Empirical

---------

* Changed all coordinates from x+yj to 2-D numpy arrays.

---------

* Improved the empirical interpolation interface.
- It no longer tries to perform interpolations, but leaves that to the
calling function.
- More and improved examples are added, copying a reference text, see
Aanonsen, "Empirical Interpolation with Application to Reduced Basis
Approximations", Chapter 3. 2009, Norwegian University of Science and
Technology.

* Quadrature points:
- Added a linear quadrature function.
- Added a function for genericly transforming quadrature from [-1, 1] to some
other range [a, b].
- Added a common selection method for quadratures.

* Added a couple of mesh classes for convenience. Currently only 1- and 2-
dimensional classes are implemented, but if they prove useful then a generic
version may be called for.

* Removed some of the interpolation code. It has been deemed relatively
unnecessary.

* Various asthetics:
- Added pylint configuration file, and improved from some of the suggestions.
- Made various improvements to the testing functions.

--------

What works:

* Basic support for empirical interpolation method is implemented. The API may
change significantly in future releases.

* Method of fundamental solutions, along with basic domain/segment codes.

* Quadrature generators all pass some basic tests (code is thanks to mpspack
and lglnodes.m):
- Periodic trapezoid
- Trapezoid
- Gaussian
- Clenshaw-Curtis
- Legendre-Gauss-Lobatto

* Interpolation codes have been written, using the following algorithms:
- Newton interpolating polynomial (nested form)
- Lagrange interpolating polynomial (uses slow method for cardinal
polynomials, probably numerically unstable for large number of nodes)
- Lagrange bivariate (also uses a slow method for cardinal polynomials, does
not yet interface well with the EI class)

* Example scripts for empirical interpolation and, method of fundamental
solutions.

* A variety of unit tests have been implemented.

* A few of the different classes (Domain, Segment, Scattering) can plot some of
their features using matplotlib.

* Restructured package layout (and changed name from emfs to empirical).

--------

* Initial version

What works:

* The examples/tut_scatt.py script is a port of the mpspack script of the same
name. While it is slightly different in some ways, it does successfully
calculate a plane wave scattering problem, and display the resultant fields.

* All of the unit tests written so far run successfully. However, there is only
60% coverage with these, and even that is not a thorough coverage of all the
methods called.