ENH: Raw `pd.Rolling.apply` with index
In many cases (especially in non-uniform indexed time-series), one needs to know the index of the series during a `rolling` operation. While it is possible to obtain the index & values in the non-`raw` mode, it isn't possible to use the optimizations of the "numba" engine with that. 

I propose a new `raw`-argument mode (say `df.rolling('1min').apply(..., raw='with_index', engine="numba")`), that assumes a second argument for the index. For example:

```python
def normalized_ptp(values, index_values):
    return values.ptp() / index_values.view('int64').ptp()

time_series.rolling('1min').apply(normalized_ptp, 
                                  raw='with_index', 
                                  engine='numba', 
                                  engine_kwargs=dict(nopython=True)
)
```
