diff --git a/src/libecalc/common/time_utils.py b/src/libecalc/common/time_utils.py index c38d72f275..47fc97188b 100644 --- a/src/libecalc/common/time_utils.py +++ b/src/libecalc/common/time_utils.py @@ -1,6 +1,7 @@ from __future__ import annotations import enum +from collections.abc import Iterator from dataclasses import dataclass from datetime import date, datetime, timedelta from typing import Any, Optional, Self, Union @@ -160,7 +161,7 @@ def create_periods(times: list[datetime], include_before: bool = True, include_a return Periods(periods=periods) - def __iter__(self): + def __iter__(self) -> Iterator[Period]: return self.periods.__iter__() def get_period(self, period: Period) -> Optional[Period]: diff --git a/src/libecalc/presentation/simple_result/simple.py b/src/libecalc/presentation/simple_result/simple.py index 2419c8eeea..6f5845cb82 100644 --- a/src/libecalc/presentation/simple_result/simple.py +++ b/src/libecalc/presentation/simple_result/simple.py @@ -4,7 +4,6 @@ from libecalc.common.component_info.component_level import ComponentLevel from libecalc.common.component_type import ComponentType -from libecalc.common.errors.exceptions import ProgrammingError from libecalc.common.logger import logger from libecalc.common.string.string_utils import to_camel_case from libecalc.common.time_utils import Period, Periods @@ -132,11 +131,11 @@ def fit_to_periods( delta profile comparisons easier/possible. Args: - component (SimpleComponentResult): The component that should be fitted. - periods (Periods): The target periods. The provided periods should all exist in the component. + component (SimpleComponentResult): The component that should be fitted (should intersect all periods in the periods list). Can have periods before and after the *periods*. Those will be trimmed. + periods (Periods): The target periods. The provided periods should all exist in the component. If the above component are missing periods, we will extrapolate with values from the bigger period Returns: - SimpleComponentResult: The component with the new periods. + SimpleComponentResult: The component with the new periods, ie. start and end will be trimmed, and mid-periods will be added if the component has a longer period than the global period. """ power = [] energy_usage = [] @@ -145,11 +144,17 @@ def fit_to_periods( emission.name: SimpleEmissionResult(name=emission.name, rate=[]) for emission in component.emissions.values() } + # We loop through a components periods and try to fit it to the common global period. for period_index, _period in enumerate(component.periods): period = Period.intersection(_period, periods.period) + # If the period is not in the global period, we skip it. ie before or after the global common period. if period: start = periods.start_dates.index(period.start) end = periods.end_dates.index(period.end) + + # In case we have a longer period in component than the global period, we have to add missing steps/periods + # e.g. if the common period has 2022-2023, but the component has 2022-2024, we have to loop twice to extrapolate the missing period with same values as the bigger period. + # Usually this will only loop once. for _ in range(start, end + 1): if component.power is not None: power.append(component.power[period_index]) @@ -159,10 +164,9 @@ def fit_to_periods( # Assume index exist if emission exist emission.rate.append(component.emissions[emission.name].rate[period_index]) else: - # This is a developer error, we should provide the correct period. - raise ProgrammingError( - f"Provided periods includes period not found in component {component.id}. " - f"Extraneous period: {period}. This should not happen, contact support." + # We do not trim extraneous periods in beginning and end for a component. We only try to fit to the common global period. + logger.warning( + f"Period {component.periods[period_index]} from {component.name} not in {periods.period}. Skipping." ) return cls( @@ -256,7 +260,7 @@ def fit_to_periods( cls, model: "SimpleResultData", periods: Periods, - ): + ) -> "SimpleResultData": """ Fit result to periods. Only a subset or the same set of periods is supported. Args: @@ -323,7 +327,7 @@ def normalize_components( reference_model: "SimpleResultData", changed_model: "SimpleResultData", exclude: Optional[list[ComponentType]] = None, - ): + ) -> tuple["SimpleResultData", "SimpleResultData"]: if exclude is None: exclude = [] @@ -367,6 +371,11 @@ def delta_profile( changed_model: "SimpleResultData", ) -> tuple["SimpleResultData", "SimpleResultData", "SimpleResultData", list[str]]: """ + Calculate delta profile between two models. We will make sure that both models have the same + periods and components before calculating the delta profile. Different start and end will be trimmed. + This delta-profile methods supports both periods with fixed interval (monthly, yearly) and irregular intervals (data-defined). + The resulting delta-profile will have the union of the periods from both models, except differing start and end, that will be trimmed, + in order to have same start and end for both models. Args: reference_model: @@ -379,12 +388,14 @@ def delta_profile( first_date = max(changed_model.periods.first_date, reference_model.periods.first_date) last_date = min(changed_model.periods.last_date, reference_model.periods.last_date) + # union of the dates in the 2 models all_dates_in_models = sorted( {date for date in reference_model.periods.all_dates if first_date <= date <= last_date}.union( {date for date in changed_model.periods.all_dates if first_date <= date <= last_date} ) ) - # define new periods using all dates in both models + + # define new periods using all dates in both models, skip extra periods before and after periods = Periods.create_periods( times=all_dates_in_models, include_after=False, @@ -396,6 +407,8 @@ def delta_profile( reference_model=reference_model, changed_model=changed_model ) + # Now as we have found the union of the dates/periods in the models, and trimming + # start end, if they differ, we can fill out missing periods for the models changed_model = cls.fit_to_periods(changed_model, periods) reference_model = cls.fit_to_periods(reference_model, periods)