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ethos_penalps.post_processing.load_profile_post_processor

ethos_penalps.post_processing.load_profile_post_processor#

Module Contents#

Classes#

CarpetPlotMatrix

LoadProfilePostProcessor

Attributes#

itcs_logger

ethos_penalps.post_processing.load_profile_post_processor.itcs_logger#
class ethos_penalps.post_processing.load_profile_post_processor.CarpetPlotMatrix#
data_frame: pandas.DataFrame#
start_date_time_series: datetime.datetime#
end_date_time_series: datetime.datetime#
x_axis_time_period_timedelta: datetime.timedelta#
resample_frequency: str = '1min'#
class ethos_penalps.post_processing.load_profile_post_processor.LoadProfilePostProcessor#
convert_lpg_load_profile_to_data_frame_matrix(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry], start_date_time_series: datetime.datetime, end_date_time_series: datetime.datetime, x_axis_time_period_timedelta: datetime.timedelta = datetime.timedelta(weeks=1), resample_frequency: str = '1min') → CarpetPlotMatrix#
convert_list_of_load_profile_entries_to_data_frame(list_of_load_profile_entries: list[ethos_penalps.data_classes.LoadProfileEntry]) → pandas.DataFrame#
convert_lpg_load_profile_to_carpet_plot(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')#
invert_list(list_to_invert: list)#
convert_time_series_to_power_matrix_with_period_columns(input_data_frame: pandas.DataFrame, periodic_time_delta: datetime.timedelta, x_row_date_format: str = '%M', period_name='Hour')#

_summary_

Parameters:

  • input_data_frame (pandas.DataFrame) – _description_

  • periodic_time_delta (datetime.timedelta) – _description_

  • x_row_date_format (str, optional) – _description_, defaults to “%M”

  • period_name (str, optional) – _description_, defaults to “Hour”

Raises:

  • Exception – _description_

  • Exception – _description_

Returns:

_description_

Return type:

_type_

convert_time_series_to_energy_matrix_with_period_columns(input_data_frame: pandas.DataFrame, periodic_time_delta: datetime.datetime, x_row_date_format: str = '%M', period_name='Hour')#
get_energy_amount_from_data_frame(load_profile_data_frame: pandas.DataFrame) → float#
get_energy_amount_from_energy_plotting_matrix(load_profile_data_frame: pandas.DataFrame) → float#
get_energy_amount_from_power_matrix(load_profile_data_frame: pandas.DataFrame, period_length: datetime.timedelta) → float#
plot_load_profile_carpet_from_data_frame_matrix(carpet_plot_load_profile_matrix: CarpetPlotMatrix, load_type_name: str) → matplotlib.figure.Figure#
combine_matrix_data_frames(list_of_carpet_plot_matrices: list[CarpetPlotMatrix]) → CarpetPlotMatrix#
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