gstlal_etg: added hdf file saving ala h5py, added utility functions and classes to idq_aggregator.py

- V1 must be removed from the coinc rates model in the denominator because
  all coincs will be made to be V1-less in the .__call__() method

- in gstpythonplugin

- from /usr/lib in Debian packages

- use AC_SUBST macro itself to set variable's value

- update gstlal-ugly's .spec and debian/install files to account for addition of library code to the package
- alphabetize gstlal and gstlal-inspiral's files
- don't package the /usr/include/ directory, only its contents

 - "gds-dev" --> "gds-devel" in .spec
 - remove multirate's Makefile.in from configure.ac as it's not packaged

- ax_append_flag.m4

- ax_require_defined.m4

- don't recurse into directory at all.  silences 00boot warning "variable 'libgstmultirate_la_SOURCES' is defined but no program or library has 'libgstmultirate_la' as canonical name (possible typo)"
- (plugin disabled because has not been ported to 1.x, so won't build)

- because we require a 1.x version of gstreamer

- I don't know if this breaks it on other people's systems, but it's broken
  without this update on mine, so ... ?

- This reverts commit 8d438d4a.
- and fixes the feature
- the problem was start-up transients in the whitener leading to bad PSDs
  at the start of analysis jobs.  this was never a problem before because
  we were averaging over the entire experiment, but now the ranking
  statistic believes there is a brief period of insanely high sensitivity
  at the start of segments, and anything found during that time was given
  far too high a significance.  the fix is to have lloidparts check the
  n-samples property of the whitener that provides the PSD, and disregard
  PSDs until it gets close to the configured average-samples value.

- sort of amazed this ever worked, the mask vector was nonsense

- diagnostic tool to investigate relationship between internal dataless
  ranking statistic used with --min-log-L feature of gstlal_inspiral, and
  the true data-defined ranking statistic.  can be used to test
  improvements to the dataless ranking statistic, or check effect of a
  choice of --min-log-L threshold.

- by finishing the construction of the dataless ranking statistic
- this makes gstlal_inspiral_fake_diststats obsolete, so it is deleted

- restore to a functioning state following addition of template bank info to ranking statistic objects
- this actually breaks the dag's final web pages, but what the dag was making plot_background plot before was actually always a little bit nonsense
- the problem was it was using the contents of the final maginalized-across-everything ranking stat data file to provide the SNR, \chi^2 PDFs, but those aren't meaningful, they're marginalized across all bank fragments, and do not reflect the ranking statistic used to actually rank anything.  also, when you add them together the total event count appears much higher than it would've been for any individual bank fragment causing the density estimation kernel to come into tighter focus, giving an inaccurate impression of the actual amount of blurry smoothing used when ranking candidates.
- the patch to add template bank information to the ranking statistic included a safety check to prevent ranking statistic marginalization across template bank bin, and the marginalization jobs were taught not to do it, so the final marginalized-across-everything file no longer contains SNR, \chi^2 PDFs at all.
- this patch to plot_background teaches it how to extract ranking statistic data from a collection of files which it indexes internally by template ID, allowing it to generate per-bank-fragment SNR and \chi^2 PDF plots, except the web pages don't know about the new plots or their names.  that can be fixed later

- teach plot_likelihood_ratio_pdf() how to skip extinction model if it cannot be constructed

- with V(t) weighting in the numerator, some candidates get rejected by the
  ranking statistic because they get collected during times when the PSD
  hadn't settled yet.  this results in a spike of zero-lag events at ln L =
  -inf, which can confuse the extinction model's attempt to find the mode
  of the zero-lag ranking statistic's distribution when fitting the
  extinction model to the data.
- this patch zeros the first few bin counts of the zero-lag PDF before the
  fit, and makes an equivalent adjustment to the threshold selection in
  FAPFAR, in order to (help) make sure the mode select for these is the
  mode we care about.

- in mapping ln L to false-alarm rates
- because we are interested in counts *above* thresholds, not below, the count in a bin should be associated with the lower boundary of the bin, not the upper boundary

- don't allow .new_with_extinction() if the zero-lag counts are all 0
- init FARFAR.__init__(), provide a more useful error message if the zero-lag counts are all 0 (FAPFAR would not initialize, so it would not produce incorrect results, but the failure messages were cryptic)

- sort of:  it now only shows quantities related to noise rates.  a new plot will be constructed for signal related quantities.