114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
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"""Obtain and work with time series data."""
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import datetime
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import pandas as pd
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from urban_meal_delivery import config
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from urban_meal_delivery import db
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def aggregate_orders(grid: db.Grid, time_step: int) -> pd.DataFrame: # pragma: no cover
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"""Obtain a time series of the ad-hoc `Order` totals.
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Args:
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grid: pixel grid used to aggregate orders spatially
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time_step: interval length (in minutes) into which orders are aggregated
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Returns:
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order_totals: `DataFrame` with a `MultiIndex` of the "pixel_id"s and
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beginnings of the intervals (i.e., "start_at"s); the sole column
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with data is "total_orders"
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"""
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# `data` is probably missing "pixel_id"-"start_at" pairs.
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# This happens whenever there is no demand in the `Pixel` in the given `time_step`.
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data = pd.read_sql_query(
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f"""-- # noqa:WPS221
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SELECT
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pixel_id,
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start_at,
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COUNT(*) AS total_orders
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FROM (
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SELECT
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pixel_id,
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placed_at_without_seconds - minutes_to_be_cut AS start_at
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FROM (
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SELECT
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pixels.pixel_id,
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DATE_TRUNC('MINUTE', orders.placed_at) AS placed_at_without_seconds,
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((
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EXTRACT(MINUTES FROM orders.placed_at)::INTEGER % {time_step}
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)::TEXT || ' MINUTES')::INTERVAL
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AS minutes_to_be_cut
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FROM (
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SELECT
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id,
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placed_at,
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pickup_address_id
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FROM
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{config.CLEAN_SCHEMA}.orders
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INNER JOIN (
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SELECT
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id AS address_id
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FROM
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{config.CLEAN_SCHEMA}.addresses
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WHERE
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city_id = {grid.city.id}
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) AS in_city
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ON orders.pickup_address_id = in_city.address_id
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WHERE
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ad_hoc IS TRUE
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) AS
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orders
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INNER JOIN (
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SELECT
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address_id,
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pixel_id
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FROM
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{config.CLEAN_SCHEMA}.addresses_pixels
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WHERE
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grid_id = {grid.id}
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AND
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city_id = {grid.city.id} -- city_id is redundant -> sanity check
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) AS pixels
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ON orders.pickup_address_id = pixels.address_id
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) AS placed_at_aggregated_into_start_at
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) AS pixel_start_at_combinations
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GROUP BY
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pixel_id,
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start_at
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ORDER BY
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pixel_id,
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start_at;
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""",
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con=db.connection,
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index_col=['pixel_id', 'start_at'],
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)
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if data.empty:
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return data
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# Calculate the first and last "start_at" value ...
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start_day = data.index.levels[1].min().date()
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start = datetime.datetime(
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start_day.year, start_day.month, start_day.day, config.SERVICE_START,
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)
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end_day = data.index.levels[1].max().date()
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end = datetime.datetime(
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end_day.year, end_day.month, end_day.day, config.SERVICE_END,
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)
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# ... and all possible `tuple`s of "pixel_id"-"start_at" combinations.
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# The "start_at" values must lie within the operating hours.
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gen = (
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(pixel_id, start_at)
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for pixel_id in sorted(data.index.levels[0])
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for start_at in pd.date_range(start, end, freq=f'{time_step}T')
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if config.SERVICE_START <= start_at.time().hour < config.SERVICE_END
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)
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# Re-index `data` filling in `0`s where there is no demand.
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index = pd.MultiIndex.from_tuples(gen)
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index.names = ['pixel_id', 'start_at']
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return data.reindex(index, fill_value=0)
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