plot_event_dynamics()

plot_event_dynamics() visualizes event timing patterns as survival-style or binned event-rate curves.

It is Sequenzo's closest counterpart to TraMineRextras::seqedplot().

Function Usage

plot_event_dynamics(
    event_sequences,
    group_labels=None,
    num_bins=20,
    time_range=None,
    title="auto",
    curve_type="survival",
    excluded_events=None,
    show_legend="auto",
    color_palette=None,
    figsize=(12, 8),
    linewidth=2.0,
    fontsize=11,
    x_label=None,
    y_label=None,
    save_as=None,
    dpi=200,
    show=False
)

TraMineR Parameter Mapping

• event_sequences -> TraMineR seqe object
• group_labels -> TraMineR group
• num_bins -> TraMineR breaks
• time_range -> TraMineR ages
• curve_type -> TraMineR type
• excluded_events -> TraMineR ignore
• show_legend -> TraMineR with.legend
• color_palette -> TraMineR cpal
• x_label / y_label -> TraMineR xlab / ylab

Entry Parameters

Parameter	Required	Type	Description
event_sequences	✓	EventSequenceData / EventSequenceList	The full event-sequence dataset to visualize.
curve_type	✗	str	"survival" or "hazard".
group_labels	✗	array-like	Optional grouping variable for panel comparison.
x_label, y_label	✗	str	Axis label overrides.
save_as	✗	str	Save path; .png is auto-appended if missing.
dpi	✗	int	Save resolution (default 200).
show	✗	bool	If True, calls plt.show() inside the function.

What It Does

• Survival mode: plots the probability that the first occurrence of an event happens after time t.
• Hazard-style mode: summarizes event occurrence by time bins, for example as a binned event rate or mean event count per bin.
• Supports grouped panels and event-level legends.

The hazard-style view is descriptive. It should not be interpreted as a full event-history hazard model.

Example (Step by Step)

from sequenzo.event_sequences import plot_event_dynamics

# Step 1: Survival-style curves
plot_event_dynamics(
    eseq,
    group_labels=df["sex"],
    curve_type="survival",
    x_label="Time",
    y_label="Survival probability",
    save_as="outputs/event_dynamics_survival",
    dpi=300,
    show=True
)

# Step 2: Hazard-style curves
plot_event_dynamics(
    eseq,
    group_labels=df["sex"],
    curve_type="hazard",
    num_bins=8,
    x_label="Time",
    y_label="Mean number of events",
    save_as="outputs/event_dynamics_hazard",
    dpi=300,
    show=True
)

R Counterpart

• Closest R function: TraMineRextras::seqedplot()
• Mapping note: Conceptually follows the same idea of showing event timing and dynamics, but it is not a one-to-one replacement for every TraMineRextras::seqedplot() workflow.

See Also

• Event Sequences Introduction explains the workflow and data structures.
• Quickstart Example shows a complete event-mining run.
• Helper Functions lists supporting utilities.

Authors

Code: Yuqi Liang

Documentation: Yuqi Liang

Reference

Ritschard, G., Bürgin, R., & Studer, M. (2013). Exploratory Mining of Life Event Histories. In J. J. McArdle & G. Ritschard (Eds.), Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences (pp. 221–253). Routledge.