Section_python.txt
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Section_python.txt
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			:line

			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 need to do two things.
			You need to build LAMMPS as a dynamic shared library, so it can be
			loaded by Python. And you need to tell Python how to find the library
			and the Python wrapper file python/lammps.py. 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.

			:line
			:line

			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 liblammps_foo.so in the src directory, as
			well as a soft link liblammps.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.

			:line

			11.2 Installing the Python wrapper into Python :link(py_2),h4

			For Python to invoke LAMMPS, there are 2 files it needs to know about:

			python/lammps.py
			src/liblammps.so :ul

			Lammps.py is the Python wrapper on the LAMMPS library interface.
			Liblammps.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 use the python/install.py script, you need to invoke 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 \[libdir\] \[pydir\] :pre

			The optional libdir is where to copy the LAMMPS shared library to; the
			default is /usr/local/lib. The optional pydir is where to copy the
			lammps.py file to; the default is the site-packages directory of the
			version of Python that is running the install script.

			Note that libdir must be a location that is in your default
			LD_LIBRARY_PATH, like /usr/local/lib or /usr/lib. And pydir must be a
			location that Python looks in by default for imported modules, like
			its site-packages dir. If you want to copy these files to
			non-standard locations, such as within your own user space, you will
			need to set your PYTHONPATH and LD_LIBRARY_PATH environment variables
			accordingly, as above.

			If the install.py script does not allow you to copy files into system
			directories, prefix the python command with "sudo". If you do this,
			make sure that the Python that root runs is the same as the Python you
			run. E.g. you may need to do something like

			% sudo /usr/local/bin/python install.py \[libdir\] \[pydir\] :pre

			You can also invoke install.py from the make command in the src
			directory as

			% make install-python :pre

			In this mode you cannot append optional arguments. Again, you may
			need to prefix this with "sudo". In this mode you cannot control
			which Python is invoked by root.

			Note that if you want Python to be able to load different versions of
			the LAMMPS shared library (see "this section"_#py_5 below), you will
			need to manually copy files like liblammps_g++.so into the appropriate
			system directory. This is not needed if you set the LD_LIBRARY_PATH
			environment variable as described above.

			: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
			2nd command 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 LAMMPS shared library or
			one of the auxiliary shared libraries it depends on, or if something
			about the library is incompatible with your Python. The error message
			should give you an indication of what went wrong.

			You can also test the load directly in Python as follows, without
			first importing from the lammps.py file:

			>>> from ctypes import CDLL
			>>> CDLL("liblammps.so") :pre

			If an error occurs, carefully go thru the steps in "Section_start
			5"_Section_start.html#start_5 and above about building a shared
			library and about insuring Python can find the necessary two files
			it needs.

			[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.

			:line
			:line

			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 liblammps.so library
			lmp = lammps("g++") # create a LAMMPS object using the liblammps_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
			:line

			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)

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