update the Bloscapck plug

Curiously, using smaller arrays and LZ4, things are a bit more competitive with cPickle:

```In [74]: a = np.linspace(0, 1, 1e7)

In [75]: %time c = blp.pack_ndarray_str(a, blosc_args=blp.BloscArgs(clevel=9, cname='lz4'))
CPU times: user 89.1 ms, sys: 6.2 ms, total: 95.3 ms
Wall time: 61.4 ms

In [76]: %time ap = cPickle.dumps(a, protocol=-1)
CPU times: user 37.2 ms, sys: 21.3 ms, total: 58.5 ms
Wall time: 58.7 ms

In [77]: %time b = blp.unpack_ndarray_str(c)
CPU times: user 57 ms, sys: 10.1 ms, total: 67.1 ms
Wall time: 51.5 ms

In [78]: %time cp = cPickle.loads(ap)
CPU times: user 24.2 ms, sys: 11.5 ms, total: 35.7 ms
Wall time: 35.6 ms

In [79]: 1.*len(ap) / len(c)
Out[79]: 12.84764215651164
```
or using the classical arange:

```
In [97]: a = np.arange(1e7)

In [98]: %time c = blp.pack_ndarray_str(a, blosc_args=blp.BloscArgs(clevel=9, cname='blosclz'))
CPU times: user 62.1 ms, sys: 3.74 ms, total: 65.8 ms
Wall time: 41 ms

In [99]: %time c = blp.pack_ndarray_str(a, blosc_args=blp.BloscArgs(clevel=9, cname='lz4'))
CPU times: user 43.9 ms, sys: 3.12 ms, total: 47 ms
Wall time: 29 ms

In [100]: %time b = blp.unpack_ndarray_str(c)
CPU times: user 46.2 ms, sys: 12.9 ms, total: 59.1 ms
Wall time: 42.9 ms

In [101]: %time ap = cPickle.dumps(a, protocol=-1)
CPU times: user 38.6 ms, sys: 21.8 ms, total: 60.4 ms
Wall time: 60.6 ms

In [102]: %time cp = cPickle.loads(ap)
CPU times: user 27.9 ms, sys: 14 ms, total: 41.8 ms
Wall time: 41.9 ms
```

so, 'pickles' with blospack can be up to 2x faster than with a regular cPickle, and similar in speed for 'unpickling'.  Not bad.  I think I am going to rephrase this a bit after merging.