logging.EventLogger

logging.EventLogger(self, event_model=BaseEvent, env=None, run_number=None)

Methods

Name Description
generate_dfg Generate a Directly-Follows Graph (DFG) from the simulation data.
get_events_by_entity Return all events associated with a specific entity_id.
get_events_by_event_name Return all events of a specific event_type.
get_events_by_event_type Return all events of a specific event_type.
get_events_by_run Return all events associated with a specific entity_id.
log_arrival Helper to log an arrival event with the correct event_type and event fields.
log_custom_event Log a custom event. The ‘event’ here can be any string describing the queue event.
log_departure Helper to log a departure event with the correct event_type and event fields.
log_queue Log a queue event. The ‘event’ here can be any string describing the queue event.
log_resource_use_end Log the end of resource use. Requires resource_id.
log_resource_use_start Log the start of resource use. Requires resource_id.
plot_entity_timeline Plot a timeline of events for a given entity.
to_csv Write the log to a CSV file.
to_dataframe Convert the event log to a pandas DataFrame.
to_json Write the event log to a JSON file or file-like buffer.
to_json_string Return the event log as a pretty JSON string.

generate_dfg

logging.EventLogger.generate_dfg(
    output_format='graphviz-object',
    input_time_format='minutes',
    **kwargs,
)

Generate a Directly-Follows Graph (DFG) from the simulation data.

This method converts the object to a dataframe, appends simulation timestamps, discovers transitions between activities, and renders the result using the specified visualization backend.

Parameters

Name Type Description Default
output_format DFGType The format of the returned graph. Supported values are: - “graphviz-object”: Returns a Graphviz object for rendering. - “graphviz-image”: Returns a static image of the graph. - “cytoscape-jupyter”: Returns an interactive Cytoscape widget for Jupyter notebooks. - “cytoscape-streamlit”: Returns a Cytoscape component compatible with Streamlit. By default “graphviz-object”. 'graphviz-object'
input_time_format str The time unit used to calculate durations and timestamps, by default “minutes”. 'minutes'
**kwargs Arbitrary keyword arguments passed to the underlying rendering functions (dfg_to_graphviz, dfg_to_cytoscape, etc.). {}

Returns

Name Type Description
graphviz.Source or ipycytoscape.CytoscapeWidget or bytes The rendered graph object in the format specified by output_format.

Raises

Name Type Description
ValueError If the provided output_format is not a valid DFGType.

Notes

This function is a wrapper. For detailed information on how nodes and edges are calculated, or for specific rendering parameters available in **kwargs, please refer to the documentation for:

  • :func:discover_dfg: For edge discovery logic.
  • :func:dfg_to_graphviz: For Graphviz-specific styling kwargs.
  • :func:dfg_to_cytoscape: For jupyter cytoscape styling kwargs.
  • :func:dfg_to_cytoscape_streamlit: For streamlit cytoscape styling kwargs.

get_events_by_entity

logging.EventLogger.get_events_by_entity(entity_id, as_dataframe=True)

Return all events associated with a specific entity_id.

get_events_by_event_name

logging.EventLogger.get_events_by_event_name(event_name, as_dataframe=True)

Return all events of a specific event_type.

get_events_by_event_type

logging.EventLogger.get_events_by_event_type(event_type, as_dataframe=True)

Return all events of a specific event_type.

get_events_by_run

logging.EventLogger.get_events_by_run(run_number, as_dataframe=True)

Return all events associated with a specific entity_id.

log_arrival

logging.EventLogger.log_arrival(
    entity_id,
    time=None,
    pathway=None,
    run_number=None,
    **extra_fields,
)

Helper to log an arrival event with the correct event_type and event fields.

log_custom_event

logging.EventLogger.log_custom_event(
    entity_id,
    event_type,
    event,
    time=None,
    pathway=None,
    run_number=None,
    **extra_fields,
)

Log a custom event. The ‘event’ here can be any string describing the queue event. An ‘event_type’ must also be passed, but can be any string of the user’s choosing.

log_departure

logging.EventLogger.log_departure(
    entity_id,
    time=None,
    pathway=None,
    run_number=None,
    **extra_fields,
)

Helper to log a departure event with the correct event_type and event fields.

log_queue

logging.EventLogger.log_queue(
    entity_id,
    event,
    time=None,
    pathway=None,
    run_number=None,
    **extra_fields,
)

Log a queue event. The ‘event’ here can be any string describing the queue event.

log_resource_use_end

logging.EventLogger.log_resource_use_end(
    entity_id,
    resource_id,
    time=None,
    pathway=None,
    run_number=None,
    **extra_fields,
)

Log the end of resource use. Requires resource_id.

log_resource_use_start

logging.EventLogger.log_resource_use_start(
    entity_id,
    resource_id,
    time=None,
    pathway=None,
    run_number=None,
    **extra_fields,
)

Log the start of resource use. Requires resource_id.

plot_entity_timeline

logging.EventLogger.plot_entity_timeline(
    entity_id,
    split_by_entity_type=False,
    show_labels=False,
)

Plot a timeline of events for a given entity.

This method visualizes the sequence of events for a specified entity from the event log as a scatter plot. The timeline is plotted using Plotly, with events displayed along the time axis. Events can be split vertically by their type or shown by event labels. Optionally, labels can be displayed directly on the plot.

Parameters

Name Type Description Default
entity_id any Identifier of the entity whose events should be plotted. required
split_by_entity_type bool If True, the y-axis shows event types to separate events vertically. If False, the y-axis shows the event labels. False
show_labels bool If True, the event labels are displayed as text on the plot. If False, no labels are shown. False

Raises

Name Type Description
ValueError If the event log is empty.
ValueError If no events are found for the given entity_id.

See Also

to_dataframe : Convert the event log into a DataFrame for analysis.

Notes

  • The plot is displayed using plotly.express.scatter.
  • The y-axis is treated as categorical to improve readability.
  • Marker styling includes a fixed size and outline color for clarity.

to_csv

logging.EventLogger.to_csv(path_or_buffer)

Write the log to a CSV file.

to_dataframe

logging.EventLogger.to_dataframe()

Convert the event log to a pandas DataFrame.

to_json

logging.EventLogger.to_json(path_or_buffer, indent=2)

Write the event log to a JSON file or file-like buffer.

to_json_string

logging.EventLogger.to_json_string(indent=2)

Return the event log as a pretty JSON string.

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