- add `.low80`, `.high80`, `.low95`, and `.high95` columns
- add check contraints for the confidence intervals
- rename the `.method` column into `.model` for consistency
- add a Jupyter notebook that allows to install all project-external
dependencies regarding R and R packages
- adjust the GitHub Action workflow to also install R and the R packages
used within the project
- add a `init_r` module that initializes all R packages globally
once the `urban_meal_delivery` package is imported
- the main purpose of this class is to manage querying the order totals
from the database and slice various kinds of time series out of the
data
- the class holds the former `aggregate_orders()` function as a method
- modularize the corresponding tests
- add `tests.config` with globals used when testing to provide a
single source of truth for various settings
- the function queries the database and aggregates the ad-hoc orders
by pixel and time steps into a demand time series
- implement "heavy" integration tests for `aggregate_orders()`
- make `pandas` a package dependency
- streamline the `Config`
- reorganize `urban_meal_delivery.console` into a sub-package
- move `tests.db.conftest` fixtures into `tests.conftest`
=> some integration tests regarding CLI scripts need a database
- add `urban_meal_delivery.console.decorators.db_revision` decorator
to ensure the database is at a certain state before a CLI script runs
- refactor the `urban_meal_delivery.db.grids.Grid.gridify()` constructor:
- bug fix: even empty `Pixel`s end up in the database temporarily
=> create `Pixel` objects only if an `Address` is to be assigned
to it
- streamline code and docstring
- add further test cases
- the purpose of this constructor method is to generate all `Pixel`s
for a `Grid` that have at least one `Address` assigned to them
- fix missing `UniqueConstraint` in `Grid` class => it was not possible
to create two `Grid`s with the same `.side_length` in different cities
- change the `City.viewport` property into two separate `City.southwest`
and `City.northeast` properties; also add `City.total_x` and
`City.total_y` properties for convenience
- the old `UTMCoordinate` class becomes the new `Location` class
- its main purpose is to represent locations in both lat-long
coordinates as well as in the UTM system
- remove `Address.__init__()` and `City.__init__()` methods as they
are not executed for entries retrieved from the database
- simplfiy the `Location.__init__()` => remove `relative_to` argument
- remove the factory functions for creating engines and sessions
- define global engine, connection, and session objects to be used
everywhere in the urban_meal_delivery package
- add Grid, Pixel, and AddressPixelAssociation ORM models
- each Grid belongs to a City an is characterized by the side_length
of all the square Pixels contained in it
- Pixels aggregate Addresses => many-to-many relationship (that is
modeled with SQLAlchemy's Association Pattern to implement a couple
of constraints)
- the Address.x and Address.y properties use the UTMCoordinate class
behind the scenes
- x and y are simple coordinates in an x-y plane
- the (0, 0) origin is the southwest corner of Address.city.viewport
- the class is a utility to abstract working with latitude-longitude
coordinates in their UTM representation (~ "cartesian plane")
- the class's .x and .y properties enable working with simple x-y
coordinates where the (0, 0) origin is the lower-left of a city's
viewport
- create `*Factory` classes with fakerboy and faker that generate
randomized instances of the ORM models
- add new pytest marker: "db" are the integration tests involving the
database whereas "e2e" will be all other integration tests
- streamline the docstrings in the ORM models
As a lot of the integration tests populate the database with test data,
it is deemed safer to run the tests in random order to uncover potential
dependencies between distinct test cases.
Because of how the `db_session` fixture is designed, this should already
be taken care of.
- clean the raw data given by the undisclosed meal delivery platform:
+ keep data only for the three target citis:
* Bordeaux
* Lyon
* Paris
+ merge duplicates
* it appears as redundant addresses were created
for each order by the same customer
=> significant reduction in the number of addresses
* propagate the merges to the other tables
that reference records merged away
+ cast data types and keep their scopes narrow
+ normalize the data
+ remove obvious outliers
+ adjust/discard unplausible values
- map the cleaned data onto the ORM models
- store the cleaned data in a new database schema
