flat

flat ¤

XES-flat exporter — produces a pandas DataFrame with case:concept:name, concept:name, time:timestamp, lifecycle:transition, org:resource columns. ts-shape itself does not import pm4py; users pass this DataFrame to pm4py.format_dataframe themselves.

to_event_log_xes ¤

to_event_log_xes(
    eventlog: EventLog,
    *,
    case_object_type: str = "asset",
    lifecycle: str = "single"
) -> pd.DataFrame

Flatten the event log into an XES-style DataFrame.

A trace is built per object of case_object_type — each event linked to that object becomes one (or two) rows in the trace.

lifecycle="single" — interval events become one row with lifecycle:transition="complete". time:timestamp is the interval-end; start_timestamp is exposed verbatim.

lifecycle="two_row" — interval events expand into a start row (using ts_shape:start_timestamp) and a complete row, paired by concept:instance.

Parameters:

Name	Type	Description	Default
`eventlog`	`EventLog`	The event log to flatten.	required
`case_object_type`	`str`	Object type whose objects become the traces.	`'asset'`
`lifecycle`	`str`	`"single"` or `"two_row"`.	`'single'`

Returns:

Type	Description
`DataFrame`	DataFrame with `case:concept:name`, `concept:name`,
`DataFrame`	`time:timestamp`, `lifecycle:transition` (and `org:resource`
`DataFrame`	when the log links operators), sorted by case and time.

Raises:

Type	Description
`ValueError`	When the log has no objects, no object of `case_object_type`, or `lifecycle` is unknown.