Update to 1.1.1.2

Changes from https://github.com/bos/text:
1.1.1.2
 - updated upperBound to return upper instead of lower bound from size hint
Bump 2.0.2-RC2 to 2.0.2-RC3
-------------
This is the third release candidate for TQSL 2.0.2.

Changes since -RC2:

Ensure that station locations added from the "sign a log" selection dialog
are added to the list of station locations.

Allow restore from a .tbk file to restore multiple pending callsign
certificates for a given callsign.

Change message on upload dialog box when a certificate request is being
uploaded to indicate that it's a callsign certificate request, not a log file
being uploaded.

When a renewed callsign certificate is installed, delete any older versions
for that callsign.

Correct defect that could cause errors when using files in directories
containing non-ASCII characters on Windows.

Correctly account for errors when signing a log so that the number of QSOs
processed is displayed correctly. (For some cases where QSOs caused errors
which the user chose to ignore, the number of QSOs processed could show up as
a negative number.)
  ( .. ommitted .. Binary download for Windows/Mac/Linux URL)
Please let us know if you see any problems with this release. I will be
announcing this as a beta on ARRL-LOTW in a week or so.
(By Rick Murphy K1MU/4, Annandale VA USA)
Mark packages that are not ready for python-3.3 also not ready for 3.4,
until proven otherwise.
Import py27-blosc-1.2.3 as wip/py-blosc.

Blosc is a high performance compressor optimized for binary data
Import py27-bloscpack-0.6.0 as wip/py-bloscpack.

Command line interface to and serialization format for Blosc
No need to maintain "trac" here anymore.
Remove patch that was integrated upstream.
dd new file from upstream.
delete missing clone!
Update 1.5.2.
Import py27-shutilwhich-1.0.1 as wip/py-shutilwhich.

Shutil.which for those not using Python 3.3 yet
Import py27-faker-0.0.4 as wip/py-faker.

A Python library for generating fake user data
Perl's got one, Ruby's got one, now Pythonistas envy no longer
Import py27-arrow-0.4.2 as wip/py-arrow.

Arrow is a Python library that offers a sensible, human-friendly approach to
creating, manipulating, formatting and converting dates, times, and timestamps
It implements and updates the datetime type, plugging gaps in functionality, and
provides an intelligent module API that supports many common creation scenarios
Simply put, it helps you work with dates and times with fewer imports and
a lot less code.
Update for git changes.
Add a patch for python-3 support.
+ llvm-3.4.1, meta-tracker-1.0.1.
Import py27-drawnow-0.44 as wip/py-drawnow.

Matlab has a great feature where it allows you to update a figure. You can
simply call drawnow and have your figure update. This is nice if you're running
a simulation and want to see the results every iteration. It'd sure be nice if
Python/matplotlib had a similar feature to update the plot each iteration.
This package adds that functionality to matplotlib.
Comment ``.include "../../devel/ocaml-findlib/buildlink3.mk".''

This commit stops automatic include of ocaml-findlib/buildlink3.mk,
because wrapper script(ocamlfind.sh) does not work when "-pp" option
is specified.
Add .include "../../devel/ocaml-findlib/buildlink3.mk".
+ py-tortoisehg-3.0.
Import py27-neo-0.3.3 as wip/py-neo.

Neo is a package for representing electrophysiology data in Python, together
with support for reading a wide range of neurophysiology file formats, including
Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for
writing to a subset of these formats plus non-proprietary formats including
HDF5.

The goal of Neo is to improve interoperability between Python tools for
analyzing, visualizing and generating electrophysiology data (such as
OpenElectrophy, NeuroTools, G-node, Helmholtz, PyNN) by providing a common,
shared object model. In order to be as lightweight a dependency as possible,
Neo is deliberately limited to represention of data, with no functions for data
analysis or visualization.

Neo implements a hierarchical data model well adapted to intracellular and
extracellular electrophysiology and EEG data with support for multi-electrodes
(for example tetrodes). Neo's data objects build on the quantities_ package,
which in turn builds on NumPy by adding support for physical dimensions. Thus
neo objects behave just like normal NumPy arrays, but with additional metadata,
checks for dimensional consistency and automatic unit conversion.
or installation instructions