Common processing step: count_hourly_booleans

[ahobeost]

When running simulations, booleans can be saved to timesteps, or as a mean hourly value.
The following code converts the mean hourly values to a total nr of hours in the True/on or False/off position.

import pandas as _pd
import pathlib as _pl
import pytest as _pt


def count_hourly_booleans(df_hourly: _pd.DataFrame, boolean_column: str) -> tuple[float, float]:
    """Helps convert averaged hourly boolean values to total hours True/on and False/off."""
    if boolean_column not in df_hourly.columns:
        raise ValueError(f"Incorrect boolean_column provided: {boolean_column}")

    count_series = df_hourly[boolean_column].value_counts()
    
    n_min_per_hour = 60
    n_minutes_on = 0
    n_minutes_off = 0
    for mean_boolean, n_hours in count_series.items():
        n_minutes_on += mean_boolean * n_min_per_hour * n_hours
        n_minutes_off += (1 - mean_boolean) * n_min_per_hour * n_hours

    total_hours_on = n_minutes_on / n_min_per_hour
    total_hours_off = n_minutes_off / n_min_per_hour

    return total_hours_on, total_hours_off


file_path = _pl.Path("T:\\ahobe\\Boolean_count") / "RESULTS_HR_cleaned.Prt"
df = _pd.read_csv(file_path, delimiter="\t")
df = df.rename(columns=lambda x: x.strip())

on, off = count_hourly_booleans(df, "Coll_TES_full")
print(f"\n\nOn: {on}")
print(f"Off: {off}")
# expected: 1795 times 1 and 6965 times 0

with _pt.raises(ValueError):
    count_hourly_booleans(df, "NO_SUCH_COLUMN")

[ahobeost]

@MathildeWirtz