## plot_survival()


Plot Kaplan-Meier survival curve(s) with Altair.


Usage

``` python
plot_survival(
    km,
    *,
    conf_int=True,
    censor_marks=True,
    risk_table=False,
    times=None,
    xlab="Time",
    ylab="Survival probability",
    width=500,
    height=300
)
```


Renders one or more Kaplan-Meier survival curves as an interactive Vega-Lite chart. Each curve shows the proportion of subjects surviving (event-free) over time as a right-continuous step function, with an optional shaded confidence band and censoring marks. Stratified fits produce one colored curve per group with a legend.

The result is a composable Altair object: layer, facet, or restyle it with Altair's API, and it renders interactively (tooltips and zoom) in notebooks and browsers. Pass `risk_table=True` to stack an aligned numbers-at-risk table beneath the curve (the x-axis scale is shared). Requires Altair (install with `pip install greenwood[altair]`).


## Parameters


`km: KaplanMeier`  
A fitted [KaplanMeier](KaplanMeier.md#greenwood.KaplanMeier) object, unstratified (single curve) or stratified.

`conf_int: bool = ``True`  
If `True` (default), draw the point-wise confidence band as a shaded step area.

`censor_marks: bool = ``True`  
If `True` (default), mark censoring times with `+` symbols on the curve.

`risk_table: bool = ``False`  
If `True`, return an `alt.VConcatChart` stacking the curve over an aligned numbers-at-risk table. If `False` (default), return only the curve.

`times: Any = None`  
Query times for the numbers-at-risk table (used only if `risk_table=True`). Defaults to six evenly spaced, rounded times from 0 to the maximum observed follow-up time.

`xlab: str = ``"Time"`  
Axis labels (defaults `"Time"` and `"Survival probability"`).

`ylab: str = ``"Time"`  
Axis labels (defaults `"Time"` and `"Survival probability"`).

`width: int = ``500`  
Curve dimensions in pixels (defaults 500 x 300).

`height: int = ``500`  
Curve dimensions in pixels (defaults 500 x 300).


## Returns


`An ``alt.LayerChart`` (or an ``alt.VConcatChart`` combining the curve and table if`  

`risk_table=True``).`  


## Examples


``` python
import greenwood as gw

lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
km = gw.KaplanMeier().fit(y, by=lung["sex"])

gw.viz.altair.plot_survival(km, risk_table=True)
```