Function reference

All-In-One Animation Functions

Generate an animated visualization of patient flow through a system.

prep.reshape_for_animations	Reshape event log data for animation purposes.
prep.generate_animation_df	Generate a DataFrame for animation purposes by adding position information to entity data.
animation.generate_animation	Generate an animated visualization of patient flow through a system.

animation.add_repeating_overlay	Add a repeating overlay (rectangle and text) to an animated Plotly figure
animation.add_subplot_panels	Re-home a vidigi animation into the top row of a stacked subplot grid.
animation.add_synchronised_trace	Add extra traces to an animation, kept in step with its frames.
animation.add_synchronised_trace_from_dataframe	Add a synchronised trace built from a long-form DataFrame.

resources.VidigiResource	A simple resource class with an ID attribute for use in VidigiStore and VidigiPriorityStore.
resources.VidigiStore	A wrapper around SimPy’s Store that allows using a context manager pattern
resources.VidigiPriorityStore	An optimized SimPy priority store that eliminates delays between resource
resources.populate_store	Populate a SimPy Store (or VidigiPriorityStore) with VidigiResource objects.

logging.EventLogger	Records simulation events for later reshaping into animations and statistics.
logging.TrialLogger	A container and analysis utility for managing multiple event logs from repeated

Numbers-in, DataFrames-out - the compute twin behind every function in Analysis Plots below.

analysis.event_durations	Pair occurrences of two events per entity and compute the duration between them.
analysis.entity_metric_by_arrival	Pair two events per entity, as `event_durations` does, and attach each
analysis.replication_means	Reduce a per-entity durations frame to one value per replication.
analysis.mean_confidence_interval	Confidence interval for a mean, computed over independent replicate values.
analysis.replication_precision	Running confidence-interval precision as replications accumulate.
analysis.queue_size_over_time	Compute the size of one or more queues at regular snapshots, across every run.
analysis.resource_use_intervals	Pair `resource_use`/`resource_use_end` rows into one interval per bout of use.
analysis.resource_utilisation	Summarise resource use into busy time, mean-in-use and utilisation, per run.
analysis.resource_occupancy_over_time	Compute how many units of each resource step were busy at regular snapshots.
analysis.activity_occupancy_stats	Summarise how many entities were present at each step of a process.
analysis.welch_moving_average	Smooth an ensemble-averaged series to visually locate a warm-up length.
analysis.compare_replication_values	Compare two independent samples of per-replication values.
analysis.flag_outlier_runs	Flag replications whose value is a statistical outlier relative to the rest.
analysis.event_occurrence_rate	Proportion of replications in which an event occurs at least once.

go.Figure-returning charts, each a thin wrapper over the matching function in Analysis Functions above.

plots.plot_queue_size	Plot the size of one or more queues over time, across every run.
plots.plot_duration_distribution	Plot the distribution of durations between two events.
plots.plot_metric_bar	Plot a bar chart of event duration statistics for a list of event pairs.
plots.plot_metric	Plot event duration statistics for a list of event pairs, as a bar, box or violin.
plots.plot_resource_utilisation	Plot a bar chart of resource utilisation, one bar per group, across runs.
plots.plot_resource_utilisation_over_time	Plot how many units of each resource step were in use over time, across runs.
plots.plot_warm_up_diagnostic	Plot a Welch (or cumulative-mean) diagnostic for choosing `warm_up=`.
plots.plot_replication_analysis	Plot cumulative-mean precision against replication count.
plots.plot_outlier_runs	Horizontal beeswarm of per-replication values, flagging outlier runs.
plots.plot_metric_vs_arrival_time	Plot a duration/metric against the arrival time of the entity it belongs to.
plots.plot_scenario_comparison	Bar chart comparing a duration statistic between two scenarios.
plots.plot_resource_utilisation_comparison	Bar chart comparing a resource utilisation metric between two scenarios.

utils.EventPosition	Pydantic model for a single event position.
utils.ArrivalPosition	`EventPosition` for the mandatory `"arrival"` event.
utils.ExitPosition	`EventPosition` for the mandatory `"depart"` event.
utils.create_event_position_df	Creates a DataFrame for event positions from a list of EventPosition objects.

process_mapping.add_sim_timestamp	Add a pseudo-timestamp column to a simulation event log.
process_mapping.discover_dfg	Discover a Directly-Follows Graph (DFG) from an event log.
process_mapping.dfg_to_graphviz	Render a Directly-Follows Graph (DFG) as a Graphviz diagram.
process_mapping.process_nodes_and_edges_for_cytoscape	Convert DFG node and edge tables into Cytoscape-compatible elements.
process_mapping.dfg_to_cytoscape	Convert DFG node/edge tables to interactive Cytoscape widget.
process_mapping.dfg_to_cytoscape_streamlit	Render a Directly-Follows Graph (DFG) interactively in Streamlit using Cytoscape.

Programmatically triggers all ‘Play’ buttons in Plotly animations embedded in Streamlit using JavaScript.

ciw.event_log_from_ciw_recs	Build an event log from a `ciw.data_record` object.
ciw.event_logger_from_ciw_recs	Build a vidigi `EventLogger` from a single `ciw.data_record` object.
ciw.trial_logger_from_ciw_recs	Build a vidigi `TrialLogger` from several ciw runs’ records.

A SimPy store that processes requests with priority.