@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 ...\n\n        ... weekly time steps (i.e., product of `7` and the number of daily time steps)\n        and the `train_horizon` in weeks; however, this assertion only holds if\n        we predict the first `time_step` of the day.\n\n        The time series with the actual order counts always holds `1` value.\n        '
    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)
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.
@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 ...\n\n        ... weekly time steps (i.e., product of `7` and the number of daily time steps)\n        and the `train_horizon` in weeks; however, this assertion only holds if\n        we predict the first `time_step` of the day.\n\n        The time series with the actual order counts always holds `1` value.\n        '
    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)