ehrapy.plot.sankey_diagram_time

ehrapy.plot.sankey_diagram_time(edata, *, var_name, layer, state_labels=None, node_width=20, node_padding=10, node_color=None, edge_color=None, label_position='right', show_values=True, title=None, width=600, height=400, **kwargs)

Create a Sankey diagram showing patient state transitions over time.

Each node represents a state at a specific time point, and flows show the number of patients transitioning between states. Visualizes how patients transition between different states (e.g. disease severity, treatment status) across consecutive time points.

Parameters:

• edata (EHRData) – Central data object.

• var_name (str) – Variable name from edata.var_names to visualize

• layer (str) – Name of the layer in edata.layers containing the feature data to visualize.

• state_labels (dict[int, str] | None, default: None) – Mapping from numeric state values to readable labels. If None, state values will be displayed as strings of their numeric codes (e.g., “0”, “1”, “2”).

• node_width (int | float, default: 20) – Width of the nodes in the Sankey diagram.

• node_padding (int | float, default: 10) – Padding between nodes in the Sankey diagram.

• node_color (str | None, default: None) – Color of the nodes. If None, default coloring is used.

• edge_color (str | None, default: None) – Color of the edges. If None, default coloring is used.

• label_position (str | None, default: 'right') – Position of the labels on the nodes. Options are ‘left’, ‘right’, ‘outer’, or ‘inner’.

• show_values (bool, default: True) – Whether to display the values on the edges.

• title (str | None, default: None) – Title of the Sankey diagram.

• width (int | None, default: 600) – Width of the Sankey diagram.

• height (int | None, default: 400) – Height of the Sankey diagram.

• **kwargs – Additional styling options passed to holoviews.element.sankey.Sankey.

Return type:

Sankey

Examples

>>> import ehrapy as ep
>>> import ehrdata as ed
>>> edata = ed.dt.ehrdata_blobs(base_timepoints=5, n_variables=1, n_observations=5, random_state=59)
>>> edata.layers["tem_data"] = edata.layers["tem_data"].astype(int)
>>> state_labels = {-2: "no", -3: "mild", -4: "moderate", -5: "severe", -6: "critical"}
>>> ep.pl.sankey_diagram_time(
...     edata,
...     var_name="feature_0",
...     layer="tem_data",
...     state_labels=state_labels,
... )