README

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Histogram-Free Multicanonical Sampling (using numerical integration as an example)
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This code is distributed with the paper "Histogram-free multicanonical Monte 
Carlo sampling to calculate the density of states", published in Computer Physics 
Communications (https://www.sciencedirect.com/science/article/pii/S0010465518303485).

The program implements a novel Monte Carlo algorithm to obtain the density of 
states of a physical system, expanded in a chosen basis set. It can be regarded as 
a descendant and a hybrid method closely related to the multicanonical method and 
Wang-Landau sampling. But unlike these existing algorithms that return the density 
of states as a numerical array, this algorithm avoids binning of a continuous 
variable and is able to express the density of states as a closed-form expression. 
It is thus suitable for the study of the statistical mechanics and thermodynamic 
properties of physical systems where the density of a continuous state variable is 
of interest.

Contacts:
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  - Alfred C. K. Farris  (Oxford College of Emory University, alfred.farris@emory.edu)
  - Ying Wai Li          (Los Alamos National Laboratory, yingwaili@lanl.gov)


Basic Information:
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  - Main program: HistogramFreeMUCA.py
  - Input file:   input.txt


System Requirements:
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  - Python 3.6 or above
  - Python libraries: NumPy, SymPy, Matplotlib


To Run the Program:
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  python HistogramFreeMUCA.py input.txt


License Information:
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  BSD 3-clause


Reference to the Paper:
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  Computer Physics Communiations 235, pp.297-304 (2018).
  https://www.sciencedirect.com/science/article/pii/S0010465518303485