runtime_accounting

runtime_accounting ¤

Runtime / operating-hours accounting for equipment.

Computes operating-time metrics from a single boolean run signal:

total run time, idle time and utilization,
equipment start count and longest continuous run,
run time per calendar window,
a cumulative operating-hours meter (like a physical hour meter).

Per-sample duration is the gap to the next sample, summed over True samples -- the same approach as OEECalculator.calculate_availability. This is distinct from DutyCycleEvents (duty percentage and cycle counts): here the focus is absolute run time and an hour-meter reading.

RuntimeAccountingEvents ¤

RuntimeAccountingEvents(
    dataframe: DataFrame,
    run_uuid: str,
    *,
    event_uuid: str = "prod:runtime",
    value_column: str = "value_bool",
    time_column: str = "systime"
)

Bases: Base

Operating-hours accounting from one boolean run signal.

Example usage::

rt = RuntimeAccountingEvents(df, run_uuid="machine:running")
rt.runtime_summary()
rt.runtime_per_window(window="1D")
rt.operating_hours_meter(window="1h")

Initialize the runtime-accounting analyser.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`run_uuid`	`str`	UUID of the boolean run-state signal (True = running).	required
`event_uuid`	`str`	UUID to tag derived events with.	`'prod:runtime'`
`value_column`	`str`	Column holding the boolean run state.	`'value_bool'`
`time_column`	`str`	Name of the timestamp column.	`'systime'`

runtime_summary ¤

runtime_summary() -> pd.DataFrame

Overall operating-time summary across the whole dataset.

Returns:

Type	Description
`DataFrame`	Summary-shape DataFrame (one row) with columns: start, end,
`DataFrame`	duration_seconds, run_seconds, run_hours, idle_seconds,
`DataFrame`	start_count, longest_run_seconds, mean_run_seconds,
`DataFrame`	utilization_pct.

runtime_per_window ¤

runtime_per_window(window: str = '1D') -> pd.DataFrame

Run time per calendar window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window (e.g. `"1D"`, `"8h"`).	`'1D'`

Returns:

Type	Description
`DataFrame`	Summary-shape DataFrame with columns: start, end, duration_seconds,
`DataFrame`	run_seconds, run_hours, start_count, utilization_pct.

operating_hours_meter ¤

operating_hours_meter(window: str = '1h') -> pd.DataFrame

Cumulative operating-hours meter, sampled per window.

Mirrors a physical equipment hour meter: a monotonically increasing total of run hours.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window for the meter readings.	`'1h'`

Returns:

Type	Description
`DataFrame`	Summary-shape DataFrame with columns: start, end, duration_seconds,
`DataFrame`	run_seconds, cumulative_run_hours.