Pad to urlInterval in put_timeseries()

I'm not sure this is functionally different from what there was previously. Is this what you were suggesting in your comment on this PR @jmfee?

TimeseriesFactory.get_timeseries() pads to the requested interval with NaNs,
filling in start/end gaps. TimeseriesFactory.put_timeseries() is supposed to
do this (according to Jeremy Fee), but it was trimming NaNs from the start/
end before writing to file. This PR fixes this by using ObsPy's Stream.trim()
method, in a similar manner to TimesereisFactory.get_timeseries(), to pad to
the specified start and end of the urlInterval..

Issue#134

Patch waveserver

Update to MetaData.py based on recent measurements

We recently reviewed our various data products and found discrepancy in some of the metadata.  We have updated these values based on recent measurements.  I've modified these values in ObservatoryMetadata.py to be consistent with our other data products.

SqDist local smoothing

By default, each element of the SQ vector (i.e., shat returned from additive())
is treated as if it is independent of its nearest neighbors in time. This is
not generally true, but to treat this in a completely statistically robust
manner would be computationally prohibitive. Instead, we apply a Gaussian
smoother/filter to a limited set of SQ elements in the neighborhood of the
current time step. The configuration parameter 'smooth' specifies the period
(in samples) at which one requires the amplitude to be attenuated by half.
Longer periods will be less attenuated, while shorter periods will be more
attenuated.

Also, added the running estimate of the standard error (i.e., sigmahat) to
the stream returned by process_one().

Added command-line option to set Z-score threshold

Fixes #131

Further clarification on forgetting factors

Updates after doisslintercept

In response to Abe Claycomb's technical algorithm review, I attempted to
further clarify how alpha, beta, and gamma work. This includes a link to
a third-party website that does a good job of explaining what the "average
age" of an exponentially smoothed series is. SqDist.md will need to be
modified at least once more when the OFR is complete, so that it can
link to the official USGS documentation in addition to all the supporting
documents and websites.

Add DOI SSL intercept certificate, update Dockerfile to add certificate for building within DOI network

Update IMFV122 empty values

IPython notebook updates

- improved explanation of exponential smoothing, and how the alpha,
  beta, and gamma parameters work in SqDist.md.
- cleaned up command-line examples in SqDist_usage.md, and incorporated
  a description of the programming interface that was previously in the
  IPython/Jupyter notebook.
- removed programming interface description, and cleaned up markdown
  accordingly; re-ran entire notebook and regenerated graphics.

Update update