Sampling at pre-defined time values
Consider a time-series, e.g.:

```
                                 some_quantity
timestamp                 
2014-10-06 18:00:40.063000-04:00      0.568000
2014-10-06 18:00:41.361000-04:00      3.014770
2014-10-06 18:00:42.896000-04:00      0.878154
2014-10-06 18:00:43.040000-04:00      0.723077
2014-10-06 18:00:44.791000-04:00      0.723077
2014-10-06 18:00:45.496000-04:00      0.309539
2014-10-06 18:00:45.799000-04:00      3.032000
2014-10-06 18:00:47.470000-04:00      3.014770
2014-10-06 18:00:48.092000-04:00      1.584616
```

and an independent set of timestamps, e.g.:

```
start_time = datetime.datetime(year   = 2014, 
                               month  = 10, 
                               day    = 6, 
                               hour   = 18, 
                               tzinfo = pytz.timezone('US/Eastern')

# Finish 400 seconds later
end_time    = start_time + datetime.timedelta(seconds=400)

new_timestamps = pd.date_range(start = start_time, 
                                  end   = end_time, 
                                  freq  = '2.5s')
```

Is there any way to sample the original time-series so that, for each of the new timestamps, we get the sum of values of the original time-series between the new time-stamps?

Another way to put it, is there any way to call `resample` by passing it a series of timestamps on which we want to do the sampling? e.g. something like `my_series.resample(how='sum', new_timestamps)` 
