Trackpy

This release is strongly recommended for all users, as it brings major improvements in speed and 3D feature-finding, and many other functionality and accuracy improvements.

Please be sure to read the "What's New" page in the [documentation](http://soft-matter.github.io/trackpy/0.3.0/) before upgrading, especially the item under "Enhancements" about calculating masses. You may have to make minor edits to your scripts and notebooks to use them with v0.3.

If you are an Anaconda user, upgrading is as simple as "conda update -c soft-matter trackpy". Other methods are shown on the Installation page in the [documentation](http://soft-matter.github.io/trackpy/0.3.0/).

This is a simple maintenance release, introducing no functionality changes. The packaging and installation are simplified.

Henceforth, to install trackpy on any platform, we recommend `conda install -c soft-matter trackpy`, but `pip install trackpy` is also supported.

This release contains some important improvements, cleanup, and bug fixes:
- Compatibility with Python 3, official support for Python 2.7 and 3.4
- More efficient storage of frame-wise data in HDF format (~40X smaller files)
- Better-looking and more flexible `annotate` function for inspecting feature locations
- Cleaner installation and version-number handling (thanks, leouieda )
- Various minor bug fixes

This is primarily a bugfix release. Changes include:
- Fixed bug in `annotate` when specifying a single threshold value.
- Added preliminary (passing) tests of 3D feature-identification capabilities.
- Fixed error in building API reference documentation.
- Removed ephemeral `MANIFEST` file from the distribution.

This release includes some enhancements, bug fixes, and cleanup:
- Enhancements and bug fixes to the new prediction framework
- Ability to use `locate` float-type image data (crucial for compatibility with the new PIMS release, v0.2)
- Faster access to on-disk framewise storage for streaming analysis
- Enhanced multi-color `annotate`

The version contains significant enhancements, including:
- Much-improved feature-finding, merged from Daniel Allan's `mr` project (`feature.py`, replacing `identification.py`) with uncertainty estimation, along with tools for filtering, analyzing, and plotting trajectories
- Prediction framework for tracking particles whose motion is correlated between frames (Nathan Keim)
- KDTree-based linking, merged from Nathan Keim's branch of trackpy, which is 2X faster on typical data
- Numba-accelerated linking and feature-finding, falling back on pure Python if numba is not available
- Features for processing large data sets "out of core" (on disk)
- Access to different linking strategies through keyword arguments (Type `help(link)` or `help(link_df)` for details.)
- Simple, fast way to read and write data in files; easily extensible to formats used by individual research groups
- A set of examples and guides, [provided separately](https://github.com/soft-matter/trackpy-examples)