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This directory contains Python code which wraps LAMMPS as a library
and allows the LAMMPS library interface to be invoked from Python,
either from a Python script or using Python interactively.
Details on the Python interface to LAMMPS and how to build LAMMPS as a
shared library, for use with Python, are given in
doc/Section_python.html and in doc/Section_start.html#start_5.
Basically you need to follow these steps in the src directory:
% make g++ mode=shlib # build for whatever machine target you wish
% make install-python # may need to do this via sudo
You can replace the last step by a one-time setting of environment
variables in your shell script. Or you can run the python/install.py
script directly to give you more control over where the two relevant
files are installed. See doc/Section_python.html for details.
You should then be able to launch Python and instantiate an instance
of LAMMPS:
% python
>>> from lammps import lammps
>>> lmp = lammps()
If that gives no errors, you have succesfully wrapped LAMMPS with
Python. See doc/Section_python.html#py_7 for tests you can then use
to run LAMMPS both in serial or parallel thru Python.
Note that you can also invoke Python code from within a LAMMPS input
script, using the "python" command. See the doc/python.html doc page
for details. The Python code you invoke can also call back to LAMMPS
using the same interface described here for wrapping LAMMPS.
-------------------------------------------------------------------
Alternative Python wrapper for LAMMPS
In addition to the original wrapper, you can use the PyLammps or IPyLammps
wrappers to create simpler Python scripts and IPython notebooks.
>>> from lammps import lammps, PyLammps, IPyLammps
>>> lmp = lammps()
>>> L = PyLammps(lmp)
Using these higher-level wrappers translates any function call into their
corresponding LAMMPS command. Arguments are either passed as one
string or individual parameters. Parameter lists are automatically
joined to one command string. E.g.,
>>> L.velocity("all create", 1.44, 87287, "loop geom")
is translated into the following call in the original interface:
>>> lmp.command("velocity all create 1.44 87287 loop geom")
It also gives you an easier way to query the system state, evaluate variable
expressions and access atom data. See examples/ipython/interface_usage.ipynb
for more details.
NOTE: this alternative wrapper was primarily developed for Python 3. While it
is mostly compatible to Python 2, not all functionality is available due to
language restrictions. E.g., 'print' is a reserved keyword in Python 2, which
is why the LAMMPS 'print' command is only available as 'lmp_print'.
-------------------------------------------------------------------
Once you have successfully wrapped LAMMPS, you can run the Python
scripts in the examples sub-directory:
trivial.py read/run a LAMMPS input script thru Python
demo.py invoke various LAMMPS library interface routines
simple.py parallel example, mimicing examples/COUPLE/simple/simple.cpp
split.py parallel example
mc.py Monte Carlo energy relaxation wrapper on LAMMPS
gui.py GUI go/stop/temperature-slider to control LAMMPS
plot.py real-time temeperature plot with GnuPlot via Pizza.py
viz_tool.py real-time viz via some viz package
vizplotgui_tool.py combination of viz.py and plot.py and gui.py
For the viz_tool.py and vizplotgui_tool.py commands, replace "tool"
with "gl" or "atomeye" or "pymol", depending on what visualization
package you have installed. We hope to add a VMD option soon.
Note that for GL, you need to be able to run the Pizza.py GL tool,
which is included in the pizza sub-directory. See the Pizza.py doc
pages for more info:
http://www.sandia.gov/~sjplimp/pizza.html
Note that for AtomEye, you need version 3, and their is a line in the
scripts that specifies the path and name of the executable. See
the AtomEye WWW pages for more details:
http://mt.seas.upenn.edu/Archive/Graphics/A
http://mt.seas.upenn.edu/Archive/Graphics/A3/A3.html
The latter link is to AtomEye 3 which has the scriping
capability needed by these Python scripts.
Note that for PyMol, you need to have built and installed the
open-source version of PyMol in your Python, so that you can import it
from a Python script. See the PyMol WWW pages for more details:
http://www.pymol.org
http://sourceforge.net/scm/?type=svn&group_id=4546
The latter link is to the open-source version.
-------------------------------------------------------------------
Each example script has more documentation in the file that explains
how to use it and what it is doing.
You can run a particular script in either of the following ways:
% trivial.py in.trivial
% python -i trivial.py in.trivial
The former assumes that you have changed the first line of the script
to point to the Python installed on your box and made the script
exectable (e.g. chmod +x trivial.py).
The example scripts take the following arguments. The in.* args are
LAMPS input scripts.
trivial.py in.trivial
demo.py
simple.py in.simple # can run in parallel (see below)
split.py in.simple # can run in parallel (see below)
gui.py in.gui 100
plot.py in.plot 10 1000 thermo_temp
viz_tool.py in.viz 100 5000
vizplotgui_tool.py in.viz 100 thermo_temp
To run LAMMPS in parallel from Python, so something like this:
% mpirun -np 4 simple.py in.simple
% mpirun -np 4 python split.py in.simple
If you run simple.py as-is, this will invoke P instances of a
one-processor run, where both Python and LAMMPS will run on single
processors. Each running job will read the same input file, and write
to same log.lammps file, which isn't too useful.
However, if you have either the Pypar or mpi4py packages installed in
your Python, and uncomment the Pypar or mpi4py code in simple.py, then
the above commands will invoke 1 instance of a P-processor run. Both
Python and LAMMPS will run on P processors. The job will read the
input file and write a single log.lammps file.
The split.py script can also be run in parallel. It uses mpi4py
version 2.0.0 (or later), which makes it possible to pass a
communicator when creating the LAMMPS object and thus run multiple
instances of LAMMPS at the same time, each on a different subset of
MPI ranks. Or run LAMMPS on one subset and some other program on the
rest of the MPI ranks, concurrently. See comments in the split.py
script for more details.
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