forked from lijiext/lammps
840 lines
34 KiB
Plaintext
840 lines
34 KiB
Plaintext
"Previous Section"_Section_modify.html - "LAMMPS WWW Site"_lws - "LAMMPS Documentation"_ld - "LAMMPS Commands"_lc - "Next Section"_Section_errors.html :c
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:link(lws,http://lammps.sandia.gov)
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:link(ld,Manual.html)
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:link(lc,Section_commands.html#comm)
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:line
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11. Python interface to LAMMPS :h3
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LAMMPS can work together with Python in two ways. First, Python can
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wrap LAMMPS through the "LAMMPS library
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interface"_Section_howto.html#howto_19, so that a Python script can
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create one or more instances of LAMMPS and launch one or more
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simulations. In Python lingo, this is "extending" Python with LAMMPS.
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Second, LAMMPS can use the Python interpreter, so that a LAMMPS input
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script can invoke Python code, and pass information back-and-forth
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between the input script and Python functions you write. The Python
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code can also callback to LAMMPS to query or change its attributes.
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In Python lingo, this is "embedding" Python in LAMMPS.
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This section describes how to do both.
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11.1 "Overview of running LAMMPS from Python"_#py_1
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11.2 "Overview of using Python from a LAMMPS script"_#py_2
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11.3 "Building LAMMPS as a shared library"_#py_3
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11.4 "Installing the Python wrapper into Python"_#py_4
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11.5 "Extending Python with MPI to run in parallel"_#py_5
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11.6 "Testing the Python-LAMMPS interface"_#py_6
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11.7 "Using LAMMPS from Python"_#py_7
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11.8 "Example Python scripts that use LAMMPS"_#py_8 :ul
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If you are not familiar with it, "Python"_http://www.python.org is a
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powerful scripting and programming language which can essentially do
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anything that faster, lower-level languages like C or C++ can do, but
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typically with much fewer lines of code. When used in embedded mode,
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Python can perform operations that the simplistic LAMMPS input script
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syntax cannot. Python can be also be used as a "glue" language to
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drive a program through its library interface, or to hook multiple
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pieces of software together, such as a simulation package plus a
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visualization package, or to run a coupled multiscale or multiphysics
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model.
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See "Section_howto 10"_Section_howto.html#howto_10 of the manual and
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the couple directory of the distribution for more ideas about coupling
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LAMMPS to other codes. See "Section_howto
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19"_Section_howto.html#howto_19 for a description of the LAMMPS
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library interface provided in src/library.cpp and src/library.h, and
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how to extend it for your needs. As described below, that interface
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is what is exposed to Python either when calling LAMMPS from Python or
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when calling Python from a LAMMPS input script and then calling back
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to LAMMPS from Python code. The library interface is designed to be
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easy to add functions to. Thus the Python interface to LAMMPS is also
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easy to extend as well.
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If you create interesting Python scripts that run LAMMPS or
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interesting Python functions that can be called from a LAMMPS input
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script, that you think would be useful to other users, please "email
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them to the developers"_http://lammps.sandia.gov/authors.html. We can
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include them in the LAMMPS distribution.
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:line
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:line
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11.1 Overview of running LAMMPS from Python :link(py_1),h4
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The LAMMPS distribution includes a python directory with all you need
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to run LAMMPS from Python. The python/lammps.py file wraps the LAMMPS
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library interface, with one wrapper function per LAMMPS library
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function. This file makes it is possible to do the following either
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from a Python script, or interactively from a Python prompt: create
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one or more instances of LAMMPS, invoke LAMMPS commands or give it an
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input script, run LAMMPS incrementally, extract LAMMPS results, an
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modify internal LAMMPS variables. From a Python script you can do
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this in serial or parallel. Running Python interactively in parallel
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does not generally work, unless you have a version of Python that
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extends standard Python to enable multiple instances of Python to read
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what you type.
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To do all of this, you must first build LAMMPS as a shared library,
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then insure that your Python can find the python/lammps.py file and
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the shared library. These steps are explained in subsequent sections
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11.3 and 11.4. Sections 11.5 and 11.6 discuss using MPI from a
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parallel Python program and how to test that you are ready to use
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LAMMPS from Python. Section 11.7 lists all the functions in the
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current LAMMPS library interface and how to call them from Python.
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Section 11.8 gives some examples of coupling LAMMPS to other tools via
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Python. For example, LAMMPS can easily be coupled to a GUI or other
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visualization tools that display graphs or animations in real time as
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LAMMPS runs. Examples of such scripts are inlcluded in the python
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directory.
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Two advantages of using Python to run LAMMPS are how concise the
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language is, and that it can be run interactively, enabling rapid
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development and debugging of programs. If you use it to mostly invoke
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costly operations within LAMMPS, such as running a simulation for a
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reasonable number of timesteps, then the overhead cost of invoking
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LAMMPS thru Python will be negligible.
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The Python wrapper for LAMMPS uses the amazing and magical (to me)
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"ctypes" package in Python, which auto-generates the interface code
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needed between Python and a set of C interface routines for a library.
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Ctypes is part of standard Python for versions 2.5 and later. You can
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check which version of Python you have installed, by simply typing
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"python" at a shell prompt.
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:line
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11.2 Overview of using Python from a LAMMPS script :link(py_2),h4
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NOTE: It is not currently possible to use the "python"_python.html
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command described in this section with Python 3, only with Python 2.
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The C API changed from Python 2 to 3 and the LAMMPS code is not
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compatible with both.
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LAMMPS has a "python"_python.html command which can be used in an
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input script to define and execute a Python function that you write
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the code for. The Python function can also be assigned to a LAMMPS
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python-style variable via the "variable"_variable.html command. Each
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time the variable is evaluated, either in the LAMMPS input script
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itself, or by another LAMMPS command that uses the variable, this will
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trigger the Python function to be invoked.
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The Python code for the function can be included directly in the input
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script or in an auxiliary file. The function can have arguments which
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are mapped to LAMMPS variables (also defined in the input script) and
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it can return a value to a LAMMPS variable. This is thus a mechanism
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for your input script to pass information to a piece of Python code,
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ask Python to execute the code, and return information to your input
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script.
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Note that a Python function can be arbitrarily complex. It can import
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other Python modules, instantiate Python classes, call other Python
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functions, etc. The Python code that you provide can contain more
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code than the single function. It can contain other functions or
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Python classes, as well as global variables or other mechanisms for
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storing state between calls from LAMMPS to the function.
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The Python function you provide can consist of "pure" Python code that
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only performs operations provided by standard Python. However, the
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Python function can also "call back" to LAMMPS through its
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Python-wrapped library interface, in the manner described in the
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previous section 11.1. This means it can issue LAMMPS input script
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commands or query and set internal LAMMPS state. As an example, this
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can be useful in an input script to create a more complex loop with
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branching logic, than can be created using the simple looping and
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branching logic enabled by the "next"_next.html and "if"_if.html
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commands.
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See the "python"_python.html doc page and the "variable"_variable.html
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doc page for its python-style variables for more info, including
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examples of Python code you can write for both pure Python operations
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and callbacks to LAMMPS.
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To run pure Python code from LAMMPS, you only need to build LAMMPS
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with the PYTHON package installed:
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make yes-python
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make machine :pre
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Note that this will link LAMMPS with the Python library on your
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system, which typically requires several auxiliary system libraries to
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also be linked. The list of these libraries and the paths to find
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them are specified in the lib/python/Makefile.lammps file. You need
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to insure that file contains the correct information for your version
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of Python and your machine to successfully build LAMMPS. See the
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lib/python/README file for more info.
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If you want to write Python code with callbacks to LAMMPS, then you
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must also follow the steps overviewed in the preceeding section (11.1)
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for running LAMMPS from Python. I.e. you must build LAMMPS as a
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shared library and insure that Python can find the python/lammps.py
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file and the shared library.
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:line
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11.3 Building LAMMPS as a shared library :link(py_3),h4
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Instructions on how to build LAMMPS as a shared library are given in
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"Section_start 5"_Section_start.html#start_5. A shared library is one
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that is dynamically loadable, which is what Python requires to wrap
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LAMMPS. On Linux this is a library file that ends in ".so", not ".a".
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From the src directory, type
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make foo mode=shlib :pre
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where foo is the machine target name, such as linux or g++ or serial.
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This should create the file liblammps_foo.so in the src directory, as
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well as a soft link liblammps.so, which is what the Python wrapper will
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load by default. Note that if you are building multiple machine
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versions of the shared library, the soft link is always set to the
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most recently built version.
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NOTE: If you are building LAMMPS with an MPI or FFT library or other
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auxiliary libraries (used by various packages), then all of these
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extra libraries must also be shared libraries. If the LAMMPS
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shared-library build fails with an error complaining about this, see
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"Section_start 5"_Section_start.html#start_5 for more details.
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:line
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11.4 Installing the Python wrapper into Python :link(py_4),h4
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For Python to invoke LAMMPS, there are 2 files it needs to know about:
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python/lammps.py
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src/liblammps.so :ul
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Lammps.py is the Python wrapper on the LAMMPS library interface.
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Liblammps.so is the shared LAMMPS library that Python loads, as
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described above.
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You can insure Python can find these files in one of two ways:
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set two environment variables
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run the python/install.py script :ul
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If you set the paths to these files as environment variables, you only
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have to do it once. For the csh or tcsh shells, add something like
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this to your ~/.cshrc file, one line for each of the two files:
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setenv PYTHONPATH $\{PYTHONPATH\}:/home/sjplimp/lammps/python
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setenv LD_LIBRARY_PATH $\{LD_LIBRARY_PATH\}:/home/sjplimp/lammps/src :pre
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If you use the python/install.py script, you need to invoke it every
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time you rebuild LAMMPS (as a shared library) or make changes to the
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python/lammps.py file.
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You can invoke install.py from the python directory as
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% python install.py \[libdir\] \[pydir\] :pre
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The optional libdir is where to copy the LAMMPS shared library to; the
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default is /usr/local/lib. The optional pydir is where to copy the
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lammps.py file to; the default is the site-packages directory of the
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version of Python that is running the install script.
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Note that libdir must be a location that is in your default
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LD_LIBRARY_PATH, like /usr/local/lib or /usr/lib. And pydir must be a
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location that Python looks in by default for imported modules, like
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its site-packages dir. If you want to copy these files to
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non-standard locations, such as within your own user space, you will
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need to set your PYTHONPATH and LD_LIBRARY_PATH environment variables
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accordingly, as above.
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If the install.py script does not allow you to copy files into system
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directories, prefix the python command with "sudo". If you do this,
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make sure that the Python that root runs is the same as the Python you
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run. E.g. you may need to do something like
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% sudo /usr/local/bin/python install.py \[libdir\] \[pydir\] :pre
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You can also invoke install.py from the make command in the src
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directory as
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% make install-python :pre
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In this mode you cannot append optional arguments. Again, you may
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need to prefix this with "sudo". In this mode you cannot control
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which Python is invoked by root.
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Note that if you want Python to be able to load different versions of
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the LAMMPS shared library (see "this section"_#py_5 below), you will
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need to manually copy files like liblammps_g++.so into the appropriate
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system directory. This is not needed if you set the LD_LIBRARY_PATH
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environment variable as described above.
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:line
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11.5 Extending Python with MPI to run in parallel :link(py_5),h4
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If you wish to run LAMMPS in parallel from Python, you need to extend
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your Python with an interface to MPI. This also allows you to
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make MPI calls directly from Python in your script, if you desire.
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There are several Python packages available that purport to wrap MPI
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as a library and allow MPI functions to be called from Python.
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These include
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"pyMPI"_http://pympi.sourceforge.net/
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"maroonmpi"_http://code.google.com/p/maroonmpi/
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"mpi4py"_http://code.google.com/p/mpi4py/
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"myMPI"_http://nbcr.sdsc.edu/forum/viewtopic.php?t=89&sid=c997fefc3933bd66204875b436940f16
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"Pypar"_http://code.google.com/p/pypar :ul
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All of these except pyMPI work by wrapping the MPI library and
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exposing (some portion of) its interface to your Python script. This
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means Python cannot be used interactively in parallel, since they do
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not address the issue of interactive input to multiple instances of
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Python running on different processors. The one exception is pyMPI,
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which alters the Python interpreter to address this issue, and (I
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believe) creates a new alternate executable (in place of "python"
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itself) as a result.
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In principle any of these Python/MPI packages should work to invoke
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LAMMPS in parallel and to make MPI calls themselves from a Python
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script which is itself running in parallel. However, when I
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downloaded and looked at a few of them, their documentation was
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incomplete and I had trouble with their installation. It's not clear
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if some of the packages are still being actively developed and
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supported.
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The packages Pypar and mpi4py have both been successfully tested with
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LAMMPS. Pypar is simpler and easy to set up and use, but supports
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only a subset of MPI. Mpi4py is more MPI-feature complete, but also a
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bit more complex to use. As of version 2.0.0, mpi4py is the only
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python MPI wrapper that allows passing a custom MPI communicator to
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the LAMMPS constructor, which means one can easily run one or more
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LAMMPS instances on subsets of the total MPI ranks.
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:line
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Pypar requires the ubiquitous "Numpy package"_http://numpy.scipy.org
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be installed in your Python. After launching Python, type
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import numpy :pre
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to see if it is installed. If not, here is how to install it (version
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1.3.0b1 as of April 2009). Unpack the numpy tarball and from its
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top-level directory, type
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python setup.py build
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sudo python setup.py install :pre
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The "sudo" is only needed if required to copy Numpy files into your
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Python distribution's site-packages directory.
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To install Pypar (version pypar-2.1.4_94 as of Aug 2012), unpack it
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and from its "source" directory, type
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python setup.py build
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sudo python setup.py install :pre
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Again, the "sudo" is only needed if required to copy Pypar files into
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your Python distribution's site-packages directory.
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If you have successully installed Pypar, you should be able to run
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Python and type
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import pypar :pre
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without error. You should also be able to run python in parallel
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on a simple test script
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% mpirun -np 4 python test.py :pre
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where test.py contains the lines
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import pypar
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print "Proc %d out of %d procs" % (pypar.rank(),pypar.size()) :pre
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and see one line of output for each processor you run on.
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NOTE: To use Pypar and LAMMPS in parallel from Python, you must insure
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both are using the same version of MPI. If you only have one MPI
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installed on your system, this is not an issue, but it can be if you
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have multiple MPIs. Your LAMMPS build is explicit about which MPI it
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is using, since you specify the details in your lo-level
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src/MAKE/Makefile.foo file. Pypar uses the "mpicc" command to find
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information about the MPI it uses to build against. And it tries to
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load "libmpi.so" from the LD_LIBRARY_PATH. This may or may not find
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the MPI library that LAMMPS is using. If you have problems running
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both Pypar and LAMMPS together, this is an issue you may need to
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address, e.g. by moving other MPI installations so that Pypar finds
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the right one.
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:line
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To install mpi4py (version mpi4py-2.0.0 as of Oct 2015), unpack it
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and from its main directory, type
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python setup.py build
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sudo python setup.py install :pre
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Again, the "sudo" is only needed if required to copy mpi4py files into
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your Python distribution's site-packages directory. To install with
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user privilege into the user local directory type
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python setup.py install --user :pre
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If you have successully installed mpi4py, you should be able to run
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Python and type
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from mpi4py import MPI :pre
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without error. You should also be able to run python in parallel
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on a simple test script
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% mpirun -np 4 python test.py :pre
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where test.py contains the lines
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from mpi4py import MPI
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comm = MPI.COMM_WORLD
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print "Proc %d out of %d procs" % (comm.Get_rank(),comm.Get_size()) :pre
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and see one line of output for each processor you run on.
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NOTE: To use mpi4py and LAMMPS in parallel from Python, you must
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insure both are using the same version of MPI. If you only have one
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MPI installed on your system, this is not an issue, but it can be if
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you have multiple MPIs. Your LAMMPS build is explicit about which MPI
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it is using, since you specify the details in your lo-level
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src/MAKE/Makefile.foo file. Mpi4py uses the "mpicc" command to find
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information about the MPI it uses to build against. And it tries to
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load "libmpi.so" from the LD_LIBRARY_PATH. This may or may not find
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the MPI library that LAMMPS is using. If you have problems running
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both mpi4py and LAMMPS together, this is an issue you may need to
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address, e.g. by moving other MPI installations so that mpi4py finds
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the right one.
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:line
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11.6 Testing the Python-LAMMPS interface :link(py_6),h4
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To test if LAMMPS is callable from Python, launch Python interactively
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and type:
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>>> from lammps import lammps
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>>> lmp = lammps() :pre
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If you get no errors, you're ready to use LAMMPS from Python. If the
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2nd command fails, the most common error to see is
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OSError: Could not load LAMMPS dynamic library :pre
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which means Python was unable to load the LAMMPS shared library. This
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typically occurs if the system can't find the LAMMPS shared library or
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one of the auxiliary shared libraries it depends on, or if something
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about the library is incompatible with your Python. The error message
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should give you an indication of what went wrong.
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You can also test the load directly in Python as follows, without
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first importing from the lammps.py file:
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>>> from ctypes import CDLL
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>>> CDLL("liblammps.so") :pre
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If an error occurs, carefully go thru the steps in "Section_start
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5"_Section_start.html#start_5 and above about building a shared
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library and about insuring Python can find the necessary two files
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it needs.
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[Test LAMMPS and Python in serial:] :h5
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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 in.lj :pre
|
|
|
|
[Test LAMMPS and Python in parallel:] :h5
|
|
|
|
To run LAMMPS in parallel, assuming you have installed the
|
|
"Pypar"_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
|
|
|
|
To run LAMMPS in parallel, assuming you have installed the
|
|
"mpi4py"_mpi4py package as discussed above, create a test.py file
|
|
containing these lines:
|
|
|
|
from mpi4py import MPI
|
|
from lammps import lammps
|
|
lmp = lammps()
|
|
lmp.file("in.lj")
|
|
me = MPI.COMM_WORLD.Get_rank()
|
|
nprocs = MPI.COMM_WORLD.Get_size()
|
|
print "Proc %d out of %d procs has" % (me,nprocs),lmp
|
|
MPI.Finalize() :pre
|
|
|
|
You can either script 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 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
|
|
|
|
11.7 Using LAMMPS from Python :link(py_7),h4
|
|
|
|
As described above, 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 files src/library.cpp and src/library.h 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
|
|
4 optional args are allowed: name, cmdargs, ptr, comm
|
|
lmp = lammps(ptr=lmpptr) # use lmpptr as previously created LAMMPS object
|
|
lmp = lammps(comm=split) # create a LAMMPS object with a custom communicator, requires mpi4py 2.0.0 or later
|
|
lmp = lammps(name="g++") # create a LAMMPS object using the liblammps_g++.so library
|
|
lmp = lammps(name="g++",cmdargs=list) # add LAMMPS command-line args, e.g. list = \["-echo","screen"\] :pre
|
|
|
|
lmp.close() # destroy a LAMMPS object :pre
|
|
|
|
version = lmp.version() # return the numerical version id, e.g. LAMMPS 2 Sep 2015 -> 20150902
|
|
|
|
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
|
|
|
|
flag = lmp.set_variable(name,value) # set existing named string-style variable to value, flag = 0 if successful
|
|
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
|
|
|
|
The lines
|
|
|
|
from lammps import lammps
|
|
lmp = lammps() :pre
|
|
|
|
create an instance of LAMMPS, wrapped in a Python class by the lammps
|
|
Python module, and return an instance of the Python class as lmp. It
|
|
is used to make all subequent calls to the LAMMPS library.
|
|
|
|
Additional arguments can be used to tell Python the name of the shared
|
|
library to load or to pass arguments to the LAMMPS instance, the same
|
|
as if LAMMPS were launched from a command-line prompt.
|
|
|
|
If the ptr argument is set like this:
|
|
|
|
lmp = lammps(ptr=lmpptr) :pre
|
|
|
|
then lmpptr must be an argument passed to Python via the LAMMPS
|
|
"python"_python.html command, when it is used to define a Python
|
|
function that is invoked by the LAMMPS input script. This mode of
|
|
using Python with LAMMPS is described above in 11.2. The variable
|
|
lmpptr refers to the instance of LAMMPS that called the embedded
|
|
Python interpreter. Using it as an argument to lammps() allows the
|
|
returned Python class instance "lmp" to make calls to that instance of
|
|
LAMMPS. See the "python"_python.html command doc page for examples
|
|
using this syntax.
|
|
|
|
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
|
|
|
|
11.8 Example Python scripts that use LAMMPS :link(py_8),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, run in parallel, similar to examples/COUPLE/simple/simple.cpp,
|
|
split.py, same as simple.py but running in parallel on a subset of procs,
|
|
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)
|