Utility classes and functions
=============================

Trilinear interpolation
-----------------------

The class :py:class:`~.LinearInterpolationNOGrid` supports linear, bilinear
and trilinear interpolation for data on an even grid in 1D, 2D or 3D, with
orthogonal or non-orthogonal lattice vectors. Please see the code reference
for documentation.

Code reference
--------------

.. automodule:: envtb.quantumcapacitance.utilities
   :members:
   
Matplotlib tips
---------------
http://www.packtpub.com/article/advanced-matplotlib-part1

::

    fig1 = pyplot.figure()
    fig2 = pyplot.figure()
    ax1 = fig1.add_subplot(2,1,1)
    ax2 = fig1.add_subplot(2,1,2)
    l, = ax2.plot(x, y)
    m, = ax1.plot(x, y)
    w, = ax1.plot(x, y+3)
    t = ax2.set_title('random numbers')
    l.set_color('red')
    m.set_color('blue')
    ax3=fig2.add_subplot(1,1,1)
    n, = ax3.plot(x, y)
    w.set_color('red')
    fig1.legend([m,w], ["line 2", "line 1"])
    ax2.legend([l],["line 3"])
    
The pyplot.someFunction() commands are equivalent to::
    ax=pyplot.gca()
    ax.someFunction()
i.e. they are applied to the current axes.

The standard procedure would be:

* Set up the plot "window" using figure() and subplot()
* Execute the plot function (e.g. plot_bandstructure())
* If the function returns lists of lines, you can style them.
* The axes have a lines property which is a list of all the lines. You
  can style them here too.
* Set other properties, like x and y limits, legend, other texts that
  belong to the figure or the subplot (=axes).


Workflow recommendation
-----------------------

This is not a utility class, but it's the best place to put this information.

(Work on this!)

* IPython web interface
* Use ? for documentation
* combine plots and use pyplot.show()
* handle data, numpy.savetxt