L-PICOLA v1.2, December 2015
    Authors: Cullan Howlett & Marc Manera (UWA, Perth; UCL, London)

    This code is L-PICOLA: A Lightcone-enabled Parallel Implementation 
    of the COLA (COmoving Lagrangian Acceleration) method, as described in 
    Howlett. C., Manera. M. & Percival. W. J. (2015). Please read this paper
    for full details of how the code works and the efforts that have been made
    to produce this code. We ask that any work making use of L-PICOLA 
    reference this paper.

    We have included a comprehensive user guide with this distribution, 
    explaining the multitude of options available when using the code. We 
    have provided explanation of each of the various compilation options 
    and run parameters therein, however as a basic start please look at the 
    information on the webpage associated with L-PICOLA.
    
    You can find the User Guide and code paper in the directory 'Documentation'.
    The source code is in the 'src' directory. Any files you may need to run 
    L-PICOLA, such as 'run_parameters.dat', are in the 'files' directory.
    Finally, we have put a few short codes you may find useful in preparing,
    and using the output from, L-PICOLA, in the 'utilities' directory.
    
    In L-PICOLA, The COLA method is applied to a PM-based N-body code as in the 
    public release of COLAcode (Dec 2012), albeit with many changes and 
    improvements. In this sense, L-PICOLA can be seen as an  amalgamation of 
    both COLAcode and 2LPTic where a substantial amount of  work has been 
    put in to both to make them compatible and simultaneously improve both. 
    Obviously this means that this work relies fundamentally on the work others 
    have put into creating the aforementioned codes, and so we kindly suggest 
    that any work using this code also reference the following papers on top 
    of the main code paper above:

      Solving Large Scale Structure in Ten Easy Steps with COLA,
      Tassev, S., Zaldarriaga, M. & Eisenstein, D. 2013,
      JCAP, 6, 036, (arXiv:1301.0322).

      Large-scale Bias and Efficient Generation of Initial Conditions for 
      Nonlocal Primordial non-Gaussianity
      Scoccimarro, R., Hui, L., Manera, M. & Chan, K. C., 2012, 
      Phys. Rev. D, 85, 083002, (arXiv:1108.5512).