## viz.plot_survival()


Plot Kaplan-Meier survival curve(s).


Usage

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


Renders one or more Kaplan-Meier survival curves as a publication-ready visualization. By default uses interactive Altair (Vega-Lite) charts with optional plotnine (ggplot2) support. 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.


## 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.

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

`risk_table: bool = ``False`  
If `True`, return a visualization 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`  
Plot dimensions (in pixels for Altair, inches for plotnine; defaults 500x300 pixels).

`height: int = ``500`  
Plot dimensions (in pixels for Altair, inches for plotnine; defaults 500x300 pixels).

`backend: str = ``"altair"`  
Plotting backend: `"altair"` (default, interactive Vega-Lite) or `"plotnine"` (ggplot2-style). Requires the corresponding extra: `pip install greenwood[altair]` or `pip install greenwood[plotnine]`.


## Returns


`altair.LayerChart or altair.VConcatChart or plotnine.ggplot`  
An Altair chart (if `backend="altair"`) or a plotnine ggplot object (if `backend="plotnine"`). With `risk_table=True` the Altair variant is a `VConcatChart` stacking the curve over an aligned numbers-at-risk table.


## 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"])

# Interactive Altair (default)
gw.plot_survival(km, risk_table=True)

# ggplot2-style (if plotnine is installed)
gw.plot_survival(km, backend="plotnine", risk_table=True)
```


<figure class="figure">
<p><img src="viz.plot_survival_files/figure-html/cell-2-output-1.png" class="figure-img" width="672" height="480" /></p>
</figure>