energy_analysis_toolbox.load_profiles.localization module#

Define Mixins to manage localized timeseries.

class energy_analysis_toolbox.timeseries.profiles.localization.LocalizedMeanProfile(period: str = 'D', *, is_max: bool = True, window: int = 1)[source]#

Bases: LocalizedProfileMixin, MeanProfile

Compute the mean profile with timezone data.

see MeanProfile

class energy_analysis_toolbox.timeseries.profiles.localization.LocalizedProfileMixin[source]#

Bases: object

A profile version where time-zoned data is managed, including DSTs.

The compute method of the profile class with which this mixin is combined is overloaded in such a way that passing time-localized data is possible. On and across DSTs, the data remains aligned “on the clock” (VS on the sun).

compute(history: DataFrame, time: Timestamp, **kwargs) → DataFrame[source]#

Compute the profile at time from the history.

The computation is managed as follows :

just fall back to the parent compute method in case the data is time-naive.
Else, unlocalize the history and target date before passing them to the parent class’ compute such that the data remains aligned based on “local time” for each day.
Relocalize the resulting profile to the history timezone. This part takes care of DST days :
- In case the target date is summer DST (23h long day) the extra profile hours is just dumped.
- In case the target date is winter DST(25h long day), the data for the 2am to 3am period is associated with the first occurrence of this time-of-day and a gap is let on the second.

Warning

If winter DST is included in the history, the timestamps from 2am to 3am on this day appears “twice” in the time-naive history passed to the aggregation. Therefore, the aggregation should be robust to this situation.