Dense quiver speedups

This merge request tackles the issue mentioned in reed.essick/iDQ#57, by doing the following things:

• Extend SelectLoudest to take in multiple gps times as well.
• Implements a DenseQuiver that handles the vectorize() step in bulk rather than delegating to each FeatureVector.
• Have a check in QuiverFactory.unlabeled() which determines which quiver to create based on how densely populated the times requested are.

There's also a couple of things I changed. In GstlalHDF5ClassifierData, there was an edge case in the object's initialization where I assumed self.segs wasn't empty, and so that's handled better now. Also in sklearn.py, I delegate to QuiverFactory.unlabeled() rather than just calling QuiverFactory to produce unlabeled quivers.

I'm leaving this as a work in progress because there's probably edge cases that haven't been handled properly (still doing some testing), and the exact way I went about a few things may be still moved around a bit, depending on what your thoughts are. There's also some commented code I still need to remove.

As for speed, I'm using a 20 second stride to compare speedups. Without any changes, I'm able to produce a 20 second stride of timeseries sampled at 16 Hz in about 12s. Now, that same 20 second stride sampled at 128 Hz takes about 6-7s.

Closes reed.essick/iDQ#57.