prep.reshape_for_animations

prep.reshape_for_animations(
    event_log,
    every_x_time_units=10,
    limit_duration=10 * 60 * 24,
    step_snapshot_max=50,
    time_col_name='time',
    entity_col_name='entity_id',
    event_type_col_name='event_type',
    event_col_name='event',
    pathway_col_name=None,
    debug_mode=False,
)

Reshape event log data for animation purposes.

This function processes an event log to create a series of snapshots at regular time intervals, suitable for creating animations of patient flow through a system.

Parameters

Name	Type	Description	Default
event_log	pd.DataFrame	The input event log containing entity events and timestamps in the form of a number of time units since the simulation began.	required
every_x_time_units	int	The time interval between snapshots in preferred time units (default is 10).	10
limit_duration	int	The maximum duration to consider in preferred time units (default is 10 days).	10 * 60 * 24
step_snapshot_max	int	The maximum number of entities to include in each snapshot for each event (default is 50).	50
time_col_name	str	Name of the column in event_log that contains the timestamp of each event. Timestamps should represent the number of time units since the simulation began.	"time"
entity_col_name	str	Name of the column in event_log that contains the unique identifier for each entity (e.g., “entity_id”, “entity”, “patient”, “patient_id”, “customer”, “ID”).	"entity_id"
event_type_col_name	str	Name of the column in event_log that specifies the category of the event. Supported event types include ‘arrival_departure’, ‘resource_use’, ‘resource_use_end’, and ‘queue’.	"event_type"
event_col_name	str	Name of the column in event_log that specifies the actual event that occurred.	"event"
pathway_col_name	str	Name of the column in event_log that identifies the specific pathway or process flow the entity is following. If None, it is assumed that pathway information is not present.	None
debug_mode	bool	If True, print debug information during processing (default is False).	False

Returns

Name	Type	Description
	DataFrame	A reshaped DataFrame containing snapshots of entity positions at regular time intervals, sorted by minute and event.

Notes

• The function creates snapshots of entity positions at specified time intervals.
• It handles entities who are present in the system at each snapshot time.
• Entities are ranked within each event based on their arrival order.
• A maximum number of patients per event can be set to limit the number of entities who will be displayed on screen within any one event type at a time.
• An ‘exit’ event is added for each entity at the end of their journey.
• The function uses memory management techniques (del and gc.collect()) to handle large datasets.

TODO

• Add behavior for when limit_duration is None.
• Consider adding ‘first step’ and ‘last step’ parameters.
• Implement pathway order and precedence columns.
• Fix the automatic exit at the end of the simulation run for all entities.