pyrc.postprocessing.parse_scop_files

add_heat_dates(df: DataFrame)

For each array in column “heat_amount” it adds an array representing the time for each entry.

It also cleans the heat_amount arrays from unnecessary values (from overhanging time ranges and stuff).

Used before plotting the data.

Parameters:

df

Returns:

The same DataFrame but with new column “heat_amount_time” and a cleaned up version of the column “heat_amount”

Return type:

pd.DataFrame

get_data_subset(df, period_type=None, orientation=None, volume_flow=None, vl=None, scop_type=None)

Filter DataFrame based on criteria.

Parameters:

• df (pd.DataFrame) – Input DataFrame from parse_scop_file

• period_type (str, optional) – Filter by period type (day, week, year)

• orientation (str, optional) – Filter by orientation (Ost, Süd, …)

• volume_flow (int | list, optional) – Filter by volume flow.

• vl (int | list, optional) – Filter by VL value(s)

• scop_type (str | list, optional) – The type of the SCOP values (e.g. preheat, complete, …)

Returns:

Filtered DataFrame

Return type:

pd.DataFrame

parse_scop_file(filenames: dict) → DataFrame

Parse SCOP data file and extract values for each VL and version/subversion.

Parameters:

filenames (dict[str, str]) – Names (keys) and paths (values) of the SCOP files.

Returns:

DataFrame with columns: period_type, date, subversion, VL, value, time_steps

Return type:

pd.DataFrame