Iteration updated to find rho using diracsolver

U,rho, U1 and rho1 saved for each iteration and errors saved. No plot
commands and specific imports from diracsolver should prevent crashing
on Andrey's box.
Cosmetic changes - no edit to function
Added mkrpa2.py : RPA kernel

This module calculates the kernel that described RPA response
for a given grid. The function RPA_inter(r) gives the kernel
for E_F = 0, and the function RPA_intra(r, kF) calculates
the _extra_ contribution due to finite Fermi energy.
The kernels are automagically saved in data files to avoid recalculations.

The module also provides testing routines which give some insight about
the response to "topped off" Coulomb potential.
Merge branch 'closing_the_loop' of https://github.com/avshytov/Coulomb-Impurity into closing_the_loop
Various fixes in mkrpa2.py:

    -- Changed regularisation parameter in interband contribution.
       Paradoxically, using a smaller value of epsilon (1e-3) gives
       poor accuracy. This is probably due to the features in the kernel
       being way too sharp.

    -- Use grid parameters in file names

    -- In testing routine, calculate the total charge

    -- Use dimensionless integration variables in RPA_inter.
       In retrospect, the advantage is not clear. It might help
       if I decide to accelerate this routine, though.

    -- Handling of [0, r[0]] interval in RPA_intra

    -- Overall, much better accuracy was now achieved. However,
       RPA_intra is way too slow, and I have to think of some
       method to accelerate it.
Various fixes in mkrpa2.py:

    -- Changed regularisation parameter in interband contribution.
       Paradoxically, using a smaller value of epsilon (1e-3) gives
       poor accuracy. This is probably due to the features in the kernel
       being way too sharp.

    -- Use grid parameters in file names

    -- In testing routine, calculate the total charge

    -- Use dimensionless integration variables in RPA_inter.
       In retrospect, the advantage is not clear. It might help
       if I decide to accelerate this routine, though.

    -- Handling of [0, r[0]] interval in RPA_intra

    -- Overall, much better accuracy was now achieved. However,
       RPA_intra is way too slow, and I have to think of some
       method to accelerate it.
Faster version of RPA_intra.

Instead of integrating the RPA intraband response for each pair of
values (r, r'), one can find the response as a function of
distance between the two points in space, R = sqrt(r1**2 + ...).
Then, the response can be obtained by integrating this quantity
as a function of relative angle theta. For this integration,
it is sufficient to employ spline interpolation of the response
as a function of R. This spline interpolation has to be calculated
only once.
Correct solution with Dirac equation and use of RPA kernel

Background rho error corrected and RPA kernel added to deal with
all RPA contributions. N and alpha included in calculation. Currently
convergent to promising screened potential without N and alpha, but
divergent so far with them.
Minor fix: if kF == 0, mkrpa2.RPA_intra was failing because of zero-sized spline
Fixes in iteration.py:

-- Proper initialization of the kernel (use the mkrpa2.py, Luke)
-- Sign error in kF
-- Grid error in RPA_resp
A script to view iteration progress. Takes filename template as an arg
Allow to specify multiple files and/or templates on the cmd line
Silly bug fixed
Silly bug again
A different method of computing density: temp-broadening
Various fixes in iteration.py, too numerous to list explicitly
More analysis in view-iter.py
typos in mkrpa2.py
Data directory added
Removed unneeded data files