# event_time()


Create a vector of event times.


Usage

``` python
event_time(
    time,
    status,
    time_max=None,
)
```


`event_time()` creates a vector of event times that may be exact, or right-, left-, or interval-censored. It validates its input and raises an error describing the first problem found.


## Parameters


`time: Any`  
Non-negative event times (numbers or booleans, with `None` or `nan` for missing values). For interval-censored values, this is the lower end of the interval. Accepts a list, a NumPy array, or a DataFrame series. A single number is treated as a vector of length 1.

`status: Any`  
Status codes, the same length as `time`: `"e"` (exact event), `"r"` (right-censored), `"l"` (left-censored), or `"i"` (interval-censored). It can be missing (`None`) only where `time` is missing. Codes are case-sensitive, and categorical input is rejected.

`time_max: Any = None`  
Upper ends of the intervals, the same length as `time`. Must be missing for values that are not interval-censored, and greater than `time` otherwise. The default `None` sets all values to missing.


## Returns


`EventTime`  
The event-time vector.


## Raises


`TypeError`  
If `time` or `time_max` isn't numeric, or `status` isn't a character vector.

`ValueError`  
If the lengths differ, or a value breaks one of the rules above.


## Notes

Errors carry two extra attributes: `check`, a stable identifier for the rule that failed (such as `"status_invalid_value"`), and `locations`, a 0-based integer array of the offending positions (empty when the rule isn't about particular positions). Only the first failing rule is reported, but with every location that breaks it.


## Examples


``` python
import greenwood as gw

x = gw.event_time(
    time=[7, 5, 3, 2, None],
    status=["e", "r", "l", "i", None],
    time_max=[None, None, None, 4, None],
)
x
```


    <event_time[5]>
    7      5+     3-     [2, 4] <NA>


To start from a data frame, name the columns in a formula (or with [Outcome.event_time()](Outcome.md#greenwood.Outcome.event_time)) and pass `data=` to `fit()`, which works on every DataFrame backend:


``` python
import polars as pl

df = pl.DataFrame({
    "times": [0.14, 0.15, 0.44, 0.76, 1.18],
    "status": ["l", "e", "e", "e", "r"],
})
gw.Turnbull().fit("event_time(times, status)", data=df)
```


    Turnbull (self-consistent NPMLE for interval-censored data)

         n  atoms  ambiguous  iters  converged
    all  5      4          1      1       True