Incorporate a vbench run into Travis-CI
A seperate 2.7 env is spawned, which is allowed to fail without
failing the entire build. The results are posted to a public gist as 
a json file, and the gist url is printed in the log.

A helper function in vb_suite/perf_HEAD.py accepts a travis
build number, It then locates the VBENCH job within the build, fetches the log 
through the Travis API, extract the gist raw_url from the log, fetches the results
as json and converts it into  a pandas dataframe.

It remains to be seen whether the results are consistent
enough between builds over time to actually make this useful.

Here's how to gather the results from a few consecutive test builds I ran:

``` python
%cd vb_suite

import pandas as pd
from perf_HEAD import get_build_results
builds=[3393087,3393105,3393122,3393125,3393130]
dfs=[get_build_results(x) for x in builds]
dfs2=[x[['timing']] for x in dfs]
for df,b in zip(dfs2,builds):
    df.columns=[str(b)]
df = dfs2[0]
for other in dfs2[1:]:
    df=df.join(other,how='outer')
df[:20]

                                   3393087     3393105     3393122     3393125     3393130
name                                                                                      
append_frame_single_homogenous    0.641988    0.340924    0.524198    0.400884    0.373756
append_frame_single_mixed         2.159144    1.354964    1.639278    1.421034    1.301402
concat_series_axis1             131.367922   56.542015   92.097044   75.879002   71.159506
ctor_index_array_string           0.009129    0.004975    0.005761    0.006048    0.007652
dataframe_get_value               0.001885    0.001201    0.001403    0.001305    0.001331
dataframe_getitem_scalar          0.005543    0.003445    0.003394    0.003432    0.003307
dataframe_reindex_columns         0.494599    0.233001    0.370013    0.334528    0.316710
dataframe_reindex_daterange       0.683607    0.329319    0.552313    0.506124    0.439592
datamatrix_getitem_scalar         0.004519    0.003790    0.003355    0.003436    0.003317
datetimeindex_add_offset          0.439327    0.227953    0.327404    0.410341    0.312527
datetimeindex_normalize          73.160005   51.629400   60.980701   59.092712   58.182502
frame_boolean_row_select          0.769411    0.596314    0.723754    0.564437    0.653430
frame_constructor_ndarray         0.064187    0.028872    0.033200    0.033250    0.042343
frame_ctor_list_of_dict         140.382051   86.547899  114.851952  127.656937  114.904165
frame_ctor_nested_dict          124.281883   73.726416   97.840071  106.689930  111.264944
frame_ctor_nested_dict_int64    229.266882  130.098104  143.503904  183.116913  152.652025
frame_fancy_lookup                2.752171    2.173202    2.223320    1.820600    2.148161
frame_fancy_lookup_all          135.908127  113.823605  117.978096  108.165979  113.741159
frame_fillna_inplace             25.430799   16.801000   20.938993   24.916101   21.809912
frame_fillna_many_columns_pad    23.508501   16.926599   20.899606   19.706178   17.867088
```
