This git repository is only updated with new releases of fullrmc. A private repository is used for development.
FUndamental Library Language for Reverse Monte Carlo or fullrmc is a molecular/atomic stochastic fitting platform to reverse modeling experimental data. fullrmc is not a standard RMC software but exceeds in its capabilities and functionalities traditional RMC and Metropolis-Hastings algoritm. Therefore RMC appellation in fullrmc, is not accurate but it’s retained to respecting the community terminology. RMC is probably best known for its applications in condensed matter physics and solid state chemistry. RMC is used to solve an inverse problem whereby an atomic model is adjusted until its atoms position have the greatest consistency with a set of experimental data. fullrmc is a python package with its core and calculation modules optimized and compiled in Cython/c. fullrmc’s Engine sub-module is the main module that contains the definition of ‘Engine’ which is the main and only class used to launch the stochastic calculation. fullrmc is a fully object-oriented package where everything can be overloaded allowing easy development, implementation and maintenance of the code. It's core sub-package and modules are fully optimized written in cython/C. fullrmc is unique in its approach, among other functionalities:
https://groups.google.com/forum/#!forum/fullrmc
numpy and cython must be installed and updated manually.
pip install -U "numpy>=1.17.4"
pip install -U "cython>=0.29.14"
pip install fullrmc
Ensure all fullrmc required packages are installed and up to data by executing the following python script:
# check whether all packages are already installed
from __future__ import print_function
from pkg_resources import parse_version as PV
for name, ver in [('numpy' ,'1.17.4') ,
('cython' ,'0.29.14'),
('pyrep' ,'3.2.0') ,
('pdbparser' ,'0.1.8') ,
('pysimplelog','2.0.0') ,
('pylocker' ,'3.0.0') ,
('matplotlib' ,'3.1.2' )]:
try:
lib = __import__(name)
except:
print('%s must be installed for fullrmc to run properly.'%(name))
else:
if PV(lib.__version__) < PV(ver):
print('%s installed version %s is below minimum suggested version %s. Updating %s is highly) recommended.'%(name, lib.__version__, ver, name)
else:
print('%s is installed properly and minimum version requirement is met.'%(name))
Locate python's site-packages by executing the following python script:
import os
os.path.join(os.path.dirname(os.__file__), 'site_packages')
Navigate to site_packages folder and clone git repository from command line:
cd .../site_packages
git clone https://github.com/bachiraoun/fullrmc.git
Change directory to .../site_packages/fullrmc/Extensions. Then compile fullrmc extensions from command line as the following:
cd .../site_packages/fullrmc/Extensions
python setup.py build_ext --inplace
http://bachiraoun.github.io/fullrmc/
If you use fullrmc in a scientific publication, we would appreciate citations to the following paper:
Text entry:
Bachir Aoun; Fullrmc, a Rigid Body Reverse Monte Carlo Modeling Package Enabled with Machine Learning and Artificial Intelligence; J. Comput. Chem. 2016, 37, 1102–1111. DOI: 10.1002/jcc.24304
Bibtex entry:
@article {JCC:JCC24304,
author = {Aoun, Bachir},
title = {Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence},
journal = {Journal of Computational Chemistry},
volume = {37},
number = {12},
issn = {1096-987X},
url = {http://dx.doi.org/10.1002/jcc.24304},
doi = {10.1002/jcc.24304},
pages = {1102--1111},
keywords = {reverse monte carlo, rigid body, machine learning, pair distribution function, modeling},
year = {2016},
}
EndNote entry:
Provider: John Wiley & Sons, Ltd
Content:text/plain; charset="UTF-8"
TY - JOUR
AU - Aoun, Bachir
TI - Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence
JO - Journal of Computational Chemistry
JA - J. Comput. Chem.
VL - 37
IS - 12
SN - 1096-987X
UR - http://dx.doi.org/10.1002/jcc.24304
DO - 10.1002/jcc.24304
SP - 1102
EP - 1111
KW - reverse Monte Carlo
KW - rigid body
KW - machine learning
KW - pair distribution function
KW - modeling
PY - 2016
ER -