ENH: resample(..., base='start') for automaticly determining base.
(edit):


```python
import pandas as pd
import datetime as dt
import numpy as np

datetime_start = dt.datetime(2014, 9, 1, 9, 30)
datetime_end = dt.datetime(2014, 9, 1, 16, 0)

tt = pd.date_range(datetime_start, datetime_end, freq='1Min')
df = pd.DataFrame(np.arange(len(tt)), index=tt, columns=['A'])
```

The first item is at 9:30, which is not divisible by 8 minutes. We'd like `df.resample('8T', base='start').first()` to be equivalent to `df.resample('8T', base=2)`.

```python
In [13]: df.resample("8T", base=2).first()
Out[13]:
                       A
2014-09-01 09:30:00    0
2014-09-01 09:38:00    8
2014-09-01 09:46:00   16
2014-09-01 09:54:00   24
2014-09-01 10:02:00   32
```


---

It seems that for 1Min bar data, resample() with sampling frequency of any multiple of 8 has a bug. The code below illustrates the bug when resampling is done at [3, 5, 6, 8, 16] Min. For both 3 and 5 frequency, the first entry of the resampled dataframe index starts at the base timestamp (9:30 in this case) while for frequencies 8 and 16, the resampled index starts at 9:26 and 9:18 respectively.

``` Python
import pandas as pd
import datetime as dt
import numpy as np

datetime_start = dt.datetime(2014, 9, 1, 9, 30)
datetime_end = dt.datetime(2014, 9, 1, 16, 0)

tt = pd.date_range(datetime_start, datetime_end, freq='1Min')
df = pd.DataFrame(np.arange(len(tt)), index=tt, columns=['A'])

for freq in [3, 5, 6, 8, 16]:
    print(freq)
    print(df.resample(str(freq) + 'Min', how='first', base=30).head(2))
```

produces the following output:

```
3
                     A
2014-09-01 09:30:00  0
2014-09-01 09:33:00  3
5
                     A
2014-09-01 09:30:00  0
2014-09-01 09:35:00  5
6
                     A
2014-09-01 09:30:00  0
2014-09-01 09:36:00  6
8
                     A
2014-09-01 09:26:00  0
2014-09-01 09:34:00  4
16
                     A
2014-09-01 09:18:00  0
2014-09-01 09:34:00  4
```
