ethos_penalps.post_processing.load_profile_entry_post_processor

Module Contents

Classes

LoadProfileEntryPostProcessor

Attributes

itcs_logger

ethos_penalps.post_processing.load_profile_entry_post_processor.itcs_logger

class ethos_penalps.post_processing.load_profile_entry_post_processor.LoadProfileEntryPostProcessor

convert_time_series_to_resampled_load_profile_meta_data(object_name: str, object_type: str, list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], start_date: datetime.datetime, end_date: datetime.datetime, x_axis_time_period_timedelta: datetime.timedelta = datetime.timedelta(weeks=1), resample_frequency: str = '1min') → ethos_penalps.data_classes.LoadProfileDataFrameMetaInformation

homogenize_list_of_load_profiles_entries(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], start_date_time_series: datetime.datetime, end_date_time_series: datetime.datetime, resample_frequency: str = '1min') → list[ethos_penalps.data_classes.LoadProfileEntry]

fill_from_date_to_start(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], start_date: datetime.datetime, energy_quantity_at_start: float = 0, power_value_to_fill: float = 0) → list[ethos_penalps.data_classes.LoadProfileEntry]

fill_to_end_date(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], end_date: datetime.datetime, energy_quantity_at_start: float = 0, power_value_to_fill: float = 0) → list[ethos_penalps.data_classes.LoadProfileEntry]

resample_load_profile_to_target_frequency(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], start_time: datetime.datetime, end_time: datetime.datetime, frequency: str = 'min') → list[ethos_penalps.data_classes.LoadProfileEntry]

_summary_

Parameters:

• list_of_load_profile_entries (list[LoadProfileEntry]) – _description_

• start_time (datetime.datetime) – _description_

• end_time (datetime.datetime) – _description_

• frequency (str, optional) –

  • T, min minutely frequency

  • S secondly frequency

  • H hourly frequency

  • D calendar day frequency

  • W weekly frequency

  • M month end frequency

  • https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases

  • defaults to “min”

Raises:

• Exception – _description_

• Exception – _description_

• Exception – _description_

• Exception – _description_

• Exception – _description_

Returns:

_description_

Return type:

pandas.DataFrame

get_energy_amount_from_list_of_load_profile_entries(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry]) → float

fill_gaps_in_time_series_with_0_values(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], energy_value_to_fill: float = 0, power_value_to_fill: float = 0) → list[ethos_penalps.data_classes.LoadProfileEntry]

check_load_profile_for_consistency_and_extract_information(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry])

invert_list(list_to_invert: list)

determine_earliest_start_date_from_list_of_list_of_load_profile_entries(end_date: datetime.datetime, period: datetime.timedelta, list_of_list_of_load_profile_entries: list[list[ethos_penalps.data_classes.LoadProfileEntry]])

determine_new_start_date(start_date: datetime.datetime, end_date: datetime.datetime, period: datetime.timedelta) → datetime.datetime

check_if_list_of_load_profile_entries_has_gaps(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry])