136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
"""Provide package-wide configuration.
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This module provides utils to create new `Config` objects
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on the fly, mainly for testing and migrating!
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Within this package, use the `config` proxy at the package's top level
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to access the current configuration!
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"""
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import datetime
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import os
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import random
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import string
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import warnings
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def random_schema_name() -> str:
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"""Generate a random PostgreSQL schema name for testing."""
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return 'temp_{name}'.format(
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name=''.join(
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(random.choice(string.ascii_lowercase) for _ in range(10)), # noqa:S311
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),
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)
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class Config:
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"""Configuration that applies in all situations."""
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# Application-specific settings
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# -----------------------------
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# Date after which the real-life data is discarded.
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CUTOFF_DAY = datetime.datetime(2017, 2, 1)
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# If a scheduled pre-order is made within this
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# time horizon, we treat it as an ad-hoc order.
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QUASI_AD_HOC_LIMIT = datetime.timedelta(minutes=45)
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# Operating hours of the platform.
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SERVICE_START = 11
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SERVICE_END = 23
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# Side lengths (in meters) for which pixel grids are created.
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# They are the basis for the aggregated demand forecasts.
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GRID_SIDE_LENGTHS = [707, 1000, 1414]
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# Time steps (in minutes) used to aggregate the
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# individual orders into time series.
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TIME_STEPS = [60]
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# Training horizons (in full weeks) used to train the forecasting models.
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# For now, we only use 8 weeks as that was the best performing in
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# a previous study (note:4f79e8fa).
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TRAIN_HORIZONS = [8]
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# The demand forecasting methods used in the simulations.
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FORECASTING_METHODS = ['hets', 'rtarima']
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# Colors for the visualizations ins `folium`.
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RESTAURANT_COLOR = 'red'
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CUSTOMER_COLOR = 'blue'
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# Implementation-specific settings
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# --------------------------------
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DATABASE_URI = os.getenv('DATABASE_URI')
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# The PostgreSQL schema that holds the tables with the original data.
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ORIGINAL_SCHEMA = os.getenv('ORIGINAL_SCHEMA') or 'public'
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# The PostgreSQL schema that holds the tables with the cleaned data.
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CLEAN_SCHEMA = os.getenv('CLEAN_SCHEMA') or 'clean'
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ALEMBIC_TABLE = 'alembic_version'
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ALEMBIC_TABLE_SCHEMA = 'public'
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R_LIBS_PATH = os.getenv('R_LIBS')
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def __repr__(self) -> str:
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"""Non-literal text representation."""
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return '<configuration>'
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class ProductionConfig(Config):
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"""Configuration for the real dataset."""
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TESTING = False
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class TestingConfig(Config):
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"""Configuration for the test suite."""
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TESTING = True
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DATABASE_URI = os.getenv('DATABASE_URI_TESTING') or Config.DATABASE_URI
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CLEAN_SCHEMA = os.getenv('CLEAN_SCHEMA_TESTING') or random_schema_name()
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def make_config(env: str = 'production') -> Config:
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"""Create a new `Config` object.
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Args:
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env: either 'production' or 'testing'
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Returns:
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config: a namespace with all configurations
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Raises:
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ValueError: if `env` is not as specified
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""" # noqa:DAR203
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config: Config # otherwise mypy is confused
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if env.strip().lower() == 'production':
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config = ProductionConfig()
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elif env.strip().lower() == 'testing':
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config = TestingConfig()
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else:
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raise ValueError("Must be either 'production' or 'testing'")
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# Without a PostgreSQL database the package cannot work.
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# As pytest sets the "TESTING" environment variable explicitly,
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# the warning is only emitted if the code is not run by pytest.
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# We see the bad configuration immediately as all "db" tests fail.
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if config.DATABASE_URI is None and not os.getenv('TESTING'):
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warnings.warn('Bad configurartion: no DATABASE_URI set in the environment')
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# Some functionalities require R and some packages installed.
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# To ensure isolation and reproducibility, the projects keeps the R dependencies
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# in a project-local folder that must be set in the environment.
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if config.R_LIBS_PATH is None and not os.getenv('TESTING'):
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warnings.warn('Bad configuration: no R_LIBS set in the environment')
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return config
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config = make_config('testing' if os.getenv('TESTING') else 'production')
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