRICT',
-        ),
         sa.ForeignKeyConstraint(
             ['courier_id'], ['couriers.id'], onupdate='RESTRICT', ondelete='RESTRICT',
         ),
@@ -100,6 +88,14 @@ class Order(meta.Base):  # noqa:WPS214
             onupdate='RESTRICT',
             ondelete='RESTRICT',
         ),
+        sa.ForeignKeyConstraint(
+            # This foreign key ensures that there is only
+            # one `.pickup_address` per `.restaurant`
+            ['restaurant_id', 'pickup_address_id'],
+            ['restaurants.id', 'restaurants.address_id'],
+            onupdate='RESTRICT',
+            ondelete='RESTRICT',
+        ),
         sa.ForeignKeyConstraint(
             ['delivery_address_id'],
             ['addresses.id'],
@@ -308,29 +304,33 @@ class Order(meta.Base):  # noqa:WPS214
 
     # Relationships
     customer = orm.relationship('Customer', back_populates='orders')
-    restaurant = orm.relationship('Restaurant', back_populates='orders')
+    restaurant = orm.relationship(
+        'Restaurant',
+        back_populates='orders',
+        primaryjoin='Restaurant.id == Order.restaurant_id',
+    )
     courier = orm.relationship('Courier', back_populates='orders')
     pickup_address = orm.relationship(
         'Address',
         back_populates='orders_picked_up',
-        foreign_keys='[Order._pickup_address_id]',
+        foreign_keys='[Order.pickup_address_id]',
     )
     delivery_address = orm.relationship(
         'Address',
         back_populates='orders_delivered',
-        foreign_keys='[Order._delivery_address_id]',
+        foreign_keys='[Order.delivery_address_id]',
     )
 
     # Convenience properties
 
     @property
     def scheduled(self) -> bool:
-        """Inverse of Order.ad_hoc."""
+        """Inverse of `.ad_hoc`."""
         return not self.ad_hoc
 
     @property
     def completed(self) -> bool:
-        """Inverse of Order.cancelled."""
+        """Inverse of `.cancelled`."""
         return not self.cancelled
 
     @property
@@ -353,9 +353,9 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def time_to_accept(self) -> datetime.timedelta:
-        """Time until a courier accepted an order.
+        """Time until the `.courier` accepted the order.
 
-        This adds the time it took the UDP to notify a courier.
+        This measures the time it took the UDP to notify the `.courier` after dispatch.
         """
         if not self.dispatch_at:
             raise RuntimeError('dispatch_at is not set')
@@ -365,9 +365,9 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def time_to_react(self) -> datetime.timedelta:
-        """Time a courier took to accept an order.
+        """Time the `.courier` took to accept an order.
 
-        This time is a subset of Order.time_to_accept.
+        A subset of `.time_to_accept`.
         """
         if not self.courier_notified_at:
             raise RuntimeError('courier_notified_at is not set')
@@ -377,7 +377,7 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def time_to_pickup(self) -> datetime.timedelta:
-        """Time from a courier's acceptance to arrival at the pickup location."""
+        """Time from the `.courier`'s acceptance to arrival at `.pickup_address`."""
         if not self.courier_accepted_at:
             raise RuntimeError('courier_accepted_at is not set')
         if not self.reached_pickup_at:
@@ -386,7 +386,7 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def time_at_pickup(self) -> datetime.timedelta:
-        """Time a courier stayed at the pickup location."""
+        """Time the `.courier` stayed at the `.pickup_address`."""
         if not self.reached_pickup_at:
             raise RuntimeError('reached_pickup_at is not set')
         if not self.pickup_at:
@@ -405,13 +405,13 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def courier_early(self) -> datetime.timedelta:
-        """Time by which a courier is early for pickup.
+        """Time by which the `.courier` is early for pickup.
 
-        Measured relative to Order.scheduled_pickup_at.
+        Measured relative to `.scheduled_pickup_at`.
 
-        0 if the courier is on time or late.
+        `datetime.timedelta(seconds=0)` if the `.courier` is on time or late.
 
-        Goes together with Order.courier_late.
+        Goes together with `.courier_late`.
         """
         return max(
             datetime.timedelta(), self.scheduled_pickup_at - self.reached_pickup_at,
@@ -419,13 +419,13 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def courier_late(self) -> datetime.timedelta:
-        """Time by which a courier is late for pickup.
+        """Time by which the `.courier` is late for pickup.
 
-        Measured relative to Order.scheduled_pickup_at.
+        Measured relative to `.scheduled_pickup_at`.
 
-        0 if the courier is on time or early.
+        `datetime.timedelta(seconds=0)` if the `.courier` is on time or early.
 
-        Goes together with Order.courier_early.
+        Goes together with `.courier_early`.
         """
         return max(
             datetime.timedelta(), self.reached_pickup_at - self.scheduled_pickup_at,
@@ -433,31 +433,31 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def restaurant_early(self) -> datetime.timedelta:
-        """Time by which a restaurant is early for pickup.
+        """Time by which the `.restaurant` is early for pickup.
 
-        Measured relative to Order.scheduled_pickup_at.
+        Measured relative to `.scheduled_pickup_at`.
 
-        0 if the restaurant is on time or late.
+        `datetime.timedelta(seconds=0)` if the `.restaurant` is on time or late.
 
-        Goes together with Order.restaurant_late.
+        Goes together with `.restaurant_late`.
         """
         return max(datetime.timedelta(), self.scheduled_pickup_at - self.pickup_at)
 
     @property
     def restaurant_late(self) -> datetime.timedelta:
-        """Time by which a restaurant is late for pickup.
+        """Time by which the `.restaurant` is late for pickup.
 
-        Measured relative to Order.scheduled_pickup_at.
+        Measured relative to `.scheduled_pickup_at`.
 
-        0 if the restaurant is on time or early.
+        `datetime.timedelta(seconds=0)` if the `.restaurant` is on time or early.
 
-        Goes together with Order.restaurant_early.
+        Goes together with `.restaurant_early`.
         """
         return max(datetime.timedelta(), self.pickup_at - self.scheduled_pickup_at)
 
     @property
     def time_to_delivery(self) -> datetime.timedelta:
-        """Time a courier took from pickup to delivery location."""
+        """Time the `.courier` took from `.pickup_address` to `.delivery_address`."""
         if not self.pickup_at:
             raise RuntimeError('pickup_at is not set')
         if not self.reached_delivery_at:
@@ -466,7 +466,7 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def time_at_delivery(self) -> datetime.timedelta:
-        """Time a courier stayed at the delivery location."""
+        """Time the `.courier` stayed at the `.delivery_address`."""
         if not self.reached_delivery_at:
             raise RuntimeError('reached_delivery_at is not set')
         if not self.delivery_at:
@@ -475,20 +475,20 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def courier_waited_at_delivery(self) -> datetime.timedelta:
-        """Time a courier waited at the delivery location."""
+        """Time the `.courier` waited at the `.delivery_address`."""
         if self._courier_waited_at_delivery:
             return self.time_at_delivery
         return datetime.timedelta()
 
     @property
     def delivery_early(self) -> datetime.timedelta:
-        """Time by which a scheduled order was early.
+        """Time by which a `.scheduled` order was early.
 
-        Measured relative to Order.scheduled_delivery_at.
+        Measured relative to `.scheduled_delivery_at`.
 
-        0 if the delivery is on time or late.
+        `datetime.timedelta(seconds=0)` if the delivery is on time or late.
 
-        Goes together with Order.delivery_late.
+        Goes together with `.delivery_late`.
         """
         if not self.scheduled:
             raise AttributeError('Makes sense only for scheduled orders')
@@ -496,13 +496,13 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def delivery_late(self) -> datetime.timedelta:
-        """Time by which a scheduled order was late.
+        """Time by which a `.scheduled` order was late.
 
-        Measured relative to Order.scheduled_delivery_at.
+        Measured relative to `.scheduled_delivery_at`.
 
-        0 if the delivery is on time or early.
+        `datetime.timedelta(seconds=0)` if the delivery is on time or early.
 
-        Goes together with Order.delivery_early.
+        Goes together with `.delivery_early`.
         """
         if not self.scheduled:
             raise AttributeError('Makes sense only for scheduled orders')
@@ -510,7 +510,7 @@ class Order(meta.Base):  # noqa:WPS214
 
     @property
     def total_time(self) -> datetime.timedelta:
-        """Time from order placement to delivery for an ad-hoc order."""
+        """Time from order placement to delivery for an `.ad_hoc` order."""
         if self.scheduled:
             raise AttributeError('Scheduled orders have no total_time')
         if self.cancelled:

src/urban_meal_delivery/db/pixels.py

@@ -0,0 +1,261 @@
+"""Provide the ORM's `Pixel` model."""
+
+from __future__ import annotations
+
+from typing import List
+
+import folium
+import sqlalchemy as sa
+import utm
+from sqlalchemy import orm
+
+from urban_meal_delivery import config
+from urban_meal_delivery import db
+from urban_meal_delivery.db import meta
+from urban_meal_delivery.db import utils
+
+
+class Pixel(meta.Base):
+    """A pixel in a `Grid`.
+
+    Square pixels aggregate `Address` objects within a `City`.
+    Every `Address` belongs to exactly one `Pixel` in a `Grid`.
+
+    Every `Pixel` has a unique `n_x`-`n_y` coordinate within the `Grid`.
+    """
+
+    __tablename__ = 'pixels'
+
+    # Columns
+    id = sa.Column(sa.Integer, primary_key=True, autoincrement=True)  # noqa:WPS125
+    grid_id = sa.Column(sa.SmallInteger, nullable=False, index=True)
+    n_x = sa.Column(sa.SmallInteger, nullable=False, index=True)
+    n_y = sa.Column(sa.SmallInteger, nullable=False, index=True)
+
+    # Constraints
+    __table_args__ = (
+        sa.ForeignKeyConstraint(
+            ['grid_id'], ['grids.id'], onupdate='RESTRICT', ondelete='RESTRICT',
+        ),
+        sa.CheckConstraint('0 <= n_x', name='n_x_is_positive'),
+        sa.CheckConstraint('0 <= n_y', name='n_y_is_positive'),
+        # Needed by a `ForeignKeyConstraint` in `AddressPixelAssociation`.
+        sa.UniqueConstraint('id', 'grid_id'),
+        # Each coordinate within the same `grid` is used at most once.
+        sa.UniqueConstraint('grid_id', 'n_x', 'n_y'),
+    )
+
+    # Relationships
+    grid = orm.relationship('Grid', back_populates='pixels')
+    addresses = orm.relationship('AddressPixelAssociation', back_populates='pixel')
+    forecasts = orm.relationship('Forecast', back_populates='pixel')
+
+    def __repr__(self) -> str:
+        """Non-literal text representation."""
+        return '<{cls}: ({x}|{y})>'.format(
+            cls=self.__class__.__name__, x=self.n_x, y=self.n_y,
+        )
+
+    # Convenience properties
+
+    @property
+    def side_length(self) -> int:
+        """The length of one side of a pixel in meters."""
+        return self.grid.side_length
+
+    @property
+    def area(self) -> float:
+        """The area of a pixel in square kilometers."""
+        return self.grid.pixel_area
+
+    @property
+    def northeast(self) -> utils.Location:
+        """The pixel's northeast corner, relative to `.grid.city.southwest`.
+
+        Implementation detail: This property is cached as none of the
+        underlying attributes to calculate the value are to be changed.
+        """
+        if not hasattr(self, '_northeast'):  # noqa:WPS421  note:d334120e
+            # The origin is the southwest corner of the `.grid.city`'s viewport.
+            easting_origin = self.grid.city.southwest.easting
+            northing_origin = self.grid.city.southwest.northing
+
+            # `+1` as otherwise we get the pixel's `.southwest` corner.
+            easting = easting_origin + ((self.n_x + 1) * self.side_length)
+            northing = northing_origin + ((self.n_y + 1) * self.side_length)
+            zone, band = self.grid.city.southwest.zone_details
+            latitude, longitude = utm.to_latlon(easting, northing, zone, band)
+
+            self._northeast = utils.Location(latitude, longitude)
+            self._northeast.relate_to(self.grid.city.southwest)
+
+        return self._northeast
+
+    @property
+    def southwest(self) -> utils.Location:
+        """The pixel's northeast corner, relative to `.grid.city.southwest`.
+
+        Implementation detail: This property is cached as none of the
+        underlying attributes to calculate the value are to be changed.
+        """
+        if not hasattr(self, '_southwest'):  # noqa:WPS421  note:d334120e
+            # The origin is the southwest corner of the `.grid.city`'s viewport.
+            easting_origin = self.grid.city.southwest.easting
+            northing_origin = self.grid.city.southwest.northing
+
+            easting = easting_origin + (self.n_x * self.side_length)
+            northing = northing_origin + (self.n_y * self.side_length)
+            zone, band = self.grid.city.southwest.zone_details
+            latitude, longitude = utm.to_latlon(easting, northing, zone, band)
+
+            self._southwest = utils.Location(latitude, longitude)
+            self._southwest.relate_to(self.grid.city.southwest)
+
+        return self._southwest
+
+    @property
+    def restaurants(self) -> List[db.Restaurant]:  # pragma: no cover
+        """Obtain all `Restaurant`s in `self`."""
+        if not hasattr(self, '_restaurants'):  # noqa:WPS421  note:d334120e
+            self._restaurants = (  # noqa:ECE001
+                db.session.query(db.Restaurant)
+                .join(
+                    db.AddressPixelAssociation,
+                    db.Restaurant.address_id == db.AddressPixelAssociation.address_id,
+                )
+                .filter(db.AddressPixelAssociation.pixel_id == self.id)
+                .all()
+            )
+
+        return self._restaurants
+
+    def clear_map(self) -> Pixel:  # pragma: no cover
+        """Shortcut to the `.city.clear_map()` method.
+
+        Returns:
+            self: enabling method chaining
+        """  # noqa:D402,DAR203
+        self.grid.city.clear_map()
+        return self
+
+    @property  # pragma: no cover
+    def map(self) -> folium.Map:  # noqa:WPS125
+        """Shortcut to the `.city.map` object."""
+        return self.grid.city.map
+
+    def draw(  # noqa:C901,WPS210,WPS231
+        self, restaurants: bool = True, order_counts: bool = False,  # pragma: no cover
+    ) -> folium.Map:
+        """Draw the pixel on the `.grid.city.map`.
+
+        Args:
+            restaurants: include the restaurants
+            order_counts: show the number of orders at a restaurant
+
+        Returns:
+            `.grid.city.map` for convenience in interactive usage
+        """
+        bounds = (
+            (self.southwest.latitude, self.southwest.longitude),
+            (self.northeast.latitude, self.northeast.longitude),
+        )
+        info_text = f'Pixel({self.n_x}|{self.n_y})'
+
+        # Make the `Pixel`s look like a checkerboard.
+        if (self.n_x + self.n_y) % 2:
+            color = '#808000'
+        else:
+            color = '#ff8c00'
+
+        marker = folium.Rectangle(
+            bounds=bounds,
+            color='gray',
+            opacity=0.2,
+            weight=5,
+            fill_color=color,
+            fill_opacity=0.2,
+            popup=info_text,
+            tooltip=info_text,
+        )
+        marker.add_to(self.grid.city.map)
+
+        if restaurants:
+            # Obtain all primary `Address`es in the city that host `Restaurant`s
+            # and are in the `self` `Pixel`.
+            addresses = (  # noqa:ECE001
+                db.session.query(db.Address)
+                .filter(
+                    db.Address.id.in_(
+                        (
+                            db.session.query(db.Address.primary_id)
+                            .join(
+                                db.Restaurant,
+                                db.Address.id == db.Restaurant.address_id,
+                            )
+                            .join(
+                                db.AddressPixelAssociation,
+                                db.Address.id == db.AddressPixelAssociation.address_id,
+                            )
+                            .filter(db.AddressPixelAssociation.pixel_id == self.id)
+                        )
+                        .distinct()
+                        .all(),
+                    ),
+                )
+                .all()
+            )
+
+            for address in addresses:
+                # Show the restaurant's name if there is only one.
+                # Otherwise, list all the restaurants' ID's.
+                restaurants = (  # noqa:ECE001
+                    db.session.query(db.Restaurant)
+                    .join(db.Address, db.Restaurant.address_id == db.Address.id)
+                    .filter(db.Address.primary_id == address.id)
+                    .all()
+                )
+                if len(restaurants) == 1:  # type:ignore
+                    tooltip = (
+                        f'{restaurants[0].name} (#{restaurants[0].id})'  # type:ignore
+                    )
+                else:
+                    tooltip = 'Restaurants ' + ', '.join(  # noqa:WPS336
+                        f'#{restaurant.id}' for restaurant in restaurants  # type:ignore
+                    )
+
+                if order_counts:
+                    # Calculate the number of orders for ALL restaurants ...
+                    n_orders = (  # noqa:ECE001
+                        db.session.query(db.Order.id)
+                        .join(db.Address, db.Order.pickup_address_id == db.Address.id)
+                        .filter(db.Address.primary_id == address.id)
+                        .count()
+                    )
+                    # ... and adjust the size of the red dot on the `.map`.
+                    if n_orders >= 1000:
+                        radius = 20  # noqa:WPS220
+                    elif n_orders >= 500:
+                        radius = 15  # noqa:WPS220
+                    elif n_orders >= 100:
+                        radius = 10  # noqa:WPS220
+                    elif n_orders >= 10:
+                        radius = 5  # noqa:WPS220
+                    else:
+                        radius = 1  # noqa:WPS220
+
+                    tooltip += f' | n_orders={n_orders}'  # noqa:WPS336
+
+                    address.draw(
+                        radius=radius,
+                        color=config.RESTAURANT_COLOR,
+                        fill_color=config.RESTAURANT_COLOR,
+                        fill_opacity=0.3,
+                        tooltip=tooltip,
+                    )
+
+                else:
+                    address.draw(
+                        radius=1, color=config.RESTAURANT_COLOR, tooltip=tooltip,
+                    )
+
+        return self.map

src/urban_meal_delivery/db/restaurants.py

@@ -1,15 +1,23 @@
-"""Provide the ORM's Restaurant model."""
+"""Provide the ORM's `Restaurant` model."""
 
+from __future__ import annotations
+
+import folium
 import sqlalchemy as sa
 from sqlalchemy import orm
 
+from urban_meal_delivery import config
+from urban_meal_delivery import db
 from urban_meal_delivery.db import meta
 
 
 class Restaurant(meta.Base):
-    """A Restaurant selling meals on the UDP."""
+    """A restaurant selling meals on the UDP.
 
-    # pylint:disable=too-few-public-methods
+    In the historic dataset, a `Restaurant` may have changed its `Address`
+    throughout its life time. The ORM model only stores the current one,
+    which in most cases is also the only one.
+    """
 
     __tablename__ = 'restaurants'
 
@@ -18,8 +26,8 @@ class Restaurant(meta.Base):
         sa.SmallInteger, primary_key=True, autoincrement=False,
     )
     created_at = sa.Column(sa.DateTime, nullable=False)
-    name = sa.Column(sa.Unicode(length=45), nullable=False)  # noqa:WPS432
+    name = sa.Column(sa.Unicode(length=45), nullable=False)
-    _address_id = sa.Column('address_id', sa.Integer, nullable=False, index=True)
+    address_id = sa.Column(sa.Integer, nullable=False, index=True)
     estimated_prep_duration = sa.Column(sa.SmallInteger, nullable=False)
 
     # Constraints
@@ -31,12 +39,103 @@ class Restaurant(meta.Base):
             '0 <= estimated_prep_duration AND estimated_prep_duration <= 2400',
             name='realistic_estimated_prep_duration',
         ),
+        # Needed by a `ForeignKeyConstraint` in `Order`.
+        sa.UniqueConstraint('id', 'address_id'),
     )
 
     # Relationships
-    address = orm.relationship('Address', back_populates='restaurant')
+    address = orm.relationship('Address', back_populates='restaurants')
     orders = orm.relationship('Order', back_populates='restaurant')
 
     def __repr__(self) -> str:
         """Non-literal text representation."""
         return '<{cls}({name})>'.format(cls=self.__class__.__name__, name=self.name)
+
+    def clear_map(self) -> Restaurant:  # pragma: no cover
+        """Shortcut to the `.address.city.clear_map()` method.
+
+        Returns:
+            self: enabling method chaining
+        """  # noqa:D402,DAR203
+        self.address.city.clear_map()
+        return self
+
+    @property  # pragma: no cover
+    def map(self) -> folium.Map:  # noqa:WPS125
+        """Shortcut to the `.address.city.map` object."""
+        return self.address.city.map
+
+    def draw(  # noqa:WPS231
+        self, customers: bool = True, order_counts: bool = False,  # pragma: no cover
+    ) -> folium.Map:
+        """Draw the restaurant on the `.address.city.map`.
+
+        By default, the restaurant's delivery locations are also shown.
+
+        Args:
+            customers: show the restaurant's delivery locations
+            order_counts: show the number of orders at the delivery locations;
+                only useful if `customers=True`
+
+        Returns:
+            `.address.city.map` for convenience in interactive usage
+        """
+        if customers:
+            # Obtain all primary `Address`es in the city that
+            # received at least one delivery from `self`.
+            delivery_addresses = (  # noqa:ECE001
+                db.session.query(db.Address)
+                .filter(
+                    db.Address.id.in_(
+                        db.session.query(db.Address.primary_id)  # noqa:WPS221
+                        .join(db.Order, db.Address.id == db.Order.delivery_address_id)
+                        .filter(db.Order.restaurant_id == self.id)
+                        .distinct()
+                        .all(),
+                    ),
+                )
+                .all()
+            )
+
+            for address in delivery_addresses:
+                if order_counts:
+                    n_orders = (  # noqa:ECE001
+                        db.session.query(db.Order)
+                        .join(db.Address, db.Order.delivery_address_id == db.Address.id)
+                        .filter(db.Order.restaurant_id == self.id)
+                        .filter(db.Address.primary_id == address.id)
+                        .count()
+                    )
+                    if n_orders >= 25:
+                        radius = 20  # noqa:WPS220
+                    elif n_orders >= 10:
+                        radius = 15  # noqa:WPS220
+                    elif n_orders >= 5:
+                        radius = 10  # noqa:WPS220
+                    elif n_orders > 1:
+                        radius = 5  # noqa:WPS220
+                    else:
+                        radius = 1  # noqa:WPS220
+
+                    address.draw(
+                        radius=radius,
+                        color=config.CUSTOMER_COLOR,
+                        fill_color=config.CUSTOMER_COLOR,
+                        fill_opacity=0.3,
+                        tooltip=f'n_orders={n_orders}',
+                    )
+
+                else:
+                    address.draw(
+                        radius=1, color=config.CUSTOMER_COLOR,
+                    )
+
+        self.address.draw(
+            radius=20,
+            color=config.RESTAURANT_COLOR,
+            fill_color=config.RESTAURANT_COLOR,
+            fill_opacity=0.3,
+            tooltip=f'{self.name} (#{self.id}) | n_orders={len(self.orders)}',
+        )
+
+        return self.map

src/urban_meal_delivery/db/utils/__init__.py

@@ -0,0 +1,5 @@
+"""Utilities used by the ORM models."""
+
+from urban_meal_delivery.db.utils.colors import make_random_cmap
+from urban_meal_delivery.db.utils.colors import rgb_to_hex
+from urban_meal_delivery.db.utils.locations import Location

src/urban_meal_delivery/db/utils/colors.py

@@ -0,0 +1,69 @@
+"""Utilities for drawing maps with `folium`."""
+
+import colorsys
+
+import numpy as np
+from matplotlib import colors
+
+
+def make_random_cmap(
+    n_colors: int, bright: bool = True,  # pragma: no cover
+) -> colors.LinearSegmentedColormap:
+    """Create a random `Colormap` with `n_colors` different colors.
+
+    Args:
+        n_colors: number of of different colors; size of `Colormap`
+        bright: `True` for strong colors, `False` for pastel colors
+
+    Returns:
+        colormap
+    """
+    np.random.seed(42)
+
+    if bright:
+        hsv_colors = [
+            (
+                np.random.uniform(low=0.0, high=1),
+                np.random.uniform(low=0.2, high=1),
+                np.random.uniform(low=0.9, high=1),
+            )
+            for _ in range(n_colors)
+        ]
+
+        rgb_colors = []
+        for color in hsv_colors:
+            rgb_colors.append(colorsys.hsv_to_rgb(*color))
+
+    else:
+        low = 0.0
+        high = 0.66
+
+        rgb_colors = [
+            (
+                np.random.uniform(low=low, high=high),
+                np.random.uniform(low=low, high=high),
+                np.random.uniform(low=low, high=high),
+            )
+            for _ in range(n_colors)
+        ]
+
+    return colors.LinearSegmentedColormap.from_list(
+        'random_color_map', rgb_colors, N=n_colors,
+    )
+
+
+def rgb_to_hex(*args: float) -> str:  # pragma: no cover
+    """Convert RGB colors into hexadecimal notation.
+
+    Args:
+        *args: percentages (0% - 100%) for the RGB channels
+
+    Returns:
+        hexadecimal_representation
+    """
+    red, green, blue = (
+        int(255 * args[0]),
+        int(255 * args[1]),
+        int(255 * args[2]),
+    )
+    return f'#{red:02x}{green:02x}{blue:02x}'  # noqa:WPS221

src/urban_meal_delivery/db/utils/locations.py

@@ -0,0 +1,142 @@
+"""A `Location` class to unify working with coordinates."""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import utm
+
+
+class Location:
+    """A location represented in WGS84 and UTM coordinates.
+
+    WGS84:
+        - "conventional" system with latitude-longitude pairs
+        - assumes earth is a sphere and models the location in 3D
+
+    UTM:
+        - the Universal Transverse Mercator sytem
+        - projects WGS84 coordinates onto a 2D map
+        - can be used for visualizations and calculations directly
+        - distances are in meters
+
+    Further info how WGS84 and UTM are related:
+        https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system
+    """
+
+    def __init__(self, latitude: float, longitude: float) -> None:
+        """Create a location from a WGS84-conforming `latitude`-`longitude` pair."""
+        # The SQLAlchemy columns come as `Decimal`s due to the `DOUBLE_PRECISION`.
+        self._latitude = float(latitude)
+        self._longitude = float(longitude)
+
+        easting, northing, zone, band = utm.from_latlon(self._latitude, self._longitude)
+
+        # `.easting` and `.northing` as `int`s are precise enough.
+        self._easting = int(easting)
+        self._northing = int(northing)
+        self._zone = zone
+        self._band = band.upper()
+
+        self._normalized_easting: Optional[int] = None
+        self._normalized_northing: Optional[int] = None
+
+    def __repr__(self) -> str:
+        """A non-literal text representation in the UTM system.
+
+        Convention is {ZONE} {EASTING} {NORTHING}.
+
+        Example:
+            `<Location: 17T 630084 4833438>'`
+        """
+        return f'<Location: {self.zone} {self.easting} {self.northing}>'  # noqa:WPS221
+
+    @property
+    def latitude(self) -> float:
+        """The latitude of the location in degrees (WGS84).
+
+        Between -90 and +90 degrees.
+        """
+        return self._latitude
+
+    @property
+    def longitude(self) -> float:
+        """The longitude of the location in degrees (WGS84).
+
+        Between -180 and +180 degrees.
+        """
+        return self._longitude
+
+    @property
+    def easting(self) -> int:
+        """The easting of the location in meters (UTM)."""
+        return self._easting
+
+    @property
+    def northing(self) -> int:
+        """The northing of the location in meters (UTM)."""
+        return self._northing
+
+    @property
+    def zone(self) -> str:
+        """The UTM zone of the location."""
+        return f'{self._zone}{self._band}'
+
+    @property
+    def zone_details(self) -> Tuple[int, str]:
+        """The UTM zone of the location as the zone number and the band."""
+        return (self._zone, self._band)
+
+    def __eq__(self, other: object) -> bool:
+        """Check if two `Location` objects are the same location."""
+        if not isinstance(other, Location):
+            return NotImplemented
+
+        if self.zone != other.zone:
+            raise ValueError('locations must be in the same zone, including the band')
+
+        return (self.easting, self.northing) == (other.easting, other.northing)
+
+    @property
+    def x(self) -> int:  # noqa:WPS111
+        """The `.easting` of the location in meters, relative to some origin.
+
+        The origin, which defines the `(0, 0)` coordinate, is set with `.relate_to()`.
+        """
+        if self._normalized_easting is None:
+            raise RuntimeError('an origin to relate to must be set first')
+
+        return self._normalized_easting
+
+    @property
+    def y(self) -> int:  # noqa:WPS111
+        """The `.northing` of the location in meters, relative to some origin.
+
+        The origin, which defines the `(0, 0)` coordinate, is set with `.relate_to()`.
+        """
+        if self._normalized_northing is None:
+            raise RuntimeError('an origin to relate to must be set first')
+
+        return self._normalized_northing
+
+    def relate_to(self, other: Location) -> None:
+        """Make the origin in the lower-left corner relative to `other`.
+
+        The `.x` and `.y` properties are the `.easting` and `.northing` values
+        of `self` minus the ones from `other`. So, `.x` and `.y` make up a
+        Cartesian coordinate system where the `other` origin is `(0, 0)`.
+
+        To prevent semantic errors in calculations based on the `.x` and `.y`
+        properties, the `other` origin may only be set once!
+        """
+        if self._normalized_easting is not None:
+            raise RuntimeError('the `other` origin may only be set once')
+
+        if not isinstance(other, Location):
+            raise TypeError('`other` is not a `Location` object')
+
+        if self.zone != other.zone:
+            raise ValueError('`other` must be in the same zone, including the band')
+
+        self._normalized_easting = self.easting - other.easting
+        self._normalized_northing = self.northing - other.northing

src/urban_meal_delivery/forecasts/__init__.py

@@ -0,0 +1,29 @@
+"""Demand forecasting utilities.
+
+This sub-package is divided into further sub-packages and modules as follows:
+
+`methods` contains various time series related statistical methods, implemented
+as plain `function` objects that are used to predict into the future given a
+time series of historic order counts. The methods are context-agnostic, meaning
+that they only take and return `pd.Series/DataFrame`s holding numbers and
+are not concerned with how these numbers were generated or what they mean.
+Some functions, like `arima.predict()` or `ets.predict()` wrap functions called
+in R using the `rpy2` library. Others, like `extrapolate_season.predict()`, are
+written in plain Python.
+
+`timify` defines an `OrderHistory` class that abstracts away the communication
+with the database and provides `pd.Series` objects with the order counts that
+are fed into the `methods`. In particular, it uses SQL statements behind the
+scenes to calculate the historic order counts on a per-`Pixel` level. Once the
+data is loaded from the database, an `OrderHistory` instance provides various
+ways to slice out, or generate, different kinds of order time series (e.g.,
+"horizontal" vs. "vertical" time series).
+
+`models` defines various forecasting `*Model`s that combine a given kind of
+time series with one of the forecasting `methods`. For example, the ETS method
+applied to a horizontal time series is implemented in the `HorizontalETSModel`.
+"""
+
+from urban_meal_delivery.forecasts import methods
+from urban_meal_delivery.forecasts import models
+from urban_meal_delivery.forecasts import timify

src/urban_meal_delivery/forecasts/methods/__init__.py

@@ -0,0 +1,6 @@
+"""Various forecasting methods implemented as functions."""
+
+from urban_meal_delivery.forecasts.methods import arima
+from urban_meal_delivery.forecasts.methods import decomposition
+from urban_meal_delivery.forecasts.methods import ets
+from urban_meal_delivery.forecasts.methods import extrapolate_season

src/urban_meal_delivery/forecasts/methods/arima.py

@@ -0,0 +1,76 @@
+"""A wrapper around R's "auto.arima" function."""
+
+import pandas as pd
+from rpy2 import robjects
+from rpy2.robjects import pandas2ri
+
+
+def predict(
+    training_ts: pd.Series,
+    forecast_interval: pd.DatetimeIndex,
+    *,
+    frequency: int,
+    seasonal_fit: bool = False,
+) -> pd.DataFrame:
+    """Predict with an automatically chosen ARIMA model.
+
+    Note: The function does not check if the `forecast_interval`
+    extends the `training_ts`'s interval without a gap!
+
+    Args:
+        training_ts: past observations to be fitted
+        forecast_interval: interval into which the `training_ts` is forecast;
+            its length becomes the step size `h` in the forecasting model in R
+        frequency: frequency of the observations in the `training_ts`
+        seasonal_fit: if a seasonal ARIMA model should be fitted
+
+    Returns:
+        predictions: point forecasts (i.e., the "prediction" column) and
+            confidence intervals (i.e, the four "low/high80/95" columns)
+
+    Raises:
+        ValueError: if `training_ts` contains `NaN` values
+    """
+    # Initialize R only if necessary as it is tested only in nox's
+    # "ci-tests-slow" session and "ci-tests-fast" should not fail.
+    from urban_meal_delivery import init_r  # noqa:F401,WPS433
+
+    # Re-seed R every time it is used to ensure reproducibility.
+    robjects.r('set.seed(42)')
+
+    if training_ts.isnull().any():
+        raise ValueError('`training_ts` must not contain `NaN` values')
+
+    # Copy the data from Python to R.
+    robjects.globalenv['data'] = robjects.r['ts'](
+        pandas2ri.py2rpy(training_ts), frequency=frequency,
+    )
+
+    seasonal = 'TRUE' if bool(seasonal_fit) else 'FALSE'
+    n_steps_ahead = len(forecast_interval)
+
+    # Make the predictions in R.
+    result = robjects.r(
+        f"""
+        as.data.frame(
+            forecast(
+                auto.arima(data, approximation = TRUE, seasonal = {seasonal:s}),
+                h = {n_steps_ahead:d}
+            )
+        )
+        """,
+    )
+
+    # Convert the results into a nice `pd.DataFrame` with the right `.index`.
+    forecasts = pandas2ri.rpy2py(result)
+    forecasts.index = forecast_interval
+
+    return forecasts.round(5).rename(
+        columns={
+            'Point Forecast': 'prediction',
+            'Lo 80': 'low80',
+            'Hi 80': 'high80',
+            'Lo 95': 'low95',
+            'Hi 95': 'high95',
+        },
+    )

src/urban_meal_delivery/forecasts/methods/decomposition.py

@@ -0,0 +1,181 @@
+"""Seasonal-trend decomposition procedure based on LOESS (STL).
+
+This module defines a `stl()` function that wraps R's STL decomposition function
+using the `rpy2` library.
+"""
+
+import math
+
+import pandas as pd
+from rpy2 import robjects
+from rpy2.robjects import pandas2ri
+
+
+def stl(  # noqa:C901,WPS210,WPS211,WPS231
+    time_series: pd.Series,
+    *,
+    frequency: int,
+    ns: int,
+    nt: int = None,
+    nl: int = None,
+    ds: int = 0,
+    dt: int = 1,
+    dl: int = 1,
+    js: int = None,
+    jt: int = None,
+    jl: int = None,
+    ni: int = 2,
+    no: int = 0,  # noqa:WPS110
+) -> pd.DataFrame:
+    """Decompose a time series into seasonal, trend, and residual components.
+
+    This is a Python wrapper around the corresponding R function.
+
+    Further info on the STL method:
+        https://www.nniiem.ru/file/news/2016/stl-statistical-model.pdf
+        https://otexts.com/fpp2/stl.html
+
+    Further info on the R's "stl" function:
+        https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/stl
+
+    Args:
+        time_series: time series with a `DateTime` based index;
+            must not contain `NaN` values
+        frequency: frequency of the observations in the `time_series`
+        ns: smoothing parameter for the seasonal component
+            (= window size of the seasonal smoother);
+            must be odd and `>= 7` so that the seasonal component is smooth;
+            the greater `ns`, the smoother the seasonal component;
+            so, this is a hyper-parameter optimized in accordance with the application
+        nt: smoothing parameter for the trend component
+            (= window size of the trend smoother);
+            must be odd and `>= (1.5 * frequency) / [1 - (1.5 / ns)]`;
+            the latter threshold is the default value;
+            the greater `nt`, the smoother the trend component
+        nl: smoothing parameter for the low-pass filter;
+            must be odd and `>= frequency`;
+            the least odd number `>= frequency` is the default
+        ds: degree of locally fitted polynomial in seasonal smoothing;
+            must be `0` or `1`
+        dt: degree of locally fitted polynomial in trend smoothing;
+            must be `0` or `1`
+        dl: degree of locally fitted polynomial in low-pass smoothing;
+            must be `0` or `1`
+        js: number of steps by which the seasonal smoother skips ahead
+            and then linearly interpolates between observations;
+            if set to `1`, the smoother is evaluated at all points;
+            to make the STL decomposition faster, increase this value;
+            by default, `js` is the smallest integer `>= 0.1 * ns`
+        jt: number of steps by which the trend smoother skips ahead
+            and then linearly interpolates between observations;
+            if set to `1`, the smoother is evaluated at all points;
+            to make the STL decomposition faster, increase this value;
+            by default, `jt` is the smallest integer `>= 0.1 * nt`
+        jl: number of steps by which the low-pass smoother skips ahead
+            and then linearly interpolates between observations;
+            if set to `1`, the smoother is evaluated at all points;
+            to make the STL decomposition faster, increase this value;
+            by default, `jl` is the smallest integer `>= 0.1 * nl`
+        ni: number of iterations of the inner loop that updates the
+            seasonal and trend components;
+            usually, a low value (e.g., `2`) suffices
+        no: number of iterations of the outer loop that handles outliers;
+            also known as the "robustness" loop;
+            if no outliers need to be handled, set `no=0`;
+            otherwise, `no=5` or `no=10` combined with `ni=1` is a good choice
+
+    Returns:
+        result: a DataFrame with three columns ("seasonal", "trend", and "residual")
+            providing time series of the individual components
+
+    Raises:
+        ValueError: some argument does not adhere to the specifications above
+    """
+    # Validate all arguments and set default values.
+
+    if time_series.isnull().any():
+        raise ValueError('`time_series` must not contain `NaN` values')
+
+    if ns % 2 == 0 or ns < 7:
+        raise ValueError('`ns` must be odd and `>= 7`')
+
+    default_nt = math.ceil((1.5 * frequency) / (1 - (1.5 / ns)))
+    if nt is not None:
+        if nt % 2 == 0 or nt < default_nt:
+            raise ValueError(
+                '`nt` must be odd and `>= (1.5 * frequency) / [1 - (1.5 / ns)]`, '
+                + 'which is {0}'.format(default_nt),
+            )
+    else:
+        nt = default_nt
+        if nt % 2 == 0:  # pragma: no cover => hard to construct edge case
+            nt += 1
+
+    if nl is not None:
+        if nl % 2 == 0 or nl < frequency:
+            raise ValueError('`nl` must be odd and `>= frequency`')
+    elif frequency % 2 == 0:
+        nl = frequency + 1
+    else:  # pragma: no cover => hard to construct edge case
+        nl = frequency
+
+    if ds not in {0, 1}:
+        raise ValueError('`ds` must be either `0` or `1`')
+    if dt not in {0, 1}:
+        raise ValueError('`dt` must be either `0` or `1`')
+    if dl not in {0, 1}:
+        raise ValueError('`dl` must be either `0` or `1`')
+
+    if js is not None:
+        if js <= 0:
+            raise ValueError('`js` must be positive')
+    else:
+        js = math.ceil(ns / 10)
+
+    if jt is not None:
+        if jt <= 0:
+            raise ValueError('`jt` must be positive')
+    else:
+        jt = math.ceil(nt / 10)
+
+    if jl is not None:
+        if jl <= 0:
+            raise ValueError('`jl` must be positive')
+    else:
+        jl = math.ceil(nl / 10)
+
+    if ni <= 0:
+        raise ValueError('`ni` must be positive')
+
+    if no < 0:
+        raise ValueError('`no` must be non-negative')
+    elif no > 0:
+        robust = True
+    else:
+        robust = False
+
+    # Initialize R only if necessary as it is tested only in nox's
+    # "ci-tests-slow" session and "ci-tests-fast" should not fail.
+    from urban_meal_delivery import init_r  # noqa:F401,WPS433
+
+    # Re-seed R every time it is used to ensure reproducibility.
+    robjects.r('set.seed(42)')
+
+    # Call the STL function in R.
+    ts = robjects.r['ts'](pandas2ri.py2rpy(time_series), frequency=frequency)
+    result = robjects.r['stl'](
+        ts, ns, ds, nt, dt, nl, dl, js, jt, jl, robust, ni, no,  # noqa:WPS221
+    )
+
+    # Unpack the result to a `pd.DataFrame`.
+    result = pandas2ri.rpy2py(result[0])
+    result = pd.DataFrame(
+        data={
+            'seasonal': result[:, 0],
+            'trend': result[:, 1],
+            'residual': result[:, 2],
+        },
+        index=time_series.index,
+    )
+
+    return result.round(5)

src/urban_meal_delivery/forecasts/methods/ets.py

@@ -0,0 +1,77 @@
+"""A wrapper around R's "ets" function."""
+
+import pandas as pd
+from rpy2 import robjects
+from rpy2.robjects import pandas2ri
+
+
+def predict(
+    training_ts: pd.Series,
+    forecast_interval: pd.DatetimeIndex,
+    *,
+    frequency: int,
+    seasonal_fit: bool = False,
+) -> pd.DataFrame:
+    """Predict with an automatically calibrated ETS model.
+
+    Note: The function does not check if the `forecast_interval`
+    extends the `training_ts`'s interval without a gap!
+
+    Args:
+        training_ts: past observations to be fitted
+        forecast_interval: interval into which the `training_ts` is forecast;
+            its length becomes the step size `h` in the forecasting model in R
+        frequency: frequency of the observations in the `training_ts`
+        seasonal_fit: if a "ZZZ" (seasonal) or a "ZZN" (non-seasonal)
+            type ETS model should be fitted
+
+    Returns:
+        predictions: point forecasts (i.e., the "prediction" column) and
+            confidence intervals (i.e, the four "low/high80/95" columns)
+
+    Raises:
+        ValueError: if `training_ts` contains `NaN` values
+    """
+    # Initialize R only if necessary as it is tested only in nox's
+    # "ci-tests-slow" session and "ci-tests-fast" should not fail.
+    from urban_meal_delivery import init_r  # noqa:F401,WPS433
+
+    # Re-seed R every time it is used to ensure reproducibility.
+    robjects.r('set.seed(42)')
+
+    if training_ts.isnull().any():
+        raise ValueError('`training_ts` must not contain `NaN` values')
+
+    # Copy the data from Python to R.
+    robjects.globalenv['data'] = robjects.r['ts'](
+        pandas2ri.py2rpy(training_ts), frequency=frequency,
+    )
+
+    model = 'ZZZ' if bool(seasonal_fit) else 'ZZN'
+    n_steps_ahead = len(forecast_interval)
+
+    # Make the predictions in R.
+    result = robjects.r(
+        f"""
+        as.data.frame(
+            forecast(
+                ets(data, model = "{model:s}"),
+                h = {n_steps_ahead:d}
+            )
+        )
+        """,
+    )
+
+    # Convert the results into a nice `pd.DataFrame` with the right `.index`.
+    forecasts = pandas2ri.rpy2py(result)
+    forecasts.index = forecast_interval
+
+    return forecasts.round(5).rename(
+        columns={
+            'Point Forecast': 'prediction',
+            'Lo 80': 'low80',
+            'Hi 80': 'high80',
+            'Lo 95': 'low95',
+            'Hi 95': 'high95',
+        },
+    )

src/urban_meal_delivery/forecasts/methods/extrapolate_season.py

@@ -0,0 +1,72 @@
+"""Forecast by linear extrapolation of a seasonal component."""
+
+import pandas as pd
+from statsmodels.tsa import api as ts_stats
+
+
+def predict(
+    training_ts: pd.Series, forecast_interval: pd.DatetimeIndex, *, frequency: int,
+) -> pd.DataFrame:
+    """Extrapolate a seasonal component with a linear model.
+
+    A naive forecast for each time unit of the day is calculated by linear
+    extrapolation from all observations of the same time of day and on the same
+    day of the week (i.e., same seasonal lag).
+
+    Note: The function does not check if the `forecast_interval`
+    extends the `training_ts`'s interval without a gap!
+
+    Args:
+        training_ts: past observations to be fitted;
+            assumed to be a seasonal component after time series decomposition
+        forecast_interval: interval into which the `training_ts` is forecast;
+            its length becomes the numbers of time steps to be forecast
+        frequency: frequency of the observations in the `training_ts`
+
+    Returns:
+        predictions: point forecasts (i.e., the "prediction" column);
+            includes the four "low/high80/95" columns for the confidence intervals
+            that only contain `NaN` values as this method does not make
+            any statistical assumptions about the time series process
+
+    Raises:
+        ValueError: if `training_ts` contains `NaN` values or some predictions
+            could not be made for time steps in the `forecast_interval`
+    """
+    if training_ts.isnull().any():
+        raise ValueError('`training_ts` must not contain `NaN` values')
+
+    extrapolated_ts = pd.Series(index=forecast_interval, dtype=float)
+    seasonal_lag = frequency * (training_ts.index[1] - training_ts.index[0])
+
+    for lag in range(frequency):
+        # Obtain all `observations` of the same seasonal lag and
+        # fit a straight line through them (= `trend`).
+        observations = training_ts[slice(lag, 999_999_999, frequency)]
+        trend = observations - ts_stats.detrend(observations)
+
+        # Create a point forecast by linear extrapolation
+        # for one or even more time steps ahead.
+        slope = trend[-1] - trend[-2]
+        prediction = trend[-1] + slope
+        idx = observations.index.max() + seasonal_lag
+        while idx <= forecast_interval.max():
+            if idx in forecast_interval:
+                extrapolated_ts.loc[idx] = prediction
+            prediction += slope
+            idx += seasonal_lag
+
+    # Sanity check.
+    if extrapolated_ts.isnull().any():  # pragma: no cover
+        raise ValueError('missing predictions in the `forecast_interval`')
+
+    return pd.DataFrame(
+        data={
+            'prediction': extrapolated_ts.round(5),
+            'low80': float('NaN'),
+            'high80': float('NaN'),
+            'low95': float('NaN'),
+            'high95': float('NaN'),
+        },
+        index=forecast_interval,
+    )

src/urban_meal_delivery/forecasts/models/__init__.py

@@ -0,0 +1,37 @@
+"""Define the forecasting `*Model`s used in this project.
+
+`*Model`s are different from plain forecasting `methods` in that they are tied
+to a given kind of historic order time series, as provided by the `OrderHistory`
+class in the `timify` module. For example, the ARIMA model applied to a vertical
+time series becomes the `VerticalARIMAModel`.
+
+An overview of the `*Model`s used for tactical forecasting can be found in section
+"3.6 Forecasting Models" in the paper "Real-time Demand Forecasting for an Urban
+Delivery Platform" that is part of the `urban-meal-delivery` research project.
+
+For the paper check:
+    https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+    https://www.sciencedirect.com/science/article/pii/S1366554520307936
+
+This sub-package is organized as follows. The `base` module defines an abstract
+`ForecastingModelABC` class that unifies how the concrete `*Model`s work.
+While the abstact `.predict()` method returns a `pd.DataFrame` (= basically,
+the result of one of the forecasting `methods`, the concrete `.make_forecast()`
+method converts the results into `Forecast` (=ORM) objects.
+Also, `.make_forecast()` implements a caching strategy where already made
+`Forecast`s are loaded from the database instead of calculating them again,
+which could be a heavier computation.
+
+The `tactical` sub-package contains all the `*Model`s used to implement the
+UDP's predictive routing strategy.
+
+A future `planning` sub-package will contain the `*Model`s used to plan the
+`Courier`'s shifts a week ahead.
+"""  # noqa:RST215
+
+from urban_meal_delivery.forecasts.models.base import ForecastingModelABC
+from urban_meal_delivery.forecasts.models.tactical.horizontal import HorizontalETSModel
+from urban_meal_delivery.forecasts.models.tactical.horizontal import HorizontalSMAModel
+from urban_meal_delivery.forecasts.models.tactical.other import TrivialModel
+from urban_meal_delivery.forecasts.models.tactical.realtime import RealtimeARIMAModel
+from urban_meal_delivery.forecasts.models.tactical.vertical import VerticalARIMAModel

src/urban_meal_delivery/forecasts/models/base.py

@@ -0,0 +1,116 @@
+"""The abstract blueprint for a forecasting `*Model`."""
+
+import abc
+import datetime as dt
+
+import pandas as pd
+
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import timify
+
+
+class ForecastingModelABC(abc.ABC):
+    """An abstract interface of a forecasting `*Model`."""
+
+    def __init__(self, order_history: timify.OrderHistory) -> None:
+        """Initialize a new forecasting model.
+
+        Args:
+            order_history: an abstraction providing the time series data
+        """
+        self._order_history = order_history
+
+    @property
+    @abc.abstractmethod
+    def name(self) -> str:
+        """The name of the model.
+
+        Used to identify `Forecast`s of the same `*Model` in the database.
+        So, these must be chosen carefully and must not be changed later on!
+
+        Example: "hets" or "varima" for tactical demand forecasting
+        """
+
+    @abc.abstractmethod
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Concrete implementation of how a `*Model` makes a prediction.
+
+        This method is called by the unified `*Model.make_forecast()` method,
+        which implements the caching logic with the database.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actuals, predictions, and possibly 80%/95% confidence intervals;
+                includes a row for the time step starting at `predict_at` and
+                may contain further rows for other time steps on the same day
+        """  # noqa:DAR202
+
+    def make_forecast(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> db.Forecast:
+        """Make a forecast for the time step starting at `predict_at`.
+
+        Important: This method uses a unified `predict_at` argument.
+        Some `*Model`s, in particular vertical ones, are only trained once per
+        day and then make a prediction for all time steps on that day, and
+        therefore, work with a `predict_day` argument instead of `predict_at`
+        behind the scenes. Then, all `Forecast`s are stored into the database
+        and only the one starting at `predict_at` is returned.
+
+        Args:
+            pixel: pixel in which the `Forecast` is made
+            predict_at: time step (i.e., "start_at") to make the `Forecast` for
+            train_horizon: weeks of historic data used to forecast `predict_at`
+
+        Returns:
+            actual, prediction, and possibly 80%/95% confidence intervals
+                for the time step starting at `predict_at`
+
+        # noqa:DAR401 RuntimeError
+        """
+        if (  # noqa:WPS337
+            cached_forecast := db.session.query(db.Forecast)  # noqa:ECE001,WPS221
+            .filter_by(pixel=pixel)
+            .filter_by(start_at=predict_at)
+            .filter_by(time_step=self._order_history.time_step)
+            .filter_by(train_horizon=train_horizon)
+            .filter_by(model=self.name)
+            .first()
+        ) :
+            return cached_forecast
+
+        # Horizontal and real-time `*Model`s return a `pd.DataFrame` with one
+        # row corresponding to the time step starting at `predict_at` whereas
+        # vertical models return several rows, covering all time steps of a day.
+        predictions = self.predict(pixel, predict_at, train_horizon)
+
+        # Convert the `predictions` into a `list` of `Forecast` objects.
+        forecasts = db.Forecast.from_dataframe(
+            pixel=pixel,
+            time_step=self._order_history.time_step,
+            train_horizon=train_horizon,
+            model=self.name,
+            data=predictions,
+        )
+
+        # We persist all `Forecast`s into the database to
+        # not have to run the same model training again.
+        db.session.add_all(forecasts)
+        db.session.commit()
+
+        # The one `Forecast` object asked for must be in `forecasts`
+        # if the concrete `*Model.predict()` method works correctly; ...
+        for forecast in forecasts:
+            if forecast.start_at == predict_at:
+                return forecast
+
+        # ..., however, we put in a loud error, just in case.
+        raise RuntimeError(  # pragma: no cover
+            '`Forecast` for `predict_at` was not returned by `*Model.predict()`',
+        )

src/urban_meal_delivery/forecasts/models/tactical/__init__.py

@@ -0,0 +1,16 @@
+"""Forecasting `*Model`s to predict demand for tactical purposes.
+
+The `*Model`s in this module predict only a small number (e.g., one)
+of time steps into the near future and are used to implement the UDP's
+predictive routing strategies.
+
+They are classified into "horizontal", "vertical", and "real-time" models
+with respect to what historic data they are trained on and how often they
+are re-trained on the day to be predicted. For the details, check section
+"3.6 Forecasting Models" in the paper "Real-time Demand Forecasting for an
+Urban Delivery Platform".
+
+For the paper check:
+    https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+    https://www.sciencedirect.com/science/article/pii/S1366554520307936
+"""  # noqa:RST215

src/urban_meal_delivery/forecasts/models/tactical/horizontal.py

@@ -0,0 +1,130 @@
+"""Horizontal forecasting `*Model`s to predict demand for tactical purposes.
+
+Horizontal `*Model`s take the historic order counts only from time steps
+corresponding to the same time of day as the one to be predicted (i.e., the
+one starting at `predict_at`). Then, they make a prediction for only one day
+into the future. Thus, the training time series have a `frequency` of `7`, the
+number of days in a week.
+"""  # noqa:RST215
+
+import datetime as dt
+
+import pandas as pd
+
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import methods
+from urban_meal_delivery.forecasts.models import base
+
+
+class HorizontalETSModel(base.ForecastingModelABC):
+    """The ETS model applied on a horizontal time series."""
+
+    name = 'hets'
+
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Predict demand for a time step.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actual order counts (i.e., the "actual" column),
+                point forecasts (i.e., the "prediction" column), and
+                confidence intervals (i.e, the four "low/high/80/95" columns);
+                contains one row for the `predict_at` time step
+
+        # noqa:DAR401 RuntimeError
+        """
+        # Generate the historic (and horizontal) order time series.
+        training_ts, frequency, actuals_ts = self._order_history.make_horizontal_ts(
+            pixel_id=pixel.id, predict_at=predict_at, train_horizon=train_horizon,
+        )
+
+        # Sanity check.
+        if frequency != 7:  # pragma: no cover
+            raise RuntimeError('`frequency` should be `7`')
+
+        # Make `predictions` with the seasonal ETS method ("ZZZ" model).
+        predictions = methods.ets.predict(
+            training_ts=training_ts,
+            forecast_interval=actuals_ts.index,
+            frequency=frequency,  # `== 7`, the number of weekdays
+            seasonal_fit=True,  # because there was no decomposition before
+        )
+
+        predictions.insert(loc=0, column='actual', value=actuals_ts)
+
+        # Sanity checks.
+        if predictions.isnull().any().any():  # pragma: no cover
+            raise RuntimeError('missing predictions in hets model')
+        if predict_at not in predictions.index:  # pragma: no cover
+            raise RuntimeError('missing prediction for `predict_at`')
+
+        return predictions
+
+
+class HorizontalSMAModel(base.ForecastingModelABC):
+    """A simple moving average model applied on a horizontal time series."""
+
+    name = 'hsma'
+
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Predict demand for a time step.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actual order counts (i.e., the "actual" column) and
+                point forecasts (i.e., the "prediction" column);
+                this model does not support confidence intervals;
+                contains one row for the `predict_at` time step
+
+        # noqa:DAR401 RuntimeError
+        """
+        # Generate the historic (and horizontal) order time series.
+        training_ts, frequency, actuals_ts = self._order_history.make_horizontal_ts(
+            pixel_id=pixel.id, predict_at=predict_at, train_horizon=train_horizon,
+        )
+
+        # Sanity checks.
+        if frequency != 7:  # pragma: no cover
+            raise RuntimeError('`frequency` should be `7`')
+        if len(actuals_ts) != 1:  # pragma: no cover
+            raise RuntimeError(
+                'the hsma model can only predict one step into the future',
+            )
+
+        # The "prediction" is calculated as the `np.mean()`.
+        # As the `training_ts` covers only full week horizons,
+        # no adjustment regarding the weekly seasonality is needed.
+        predictions = pd.DataFrame(
+            data={
+                'actual': actuals_ts,
+                'prediction': training_ts.values.mean(),
+                'low80': float('NaN'),
+                'high80': float('NaN'),
+                'low95': float('NaN'),
+                'high95': float('NaN'),
+            },
+            index=actuals_ts.index,
+        )
+
+        # Sanity checks.
+        if (  # noqa:WPS337
+            predictions[['actual', 'prediction']].isnull().any().any()
+        ):  # pragma: no cover
+
+            raise RuntimeError('missing predictions in hsma model')
+        if predict_at not in predictions.index:  # pragma: no cover
+            raise RuntimeError('missing prediction for `predict_at`')
+
+        return predictions

src/urban_meal_delivery/forecasts/models/tactical/other.py

@@ -0,0 +1,75 @@
+"""Forecasting `*Model`s to predict demand for tactical purposes ...
+
+... that cannot be classified into either "horizontal", "vertical",
+or "real-time".
+"""  # noqa:RST215
+
+import datetime as dt
+
+import pandas as pd
+
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts.models import base
+
+
+class TrivialModel(base.ForecastingModelABC):
+    """A trivial model predicting `0` demand.
+
+    No need to distinguish between a "horizontal", "vertical", or
+    "real-time" model here as all give the same prediction for all time steps.
+    """
+
+    name = 'trivial'
+
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Predict demand for a time step.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actual order counts (i.e., the "actual" column) and
+                point forecasts (i.e., the "prediction" column);
+                this model does not support confidence intervals;
+                contains one row for the `predict_at` time step
+
+        # noqa:DAR401 RuntimeError
+        """
+        # Generate the historic order time series mainly to check if a valid
+        # `training_ts` exists (i.e., the demand history is long enough).
+        _, frequency, actuals_ts = self._order_history.make_horizontal_ts(
+            pixel_id=pixel.id, predict_at=predict_at, train_horizon=train_horizon,
+        )
+
+        # Sanity checks.
+        if frequency != 7:  # pragma: no cover
+            raise RuntimeError('`frequency` should be `7`')
+        if len(actuals_ts) != 1:  # pragma: no cover
+            raise RuntimeError(
+                'the trivial model can only predict one step into the future',
+            )
+
+        # The "prediction" is simply `0.0`.
+        predictions = pd.DataFrame(
+            data={
+                'actual': actuals_ts,
+                'prediction': 0.0,
+                'low80': float('NaN'),
+                'high80': float('NaN'),
+                'low95': float('NaN'),
+                'high95': float('NaN'),
+            },
+            index=actuals_ts.index,
+        )
+
+        # Sanity checks.
+        if predictions['actual'].isnull().any():  # pragma: no cover
+            raise RuntimeError('missing actuals in trivial model')
+        if predict_at not in predictions.index:  # pragma: no cover
+            raise RuntimeError('missing prediction for `predict_at`')
+
+        return predictions

src/urban_meal_delivery/forecasts/models/tactical/realtime.py

@@ -0,0 +1,117 @@
+"""Real-time forecasting `*Model`s to predict demand for tactical purposes.
+
+Real-time `*Model`s take order counts of all time steps in the training data
+and make a prediction for only one time step on the day to be predicted (i.e.,
+the one starting at `predict_at`). Thus, the training time series have a
+`frequency` of the number of weekdays, `7`, times the number of time steps on a
+day. For example, for 60-minute time steps, the `frequency` becomes `7 * 12`
+(= operating hours from 11 am to 11 pm), which is `84`. Real-time `*Model`s
+train the forecasting `methods` on a seasonally decomposed time series internally.
+"""  # noqa:RST215
+
+import datetime as dt
+
+import pandas as pd
+
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import methods
+from urban_meal_delivery.forecasts.models import base
+
+
+class RealtimeARIMAModel(base.ForecastingModelABC):
+    """The ARIMA model applied on a real-time time series."""
+
+    name = 'rtarima'
+
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Predict demand for a time step.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actual order counts (i.e., the "actual" column),
+                point forecasts (i.e., the "prediction" column), and
+                confidence intervals (i.e, the four "low/high/80/95" columns);
+                contains one row for the `predict_at` time step
+
+        # noqa:DAR401 RuntimeError
+        """
+        # Generate the historic (and real-time) order time series.
+        training_ts, frequency, actuals_ts = self._order_history.make_realtime_ts(
+            pixel_id=pixel.id, predict_at=predict_at, train_horizon=train_horizon,
+        )
+
+        # Decompose the `training_ts` to make predictions for the seasonal
+        # component and the seasonally adjusted observations separately.
+        decomposed_training_ts = methods.decomposition.stl(
+            time_series=training_ts,
+            frequency=frequency,
+            # "Periodic" `ns` parameter => same seasonal component value
+            # for observations of the same lag.
+            ns=999,
+        )
+
+        # Make predictions for the seasonal component by linear extrapolation.
+        seasonal_predictions = methods.extrapolate_season.predict(
+            training_ts=decomposed_training_ts['seasonal'],
+            forecast_interval=actuals_ts.index,
+            frequency=frequency,
+        )
+
+        # Make predictions with the ARIMA model on the seasonally adjusted time series.
+        seasonally_adjusted_predictions = methods.arima.predict(
+            training_ts=(
+                decomposed_training_ts['trend'] + decomposed_training_ts['residual']
+            ),
+            forecast_interval=actuals_ts.index,
+            # Because the seasonality was taken out before,
+            # the `training_ts` has, by definition, a `frequency` of `1`.
+            frequency=1,
+            seasonal_fit=False,
+        )
+
+        # The overall `predictions` are the sum of the separate predictions above.
+        # As the linear extrapolation of the seasonal component has no
+        # confidence interval, we put the one from the ARIMA model around
+        # the extrapolated seasonal component.
+        predictions = pd.DataFrame(
+            data={
+                'actual': actuals_ts,
+                'prediction': (
+                    seasonal_predictions['prediction']  # noqa:WPS204
+                    + seasonally_adjusted_predictions['prediction']
+                ),
+                'low80': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['low80']
+                ),
+                'high80': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['high80']
+                ),
+                'low95': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['low95']
+                ),
+                'high95': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['high95']
+                ),
+            },
+            index=actuals_ts.index,
+        )
+
+        # Sanity checks.
+        if len(predictions) != 1:  # pragma: no cover
+            raise RuntimeError('real-time models should predict exactly one time step')
+        if predictions.isnull().any().any():  # pragma: no cover
+            raise RuntimeError('missing predictions in rtarima model')
+        if predict_at not in predictions.index:  # pragma: no cover
+            raise RuntimeError('missing prediction for `predict_at`')
+
+        return predictions

src/urban_meal_delivery/forecasts/models/tactical/vertical.py

@@ -0,0 +1,119 @@
+"""Vertical forecasting `*Model`s to predict demand for tactical purposes.
+
+Vertical `*Model`s take order counts of all time steps in the training data
+and make a prediction for all time steps on the day to be predicted at once.
+Thus, the training time series have a `frequency` of the number of weekdays,
+`7`, times the number of time steps on a day. For example, with 60-minute time
+steps, the `frequency` becomes `7 * 12` (= operating hours from 11 am to 11 pm),
+which is `84`. Vertical `*Model`s train the forecasting `methods` on a seasonally
+decomposed time series internally.
+"""  # noqa:RST215
+
+import datetime as dt
+
+import pandas as pd
+
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import methods
+from urban_meal_delivery.forecasts.models import base
+
+
+class VerticalARIMAModel(base.ForecastingModelABC):
+    """The ARIMA model applied on a vertical time series."""
+
+    name = 'varima'
+
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Predict demand for a time step.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actual order counts (i.e., the "actual" column),
+                point forecasts (i.e., the "prediction" column), and
+                confidence intervals (i.e, the four "low/high/80/95" columns);
+                contains several rows, including one for the `predict_at` time step
+
+        # noqa:DAR401 RuntimeError
+        """
+        # Generate the historic (and vertical) order time series.
+        training_ts, frequency, actuals_ts = self._order_history.make_vertical_ts(
+            pixel_id=pixel.id,
+            predict_day=predict_at.date(),
+            train_horizon=train_horizon,
+        )
+
+        # Decompose the `training_ts` to make predictions for the seasonal
+        # component and the seasonally adjusted observations separately.
+        decomposed_training_ts = methods.decomposition.stl(
+            time_series=training_ts,
+            frequency=frequency,
+            # "Periodic" `ns` parameter => same seasonal component value
+            # for observations of the same lag.
+            ns=999,
+        )
+
+        # Make predictions for the seasonal component by linear extrapolation.
+        seasonal_predictions = methods.extrapolate_season.predict(
+            training_ts=decomposed_training_ts['seasonal'],
+            forecast_interval=actuals_ts.index,
+            frequency=frequency,
+        )
+
+        # Make predictions with the ARIMA model on the seasonally adjusted time series.
+        seasonally_adjusted_predictions = methods.arima.predict(
+            training_ts=(
+                decomposed_training_ts['trend'] + decomposed_training_ts['residual']
+            ),
+            forecast_interval=actuals_ts.index,
+            # Because the seasonality was taken out before,
+            # the `training_ts` has, by definition, a `frequency` of `1`.
+            frequency=1,
+            seasonal_fit=False,
+        )
+
+        # The overall `predictions` are the sum of the separate predictions above.
+        # As the linear extrapolation of the seasonal component has no
+        # confidence interval, we put the one from the ARIMA model around
+        # the extrapolated seasonal component.
+        predictions = pd.DataFrame(
+            data={
+                'actual': actuals_ts,
+                'prediction': (
+                    seasonal_predictions['prediction']  # noqa:WPS204
+                    + seasonally_adjusted_predictions['prediction']
+                ),
+                'low80': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['low80']
+                ),
+                'high80': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['high80']
+                ),
+                'low95': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['low95']
+                ),
+                'high95': (
+                    seasonal_predictions['prediction']
+                    + seasonally_adjusted_predictions['high95']
+                ),
+            },
+            index=actuals_ts.index,
+        )
+
+        # Sanity checks.
+        if len(predictions) <= 1:  # pragma: no cover
+            raise RuntimeError('vertical models should predict several time steps')
+        if predictions.isnull().any().any():  # pragma: no cover
+            raise RuntimeError('missing predictions in varima model')
+        if predict_at not in predictions.index:  # pragma: no cover
+            raise RuntimeError('missing prediction for `predict_at`')
+
+        return predictions

src/urban_meal_delivery/forecasts/timify.py

@@ -0,0 +1,560 @@
+"""Obtain and work with time series data."""
+
+from __future__ import annotations
+
+import datetime as dt
+from typing import Tuple
+
+import pandas as pd
+import sqlalchemy as sa
+
+from urban_meal_delivery import config
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import models
+
+
+class OrderHistory:
+    """Generate time series from the `Order` model in the database.
+
+    The purpose of this class is to abstract away the managing of the order data
+    in memory and the slicing the data into various kinds of time series.
+    """
+
+    def __init__(self, grid: db.Grid, time_step: int) -> None:
+        """Initialize a new `OrderHistory` object.
+
+        Args:
+            grid: pixel grid used to aggregate orders spatially
+            time_step: interval length (in minutes) into which orders are aggregated
+
+        # noqa:DAR401 RuntimeError
+        """
+        self._grid = grid
+        self._time_step = time_step
+
+        # Number of daily time steps must be a whole multiple of `time_step` length.
+        n_daily_time_steps = (
+            60 * (config.SERVICE_END - config.SERVICE_START) / time_step
+        )
+        if n_daily_time_steps != int(n_daily_time_steps):  # pragma: no cover
+            raise RuntimeError('Internal error: configuration has invalid TIME_STEPS')
+        self._n_daily_time_steps = int(n_daily_time_steps)
+
+        # The `_data` are populated by `.aggregate_orders()`.
+        self._data = None
+
+    @property
+    def time_step(self) -> int:
+        """The length of one time step."""
+        return self._time_step
+
+    @property
+    def totals(self) -> pd.DataFrame:
+        """The order totals by `Pixel` and `.time_step`.
+
+        The returned object should not be mutated!
+
+        Returns:
+            order_totals: a one-column `DataFrame` with a `MultiIndex` of the
+                "pixel_id"s and "start_at"s (i.e., beginnings of the intervals);
+                the column with data is "n_orders"
+        """
+        if self._data is None:
+            self._data = self.aggregate_orders()
+
+        return self._data
+
+    def aggregate_orders(self) -> pd.DataFrame:  # pragma: no cover
+        """Generate and load all order totals from the database."""
+        # `data` is probably missing "pixel_id"-"start_at" pairs.
+        # This happens when there is no demand in the `Pixel` in the given `time_step`.
+        data = pd.read_sql_query(
+            sa.text(
+                f"""  -- # noqa:WPS221
+                SELECT
+                    pixel_id,
+                    start_at,
+                    COUNT(*) AS n_orders
+                FROM (
+                    SELECT
+                        pixel_id,
+                        placed_at_without_seconds - minutes_to_be_cut AS start_at
+                    FROM (
+                        SELECT
+                            pixels.pixel_id,
+                            DATE_TRUNC('MINUTE', orders.placed_at)
+                                AS placed_at_without_seconds,
+                            ((
+                                EXTRACT(MINUTES FROM orders.placed_at)::INTEGER
+                                    % {self._time_step}
+                            )::TEXT || ' MINUTES')::INTERVAL
+                                AS minutes_to_be_cut
+                        FROM (
+                            SELECT
+                                id,
+                                placed_at,
+                                pickup_address_id
+                            FROM
+                                {config.CLEAN_SCHEMA}.orders
+                            INNER JOIN (
+                                SELECT
+                                    id AS address_id
+                                FROM
+                                    {config.CLEAN_SCHEMA}.addresses
+                                WHERE
+                                    city_id = {self._grid.city.id}
+                            ) AS in_city
+                                ON orders.pickup_address_id = in_city.address_id
+                            WHERE
+                                ad_hoc IS TRUE
+                        ) AS
+                            orders
+                        INNER JOIN (
+                            SELECT
+                                address_id,
+                                pixel_id
+                            FROM
+                                {config.CLEAN_SCHEMA}.addresses_pixels
+                            WHERE
+                                grid_id = {self._grid.id}
+                                AND
+                                city_id = {self._grid.city.id} -- -> sanity check
+                        ) AS pixels
+                            ON orders.pickup_address_id = pixels.address_id
+                    ) AS placed_at_aggregated_into_start_at
+                ) AS pixel_start_at_combinations
+                GROUP BY
+                    pixel_id,
+                    start_at
+                ORDER BY
+                    pixel_id,
+                    start_at;
+                """,
+            ),  # noqa:WPS355
+            con=db.connection,
+            index_col=['pixel_id', 'start_at'],
+        )
+
+        if data.empty:
+            return data
+
+        # Calculate the first and last "start_at" value ...
+        start_day = data.index.levels[1].min().date()
+        start = dt.datetime(
+            start_day.year, start_day.month, start_day.day, config.SERVICE_START,
+        )
+        end_day = data.index.levels[1].max().date()
+        end = dt.datetime(end_day.year, end_day.month, end_day.day, config.SERVICE_END)
+        # ... and all possible `tuple`s of "pixel_id"-"start_at" combinations.
+        # The "start_at" values must lie within the operating hours.
+        gen = (
+            (pixel_id, start_at)
+            for pixel_id in sorted(data.index.levels[0])
+            for start_at in pd.date_range(start, end, freq=f'{self._time_step}T')
+            if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+        )
+
+        # Re-index `data` filling in `0`s where there is no demand.
+        index = pd.MultiIndex.from_tuples(gen)
+        index.names = ['pixel_id', 'start_at']
+
+        return data.reindex(index, fill_value=0)
+
+    def first_order_at(self, pixel_id: int) -> dt.datetime:
+        """Get the time step with the first order in a pixel.
+
+        Args:
+            pixel_id: pixel for which to get the first order
+
+        Returns:
+            minimum "start_at" from when orders take place
+
+        Raises:
+            LookupError: `pixel_id` not in `grid`
+
+        # noqa:DAR401 RuntimeError
+        """
+        try:
+            intra_pixel = self.totals.loc[pixel_id]
+        except KeyError:
+            raise LookupError('The `pixel_id` is not in the `grid`') from None
+
+        first_order = intra_pixel[intra_pixel['n_orders'] > 0].index.min()
+
+        # Sanity check: without an `Order`, the `Pixel` should not exist.
+        if first_order is pd.NaT:  # pragma: no cover
+            raise RuntimeError('no orders in the pixel')
+
+        # Return a proper `datetime.datetime` object.
+        return dt.datetime(
+            first_order.year,
+            first_order.month,
+            first_order.day,
+            first_order.hour,
+            first_order.minute,
+        )
+
+    def last_order_at(self, pixel_id: int) -> dt.datetime:
+        """Get the time step with the last order in a pixel.
+
+        Args:
+            pixel_id: pixel for which to get the last order
+
+        Returns:
+            maximum "start_at" from when orders take place
+
+        Raises:
+            LookupError: `pixel_id` not in `grid`
+
+        # noqa:DAR401 RuntimeError
+        """
+        try:
+            intra_pixel = self.totals.loc[pixel_id]
+        except KeyError:
+            raise LookupError('The `pixel_id` is not in the `grid`') from None
+
+        last_order = intra_pixel[intra_pixel['n_orders'] > 0].index.max()
+
+        # Sanity check: without an `Order`, the `Pixel` should not exist.
+        if last_order is pd.NaT:  # pragma: no cover
+            raise RuntimeError('no orders in the pixel')
+
+        # Return a proper `datetime.datetime` object.
+        return dt.datetime(
+            last_order.year,
+            last_order.month,
+            last_order.day,
+            last_order.hour,
+            last_order.minute,
+        )
+
+    def make_horizontal_ts(  # noqa:WPS210
+        self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
+    ) -> Tuple[pd.Series, int, pd.Series]:
+        """Slice a horizontal time series out of the `.totals`.
+
+        Create a time series covering `train_horizon` weeks that can be used
+        for training a forecasting model to predict the demand at `predict_at`.
+
+        For explanation of the terms "horizontal", "vertical", and "real-time"
+        in the context of time series, see section 3.2 in the following paper:
+        https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+
+        Args:
+            pixel_id: pixel in which the time series is aggregated
+            predict_at: time step (i.e., "start_at") for which a prediction is made
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            training time series, frequency, actual order count at `predict_at`
+
+        Raises:
+            LookupError: `pixel_id` not in `grid` or `predict_at` not in `.totals`
+            RuntimeError: desired time series slice is not entirely in `.totals`
+        """
+        try:
+            intra_pixel = self.totals.loc[pixel_id]
+        except KeyError:
+            raise LookupError('The `pixel_id` is not in the `grid`') from None
+
+        if predict_at >= config.CUTOFF_DAY:  # pragma: no cover
+            raise RuntimeError('Internal error: cannot predict beyond the given data')
+
+        # The first and last training day are just before the `predict_at` day
+        # and span exactly `train_horizon` weeks covering only the times of the
+        # day equal to the hour/minute of `predict_at`.
+        first_train_day = predict_at.date() - dt.timedelta(weeks=train_horizon)
+        first_start_at = dt.datetime(
+            first_train_day.year,
+            first_train_day.month,
+            first_train_day.day,
+            predict_at.hour,
+            predict_at.minute,
+        )
+        last_train_day = predict_at.date() - dt.timedelta(days=1)
+        last_start_at = dt.datetime(
+            last_train_day.year,
+            last_train_day.month,
+            last_train_day.day,
+            predict_at.hour,
+            predict_at.minute,
+        )
+
+        # The frequency is the number of weekdays.
+        frequency = 7
+
+        # Take only the counts at the `predict_at` time.
+        training_ts = intra_pixel.loc[
+            first_start_at : last_start_at : self._n_daily_time_steps,  # type:ignore
+            'n_orders',
+        ]
+        if len(training_ts) != frequency * train_horizon:
+            raise RuntimeError('Not enough historic data for `predict_at`')
+
+        actuals_ts = intra_pixel.loc[[predict_at], 'n_orders']
+        if not len(actuals_ts):  # pragma: no cover
+            raise LookupError('`predict_at` is not in the order history')
+
+        return training_ts, frequency, actuals_ts
+
+    def make_vertical_ts(  # noqa:WPS210
+        self, pixel_id: int, predict_day: dt.date, train_horizon: int,
+    ) -> Tuple[pd.Series, int, pd.Series]:
+        """Slice a vertical time series out of the `.totals`.
+
+        Create a time series covering `train_horizon` weeks that can be used
+        for training a forecasting model to predict the demand on the `predict_day`.
+
+        For explanation of the terms "horizontal", "vertical", and "real-time"
+        in the context of time series, see section 3.2 in the following paper:
+        https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+
+        Args:
+            pixel_id: pixel in which the time series is aggregated
+            predict_day: day for which predictions are made
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            training time series, frequency, actual order counts on `predict_day`
+
+        Raises:
+            LookupError: `pixel_id` not in `grid` or `predict_day` not in `.totals`
+            RuntimeError: desired time series slice is not entirely in `.totals`
+        """
+        try:
+            intra_pixel = self.totals.loc[pixel_id]
+        except KeyError:
+            raise LookupError('The `pixel_id` is not in the `grid`') from None
+
+        if predict_day >= config.CUTOFF_DAY.date():  # pragma: no cover
+            raise RuntimeError('Internal error: cannot predict beyond the given data')
+
+        # The first and last training day are just before the `predict_day`
+        # and span exactly `train_horizon` weeks covering all times of the day.
+        first_train_day = predict_day - dt.timedelta(weeks=train_horizon)
+        first_start_at = dt.datetime(
+            first_train_day.year,
+            first_train_day.month,
+            first_train_day.day,
+            config.SERVICE_START,
+            0,
+        )
+        last_train_day = predict_day - dt.timedelta(days=1)
+        last_start_at = dt.datetime(
+            last_train_day.year,
+            last_train_day.month,
+            last_train_day.day,
+            config.SERVICE_END,  # subtract one `time_step` below
+            0,
+        ) - dt.timedelta(minutes=self._time_step)
+
+        # The frequency is the number of weekdays times the number of daily time steps.
+        frequency = 7 * self._n_daily_time_steps
+
+        # Take all the counts between `first_train_day` and `last_train_day`.
+        training_ts = intra_pixel.loc[
+            first_start_at:last_start_at,  # type:ignore
+            'n_orders',
+        ]
+        if len(training_ts) != frequency * train_horizon:
+            raise RuntimeError('Not enough historic data for `predict_day`')
+
+        first_prediction_at = dt.datetime(
+            predict_day.year,
+            predict_day.month,
+            predict_day.day,
+            config.SERVICE_START,
+            0,
+        )
+        last_prediction_at = dt.datetime(
+            predict_day.year,
+            predict_day.month,
+            predict_day.day,
+            config.SERVICE_END,  # subtract one `time_step` below
+            0,
+        ) - dt.timedelta(minutes=self._time_step)
+
+        actuals_ts = intra_pixel.loc[
+            first_prediction_at:last_prediction_at,  # type:ignore
+            'n_orders',
+        ]
+        if not len(actuals_ts):  # pragma: no cover
+            raise LookupError('`predict_day` is not in the order history')
+
+        return training_ts, frequency, actuals_ts
+
+    def make_realtime_ts(  # noqa:WPS210
+        self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
+    ) -> Tuple[pd.Series, int, pd.Series]:
+        """Slice a vertical real-time time series out of the `.totals`.
+
+        Create a time series covering `train_horizon` weeks that can be used
+        for training a forecasting model to predict the demand at `predict_at`.
+
+        For explanation of the terms "horizontal", "vertical", and "real-time"
+        in the context of time series, see section 3.2 in the following paper:
+        https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+
+        Args:
+            pixel_id: pixel in which the time series is aggregated
+            predict_at: time step (i.e., "start_at") for which a prediction is made
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            training time series, frequency, actual order count at `predict_at`
+
+        Raises:
+            LookupError: `pixel_id` not in `grid` or `predict_at` not in `.totals`
+            RuntimeError: desired time series slice is not entirely in `.totals`
+        """
+        try:
+            intra_pixel = self.totals.loc[pixel_id]
+        except KeyError:
+            raise LookupError('The `pixel_id` is not in the `grid`') from None
+
+        if predict_at >= config.CUTOFF_DAY:  # pragma: no cover
+            raise RuntimeError('Internal error: cannot predict beyond the given data')
+
+        # The first and last training day are just before the `predict_at` day
+        # and span exactly `train_horizon` weeks covering all times of the day,
+        # including times on the `predict_at` day that are earlier than `predict_at`.
+        first_train_day = predict_at.date() - dt.timedelta(weeks=train_horizon)
+        first_start_at = dt.datetime(
+            first_train_day.year,
+            first_train_day.month,
+            first_train_day.day,
+            config.SERVICE_START,
+            0,
+        )
+        # Predicting the first time step on the `predict_at` day is a corner case.
+        # Then, the previous day is indeed the `last_train_day`. Predicting any
+        # other time step implies that the `predict_at` day is the `last_train_day`.
+        # `last_train_time` is the last "start_at" before the one being predicted.
+        if predict_at.hour == config.SERVICE_START:
+            last_train_day = predict_at.date() - dt.timedelta(days=1)
+            last_train_time = dt.time(config.SERVICE_END, 0)
+        else:
+            last_train_day = predict_at.date()
+            last_train_time = predict_at.time()
+        last_start_at = dt.datetime(
+            last_train_day.year,
+            last_train_day.month,
+            last_train_day.day,
+            last_train_time.hour,
+            last_train_time.minute,
+        ) - dt.timedelta(minutes=self._time_step)
+
+        # The frequency is the number of weekdays times the number of daily time steps.
+        frequency = 7 * self._n_daily_time_steps
+
+        # Take all the counts between `first_train_day` and `last_train_day`,
+        # including the ones on the `predict_at` day prior to `predict_at`.
+        training_ts = intra_pixel.loc[
+            first_start_at:last_start_at,  # type:ignore
+            'n_orders',
+        ]
+        n_time_steps_on_predict_day = (
+            (
+                predict_at
+                - dt.datetime(
+                    predict_at.year,
+                    predict_at.month,
+                    predict_at.day,
+                    config.SERVICE_START,
+                    0,
+                )
+            ).seconds
+            // 60  # -> minutes
+            // self._time_step
+        )
+        if len(training_ts) != frequency * train_horizon + n_time_steps_on_predict_day:
+            raise RuntimeError('Not enough historic data for `predict_day`')
+
+        actuals_ts = intra_pixel.loc[[predict_at], 'n_orders']
+        if not len(actuals_ts):  # pragma: no cover
+            raise LookupError('`predict_at` is not in the order history')
+
+        return training_ts, frequency, actuals_ts
+
+    def avg_daily_demand(
+        self, pixel_id: int, predict_day: dt.date, train_horizon: int,
+    ) -> float:
+        """Calculate the average daily demand (ADD) for a `Pixel`.
+
+        The ADD is defined as the average number of daily `Order`s in a
+        `Pixel` within the training horizon preceding the `predict_day`.
+
+        The ADD is primarily used for the rule-based heuristic to determine
+        the best forecasting model for a `Pixel` on the `predict_day`.
+
+        Implementation note: To calculate the ADD, the order counts are
+        generated as a vertical time series. That must be so as we need to
+        include all time steps of the days before the `predict_day` and
+        no time step of the latter.
+
+        Args:
+            pixel_id: pixel for which the ADD is calculated
+            predict_day: following the `train_horizon` on which the ADD is calculated
+            train_horizon: time horizon over which the ADD is calculated
+
+        Returns:
+            average number of orders per day
+        """
+        training_ts, _, _ = self.make_vertical_ts(  # noqa:WPS434
+            pixel_id=pixel_id, predict_day=predict_day, train_horizon=train_horizon,
+        )
+
+        first_day = training_ts.index.min().date()
+        last_day = training_ts.index.max().date()
+        # `+1` as both `first_day` and `last_day` are included.
+        n_days = (last_day - first_day).days + 1
+
+        return round(training_ts.sum() / n_days, 1)
+
+    def choose_tactical_model(
+        self, pixel_id: int, predict_day: dt.date, train_horizon: int,
+    ) -> models.ForecastingModelABC:
+        """Choose the most promising forecasting `*Model` for tactical purposes.
+
+        The rules are deduced from "Table 1: Top-3 models by ..." in the article
+        "Real-time demand forecasting for an urban delivery platform", the first
+        research paper published for this `urban-meal-delivery` project.
+
+        According to the research findings in the article "Real-time demand
+        forecasting for an urban delivery platform", the best model is a function
+        of the average daily demand (ADD) and the length of the training horizon.
+
+        For the paper check:
+            https://github.com/webartifex/urban-meal-delivery-demand-forecasting/blob/main/paper.pdf
+            https://www.sciencedirect.com/science/article/pii/S1366554520307936
+
+        Args:
+            pixel_id: pixel for which a forecasting `*Model` is chosen
+            predict_day: day for which demand is to be predicted with the `*Model`
+            train_horizon: time horizon available for training the `*Model`
+
+        Returns:
+            most promising forecasting `*Model`
+
+        # noqa:DAR401 RuntimeError
+        """  # noqa:RST215
+        add = self.avg_daily_demand(
+            pixel_id=pixel_id, predict_day=predict_day, train_horizon=train_horizon,
+        )
+
+        # For now, we only make forecasts with 8 weeks
+        # as the training horizon (note:4f79e8fa).
+        if train_horizon == 8:
+            if add >= 25:  # = "high demand"
+                return models.HorizontalETSModel(order_history=self)
+            elif add >= 10:  # = "medium demand"
+                return models.HorizontalETSModel(order_history=self)
+            elif add >= 2.5:  # = "low demand"
+                return models.HorizontalSMAModel(order_history=self)
+
+            # = "no demand"
+            return models.TrivialModel(order_history=self)
+
+        raise RuntimeError(
+            'no rule for the given average daily demand and training horizon',
+        )

src/urban_meal_delivery/init_r.py

@@ -0,0 +1,28 @@
+"""Initialize the R dependencies.
+
+The purpose of this module is to import all the R packages that are installed
+into a sub-folder (see `config.R_LIBS_PATH`) in the project's root directory.
+
+The Jupyter notebook "research/r_dependencies.ipynb" can be used to install all
+R dependencies on a Ubuntu/Debian based system.
+"""
+
+from rpy2.rinterface_lib import callbacks as rcallbacks
+from rpy2.robjects import packages as rpackages
+
+
+# Suppress R's messages to stdout and stderr.
+# Source: https://stackoverflow.com/a/63220287
+rcallbacks.consolewrite_print = lambda msg: None  # pragma: no cover
+rcallbacks.consolewrite_warnerror = lambda msg: None  # pragma: no cover
+
+
+# For clarity and convenience, re-raise the error that results from missing R
+# dependencies with clearer instructions as to how to deal with it.
+try:  # noqa:WPS229
+    rpackages.importr('forecast')
+    rpackages.importr('zoo')
+
+except rpackages.PackageNotInstalledError:  # pragma: no cover
+    msg = 'See the "research/r_dependencies.ipynb" notebook!'
+    raise rpackages.PackageNotInstalledError(msg) from None

tests/config.py

@@ -0,0 +1,34 @@
+"""Globals used when testing."""
+
+import datetime as dt
+
+from urban_meal_delivery import config
+
+
+# The day on which most test cases take place.
+YEAR, MONTH, DAY = 2016, 7, 1
+
+# The hour when most test cases take place.
+NOON = 12
+
+# `START` and `END` constitute a 57-day time span, 8 full weeks plus 1 day.
+# That implies a maximum `train_horizon` of `8` as that needs full 7-day weeks.
+START = dt.datetime(YEAR, MONTH, DAY, config.SERVICE_START, 0)
+_end = START + dt.timedelta(days=56)  # `56` as `START` is not included
+END = dt.datetime(_end.year, _end.month, _end.day, config.SERVICE_END, 0)
+
+# Default time steps (in minutes), for example, for `OrderHistory` objects.
+LONG_TIME_STEP = 60
+SHORT_TIME_STEP = 30
+TIME_STEPS = (SHORT_TIME_STEP, LONG_TIME_STEP)
+# The `frequency` of vertical time series is the number of days in a week, 7,
+# times the number of time steps per day. With 12 operating hours (11 am - 11 pm)
+# the `frequency`s are 84 and 168 for the `LONG/SHORT_TIME_STEP`s.
+VERTICAL_FREQUENCY_LONG = 7 * 12
+VERTICAL_FREQUENCY_SHORT = 7 * 24
+
+# Default training horizons, for example, for
+# `OrderHistory.make_horizontal_time_series()`.
+LONG_TRAIN_HORIZON = 8
+SHORT_TRAIN_HORIZON = 2
+TRAIN_HORIZONS = (SHORT_TRAIN_HORIZON, LONG_TRAIN_HORIZON)

tests/conftest.py

@@ -1,12 +1,116 @@
-"""Utils for testing the entire package."""
+"""Fixtures for testing the entire package.
+
+The ORM related fixtures are placed here too as some integration tests
+in the CLI layer need access to the database.
+"""
 
 import os
 
+import pytest
+import sqlalchemy as sa
+from alembic import command as migrations_cmd
+from alembic import config as migrations_config
+from sqlalchemy import orm
+
+from tests.db import fake_data
 from urban_meal_delivery import config
+from urban_meal_delivery import db
 
 
+# The TESTING environment variable is set
+# in setup.cfg in pytest's config section.
 if not os.getenv('TESTING'):
     raise RuntimeError('Tests must be executed with TESTING set in the environment')
 
 if not config.TESTING:
     raise RuntimeError('The testing configuration was not loaded')
+
+
+@pytest.fixture(scope='session', params=['all_at_once', 'sequentially'])
+def db_connection(request):
+    """Create all tables given the ORM models.
+
+    The tables are put into a distinct PostgreSQL schema
+    that is removed after all tests are over.
+
+    The database connection used to do that is yielded.
+
+    There are two modes for this fixture:
+
+    - "all_at_once": build up the tables all at once with MetaData.create_all()
+    - "sequentially": build up the tables sequentially with `alembic upgrade head`
+
+    This ensures that Alembic's migration files are consistent.
+    """
+    # We need a fresh database connection for each of the two `params`.
+    # Otherwise, the first test of the parameter run second will fail.
+    engine = sa.create_engine(config.DATABASE_URI)
+    connection = engine.connect()
+
+    # Monkey patch the package's global `engine` and `connection` objects,
+    # just in case if it is used somewhere in the code base.
+    db.engine = engine
+    db.connection = connection
+
+    if request.param == 'all_at_once':
+        connection.execute(f'CREATE SCHEMA {config.CLEAN_SCHEMA};')
+        db.Base.metadata.create_all(connection)
+    else:
+        cfg = migrations_config.Config('alembic.ini')
+        migrations_cmd.upgrade(cfg, 'head')
+
+    try:
+        yield connection
+
+    finally:
+        connection.execute(f'DROP SCHEMA {config.CLEAN_SCHEMA} CASCADE;')
+
+        if request.param == 'sequentially':
+            tmp_alembic_version = f'{config.ALEMBIC_TABLE}_{config.CLEAN_SCHEMA}'
+            connection.execute(
+                f'DROP TABLE {config.ALEMBIC_TABLE_SCHEMA}.{tmp_alembic_version};',
+            )
+
+        connection.close()
+
+
+@pytest.fixture
+def db_session(db_connection):
+    """A SQLAlchemy session that rolls back everything after a test case."""
+    # Begin the outermost transaction
+    # that is rolled back at the end of each test case.
+    transaction = db_connection.begin()
+
+    # Create a session bound to the same connection as the `transaction`.
+    # Using any other session would not result in the roll back.
+    session = orm.sessionmaker()(bind=db_connection)
+
+    # Monkey patch the package's global `session` object,
+    # which is used heavily in the code base.
+    db.session = session
+
+    try:
+        yield session
+
+    finally:
+        session.close()
+        transaction.rollback()
+
+
+# Import the fixtures from the `fake_data` sub-package.
+
+make_address = fake_data.make_address
+make_courier = fake_data.make_courier
+make_customer = fake_data.make_customer
+make_order = fake_data.make_order
+make_restaurant = fake_data.make_restaurant
+
+address = fake_data.address
+city = fake_data.city
+city_data = fake_data.city_data
+courier = fake_data.courier
+customer = fake_data.customer
+order = fake_data.order
+restaurant = fake_data.restaurant
+grid = fake_data.grid
+pixel = fake_data.pixel

tests/console/__init__.py

@@ -0,0 +1,5 @@
+"""Test the CLI scripts in the urban-meal-delivery package.
+
+Some tests require a database. Therefore, the corresponding code is excluded
+from coverage reporting with "pragma: no cover" (grep:b1f68d24).
+"""

tests/console/conftest.py

@@ -0,0 +1,10 @@
+"""Fixture for testing the CLI scripts."""
+
+import pytest
+from click import testing as click_testing
+
+
+@pytest.fixture
+def cli() -> click_testing.CliRunner:
+    """Initialize Click's CLI Test Runner."""
+    return click_testing.CliRunner()

tests/console/test_gridify.py

@@ -0,0 +1,48 @@
+"""Integration test for the `urban_meal_delivery.console.gridify` module."""
+
+import pytest
+
+import urban_meal_delivery
+from urban_meal_delivery import db
+from urban_meal_delivery.console import gridify
+
+
+@pytest.mark.db
+def test_two_pixels_with_two_addresses(  # noqa:WPS211
+    cli, db_session, monkeypatch, city, make_address, make_restaurant, make_order,
+):
+    """Two `Address` objects in distinct `Pixel` objects.
+
+    This is roughly the same test case as
+    `tests.db.test_grids.test_two_pixels_with_two_addresses`.
+    The difference is that the result is written to the database.
+    """
+    # Create two `Address` objects in distinct `Pixel`s.
+    # One `Address` in the lower-left `Pixel`, ...
+    address1 = make_address(latitude=48.8357377, longitude=2.2517412)
+    # ... and another one in the upper-right one.
+    address2 = make_address(latitude=48.8898312, longitude=2.4357622)
+
+    # Locate a `Restaurant` at the two `Address` objects and
+    # place one `Order` for each of them so that the `Address`
+    # objects are used as `Order.pickup_address`s.
+    restaurant1 = make_restaurant(address=address1)
+    restaurant2 = make_restaurant(address=address2)
+    order1 = make_order(restaurant=restaurant1)
+    order2 = make_order(restaurant=restaurant2)
+
+    db_session.add(order1)
+    db_session.add(order2)
+    db_session.commit()
+
+    side_length = max(city.total_x // 2, city.total_y // 2) + 1
+
+    # Hack the configuration regarding the grids to be created.
+    monkeypatch.setattr(urban_meal_delivery.config, 'GRID_SIDE_LENGTHS', [side_length])
+
+    result = cli.invoke(gridify.gridify)
+
+    assert result.exit_code == 0
+
+    assert db_session.query(db.Grid).count() == 1
+    assert db_session.query(db.Pixel).count() == 2

tests/console/test_main.py

@@ -1,34 +1,31 @@
-"""Test the package's `umd` command-line client."""
+"""Test the package's top-level `umd` CLI command."""
 
 import click
 import pytest
-from click import testing as click_testing
 
-from urban_meal_delivery import console
+from urban_meal_delivery.console import main
 
 
 class TestShowVersion:
-    """Test console.show_version().
+    """Test `console.main.show_version()`.
 
     The function is used as a callback to a click command option.
 
-    show_version() prints the name and version of the installed package to
+    `show_version()` prints the name and version of the installed package to
     stdout. The output looks like this: "{pkg_name}, version {version}".
 
     Development (= non-final) versions are indicated by appending a
     " (development)" to the output.
     """
 
-    # pylint:disable=no-self-use
-
     @pytest.fixture
     def ctx(self) -> click.Context:
-        """Context around the console.main Command."""
+        """Context around the `main.entry_point` Command."""
-        return click.Context(console.main)
+        return click.Context(main.entry_point)
 
     def test_no_version(self, capsys, ctx):
-        """The the early exit branch without any output."""
+        """Test the early exit branch without any output."""
-        console.show_version(ctx, _param='discarded', value=False)
+        main.show_version(ctx, _param='discarded', value=False)
 
         captured = capsys.readouterr()
 
@@ -37,10 +34,10 @@ class TestShowVersion:
     def test_final_version(self, capsys, ctx, monkeypatch):
         """For final versions, NO "development" warning is emitted."""
         version = '1.2.3'
-        monkeypatch.setattr(console.urban_meal_delivery, '__version__', version)
+        monkeypatch.setattr(main.urban_meal_delivery, '__version__', version)
 
         with pytest.raises(click.exceptions.Exit):
-            console.show_version(ctx, _param='discarded', value=True)
+            main.show_version(ctx, _param='discarded', value=True)
 
         captured = capsys.readouterr()
 
@@ -49,37 +46,29 @@ class TestShowVersion:
     def test_develop_version(self, capsys, ctx, monkeypatch):
         """For develop versions, a warning thereof is emitted."""
         version = '1.2.3.dev0'
-        monkeypatch.setattr(console.urban_meal_delivery, '__version__', version)
+        monkeypatch.setattr(main.urban_meal_delivery, '__version__', version)
 
         with pytest.raises(click.exceptions.Exit):
-            console.show_version(ctx, _param='discarded', value=True)
+            main.show_version(ctx, _param='discarded', value=True)
 
         captured = capsys.readouterr()
 
         assert captured.out.strip().endswith(f', version {version} (development)')
 
 
-class TestCLI:
+class TestCLIWithoutCommand:
-    """Test the `umd` CLI utility.
+    """Test the `umd` CLI utility, invoked without any specific command.
 
     The test cases are integration tests.
     Therefore, they are not considered for coverage reporting.
     """
 
-    # pylint:disable=no-self-use
-
-    @pytest.fixture
-    def cli(self) -> click_testing.CliRunner:
-        """Initialize Click's CLI Test Runner."""
-        return click_testing.CliRunner()
-
     @pytest.mark.no_cover
     def test_no_options(self, cli):
         """Exit with 0 status code and no output if run without options."""
-        result = cli.invoke(console.main)
+        result = cli.invoke(main.entry_point)
 
         assert result.exit_code == 0
-        assert result.output == ''
 
     # The following test cases validate the --version / -V option.
 
@@ -90,9 +79,9 @@ class TestCLI:
     def test_final_version(self, cli, monkeypatch, option):
         """For final versions, NO "development" warning is emitted."""
         version = '1.2.3'
-        monkeypatch.setattr(console.urban_meal_delivery, '__version__', version)
+        monkeypatch.setattr(main.urban_meal_delivery, '__version__', version)
 
-        result = cli.invoke(console.main, option)
+        result = cli.invoke(main.entry_point, option)
 
         assert result.exit_code == 0
         assert result.output.strip().endswith(f', version {version}')
@@ -102,9 +91,9 @@ class TestCLI:
     def test_develop_version(self, cli, monkeypatch, option):
         """For develop versions, a warning thereof is emitted."""
         version = '1.2.3.dev0'
-        monkeypatch.setattr(console.urban_meal_delivery, '__version__', version)
+        monkeypatch.setattr(main.urban_meal_delivery, '__version__', version)
 
-        result = cli.invoke(console.main, option)
+        result = cli.invoke(main.entry_point, option)
 
         assert result.exit_code == 0
         assert result.output.strip().endswith(f', version {version} (development)')

tests/db/conftest.py

@@ -1,263 +0,0 @@
-"""Utils for testing the ORM layer."""
-
-import datetime
-
-import pytest
-from alembic import command as migrations_cmd
-from alembic import config as migrations_config
-
-from urban_meal_delivery import config
-from urban_meal_delivery import db
-
-
-@pytest.fixture(scope='session', params=['all_at_once', 'sequentially'])
-def db_engine(request):
-    """Create all tables given the ORM models.
-
-    The tables are put into a distinct PostgreSQL schema
-    that is removed after all tests are over.
-
-    The engine used to do that is yielded.
-
-    There are two modes for this fixture:
-
-    - "all_at_once": build up the tables all at once with MetaData.create_all()
-    - "sequentially": build up the tables sequentially with `alembic upgrade head`
-
-    This ensures that Alembic's migration files are consistent.
-    """
-    engine = db.make_engine()
-
-    if request.param == 'all_at_once':
-        engine.execute(f'CREATE SCHEMA {config.CLEAN_SCHEMA};')
-        db.Base.metadata.create_all(engine)
-    else:
-        cfg = migrations_config.Config('alembic.ini')
-        migrations_cmd.upgrade(cfg, 'head')
-
-    try:
-        yield engine
-
-    finally:
-        engine.execute(f'DROP SCHEMA {config.CLEAN_SCHEMA} CASCADE;')
-
-        if request.param == 'sequentially':
-            tmp_alembic_version = f'{config.ALEMBIC_TABLE}_{config.CLEAN_SCHEMA}'
-            engine.execute(
-                f'DROP TABLE {config.ALEMBIC_TABLE_SCHEMA}.{tmp_alembic_version};',
-            )
-
-
-@pytest.fixture
-def db_session(db_engine):
-    """A SQLAlchemy session that rolls back everything after a test case."""
-    connection = db_engine.connect()
-    # Begin the outer most transaction
-    # that is rolled back at the end of the test.
-    transaction = connection.begin()
-    # Create a session bound on the same connection as the transaction.
-    # Using any other session would not work.
-    Session = db.make_session_factory()  # noqa:N806
-    session = Session(bind=connection)
-
-    try:
-        yield session
-
-    finally:
-        session.close()
-        transaction.rollback()
-        connection.close()
-
-
-@pytest.fixture
-def address_data():
-    """The data for an Address object in Paris."""
-    return {
-        'id': 1,
-        '_primary_id': 1,  # => "itself"
-        'created_at': datetime.datetime(2020, 1, 2, 3, 4, 5),
-        'place_id': 'ChIJxSr71vZt5kcRoFHY4caCCxw',
-        'latitude': 48.85313,
-        'longitude': 2.37461,
-        '_city_id': 1,
-        'city_name': 'St. German',
-        'zip_code': '75011',
-        'street': '42 Rue De Charonne',
-        'floor': None,
-    }
-
-
-@pytest.fixture
-def address(address_data, city):
-    """An Address object."""
-    address = db.Address(**address_data)
-    address.city = city
-    return address
-
-
-@pytest.fixture
-def address2_data():
-    """The data for an Address object in Paris."""
-    return {
-        'id': 2,
-        '_primary_id': 2,  # => "itself"
-        'created_at': datetime.datetime(2020, 1, 2, 4, 5, 6),
-        'place_id': 'ChIJs-9a6QZy5kcRY8Wwk9Ywzl8',
-        'latitude': 48.852196,
-        'longitude': 2.373937,
-        '_city_id': 1,
-        'city_name': 'Paris',
-        'zip_code': '75011',
-        'street': 'Rue De Charonne 3',
-        'floor': 2,
-    }
-
-
-@pytest.fixture
-def address2(address2_data, city):
-    """An Address object."""
-    address2 = db.Address(**address2_data)
-    address2.city = city
-    return address2
-
-
-@pytest.fixture
-def city_data():
-    """The data for the City object modeling Paris."""
-    return {
-        'id': 1,
-        'name': 'Paris',
-        'kml': "<?xml version='1.0' encoding='UTF-8'?> ...",
-        '_center_latitude': 48.856614,
-        '_center_longitude': 2.3522219,
-        '_northeast_latitude': 48.9021449,
-        '_northeast_longitude': 2.4699208,
-        '_southwest_latitude': 48.815573,
-        '_southwest_longitude': 2.225193,
-        'initial_zoom': 12,
-    }
-
-
-@pytest.fixture
-def city(city_data):
-    """A City object."""
-    return db.City(**city_data)
-
-
-@pytest.fixture
-def courier_data():
-    """The data for a Courier object."""
-    return {
-        'id': 1,
-        'created_at': datetime.datetime(2020, 1, 2, 3, 4, 5),
-        'vehicle': 'bicycle',
-        'historic_speed': 7.89,
-        'capacity': 100,
-        'pay_per_hour': 750,
-        'pay_per_order': 200,
-    }
-
-
-@pytest.fixture
-def courier(courier_data):
-    """A Courier object."""
-    return db.Courier(**courier_data)
-
-
-@pytest.fixture
-def customer_data():
-    """The data for the Customer object."""
-    return {'id': 1}
-
-
-@pytest.fixture
-def customer(customer_data):
-    """A Customer object."""
-    return db.Customer(**customer_data)
-
-
-@pytest.fixture
-def order_data():
-    """The data for an ad-hoc Order object."""
-    return {
-        'id': 1,
-        '_delivery_id': 1,
-        '_customer_id': 1,
-        'placed_at': datetime.datetime(2020, 1, 2, 11, 55, 11),
-        'ad_hoc': True,
-        'scheduled_delivery_at': None,
-        'scheduled_delivery_at_corrected': None,
-        'first_estimated_delivery_at': datetime.datetime(2020, 1, 2, 12, 35, 0),
-        'cancelled': False,
-        'cancelled_at': None,
-        'cancelled_at_corrected': None,
-        'sub_total': 2000,
-        'delivery_fee': 250,
-        'total': 2250,
-        '_restaurant_id': 1,
-        'restaurant_notified_at': datetime.datetime(2020, 1, 2, 12, 5, 5),
-        'restaurant_notified_at_corrected': False,
-        'restaurant_confirmed_at': datetime.datetime(2020, 1, 2, 12, 5, 25),
-        'restaurant_confirmed_at_corrected': False,
-        'estimated_prep_duration': 900,
-        'estimated_prep_duration_corrected': False,
-        'estimated_prep_buffer': 480,
-        '_courier_id': 1,
-        'dispatch_at': datetime.datetime(2020, 1, 2, 12, 5, 1),
-        'dispatch_at_corrected': False,
-        'courier_notified_at': datetime.datetime(2020, 1, 2, 12, 6, 2),
-        'courier_notified_at_corrected': False,
-        'courier_accepted_at': datetime.datetime(2020, 1, 2, 12, 6, 17),
-        'courier_accepted_at_corrected': False,
-        'utilization': 50,
-        '_pickup_address_id': 1,
-        'reached_pickup_at': datetime.datetime(2020, 1, 2, 12, 16, 21),
-        'pickup_at': datetime.datetime(2020, 1, 2, 12, 18, 1),
-        'pickup_at_corrected': False,
-        'pickup_not_confirmed': False,
-        'left_pickup_at': datetime.datetime(2020, 1, 2, 12, 19, 45),
-        'left_pickup_at_corrected': False,
-        '_delivery_address_id': 2,
-        'reached_delivery_at': datetime.datetime(2020, 1, 2, 12, 27, 33),
-        'delivery_at': datetime.datetime(2020, 1, 2, 12, 29, 55),
-        'delivery_at_corrected': False,
-        'delivery_not_confirmed': False,
-        '_courier_waited_at_delivery': False,
-        'logged_delivery_distance': 500,
-        'logged_avg_speed': 7.89,
-        'logged_avg_speed_distance': 490,
-    }
-
-
-@pytest.fixture
-def order(  # noqa:WPS211 pylint:disable=too-many-arguments
-    order_data, customer, restaurant, courier, address, address2,
-):
-    """An Order object."""
-    order = db.Order(**order_data)
-    order.customer = customer
-    order.restaurant = restaurant
-    order.courier = courier
-    order.pickup_address = address
-    order.delivery_address = address2
-    return order
-
-
-@pytest.fixture
-def restaurant_data():
-    """The data for the Restaurant object."""
-    return {
-        'id': 1,
-        'created_at': datetime.datetime(2020, 1, 2, 3, 4, 5),
-        'name': 'Vevay',
-        '_address_id': 1,
-        'estimated_prep_duration': 1000,
-    }
-
-
-@pytest.fixture
-def restaurant(restaurant_data, address):
-    """A Restaurant object."""
-    restaurant = db.Restaurant(**restaurant_data)
-    restaurant.address = address
-    return restaurant

tests/db/fake_data/__init__.py

@@ -0,0 +1,16 @@
+"""Fixtures for testing the ORM layer with fake data."""
+
+from tests.db.fake_data.fixture_makers import make_address
+from tests.db.fake_data.fixture_makers import make_courier
+from tests.db.fake_data.fixture_makers import make_customer
+from tests.db.fake_data.fixture_makers import make_order
+from tests.db.fake_data.fixture_makers import make_restaurant
+from tests.db.fake_data.static_fixtures import address
+from tests.db.fake_data.static_fixtures import city
+from tests.db.fake_data.static_fixtures import city_data
+from tests.db.fake_data.static_fixtures import courier
+from tests.db.fake_data.static_fixtures import customer
+from tests.db.fake_data.static_fixtures import grid
+from tests.db.fake_data.static_fixtures import order
+from tests.db.fake_data.static_fixtures import pixel
+from tests.db.fake_data.static_fixtures import restaurant

tests/db/fake_data/factories.py

@@ -0,0 +1,378 @@
+"""Factories to create instances for the SQLAlchemy models."""
+
+import datetime as dt
+import random
+import string
+
+import factory
+import faker
+from factory import alchemy
+from geopy import distance
+
+from tests import config as test_config
+from urban_meal_delivery import db
+
+
+def _random_timespan(  # noqa:WPS211
+    *,
+    min_hours=0,
+    min_minutes=0,
+    min_seconds=0,
+    max_hours=0,
+    max_minutes=0,
+    max_seconds=0,
+):
+    """A randomized `timedelta` object between the specified arguments."""
+    total_min_seconds = min_hours * 3600 + min_minutes * 60 + min_seconds
+    total_max_seconds = max_hours * 3600 + max_minutes * 60 + max_seconds
+    return dt.timedelta(seconds=random.randint(total_min_seconds, total_max_seconds))
+
+
+def _early_in_the_morning():
+    """A randomized `datetime` object early in the morning."""
+    early = dt.datetime(test_config.YEAR, test_config.MONTH, test_config.DAY, 3, 0)
+    return early + _random_timespan(max_hours=2)
+
+
+class AddressFactory(alchemy.SQLAlchemyModelFactory):
+    """Create instances of the `db.Address` model."""
+
+    class Meta:
+        model = db.Address
+        sqlalchemy_get_or_create = ('id',)
+
+    id = factory.Sequence(lambda num: num)  # noqa:WPS125
+    created_at = factory.LazyFunction(_early_in_the_morning)
+
+    # When testing, all addresses are considered primary ones.
+    # As non-primary addresses have no different behavior and
+    # the property is only kept from the original dataset for
+    # completeness sake, that is ok to do.
+    primary_id = factory.LazyAttribute(lambda obj: obj.id)
+
+    # Mimic a Google Maps Place ID with just random characters.
+    place_id = factory.LazyFunction(
+        lambda: ''.join(random.choice(string.ascii_lowercase) for _ in range(20)),
+    )
+
+    # Place the addresses somewhere in downtown Paris.
+    latitude = factory.Faker('coordinate', center=48.855, radius=0.01)
+    longitude = factory.Faker('coordinate', center=2.34, radius=0.03)
+    # city -> set by the `make_address` fixture as there is only one `city`
+    city_name = 'Paris'
+    zip_code = factory.LazyFunction(lambda: random.randint(75001, 75020))
+    street = factory.Faker('street_address', locale='fr_FR')
+
+
+class CourierFactory(alchemy.SQLAlchemyModelFactory):
+    """Create instances of the `db.Courier` model."""
+
+    class Meta:
+        model = db.Courier
+        sqlalchemy_get_or_create = ('id',)
+
+    id = factory.Sequence(lambda num: num)  # noqa:WPS125
+    created_at = factory.LazyFunction(_early_in_the_morning)
+    vehicle = 'bicycle'
+    historic_speed = 7.89
+    capacity = 100
+    pay_per_hour = 750
+    pay_per_order = 200
+
+
+class CustomerFactory(alchemy.SQLAlchemyModelFactory):
+    """Create instances of the `db.Customer` model."""
+
+    class Meta:
+        model = db.Customer
+        sqlalchemy_get_or_create = ('id',)
+
+    id = factory.Sequence(lambda num: num)  # noqa:WPS125
+
+
+_restaurant_names = faker.Faker()
+
+
+class RestaurantFactory(alchemy.SQLAlchemyModelFactory):
+    """Create instances of the `db.Restaurant` model."""
+
+    class Meta:
+        model = db.Restaurant
+        sqlalchemy_get_or_create = ('id',)
+
+    id = factory.Sequence(lambda num: num)  # noqa:WPS125
+    created_at = factory.LazyFunction(_early_in_the_morning)
+    name = factory.LazyFunction(
+        lambda: f"{_restaurant_names.first_name()}'s Restaurant",
+    )
+    # address -> set by the `make_restaurant` fixture as there is only one `city`
+    estimated_prep_duration = 1000
+
+
+class AdHocOrderFactory(alchemy.SQLAlchemyModelFactory):
+    """Create instances of the `db.Order` model.
+
+    This factory creates ad-hoc `Order`s while the `ScheduledOrderFactory`
+    below creates pre-orders. They are split into two classes mainly
+    because the logic regarding how the timestamps are calculated from
+    each other differs.
+
+    See the docstring in the contained `Params` class for
+    flags to adapt how the `Order` is created.
+    """
+
+    class Meta:
+        model = db.Order
+        sqlalchemy_get_or_create = ('id',)
+
+    class Params:
+        """Define flags that overwrite some attributes.
+
+        The `factory.Trait` objects in this class are executed after all
+        the normal attributes in the `OrderFactory` classes were evaluated.
+
+        Flags:
+            cancel_before_pickup
+            cancel_after_pickup
+        """
+
+        # Timestamps after `cancelled_at` are discarded
+        # by the `post_generation` hook at the end of the `OrderFactory`.
+        cancel_ = factory.Trait(  # noqa:WPS120 -> leading underscore does not work
+            cancelled=True, cancelled_at_corrected=False,
+        )
+        cancel_before_pickup = factory.Trait(
+            cancel_=True,
+            cancelled_at=factory.LazyAttribute(
+                lambda obj: obj.dispatch_at
+                + _random_timespan(
+                    max_seconds=(obj.pickup_at - obj.dispatch_at).total_seconds(),
+                ),
+            ),
+        )
+        cancel_after_pickup = factory.Trait(
+            cancel_=True,
+            cancelled_at=factory.LazyAttribute(
+                lambda obj: obj.pickup_at
+                + _random_timespan(
+                    max_seconds=(obj.delivery_at - obj.pickup_at).total_seconds(),
+                ),
+            ),
+        )
+
+    # Generic attributes
+    id = factory.Sequence(lambda num: num)  # noqa:WPS125
+    # customer -> set by the `make_order` fixture for better control
+
+    # Attributes regarding the specialization of an `Order`: ad-hoc or scheduled.
+    # Ad-hoc `Order`s are placed between 11.45 and 14.15.
+    placed_at = factory.LazyFunction(
+        lambda: dt.datetime(
+            test_config.YEAR, test_config.MONTH, test_config.DAY, 11, 45,
+        )
+        + _random_timespan(max_hours=2, max_minutes=30),
+    )
+    ad_hoc = True
+    scheduled_delivery_at = None
+    scheduled_delivery_at_corrected = None
+    # Without statistical info, we assume an ad-hoc `Order` delivered after 45 minutes.
+    first_estimated_delivery_at = factory.LazyAttribute(
+        lambda obj: obj.placed_at + dt.timedelta(minutes=45),
+    )
+
+    # Attributes regarding the cancellation of an `Order`.
+    # May be overwritten with the `cancel_before_pickup` or `cancel_after_pickup` flags.
+    cancelled = False
+    cancelled_at = None
+    cancelled_at_corrected = None
+
+    # Price-related attributes -> sample realistic prices
+    sub_total = factory.LazyFunction(lambda: 100 * random.randint(15, 25))
+    delivery_fee = 250
+    total = factory.LazyAttribute(lambda obj: obj.sub_total + obj.delivery_fee)
+
+    # Restaurant-related attributes
+    # restaurant -> set by the `make_order` fixture for better control
+    restaurant_notified_at = factory.LazyAttribute(
+        lambda obj: obj.placed_at + _random_timespan(min_seconds=30, max_seconds=90),
+    )
+    restaurant_notified_at_corrected = False
+    restaurant_confirmed_at = factory.LazyAttribute(
+        lambda obj: obj.restaurant_notified_at
+        + _random_timespan(min_seconds=30, max_seconds=150),
+    )
+    restaurant_confirmed_at_corrected = False
+    # Use the database defaults of the historic data.
+    estimated_prep_duration = 900
+    estimated_prep_duration_corrected = False
+    estimated_prep_buffer = 480
+
+    # Dispatch-related columns
+    # courier -> set by the `make_order` fixture for better control
+    dispatch_at = factory.LazyAttribute(
+        lambda obj: obj.placed_at + _random_timespan(min_seconds=600, max_seconds=1080),
+    )
+    dispatch_at_corrected = False
+    courier_notified_at = factory.LazyAttribute(
+        lambda obj: obj.dispatch_at
+        + _random_timespan(min_seconds=100, max_seconds=140),
+    )
+    courier_notified_at_corrected = False
+    courier_accepted_at = factory.LazyAttribute(
+        lambda obj: obj.courier_notified_at
+        + _random_timespan(min_seconds=15, max_seconds=45),
+    )
+    courier_accepted_at_corrected = False
+    # Sample a realistic utilization.
+    utilization = factory.LazyFunction(lambda: random.choice([50, 60, 70, 80, 90, 100]))
+
+    # Pickup-related attributes
+    # pickup_address -> aligned with `restaurant.address` by the `make_order` fixture
+    reached_pickup_at = factory.LazyAttribute(
+        lambda obj: obj.courier_accepted_at
+        + _random_timespan(min_seconds=300, max_seconds=600),
+    )
+    pickup_at = factory.LazyAttribute(
+        lambda obj: obj.reached_pickup_at
+        + _random_timespan(min_seconds=120, max_seconds=600),
+    )
+    pickup_at_corrected = False
+    pickup_not_confirmed = False
+    left_pickup_at = factory.LazyAttribute(
+        lambda obj: obj.pickup_at + _random_timespan(min_seconds=60, max_seconds=180),
+    )
+    left_pickup_at_corrected = False
+
+    # Delivery-related attributes
+    # delivery_address -> set by the `make_order` fixture as there is only one `city`
+    reached_delivery_at = factory.LazyAttribute(
+        lambda obj: obj.left_pickup_at
+        + _random_timespan(min_seconds=240, max_seconds=480),
+    )
+    delivery_at = factory.LazyAttribute(
+        lambda obj: obj.reached_delivery_at
+        + _random_timespan(min_seconds=240, max_seconds=660),
+    )
+    delivery_at_corrected = False
+    delivery_not_confirmed = False
+    _courier_waited_at_delivery = factory.LazyAttribute(
+        lambda obj: False if obj.delivery_at else None,
+    )
+
+    # Statistical attributes -> calculate realistic stats
+    logged_delivery_distance = factory.LazyAttribute(
+        lambda obj: distance.great_circle(  # noqa:WPS317
+            (obj.pickup_address.latitude, obj.pickup_address.longitude),
+            (obj.delivery_address.latitude, obj.delivery_address.longitude),
+        ).meters,
+    )
+    logged_avg_speed = factory.LazyAttribute(  # noqa:ECE001
+        lambda obj: round(
+            (
+                obj.logged_avg_speed_distance
+                / (obj.delivery_at - obj.pickup_at).total_seconds()
+            ),
+            2,
+        ),
+    )
+    logged_avg_speed_distance = factory.LazyAttribute(
+        lambda obj: 0.95 * obj.logged_delivery_distance,
+    )
+
+    @factory.post_generation
+    def post(  # noqa:C901,WPS231
+        obj, create, extracted, **kwargs,  # noqa:B902,N805
+    ):
+        """Discard timestamps that occur after cancellation."""
+        if obj.cancelled:
+            if obj.cancelled_at <= obj.restaurant_notified_at:
+                obj.restaurant_notified_at = None
+                obj.restaurant_notified_at_corrected = None
+            if obj.cancelled_at <= obj.restaurant_confirmed_at:
+                obj.restaurant_confirmed_at = None
+                obj.restaurant_confirmed_at_corrected = None
+            if obj.cancelled_at <= obj.dispatch_at:
+                obj.dispatch_at = None
+                obj.dispatch_at_corrected = None
+            if obj.cancelled_at <= obj.courier_notified_at:
+                obj.courier_notified_at = None
+                obj.courier_notified_at_corrected = None
+            if obj.cancelled_at <= obj.courier_accepted_at:
+                obj.courier_accepted_at = None
+                obj.courier_accepted_at_corrected = None
+            if obj.cancelled_at <= obj.reached_pickup_at:
+                obj.reached_pickup_at = None
+            if obj.cancelled_at <= obj.pickup_at:
+                obj.pickup_at = None
+                obj.pickup_at_corrected = None
+                obj.pickup_not_confirmed = None
+            if obj.cancelled_at <= obj.left_pickup_at:
+                obj.left_pickup_at = None
+                obj.left_pickup_at_corrected = None
+            if obj.cancelled_at <= obj.reached_delivery_at:
+                obj.reached_delivery_at = None
+            if obj.cancelled_at <= obj.delivery_at:
+                obj.delivery_at = None
+                obj.delivery_at_corrected = None
+                obj.delivery_not_confirmed = None
+                obj._courier_waited_at_delivery = None
+
+
+class ScheduledOrderFactory(AdHocOrderFactory):
+    """Create instances of the `db.Order` model.
+
+    This class takes care of the various timestamps for pre-orders.
+
+    Pre-orders are placed long before the test day's lunch time starts.
+    All timestamps are relative to either `.dispatch_at` or `.restaurant_notified_at`
+    and calculated backwards from `.scheduled_delivery_at`.
+    """
+
+    # Attributes regarding the specialization of an `Order`: ad-hoc or scheduled.
+    placed_at = factory.LazyFunction(_early_in_the_morning)
+    ad_hoc = False
+    # Discrete `datetime` objects in the "core" lunch time are enough.
+    scheduled_delivery_at = factory.LazyFunction(
+        lambda: random.choice(
+            [
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 12, 0,
+                ),
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 12, 15,
+                ),
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 12, 30,
+                ),
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 12, 45,
+                ),
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 13, 0,
+                ),
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 13, 15,
+                ),
+                dt.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, 13, 30,
+                ),
+            ],
+        ),
+    )
+    scheduled_delivery_at_corrected = False
+    # Assume the `Order` is on time.
+    first_estimated_delivery_at = factory.LazyAttribute(
+        lambda obj: obj.scheduled_delivery_at,
+    )
+
+    # Restaurant-related attributes
+    restaurant_notified_at = factory.LazyAttribute(
+        lambda obj: obj.scheduled_delivery_at
+        - _random_timespan(min_minutes=45, max_minutes=50),
+    )
+
+    # Dispatch-related attributes
+    dispatch_at = factory.LazyAttribute(
+        lambda obj: obj.scheduled_delivery_at
+        - _random_timespan(min_minutes=40, max_minutes=45),
+    )

tests/db/fake_data/fixture_makers.py

@@ -0,0 +1,105 @@
+"""Fixture factories for testing the ORM layer with fake data."""
+
+import pytest
+
+from tests.db.fake_data import factories
+
+
+@pytest.fixture
+def make_address(city):
+    """Replaces `AddressFactory.build()`: Create an `Address` in the `city`."""
+    # Reset the identifiers before every test.
+    factories.AddressFactory.reset_sequence(1)
+
+    def func(**kwargs):
+        """Create an `Address` object in the `city`."""
+        return factories.AddressFactory.build(city=city, **kwargs)
+
+    return func
+
+
+@pytest.fixture
+def make_courier():
+    """Replaces `CourierFactory.build()`: Create a `Courier`."""
+    # Reset the identifiers before every test.
+    factories.CourierFactory.reset_sequence(1)
+
+    def func(**kwargs):
+        """Create a new `Courier` object."""
+        return factories.CourierFactory.build(**kwargs)
+
+    return func
+
+
+@pytest.fixture
+def make_customer():
+    """Replaces `CustomerFactory.build()`: Create a `Customer`."""
+    # Reset the identifiers before every test.
+    factories.CustomerFactory.reset_sequence(1)
+
+    def func(**kwargs):
+        """Create a new `Customer` object."""
+        return factories.CustomerFactory.build(**kwargs)
+
+    return func
+
+
+@pytest.fixture
+def make_restaurant(make_address):
+    """Replaces `RestaurantFactory.build()`: Create a `Restaurant`."""
+    # Reset the identifiers before every test.
+    factories.RestaurantFactory.reset_sequence(1)
+
+    def func(address=None, **kwargs):
+        """Create a new `Restaurant` object.
+
+        If no `address` is provided, a new `Address` is created.
+        """
+        if address is None:
+            address = make_address()
+
+        return factories.RestaurantFactory.build(address=address, **kwargs)
+
+    return func
+
+
+@pytest.fixture
+def make_order(make_address, make_courier, make_customer, make_restaurant):
+    """Replaces `OrderFactory.build()`: Create a `Order`."""
+    # Reset the identifiers before every test.
+    factories.AdHocOrderFactory.reset_sequence(1)
+
+    def func(scheduled=False, restaurant=None, courier=None, **kwargs):
+        """Create a new `Order` object.
+
+        Each `Order` is made by a new `Customer` with a unique `Address` for delivery.
+
+        Args:
+            scheduled: if an `Order` is a pre-order
+            restaurant: who receives the `Order`; defaults to a new `Restaurant`
+            courier: who delivered the `Order`; defaults to a new `Courier`
+            kwargs: additional keyword arguments forwarded to the `OrderFactory`
+
+        Returns:
+            order
+        """
+        if scheduled:
+            factory_cls = factories.ScheduledOrderFactory
+        else:
+            factory_cls = factories.AdHocOrderFactory
+
+        if restaurant is None:
+            restaurant = make_restaurant()
+        if courier is None:
+            courier = make_courier()
+
+        return factory_cls.build(
+            customer=make_customer(),  # assume a unique `Customer` per order
+            restaurant=restaurant,
+            courier=courier,
+            pickup_address=restaurant.address,  # no `Address` history
+            delivery_address=make_address(),  # unique `Customer` => new `Address`
+            **kwargs,
+        )
+
+    return func

tests/db/fake_data/static_fixtures.py

@@ -0,0 +1,70 @@
+"""Fake data for testing the ORM layer."""
+
+import pytest
+
+from urban_meal_delivery import db
+
+
+@pytest.fixture
+def city_data():
+    """The data for the one and only `City` object as a `dict`."""
+    return {
+        'id': 1,
+        'name': 'Paris',
+        'kml': "<?xml version='1.0' encoding='UTF-8'?> ...",
+        'center_latitude': 48.856614,
+        'center_longitude': 2.3522219,
+        'northeast_latitude': 48.9021449,
+        'northeast_longitude': 2.4699208,
+        'southwest_latitude': 48.815573,
+        'southwest_longitude': 2.225193,
+        'initial_zoom': 12,
+    }
+
+
+@pytest.fixture
+def city(city_data):
+    """The one and only `City` object."""
+    return db.City(**city_data)
+
+
+@pytest.fixture
+def address(make_address):
+    """An `Address` object in the `city`."""
+    return make_address()
+
+
+@pytest.fixture
+def courier(make_courier):
+    """A `Courier` object."""
+    return make_courier()
+
+
+@pytest.fixture
+def customer(make_customer):
+    """A `Customer` object."""
+    return make_customer()
+
+
+@pytest.fixture
+def restaurant(address, make_restaurant):
+    """A `Restaurant` object located at the `address`."""
+    return make_restaurant(address=address)
+
+
+@pytest.fixture
+def order(make_order, restaurant):
+    """An `Order` object for the `restaurant`."""
+    return make_order(restaurant=restaurant)
+
+
+@pytest.fixture
+def grid(city):
+    """A `Grid` with a pixel area of 1 square kilometer."""
+    return db.Grid(city=city, side_length=1000)
+
+
+@pytest.fixture
+def pixel(grid):
+    """The `Pixel` in the lower-left corner of the `grid`."""
+    return db.Pixel(id=1, grid=grid, n_x=0, n_y=0)

tests/db/test_addresses.py

@@ -1,141 +1,154 @@
-"""Test the ORM's Address model."""
+"""Test the ORM's `Address` model."""
 
 import pytest
+import sqlalchemy as sqla
 from sqlalchemy import exc as sa_exc
-from sqlalchemy.orm import exc as orm_exc
 
 from urban_meal_delivery import db
+from urban_meal_delivery.db import utils
 
 
 class TestSpecialMethods:
-    """Test special methods in Address."""
+    """Test special methods in `Address`."""
 
-    # pylint:disable=no-self-use
+    def test_create_address(self, address):
-
+        """Test instantiation of a new `Address` object."""
-    def test_create_address(self, address_data):
+        assert address is not None
-        """Test instantiation of a new Address object."""
-        result = db.Address(**address_data)
-
-        assert result is not None
-
-    def test_text_representation(self, address_data):
-        """Address has a non-literal text representation."""
-        address = db.Address(**address_data)
-        street = address_data['street']
-        city_name = address_data['city_name']
 
+    def test_text_representation(self, address):
+        """`Address` has a non-literal text representation."""
         result = repr(address)
 
-        assert result == f'<Address({street} in {city_name})>'
+        assert result == f'<Address({address.street} in {address.city_name})>'
 
 
-@pytest.mark.e2e
+@pytest.mark.db
 @pytest.mark.no_cover
 class TestConstraints:
-    """Test the database constraints defined in Address."""
+    """Test the database constraints defined in `Address`."""
 
-    # pylint:disable=no-self-use
+    def test_insert_into_database(self, db_session, address):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Address).count() == 0
 
-    def test_insert_into_database(self, address, db_session):
-        """Insert an instance into the database."""
         db_session.add(address)
         db_session.commit()
 
-    def test_dublicate_primary_key(self, address, address_data, city, db_session):
+        assert db_session.query(db.Address).count() == 1
-        """Can only add a record once."""
+
+    def test_delete_a_referenced_address(self, db_session, address, make_address):
+        """Remove a record that is referenced with a FK."""
         db_session.add(address)
+        # Fake another_address that has the same `.primary_id` as `address`.
+        db_session.add(make_address(primary_id=address.id))
         db_session.commit()
 
-        another_address = db.Address(**address_data)
+        db_session.delete(address)
-        another_address.city = city
-        db_session.add(another_address)
 
-        with pytest.raises(orm_exc.FlushError):
+        with pytest.raises(
+            sa_exc.IntegrityError, match='fk_addresses_to_addresses_via_primary_id',
+        ):
             db_session.commit()
 
-    def test_delete_a_referenced_address(self, address, address_data, db_session):
+    def test_delete_a_referenced_city(self, db_session, address):
         """Remove a record that is referenced with a FK."""
         db_session.add(address)
         db_session.commit()
 
-        # Fake a second address that belongs to the same primary address.
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
-        address_data['id'] += 1
+        stmt = sqla.delete(db.City).where(db.City.id == address.city.id)
-        another_address = db.Address(**address_data)
-        db_session.add(another_address)
-        db_session.commit()
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(
-            db_session.execute(
+            sa_exc.IntegrityError, match='fk_addresses_to_cities_via_city_id',
-                db.Address.__table__.delete().where(  # noqa:WPS609
+        ):
-                    db.Address.id == address.id,
+            db_session.execute(stmt)
-                ),
-            )
-
-    def test_delete_a_referenced_city(self, address, city, db_session):
-        """Remove a record that is referenced with a FK."""
-        db_session.add(address)
-        db_session.commit()
-
-        with pytest.raises(sa_exc.IntegrityError):
-            db_session.execute(
-                db.City.__table__.delete().where(db.City.id == city.id),  # noqa:WPS609
-            )
 
     @pytest.mark.parametrize('latitude', [-91, 91])
-    def test_invalid_latitude(self, address, db_session, latitude):
+    def test_invalid_latitude(self, db_session, address, latitude):
         """Insert an instance with invalid data."""
         address.latitude = latitude
         db_session.add(address)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(
+            sa_exc.IntegrityError, match='latitude_between_90_degrees',
+        ):
             db_session.commit()
 
     @pytest.mark.parametrize('longitude', [-181, 181])
-    def test_invalid_longitude(self, address, db_session, longitude):
+    def test_invalid_longitude(self, db_session, address, longitude):
         """Insert an instance with invalid data."""
         address.longitude = longitude
         db_session.add(address)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(
+            sa_exc.IntegrityError, match='longitude_between_180_degrees',
+        ):
             db_session.commit()
 
     @pytest.mark.parametrize('zip_code', [-1, 0, 9999, 100000])
-    def test_invalid_zip_code(self, address, db_session, zip_code):
+    def test_invalid_zip_code(self, db_session, address, zip_code):
         """Insert an instance with invalid data."""
         address.zip_code = zip_code
         db_session.add(address)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='valid_zip_code'):
             db_session.commit()
 
     @pytest.mark.parametrize('floor', [-1, 41])
-    def test_invalid_floor(self, address, db_session, floor):
+    def test_invalid_floor(self, db_session, address, floor):
         """Insert an instance with invalid data."""
         address.floor = floor
         db_session.add(address)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_floor'):
             db_session.commit()
 
 
 class TestProperties:
-    """Test properties in Address."""
+    """Test properties in `Address`."""
 
-    # pylint:disable=no-self-use
+    def test_is_primary(self, address):
-
+        """Test `Address.is_primary` property."""
-    def test_is_primary(self, address_data):
+        assert address.id == address.primary_id
-        """Test Address.is_primary property."""
-        address = db.Address(**address_data)
 
         result = address.is_primary
 
         assert result is True
 
-    def test_is_not_primary(self, address_data):
+    def test_is_not_primary(self, address):
-        """Test Address.is_primary property."""
+        """Test `Address.is_primary` property."""
-        address_data['_primary_id'] = 999
+        address.primary_id = 999
-        address = db.Address(**address_data)
 
         result = address.is_primary
 
         assert result is False
+
+    def test_location(self, address):
+        """Test `Address.location` property."""
+        latitude = float(address.latitude)
+        longitude = float(address.longitude)
+
+        result = address.location
+
+        assert isinstance(result, utils.Location)
+        assert result.latitude == pytest.approx(latitude)
+        assert result.longitude == pytest.approx(longitude)
+
+    def test_location_is_cached(self, address):
+        """Test `Address.location` property."""
+        result1 = address.location
+        result2 = address.location
+
+        assert result1 is result2
+
+    def test_x_is_positive(self, address):
+        """Test `Address.x` property."""
+        result = address.x
+
+        assert result > 0
+
+    def test_y_is_positive(self, address):
+        """Test `Address.y` property."""
+        result = address.y
+
+        assert result > 0

tests/db/test_addresses_pixels.py

@@ -0,0 +1,135 @@
+"""Test the ORM's `AddressPixelAssociation` model.
+
+Implementation notes:
+    The test suite has 100% coverage without the test cases in this module.
+    That is so as the `AddressPixelAssociation` model is imported into the
+    `urban_meal_delivery.db` namespace so that the `Address` and `Pixel` models
+    can find it upon initialization. Yet, none of the other unit tests run any
+    code associated with it. Therefore, we test it here as non-e2e tests and do
+    not measure its coverage.
+"""
+
+import pytest
+import sqlalchemy as sqla
+from sqlalchemy import exc as sa_exc
+
+from urban_meal_delivery import db
+
+
+@pytest.fixture
+def assoc(address, pixel):
+    """An association between `address` and `pixel`."""
+    return db.AddressPixelAssociation(address=address, pixel=pixel)
+
+
+@pytest.mark.no_cover
+class TestSpecialMethods:
+    """Test special methods in `Pixel`."""
+
+    def test_create_an_address_pixel_association(self, assoc):
+        """Test instantiation of a new `AddressPixelAssociation` object."""
+        assert assoc is not None
+
+
+@pytest.mark.db
+@pytest.mark.no_cover
+class TestConstraints:
+    """Test the database constraints defined in `AddressPixelAssociation`.
+
+    The foreign keys to `City` and `Grid` are tested via INSERT and not
+    DELETE statements as the latter would already fail because of foreign
+    keys defined in `Address` and `Pixel`.
+    """
+
+    def test_insert_into_database(self, db_session, assoc):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.AddressPixelAssociation).count() == 0
+
+        db_session.add(assoc)
+        db_session.commit()
+
+        assert db_session.query(db.AddressPixelAssociation).count() == 1
+
+    def test_delete_a_referenced_address(self, db_session, assoc):
+        """Remove a record that is referenced with a FK."""
+        db_session.add(assoc)
+        db_session.commit()
+
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
+        stmt = sqla.delete(db.Address).where(db.Address.id == assoc.address.id)
+
+        with pytest.raises(
+            sa_exc.IntegrityError,
+            match='fk_addresses_pixels_to_addresses_via_address_id_city_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_reference_an_invalid_city(self, db_session, address, pixel):
+        """Insert a record with an invalid foreign key."""
+        db_session.add(address)
+        db_session.add(pixel)
+        db_session.commit()
+
+        # Must insert without ORM as otherwise SQLAlchemy figures out
+        # that something is wrong before any query is sent to the database.
+        stmt = sqla.insert(db.AddressPixelAssociation).values(
+            address_id=address.id,
+            city_id=999,
+            grid_id=pixel.grid.id,
+            pixel_id=pixel.id,
+        )
+
+        with pytest.raises(
+            sa_exc.IntegrityError,
+            match='fk_addresses_pixels_to_addresses_via_address_id_city_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_reference_an_invalid_grid(self, db_session, address, pixel):
+        """Insert a record with an invalid foreign key."""
+        db_session.add(address)
+        db_session.add(pixel)
+        db_session.commit()
+
+        # Must insert without ORM as otherwise SQLAlchemy figures out
+        # that something is wrong before any query is sent to the database.
+        stmt = sqla.insert(db.AddressPixelAssociation).values(
+            address_id=address.id,
+            city_id=address.city.id,
+            grid_id=999,
+            pixel_id=pixel.id,
+        )
+
+        with pytest.raises(
+            sa_exc.IntegrityError,
+            match='fk_addresses_pixels_to_grids_via_grid_id_city_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_delete_a_referenced_pixel(self, db_session, assoc):
+        """Remove a record that is referenced with a FK."""
+        db_session.add(assoc)
+        db_session.commit()
+
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
+        stmt = sqla.delete(db.Pixel).where(db.Pixel.id == assoc.pixel.id)
+
+        with pytest.raises(
+            sa_exc.IntegrityError,
+            match='fk_addresses_pixels_to_pixels_via_pixel_id_grid_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_put_an_address_on_a_grid_twice(self, db_session, address, assoc, pixel):
+        """Insert a record that violates a unique constraint."""
+        db_session.add(assoc)
+        db_session.commit()
+
+        # Create a neighboring `Pixel` and put the same `address` as in `pixel` in it.
+        neighbor = db.Pixel(grid=pixel.grid, n_x=pixel.n_x, n_y=pixel.n_y + 1)
+        another_assoc = db.AddressPixelAssociation(address=address, pixel=neighbor)
+
+        db_session.add(another_assoc)
+
+        with pytest.raises(sa_exc.IntegrityError, match='duplicate key value'):
+            db_session.commit()

tests/db/test_cities.py

@@ -1,99 +1,96 @@
-"""Test the ORM's City model."""
+"""Test the ORM's `City` model."""
 
 import pytest
-from sqlalchemy.orm import exc as orm_exc
 
 from urban_meal_delivery import db
+from urban_meal_delivery.db import utils
 
 
 class TestSpecialMethods:
-    """Test special methods in City."""
+    """Test special methods in `City`."""
 
-    # pylint:disable=no-self-use
+    def test_create_city(self, city):
-
+        """Test instantiation of a new `City` object."""
-    def test_create_city(self, city_data):
+        assert city is not None
-        """Test instantiation of a new City object."""
-        result = db.City(**city_data)
-
-        assert result is not None
-
-    def test_text_representation(self, city_data):
-        """City has a non-literal text representation."""
-        city = db.City(**city_data)
-        name = city_data['name']
 
+    def test_text_representation(self, city):
+        """`City` has a non-literal text representation."""
         result = repr(city)
 
-        assert result == f'<City({name})>'
+        assert result == f'<City({city.name})>'
 
 
-@pytest.mark.e2e
+@pytest.mark.db
 @pytest.mark.no_cover
 class TestConstraints:
-    """Test the database constraints defined in City."""
+    """Test the database constraints defined in `City`."""
 
-    # pylint:disable=no-self-use
+    def test_insert_into_database(self, db_session, city):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.City).count() == 0
 
-    def test_insert_into_database(self, city, db_session):
-        """Insert an instance into the database."""
         db_session.add(city)
         db_session.commit()
 
-    def test_dublicate_primary_key(self, city, city_data, db_session):
+        assert db_session.query(db.City).count() == 1
-        """Can only add a record once."""
-        db_session.add(city)
-        db_session.commit()
-
-        another_city = db.City(**city_data)
-        db_session.add(another_city)
-
-        with pytest.raises(orm_exc.FlushError):
-            db_session.commit()
 
 
 class TestProperties:
-    """Test properties in City."""
+    """Test properties in `City`."""
 
-    # pylint:disable=no-self-use
+    def test_center(self, city, city_data):
+        """Test `City.center` property."""
+        result = city.center
 
-    def test_location_data(self, city_data):
+        assert isinstance(result, utils.Location)
-        """Test City.location property."""
+        assert result.latitude == pytest.approx(city_data['center_latitude'])
-        city = db.City(**city_data)
+        assert result.longitude == pytest.approx(city_data['center_longitude'])
 
-        result = city.location
+    def test_center_is_cached(self, city):
+        """Test `City.center` property."""
+        result1 = city.center
+        result2 = city.center
 
-        assert isinstance(result, dict)
+        assert result1 is result2
-        assert len(result) == 2
-        assert result['latitude'] == pytest.approx(city_data['_center_latitude'])
-        assert result['longitude'] == pytest.approx(city_data['_center_longitude'])
 
-    def test_viewport_data_overall(self, city_data):
+    def test_northeast(self, city, city_data):
-        """Test City.viewport property."""
+        """Test `City.northeast` property."""
-        city = db.City(**city_data)
+        result = city.northeast
 
-        result = city.viewport
+        assert isinstance(result, utils.Location)
+        assert result.latitude == pytest.approx(city_data['northeast_latitude'])
+        assert result.longitude == pytest.approx(city_data['northeast_longitude'])
 
-        assert isinstance(result, dict)
+    def test_northeast_is_cached(self, city):
-        assert len(result) == 2
+        """Test `City.northeast` property."""
+        result1 = city.northeast
+        result2 = city.northeast
 
-    def test_viewport_data_northeast(self, city_data):
+        assert result1 is result2
-        """Test City.viewport property."""
-        city = db.City(**city_data)
 
-        result = city.viewport['northeast']
+    def test_southwest(self, city, city_data):
+        """Test `City.southwest` property."""
+        result = city.southwest
 
-        assert isinstance(result, dict)
+        assert isinstance(result, utils.Location)
-        assert len(result) == 2
+        assert result.latitude == pytest.approx(city_data['southwest_latitude'])
-        assert result['latitude'] == pytest.approx(city_data['_northeast_latitude'])
+        assert result.longitude == pytest.approx(city_data['southwest_longitude'])
-        assert result['longitude'] == pytest.approx(city_data['_northeast_longitude'])
 
-    def test_viewport_data_southwest(self, city_data):
+    def test_southwest_is_cached(self, city):
-        """Test City.viewport property."""
+        """Test `City.southwest` property."""
-        city = db.City(**city_data)
+        result1 = city.southwest
+        result2 = city.southwest
 
-        result = city.viewport['southwest']
+        assert result1 is result2
 
-        assert isinstance(result, dict)
+    def test_total_x(self, city):
-        assert len(result) == 2
+        """Test `City.total_x` property."""
-        assert result['latitude'] == pytest.approx(city_data['_southwest_latitude'])
+        result = city.total_x
-        assert result['longitude'] == pytest.approx(city_data['_southwest_longitude'])
+
+        assert result > 18_000
+
+    def test_total_y(self, city):
+        """Test `City.total_y` property."""
+        result = city.total_y
+
+        assert result > 9_000

tests/db/test_couriers.py

@@ -1,125 +1,107 @@
-"""Test the ORM's Courier model."""
+"""Test the ORM's `Courier` model."""
 
 import pytest
 from sqlalchemy import exc as sa_exc
-from sqlalchemy.orm import exc as orm_exc
 
 from urban_meal_delivery import db
 
 
 class TestSpecialMethods:
-    """Test special methods in Courier."""
+    """Test special methods in `Courier`."""
 
-    # pylint:disable=no-self-use
+    def test_create_courier(self, courier):
-
+        """Test instantiation of a new `Courier` object."""
-    def test_create_courier(self, courier_data):
+        assert courier is not None
-        """Test instantiation of a new Courier object."""
-        result = db.Courier(**courier_data)
-
-        assert result is not None
-
-    def test_text_representation(self, courier_data):
-        """Courier has a non-literal text representation."""
-        courier_data['id'] = 1
-        courier = db.Courier(**courier_data)
-        id_ = courier_data['id']
 
+    def test_text_representation(self, courier):
+        """`Courier` has a non-literal text representation."""
         result = repr(courier)
 
-        assert result == f'<Courier(#{id_})>'
+        assert result == f'<Courier(#{courier.id})>'
 
 
-@pytest.mark.e2e
+@pytest.mark.db
 @pytest.mark.no_cover
 class TestConstraints:
-    """Test the database constraints defined in Courier."""
+    """Test the database constraints defined in `Courier`."""
 
-    # pylint:disable=no-self-use
+    def test_insert_into_database(self, db_session, courier):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Courier).count() == 0
 
-    def test_insert_into_database(self, courier, db_session):
-        """Insert an instance into the database."""
         db_session.add(courier)
         db_session.commit()
 
-    def test_dublicate_primary_key(self, courier, courier_data, db_session):
+        assert db_session.query(db.Courier).count() == 1
-        """Can only add a record once."""
-        db_session.add(courier)
-        db_session.commit()
 
-        another_courier = db.Courier(**courier_data)
+    def test_invalid_vehicle(self, db_session, courier):
-        db_session.add(another_courier)
-
-        with pytest.raises(orm_exc.FlushError):
-            db_session.commit()
-
-    def test_invalid_vehicle(self, courier, db_session):
         """Insert an instance with invalid data."""
         courier.vehicle = 'invalid'
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='available_vehicle_types'):
             db_session.commit()
 
-    def test_negative_speed(self, courier, db_session):
+    def test_negative_speed(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.historic_speed = -1
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_speed'):
             db_session.commit()
 
-    def test_unrealistic_speed(self, courier, db_session):
+    def test_unrealistic_speed(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.historic_speed = 999
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_speed'):
             db_session.commit()
 
-    def test_negative_capacity(self, courier, db_session):
+    def test_negative_capacity(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.capacity = -1
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='capacity_under_200_liters'):
             db_session.commit()
 
-    def test_too_much_capacity(self, courier, db_session):
+    def test_too_much_capacity(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.capacity = 999
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='capacity_under_200_liters'):
             db_session.commit()
 
-    def test_negative_pay_per_hour(self, courier, db_session):
+    def test_negative_pay_per_hour(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.pay_per_hour = -1
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_pay_per_hour'):
             db_session.commit()
 
-    def test_too_much_pay_per_hour(self, courier, db_session):
+    def test_too_much_pay_per_hour(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.pay_per_hour = 9999
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_pay_per_hour'):
             db_session.commit()
 
-    def test_negative_pay_per_order(self, courier, db_session):
+    def test_negative_pay_per_order(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.pay_per_order = -1
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_pay_per_order'):
             db_session.commit()
 
-    def test_too_much_pay_per_order(self, courier, db_session):
+    def test_too_much_pay_per_order(self, db_session, courier):
         """Insert an instance with invalid data."""
         courier.pay_per_order = 999
         db_session.add(courier)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(sa_exc.IntegrityError, match='realistic_pay_per_order'):
             db_session.commit()

tests/db/test_customer.py

@@ -1,51 +1,34 @@
-"""Test the ORM's Customer model."""
+"""Test the ORM's `Customer` model."""
 
 import pytest
-from sqlalchemy.orm import exc as orm_exc
 
 from urban_meal_delivery import db
 
 
 class TestSpecialMethods:
-    """Test special methods in Customer."""
+    """Test special methods in `Customer`."""
 
-    # pylint:disable=no-self-use
+    def test_create_customer(self, customer):
-
+        """Test instantiation of a new `Customer` object."""
-    def test_create_customer(self, customer_data):
+        assert customer is not None
-        """Test instantiation of a new Customer object."""
-        result = db.Customer(**customer_data)
-
-        assert result is not None
-
-    def test_text_representation(self, customer_data):
-        """Customer has a non-literal text representation."""
-        customer = db.Customer(**customer_data)
-        id_ = customer_data['id']
 
+    def test_text_representation(self, customer):
+        """`Customer` has a non-literal text representation."""
         result = repr(customer)
 
-        assert result == f'<Customer(#{id_})>'
+        assert result == f'<Customer(#{customer.id})>'
 
 
-@pytest.mark.e2e
+@pytest.mark.db
 @pytest.mark.no_cover
 class TestConstraints:
-    """Test the database constraints defined in Customer."""
+    """Test the database constraints defined in `Customer`."""
 
-    # pylint:disable=no-self-use
+    def test_insert_into_database(self, db_session, customer):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Customer).count() == 0
 
-    def test_insert_into_database(self, customer, db_session):
-        """Insert an instance into the database."""
         db_session.add(customer)
         db_session.commit()
 
-    def test_dublicate_primary_key(self, customer, customer_data, db_session):
+        assert db_session.query(db.Customer).count() == 1
-        """Can only add a record once."""
-        db_session.add(customer)
-        db_session.commit()
-
-        another_customer = db.Customer(**customer_data)
-        db_session.add(another_customer)
-
-        with pytest.raises(orm_exc.FlushError):
-            db_session.commit()

tests/db/test_forecasts.py

@@ -0,0 +1,505 @@
+"""Test the ORM's `Forecast` model."""
+
+import datetime as dt
+
+import pandas as pd
+import pytest
+import sqlalchemy as sqla
+from sqlalchemy import exc as sa_exc
+
+from tests import config as test_config
+from urban_meal_delivery import db
+
+
+MODEL = 'hets'
+
+
+@pytest.fixture
+def forecast(pixel):
+    """A `forecast` made in the `pixel` at `NOON`."""
+    start_at = dt.datetime(
+        test_config.END.year,
+        test_config.END.month,
+        test_config.END.day,
+        test_config.NOON,
+    )
+
+    return db.Forecast(
+        pixel=pixel,
+        start_at=start_at,
+        time_step=test_config.LONG_TIME_STEP,
+        train_horizon=test_config.LONG_TRAIN_HORIZON,
+        model=MODEL,
+        actual=12,
+        prediction=12.3,
+        low80=1.23,
+        high80=123.4,
+        low95=0.123,
+        high95=1234.5,
+    )
+
+
+class TestSpecialMethods:
+    """Test special methods in `Forecast`."""
+
+    def test_create_forecast(self, forecast):
+        """Test instantiation of a new `Forecast` object."""
+        assert forecast is not None
+
+    def test_text_representation(self, forecast):
+        """`Forecast` has a non-literal text representation."""
+        result = repr(forecast)
+
+        assert (
+            result
+            == f'<Forecast: {forecast.prediction} for pixel ({forecast.pixel.n_x}|{forecast.pixel.n_y}) at {forecast.start_at}>'  # noqa:E501
+        )
+
+
+@pytest.mark.db
+@pytest.mark.no_cover
+class TestConstraints:
+    """Test the database constraints defined in `Forecast`."""
+
+    def test_insert_into_database(self, db_session, forecast):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Forecast).count() == 0
+
+        db_session.add(forecast)
+        db_session.commit()
+
+        assert db_session.query(db.Forecast).count() == 1
+
+    def test_delete_a_referenced_pixel(self, db_session, forecast):
+        """Remove a record that is referenced with a FK."""
+        db_session.add(forecast)
+        db_session.commit()
+
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
+        stmt = sqla.delete(db.Pixel).where(db.Pixel.id == forecast.pixel.id)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='fk_forecasts_to_pixels_via_pixel_id',
+        ):
+            db_session.execute(stmt)
+
+    @pytest.mark.parametrize('hour', [10, 23])
+    def test_invalid_start_at_outside_operating_hours(
+        self, db_session, forecast, hour,
+    ):
+        """Insert an instance with invalid data."""
+        forecast.start_at = dt.datetime(
+            forecast.start_at.year,
+            forecast.start_at.month,
+            forecast.start_at.day,
+            hour,
+        )
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='within_operating_hours',
+        ):
+            db_session.commit()
+
+    def test_invalid_start_at_not_quarter_of_hour(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        forecast.start_at += dt.timedelta(minutes=1)
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='must_be_quarters_of_the_hour',
+        ):
+            db_session.commit()
+
+    def test_invalid_start_at_seconds_set(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        forecast.start_at += dt.timedelta(seconds=1)
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='no_seconds',
+        ):
+            db_session.commit()
+
+    def test_invalid_start_at_microseconds_set(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        forecast.start_at += dt.timedelta(microseconds=1)
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='no_microseconds',
+        ):
+            db_session.commit()
+
+    @pytest.mark.parametrize('value', [-1, 0])
+    def test_positive_time_step(self, db_session, forecast, value):
+        """Insert an instance with invalid data."""
+        forecast.time_step = value
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='time_step_must_be_positive',
+        ):
+            db_session.commit()
+
+    @pytest.mark.parametrize('value', [-1, 0])
+    def test_positive_train_horizon(self, db_session, forecast, value):
+        """Insert an instance with invalid data."""
+        forecast.train_horizon = value
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='training_horizon_must_be_positive',
+        ):
+            db_session.commit()
+
+    def test_non_negative_actuals(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        forecast.actual = -1
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='actuals_must_be_non_negative',
+        ):
+            db_session.commit()
+
+    def test_set_prediction_without_ci(self, db_session, forecast):
+        """Sanity check to see that the check constraint ...
+
+        ... "prediction_must_be_within_ci" is not triggered.
+        """
+        forecast.low80 = None
+        forecast.high80 = None
+        forecast.low95 = None
+        forecast.high95 = None
+
+        db_session.add(forecast)
+        db_session.commit()
+
+    def test_ci80_with_missing_low(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.high80 is not None
+
+        forecast.low80 = None
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci_upper_and_lower_bounds',
+        ):
+            db_session.commit()
+
+    def test_ci95_with_missing_low(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.high95 is not None
+
+        forecast.low95 = None
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci_upper_and_lower_bounds',
+        ):
+            db_session.commit()
+
+    def test_ci80_with_missing_high(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low80 is not None
+
+        forecast.high80 = None
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci_upper_and_lower_bounds',
+        ):
+            db_session.commit()
+
+    def test_ci95_with_missing_high(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low95 is not None
+
+        forecast.high95 = None
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci_upper_and_lower_bounds',
+        ):
+            db_session.commit()
+
+    def test_prediction_smaller_than_low80_with_ci95_set(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low95 is not None
+        assert forecast.high95 is not None
+
+        forecast.prediction = forecast.low80 - 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_smaller_than_low80_without_ci95_set(
+        self, db_session, forecast,
+    ):
+        """Insert an instance with invalid data."""
+        forecast.low95 = None
+        forecast.high95 = None
+
+        forecast.prediction = forecast.low80 - 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_smaller_than_low95_with_ci80_set(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low80 is not None
+        assert forecast.high80 is not None
+
+        forecast.prediction = forecast.low95 - 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_smaller_than_low95_without_ci80_set(
+        self, db_session, forecast,
+    ):
+        """Insert an instance with invalid data."""
+        forecast.low80 = None
+        forecast.high80 = None
+
+        forecast.prediction = forecast.low95 - 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_greater_than_high80_with_ci95_set(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low95 is not None
+        assert forecast.high95 is not None
+
+        forecast.prediction = forecast.high80 + 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_greater_than_high80_without_ci95_set(
+        self, db_session, forecast,
+    ):
+        """Insert an instance with invalid data."""
+        forecast.low95 = None
+        forecast.high95 = None
+
+        forecast.prediction = forecast.high80 + 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_greater_than_high95_with_ci80_set(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low80 is not None
+        assert forecast.high80 is not None
+
+        forecast.prediction = forecast.high95 + 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_prediction_greater_than_high95_without_ci80_set(
+        self, db_session, forecast,
+    ):
+        """Insert an instance with invalid data."""
+        forecast.low80 = None
+        forecast.high80 = None
+
+        forecast.prediction = forecast.high95 + 0.001
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='prediction_must_be_within_ci',
+        ):
+            db_session.commit()
+
+    def test_ci80_upper_bound_greater_than_lower_bound(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low80 is not None
+        assert forecast.high80 is not None
+
+        # Do not trigger the "ci95_must_be_wider_than_ci80" constraint.
+        forecast.low95 = None
+        forecast.high95 = None
+
+        forecast.low80, forecast.high80 = (  # noqa:WPS414
+            forecast.high80,
+            forecast.low80,
+        )
+
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci_upper_bound_greater_than_lower_bound',
+        ):
+            db_session.commit()
+
+    def test_ci95_upper_bound_greater_than_lower_bound(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low95 is not None
+        assert forecast.high95 is not None
+
+        # Do not trigger the "ci95_must_be_wider_than_ci80" constraint.
+        forecast.low80 = None
+        forecast.high80 = None
+
+        forecast.low95, forecast.high95 = (  # noqa:WPS414
+            forecast.high95,
+            forecast.low95,
+        )
+
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci_upper_bound_greater_than_lower_bound',
+        ):
+            db_session.commit()
+
+    def test_ci95_is_wider_than_ci80_at_low_end(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.low80 is not None
+        assert forecast.low95 is not None
+
+        forecast.low80, forecast.low95 = (forecast.low95, forecast.low80)  # noqa:WPS414
+
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci95_must_be_wider_than_ci80',
+        ):
+            db_session.commit()
+
+    def test_ci95_is_wider_than_ci80_at_high_end(self, db_session, forecast):
+        """Insert an instance with invalid data."""
+        assert forecast.high80 is not None
+        assert forecast.high95 is not None
+
+        forecast.high80, forecast.high95 = (  # noqa:WPS414
+            forecast.high95,
+            forecast.high80,
+        )
+
+        db_session.add(forecast)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='ci95_must_be_wider_than_ci80',
+        ):
+            db_session.commit()
+
+    def test_two_predictions_for_same_forecasting_setting(self, db_session, forecast):
+        """Insert a record that violates a unique constraint."""
+        db_session.add(forecast)
+        db_session.commit()
+
+        another_forecast = db.Forecast(
+            pixel=forecast.pixel,
+            start_at=forecast.start_at,
+            time_step=forecast.time_step,
+            train_horizon=forecast.train_horizon,
+            model=forecast.model,
+            actual=forecast.actual,
+            prediction=2,
+            low80=1,
+            high80=3,
+            low95=0,
+            high95=4,
+        )
+        db_session.add(another_forecast)
+
+        with pytest.raises(sa_exc.IntegrityError, match='duplicate key value'):
+            db_session.commit()
+
+
+class TestFromDataFrameConstructor:
+    """Test the alternative `Forecast.from_dataframe()` constructor."""
+
+    @pytest.fixture
+    def prediction_data(self):
+        """A `pd.DataFrame` as returned by `*Model.predict()` ...
+
+        ... and used as the `data` argument to `Forecast.from_dataframe()`.
+
+        We assume the `data` come from some vertical forecasting `*Model`
+        and contain several rows (= `3` in this example) corresponding
+        to different time steps centered around `NOON`.
+        """
+        noon_start_at = dt.datetime(
+            test_config.END.year,
+            test_config.END.month,
+            test_config.END.day,
+            test_config.NOON,
+        )
+
+        index = pd.Index(
+            [
+                noon_start_at - dt.timedelta(minutes=test_config.LONG_TIME_STEP),
+                noon_start_at,
+                noon_start_at + dt.timedelta(minutes=test_config.LONG_TIME_STEP),
+            ],
+        )
+        index.name = 'start_at'
+
+        return pd.DataFrame(
+            data={
+                'actual': (11, 12, 13),
+                'prediction': (11.3, 12.3, 13.3),
+                'low80': (1.123, 1.23, 1.323),
+                'high80': (112.34, 123.4, 132.34),
+                'low95': (0.1123, 0.123, 0.1323),
+                'high95': (1123.45, 1234.5, 1323.45),
+            },
+            index=index,
+        )
+
+    def test_convert_dataframe_into_orm_objects(self, pixel, prediction_data):
+        """Call `Forecast.from_dataframe()`."""
+        forecasts = db.Forecast.from_dataframe(
+            pixel=pixel,
+            time_step=test_config.LONG_TIME_STEP,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+            model=MODEL,
+            data=prediction_data,
+        )
+
+        assert len(forecasts) == 3
+        for forecast in forecasts:
+            assert isinstance(forecast, db.Forecast)
+
+    @pytest.mark.db
+    def test_persist_predictions_into_database(
+        self, db_session, pixel, prediction_data,
+    ):
+        """Call `Forecast.from_dataframe()` and persist the results."""
+        forecasts = db.Forecast.from_dataframe(
+            pixel=pixel,
+            time_step=test_config.LONG_TIME_STEP,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+            model=MODEL,
+            data=prediction_data,
+        )
+
+        db_session.add_all(forecasts)
+        db_session.commit()

tests/db/test_grids.py

@@ -0,0 +1,239 @@
+"""Test the ORM's `Grid` model."""
+
+import pytest
+import sqlalchemy as sqla
+from sqlalchemy import exc as sa_exc
+
+from urban_meal_delivery import db
+
+
+class TestSpecialMethods:
+    """Test special methods in `Grid`."""
+
+    def test_create_grid(self, grid):
+        """Test instantiation of a new `Grid` object."""
+        assert grid is not None
+
+    def test_text_representation(self, grid):
+        """`Grid` has a non-literal text representation."""
+        result = repr(grid)
+
+        assert result == f'<Grid: {grid.pixel_area} sqr. km>'
+
+
+@pytest.mark.db
+@pytest.mark.no_cover
+class TestConstraints:
+    """Test the database constraints defined in `Grid`."""
+
+    def test_insert_into_database(self, db_session, grid):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Grid).count() == 0
+
+        db_session.add(grid)
+        db_session.commit()
+
+        assert db_session.query(db.Grid).count() == 1
+
+    def test_delete_a_referenced_city(self, db_session, grid):
+        """Remove a record that is referenced with a FK."""
+        db_session.add(grid)
+        db_session.commit()
+
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
+        stmt = sqla.delete(db.City).where(db.City.id == grid.city.id)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='fk_grids_to_cities_via_city_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_two_grids_with_identical_side_length(self, db_session, grid):
+        """Insert a record that violates a unique constraint."""
+        db_session.add(grid)
+        db_session.commit()
+
+        # Create a `Grid` with the same `.side_length` in the same `.city`.
+        another_grid = db.Grid(city=grid.city, side_length=grid.side_length)
+        db_session.add(another_grid)
+
+        with pytest.raises(sa_exc.IntegrityError, match='duplicate key value'):
+            db_session.commit()
+
+
+class TestProperties:
+    """Test properties in `Grid`."""
+
+    def test_pixel_area(self, grid):
+        """Test `Grid.pixel_area` property."""
+        result = grid.pixel_area
+
+        assert result == 1.0
+
+
+class TestGridification:
+    """Test the `Grid.gridify()` constructor."""
+
+    @pytest.fixture
+    def addresses_mock(self, mocker, monkeypatch):
+        """A `Mock` whose `.return_value` are to be set ...
+
+        ... to the addresses that are gridified. The addresses are
+        all considered `Order.pickup_address` attributes for some orders.
+        """
+        mock = mocker.Mock()
+        query = (  # noqa:ECE001
+            mock.query.return_value.join.return_value.filter.return_value.all  # noqa:E501,WPS219
+        )
+        monkeypatch.setattr(db, 'session', mock)
+
+        return query
+
+    @pytest.mark.no_cover
+    def test_no_pixel_without_addresses(self, city, addresses_mock):
+        """Without orders, there are no `Pixel` objects on the `grid`.
+
+        This test case skips the `for`-loop inside `Grid.gridify()`.
+        """
+        addresses_mock.return_value = []
+
+        # The chosen `side_length` would result in one `Pixel` if there were orders.
+        # `+1` as otherwise there would be a second pixel in one direction.
+        side_length = max(city.total_x, city.total_y) + 1
+
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert len(result.pixels) == 0  # noqa:WPS507
+
+    def test_one_pixel_with_one_address(self, city, order, addresses_mock):
+        """At the very least, there must be one `Pixel` ...
+
+        ... if the `side_length` is greater than both the
+        horizontal and vertical distances of the viewport.
+        """
+        addresses_mock.return_value = [order.pickup_address]
+
+        # `+1` as otherwise there would be a second pixel in one direction.
+        side_length = max(city.total_x, city.total_y) + 1
+
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert len(result.pixels) == 1
+
+    def test_one_pixel_with_two_addresses(self, city, make_order, addresses_mock):
+        """At the very least, there must be one `Pixel` ...
+
+        ... if the `side_length` is greater than both the
+        horizontal and vertical distances of the viewport.
+
+        This test case is necessary as `test_one_pixel_with_one_address`
+        does not have to re-use an already created `Pixel` object internally.
+        """
+        orders = [make_order(), make_order()]
+        addresses_mock.return_value = [order.pickup_address for order in orders]
+
+        # `+1` as otherwise there would be a second pixel in one direction.
+        side_length = max(city.total_x, city.total_y) + 1
+
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert len(result.pixels) == 1
+
+    def test_no_pixel_with_one_address_too_far_south(self, city, order, addresses_mock):
+        """An `address` outside the `city`'s viewport is discarded."""
+        # Move the `address` just below `city.southwest`.
+        order.pickup_address.latitude = city.southwest.latitude - 0.1
+        addresses_mock.return_value = [order.pickup_address]
+
+        # `+1` as otherwise there would be a second pixel in one direction.
+        side_length = max(city.total_x, city.total_y) + 1
+
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert len(result.pixels) == 0  # noqa:WPS507
+
+    @pytest.mark.no_cover
+    def test_no_pixel_with_one_address_too_far_west(self, city, order, addresses_mock):
+        """An `address` outside the `city`'s viewport is discarded.
+
+        This test is a logical sibling to
+        `test_no_pixel_with_one_address_too_far_south` and therefore redundant.
+        """
+        # Move the `address` just left to `city.southwest`.
+        order.pickup_address.longitude = city.southwest.longitude - 0.1
+        addresses_mock.return_value = [order.pickup_address]
+
+        # `+1` as otherwise there would be a second pixel in one direction.
+        side_length = max(city.total_x, city.total_y) + 1
+
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert len(result.pixels) == 0  # noqa:WPS507
+
+    @pytest.mark.no_cover
+    def test_two_pixels_with_two_addresses(self, city, make_address, addresses_mock):
+        """Two `Address` objects in distinct `Pixel` objects.
+
+        This test is more of a sanity check.
+        """
+        # Create two `Address` objects in distinct `Pixel`s.
+        addresses_mock.return_value = [
+            # One `Address` in the lower-left `Pixel`, ...
+            make_address(latitude=48.8357377, longitude=2.2517412),
+            # ... and another one in the upper-right one.
+            make_address(latitude=48.8898312, longitude=2.4357622),
+        ]
+
+        side_length = max(city.total_x // 2, city.total_y // 2) + 1
+
+        # By assumption of the test data.
+        n_pixels_x = (city.total_x // side_length) + 1
+        n_pixels_y = (city.total_y // side_length) + 1
+        assert n_pixels_x * n_pixels_y == 4
+
+        # Create a `Grid` with at most four `Pixel`s.
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert len(result.pixels) == 2
+
+    @pytest.mark.db
+    @pytest.mark.no_cover
+    @pytest.mark.parametrize('side_length', [250, 500, 1_000, 2_000, 4_000, 8_000])
+    def test_make_random_grids(  # noqa:WPS211,WPS218
+        self, db_session, city, make_address, make_restaurant, make_order, side_length,
+    ):
+        """With 100 random `Address` objects, a grid must have ...
+
+        ... between 1 and a deterministic upper bound of `Pixel` objects.
+
+        This test creates confidence that the created `Grid`
+        objects adhere to the database constraints.
+        """
+        addresses = [make_address() for _ in range(100)]
+        restaurants = [make_restaurant(address=address) for address in addresses]
+        orders = [make_order(restaurant=restaurant) for restaurant in restaurants]
+        db_session.add_all(orders)
+
+        n_pixels_x = (city.total_x // side_length) + 1
+        n_pixels_y = (city.total_y // side_length) + 1
+
+        result = db.Grid.gridify(city=city, side_length=side_length)
+
+        assert isinstance(result, db.Grid)
+        assert 1 <= len(result.pixels) <= n_pixels_x * n_pixels_y
+
+        # Sanity checks for `Pixel.southwest` and `Pixel.northeast`.
+        for pixel in result.pixels:
+            assert abs(pixel.southwest.x - pixel.n_x * side_length) < 2
+            assert abs(pixel.southwest.y - pixel.n_y * side_length) < 2
+            assert abs(pixel.northeast.x - (pixel.n_x + 1) * side_length) < 2
+            assert abs(pixel.northeast.y - (pixel.n_y + 1) * side_length) < 2
+
+        db_session.add(result)
+        db_session.commit()

tests/db/test_orders.py

@@ -1,57 +1,40 @@
-"""Test the ORM's Order model."""
+"""Test the ORM's `Order` model."""
 
 import datetime
+import random
 
 import pytest
-from sqlalchemy.orm import exc as orm_exc
 
 from urban_meal_delivery import db
 
 
 class TestSpecialMethods:
-    """Test special methods in Order."""
+    """Test special methods in `Order`."""
 
-    # pylint:disable=no-self-use
+    def test_create_order(self, order):
-
+        """Test instantiation of a new `Order` object."""
-    def test_create_order(self, order_data):
+        assert order is not None
-        """Test instantiation of a new Order object."""
-        result = db.Order(**order_data)
-
-        assert result is not None
-
-    def test_text_representation(self, order_data):
-        """Order has a non-literal text representation."""
-        order = db.Order(**order_data)
-        id_ = order_data['id']
 
+    def test_text_representation(self, order):
+        """`Order` has a non-literal text representation."""
         result = repr(order)
 
-        assert result == f'<Order(#{id_})>'
+        assert result == f'<Order(#{order.id})>'
 
 
-@pytest.mark.e2e
+@pytest.mark.db
 @pytest.mark.no_cover
 class TestConstraints:
-    """Test the database constraints defined in Order."""
+    """Test the database constraints defined in `Order`."""
 
-    # pylint:disable=no-self-use
+    def test_insert_into_database(self, db_session, order):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Order).count() == 0
 
-    def test_insert_into_database(self, order, db_session):
-        """Insert an instance into the database."""
         db_session.add(order)
         db_session.commit()
 
-    def test_dublicate_primary_key(self, order, order_data, city, db_session):
+        assert db_session.query(db.Order).count() == 1
-        """Can only add a record once."""
-        db_session.add(order)
-        db_session.commit()
-
-        another_order = db.Order(**order_data)
-        another_order.city = city
-        db_session.add(another_order)
-
-        with pytest.raises(orm_exc.FlushError):
-            db_session.commit()
 
     # TODO (order-constraints): the various Foreign Key and Check Constraints
     # should be tested eventually. This is not of highest importance as
@@ -59,339 +42,429 @@ class TestConstraints:
 
 
 class TestProperties:
-    """Test properties in Order."""
+    """Test properties in `Order`.
 
-    # pylint:disable=no-self-use,too-many-public-methods
+    The `order` fixture uses the defaults specified in `factories.OrderFactory`
+    and provided by the `make_order` fixture.
+    """
 
-    def test_is_not_scheduled(self, order_data):
+    def test_is_ad_hoc(self, order):
-        """Test Order.scheduled property."""
+        """Test `Order.scheduled` property."""
-        order = db.Order(**order_data)
+        assert order.ad_hoc is True
 
         result = order.scheduled
 
         assert result is False
 
-    def test_is_scheduled(self, order_data):
+    def test_is_scheduled(self, make_order):
-        """Test Order.scheduled property."""
+        """Test `Order.scheduled` property."""
-        order_data['ad_hoc'] = False
+        order = make_order(scheduled=True)
-        order_data['scheduled_delivery_at'] = datetime.datetime(2020, 1, 2, 12, 30, 0)
+        assert order.ad_hoc is False
-        order_data['scheduled_delivery_at_corrected'] = False
-        order = db.Order(**order_data)
 
         result = order.scheduled
 
         assert result is True
 
-    def test_is_completed(self, order_data):
+    def test_is_completed(self, order):
-        """Test Order.completed property."""
+        """Test `Order.completed` property."""
-        order = db.Order(**order_data)
-
         result = order.completed
 
         assert result is True
 
-    def test_is_not_completed(self, order_data):
+    def test_is_not_completed1(self, make_order):
-        """Test Order.completed property."""
+        """Test `Order.completed` property."""
-        order_data['cancelled'] = True
+        order = make_order(cancel_before_pickup=True)
-        order_data['cancelled_at'] = datetime.datetime(2020, 1, 2, 12, 15, 0)
+        assert order.cancelled is True
-        order_data['cancelled_at_corrected'] = False
-        order = db.Order(**order_data)
 
         result = order.completed
 
         assert result is False
 
-    def test_is_corrected(self, order_data):
+    def test_is_not_completed2(self, make_order):
-        """Test Order.corrected property."""
+        """Test `Order.completed` property."""
-        order_data['dispatch_at_corrected'] = True
+        order = make_order(cancel_after_pickup=True)
-        order = db.Order(**order_data)
+        assert order.cancelled is True
+
+        result = order.completed
+
+        assert result is False
+
+    def test_is_not_corrected(self, order):
+        """Test `Order.corrected` property."""
+        # By default, the `OrderFactory` sets all `.*_corrected` attributes to `False`.
+        result = order.corrected
+
+        assert result is False
+
+    @pytest.mark.parametrize(
+        'column',
+        [
+            'scheduled_delivery_at',
+            'cancelled_at',
+            'restaurant_notified_at',
+            'restaurant_confirmed_at',
+            'dispatch_at',
+            'courier_notified_at',
+            'courier_accepted_at',
+            'pickup_at',
+            'left_pickup_at',
+            'delivery_at',
+        ],
+    )
+    def test_is_corrected(self, order, column):
+        """Test `Order.corrected` property."""
+        setattr(order, f'{column}_corrected', True)
 
         result = order.corrected
 
         assert result is True
 
-    def test_time_to_accept_no_dispatch_at(self, order_data):
+    def test_time_to_accept_no_dispatch_at(self, order):
-        """Test Order.time_to_accept property."""
+        """Test `Order.time_to_accept` property."""
-        order_data['dispatch_at'] = None
+        order.dispatch_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_accept)
 
-    def test_time_to_accept_no_courier_accepted(self, order_data):
+    def test_time_to_accept_no_courier_accepted(self, order):
-        """Test Order.time_to_accept property."""
+        """Test `Order.time_to_accept` property."""
-        order_data['courier_accepted_at'] = None
+        order.courier_accepted_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_accept)
 
-    def test_time_to_accept_success(self, order_data):
+    def test_time_to_accept_success(self, order):
-        """Test Order.time_to_accept property."""
+        """Test `Order.time_to_accept` property."""
-        order = db.Order(**order_data)
-
         result = order.time_to_accept
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
 
-    def test_time_to_react_no_courier_notified(self, order_data):
+    def test_time_to_react_no_courier_notified(self, order):
-        """Test Order.time_to_react property."""
+        """Test `Order.time_to_react` property."""
-        order_data['courier_notified_at'] = None
+        order.courier_notified_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_react)
 
-    def test_time_to_react_no_courier_accepted(self, order_data):
+    def test_time_to_react_no_courier_accepted(self, order):
-        """Test Order.time_to_react property."""
+        """Test `Order.time_to_react` property."""
-        order_data['courier_accepted_at'] = None
+        order.courier_accepted_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_react)
 
-    def test_time_to_react_success(self, order_data):
+    def test_time_to_react_success(self, order):
-        """Test Order.time_to_react property."""
+        """Test `Order.time_to_react` property."""
-        order = db.Order(**order_data)
-
         result = order.time_to_react
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
 
-    def test_time_to_pickup_no_reached_pickup_at(self, order_data):
+    def test_time_to_pickup_no_reached_pickup_at(self, order):
-        """Test Order.time_to_pickup property."""
+        """Test `Order.time_to_pickup` property."""
-        order_data['reached_pickup_at'] = None
+        order.reached_pickup_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_pickup)
 
-    def test_time_to_pickup_no_courier_accepted(self, order_data):
+    def test_time_to_pickup_no_courier_accepted(self, order):
-        """Test Order.time_to_pickup property."""
+        """Test `Order.time_to_pickup` property."""
-        order_data['courier_accepted_at'] = None
+        order.courier_accepted_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_pickup)
 
-    def test_time_to_pickup_success(self, order_data):
+    def test_time_to_pickup_success(self, order):
-        """Test Order.time_to_pickup property."""
+        """Test `Order.time_to_pickup` property."""
-        order = db.Order(**order_data)
-
         result = order.time_to_pickup
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
 
-    def test_time_at_pickup_no_reached_pickup_at(self, order_data):
+    def test_time_at_pickup_no_reached_pickup_at(self, order):
-        """Test Order.time_at_pickup property."""
+        """Test `Order.time_at_pickup` property."""
-        order_data['reached_pickup_at'] = None
+        order.reached_pickup_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_at_pickup)
 
-    def test_time_at_pickup_no_pickup_at(self, order_data):
+    def test_time_at_pickup_no_pickup_at(self, order):
-        """Test Order.time_at_pickup property."""
+        """Test `Order.time_at_pickup` property."""
-        order_data['pickup_at'] = None
+        order.pickup_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_at_pickup)
 
-    def test_time_at_pickup_success(self, order_data):
+    def test_time_at_pickup_success(self, order):
-        """Test Order.time_at_pickup property."""
+        """Test `Order.time_at_pickup` property."""
-        order = db.Order(**order_data)
-
         result = order.time_at_pickup
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
 
-    def test_scheduled_pickup_at_no_restaurant_notified(  # noqa:WPS118
+    def test_scheduled_pickup_at_no_restaurant_notified(self, order):  # noqa:WPS118
-        self, order_data,
+        """Test `Order.scheduled_pickup_at` property."""
-    ):
+        order.restaurant_notified_at = None
-        """Test Order.scheduled_pickup_at property."""
-        order_data['restaurant_notified_at'] = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.scheduled_pickup_at)
 
-    def test_scheduled_pickup_at_no_est_prep_duration(self, order_data):  # noqa:WPS118
+    def test_scheduled_pickup_at_no_est_prep_duration(self, order):  # noqa:WPS118
-        """Test Order.scheduled_pickup_at property."""
+        """Test `Order.scheduled_pickup_at` property."""
-        order_data['estimated_prep_duration'] = None
+        order.estimated_prep_duration = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.scheduled_pickup_at)
 
-    def test_scheduled_pickup_at_success(self, order_data):
+    def test_scheduled_pickup_at_success(self, order):
-        """Test Order.scheduled_pickup_at property."""
+        """Test `Order.scheduled_pickup_at` property."""
-        order = db.Order(**order_data)
-
         result = order.scheduled_pickup_at
 
-        assert isinstance(result, datetime.datetime)
+        assert order.placed_at < result < order.delivery_at
 
-    def test_if_courier_early_at_pickup(self, order_data):
+    def test_courier_is_early_at_pickup(self, order):
-        """Test Order.courier_early property."""
+        """Test `Order.courier_early` property."""
-        order = db.Order(**order_data)
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
+        order.estimated_prep_duration = 999_999
 
         result = order.courier_early
 
         assert bool(result) is True
 
-    def test_if_courier_late_at_pickup(self, order_data):
+    def test_courier_is_not_early_at_pickup(self, order):
-        """Test Order.courier_late property."""
+        """Test `Order.courier_early` property."""
-        # Opposite of test case before.
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
-        order = db.Order(**order_data)
+        order.estimated_prep_duration = 1
+
+        result = order.courier_early
+
+        assert bool(result) is False
+
+    def test_courier_is_late_at_pickup(self, order):
+        """Test `Order.courier_late` property."""
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
+        order.estimated_prep_duration = 1
+
+        result = order.courier_late
+
+        assert bool(result) is True
+
+    def test_courier_is_not_late_at_pickup(self, order):
+        """Test `Order.courier_late` property."""
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
+        order.estimated_prep_duration = 999_999
 
         result = order.courier_late
 
         assert bool(result) is False
 
-    def test_if_restaurant_early_at_pickup(self, order_data):
+    def test_restaurant_early_at_pickup(self, order):
-        """Test Order.restaurant_early property."""
+        """Test `Order.restaurant_early` property."""
-        order = db.Order(**order_data)
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
+        order.estimated_prep_duration = 999_999
 
         result = order.restaurant_early
 
         assert bool(result) is True
 
-    def test_if_restaurant_late_at_pickup(self, order_data):
+    def test_restaurant_is_not_early_at_pickup(self, order):
-        """Test Order.restaurant_late property."""
+        """Test `Order.restaurant_early` property."""
-        # Opposite of test case before.
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
-        order = db.Order(**order_data)
+        order.estimated_prep_duration = 1
+
+        result = order.restaurant_early
+
+        assert bool(result) is False
+
+    def test_restaurant_is_late_at_pickup(self, order):
+        """Test `Order.restaurant_late` property."""
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
+        order.estimated_prep_duration = 1
+
+        result = order.restaurant_late
+
+        assert bool(result) is True
+
+    def test_restaurant_is_not_late_at_pickup(self, order):
+        """Test `Order.restaurant_late` property."""
+        # Manipulate the attribute that determines `Order.scheduled_pickup_at`.
+        order.estimated_prep_duration = 999_999
 
         result = order.restaurant_late
 
         assert bool(result) is False
 
-    def test_time_to_delivery_no_reached_delivery_at(self, order_data):  # noqa:WPS118
+    def test_time_to_delivery_no_reached_delivery_at(self, order):  # noqa:WPS118
-        """Test Order.time_to_delivery property."""
+        """Test `Order.time_to_delivery` property."""
-        order_data['reached_delivery_at'] = None
+        order.reached_delivery_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_delivery)
 
-    def test_time_to_delivery_no_pickup_at(self, order_data):
+    def test_time_to_delivery_no_pickup_at(self, order):
-        """Test Order.time_to_delivery property."""
+        """Test `Order.time_to_delivery` property."""
-        order_data['pickup_at'] = None
+        order.pickup_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_to_delivery)
 
-    def test_time_to_delivery_success(self, order_data):
+    def test_time_to_delivery_success(self, order):
-        """Test Order.time_to_delivery property."""
+        """Test `Order.time_to_delivery` property."""
-        order = db.Order(**order_data)
-
         result = order.time_to_delivery
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
 
-    def test_time_at_delivery_no_reached_delivery_at(self, order_data):  # noqa:WPS118
+    def test_time_at_delivery_no_reached_delivery_at(self, order):  # noqa:WPS118
-        """Test Order.time_at_delivery property."""
+        """Test `Order.time_at_delivery` property."""
-        order_data['reached_delivery_at'] = None
+        order.reached_delivery_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_at_delivery)
 
-    def test_time_at_delivery_no_delivery_at(self, order_data):
+    def test_time_at_delivery_no_delivery_at(self, order):
-        """Test Order.time_at_delivery property."""
+        """Test `Order.time_at_delivery` property."""
-        order_data['delivery_at'] = None
+        order.delivery_at = None
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='not set'):
             int(order.time_at_delivery)
 
-    def test_time_at_delivery_success(self, order_data):
+    def test_time_at_delivery_success(self, order):
-        """Test Order.time_at_delivery property."""
+        """Test `Order.time_at_delivery` property."""
-        order = db.Order(**order_data)
-
         result = order.time_at_delivery
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
 
-    def test_courier_waited_at_delviery(self, order_data):
+    def test_courier_waited_at_delviery(self, order):
-        """Test Order.courier_waited_at_delivery property."""
+        """Test `Order.courier_waited_at_delivery` property."""
-        order_data['_courier_waited_at_delivery'] = True
+        order._courier_waited_at_delivery = True
-        order = db.Order(**order_data)
 
-        result = int(order.courier_waited_at_delivery.total_seconds())
+        result = order.courier_waited_at_delivery.total_seconds()
 
         assert result > 0
 
-    def test_courier_did_not_wait_at_delivery(self, order_data):
+    def test_courier_did_not_wait_at_delivery(self, order):
-        """Test Order.courier_waited_at_delivery property."""
+        """Test `Order.courier_waited_at_delivery` property."""
-        order_data['_courier_waited_at_delivery'] = False
+        order._courier_waited_at_delivery = False
-        order = db.Order(**order_data)
 
-        result = int(order.courier_waited_at_delivery.total_seconds())
+        result = order.courier_waited_at_delivery.total_seconds()
 
         assert result == 0
 
-    def test_if_delivery_early_success(self, order_data):
+    def test_ad_hoc_order_cannot_be_early(self, order):
-        """Test Order.delivery_early property."""
+        """Test `Order.delivery_early` property."""
-        order_data['ad_hoc'] = False
+        # By default, the `OrderFactory` creates ad-hoc orders.
-        order_data['scheduled_delivery_at'] = datetime.datetime(2020, 1, 2, 12, 30, 0)
+        with pytest.raises(AttributeError, match='scheduled'):
-        order_data['scheduled_delivery_at_corrected'] = False
+            int(order.delivery_early)
-        order = db.Order(**order_data)
+
+    def test_scheduled_order_delivered_early(self, make_order):
+        """Test `Order.delivery_early` property."""
+        order = make_order(scheduled=True)
+        # Schedule the order to a lot later.
+        order.scheduled_delivery_at += datetime.timedelta(hours=2)
 
         result = order.delivery_early
 
         assert bool(result) is True
 
-    def test_if_delivery_early_failure(self, order_data):
+    def test_scheduled_order_not_delivered_early(self, make_order):
-        """Test Order.delivery_early property."""
+        """Test `Order.delivery_early` property."""
-        order = db.Order(**order_data)
+        order = make_order(scheduled=True)
+        # Schedule the order to a lot earlier.
+        order.scheduled_delivery_at -= datetime.timedelta(hours=2)
 
-        with pytest.raises(AttributeError, match='scheduled'):
+        result = order.delivery_early
-            int(order.delivery_early)
 
-    def test_if_delivery_late_success(self, order_data):
+        assert bool(result) is False
+
+    def test_ad_hoc_order_cannot_be_late(self, order):
         """Test Order.delivery_late property."""
-        order_data['ad_hoc'] = False
+        # By default, the `OrderFactory` creates ad-hoc orders.
-        order_data['scheduled_delivery_at'] = datetime.datetime(2020, 1, 2, 12, 30, 0)
+        with pytest.raises(AttributeError, match='scheduled'):
-        order_data['scheduled_delivery_at_corrected'] = False
+            int(order.delivery_late)
-        order = db.Order(**order_data)
+
+    def test_scheduled_order_delivered_late(self, make_order):
+        """Test `Order.delivery_early` property."""
+        order = make_order(scheduled=True)
+        # Schedule the order to a lot earlier.
+        order.scheduled_delivery_at -= datetime.timedelta(hours=2)
+
+        result = order.delivery_late
+
+        assert bool(result) is True
+
+    def test_scheduled_order_not_delivered_late(self, make_order):
+        """Test `Order.delivery_early` property."""
+        order = make_order(scheduled=True)
+        # Schedule the order to a lot later.
+        order.scheduled_delivery_at += datetime.timedelta(hours=2)
 
         result = order.delivery_late
 
         assert bool(result) is False
 
-    def test_if_delivery_late_failure(self, order_data):
+    def test_no_total_time_for_scheduled_order(self, make_order):
-        """Test Order.delivery_late property."""
+        """Test `Order.total_time` property."""
-        order = db.Order(**order_data)
+        order = make_order(scheduled=True)
-
-        with pytest.raises(AttributeError, match='scheduled'):
-            int(order.delivery_late)
-
-    def test_no_total_time_for_pre_order(self, order_data):
-        """Test Order.total_time property."""
-        order_data['ad_hoc'] = False
-        order_data['scheduled_delivery_at'] = datetime.datetime(2020, 1, 2, 12, 30, 0)
-        order_data['scheduled_delivery_at_corrected'] = False
-        order = db.Order(**order_data)
 
         with pytest.raises(AttributeError, match='Scheduled'):
             int(order.total_time)
 
-    def test_no_total_time_for_cancelled_order(self, order_data):
+    def test_no_total_time_for_cancelled_order(self, make_order):
-        """Test Order.total_time property."""
+        """Test `Order.total_time` property."""
-        order_data['cancelled'] = True
+        order = make_order(cancel_before_pickup=True)
-        order_data['cancelled_at'] = datetime.datetime(2020, 1, 2, 12, 15, 0)
-        order_data['cancelled_at_corrected'] = False
-        order = db.Order(**order_data)
 
         with pytest.raises(RuntimeError, match='Cancelled'):
             int(order.total_time)
 
-    def test_total_time_success(self, order_data):
+    def test_total_time_success(self, order):
-        """Test Order.total_time property."""
+        """Test `Order.total_time` property."""
-        order = db.Order(**order_data)
-
         result = order.total_time
 
-        assert isinstance(result, datetime.timedelta)
+        assert result > datetime.timedelta(0)
+
+
+@pytest.mark.db
+@pytest.mark.no_cover
+def test_make_random_orders(  # noqa:C901,WPS211,WPS213,WPS231
+    db_session, make_address, make_courier, make_restaurant, make_order,
+):
+    """Sanity check the all the `make_*` fixtures.
+
+    Ensure that all generated `Address`, `Courier`, `Customer`, `Restauarant`,
+    and `Order` objects adhere to the database constraints.
+    """  # noqa:D202
+    # Generate a large number of `Order`s to obtain a large variance of data.
+    for _ in range(1_000):  # noqa:WPS122
+
+        # Ad-hoc `Order`s are far more common than pre-orders.
+        scheduled = random.choice([True, False, False, False, False])
+
+        # Randomly pass a `address` argument to `make_restaurant()` and
+        # a `restaurant` argument to `make_order()`.
+        if random.random() < 0.5:
+            address = random.choice([None, make_address()])
+            restaurant = make_restaurant(address=address)
+        else:
+            restaurant = None
+
+        # Randomly pass a `courier` argument to `make_order()`.
+        courier = random.choice([None, make_courier()])
+
+        # A tiny fraction of `Order`s get cancelled.
+        if random.random() < 0.05:
+            if random.random() < 0.5:
+                cancel_before_pickup, cancel_after_pickup = True, False
+            else:
+                cancel_before_pickup, cancel_after_pickup = False, True
+        else:
+            cancel_before_pickup, cancel_after_pickup = False, False
+
+        # Write all the generated objects to the database.
+        # This should already trigger an `IntegrityError` if the data are flawed.
+        order = make_order(
+            scheduled=scheduled,
+            restaurant=restaurant,
+            courier=courier,
+            cancel_before_pickup=cancel_before_pickup,
+            cancel_after_pickup=cancel_after_pickup,
+        )
+        db_session.add(order)
+
+    db_session.commit()

tests/db/test_pixels.py

@@ -0,0 +1,152 @@
+"""Test the ORM's `Pixel` model."""
+
+import pytest
+import sqlalchemy as sqla
+from sqlalchemy import exc as sa_exc
+
+from urban_meal_delivery import db
+
+
+class TestSpecialMethods:
+    """Test special methods in `Pixel`."""
+
+    def test_create_pixel(self, pixel):
+        """Test instantiation of a new `Pixel` object."""
+        assert pixel is not None
+
+    def test_text_representation(self, pixel):
+        """`Pixel` has a non-literal text representation."""
+        result = repr(pixel)
+
+        assert result == f'<Pixel: ({pixel.n_x}|{pixel.n_y})>'
+
+
+@pytest.mark.db
+@pytest.mark.no_cover
+class TestConstraints:
+    """Test the database constraints defined in `Pixel`."""
+
+    def test_insert_into_database(self, db_session, pixel):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Pixel).count() == 0
+
+        db_session.add(pixel)
+        db_session.commit()
+
+        assert db_session.query(db.Pixel).count() == 1
+
+    def test_delete_a_referenced_grid(self, db_session, pixel):
+        """Remove a record that is referenced with a FK."""
+        db_session.add(pixel)
+        db_session.commit()
+
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
+        stmt = sqla.delete(db.Grid).where(db.Grid.id == pixel.grid.id)
+
+        with pytest.raises(
+            sa_exc.IntegrityError, match='fk_pixels_to_grids_via_grid_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_negative_n_x(self, db_session, pixel):
+        """Insert an instance with invalid data."""
+        pixel.n_x = -1
+        db_session.add(pixel)
+
+        with pytest.raises(sa_exc.IntegrityError, match='n_x_is_positive'):
+            db_session.commit()
+
+    def test_negative_n_y(self, db_session, pixel):
+        """Insert an instance with invalid data."""
+        pixel.n_y = -1
+        db_session.add(pixel)
+
+        with pytest.raises(sa_exc.IntegrityError, match='n_y_is_positive'):
+            db_session.commit()
+
+    def test_non_unique_coordinates_within_a_grid(self, db_session, pixel):
+        """Insert an instance with invalid data."""
+        another_pixel = db.Pixel(grid=pixel.grid, n_x=pixel.n_x, n_y=pixel.n_y)
+        db_session.add(another_pixel)
+
+        with pytest.raises(sa_exc.IntegrityError, match='duplicate key value'):
+            db_session.commit()
+
+
+class TestProperties:
+    """Test properties in `Pixel`."""
+
+    def test_side_length(self, pixel):
+        """Test `Pixel.side_length` property."""
+        result = pixel.side_length
+
+        assert result == 1_000
+
+    def test_area(self, pixel):
+        """Test `Pixel.area` property."""
+        result = pixel.area
+
+        assert result == 1.0
+
+    def test_northeast(self, pixel):
+        """Test `Pixel.northeast` property."""
+        result = pixel.northeast
+
+        assert abs(result.x - pixel.side_length) < 2
+        assert abs(result.y - pixel.side_length) < 2
+
+    def test_northeast_is_cached(self, pixel):
+        """Test `Pixel.northeast` property."""
+        result1 = pixel.northeast
+        result2 = pixel.northeast
+
+        assert result1 is result2
+
+    def test_southwest(self, pixel):
+        """Test `Pixel.southwest` property."""
+        result = pixel.southwest
+
+        assert abs(result.x) < 2
+        assert abs(result.y) < 2
+
+    def test_southwest_is_cached(self, pixel):
+        """Test `Pixel.southwest` property."""
+        result1 = pixel.southwest
+        result2 = pixel.southwest
+
+        assert result1 is result2
+
+    @pytest.fixture
+    def _restaurants_mock(self, mocker, monkeypatch, restaurant):
+        """A `Mock` whose `.return_value` is `[restaurant]`."""
+        mock = mocker.Mock()
+        query = (  # noqa:ECE001
+            mock.query.return_value.join.return_value.filter.return_value.all  # noqa:E501,WPS219
+        )
+        query.return_value = [restaurant]
+        monkeypatch.setattr(db, 'session', mock)
+
+    @pytest.mark.usefixtures('_restaurants_mock')
+    def test_restaurants(self, pixel, restaurant):
+        """Test `Pixel.restaurants` property."""
+        result = pixel.restaurants
+
+        assert result == [restaurant]
+
+    @pytest.mark.usefixtures('_restaurants_mock')
+    def test_restaurants_is_cached(self, pixel):
+        """Test `Pixel.restaurants` property."""
+        result1 = pixel.restaurants
+        result2 = pixel.restaurants
+
+        assert result1 is result2
+
+    @pytest.mark.db
+    def test_restaurants_with_db(self, pixel):
+        """Test `Pixel.restaurants` property.
+
+        This is a trivial integration test.
+        """
+        result = pixel.restaurants
+
+        assert not result  # = empty `list`

tests/db/test_restaurants.py

@@ -1,80 +1,69 @@
-"""Test the ORM's Restaurant model."""
+"""Test the ORM's `Restaurant` model."""
 
 import pytest
+import sqlalchemy as sqla
 from sqlalchemy import exc as sa_exc
-from sqlalchemy.orm import exc as orm_exc
 
 from urban_meal_delivery import db
 
 
 class TestSpecialMethods:
-    """Test special methods in Restaurant."""
+    """Test special methods in `Restaurant`."""
 
-    # pylint:disable=no-self-use
+    def test_create_restaurant(self, restaurant):
-
+        """Test instantiation of a new `Restaurant` object."""
-    def test_create_restaurant(self, restaurant_data):
+        assert restaurant is not None
-        """Test instantiation of a new Restaurant object."""
-        result = db.Restaurant(**restaurant_data)
-
-        assert result is not None
-
-    def test_text_representation(self, restaurant_data):
-        """Restaurant has a non-literal text representation."""
-        restaurant = db.Restaurant(**restaurant_data)
-        name = restaurant_data['name']
 
+    def test_text_representation(self, restaurant):
+        """`Restaurant` has a non-literal text representation."""
         result = repr(restaurant)
 
-        assert result == f'<Restaurant({name})>'
+        assert result == f'<Restaurant({restaurant.name})>'
 
 
-@pytest.mark.e2e
+@pytest.mark.db
 @pytest.mark.no_cover
 class TestConstraints:
-    """Test the database constraints defined in Restaurant."""
+    """Test the database constraints defined in `Restaurant`."""
 
-    # pylint:disable=no-self-use
+    def test_insert_into_database(self, db_session, restaurant):
+        """Insert an instance into the (empty) database."""
+        assert db_session.query(db.Restaurant).count() == 0
 
-    def test_insert_into_database(self, restaurant, db_session):
-        """Insert an instance into the database."""
         db_session.add(restaurant)
         db_session.commit()
 
-    def test_dublicate_primary_key(self, restaurant, restaurant_data, db_session):
+        assert db_session.query(db.Restaurant).count() == 1
-        """Can only add a record once."""
-        db_session.add(restaurant)
-        db_session.commit()
 
-        another_restaurant = db.Restaurant(**restaurant_data)
+    def test_delete_a_referenced_address(self, db_session, restaurant):
-        db_session.add(another_restaurant)
-
-        with pytest.raises(orm_exc.FlushError):
-            db_session.commit()
-
-    def test_delete_a_referenced_address(self, restaurant, address, db_session):
         """Remove a record that is referenced with a FK."""
         db_session.add(restaurant)
         db_session.commit()
 
-        with pytest.raises(sa_exc.IntegrityError):
+        # Must delete without ORM as otherwise an UPDATE statement is emitted.
-            db_session.execute(
+        stmt = sqla.delete(db.Address).where(db.Address.id == restaurant.address.id)
-                db.Address.__table__.delete().where(  # noqa:WPS609
-                    db.Address.id == address.id,
-                ),
-            )
 
-    def test_negative_prep_duration(self, restaurant, db_session):
+        with pytest.raises(
+            sa_exc.IntegrityError, match='fk_restaurants_to_addresses_via_address_id',
+        ):
+            db_session.execute(stmt)
+
+    def test_negative_prep_duration(self, db_session, restaurant):
         """Insert an instance with invalid data."""
         restaurant.estimated_prep_duration = -1
         db_session.add(restaurant)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(
+            sa_exc.IntegrityError, match='realistic_estimated_prep_duration',
+        ):
             db_session.commit()
 
-    def test_too_high_prep_duration(self, restaurant, db_session):
+    def test_too_high_prep_duration(self, db_session, restaurant):
         """Insert an instance with invalid data."""
         restaurant.estimated_prep_duration = 2500
         db_session.add(restaurant)
 
-        with pytest.raises(sa_exc.IntegrityError):
+        with pytest.raises(
+            sa_exc.IntegrityError, match='realistic_estimated_prep_duration',
+        ):
             db_session.commit()

tests/db/utils/__init__.py

@@ -0,0 +1 @@
+"""Test the utilities for the ORM layer."""

tests/db/utils/test_locations.py

@@ -0,0 +1,195 @@
+"""Test the `Location` class."""
+
+import pytest
+
+from urban_meal_delivery.db import utils
+
+
+# All tests take place in Paris.
+MIN_EASTING, MAX_EASTING = 443_100, 461_200
+MIN_NORTHING, MAX_NORTHING = 5_407_200, 5_416_800
+ZONE = '31U'
+
+
+@pytest.fixture
+def location(address):
+    """A `Location` object based off the `address` fixture."""
+    obj = utils.Location(address.latitude, address.longitude)
+
+    assert obj.zone == ZONE  # sanity check
+
+    return obj
+
+
+@pytest.fixture
+def faraway_location():
+    """A `Location` object far away from the `location`."""
+    obj = utils.Location(latitude=0, longitude=0)
+
+    assert obj.zone != ZONE  # sanity check
+
+    return obj
+
+
+@pytest.fixture
+def origin(city):
+    """A `Location` object based off the one and only `city`."""
+    obj = city.southwest
+
+    assert obj.zone == ZONE  # sanity check
+
+    return obj
+
+
+class TestSpecialMethods:
+    """Test special methods in `Location`."""
+
+    def test_create_utm_coordinates(self, location):
+        """Test instantiation of a new `Location` object."""
+        assert location is not None
+
+    def test_text_representation(self, location):
+        """The text representation is a non-literal."""
+        result = repr(location)
+
+        assert result.startswith('<Location:')
+        assert result.endswith('>')
+
+    @pytest.mark.e2e
+    def test_coordinates_in_the_text_representation(self, location):
+        """Test the UTM convention in the non-literal text `repr()`.
+
+        Example Format:
+            `<UTM: 17T 630084 4833438>'`
+        """
+        result = repr(location)
+
+        parts = result.split(' ')
+        zone = parts[1]
+        easting = int(parts[2])
+        northing = int(parts[3][:-1])  # strip the ending ">"
+
+        assert zone == location.zone
+        assert MIN_EASTING < easting < MAX_EASTING
+        assert MIN_NORTHING < northing < MAX_NORTHING
+
+    def test_compare_utm_coordinates_to_different_data_type(self, location):
+        """Test `Location.__eq__()`."""
+        result = location == object()
+
+        assert result is False
+
+    def test_compare_utm_coordinates_to_far_away_coordinates(
+        self, location, faraway_location,
+    ):
+        """Test `Location.__eq__()`."""
+        with pytest.raises(ValueError, match='must be in the same zone'):
+            bool(location == faraway_location)
+
+    def test_compare_utm_coordinates_to_equal_coordinates(self, location, address):
+        """Test `Location.__eq__()`."""
+        same_location = utils.Location(address.latitude, address.longitude)
+
+        result = location == same_location
+
+        assert result is True
+
+    def test_compare_utm_coordinates_to_themselves(self, location):
+        """Test `Location.__eq__()`."""
+        result = location == location  # noqa:WPS312
+
+        assert result is True
+
+    def test_compare_utm_coordinates_to_different_coordinates(self, location, origin):
+        """Test `Location.__eq__()`."""
+        result = location == origin
+
+        assert result is False
+
+
+class TestProperties:
+    """Test properties in `Location`."""
+
+    def test_latitude(self, location, address):
+        """Test `Location.latitude` property."""
+        result = location.latitude
+
+        assert result == pytest.approx(float(address.latitude))
+
+    def test_longitude(self, location, address):
+        """Test `Location.longitude` property."""
+        result = location.longitude
+
+        assert result == pytest.approx(float(address.longitude))
+
+    def test_easting(self, location):
+        """Test `Location.easting` property."""
+        result = location.easting
+
+        assert MIN_EASTING < result < MAX_EASTING
+
+    def test_northing(self, location):
+        """Test `Location.northing` property."""
+        result = location.northing
+
+        assert MIN_NORTHING < result < MAX_NORTHING
+
+    def test_zone(self, location):
+        """Test `Location.zone` property."""
+        result = location.zone
+
+        assert result == ZONE
+
+    def test_zone_details(self, location):
+        """Test `Location.zone_details` property."""
+        result = location.zone_details
+
+        zone, band = result
+        assert ZONE == f'{zone}{band}'
+
+
+class TestRelateTo:
+    """Test the `Location.relate_to()` method and the `.x` and `.y` properties."""
+
+    def test_run_relate_to_twice(self, location, origin):
+        """The `.relate_to()` method must only be run once."""
+        location.relate_to(origin)
+
+        with pytest.raises(RuntimeError, match='once'):
+            location.relate_to(origin)
+
+    def test_call_relate_to_with_wrong_other_type(self, location):
+        """`other` must be another `Location`."""
+        with pytest.raises(TypeError, match='Location'):
+            location.relate_to(object())
+
+    def test_call_relate_to_with_far_away_other(
+        self, location, faraway_location,
+    ):
+        """The `other` origin must be in the same UTM zone."""
+        with pytest.raises(ValueError, match='must be in the same zone'):
+            location.relate_to(faraway_location)
+
+    def test_access_x_without_origin(self, location):
+        """`.relate_to()` must be called before `.x` can be accessed."""
+        with pytest.raises(RuntimeError, match='origin to relate to must be set'):
+            int(location.x)
+
+    def test_access_y_without_origin(self, location):
+        """`.relate_to()` must be called before `.y` can be accessed."""
+        with pytest.raises(RuntimeError, match='origin to relate to must be set'):
+            int(location.y)
+
+    def test_origin_must_be_lower_left_when_relating_to_oneself(self, location):
+        """`.x` and `.y` must be `== (0, 0)` when oneself is the origin."""
+        location.relate_to(location)
+
+        assert (location.x, location.y) == (0, 0)
+
+    @pytest.mark.e2e
+    def test_x_and_y_must_not_be_lower_left_for_address_in_city(self, location, origin):
+        """`.x` and `.y` must be `> (0, 0)` when oneself is the origin."""
+        location.relate_to(origin)
+
+        assert location.x > 0
+        assert location.y > 0

tests/forecasts/__init__.py

@@ -0,0 +1 @@
+"""Tests for the `urban_meal_delivery.forecasts` sub-package."""

tests/forecasts/conftest.py

@@ -0,0 +1,138 @@
+"""Fixtures for testing the `urban_meal_delivery.forecasts` sub-package."""
+
+import datetime as dt
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import config
+from urban_meal_delivery.forecasts import timify
+
+
+@pytest.fixture
+def horizontal_datetime_index():
+    """A `pd.Index` with `DateTime` values.
+
+    The times resemble a horizontal time series with a `frequency` of `7`.
+    All observations take place at `NOON`.
+    """
+    first_start_at = dt.datetime(
+        test_config.YEAR, test_config.MONTH, test_config.DAY, test_config.NOON, 0,
+    )
+
+    gen = (
+        start_at
+        for start_at in pd.date_range(first_start_at, test_config.END, freq='D')
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    # Sanity check.
+    # `+1` as both the `START` and `END` day are included.
+    n_days = (test_config.END - test_config.START).days + 1
+    assert len(index) == n_days
+
+    return index
+
+
+@pytest.fixture
+def horizontal_no_demand(horizontal_datetime_index):
+    """A horizontal time series with order totals: no demand."""
+    return pd.Series(0, index=horizontal_datetime_index, name='n_orders')
+
+
+@pytest.fixture
+def vertical_datetime_index():
+    """A `pd.Index` with `DateTime` values.
+
+    The times resemble a vertical time series with a
+    `frequency` of `7` times the number of daily time steps,
+    which is `12` for `LONG_TIME_STEP` values.
+    """
+    gen = (
+        start_at
+        for start_at in pd.date_range(
+            test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    # Sanity check: n_days * n_number_of_opening_hours.
+    # `+1` as both the `START` and `END` day are included.
+    n_days = (test_config.END - test_config.START).days + 1
+    assert len(index) == n_days * 12
+
+    return index
+
+
+@pytest.fixture
+def vertical_no_demand(vertical_datetime_index):
+    """A vertical time series with order totals: no demand."""
+    return pd.Series(0, index=vertical_datetime_index, name='n_orders')
+
+
+@pytest.fixture
+def good_pixel_id(pixel):
+    """A `pixel_id` that is on the `grid`."""
+    return pixel.id  # `== 1`
+
+
+@pytest.fixture
+def predict_at() -> dt.datetime:
+    """`NOON` on the day to be predicted."""
+    return dt.datetime(
+        test_config.END.year,
+        test_config.END.month,
+        test_config.END.day,
+        test_config.NOON,
+    )
+
+
+@pytest.fixture
+def order_totals(good_pixel_id):
+    """A mock for `OrderHistory.totals`.
+
+    To be a bit more realistic, we sample two pixels on the `grid`.
+
+    Uses the LONG_TIME_STEP as the length of a time step.
+    """
+    pixel_ids = [good_pixel_id, good_pixel_id + 1]
+
+    gen = (
+        (pixel_id, start_at)
+        for pixel_id in pixel_ids
+        for start_at in pd.date_range(
+            test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    # Re-index `data` filling in `0`s where there is no demand.
+    index = pd.MultiIndex.from_tuples(gen)
+    index.names = ['pixel_id', 'start_at']
+
+    df = pd.DataFrame(data={'n_orders': 1}, index=index)
+
+    # Sanity check: n_pixels * n_time_steps_per_day * n_days.
+    # `+1` as both the `START` and `END` day are included.
+    n_days = (test_config.END - test_config.START).days + 1
+    assert len(df) == 2 * 12 * n_days
+
+    return df
+
+
+@pytest.fixture
+def order_history(order_totals, grid):
+    """An `OrderHistory` object that does not need the database.
+
+    Uses the LONG_TIME_STEP as the length of a time step.
+    """
+    oh = timify.OrderHistory(grid=grid, time_step=test_config.LONG_TIME_STEP)
+    oh._data = order_totals
+
+    return oh

tests/forecasts/methods/__init__.py

@@ -0,0 +1 @@
+"""Tests for the `urban_meal_delivery.forecasts.methods` sub-package."""

tests/forecasts/methods/test_decomposition.py

@@ -0,0 +1,243 @@
+"""Test the `stl()` function."""
+
+import math
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery.forecasts.methods import decomposition
+
+
+# The "periodic" `ns` suggested for the STL method.
+NS = 999
+
+
+class TestInvalidArguments:
+    """Test `stl()` with invalid arguments."""
+
+    def test_no_nans_in_time_series(self, vertical_datetime_index):
+        """`stl()` requires a `time_series` without `NaN` values."""
+        time_series = pd.Series(dtype=float, index=vertical_datetime_index)
+
+        with pytest.raises(ValueError, match='`NaN` values'):
+            decomposition.stl(
+                time_series, frequency=test_config.VERTICAL_FREQUENCY_LONG, ns=NS,
+            )
+
+    def test_ns_not_odd(self, vertical_no_demand):
+        """`ns` must be odd and `>= 7`."""
+        with pytest.raises(ValueError, match='`ns`'):
+            decomposition.stl(
+                vertical_no_demand, frequency=test_config.VERTICAL_FREQUENCY_LONG, ns=8,
+            )
+
+    @pytest.mark.parametrize('ns', [-99, -1, 1, 5])
+    def test_ns_smaller_than_seven(self, vertical_no_demand, ns):
+        """`ns` must be odd and `>= 7`."""
+        with pytest.raises(ValueError, match='`ns`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=ns,
+            )
+
+    def test_nt_not_odd(self, vertical_no_demand):
+        """`nt` must be odd and `>= default_nt`."""
+        nt = 200
+        default_nt = math.ceil(
+            (1.5 * test_config.VERTICAL_FREQUENCY_LONG) / (1 - (1.5 / NS)),
+        )
+
+        assert nt > default_nt  # sanity check
+
+        with pytest.raises(ValueError, match='`nt`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                nt=nt,
+            )
+
+    @pytest.mark.parametrize('nt', [-99, -1, 0, 1, 99, 125])
+    def test_nt_not_at_least_the_default(self, vertical_no_demand, nt):
+        """`nt` must be odd and `>= default_nt`."""
+        # `default_nt` becomes 161.
+        default_nt = math.ceil(
+            (1.5 * test_config.VERTICAL_FREQUENCY_LONG) / (1 - (1.5 / NS)),
+        )
+
+        assert nt < default_nt  # sanity check
+
+        with pytest.raises(ValueError, match='`nt`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                nt=nt,
+            )
+
+    def test_nl_not_odd(self, vertical_no_demand):
+        """`nl` must be odd and `>= frequency`."""
+        nl = 200
+
+        assert nl > test_config.VERTICAL_FREQUENCY_LONG  # sanity check
+
+        with pytest.raises(ValueError, match='`nl`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                nl=nl,
+            )
+
+    def test_nl_at_least_the_frequency(self, vertical_no_demand):
+        """`nl` must be odd and `>= frequency`."""
+        nl = 77
+
+        assert nl < test_config.VERTICAL_FREQUENCY_LONG  # sanity check
+
+        with pytest.raises(ValueError, match='`nl`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                nl=nl,
+            )
+
+    def test_ds_not_zero_or_one(self, vertical_no_demand):
+        """`ds` must be `0` or `1`."""
+        with pytest.raises(ValueError, match='`ds`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                ds=2,
+            )
+
+    def test_dt_not_zero_or_one(self, vertical_no_demand):
+        """`dt` must be `0` or `1`."""
+        with pytest.raises(ValueError, match='`dt`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                dt=2,
+            )
+
+    def test_dl_not_zero_or_one(self, vertical_no_demand):
+        """`dl` must be `0` or `1`."""
+        with pytest.raises(ValueError, match='`dl`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                dl=2,
+            )
+
+    @pytest.mark.parametrize('js', [-1, 0])
+    def test_js_not_positive(self, vertical_no_demand, js):
+        """`js` must be positive."""
+        with pytest.raises(ValueError, match='`js`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                js=js,
+            )
+
+    @pytest.mark.parametrize('jt', [-1, 0])
+    def test_jt_not_positive(self, vertical_no_demand, jt):
+        """`jt` must be positive."""
+        with pytest.raises(ValueError, match='`jt`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                jt=jt,
+            )
+
+    @pytest.mark.parametrize('jl', [-1, 0])
+    def test_jl_not_positive(self, vertical_no_demand, jl):
+        """`jl` must be positive."""
+        with pytest.raises(ValueError, match='`jl`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                jl=jl,
+            )
+
+    @pytest.mark.parametrize('ni', [-1, 0])
+    def test_ni_not_positive(self, vertical_no_demand, ni):
+        """`ni` must be positive."""
+        with pytest.raises(ValueError, match='`ni`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                ni=ni,
+            )
+
+    def test_no_not_non_negative(self, vertical_no_demand):
+        """`no` must be non-negative."""
+        with pytest.raises(ValueError, match='`no`'):
+            decomposition.stl(
+                vertical_no_demand,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+                ns=NS,
+                no=-1,
+            )
+
+
+@pytest.mark.r
+class TestValidArguments:
+    """Test `stl()` with valid arguments."""
+
+    def test_structure_of_returned_dataframe(self, vertical_no_demand):
+        """`stl()` returns a `pd.DataFrame` with three columns."""
+        result = decomposition.stl(
+            vertical_no_demand, frequency=test_config.VERTICAL_FREQUENCY_LONG, ns=NS,
+        )
+
+        assert isinstance(result, pd.DataFrame)
+        assert list(result.columns) == ['seasonal', 'trend', 'residual']
+
+    # Run the `stl()` function with all possible combinations of arguments,
+    # including default ones and explicitly set non-default ones.
+    @pytest.mark.parametrize('nt', [None, 163])
+    @pytest.mark.parametrize('nl', [None, 777])
+    @pytest.mark.parametrize('ds', [0, 1])
+    @pytest.mark.parametrize('dt', [0, 1])
+    @pytest.mark.parametrize('dl', [0, 1])
+    @pytest.mark.parametrize('js', [None, 1])
+    @pytest.mark.parametrize('jt', [None, 1])
+    @pytest.mark.parametrize('jl', [None, 1])
+    @pytest.mark.parametrize('ni', [2, 3])
+    @pytest.mark.parametrize('no', [0, 1])
+    def test_decompose_time_series_with_no_demand(  # noqa:WPS211,WPS216
+        self, vertical_no_demand, nt, nl, ds, dt, dl, js, jt, jl, ni, no,  # noqa:WPS110
+    ):
+        """Decomposing a time series with no demand ...
+
+        ... returns a `pd.DataFrame` with three columns holding only `0.0` values.
+        """
+        decomposed = decomposition.stl(
+            vertical_no_demand,
+            frequency=test_config.VERTICAL_FREQUENCY_LONG,
+            ns=NS,
+            nt=nt,
+            nl=nl,
+            ds=ds,
+            dt=dt,
+            dl=dl,
+            js=js,
+            jt=jt,
+            jl=jl,
+            ni=ni,
+            no=no,  # noqa:WPS110
+        )
+
+        result = decomposed.sum().sum()
+
+        assert result == 0

tests/forecasts/methods/test_predictions.py

@@ -0,0 +1,130 @@
+"""Test all the `*.predict()` functions in the `methods` sub-package."""
+
+import datetime as dt
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import config
+from urban_meal_delivery.forecasts.methods import arima
+from urban_meal_delivery.forecasts.methods import ets
+from urban_meal_delivery.forecasts.methods import extrapolate_season
+
+
+@pytest.fixture
+def forecast_interval():
+    """A `pd.Index` with `DateTime` values ...
+
+    ... that takes place one day after the `START`-`END` horizon and
+    resembles an entire day (`12` "start_at" values as we use `LONG_TIME_STEP`).
+    """
+    future_day = test_config.END.date() + dt.timedelta(days=1)
+    first_start_at = dt.datetime(
+        future_day.year, future_day.month, future_day.day, config.SERVICE_START, 0,
+    )
+    end_of_day = dt.datetime(
+        future_day.year, future_day.month, future_day.day, config.SERVICE_END, 0,
+    )
+
+    gen = (
+        start_at
+        for start_at in pd.date_range(
+            first_start_at, end_of_day, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    return index
+
+
+@pytest.fixture
+def forecast_time_step():
+    """A `pd.Index` with one `DateTime` value, resembling `NOON`."""
+    future_day = test_config.END.date() + dt.timedelta(days=1)
+
+    start_at = dt.datetime(
+        future_day.year, future_day.month, future_day.day, test_config.NOON, 0,
+    )
+
+    index = pd.Index([start_at])
+    index.name = 'start_at'
+
+    return index
+
+
+@pytest.mark.r
+@pytest.mark.parametrize(
+    'func', [arima.predict, ets.predict, extrapolate_season.predict],
+)
+class TestMakePredictions:
+    """Make predictions with `arima.predict()` and `ets.predict()`."""
+
+    def test_training_data_contains_nan_values(
+        self, func, vertical_no_demand, forecast_interval,
+    ):
+        """`training_ts` must not contain `NaN` values."""
+        vertical_no_demand.iloc[0] = pd.NA
+
+        with pytest.raises(ValueError, match='must not contain `NaN`'):
+            func(
+                training_ts=vertical_no_demand,
+                forecast_interval=forecast_interval,
+                frequency=test_config.VERTICAL_FREQUENCY_LONG,
+            )
+
+    def test_structure_of_returned_dataframe(
+        self, func, vertical_no_demand, forecast_interval,
+    ):
+        """Both `.predict()` return a `pd.DataFrame` with five columns."""
+        result = func(
+            training_ts=vertical_no_demand,
+            forecast_interval=forecast_interval,
+            frequency=test_config.VERTICAL_FREQUENCY_LONG,
+        )
+
+        assert isinstance(result, pd.DataFrame)
+        assert list(result.columns) == [
+            'prediction',
+            'low80',
+            'high80',
+            'low95',
+            'high95',
+        ]
+
+    def test_predict_horizontal_time_series_with_no_demand(
+        self, func, horizontal_no_demand, forecast_time_step,
+    ):
+        """Predicting a horizontal time series with no demand ...
+
+        ... returns a `pd.DataFrame` with five columns holding only `0.0` values.
+        """
+        predictions = func(
+            training_ts=horizontal_no_demand,
+            forecast_interval=forecast_time_step,
+            frequency=7,
+        )
+
+        result = predictions.sum().sum()
+
+        assert result == 0
+
+    def test_predict_vertical_time_series_with_no_demand(
+        self, func, vertical_no_demand, forecast_interval,
+    ):
+        """Predicting a vertical time series with no demand ...
+
+        ... returns a `pd.DataFrame` with five columns holding only `0.0` values.
+        """
+        predictions = func(
+            training_ts=vertical_no_demand,
+            forecast_interval=forecast_interval,
+            frequency=test_config.VERTICAL_FREQUENCY_LONG,
+        )
+
+        result = predictions.sum().sum()
+
+        assert result == 0

tests/forecasts/test_models.py

@@ -0,0 +1,172 @@
+"""Tests for the `urban_meal_delivery.forecasts.models` sub-package."""
+
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import models
+
+
+MODELS = (
+    models.HorizontalETSModel,
+    models.HorizontalSMAModel,
+    models.RealtimeARIMAModel,
+    models.VerticalARIMAModel,
+    models.TrivialModel,
+)
+
+
+@pytest.mark.parametrize('model_cls', MODELS)
+class TestGenericForecastingModelProperties:
+    """Test everything all concrete `*Model`s have in common.
+
+    The test cases here replace testing the `ForecastingModelABC` class on its own.
+
+    As uncertainty is in the nature of forecasting, we do not test the individual
+    point forecasts or confidence intervals themselves. Instead, we confirm
+    that all the `*Model`s adhere to the `ForecastingModelABC` generically.
+    So, these test cases are more like integration tests conceptually.
+
+    Also, note that some `methods.*.predict()` functions use R behind the scenes.
+    """  # noqa:RST215
+
+    def test_create_model(self, model_cls, order_history):
+        """Test instantiation of a new and concrete `*Model` object."""
+        model = model_cls(order_history=order_history)
+
+        assert model is not None
+
+    def test_model_has_a_name(self, model_cls, order_history):
+        """Access the `*Model.name` property."""
+        model = model_cls(order_history=order_history)
+
+        result = model.name
+
+        assert isinstance(result, str)
+
+    unique_model_names = set()
+
+    def test_each_model_has_a_unique_name(self, model_cls, order_history):
+        """The `*Model.name` values must be unique across all `*Model`s.
+
+        Important: this test case has a side effect that is visible
+        across the different parametrized versions of this case!
+        """  # noqa:RST215
+        model = model_cls(order_history=order_history)
+
+        assert model.name not in self.unique_model_names
+
+        self.unique_model_names.add(model.name)
+
+    @pytest.mark.r
+    def test_make_prediction_structure(
+        self, model_cls, order_history, pixel, predict_at,
+    ):
+        """`*Model.predict()` returns a `pd.DataFrame` ...
+
+        ... with known columns.
+        """  # noqa:RST215
+        model = model_cls(order_history=order_history)
+
+        result = model.predict(
+            pixel=pixel,
+            predict_at=predict_at,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, pd.DataFrame)
+        assert list(result.columns) == [
+            'actual',
+            'prediction',
+            'low80',
+            'high80',
+            'low95',
+            'high95',
+        ]
+
+    @pytest.mark.r
+    def test_make_prediction_for_given_time_step(
+        self, model_cls, order_history, pixel, predict_at,
+    ):
+        """`*Model.predict()` returns a row for ...
+
+        ... the time step starting at `predict_at`.
+        """  # noqa:RST215
+        model = model_cls(order_history=order_history)
+
+        result = model.predict(
+            pixel=pixel,
+            predict_at=predict_at,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert predict_at in result.index
+
+    @pytest.mark.r
+    def test_make_prediction_contains_actual_values(
+        self, model_cls, order_history, pixel, predict_at,
+    ):
+        """`*Model.predict()` returns a `pd.DataFrame` ...
+
+        ... where the "actual" and "prediction" columns must not be empty.
+        """  # noqa:RST215
+        model = model_cls(order_history=order_history)
+
+        result = model.predict(
+            pixel=pixel,
+            predict_at=predict_at,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert not result['actual'].isnull().any()
+        assert not result['prediction'].isnull().any()
+
+    @pytest.mark.db
+    @pytest.mark.r
+    def test_make_forecast(  # noqa:WPS211
+        self, db_session, model_cls, order_history, pixel, predict_at,
+    ):
+        """`*Model.make_forecast()` returns a `Forecast` object."""  # noqa:RST215
+        model = model_cls(order_history=order_history)
+
+        result = model.make_forecast(
+            pixel=pixel,
+            predict_at=predict_at,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, db.Forecast)
+        assert result.pixel == pixel
+        assert result.start_at == predict_at
+        assert result.train_horizon == test_config.LONG_TRAIN_HORIZON
+
+    @pytest.mark.db
+    @pytest.mark.r
+    def test_make_forecast_is_cached(  # noqa:WPS211
+        self, db_session, model_cls, order_history, pixel, predict_at,
+    ):
+        """`*Model.make_forecast()` caches the `Forecast` object."""  # noqa:RST215
+        model = model_cls(order_history=order_history)
+
+        assert db_session.query(db.Forecast).count() == 0
+
+        result1 = model.make_forecast(
+            pixel=pixel,
+            predict_at=predict_at,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        n_cached_forecasts = db_session.query(db.Forecast).count()
+        assert n_cached_forecasts >= 1
+
+        result2 = model.make_forecast(
+            pixel=pixel,
+            predict_at=predict_at,
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert n_cached_forecasts == db_session.query(db.Forecast).count()
+
+        assert result1 == result2

tests/forecasts/timify/__init__.py

@@ -0,0 +1 @@
+"""Tests for the `urban_meal_delivery.forecasts.timify` module."""

tests/forecasts/timify/test_aggregate_orders.py

@@ -0,0 +1,386 @@
+"""Test the `OrderHistory.aggregate_orders()` method."""
+
+import datetime
+
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts import timify
+
+
+@pytest.mark.db
+class TestAggregateOrders:
+    """Test the `OrderHistory.aggregate_orders()` method.
+
+    The test cases are integration tests that model realistic scenarios.
+    """
+
+    @pytest.fixture
+    def addresses_mock(self, mocker, monkeypatch):
+        """A `Mock` whose `.return_value` are to be set ...
+
+        ... to the addresses that are gridified. The addresses are
+        all considered `Order.pickup_address` attributes for some orders.
+
+        Note: This fixture also exists in `tests.db.test_grids`.
+        """
+        mock = mocker.Mock()
+        query = (  # noqa:ECE001
+            mock.query.return_value.join.return_value.filter.return_value.all  # noqa:E501,WPS219
+        )
+        monkeypatch.setattr(db, 'session', mock)
+
+        return query
+
+    @pytest.fixture
+    def one_pixel_grid(self, db_session, city, restaurant, addresses_mock):
+        """A persisted `Grid` with one `Pixel`.
+
+        `restaurant` must be a dependency as otherwise the `restaurant.address`
+        is not put into the database as an `Order.pickup_address`.
+        """
+        addresses_mock.return_value = [restaurant.address]
+
+        # `+1` as otherwise there would be a second pixel in one direction.
+        side_length = max(city.total_x, city.total_y) + 1
+        grid = db.Grid.gridify(city=city, side_length=side_length)
+        db_session.add(grid)
+
+        assert len(grid.pixels) == 1  # sanity check
+
+        return grid
+
+    def test_no_orders(self, db_session, one_pixel_grid, restaurant):
+        """Edge case that does not occur for real-life data."""
+        db_session.commit()
+        assert len(restaurant.orders) == 0  # noqa:WPS507  sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        assert len(result) == 0  # noqa:WPS507
+
+    def test_evenly_distributed_ad_hoc_orders(
+        self, db_session, one_pixel_grid, restaurant, make_order,
+    ):
+        """12 ad-hoc orders, one per operating hour."""
+        # Create one order per hour and 12 orders in total.
+        for hour in range(11, 23):
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant,
+                placed_at=datetime.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, hour, 11,
+                ),
+            )
+            db_session.add(order)
+
+        db_session.commit()
+
+        assert len(restaurant.orders) == 12  # sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # The resulting `DataFrame` has 12 rows holding `1`s.
+        assert len(result) == 12
+        assert result['n_orders'].min() == 1
+        assert result['n_orders'].max() == 1
+        assert result['n_orders'].sum() == 12
+
+    def test_evenly_distributed_ad_hoc_orders_with_no_demand_late(  # noqa:WPS218
+        self, db_session, one_pixel_grid, restaurant, make_order,
+    ):
+        """10 ad-hoc orders, one per hour, no orders after 21."""
+        # Create one order per hour and 10 orders in total.
+        for hour in range(11, 21):
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant,
+                placed_at=datetime.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, hour, 11,
+                ),
+            )
+            db_session.add(order)
+
+        db_session.commit()
+
+        assert len(restaurant.orders) == 10  # sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # Even though there are only 10 orders, there are 12 rows in the `DataFrame`.
+        # That is so as `0`s are filled in for hours without any demand at the end.
+        assert len(result) == 12
+        assert result['n_orders'].min() == 0
+        assert result['n_orders'].max() == 1
+        assert result.iloc[:10]['n_orders'].sum() == 10
+        assert result.iloc[10:]['n_orders'].sum() == 0
+
+    def test_one_ad_hoc_order_every_other_hour(
+        self, db_session, one_pixel_grid, restaurant, make_order,
+    ):
+        """6 ad-hoc orders, one every other hour."""
+        # Create one order every other hour.
+        for hour in range(11, 23, 2):
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant,
+                placed_at=datetime.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, hour, 11,
+                ),
+            )
+            db_session.add(order)
+
+        db_session.commit()
+
+        assert len(restaurant.orders) == 6  # sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # The resulting `DataFrame` has 12 rows, 6 holding `0`s, and 6 holding `1`s.
+        assert len(result) == 12
+        assert result['n_orders'].min() == 0
+        assert result['n_orders'].max() == 1
+        assert result['n_orders'].sum() == 6
+
+    def test_one_ad_hoc_and_one_pre_order(
+        self, db_session, one_pixel_grid, restaurant, make_order,
+    ):
+        """1 ad-hoc and 1 scheduled order.
+
+        The scheduled order is discarded.
+        """
+        ad_hoc_order = make_order(
+            scheduled=False,
+            restaurant=restaurant,
+            placed_at=datetime.datetime(
+                test_config.YEAR, test_config.MONTH, test_config.DAY, 11, 11,
+            ),
+        )
+        db_session.add(ad_hoc_order)
+
+        pre_order = make_order(
+            scheduled=True,
+            restaurant=restaurant,
+            placed_at=datetime.datetime(
+                test_config.YEAR, test_config.MONTH, test_config.DAY, 9, 0,
+            ),
+            scheduled_delivery_at=datetime.datetime(
+                test_config.YEAR, test_config.MONTH, test_config.DAY, 12, 0,
+            ),
+        )
+        db_session.add(pre_order)
+
+        db_session.commit()
+
+        assert len(restaurant.orders) == 2  # sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # The resulting `DataFrame` has 12 rows, 11 holding `0`s, and one holding a `1`.
+        assert len(result) == 12
+        assert result['n_orders'].min() == 0
+        assert result['n_orders'].max() == 1
+        assert result['n_orders'].sum() == 1
+
+    def test_evenly_distributed_ad_hoc_orders_with_half_hour_time_steps(  # noqa:WPS218
+        self, db_session, one_pixel_grid, restaurant, make_order,
+    ):
+        """12 ad-hoc orders, one per hour, with 30 minute time windows.
+
+        In half the time steps, there is no demand.
+        """
+        # Create one order per hour and 10 orders in total.
+        for hour in range(11, 23):
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant,
+                placed_at=datetime.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, hour, 11,
+                ),
+            )
+            db_session.add(order)
+
+        db_session.commit()
+
+        assert len(restaurant.orders) == 12  # sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.SHORT_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # The resulting `DataFrame` has 24 rows for the 24 30-minute time steps.
+        # The rows' values are `0` and `1` alternating.
+        assert len(result) == 24
+        assert result['n_orders'].min() == 0
+        assert result['n_orders'].max() == 1
+        assert result.iloc[::2]['n_orders'].sum() == 12
+        assert result.iloc[1::2]['n_orders'].sum() == 0
+
+    def test_ad_hoc_orders_over_two_days(
+        self, db_session, one_pixel_grid, restaurant, make_order,
+    ):
+        """First day 12 ad-hoc orders, one per operating hour ...
+
+        ... and 6 orders, one every other hour on the second day.
+        In total, there are 18 orders.
+        """
+        # Create one order per hour and 12 orders in total.
+        for hour in range(11, 23):
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant,
+                placed_at=datetime.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, hour, 11,
+                ),
+            )
+            db_session.add(order)
+
+        # Create one order every other hour and 6 orders in total.
+        for hour in range(11, 23, 2):  # noqa:WPS440
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant,
+                placed_at=datetime.datetime(
+                    test_config.YEAR,
+                    test_config.MONTH,
+                    test_config.DAY + 1,
+                    hour,  # noqa:WPS441
+                    11,
+                ),
+            )
+            db_session.add(order)
+
+        db_session.commit()
+
+        assert len(restaurant.orders) == 18  # sanity check
+
+        oh = timify.OrderHistory(
+            grid=one_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # The resulting `DataFrame` has 24 rows, 12 for each day.
+        assert len(result) == 24
+        assert result['n_orders'].min() == 0
+        assert result['n_orders'].max() == 1
+        assert result['n_orders'].sum() == 18
+
+    @pytest.fixture
+    def two_pixel_grid(  # noqa:WPS211
+        self, db_session, city, make_address, make_restaurant, addresses_mock,
+    ):
+        """A persisted `Grid` with two `Pixel` objects."""
+        # One `Address` in the lower-left `Pixel`, ...
+        address1 = make_address(latitude=48.8357377, longitude=2.2517412)
+        # ... and another one in the upper-right one.
+        address2 = make_address(latitude=48.8898312, longitude=2.4357622)
+
+        addresses_mock.return_value = [address1, address2]
+
+        # Create `Restaurant`s at the two addresses.
+        make_restaurant(address=address1)
+        make_restaurant(address=address2)
+
+        # This creates four `Pixel`s, two of which have no `pickup_address`.
+        side_length = max(city.total_x // 2, city.total_y // 2) + 1
+
+        grid = db.Grid.gridify(city=city, side_length=side_length)
+
+        db_session.add(grid)
+
+        assert len(grid.pixels) == 2  # sanity check
+
+        return grid
+
+    def test_two_pixels_with_shifted_orders(  # noqa:WPS218
+        self, db_session, two_pixel_grid, make_order,
+    ):
+        """One restaurant with one order every other hour ...
+
+        ... and another restaurant with two orders per hour.
+        In total, there are 30 orders.
+        """
+        address1, address2 = two_pixel_grid.city.addresses
+        # Rarely, an `Address` may have several `Restaurant`s in the real dataset.
+        restaurant1, restaurant2 = address1.restaurants[0], address2.restaurants[0]
+
+        # Create one order every other hour for `restaurant1`.
+        for hour in range(11, 23, 2):
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant1,
+                placed_at=datetime.datetime(
+                    test_config.YEAR, test_config.MONTH, test_config.DAY, hour, 11,
+                ),
+            )
+            db_session.add(order)
+
+        # Create two orders per hour for `restaurant2`.
+        for hour in range(11, 23):  # noqa:WPS440
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant2,
+                placed_at=datetime.datetime(
+                    test_config.YEAR,
+                    test_config.MONTH,
+                    test_config.DAY,
+                    hour,  # noqa:WPS441
+                    13,
+                ),
+            )
+            db_session.add(order)
+
+            order = make_order(
+                scheduled=False,
+                restaurant=restaurant2,
+                placed_at=datetime.datetime(
+                    test_config.YEAR,
+                    test_config.MONTH,
+                    test_config.DAY,
+                    hour,  # noqa:WPS441
+                    14,
+                ),
+            )
+            db_session.add(order)
+
+        db_session.commit()
+
+        # sanity checks
+        assert len(restaurant1.orders) == 6
+        assert len(restaurant2.orders) == 24
+
+        oh = timify.OrderHistory(
+            grid=two_pixel_grid, time_step=test_config.LONG_TIME_STEP,
+        )
+
+        result = oh.aggregate_orders()
+
+        # The resulting `DataFrame` has 24 rows, 12 for each pixel.
+        assert len(result) == 24
+        assert result['n_orders'].min() == 0
+        assert result['n_orders'].max() == 2
+        assert result['n_orders'].sum() == 30

tests/forecasts/timify/test_avg_daily_demand.py

@@ -0,0 +1,143 @@
+"""Tests for the `OrderHistory.avg_daily_demand()` and ...
+
+`OrderHistory.choose_tactical_model()` methods.
+
+We test both methods together as they take the same input and are really
+two parts of the same conceptual step.
+"""
+
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery.forecasts import models
+
+
+class TestAverageDailyDemand:
+    """Tests for the `OrderHistory.avg_daily_demand()` method."""
+
+    def test_avg_daily_demand_with_constant_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """The average daily demand must be the number of time steps ...
+
+        ... if the demand is `1` at each time step.
+
+        Note: The `order_history` fixture assumes `12` time steps per day as it
+        uses `LONG_TIME_STEP=60` as the length of a time step.
+        """
+        result = order_history.avg_daily_demand(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert result == 12.0
+
+    def test_avg_daily_demand_with_no_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """Without demand, the average daily demand must be `0.0`."""
+        order_history._data.loc[:, 'n_orders'] = 0
+
+        result = order_history.avg_daily_demand(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert result == 0.0
+
+
+class TestChooseTacticalModel:
+    """Tests for the `OrderHistory.choose_tactical_model()` method."""
+
+    def test_best_model_with_high_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """With high demand, the average daily demand is `.>= 25.0`."""
+        # With 12 time steps per day, the ADD becomes `36.0`.
+        order_history._data.loc[:, 'n_orders'] = 3
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, models.HorizontalETSModel)
+
+    def test_best_model_with_medium_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """With medium demand, the average daily demand is `>= 10.0` and `< 25.0`."""
+        # With 12 time steps per day, the ADD becomes `24.0`.
+        order_history._data.loc[:, 'n_orders'] = 2
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, models.HorizontalETSModel)
+
+    def test_best_model_with_low_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """With low demand, the average daily demand is `>= 2.5` and `< 10.0`."""
+        # With 12 time steps per day, the ADD becomes `12.0` ...
+        data = order_history._data
+        data.loc[:, 'n_orders'] = 1
+
+        # ... and we set three additional time steps per day to `0`.
+        data.loc[  # noqa:ECE001
+            # all `Pixel`s, all `Order`s in time steps starting at 11 am
+            (slice(None), slice(data.index.levels[1][0], None, 12)),
+            'n_orders',
+        ] = 0
+        data.loc[  # noqa:ECE001
+            # all `Pixel`s, all `Order`s in time steps starting at 12 am
+            (slice(None), slice(data.index.levels[1][1], None, 12)),
+            'n_orders',
+        ] = 0
+        data.loc[  # noqa:ECE001
+            # all `Pixel`s, all `Order`s in time steps starting at 1 pm
+            (slice(None), slice(data.index.levels[1][2], None, 12)),
+            'n_orders',
+        ] = 0
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, models.HorizontalSMAModel)
+
+    def test_best_model_with_no_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """Without demand, the average daily demand is `< 2.5`."""
+        order_history._data.loc[:, 'n_orders'] = 0
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, models.TrivialModel)
+
+    def test_best_model_for_unknown_train_horizon(
+        self, order_history, good_pixel_id, predict_at,  # noqa:RST215
+    ):
+        """For `train_horizon`s not included in the rule-based system ...
+
+        ... the method raises a `RuntimeError`.
+        """
+        with pytest.raises(RuntimeError, match='no rule'):
+            order_history.choose_tactical_model(
+                pixel_id=good_pixel_id,
+                predict_day=predict_at.date(),
+                train_horizon=test_config.SHORT_TRAIN_HORIZON,
+            )

tests/forecasts/timify/test_make_time_series.py

@@ -0,0 +1,399 @@
+"""Test the code generating time series with the order totals.
+
+Unless otherwise noted, each `time_step` is 60 minutes long implying
+12 time steps per day (i.e., we use `LONG_TIME_STEP` by default).
+"""
+
+import datetime
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import config
+
+
+@pytest.fixture
+def good_predict_at():
+    """A `predict_at` within `START`-`END` and ...
+
+    ... a long enough history so that either `SHORT_TRAIN_HORIZON`
+    or `LONG_TRAIN_HORIZON` works.
+    """
+    return datetime.datetime(
+        test_config.END.year,
+        test_config.END.month,
+        test_config.END.day,
+        test_config.NOON,
+        0,
+    )
+
+
+@pytest.fixture
+def bad_predict_at():
+    """A `predict_at` within `START`-`END` but ...
+
+    ... not a long enough history so that both `SHORT_TRAIN_HORIZON`
+    and `LONG_TRAIN_HORIZON` do not work.
+    """
+    predict_day = test_config.END - datetime.timedelta(weeks=6, days=1)
+    return datetime.datetime(
+        predict_day.year, predict_day.month, predict_day.day, test_config.NOON, 0,
+    )
+
+
+class TestMakeHorizontalTimeSeries:
+    """Test the `OrderHistory.make_horizontal_ts()` method."""
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
+        """A `pixel_id` that is not in the `grid`."""
+        with pytest.raises(LookupError):
+            order_history.make_horizontal_ts(
+                pixel_id=999_999,
+                predict_at=good_predict_at,
+                train_horizon=train_horizon,
+            )
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_are_series(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The time series come as a `pd.Series`."""
+        result = order_history.make_horizontal_ts(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        assert isinstance(training_ts, pd.Series)
+        assert training_ts.name == 'n_orders'
+        assert isinstance(actuals_ts, pd.Series)
+        assert actuals_ts.name == 'n_orders'
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_have_correct_length(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The length of a training time series must be a multiple of `7` ...
+
+        ... whereas the time series with the actual order counts has only `1` value.
+        """
+        result = order_history.make_horizontal_ts(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        assert len(training_ts) == 7 * train_horizon
+        assert len(actuals_ts) == 1
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_frequency_is_number_of_weekdays(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The `frequency` must be `7`."""
+        result = order_history.make_horizontal_ts(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        _, frequency, _ = result  # noqa:WPS434
+
+        assert frequency == 7
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_no_long_enough_history1(
+        self, order_history, good_pixel_id, bad_predict_at, train_horizon,
+    ):
+        """If the `predict_at` day is too early in the `START`-`END` horizon ...
+
+        ... the history of order totals is not long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_horizontal_ts(
+                pixel_id=good_pixel_id,
+                predict_at=bad_predict_at,
+                train_horizon=train_horizon,
+            )
+
+    def test_no_long_enough_history2(
+        self, order_history, good_pixel_id, good_predict_at,
+    ):
+        """If the `train_horizon` is longer than the `START`-`END` horizon ...
+
+        ... the history of order totals can never be long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_horizontal_ts(
+                pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
+            )
+
+
+class TestMakeVerticalTimeSeries:
+    """Test the `OrderHistory.make_vertical_ts()` method."""
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
+        """A `pixel_id` that is not in the `grid`."""
+        with pytest.raises(LookupError):
+            order_history.make_vertical_ts(
+                pixel_id=999_999,
+                predict_day=good_predict_at.date(),
+                train_horizon=train_horizon,
+            )
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_are_series(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The time series come as `pd.Series`."""
+        result = order_history.make_vertical_ts(
+            pixel_id=good_pixel_id,
+            predict_day=good_predict_at.date(),
+            train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        assert isinstance(training_ts, pd.Series)
+        assert training_ts.name == 'n_orders'
+        assert isinstance(actuals_ts, pd.Series)
+        assert actuals_ts.name == 'n_orders'
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_have_correct_length(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The length of a training time series is the product of the ...
+
+        ... weekly time steps (i.e., product of `7` and the number of daily time steps)
+        and the `train_horizon` in weeks.
+
+        The time series with the actual order counts always holds one observation
+        per time step of a day.
+        """
+        result = order_history.make_vertical_ts(
+            pixel_id=good_pixel_id,
+            predict_day=good_predict_at.date(),
+            train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        n_daily_time_steps = (
+            60
+            * (config.SERVICE_END - config.SERVICE_START)
+            // test_config.LONG_TIME_STEP
+        )
+
+        assert len(training_ts) == 7 * n_daily_time_steps * train_horizon
+        assert len(actuals_ts) == n_daily_time_steps
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_frequency_is_number_number_of_weekly_time_steps(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The `frequency` is the number of weekly time steps."""
+        result = order_history.make_vertical_ts(
+            pixel_id=good_pixel_id,
+            predict_day=good_predict_at.date(),
+            train_horizon=train_horizon,
+        )
+
+        _, frequency, _ = result  # noqa:WPS434
+
+        n_daily_time_steps = (
+            60
+            * (config.SERVICE_END - config.SERVICE_START)
+            // test_config.LONG_TIME_STEP
+        )
+
+        assert frequency == 7 * n_daily_time_steps
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_no_long_enough_history1(
+        self, order_history, good_pixel_id, bad_predict_at, train_horizon,
+    ):
+        """If the `predict_at` day is too early in the `START`-`END` horizon ...
+
+        ... the history of order totals is not long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_vertical_ts(
+                pixel_id=good_pixel_id,
+                predict_day=bad_predict_at.date(),
+                train_horizon=train_horizon,
+            )
+
+    def test_no_long_enough_history2(
+        self, order_history, good_pixel_id, good_predict_at,
+    ):
+        """If the `train_horizon` is longer than the `START`-`END` horizon ...
+
+        ... the history of order totals can never be long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_vertical_ts(
+                pixel_id=good_pixel_id,
+                predict_day=good_predict_at.date(),
+                train_horizon=999,
+            )
+
+
+class TestMakeRealTimeTimeSeries:
+    """Test the `OrderHistory.make_realtime_ts()` method."""
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
+        """A `pixel_id` that is not in the `grid`."""
+        with pytest.raises(LookupError):
+            order_history.make_realtime_ts(
+                pixel_id=999_999,
+                predict_at=good_predict_at,
+                train_horizon=train_horizon,
+            )
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_are_series(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The time series come as `pd.Series`."""
+        result = order_history.make_realtime_ts(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        assert isinstance(training_ts, pd.Series)
+        assert training_ts.name == 'n_orders'
+        assert isinstance(actuals_ts, pd.Series)
+        assert actuals_ts.name == 'n_orders'
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_have_correct_length1(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The length of a training time series is the product of the ...
+
+        ... weekly time steps (i.e., product of `7` and the number of daily time steps)
+        and the `train_horizon` in weeks; however, this assertion only holds if
+        we predict the first `time_step` of the day.
+
+        The time series with the actual order counts always holds `1` value.
+        """
+        predict_at = datetime.datetime(
+            good_predict_at.year,
+            good_predict_at.month,
+            good_predict_at.day,
+            config.SERVICE_START,
+            0,
+        )
+        result = order_history.make_realtime_ts(
+            pixel_id=good_pixel_id, predict_at=predict_at, train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        n_daily_time_steps = (
+            60
+            * (config.SERVICE_END - config.SERVICE_START)
+            // test_config.LONG_TIME_STEP
+        )
+
+        assert len(training_ts) == 7 * n_daily_time_steps * train_horizon
+        assert len(actuals_ts) == 1
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_have_correct_length2(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The length of a training time series is the product of the ...
+
+        ... weekly time steps (i.e., product of `7` and the number of daily time steps)
+        and the `train_horizon` in weeks; however, this assertion only holds if
+        we predict the first `time_step` of the day. Predicting any other `time_step`
+        means that the training time series becomes longer by the number of time steps
+        before the one being predicted.
+
+        The time series with the actual order counts always holds `1` value.
+        """
+        assert good_predict_at.hour == test_config.NOON
+
+        result = order_history.make_realtime_ts(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        training_ts, _, actuals_ts = result
+
+        n_daily_time_steps = (
+            60
+            * (config.SERVICE_END - config.SERVICE_START)
+            // test_config.LONG_TIME_STEP
+        )
+        n_time_steps_before = (
+            60 * (test_config.NOON - config.SERVICE_START) // test_config.LONG_TIME_STEP
+        )
+
+        assert (
+            len(training_ts)
+            == 7 * n_daily_time_steps * train_horizon + n_time_steps_before
+        )
+        assert len(actuals_ts) == 1
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_frequency_is_number_number_of_weekly_time_steps(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The `frequency` is the number of weekly time steps."""
+        result = order_history.make_realtime_ts(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        _, frequency, _ = result  # noqa:WPS434
+
+        n_daily_time_steps = (
+            60
+            * (config.SERVICE_END - config.SERVICE_START)
+            // test_config.LONG_TIME_STEP
+        )
+
+        assert frequency == 7 * n_daily_time_steps
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_no_long_enough_history1(
+        self, order_history, good_pixel_id, bad_predict_at, train_horizon,
+    ):
+        """If the `predict_at` day is too early in the `START`-`END` horizon ...
+
+        ... the history of order totals is not long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_realtime_ts(
+                pixel_id=good_pixel_id,
+                predict_at=bad_predict_at,
+                train_horizon=train_horizon,
+            )
+
+    def test_no_long_enough_history2(
+        self, order_history, good_pixel_id, good_predict_at,
+    ):
+        """If the `train_horizon` is longer than the `START`-`END` horizon ...
+
+        ... the history of order totals can never be long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_realtime_ts(
+                pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
+            )

tests/forecasts/timify/test_order_history.py

@@ -0,0 +1,92 @@
+"""Test the basic functionalities in the `OrderHistory` class."""
+
+import datetime as dt
+
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery.forecasts import timify
+
+
+class TestSpecialMethods:
+    """Test the special methods in `OrderHistory`."""
+
+    def test_instantiate(self, order_history):
+        """Test `OrderHistory.__init__()`."""
+        assert order_history is not None
+
+
+class TestProperties:
+    """Test the properties in `OrderHistory`."""
+
+    @pytest.mark.parametrize('time_step', test_config.TIME_STEPS)
+    def test_time_step(self, grid, time_step):
+        """Test `OrderHistory.time_step` property."""
+        order_history = timify.OrderHistory(grid=grid, time_step=time_step)
+
+        result = order_history.time_step
+
+        assert result == time_step
+
+    def test_totals(self, order_history, order_totals):
+        """Test `OrderHistory.totals` property.
+
+        The result of the `OrderHistory.aggregate_orders()` method call
+        is cached in the `OrderHistory.totals` property.
+
+        Note: `OrderHistory.aggregate_orders()` is not called as
+        `OrderHistory._data` is already set in the `order_history` fixture.
+        """
+        result = order_history.totals
+
+        assert result is order_totals
+
+    def test_totals_is_cached(self, order_history, monkeypatch):
+        """Test `OrderHistory.totals` property.
+
+        The result of the `OrderHistory.aggregate_orders()` method call
+        is cached in the `OrderHistory.totals` property.
+
+        Note: We make `OrderHistory.aggregate_orders()` return a `sentinel`
+        that is cached into `OrderHistory._data`, which must be unset first.
+        """
+        monkeypatch.setattr(order_history, '_data', None)
+        sentinel = object()
+        monkeypatch.setattr(order_history, 'aggregate_orders', lambda: sentinel)
+
+        result1 = order_history.totals
+        result2 = order_history.totals
+
+        assert result1 is result2
+        assert result1 is sentinel
+
+
+class TestMethods:
+    """Test various methods in `OrderHistory`."""
+
+    def test_first_order_at_existing_pixel(self, order_history, good_pixel_id):
+        """Test `OrderHistory.first_order_at()` with good input."""
+        result = order_history.first_order_at(good_pixel_id)
+
+        assert result == test_config.START
+
+    def test_first_order_at_non_existing_pixel(self, order_history, good_pixel_id):
+        """Test `OrderHistory.first_order_at()` with bad input."""
+        with pytest.raises(
+            LookupError, match='`pixel_id` is not in the `grid`',
+        ):
+            order_history.first_order_at(-1)
+
+    def test_last_order_at_existing_pixel(self, order_history, good_pixel_id):
+        """Test `OrderHistory.last_order_at()` with good input."""
+        result = order_history.last_order_at(good_pixel_id)
+
+        one_time_step = dt.timedelta(minutes=test_config.LONG_TIME_STEP)
+        assert result == test_config.END - one_time_step
+
+    def test_last_order_at_non_existing_pixel(self, order_history, good_pixel_id):
+        """Test `OrderHistory.last_order_at()` with bad input."""
+        with pytest.raises(
+            LookupError, match='`pixel_id` is not in the `grid`',
+        ):
+            order_history.last_order_at(-1)

tests/test_config.py

@@ -29,6 +29,9 @@ def test_database_uri_set(env, monkeypatch):
     monkeypatch.setattr(configuration.ProductionConfig, 'DATABASE_URI', uri)
     monkeypatch.setattr(configuration.TestingConfig, 'DATABASE_URI', uri)
 
+    # Prevent that a warning is emitted for a missing R_LIBS_PATH.
+    monkeypatch.setattr(configuration.Config, 'R_LIBS_PATH', '.cache/r_libs')
+
     with pytest.warns(None) as record:
         configuration.make_config(env)
 
@@ -36,15 +39,88 @@ def test_database_uri_set(env, monkeypatch):
 
 
 @pytest.mark.parametrize('env', envs)
-def test_no_database_uri_set(env, monkeypatch):
+def test_no_database_uri_set_with_testing_env_var(env, monkeypatch):
     """Package does not work without DATABASE_URI set in the environment."""
     monkeypatch.setattr(configuration.ProductionConfig, 'DATABASE_URI', None)
     monkeypatch.setattr(configuration.TestingConfig, 'DATABASE_URI', None)
 
+    monkeypatch.setenv('TESTING', 'true')
+
+    # Prevent that a warning is emitted for a missing R_LIBS_PATH.
+    monkeypatch.setattr(configuration.Config, 'R_LIBS_PATH', '.cache/r_libs')
+
+    with pytest.warns(None) as record:
+        configuration.make_config(env)
+
+    assert len(record) == 0  # noqa:WPS441,WPS507
+
+
+@pytest.mark.parametrize('env', envs)
+def test_no_database_uri_set_without_testing_env_var(env, monkeypatch):
+    """Package does not work without DATABASE_URI set in the environment."""
+    monkeypatch.setattr(configuration.ProductionConfig, 'DATABASE_URI', None)
+    monkeypatch.setattr(configuration.TestingConfig, 'DATABASE_URI', None)
+
+    monkeypatch.delenv('TESTING', raising=False)
+
+    # Prevent that a warning is emitted for a missing R_LIBS_PATH.
+    monkeypatch.setattr(configuration.Config, 'R_LIBS_PATH', '.cache/r_libs')
+
     with pytest.warns(UserWarning, match='no DATABASE_URI'):
         configuration.make_config(env)
 
 
+@pytest.mark.parametrize('env', envs)
+def test_r_libs_path_set(env, monkeypatch):
+    """Package does NOT emit a warning if R_LIBS is set in the environment."""
+    monkeypatch.setattr(configuration.Config, 'R_LIBS_PATH', '.cache/r_libs')
+
+    # Prevent that a warning is emitted for a missing DATABASE_URI.
+    uri = 'postgresql://user:password@localhost/db'
+    monkeypatch.setattr(configuration.ProductionConfig, 'DATABASE_URI', uri)
+
+    with pytest.warns(None) as record:
+        configuration.make_config(env)
+
+    assert len(record) == 0  # noqa:WPS441,WPS507
+
+
+@pytest.mark.parametrize('env', envs)
+def test_no_r_libs_path_set_with_testing_env_var(env, monkeypatch):
+    """Package emits a warning if no R_LIBS is set in the environment ...
+
+    ... when not testing.
+    """
+    monkeypatch.setattr(configuration.Config, 'R_LIBS_PATH', None)
+    monkeypatch.setenv('TESTING', 'true')
+
+    # Prevent that a warning is emitted for a missing DATABASE_URI.
+    uri = 'postgresql://user:password@localhost/db'
+    monkeypatch.setattr(configuration.ProductionConfig, 'DATABASE_URI', uri)
+
+    with pytest.warns(None) as record:
+        configuration.make_config(env)
+
+    assert len(record) == 0  # noqa:WPS441,WPS507
+
+
+@pytest.mark.parametrize('env', envs)
+def test_no_r_libs_path_set_without_testing_env_var(env, monkeypatch):
+    """Package emits a warning if no R_LIBS is set in the environment ...
+
+    ... when not testing.
+    """
+    monkeypatch.setattr(configuration.Config, 'R_LIBS_PATH', None)
+    monkeypatch.delenv('TESTING', raising=False)
+
+    # Prevent that a warning is emitted for a missing DATABASE_URI.
+    uri = 'postgresql://user:password@localhost/db'
+    monkeypatch.setattr(configuration.ProductionConfig, 'DATABASE_URI', uri)
+
+    with pytest.warns(UserWarning, match='no R_LIBS'):
+        configuration.make_config(env)
+
+
 def test_random_testing_schema():
     """CLEAN_SCHEMA is randomized if not set explicitly."""
     result = configuration.random_schema_name()

tests/test_init_r.py

@@ -0,0 +1,19 @@
+"""Verify that the R packages are installed correctly."""
+
+import pytest
+
+
+@pytest.mark.r
+def test_r_packages_installed():
+    """Import the `urban_meal_delivery.init_r` module.
+
+    Doing this raises a `PackageNotInstalledError` if the
+    mentioned R packages are not importable.
+
+    They must be installed externally. That happens either
+    in the "research/r_dependencies.ipynb" notebook or
+    in the GitHub Actions CI.
+    """
+    from urban_meal_delivery import init_r  # noqa:WPS433
+
+    assert init_r is not None

tests/test_version.py

@@ -20,8 +20,6 @@ import urban_meal_delivery
 class TestPEP404Compliance:
     """Packaged version identifier is PEP440 compliant."""
 
-    # pylint:disable=no-self-use
-
     @pytest.fixture
     def parsed_version(self) -> str:
         """The packaged version."""
@@ -47,8 +45,6 @@ class TestPEP404Compliance:
 class TestSemanticVersioning:
     """Packaged version follows a strict subset of semantic versioning."""
 
-    # pylint:disable=no-self-use
-
     version_pattern = re.compile(
         r'^(0|([1-9]\d*))\.(0|([1-9]\d*))\.(0|([1-9]\d*))(\.dev(0|([1-9]\d*)))?$',
     )