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11. Python interface to LAMMPS :h3
This section describes how to build and use LAMMPS via a Python
interface.
11.1 "Building LAMMPS as a shared library"_#py_1
11.2 "Installing the Python wrapper into Python"_#py_2
11.3 "Extending Python with MPI to run in parallel"_#py_3
11.4 "Testing the Python-LAMMPS interface"_#py_4
11.5 "Using LAMMPS from Python"_#py_5
11.6 "Example Python scripts that use LAMMPS"_#py_6 :ul
The LAMMPS distribution includes the file python/lammps.py which wraps
the library interface to LAMMPS. This file makes it is possible to
run LAMMPS, invoke LAMMPS commands or give it an input script, extract
LAMMPS results, an modify internal LAMMPS variables, either from a
Python script or interactively from a Python prompt. You can do the
former in serial or parallel. Running Python interactively in
parallel does not generally work, unless you have a package installed
that extends your Python to enable multiple instances of Python to
read what you type.
"Python"_http://www.python.org is a powerful scripting and programming
language which can be used to wrap software like LAMMPS and other
packages. It can be used to glue multiple pieces of software
together, e.g. to run a coupled or multiscale model. See "Section
section"_Section_howto.html#howto_10 of the manual and the couple
directory of the distribution for more ideas about coupling LAMMPS to
other codes. See "Section_start 4"_Section_start.html#start_5 about
how to build LAMMPS as a library, and "Section_howto
19"_Section_howto.html#howto_19 for a description of the library
interface provided in src/library.cpp and src/library.h and how to
extend it for your needs. As described below, that interface is what
is exposed to Python. It is designed to be easy to add functions to.
This can easily extend the Python inteface as well. See details
below.
By using the Python interface, LAMMPS can also be coupled with a GUI
or other visualization tools that display graphs or animations in real
time as LAMMPS runs. Examples of such scripts are inlcluded in the
python directory.
Two advantages of using Python are how concise the language is, and
that it can be run interactively, enabling rapid development and
debugging of programs. If you use it to mostly invoke costly
operations within LAMMPS, such as running a simulation for a
reasonable number of timesteps, then the overhead cost of invoking
LAMMPS thru Python will be negligible.
Before using LAMMPS from a Python script, you have to do two things.
You need to set two environment variables. And you need to build
LAMMPS as a dynamic shared library, so it can be loaded by Python.
Both these steps are discussed below. If you wish to run LAMMPS in
parallel from Python, you also need to extend your Python with MPI.
This is also discussed below.
The Python wrapper for LAMMPS uses the amazing and magical (to me)
"ctypes" package in Python, which auto-generates the interface code
needed between Python and a set of C interface routines for a library.
Ctypes is part of standard Python for versions 2.5 and later. You can
check which version of Python you have installed, by simply typing
"python" at a shell prompt.
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11.1 Building LAMMPS as a shared library :link(py_1),h4
Instructions on how to build LAMMPS as a shared library are given in
"Section_start 5"_Section_start.html#start_5. A shared library is one
that is dynamically loadable, which is what Python requires. On Linux
this is a library file that ends in ".so", not ".a".
From the src directory, type
make makeshlib
make -f Makefile.shlib foo :pre
where foo is the machine target name, such as linux or g++ or serial.
This should create the file liblmp_foo.so in the src directory, as
well as a soft link liblmp.so, which is what the Python wrapper will
load by default. Note that if you are building multiple machine
versions of the shared library, the soft link is always set to the
most recently built version.
If this fails, see "Section_start 5"_Section_start.html#start_5 for
more details, especially if your LAMMPS build uses auxiliary libraries
like MPI or FFTW which may not be built as shared libraries on your
system.
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11.2 Installing the Python wrapper into Python :link(py_2),h4
For Python to invoke LAMMPS, there are 2 files it needs to have:
python/lammps.py
src/liblmp.so :ul
Lammps.py is the Python wrapper on the LAMMPS library interface.
Liblmp.so is the shared LAMMPS library that Python loads, as described
above.
You can insure Python can find these files in one of two ways:
set two environment variables
run the python/install.py script :ul
If you set the paths to these files as environment variables, you only
have to do it once. For the csh or tcsh shells, add something like
this to your ~/.cshrc file, one line for each of the two files:
setenv PYTHONPATH ${PYTHONPATH}:/home/sjplimp/lammps/python
setenv LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/home/sjplimp/lammps/src :pre
If you run the python/install.py script, you need to rerun it every
time you rebuild LAMMPS (as a shared library) or make changes to the
python/lammps.py file.
You can invoke install.py from the python directory as
% python install.py :pre
Prefix this command with "sudo" if it does not allow you to copy files
into the Python site-packages directory. If you do this, make sure
that the Python run by root is the same as the Python you run.
E.g. you may need to do something like
% sudo /usr/local/bin/python install.py :pre
You can also invoke install.py from the src directory as
% make install-python :pre
Again, you may need to prefix this with "sudo". In this mode you
cannot control which Python root invokes.
:line
11.3 Extending Python with MPI to run in parallel :link(py_3),h4
If you wish to run LAMMPS in parallel from Python, you need to extend
your Python with an interface to MPI. This also allows you to
make MPI calls directly from Python in your script, if you desire.
There are several Python packages available that purport to wrap MPI
as a library and allow MPI functions to be called from Python.
These include
"pyMPI"_http://pympi.sourceforge.net/
"maroonmpi"_http://code.google.com/p/maroonmpi/
"mpi4py"_http://code.google.com/p/mpi4py/
"myMPI"_http://nbcr.sdsc.edu/forum/viewtopic.php?t=89&sid=c997fefc3933bd66204875b436940f16
"Pypar"_http://code.google.com/p/pypar :ul
All of these except pyMPI work by wrapping the MPI library and
exposing (some portion of) its interface to your Python script. This
means Python cannot be used interactively in parallel, since they do
not address the issue of interactive input to multiple instances of
Python running on different processors. The one exception is pyMPI,
which alters the Python interpreter to address this issue, and (I
believe) creates a new alternate executable (in place of "python"
itself) as a result.
In principle any of these Python/MPI packages should work to invoke
LAMMPS in parallel and MPI calls themselves from a Python script which
is itself running in parallel. However, when I downloaded and looked
at a few of them, their documentation was incomplete and I had trouble
with their installation. It's not clear if some of the packages are
still being actively developed and supported.
The one I recommend, since I have successfully used it with LAMMPS, is
Pypar. Pypar requires the ubiquitous "Numpy
package"_http://numpy.scipy.org be installed in your Python. After
launching python, type
import numpy :pre
to see if it is installed. If not, here is how to install it (version
1.3.0b1 as of April 2009). Unpack the numpy tarball and from its
top-level directory, type
python setup.py build
sudo python setup.py install :pre
The "sudo" is only needed if required to copy Numpy files into your
Python distribution's site-packages directory.
To install Pypar (version pypar-2.1.4_94 as of Aug 2012), unpack it
and from its "source" directory, type
python setup.py build
sudo python setup.py install :pre
Again, the "sudo" is only needed if required to copy Pypar files into
your Python distribution's site-packages directory.
If you have successully installed Pypar, you should be able to run
Python and type
import pypar :pre
without error. You should also be able to run python in parallel
on a simple test script
% mpirun -np 4 python test.py :pre
where test.py contains the lines
import pypar
print "Proc %d out of %d procs" % (pypar.rank(),pypar.size()) :pre
and see one line of output for each processor you run on.
IMPORTANT NOTE: To use Pypar and LAMMPS in parallel from Python, you
must insure both are using the same version of MPI. If you only have
one MPI installed on your system, this is not an issue, but it can be
if you have multiple MPIs. Your LAMMPS build is explicit about which
MPI it is using, since you specify the details in your lo-level
src/MAKE/Makefile.foo file. Pypar uses the "mpicc" command to find
information about the MPI it uses to build against. And it tries to
load "libmpi.so" from the LD_LIBRARY_PATH. This may or may not find
the MPI library that LAMMPS is using. If you have problems running
both Pypar and LAMMPS together, this is an issue you may need to
address, e.g. by moving other MPI installations so that Pypar finds
the right one.
:line
11.4 Testing the Python-LAMMPS interface :link(py_4),h4
To test if LAMMPS is callable from Python, launch Python interactively
and type:
>>> from lammps import lammps
>>> lmp = lammps() :pre
If you get no errors, you're ready to use LAMMPS from Python.
If the load fails, the most common error to see is
OSError: Could not load LAMMPS dynamic library :pre
which means Python was unable to load the LAMMPS shared library. This
typically occurs if the system can't find the LAMMMPS shared library
or one of the auxiliary shared libraries it depends on.
Python (actually the operating system) isn't verbose about telling you
why the load failed, so carefully go through the steps above regarding
environment variables, and the instructions in "Section_start
5"_Section_start.html#start_5 about building a shared library and
about setting the LD_LIBRARY_PATH envirornment variable.
[Test LAMMPS and Python in serial:] :h5
To run a LAMMPS test in serial, type these lines into Python
interactively from the bench directory:
>>> from lammps import lammps
>>> lmp = lammps()
>>> lmp.file("in.lj") :pre
Or put the same lines in the file test.py and run it as
% python test.py :pre
Either way, you should see the results of running the in.lj benchmark
on a single processor appear on the screen, the same as if you had
typed something like:
lmp_g++ < in.lj :pre
[Test LAMMPS and Python in parallel:] :h5
To run LAMMPS in parallel, assuming you have installed the
"Pypar"_http://datamining.anu.edu.au/~ole/pypar package as discussed
above, create a test.py file containing these lines:
import pypar
from lammps import lammps
lmp = lammps()
lmp.file("in.lj")
print "Proc %d out of %d procs has" % (pypar.rank(),pypar.size()),lmp
pypar.finalize() :pre
You can then run it in parallel as:
% mpirun -np 4 python test.py :pre
and you should see the same output as if you had typed
% mpirun -np 4 lmp_g++ < in.lj :pre
Note that if you leave out the 3 lines from test.py that specify Pypar
commands you will instantiate and run LAMMPS independently on each of
the P processors specified in the mpirun command. In this case you
should get 4 sets of output, each showing that a LAMMPS run was made
on a single processor, instead of one set of output showing that
LAMMPS ran on 4 processors. If the 1-processor outputs occur, it
means that Pypar is not working correctly.
Also note that once you import the PyPar module, Pypar initializes MPI
for you, and you can use MPI calls directly in your Python script, as
described in the Pypar documentation. The last line of your Python
script should be pypar.finalize(), to insure MPI is shut down
correctly.
[Running Python scripts:] :h5
Note that any Python script (not just for LAMMPS) can be invoked in
one of several ways:
% python foo.script
% python -i foo.script
% foo.script :pre
The last command requires that the first line of the script be
something like this:
#!/usr/local/bin/python
#!/usr/local/bin/python -i :pre
where the path points to where you have Python installed, and that you
have made the script file executable:
% chmod +x foo.script :pre
Without the "-i" flag, Python will exit when the script finishes.
With the "-i" flag, you will be left in the Python interpreter when
the script finishes, so you can type subsequent commands. As
mentioned above, you can only run Python interactively when running
Python on a single processor, not in parallel.
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11.5 Using LAMMPS from Python :link(py_5),h4
The Python interface to LAMMPS consists of a Python "lammps" module,
the source code for which is in python/lammps.py, which creates a
"lammps" object, with a set of methods that can be invoked on that
object. The sample Python code below assumes you have first imported
the "lammps" module in your Python script, as follows:
from lammps import lammps :pre
These are the methods defined by the lammps module. If you look
at the file src/library.cpp you will see that they correspond
one-to-one with calls you can make to the LAMMPS library from a C++ or
C or Fortran program.
lmp = lammps() # create a LAMMPS object using the default liblmp.so library
lmp = lammps("g++") # create a LAMMPS object using the liblmp_g++.so library
lmp = lammps("",list) # ditto, with command-line args, e.g. list = \["-echo","screen"\]
lmp = lammps("g++",list) :pre
lmp.close() # destroy a LAMMPS object :pre
lmp.file(file) # run an entire input script, file = "in.lj"
lmp.command(cmd) # invoke a single LAMMPS command, cmd = "run 100" :pre
xlo = lmp.extract_global(name,type) # extract a global quantity
# name = "boxxlo", "nlocal", etc
# type = 0 = int
# 1 = double :pre
coords = lmp.extract_atom(name,type) # extract a per-atom quantity
# name = "x", "type", etc
# type = 0 = vector of ints
# 1 = array of ints
# 2 = vector of doubles
# 3 = array of doubles :pre
eng = lmp.extract_compute(id,style,type) # extract value(s) from a compute
v3 = lmp.extract_fix(id,style,type,i,j) # extract value(s) from a fix
# id = ID of compute or fix
# style = 0 = global data
# 1 = per-atom data
# 2 = local data
# type = 0 = scalar
# 1 = vector
# 2 = array
# i,j = indices of value in global vector or array :pre
var = lmp.extract_variable(name,group,flag) # extract value(s) from a variable
# name = name of variable
# group = group ID (ignored for equal-style variables)
# flag = 0 = equal-style variable
# 1 = atom-style variable :pre
natoms = lmp.get_natoms() # total # of atoms as int
data = lmp.gather_atoms(name,type,count) # return atom attribute of all atoms gathered into data, ordered by atom ID
# name = "x", "charge", "type", etc
# count = # of per-atom values, 1 or 3, etc
lmp.scatter_atoms(name,type,count,data) # scatter atom attribute of all atoms from data, ordered by atom ID
# name = "x", "charge", "type", etc
# count = # of per-atom values, 1 or 3, etc :pre
:line
IMPORTANT NOTE: Currently, the creation of a LAMMPS object from within
lammps.py does not take an MPI communicator as an argument. There
should be a way to do this, so that the LAMMPS instance runs on a
subset of processors if desired, but I don't know how to do it from
Pypar. So for now, it runs with MPI_COMM_WORLD, which is all the
processors. If someone figures out how to do this with one or more of
the Python wrappers for MPI, like Pypar, please let us know and we
will amend these doc pages.
Note that you can create multiple LAMMPS objects in your Python
script, and coordinate and run multiple simulations, e.g.
from lammps import lammps
lmp1 = lammps()
lmp2 = lammps()
lmp1.file("in.file1")
lmp2.file("in.file2") :pre
The file() and command() methods allow an input script or single
commands to be invoked.
The extract_global(), extract_atom(), extract_compute(),
extract_fix(), and extract_variable() methods return values or
pointers to data structures internal to LAMMPS.
For extract_global() see the src/library.cpp file for the list of
valid names. New names could easily be added. A double or integer is
returned. You need to specify the appropriate data type via the type
argument.
For extract_atom(), a pointer to internal LAMMPS atom-based data is
returned, which you can use via normal Python subscripting. See the
extract() method in the src/atom.cpp file for a list of valid names.
Again, new names could easily be added. A pointer to a vector of
doubles or integers, or a pointer to an array of doubles (double **)
or integers (int **) is returned. You need to specify the appropriate
data type via the type argument.
For extract_compute() and extract_fix(), the global, per-atom, or
local data calulated by the compute or fix can be accessed. What is
returned depends on whether the compute or fix calculates a scalar or
vector or array. For a scalar, a single double value is returned. If
the compute or fix calculates a vector or array, a pointer to the
internal LAMMPS data is returned, which you can use via normal Python
subscripting. The one exception is that for a fix that calculates a
global vector or array, a single double value from the vector or array
is returned, indexed by I (vector) or I and J (array). I,J are
zero-based indices. The I,J arguments can be left out if not needed.
See "Section_howto 15"_Section_howto.html#howto_15 of the manual for a
discussion of global, per-atom, and local data, and of scalar, vector,
and array data types. See the doc pages for individual
"computes"_compute.html and "fixes"_fix.html for a description of what
they calculate and store.
For extract_variable(), an "equal-style or atom-style
variable"_variable.html is evaluated and its result returned.
For equal-style variables a single double value is returned and the
group argument is ignored. For atom-style variables, a vector of
doubles is returned, one value per atom, which you can use via normal
Python subscripting. The values will be zero for atoms not in the
specified group.
The get_natoms() method returns the total number of atoms in the
simulation, as an int.
The gather_atoms() method returns a ctypes vector of ints or doubles
as specified by type, of length count*natoms, for the property of all
the atoms in the simulation specified by name, ordered by count and
then by atom ID. The vector can be used via normal Python
subscripting. If atom IDs are not consecutively ordered within
LAMMPS, a None is returned as indication of an error.
Note that the data structure gather_atoms("x") returns is different
from the data structure returned by extract_atom("x") in four ways.
(1) Gather_atoms() returns a vector which you index as x\[i\];
extract_atom() returns an array which you index as x\[i\]\[j\]. (2)
Gather_atoms() orders the atoms by atom ID while extract_atom() does
not. (3) Gathert_atoms() returns a list of all atoms in the
simulation; extract_atoms() returns just the atoms local to each
processor. (4) Finally, the gather_atoms() data structure is a copy
of the atom coords stored internally in LAMMPS, whereas extract_atom()
returns an array that effectively points directly to the internal
data. This means you can change values inside LAMMPS from Python by
assigning a new values to the extract_atom() array. To do this with
the gather_atoms() vector, you need to change values in the vector,
then invoke the scatter_atoms() method.
The scatter_atoms() method takes a vector of ints or doubles as
specified by type, of length count*natoms, for the property of all the
atoms in the simulation specified by name, ordered by bount and then
by atom ID. It uses the vector of data to overwrite the corresponding
properties for each atom inside LAMMPS. This requires LAMMPS to have
its "map" option enabled; see the "atom_modify"_atom_modify.html
command for details. If it is not, or if atom IDs are not
consecutively ordered, no coordinates are reset.
The array of coordinates passed to scatter_atoms() must be a ctypes
vector of ints or doubles, allocated and initialized something like
this:
from ctypes import *
natoms = lmp.get_natoms()
n3 = 3*natoms
x = (n3*c_double)()
x[0] = x coord of atom with ID 1
x[1] = y coord of atom with ID 1
x[2] = z coord of atom with ID 1
x[3] = x coord of atom with ID 2
...
x[n3-1] = z coord of atom with ID natoms
lmp.scatter_coords("x",1,3,x) :pre
Alternatively, you can just change values in the vector returned by
gather_atoms("x",1,3), since it is a ctypes vector of doubles.
:line
As noted above, these Python class methods correspond one-to-one with
the functions in the LAMMPS library interface in src/library.cpp and
library.h. This means you can extend the Python wrapper via the
following steps:
Add a new interface function to src/library.cpp and
src/library.h. :ulb,l
Rebuild LAMMPS as a shared library. :l
Add a wrapper method to python/lammps.py for this interface
function. :l
You should now be able to invoke the new interface function from a
Python script. Isn't ctypes amazing? :l,ule
:line
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11.6 Example Python scripts that use LAMMPS :link(py_6),h4
These are the Python scripts included as demos in the python/examples
directory of the LAMMPS distribution, to illustrate the kinds of
things that are possible when Python wraps LAMMPS. If you create your
own scripts, send them to us and we can include them in the LAMMPS
distribution.
trivial.py, read/run a LAMMPS input script thru Python,
demo.py, invoke various LAMMPS library interface routines,
simple.py, mimic operation of couple/simple/simple.cpp in Python,
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_tool.py and plot.py and gui.py :tb(c=2)
:line
For the viz_tool.py and vizplotgui_tool.py commands, replace "tool"
with "gl" or "atomeye" or "pymol" or "vmd", depending on what
visualization package you have installed.
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"_pizza for more info:
:link(pizza,http://www.sandia.gov/~sjplimp/pizza.html)
Note that for AtomEye, you need version 3, and there is a line in the
scripts that specifies the path and name of the executable. See the
AtomEye WWW pages "here"_atomeye or "here"_atomeye3 for more details:
http://mt.seas.upenn.edu/Archive/Graphics/A
http://mt.seas.upenn.edu/Archive/Graphics/A3/A3.html :pre
:link(atomeye,http://mt.seas.upenn.edu/Archive/Graphics/A)
:link(atomeye3,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 "here"_pymol or
"here"_pymolopen for more details:
http://www.pymol.org
http://sourceforge.net/scm/?type=svn&group_id=4546 :pre
:link(pymol,http://www.pymol.org)
:link(pymolopen,http://sourceforge.net/scm/?type=svn&group_id=4546)
The latter link is to the open-source version.
Note that for VMD, you need a fairly current version (1.8.7 works for
me) and there are some lines in the pizza/vmd.py script for 4 PIZZA
variables that have to match the VMD installation on your system.
:line
See the python/README file for instructions on how to run them and the
source code for individual scripts for comments about what they do.
Here are screenshots of the vizplotgui_tool.py script in action for
different visualization package options. Click to see larger images:
:image(JPG/screenshot_gl_small.jpg,JPG/screenshot_gl.jpg)
:image(JPG/screenshot_atomeye_small.jpg,JPG/screenshot_atomeye.jpg)
:image(JPG/screenshot_pymol_small.jpg,JPG/screenshot_pymol.jpg)
:image(JPG/screenshot_vmd_small.jpg,JPG/screenshot_vmd.jpg)