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index b3b5171e7..bd4dfcf53 100644
--- a/doc/src/Section_python.txt
+++ b/doc/src/Section_python.txt
@@ -1,826 +1,838 @@
"Previous Section"_Section_modify.html - "LAMMPS WWW Site"_lws - "LAMMPS Documentation"_ld - "LAMMPS Commands"_lc - "Next Section"_Section_errors.html :c
:link(lws,http://lammps.sandia.gov)
:link(ld,Manual.html)
:link(lc,Section_commands.html#comm)
:line
11. Python interface to LAMMPS :h3
-LAMMPS can work together with Python in two ways. First, Python can
+LAMMPS can work together with Python in three ways. First, Python can
wrap LAMMPS through the "LAMMPS library
interface"_Section_howto.html#howto_19, so that a Python script can
create one or more instances of LAMMPS and launch one or more
simulations. In Python lingo, this is "extending" Python with LAMMPS.
-Second, LAMMPS can use the Python interpreter, so that a LAMMPS input
+Second, the low-level Python interface can be used indirectly through the
+PyLammps and IPyLammps wrapper classes in Python. These wrappers try to
+simplify the usage of LAMMPS in Python by providing an object-based interface
+to common LAMMPS functionality. It also reduces the amount of code necessary to
+parameterize LAMMPS scripts through Python and makes variables and computes
+directly accessible. See "PyLammps interface"_#py_9 for more details.
+
+Third, LAMMPS can use the Python interpreter, so that a LAMMPS input
script can invoke Python code, and pass information back-and-forth
between the input script and Python functions you write. The Python
code can also callback to LAMMPS to query or change its attributes.
In Python lingo, this is "embedding" Python in LAMMPS.
-This section describes how to do both.
+This section describes how to use these three approaches.
11.1 "Overview of running LAMMPS from Python"_#py_1
11.2 "Overview of using Python from a LAMMPS script"_#py_2
11.3 "Building LAMMPS as a shared library"_#py_3
11.4 "Installing the Python wrapper into Python"_#py_4
11.5 "Extending Python with MPI to run in parallel"_#py_5
11.6 "Testing the Python-LAMMPS interface"_#py_6
11.7 "Using LAMMPS from Python"_#py_7
-11.8 "Example Python scripts that use LAMMPS"_#py_8 :ul
+11.8 "Example Python scripts that use LAMMPS"_#py_8
+11.9 "PyLammps interface"_#py_9 :ul
If you are not familiar with it, "Python"_http://www.python.org is a
powerful scripting and programming language which can essentially do
anything that faster, lower-level languages like C or C++ can do, but
typically with much fewer lines of code. When used in embedded mode,
Python can perform operations that the simplistic LAMMPS input script
syntax cannot. Python can be also be used as a "glue" language to
drive a program through its library interface, or to hook multiple
pieces of software together, such as a simulation package plus a
visualization package, or to run a coupled multiscale or multiphysics
model.
See "Section 6.10"_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
6.19"_Section_howto.html#howto_19 for a description of the LAMMPS
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 either when calling LAMMPS from Python or
when calling Python from a LAMMPS input script and then calling back
to LAMMPS from Python code. The library interface is designed to be
easy to add functions to. Thus the Python interface to LAMMPS is also
easy to extend as well.
If you create interesting Python scripts that run LAMMPS or
interesting Python functions that can be called from a LAMMPS input
script, that you think would be useful to other users, please "email
them to the developers"_http://lammps.sandia.gov/authors.html. We can
include them in the LAMMPS distribution.
:line
:line
11.1 Overview of running LAMMPS from Python :link(py_1),h4
The LAMMPS distribution includes a python directory with all you need
to run LAMMPS from Python. The python/lammps.py file wraps the LAMMPS
library interface, with one wrapper function per LAMMPS library
function. This file makes it is possible to do the following either
from a Python script, or interactively from a Python prompt: create
one or more instances of LAMMPS, invoke LAMMPS commands or give it an
input script, run LAMMPS incrementally, extract LAMMPS results, an
modify internal LAMMPS variables. From a Python script you can do
this in serial or parallel. Running Python interactively in parallel
does not generally work, unless you have a version of Python that
extends standard Python to enable multiple instances of Python to read
what you type.
To do all of this, you must first build LAMMPS as a shared library,
then insure that your Python can find the python/lammps.py file and
the shared library. These steps are explained in subsequent sections
11.3 and 11.4. Sections 11.5 and 11.6 discuss using MPI from a
parallel Python program and how to test that you are ready to use
LAMMPS from Python. Section 11.7 lists all the functions in the
current LAMMPS library interface and how to call them from Python.
Section 11.8 gives some examples of coupling LAMMPS to other tools via
Python. For example, LAMMPS can easily be coupled to 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 to run LAMMPS 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.
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
11.2 Overview of using Python from a LAMMPS script :link(py_2),h4
NOTE: It is not currently possible to use the "python"_python.html
command described in this section with Python 3, only with Python 2.
The C API changed from Python 2 to 3 and the LAMMPS code is not
compatible with both.
LAMMPS has a "python"_python.html command which can be used in an
input script to define and execute a Python function that you write
the code for. The Python function can also be assigned to a LAMMPS
python-style variable via the "variable"_variable.html command. Each
time the variable is evaluated, either in the LAMMPS input script
itself, or by another LAMMPS command that uses the variable, this will
trigger the Python function to be invoked.
The Python code for the function can be included directly in the input
script or in an auxiliary file. The function can have arguments which
are mapped to LAMMPS variables (also defined in the input script) and
it can return a value to a LAMMPS variable. This is thus a mechanism
for your input script to pass information to a piece of Python code,
ask Python to execute the code, and return information to your input
script.
Note that a Python function can be arbitrarily complex. It can import
other Python modules, instantiate Python classes, call other Python
functions, etc. The Python code that you provide can contain more
code than the single function. It can contain other functions or
Python classes, as well as global variables or other mechanisms for
storing state between calls from LAMMPS to the function.
The Python function you provide can consist of "pure" Python code that
only performs operations provided by standard Python. However, the
Python function can also "call back" to LAMMPS through its
Python-wrapped library interface, in the manner described in the
previous section 11.1. This means it can issue LAMMPS input script
commands or query and set internal LAMMPS state. As an example, this
can be useful in an input script to create a more complex loop with
branching logic, than can be created using the simple looping and
branching logic enabled by the "next"_next.html and "if"_if.html
commands.
See the "python"_python.html doc page and the "variable"_variable.html
doc page for its python-style variables for more info, including
examples of Python code you can write for both pure Python operations
and callbacks to LAMMPS.
To run pure Python code from LAMMPS, you only need to build LAMMPS
with the PYTHON package installed:
make yes-python
make machine :pre
Note that this will link LAMMPS with the Python library on your
system, which typically requires several auxiliary system libraries to
also be linked. The list of these libraries and the paths to find
them are specified in the lib/python/Makefile.lammps file. You need
to insure that file contains the correct information for your version
of Python and your machine to successfully build LAMMPS. See the
lib/python/README file for more info.
If you want to write Python code with callbacks to LAMMPS, then you
must also follow the steps overviewed in the preceeding section (11.1)
for running LAMMPS from Python. I.e. you must build LAMMPS as a
shared library and insure that Python can find the python/lammps.py
file and the shared library.
:line
11.3 Building LAMMPS as a shared library :link(py_3),h4
Instructions on how to build LAMMPS as a shared library are given in
"Section 2.5"_Section_start.html#start_5. A shared library is one
that is dynamically loadable, which is what Python requires to wrap
LAMMPS. On Linux this is a library file that ends in ".so", not ".a".
From the src directory, type
make foo mode=shlib :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.
NOTE: If you are building LAMMPS with an MPI or FFT library or other
auxiliary libraries (used by various packages), then all of these
extra libraries must also be shared libraries. If the LAMMPS
shared-library build fails with an error complaining about this, see
"Section 2.5"_Section_start.html#start_5 for more details.
:line
11.4 Installing the Python wrapper into Python :link(py_4),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
11.5 Extending Python with MPI to run in parallel :link(py_5),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. However,
development on most of them seems to be halted except on:
"mpi4py"_https://bitbucket.org/mpi4py/mpi4py
"PyPar"_https://github.com/daleroberts/pypar :ul
Both packages, PyPar and mpi4py have been successfully tested with
LAMMPS. PyPar is simpler and easy to set up and use, but supports
only a subset of MPI. Mpi4py is more MPI-feature complete, but also a
bit more complex to use. As of version 2.0.0, mpi4py is the only
python MPI wrapper that allows passing a custom MPI communicator to
the LAMMPS constructor, which means one can easily run one or more
LAMMPS instances on subsets of the total MPI ranks.
:line
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.
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
To install mpi4py (version mpi4py-2.0.0 as of Oct 2015), unpack it
and from its main directory, type
python setup.py build
sudo python setup.py install :pre
Again, the "sudo" is only needed if required to copy mpi4py files into
your Python distribution's site-packages directory. To install with
user privilege into the user local directory type
python setup.py install --user :pre
If you have successully installed mpi4py, you should be able to run
Python and type
from mpi4py import MPI :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
from mpi4py import MPI
comm = MPI.COMM_WORLD
print "Proc %d out of %d procs" % (comm.Get_rank(),comm.Get_size()) :pre
and see one line of output for each processor you run on.
NOTE: To use mpi4py 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. Mpi4py 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 mpi4py and LAMMPS together, this is an issue you may need to
address, e.g. by moving other MPI installations so that mpi4py finds
the right one.
:line
11.6 Testing the Python-LAMMPS interface :link(py_6),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
2.5"_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 in.lj :pre
[Test LAMMPS and Python in parallel:] :h5
To run LAMMPS in parallel, assuming you have installed the
"PyPar"_https://github.com/daleroberts/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"_https://bitbucket.org/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, and which are described in
"Section 6.19"_Section_howto.html#howto_19 of the manual.
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
lmp.commands_list(cmdlist) # invoke commands in cmdlist = ["run 10", "run 20"]
lmp.commands_string(multicmd) # invoke commands in multicmd = "run 10\nrun 20"
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
value = lmp.get_thermo(name) # return current value of a thermo keyword
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 to lammps() 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(), command(), commands_list(), commands_string() methods
allow an input script, a single command, or multiple 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 6.15"_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"_pymolhome or
"here"_pymolopen for more details:
http://www.pymol.org
http://sourceforge.net/scm/?type=svn&group_id=4546 :pre
:link(pymolhome,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)
+
+11.9 PyLammps interface :link(py_9),h4
+
+Please see the "PyLammps Tutorial"_tutorial_pylammps.html.
diff --git a/doc/src/tutorial_pylammps.txt b/doc/src/tutorial_pylammps.txt
new file mode 100644
index 000000000..6966bb90b
--- /dev/null
+++ b/doc/src/tutorial_pylammps.txt
@@ -0,0 +1,462 @@
+"LAMMPS WWW Site"_lws - "LAMMPS Documentation"_ld - "LAMMPS Commands"_lc :c
+
+:link(lws,http://lammps.sandia.gov)
+:link(ld,Manual.html)
+:link(lc,Section_commands.html#comm)
+
+:line
+
+PyLammps Tutorial :h1
+
+<!-- RST
+.. contents::
+END_RST -->
+
+Overview :h2
+
+PyLammps is a Python wrapper class which can be created on its own or use an
+existing lammps Python object. It creates a simpler, Python-like interface to
+common LAMMPS functionality. Unlike the original flat C-types interface, it
+exposes a discoverable API. It no longer requires knowledge of the underlying
+C++ code implementation. Finally, the IPyLammps wrapper builds on top of
+PyLammps and adds some additional features for IPython integration into IPython
+notebooks, e.g. for embedded visualization output from dump/image.
+
+Comparison of lammps and PyLammps interfaces :h3
+
+lammps.lammps :h4
+
+uses C-Types
+direct memory access to native C++ data
+provides functions to send and receive data to LAMMPS
+requires knowledge of how LAMMPS internally works (C pointers, etc) :ul
+
+lammps.PyLammps :h4
+
+higher-level abstraction built on top of original C-Types interface
+manipulation of Python objects
+communication with LAMMPS is hidden from API user
+shorter, more concise Python
+better IPython integration, designed for quick prototyping :ul
+
+
+Quick Start :h2
+
+System-wide Installation :h3
+
+Step 1: Building LAMMPS as a shared library :h4
+
+To use LAMMPS inside of Python it has to be compiled as shared library. This
+library is then loaded by the Python interface. In this example, we use the
+Make.py utility to create a Makefile with C++ exceptions, PNG, JPEG and FFMPEG
+output support enabled. Finally, we also enable the MOLECULE package and compile
+using the generated {auto} Makefile.
+
+cd $LAMMPS_DIR/src :pre
+
+# generate custom Makefile
+python2 Make.py -jpg -png -s ffmpeg exceptions -m mpi -a file :pre
+
+# add packages if necessary
+make yes-MOLECULE :pre
+
+# compile shared library using Makefile
+make mode=shlib auto :pre
+
+Step 2: Installing the LAMMPS Python package :h4
+
+PyLammps is part of the lammps Python package. To install it simply install
+that package into your current Python installation.
+
+cd $LAMMPS_DIR/python
+python install.py :pre
+
+NOTE: Recompiling the shared library requires reinstalling the Python package
+
+
+Installation inside of a virtualenv :h3
+
+You can use virtualenv to create a custom Python environment specifically tuned
+for your workflow.
+
+Benefits of using a virtualenv :h4
+
+isolation of your system Python installation from your development installation
+installation can happen in your user directory without root access (useful for HPC clusters)
+installing packages through pip allows you to get newer versions of packages than e.g., through apt-get or yum package managers (and without root access)
+you can even install specific old versions of a package if necessary :ul
+
+[Prerequisite (e.g. on Ubuntu)]
+
+apt-get install python-virtualenv :pre
+
+Creating a virtualenv with lammps installed :h4
+
+# create virtualenv name 'testing' :pre
+
+# activate 'testing' environment
+source testing/bin/activate :pre
+
+# install LAMMPS package in virtualenv
+(testing) cd $LAMMPS_DIR/python
+(testing) python install.py :pre
+
+# install other useful packages
+(testing) pip install matplotlib jupyter mpi4py :pre
+
+... :pre
+
+# return to original shell
+(testing) deactivate :pre
+
+
+Creating a new instance of PyLammps :h2
+
+To create a PyLammps object you need to first import the class from the lammps
+module. By using the default constructor, a new {lammps} instance is created.
+
+from lammps import PyLammps
+L = PyLammps() :pre
+
+You can also initialize PyLammps on top of this existing {lammps} object:
+
+from lammps import lammps, PyLammps
+lmp = lammps()
+L = PyLammps(ptr=lmp) :pre
+
+Commands :h2
+
+Sending a LAMMPS command with the existing library interfaces is done using
+the command method of the lammps object instance.
+
+For instance, let's take the following LAMMPS command:
+
+region box block 0 10 0 5 -0.5 0.5 :pre
+
+In the original interface this command can be executed with the following
+Python code if {L} was a lammps instance:
+
+L.command("region box block 0 10 0 5 -0.5 0.5") :pre
+
+With the PyLammps interface, any command can be split up into arbitrary parts
+separated by whitespace, passed as individual arguments to a region method.
+
+L.region("box block", 0, 10, 0, 5, -0.5, 0.5) :pre
+
+Note that each parameter is set as Python literal floating-point number. In the
+PyLammps interface, each command takes an arbitrary parameter list and transparently
+merges it to a single command string, separating individual parameters by whitespace.
+
+The benefit of this approach is avoiding redundant command calls and easier
+parameterization. In the original interface parametrization needed to be done
+manually by creating formatted strings.
+
+L.command("region box block %f %f %f %f %f %f" % (xlo, xhi, ylo, yhi, zlo, zhi)) :pre
+
+In contrast, methods of PyLammps accept parameters directly and will convert
+them automatically to a final command string.
+
+L.region("box block", xlo, xhi, ylo, yhi, zlo, zhi) :pre
+
+System state :h2
+
+In addition to dispatching commands directly through the PyLammps object, it
+also provides several properties which allow you to query the system state.
+
+:dlb
+
+L.system :dt
+
+Is a dictionary describing the system such as the bounding box or number of atoms :dd
+
+L.system.xlo, L.system.xhi :dt
+
+bounding box limits along x-axis :dd
+
+L.system.ylo, L.system.yhi :dt
+
+bounding box limits along y-axis :dd
+
+L.system.zlo, L.system.zhi :dt
+
+bounding box limits along z-axis :dd
+
+L.communication :dt
+
+configuration of communication subsystem, such as the number of threads or processors :dd
+
+L.communication.nthreads :dt
+
+number of threads used by each LAMMPS process :dd
+
+L.communication.nprocs :dt
+
+number of MPI processes used by LAMMPS :dd
+
+L.fixes :dt
+
+List of fixes in the current system :dd
+
+L.computes :dt
+
+List of active computes in the current system :dd
+
+L.dump :dt
+
+List of active dumps in the current system :dd
+
+L.groups :dt
+
+List of groups present in the current system :dd
+
+:dle
+
+Working with LAMMPS variables :h2
+
+LAMMPS variables can be both defined and accessed via the PyLammps interface.
+
+To define a variable you can use the "variable"_variable.html command:
+
+L.variable("a index 2") :pre
+
+A dictionary of all variables is returned by L.variables
+
+you can access an individual variable by retrieving a variable object from the
+L.variables dictionary by name
+
+a = L.variables\['a'\] :pre
+
+The variable value can then be easily read and written by accessing the value
+property of this object.
+
+print(a.value)
+a.value = 4 :pre
+
+Retrieving the value of an arbitrary LAMMPS expressions :h2
+
+LAMMPS expressions can be immediately evaluated by using the eval method. The
+passed string parameter can be any expression containing global thermo values,
+variables, compute or fix data.
+
+result = L.eval("ke") # kinetic energy
+result = L.eval("pe") # potential energy :pre
+
+result = L.eval("v_t/2.0") :pre
+
+Accessing atom data :h2
+
+All atoms in the current simulation can be accessed by using the L.atoms list.
+Each element of this list is an object which exposes its properties (id, type,
+position, velocity, force, etc.).
+
+# access first atom
+L.atoms\[0\].id
+L.atoms\[0\].type :pre
+
+# access second atom
+L.atoms\[1\].position
+L.atoms\[1\].velocity
+L.atoms\[1\].force :pre
+
+Some properties can also be used to set:
+
+# set position in 2D simulation
+L.atoms\[0\].position = (1.0, 0.0) :pre
+
+# set position in 3D simulation
+L.atoms\[0\].position = (1.0, 0.0, 1.) :pre
+
+Evaluating thermo data :h2
+
+Each simulation run usually produces thermo output based on system state,
+computes, fixes or variables. The trajectories of these values can be queried
+after a run via the L.runs list. This list contains a growing list of run data.
+The first element is the output of the first run, the second element that of
+the second run.
+
+L.run(1000)
+L.runs\[0\] # data of first 1000 time steps :pre
+
+L.run(1000)
+L.runs\[1\] # data of second 1000 time steps :pre
+
+Each run contains a dictionary of all trajectories. Each trajectory is
+accessible through its thermo name:
+
+L.runs\[0\].step # list of time steps in first run
+L.runs\[0\].ke # list of kinetic energy values in first run :pre
+
+Together with matplotlib plotting data out of LAMMPS becomes simple:
+
+import matplotlib.plot as plt
+
+steps = L.runs\[0\].step
+ke = L.runs\[0\].ke
+plt.plot(steps, ke) :pre
+
+Error handling with PyLammps :h2
+
+Compiling the shared library with C++ exception support provides a better error
+handling experience. Without exceptions the LAMMPS code will terminate the
+current Python process with an error message. C++ exceptions allow capturing
+them on the C++ side and rethrowing them on the Python side. This way you
+can handle LAMMPS errors through the Python exception handling mechanism.
+
+IMPORTANT NOTE: Capturing a LAMMPS exception in Python can still mean that the
+current LAMMPS process is in an illegal state and must be terminated. It is
+advised to save your data and terminate the Python instance as quickly as
+possible.
+
+Using PyLammps in IPython notebooks and Jupyter :h2
+
+If the LAMMPS Python package is installed for the same Python interpreter as
+IPython, you can use PyLammps directly inside of an IPython notebook inside of
+Jupyter. Jupyter is a powerful integrated development environment (IDE) for
+many dynamic languages like Python, Julia and others, which operates inside of
+any web browser. Besides auto-completion and syntax highlighting it allows you
+to create formatted documents using Markup, mathematical formulas, graphics and
+animations intermixed with executable Python code. It is a great format for
+tutorials and showcasing your latest research.
+
+To launch an instance of Jupyter simply run the following command inside your
+Python environment (this assumes you followed the Quick Start instructions):
+
+jupyter notebook :pre
+
+IPyLammps Examples :h2
+
+Examples of IPython notebooks can be found in the python/examples/pylammps
+subdirectory. To open these notebooks launch {jupyter notebook} inside this
+directory and navigate to one of them. If you compiled and installed
+a LAMMPS shared library with execeptions, PNG, JPEG and FFMPEG support
+you should be able to rerun all of these notebooks.
+
+Validating a dihedral potential :h3
+
+This example showcases how an IPython Notebook can be used to compare a simple
+LAMMPS simulation of a harmonic dihedral potential to its analytical solution.
+Four atoms are placed in the simulation and the dihedral potential is applied on
+them using a datafile. Then one of the atoms is rotated along the central axis by
+setting its position from Python, which changes the dihedral angle.
+
+phi = \[d * math.pi / 180 for d in range(360)\] :pre
+
+pos = \[(1.0, math.cos(p), math.sin(p)) for p in phi\] :pre
+
+pe = \[\]
+for p in pos:
+ L.atoms\[3\].position = p
+ L.run(0)
+ pe.append(L.eval("pe")) :pre
+
+By evaluating the potential energy for each position we can verify that
+trajectory with the analytical formula. To compare both solutions, we plot
+both trajectories over each other using matplotlib, which embeds the generated
+plot inside the IPython notebook.
+
+:c,image(JPG/pylammps_dihedral.jpg)
+
+Running a Monte Carlo relaxation :h3
+
+This second example shows how to use PyLammps to create a 2D Monte Carlo Relaxation
+simulation, computing and plotting energy terms and even embedding video output.
+
+Initially, a 2D system is created in a state with minimal energy.
+
+:c,image(JPG/pylammps_mc_minimum.jpg)
+
+It is then disordered by moving each atom by a random delta.
+
+random.seed(27848)
+deltaperturb = 0.2 :pre
+
+for i in range(L.system.natoms):
+ x, y = L.atoms\[i\].position
+ dx = deltaperturb * random.uniform(-1, 1)
+ dy = deltaperturb * random.uniform(-1, 1)
+ L.atoms\[i\].position = (x+dx, y+dy) :pre
+
+L.run(0) :pre
+
+:c,image(JPG/pylammps_mc_disordered.jpg)
+
+Finally, the Monte Carlo algorithm is implemented in Python. It continuously
+moves random atoms by a random delta and only accepts certain moves.
+
+estart = L.eval("pe")
+elast = estart :pre
+
+naccept = 0
+energies = \[estart\] :pre
+
+niterations = 3000
+deltamove = 0.1
+kT = 0.05 :pre
+
+natoms = L.system.natoms :pre
+
+for i in range(niterations):
+ iatom = random.randrange(0, natoms)
+ current_atom = L.atoms\[iatom\] :pre
+
+ x0, y0 = current_atom.position :pre
+
+ dx = deltamove * random.uniform(-1, 1)
+ dy = deltamove * random.uniform(-1, 1) :pre
+
+ current_atom.position = (x0+dx, y0+dy) :pre
+
+ L.run(1, "pre no post no") :pre
+
+ e = L.eval("pe")
+ energies.append(e) :pre
+
+ if e <= elast:
+ naccept += 1
+ elast = e
+ elif random.random() <= math.exp(natoms*(elast-e)/kT):
+ naccept += 1
+ elast = e
+ else:
+ current_atom.position = (x0, y0) :pre
+
+The energies of each iteration are collected in a Python list and finally plotted using matplotlib.
+
+:c,image(JPG/pylammps_mc_energies_plot.jpg)
+
+The IPython notebook also shows how to use dump commands and embed video files
+inside of the IPython notebook.
+
+Using PyLammps and mpi4py (Experimental) :h2
+
+PyLammps can be run in parallel using mpi4py. This python package can be installed using
+
+pip install mpi4py :pre
+
+The following is a short example which reads in an existing LAMMPS input file and
+executes it in parallel. You can find in.melt in the examples/melt folder.
+
+from mpi4py import MPI
+from lammps import PyLammps :pre
+
+L = PyLammps()
+L.file("in.melt") :pre
+
+if MPI.COMM_WORLD.rank == 0:
+ print("Potential energy: ", L.eval("pe")) :pre
+
+MPI.Finalize() :pre
+
+To run this script (melt.py) in parallel using 4 MPI processes we invoke the
+following mpirun command:
+
+mpirun -np 4 python melt.py :pre
+
+IMPORTANT NOTE: Any command must be executed by all MPI processes. However, evaluations and querying the system state is only available on rank 0.
+
+Feedback and Contributing :h2
+
+If you find this Python interface useful, please feel free to provide feedback
+and ideas on how to improve it to Richard Berger (richard.berger@temple.edu). We also
+want to encourage people to write tutorial style IPython notebooks showcasing LAMMPS usage
+and maybe their latest research results.
diff --git a/doc/src/tutorials.txt b/doc/src/tutorials.txt
index 98c748f3a..569ad892b 100644
--- a/doc/src/tutorials.txt
+++ b/doc/src/tutorials.txt
@@ -1,13 +1,14 @@
Tutorials :h1
<!-- RST
.. toctree::
:maxdepth: 1
tutorial_drude
tutorial_github
+ tutorial_pylammps
body
manifolds
END_RST -->
diff --git a/python/examples/ipython/.gitignore b/python/examples/pylammps/.gitignore
similarity index 100%
rename from python/examples/ipython/.gitignore
rename to python/examples/pylammps/.gitignore
diff --git a/python/examples/pylammps/README b/python/examples/pylammps/README
new file mode 100644
index 000000000..ac6962228
--- /dev/null
+++ b/python/examples/pylammps/README
@@ -0,0 +1,28 @@
+# Compile LAMMPS as shared library
+
+git clone https://github.com/lammps/lammps.git
+cd lammps/src
+python Make.py -m mpi -png -s ffmpeg exceptions -a file
+
+make -j 4 mode=shlib auto
+cd ../..
+
+# Install Python package
+
+virtualenv testing
+source testing/bin/activate
+
+(testing) cd lammps/python
+(testing) python install.py
+(testing) pip install jupyter matplotlib mpi4py
+
+(testing) cd ../../examples
+
+# Launch jupter and work inside browser
+
+(testing) jupyter notebook
+
+# Use Ctrl+c to stop jupyter
+
+# finally exit the virtualenv
+(testing) deactivate
diff --git a/python/examples/pylammps/dihedrals/data.dihedral b/python/examples/pylammps/dihedrals/data.dihedral
new file mode 100644
index 000000000..2ab745c3f
--- /dev/null
+++ b/python/examples/pylammps/dihedrals/data.dihedral
@@ -0,0 +1,34 @@
+Comment line
+
+4 atoms
+0 bonds
+0 angles
+1 dihedrals
+0 impropers
+
+1 atom types
+0 bond types
+0 angle types
+1 dihedral types
+0 improper types
+
+-5.0 5.0 xlo xhi
+-5.0 5.0 ylo yhi
+-5.0 5.0 zlo zhi
+0.0 0.0 0.0 xy xz yz
+
+Atoms # molecular
+
+1 1 1 -1.00000 1.00000 0.00000
+2 1 1 -0.50000 0.00000 0.00000
+3 1 1 0.50000 0.00000 0.00000
+4 1 1 1.00000 1.00000 0.00000
+
+Dihedral Coeffs
+
+1 80.0 1 2
+
+Dihedrals
+
+1 1 1 2 3 4
+
diff --git a/python/examples/pylammps/dihedrals/dihedral.ipynb b/python/examples/pylammps/dihedrals/dihedral.ipynb
new file mode 100644
index 000000000..db7e81aaf
--- /dev/null
+++ b/python/examples/pylammps/dihedrals/dihedral.ipynb
@@ -0,0 +1,1168 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Validating a dihedral potential"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from lammps import IPyLammps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LAMMPS output is captured by PyLammps wrapper\n"
+ ]
+ }
+ ],
+ "source": [
+ "L = IPyLammps()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "\n",
+ "L.units(\"real\")\n",
+ "L.atom_style(\"molecular\")\n",
+ "\n",
+ "L.boundary(\"f f f\")\n",
+ "L.neighbor(0.3, \"bin\")\n",
+ "\n",
+ "L.dihedral_style(\"harmonic\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Reading data file ...',\n",
+ " ' triclinic box = (-5 -5 -5) to (5 5 5) with tilt (0 0 0)',\n",
+ " ' 1 by 1 by 1 MPI processor grid',\n",
+ " ' reading atoms ...',\n",
+ " ' 4 atoms',\n",
+ " ' scanning dihedrals ...',\n",
+ " ' 1 = max dihedrals/atom',\n",
+ " ' reading dihedrals ...',\n",
+ " ' 1 dihedrals',\n",
+ " 'Finding 1-2 1-3 1-4 neighbors ...',\n",
+ " ' Special bond factors lj: 0 0 0 ',\n",
+ " ' Special bond factors coul: 0 0 0 ',\n",
+ " ' 0 = max # of 1-2 neighbors',\n",
+ " ' 0 = max # of 1-3 neighbors',\n",
+ " ' 0 = max # of 1-4 neighbors',\n",
+ " ' 1 = max # of special neighbors']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.read_data(\"data.dihedral\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.pair_style(\"zero\", 5)\n",
+ "L.pair_coeff(\"*\", \"*\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.mass(1, 1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.velocity(\"all\", \"set\", 0.0, 0.0, 0.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.run(0);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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OBRobp6zWKbP5ot2eU1a2Gw5CE/wGfAoW8/JeOHSo+dChdsgYGwt0dvLWt8pw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\n/x+AnPCD2jXp5gAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.image(zoom=1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1.0000000000000009, 1.0000000000000009, 0.0)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.atoms[3].position"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.atoms[3].position = (1.0, 0.0, 1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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18mSgvn7Sap00my/a7TklJbvhADTBb8CnYWHHjpcOHmw+eLANMkZHAx0d/Nqv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\nTwwAovvK6w3dMisv362uXtPr12TZoVD8TKtNS01VAmeAi8DvA18HRnJ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+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.image(zoom=1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "160.0"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.eval(\"pe\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.atoms[3].position = (1.0, 0.0, -1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.run(0);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "phi = [d * math.pi / 180 for d in range(360)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "pos = [(1.0, math.cos(p), math.sin(p)) for p in phi]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "K = 80.0\n",
+ "d = 1\n",
+ "n = 2\n",
+ "E_analytical = [K * (1 + d * math.cos(n*p)) for p in phi]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "pe = []\n",
+ "for p in pos:\n",
+ " L.atoms[3].position = p\n",
+ " L.run(0);\n",
+ " pe.append(L.eval(\"pe\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " this.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width);\n",
+ " canvas.attr('height', height);\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'];\n",
+ " var y0 = fig.canvas.height - msg['y0'];\n",
+ " var x1 = msg['x1'];\n",
+ " var y1 = fig.canvas.height - msg['y1'];\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x;\n",
+ " var y = canvas_pos.y;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7f191b1628d0>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plt.plot(range(360), pe, range(360), E_analytical)\n",
+ "plt.xlabel('angle')\n",
+ "plt.ylabel('E')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python/examples/ipython/interface_usage.ipynb b/python/examples/pylammps/interface_usage.ipynb
similarity index 100%
rename from python/examples/ipython/interface_usage.ipynb
rename to python/examples/pylammps/interface_usage.ipynb
diff --git a/python/examples/ipython/interface_usage_bonds.ipynb b/python/examples/pylammps/interface_usage_bonds.ipynb
similarity index 100%
rename from python/examples/ipython/interface_usage_bonds.ipynb
rename to python/examples/pylammps/interface_usage_bonds.ipynb
diff --git a/python/examples/pylammps/montecarlo/mc.ipynb b/python/examples/pylammps/montecarlo/mc.ipynb
new file mode 100644
index 000000000..8906e3add
--- /dev/null
+++ b/python/examples/pylammps/montecarlo/mc.ipynb
@@ -0,0 +1,1303 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Monte Carlo Relaxation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import print_function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import random, math"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup perfect system"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from lammps import IPyLammps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LAMMPS output is captured by PyLammps wrapper\n"
+ ]
+ }
+ ],
+ "source": [
+ "L = IPyLammps()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "L.units(\"lj\")\n",
+ "L.atom_style(\"atomic\")\n",
+ "L.atom_modify(\"map array sort\", 0, 0.0)\n",
+ "\n",
+ "L.dimension(2)\n",
+ "\n",
+ "L.lattice(\"hex\", 1.0)\n",
+ "L.region(\"box block\", 0, 10, 0, 5, -0.5, 0.5)\n",
+ "\n",
+ "L.create_box(1, \"box\")\n",
+ "L.create_atoms(1, \"box\")\n",
+ "L.mass(1, 1.0)\n",
+ "\n",
+ "L.pair_style(\"lj/cut\", 2.5)\n",
+ "L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
+ "L.pair_modify(\"shift\", \"yes\")\n",
+ "\n",
+ "L.neighbor(0.3, \"bin\")\n",
+ "L.neigh_modify(\"delay\", 0, \"every\", 1, \"check\", \"yes\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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mZMxZOwPKn6teRvA7ytqe2UmYE4vWwLKuqSia/cJu41pZoizrstyU5aYss7rO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\n",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.image(zoom=1.6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.run(0);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "emin = L.eval(\"pe\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "L.dump(\"3 all movie 25 movie.mp4 type type zoom 1.6 adiam 1.0\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Disorder system"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "random.seed(27848)\n",
+ "deltaperturb = 0.2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "for i in range(L.system.natoms):\n",
+ " x, y = L.atoms[i].position\n",
+ " dx = deltaperturb * random.uniform(-1, 1)\n",
+ " dy = deltaperturb * random.uniform(-1, 1)\n",
+ " L.atoms[i].position = (x+dx, y+dy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "L.run(0);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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tAb6FWWZ8/n/BxM2kG1D6nyFX987tV/d2on8JGOAnt1/XOXmsjWiCeGIBPPCM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LvAMA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HlD52pZMqwkVWW44ctB5aMnMSjWZ7ekKWTJHHs7HPO2c4nuvlb7J/IwzzFvmjxENitjEb+q\n/84I4KzwX5j/AdGW2o0uvmo2yM/6kK+oB75qoaz/MGtfArfu+fMwMxEtwwM8CRMKtTw0Uda/BubA\nV8Og6bcSEZAyj7xPnOOqEgmm+EeJdAYww7nGuUpq2vJPiUlVz9FKn8t3rc8J+CDza4l2Vmmcel/B\nr9c4YdWRSkbiE8xSxheV/Zg5lT5YoPF+1jTTspxkGYUyvo960/h941fuFvBXzlWVv33deoAPELnB\njLYK+PvKdH6E+c8S7QQPfRbmvQz9DD+IAJpuIpi7Sb/b4X//LeZXEu0Bc2BbkW6iggDNHHWQddGA\nXeTrFKfy/XUWKAkfnsNEk0kYeZLGTGAYeRK3dbYKqG4sKBF2rQKUI/H0pQLknOyCl83JXjiAuYmj\n9LwXiUHRraksVo1RkbNdN4jtTuiEz8Kti8HNtCwluCEiZnbO1eHiFbH/LjxowyikWKV1Lo0AzhQv\nAJe6evzWV+0qKIZyTO2r5qscqBief/p+niIOf5iIJspu9ly5Cnhu8LaL8DFSZqige1LXxxLMGHR7\n344aFRdJfK5VblfVVTTJvfwh4H1EUHY5DyfzD5kBfID5z51W44RBkSOaLZ2R2A8L4lPpOQhhXOHc\nJJTx20cxuITiD1bOlcyl9wUQCW+p/ukHFqEBnu4e+K8RARgDu0odWIejWABvJZoDFfAGZgAfZ/4O\nok1gS2VpskG9x3fvGHdjPp2fKYFD4DfUp/o/zJeI9oAtYBPY6EZdHQmj8idowEDyLT7+YFNo/h3z\njxA18YlgrqT4wTySKf86kA0KqFpJVLWxXgSXPFLUEbOTsYMS8gZSIU0q8p7MtZqk1COVlb91eWLt\n8CdleBSQSHDTNO2tS9NRkqRhB7UPkU0d2jvKoEEaNt8Nezvkk3yI+ZlnZkYAZ4VfZv5rRNuKAIYF\ntKHoou6mR1aG5xWQD+T/p2CCe8QXiP4U8BmRCYnuO/R8jp0bJUk/6Jaydhy7poJu7R8tB4+HA94Q\nNJHR6lXhv5oDbyO6BZTAx5i/4z41Tif4pJKR0PO5fpz5x0LXGzGzlPG9n8r3FZdQTEz4yqKyb0er\nKpdwXc+B7oQSfJHoJrAJXA0uPA28cnnDbeAIOADeSDQH3sT8OeZXhIL/pir49xgx6sGTNYovuV2H\nwG8OLslNZgAXiDa7Z67fIQ6+pkFG0Yck22cfxgyyWyFFqUeejKIcoBcIBn1q3ZU/5d0CeLSPdyKp\nBElV2vPY4s7FoKoaRhVN1+mOf/vh3WqHcvNlCVQTApGRcyPZNhM8LRc2wg97O3q3rl7FQ9U5saLn\nmAAA/Arza4gkGFxZQFupQazCCsZheN6ECPrWi/UV/gfRFHgS+MNEY6Jf8F40hdJdFf2jZFXQLWXt\nUpW1tX/kBvXDf0OUKaunn66Zet0G/jnRq4H3AAfdxHQ6FKcDddfeDRkiD5kfjQOAg3WWMTgVUcnc\nU9l71XYQRdwrVfb54PvKZ5P8z6eJxsANYEY0JsrUkTp1ktMQuo1DEu8W8ENER8CXmXeItkLBf6JE\nX/pA0oHiCyoMlczPl9bb6H1mYfutwAFjlU7U9aSeFa6AO8CXH9IEyrczfz/RTFGjjDzJVuW+evKk\nHgFgEIsfhBWeE+ZWYxYiAKicresKfHVXXT0oqtfBz3j6xBO4CSBkt3zYMzyWJV/d3o7j5rtBb4cW\njwzTUE2480YALwY+yPydREfArtJiZqvuqFOXiVf5ql6F5585+1GuXyJaAheJtok2k2SSJOMk+UEi\nAD+T5xXzhGgEtP5REGbEniDJmQz9ozzoLHvf/c1ZlomDA7DK21bMRdhvl3U3q/xF4BfDwtU48SLt\nitOHJkn/f+WZXOmTPgdQbLMKll3K+MOfz3fL+Ct1XLwq4KuAdwCfI9oDdpJkM0mmaTqKA/VkdnGo\nYY6ZR8xpcHK1BX8B+FtEh8yQHQbAZvdARqsK/tT1LUrg8/dwrzjICvJw7JED9PtrNcEC+MrDvrHP\nAxeBAqhU9Xt0YvGjRwDFmmrQrfALTuP3Yu5Jnvx6UqnVn2TV9FAAdx37+mbmHyZqdNgnwY30XnQz\nUcdb4Qa9HTr5M8yIluvrEEYADx+fZP7WkK+Yda+pfkj0UAQaKDLlx/47wCGwAH6c6A1n+RP+T6IE\nuEi0m6bbWZbqqbPMf3c2k8U17z460opDqADZqf7DnmNSDe7lO2V+i1rsPvGeg8oiY06ZE/V9Y934\nbwK3gV8K9c9JINdoj7IwXoIGjUs1cAD8vzXH+HPMf49oO64hiyp75jYCWJkTWNMH61fpuKrQj3aB\naDdNd+ScwwaFVmXkXNI006YZN03qXOI9BRs8dG9lv1jDTESHweHoFfzTARfKt/ud+7xOuqS0MZAG\nUcj5vHBmt/R9zK8l2g7n3JMnDYsfPWNdrep4F0t9EyhDiDMZkAoGMZz+ud16Te09Nj18Hbiomu9i\nb0cmi56Cp9VrvqtX3ToeBDfyhovzYzwfBwIA8AXm60QbQUQx6QYB8T71wnPBf1Cp8Jj/3Qd+lGgB\nvOkMHrDfJhoB20lyYTTalGn4MgNZt8NUFcrye7IMdf2O+XxdQDMMkKtB0P2uixeP96gAUeVGdT2t\n69Q5cg7ea8VU73l+HfBeYL7K5PWOlEMT2fIe0hFfBr4FKMIDfGzmRIG+vorT03Fx1yL48Gdy4D8C\nm0lyMcu2Zdx0XF4oqqrYy1pVSVVt1jU1DXvvvZelofXgb3w10dPMQ+s87hJAvHgV8HsPdoX4m+dL\nfoD51UQzYBmCgMmaGlvPSrr1CqUy9Cjk3ZA9vQdS4TVFdUn03aPw6d3Mf5toM7gI/Vle2tMaKLt0\nd7f+GLr94kEEuEYADxCxMiehTLcRflG9qUeHz+9JU6gZWyvzv2OV//3ph/eL/g7RSKzSeDyTTXvb\n29jaOl4yJZPRZB32comi+H6gquu3luVdg+56EHT/7I0bmM1awxetnjS5FAWKYlRV7XUPiu/4tltA\nATwBzLsO6XS9xqm5H3v3YebXhbF6pXoUR922iRNU9nn3+2pHrAB+EpglycXRaFvOeWcHW1vY3Dxu\n+JSpA9LzmSQApoBrmjh2bRIUxhvAFrAL3OguVODz86ifDk8z/3Giw1AKmqpMFK3Kwler6h/aPh4C\nFbCjNGZxmO6w8zm+Z70mao9pxvuSvT4D3AjTmYoTs1sru6+LVTwUb93Nc/X7Zo/TZfUheToeJE8z\n4JNZNk2Scdz5ACBMWq+8H90t//vzD+lRZ2AjSfZGo9nGRrtr++JF7O1hcxOjUdv9K8MP7tyJbfdj\n5h9gXjj3Fud4TYDsBvfyv7785djdxe4utraOhzhKm/t8jvlcrN6IeRyG3MobjoFJWK2+BVwEXksk\nXUsP1+S9l/m7iDaDyr43W3vlo6hTQNStOmj3/yeADaLdLNuW1a8XL+LiRVy4gO3tdtCQzAKTcw7J\nsYR57H3FnDmXhcsTT2MT2DnVfIVzjd9ifhnRgSoFjdbowbCq470nf5IqyB8k2g6a1+lgmG7vhvOg\nj7InMXj6Pu/kp5hfRbQHFN2S/jC7NXQ7qu44xV5wc/iQVFhGAA8aMgsNTIHPh/xvJvlfvdrUudS5\nNOR/01X533gRJf/7gJ/tM0R7RLM03ZlO24XUV6/i6lVcvIjt7Xb9ep7jzh3cvh2bv2Qfxci5zPvv\nS5KF9wvgHd17OfSPfvEVr8ATT+DyZTzxBHZ320FXwi4yaS4uePG+tXrMsmgh2r5JcH63ge8iOovS\n1oeYX0V0BOyskpzSmj5YN5jyplW8C5EFp+nuZIKtLTzxRLv09dIl7O62G0NlJP3t2+00GOfkJecs\ndYhUDkTRwAzYWL9a+XHFV5hnauxVtNfx5JNuw+PKArjuqnmGeZtoV3HAyrodnzhGRaz/p0/1Q3yK\n+Y8RbQPzQbPLcAZffND8mt4OqW3MV6ndjAC+OfgS0EgGIMt2JM/ey//Gba5r8r+6b6AAboT874N8\nqg1gnCRb4zE2N7G3hytX8OSTePJJXL7cEoAM/4nj0WX5V1mirrOmSb1PmROihPm7gSPgbV3VebyX\nv/Qt34KLF3H9Om7cwJUruHChXeck7CJkEFve6zoVq+d9EkZRpl3DJznT1xG99wyu+KeYv1XlGSar\nhuHoCCBRc61pUGNcAm8DRkmyNRolsxl2d3H5cnvO165hd7cdsi3Lp+I5h9VjSZqKN5CEjsJELReT\n4NKt36z5uGIZ/KqJaoceK7YedrxDmekvDM7qiBnAREmqdGAxUk0PSTccjHX1xf2X1julOOaXBV3v\n5hoBoRv0361MQ4nP8elzeB8eWwKoJc8+Gu1Mp22efXsbsxkmk9b2xQ3Xy+XK/G8vDbI3yP/eLz5N\ntEc0StOZrH2/cAGXL+PGDTz1FK5exfZ2u43o4KB11SVRs1hI4CJDTqk7cPx7gDvAf+vey/ddvYrd\nXVy6hOvX8dRTuHGjJQDvMZ9jY+N40KYMWRPDlyTSCBMNn6YB6W2uz+z3+gLzNaL9E8fqNWFK5bqe\nA7H+/16e5DSdjceYzbC3h0uXcO0annoK165hb6/dQiWZH9l4LAOoZRqMnHMYTkBqmVpc9zoGNvFS\nRAyvN0LqZjyYeUWqIFSul4EJSmYi2gc2ghsuP33TJYCeV14AX3wY1vYrzJtEO4NAhJSCy4fPkK6/\ndUfAb59Pb+DxJIDPE+0Q7WRZm2kZ5n/lsT88bPdHEnXyv95nQRqvaWD3wfK/IyAhmmYZhADkg126\nhCtXcOUKtrdBhDw/zk6INEgpRCluMQ3Or7z+MnAL+H3gq8D7d3cxm7VvfuVKGwRcuIDJBE3Tbj4q\nChwdtXQoaTEZxxjXrwfDF22f0MD0LH+1F5ipO1o1U27+sA1q+BzeBJ5h/gxRSjRJ00wIYHsbFy60\n2bArV7C7iyxDURxvHJtO24l7ado/53AU8cATtWs+1kVesjSQdTP4MSnUAM/f88n0JFXTbmChQwrp\npPvqQz3zBTOAdFUgkg2kvT33Xxjuc+f5DjyGBPAJoieASZruTCbY3m7zv1ev4vLlfv43ro+XZEjI\n/2ZAShTzv1mggQ1gdtr874eILgGUJKM0xWgEiUu2ttrQZHsbm5vyNBwbfZGrq+3VreaMiEOhgpVt\nyoB3Tybj+Oa7u9jba5nvwoV2taT3WCw61BIX0Adq6X03veJuBPxVov9+Zjc+WpZRqDcOJae9ngMf\nkgzSXvteomsAJUkm5zyZYDZrj1okQNISAbRGP7ZfqEOQ5czcHfGoTyNOe30p46EroPQbjqULMnQ8\nHJ6xkXXMRHQUJsrEWzdaf+tq4H+df/p/DO/wluTZR6NM5B+XLh2nWfb22vzvYtFW/1blfxNJtav8\nb6Yqog3wF4h+/T5/+0zcSaI0TZFlkIp0rEuL6fE+VmXbF7OsHEFQ6PvuSxupBMiSpN0gH2XvuslA\ndl5HYxf8ruOEJpGXUe/hbSMfkPJ8XzQHc7wqzxAdMTmQHHhW/Ray/peJ0iSBHHV8xW/tfSz5wrnh\nacdWO33OmgbkSkxgOCtUL7ptXSl0Hg16O2R+yVcel8jvcSOAjxFdArIkmY1GkAKg5H+ffBLXrx/n\nfw8PO/nf6TS6w5IARsj/rqSBrfv/YOI4UHTnpeEr7sAritYwSdJ/uURRtAvQxUgxN6E0rW2TUxYq\nAUiMu9g+JXZCXbdlj0B1x+8cDJ9Mv5IC+JBmWBVCX3wHMyOKsblw1f6aJzCJuzj0OQvNi/5V/sdY\nX8lzFIWs/JbTkNFmkQP0ywcdSLwPBotsjAAeIUyBhGicpiPJ/+7s4MIFXLrUyf+WJZIERdFmgdYl\nQ8LCP+o2lInrd7+ayFhQcpLnkR5U2fEr1cjJpCWA/f12E6nQQLBNlfd1mOHTqMGT8ZXo7I0YfTF5\nR0dgbr+yvPN83ntzp952aPVcsK1i+F783Hdz7wllwBFJi1/nnOfztq+iKNoc4P5+u1p5sWjpVp1z\nrebA9A5EuwUvNS2QwQjgkYbM8xvF/K+0gEqqfXMTm5ttvkUkHzH/qxzzmP/tLRSMNJDdfybkZ4j+\nQGwpjFuuZPGvfCTZ/OUclkscHODmTezvtxxQlqjrRuaSR8O06kXSjCPvL9uDRecuDi8RyhJ37uDW\nLRwcHL95VcG5yrm6Sy365RQFJi9iEHCaZG4cLhQd/6j3zzJUFaZTMLdceOtWhwOqyjdN6X0VhgA3\nal2MPud4GmMzIQYjgEcEv0p0CUCSJJIKj6n26N2LfdQpYJ38ldmQgwSIToNAaQHvz4eVrgLm0jmu\naxLff38fWYamwdERRiN431qrnnNa13nTFHHrnppRE/dDCUXV3pfOTWSSxOEhbt1CmqKqWulnWbZ/\n6a1buHMH8znyPFq9MgwHjf5vvcbqJY/wHWjCYNHS+6Kup0WB+RwHB22Tncg9ZQm4/O+3b7dHURSo\nKllr3p5DGEIwPGqo0zAYjAAeFfd/df5X8uxB798mBIb53zhqf5D2jdn20+V/xZRkYQfeoqq2xNDL\n7IeiONYjiWGSvFAggLyqcllKF2x0paZTVWoAXOl93jQTcf8PDpBlx1ZPtP8SFhwc4OAgEoAsFi4D\nu1RqAEu0gBr0CN+B9kCYC+cWQgBHR8dne+cOxuN23oaUAdQ5F3V9fM5hBPzwnMnMhsEI4BHEcf4X\nqvjZy/8KAYgF1PnfukbTHOfZw8z9XlqcFAf8JaKP3Fv+V3bVZkDBnHt/VNfj5bKdSiRGeTpFmrY1\n4TiebLFAnudlOW+apSzCDTuJ4ivaa/n6ufeLut4oisl8fmz1ZPADgKZBUbSDhqQSkOeyZj2+eVx8\nUampO+cIZZgnuvR+WteTotiaz9vRb3mO6RRZdnwUcs7LJZbLsigWsnE+rH4t1dChUu285ME/DQYj\ngLPM7fzqvXld78cB+ADNszgCjoBngc/2LbFgD9gD/sjd31IaFK882Af7o+9v/2UJv0T5bDs26oXT\nnQYB0rQy/FQvoH6hXTj/+w8SSO0AOw/lF3nRcfH9EnLx11F/vZ0X8I1Tv5v8+pfO51EYvul45Ss3\njAAeFG9+80W7SQaDml8UvQAABrdJREFUwfCSI4DXv/5v2I9kMBjOLQ6Aa0YAp8ErXvEqYN9ukMFg\nOM94dI3YI0cAX/sann/+f1+/fvv27W8sl7XdHYPBcN6R581zz/Hzz6NpHq0PRo9g3/Nb3kJXr1Ke\n86N2WAaDwXAKVBW+9CXs7+Ptb3+07O2jSACvfCV9+7e3Da0Gg8HwGBDAZIKDA3zsY0YAZ4Z/RnQd\n2AC2gW2iTaJZkmyk6SRJxmmaJUlKRADL4DPva+9lIXDlfcksryJsG18CC2AOLLur2GVH2BHwK/d/\ndD8Qxo5vqxXb67ZRu+7a91J9MBf+ZG8l6cuAmdpw1I4zJNJ72zlMOhuu2M1XvX/cilcCd4APnYcL\n871EssB2p3cU3dVp8TftHYXcgaXqgNPnLGtn3msjgAznH49VI9ibmH+UqJ1jzOyBxvuKeZIkI+dS\nopYAAM/speHL+zr21jLrh19M4VJNXdYrgotTfcLngEtAHuzpdLD+kLpko9dSRgJwyippA10A+0AR\njFTc350x9zadesUu9YAA3Bqrl58T6w/gZ5m/m2gWjm4jTPdNB0tfe0dRKgJo1pyz0L/BYATwyGE/\nDOwG4Jhr5opozDwiSoGE2uH3MvJeBj/IiJ5adXvmKgJoupYi+tofPJUpfB/zXyHaBiqgCNsnRmqf\nqvZMexGAWCX9t8ZPVYed1AB2wreYBavXG2juBzvWdXjBXavnFbscnKub8CxwCVgChQoCsu7m8RMI\nwHeX0LIiwjnwMXP/DY8F6PEbfv3DRJIF2gppljGQEWUr0yyyATg8/FWwg3nIALAyhTH/cwg8yDzk\nP0+0CWwBWyEIyAY70FcSgN5JpDMYkpSQucSvUW++sSr1od+86Vo96m6W18mlOfDR83ZV/jTRbsiJ\nHcdDa45CEwC6q1/1r78EPmLW32AE8MhimP/VaZZEPfkrMyGRAOqBIxxNwHse+ND+JFH8hL3sBFTC\nIeamXXchNbpJibxrnb9TvflUbTXSKaZeBFAPFl7H9xd2+ej5vCffRrSluHa85ijiOTeD1a+9MOjX\nzfobjAAecUj+dzt4wZO7pVmaQaG1GWRCmpD6v/OQrOEfCmZ6o0tRQyddL6SmgVXKgU8OPs+3hTef\nDcoMvYy28NxKq9eE9//4eb4kTwYO2Owm3NCNoupuJaZHhGL9P2HW32AE8Ojj1USXgCmwq7NAa1Lh\nQwJwazIhFbAAPvzwTmxXmWntnEa7k4XJo3H0fM9vXQK/uebz3OgSTKYijGj1oAIj6nKeC4Xf33gM\nNl8T7amUoK4EeLXtMlEuAnWtf77+nA0GI4BHDn+GaCfkf2dhsXi6PhWuRx/TKvdfrMCHH/ZxEREF\n2xQ5YKR2oPfcUm2d72qSUqJNdQJp2KnL6s3Tgfvvw/sfAV98jK4HEY2ALRURjlR+LO1GWvqcP2em\n32AEcO4wzP+OVpUBGrXsiQYOIKvkz9ltfxUamKnPOVpFAJGNFsDv3vOHEcO3Ed58HNglW+X+i/Uv\ngC88pheDiCahMjReRQDxHBpgDnzVrL/BCOCcQvK/291aq84CxVR4MjCFUCFC8WLlwYloquSbWZex\nauDZ036MaPg0B/TYxQHl/VDLuaaBVEmwdGXYAzXwvNl9gxHAY4AR0W6oAcZyqM4C9fK/vepfA+TA\nZx+vTMi4Gw954KbZO4PBCOAx9vjGKv+bdr3gXv4XSmNzCPxfM44Gg+FxRPYS+Z7Cczr/W68pAMa8\n/xJ4xky/wWCwCOAxiwaywYCwmGdvgK+Z3TcYDEYABoPBYHhckdgRGAwGgxGAwWAwGIwADAaDwWAE\nYDAYDAYjAIPBYDAYARgMBoPBCMBgMBgMRgAGg8FgMAIwGAwGgxGAwWAwGIwADAaDwWAEYDAYDAYj\nAIPBYDAYARgMBoPBCMBgMBgMRgAGg8FgMAIwGAwGgxGAwWAwGIwADAaDwWAEYDAYDAYjAIPBYDAY\nARgMBoPBCMBgMBgMRgAGg8FgBGAwGAwGIwCDwWAwGAEYDAaDwQjAYDAYDEYABoPBYDACMBgMBoMR\ngMFgMBiMAAwGg8FgBGAwGAwGIwCDwWAwGAEYDAaDwQjAYDAYDEYABoPBYDACMBgMBoMRgMFgMBiM\nAAwGg8FgBGAwGAwGIwCDwWAwGAEYDAaDwQjAYDAYDEYABoPBYDACMBgMBoMRgMFgMBgBGAwGg8EI\nwGAwGAxGAAaDwWAwAjAYDAaDEYDBYDAYjAAMBoPBYARgMBgMBiMAg8FgMBgBGAwGg8EIwGAwGAxG\nAAaDwWAwAjAYDAaDEYDBYDAYjAAMBoPB8OLg/wNNtOm/dIgqAQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.image(zoom=1.6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Minimize using Monte Carlo moves"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "estart = L.eval(\"pe\")\n",
+ "elast = estart"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "naccept = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "energies = [estart]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "niterations = 3000\n",
+ "deltamove = 0.1\n",
+ "kT = 0.05"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "natoms = L.system.natoms\n",
+ "\n",
+ "for i in range(niterations):\n",
+ " iatom = random.randrange(0, natoms)\n",
+ " current_atom = L.atoms[iatom]\n",
+ " \n",
+ " x0, y0 = current_atom.position\n",
+ " \n",
+ " dx = deltamove * random.uniform(-1, 1)\n",
+ " dy = deltamove * random.uniform(-1, 1)\n",
+ " \n",
+ " current_atom.position = (x0+dx, y0+dy)\n",
+ " \n",
+ " L.run(1, \"pre no post no\")\n",
+ " \n",
+ " e = L.eval(\"pe\")\n",
+ " energies.append(e)\n",
+ " \n",
+ " if e <= elast:\n",
+ " naccept += 1\n",
+ " elast = e\n",
+ " elif random.random() <= math.exp(natoms*(elast-e)/kT):\n",
+ " naccept += 1\n",
+ " elast = e\n",
+ " else:\n",
+ " current_atom.position = (x0, y0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " this.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width);\n",
+ " canvas.attr('height', height);\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'];\n",
+ " var y0 = fig.canvas.height - msg['y0'];\n",
+ " var x1 = msg['x1'];\n",
+ " var y1 = fig.canvas.height - msg['y1'];\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x;\n",
+ " var y = canvas_pos.y;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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EfCQIbn5BCAAACuGAKwda0k6WdIYSZ9IuljSbyS9JT8imJSqBeCpp+ZH4LhxPn95Afj22/nrAwDQ6AjA9Osn6W+SWiU9K+nnkrpHln9J0oIExpVVtQA0M2trywXgvvv6PAIQAIAVQwCm3zxJ10rausI6F1RpLKVUNQDNzI4/3oNugw38dTYA29tLr//WW778nXeqNkQAAFKNAEy/tP8PU/UANDN75BGzzTbz5/fe64HX1lZ6XQIQAIB8BGD6fb3M1CvJQUUkEoD//rfZ//t//jx7BHDZstLrjh3ry99+u3rjAwAgzQjA9GuXn/FbamqVdIOkLyQ2uoQC8MknzdZf359nA3DJktLrjhnjy996q3rjAwAgzQjA9PuVpOck/VDS5pnHZyQdL+lASa9Luiax0SUUgM88Y9anjz/PBuDixaXXJQABAMhHAKbfGEnrFsz7Wma+JG0saXJVR5QvkQB84QWzr37Vn2cDcNGi0uuOHu3Lx46t3vgAAEgzAjD95ij/si+S3xlkbuT1/OoNp0giAfjKK2Zrr+3PsyeBLFxYel0CEACAfARg+o2Uf8TbI/N6dUlXSXoi83ojSRMTGFdWIgH4xhtma67pz7NHAFtbS6/75pu+fMyY6o0PAIA0IwDTbyP5Ld+WSpqeeXw7M1+SdpB0UDJDk5RQAL71ltnqq/vzbABG7w8cRQACAJCPAKwNq0jaSdLhmcdVkh1OnkQC8P33Peo+/DAXgPPmlV73jTd8+ejRVR0iAACpRQCm2+ckjZb0+aQHUkEiAWiWuyVc9juAc+eWXi8bgG++Wd3xAQCQVgRg+k2V1C3pQVSQWAD26uVhd9dd/lhuCK+/TgACABBFAKbfeZJ+n/QgKkgsACdP9rDbait/nD279HqjRvnyN96o7vgAAEgrAjD9/itpiaQp8gtAPx2Z0iCxAJw+3cPuC1/wx5kzS69HAAIAkI8ATL/BFaY0SCwAZ83ysOvWzR9nzCi93muv+fLXX6/u+AAASCsCEKESC8DW1tyJIJLZp5+WXo8ABAAgHwFYG9aU9FNJp2deryPpq8kNJ09iAbhsWX4AfvJJ6fVefdWXjxpV3fEBAJBWBGD6bS2/APQ45W759gNJIxIbUb7EAtDMw27VVf3x449Lr0MAAgCQjwBMvyclnZB5Pivz+AVJHyQymmKJBuCxx5p98YseeNOnl17nlVd8+WuvVXdsAACkFQGYfjMkdc08nxmZPyeBsZSSaABefXXuI+Bp08wWLcote/FFn//yy/746quJDBEAgNQhANPvPUnrZp5nA/Drkt5JZjhFEg3Ae+4pviNI1jXXEIAAAJRCAKbfBZIek7ShPADXk3S/pHOSHFREogH4+OO5ALzkklzwmZndcIO/fuklf3zllUSGCABA6hCA6ddd0q2S2jNTm6R7M/PTINEAHDMmF4AXX+yP3/qWL7vlFgIQAIBSCMDa8WVJ28k//k2TRAPQLBeA553njxtu6PNvu81fP/98/pFBAAAaHQGIUKkJwOz01a/6NQKHD/fXTzxBAAIAEEUApt+6km6RNEbS5IIpDRIPwDFjzNZeOz8Cjz7a7K67/HlLS+6jYAAAQADWgsckPSO/FuCAgikNEg9AM7OlS/MD8Fvfyp0VfN99/vjii4kOEQCA1CAA02+O/MLPaZWKADTLD8BvfjMXgNddRwACABBFAKbfO5LWSHoQFaQyAKXcR8DZ6YUXkh4hAADpQACm3yGS/iapt/yOINEpDVITgDNmmL39ttkvf+nB9+MfE4AAAJRCAKZf9Pp/hVMapCYAs+bNKz4aKJn9739JjwwAgHQgANNv1wpTGqQuAM3Mxo4tHYCjRvnyCRPM2tuTHSMAAEkhABEqlQFo5mcGT5tmNnSoB+A66/jjxx/74wMPJD1CAACSQQDWhh0lXS/pwczrbST1S244eVIbgFEDB+aOBI4e7Y/XX5/0qAAASAYBmH5HyC8Fc72kuZl520oamdiI8tVEAJrlAvCcc/zx9NP9cdq0pEcGAEB1EYDp96b8CKAkzco8dpf0cTLDKVJzAZidDj889xwAgEZCAKbf7MjzmZnHLpHnSauZADQzu+KKXPQdeGDuOSeEAAAaCQGYfqMk9c08z0bftpJeTmY4RWoqAM3Mliwx22ST/KOBixYlPSoAAKqHAEy/oyS9L+lX8u8A/kx+d5CfJDimqJoLQDOz9dbLD8D//Ce3bPp0s2XLkhsbAACdjQCsDb+Q9LqkVknjJZ2U6Gjy1WQAzpxpNmhQfgSusop/FCyZ3XBD0iMEAKDzEIAIVZMBmFV4Ysill/rj5ZcnPTIAADoPAYhQNR2AI0aY3XdfcQhefXXSIwMAoPMQgAhV0wGYtWxZ7uifZHbVVUmPCACAzkMANqYjJD0tv8B0m6SuBcu3lPSUpPmSPpA0uMK26iIAs5YuNfvOd8yGDEl6JAAAdB4CsDHtJY/Ao1UcgGtI+lDSHyV1k7SFpCmSfldmW3UVgGZml1zi1wgEAKBeEYCNbVcVB+AASR8VzDtJ0rtltlF3AfjAA2ZrrOG3iHvppaRHAwBA/AjAdPqv/CPa5U2hSgXg5ZIeKVjve5n11iixjboLwEmT8k8IAQCg3hCA6TS4g1OoUgF4o6S/F6y3SWa9r5XYRt0FoBkBCACobwRgY4vtCGBTU5M1Nzdbc3OztbS0JP2+Dvbyy7kAXLgw6dEAABCupaXls7+rm5qaCMAa0kUea9kpVKkAPEoN/h1AM7O2tlwAPv980qMBACBeHAFMvy9LukPSJ/JYi04rq6uk7pL2zmynR+Z1F/lRvqmShkj6vPws4ElqoLOAs0rdJxgAgHpAAKbfbZKel3SgpHmZxxcknRiwzQGS2pULyezzXTLLt5CfZLJAfkmYsytsq24D8NFHzb74RbM6+EQbAIA8BGD6fSipT+b57MzjRpKeSWY4Reo2AM3MNt2UAAQA1B8CMP3mRJ5Pl/S5EvOTVPcB+MgjSY8CAIB4EYDp96akDTPPn5V0uKQ95EcG06CuA3Czzcz+9a+kRwEAQLwIwPQ7QdJBmecHS1om/77e6YmNKF9dB+DmmxOAAID6QwDWnt6SNk56EBF1HYBbbGH28MNJjwIAgHgRgAhV9wH40ENJjwIAgHgRgOnUEnle6b7AaVDXAfjtb5s9+GDSowAAIF4EYDoNjDw/V513L+A41HUAbrklAQgAqD8EIELVdQButZXZAw8kPQoAAOJFAKbfm2Xmj6rqKMqr+wC8//6kRwEAQLwIwPSbV2b+rKqOory6DsCttza7776kRwEAQLwIwPQ6JjO1Sjo68voYSRdJeju5oeWp6wDs25cABADUHwIwvSZmprbI84mSxsvvCPL95IaWp64DcJttzP75z6RHAQBAvAjA9PtX0gNYjroPwH/8I+lRAAAQLwIQoeo6AL/zHQIQAFB/CMD0W0N+zb+HxYWgq27bbc3uvTfpUQAAEC8CMP3uljRO0sXiQtBVt+22ZiNGJD0KAADiRQCm3yxJ6yQ9iArqOgC/+12ze+5JehQAAMSLAEy/iZK6JT2ICuo6ALfbjgAEANQfAjD9fi3pMkldkx5IGXUdgNtvb3b33UmPAgCAeBGA6TdF0lL5BaEnF0xpUPcBeNddSY8CAIB4EYDpN6DClAZ1HYA77GB2551JjwIAgHgRgAhV1wH4ve8RgACA+kMA1ob1JZ0l6crM6w0lbZrccPLUdQDuuKPZ3/+e9CgAAIgXAZh+/SXNl/SYpHmZeTvLLwydBnUfgMOHJz0KAADiRQCm30uSDsg8n5V5XE3StGSGU6SuA3CnnczuuCPpUQAAEC8CMP1mR57PjDyfVbhiQuo6APv1IwABAPWHAEy/sfLv/Em5ANxE0pvJDKdIXQfgzjub3X570qMAACBeBGD6nSLpVUl7yo8G9pP0nKTfJjmoiLoPwL/9LelRAAAQLwIw/bpKOlcef+2SFihddwap6wDcZRcCEABQfwjA2tJL6bsvcF0H4K67mt12W9KjAAAgXgRg+pW73MsDVR1FeXUdgLvtZvbXvyY9CgAA4kUApt/cMvNnlplfbXUfgLfemvQoAACIFwGYXv0z0wJJu0de95d0vKRJyQ0tT10H4O67E4AAgPpDAKZXe2ZqizzPvv5A0s+TG1qeug7A/v3Nbrkl6VEAABAvAjD9Ric9gOWo6wDcYw+zm29OehQAAMSLAESoug/Am25KehQAAMSLAKwNJ8nvCDI/8/g7SV0SHVFOXQfgnnsSgACA+kMApt8pkqZKOlnSfpnHDySdmuSgIuo6APfay+zGG5MeBQAA8SIA0+9tSdsUzOsraVwCYymlrgNw773Nbrgh6VEAABAvAjD9Zqv4tm9dM/PTgAAEAKDGEIDp95KkQwrmHSTplQTGUkpdB+D3v292/fVJjwIAgHgRgOn3Q0mLJd0r6eLM4yL59wHToK4D8Ac/MLvuuqRHAQBAvAjA2vBdSdfK7wt8raTtkh1OnroOwH32Mbv22qRHAQBAvAhAhCIAAQCoMQRgbfiepBsktUi6UdKOyQ4nT10H4L77ml1zTdKjAAAgXgRg+v1c/h3AOyVdmHlcKOmoJAcVUdcB+MMfml19dcfW/e1vzc47r3PHAwBAHAjA9HtH0j4F834g6d0ExlJKXQfgfvuZXXVVx9aVfAIAIO0IwPSbq9LXAZybwFhKIQAzCEAAQK0gANPvXkn7FszbJzM/Deo6AH/0I7Mrr+zYugQgAKBWEIDp9xdJCyTdI+kiSXdLmi9pmKTzIlNS6joA99/f7C9/6di6BCAAoFYQgOn3RAemkYmNrs4D8IADCEAAQP0hABGq7gNw2LCOrUsAAgBqBQGIUHUdgAceaPbnP3dsXQIQAFArCECEqusAPOggsz/9qWPrEoAAgFpBAKKUwZKWyS81My/zeEeZdes6AA8+2ANw6dLlr0sAAgBqBQGIUgZLerqD69Z1AB57bC7s3n+/8roEIACgVhCAKIUAzHj22VzYnXlm5XUJQABArSAAUcpg+Ue/0yVNlH/8+80y69Z1AJqZtbebHXaY2dChldcjAAEAtYIARCmbSeqTeb6upNslvSepR4l16z4Azfyj4EGDKq9DAAIAagUBiI7oJmmhpD1LLOspyZqamqy5udmam5utpaUl6fd17E4+2ay5ufI6BCAAIM1aWlo++7u6qamJAMRydZPUKmmvEssa4gjgwIFmxx1XeR0CEABQKzgCiFIOk/TlzPN1JN0maYKk1Uus2xABeM45Zr/6VeV1CEAAQK0gAFHK/fITQOZLmiI/CWSDMus2RACed57ZL35RetmHH/ojAQgAqBUEIEI1RABecIHZz35WPP+DDzz65s0jAAEAtYMARKiGCMCLLzY74oji+W+95dE3cyYBCACoHQQgQjVEAF5+udkhhxTPHzuWAAQA1B4CEKEaIgCHDTM74IDi+WPGePR98gkBCACoHQQgQjVEAF59tdn22/tdQaJGj/bo++gjAhAAUDsIQIRqiAC8/nqPu1tvzc0bPNjshBN8fvZkEAIQAFALCECEaogAvPFGj7uNN/boW7IkF3yS2aRJBCAAoHYQgAjVEAE4fHh+8PXpk/96/HgCEABQOwhAhGqIAGxvN5s/3+zuu/PDLzuNG0cAAgBqBwGIUA0RgFHjx5ttt11+AG62GQEIAKgdBCBCNVwARpU6GggAQNoRgAjV0AE4YwYBCACoPQQgQjV0AJoRgACA2kMAIlTDB+DChfkB+OKLSY8IAIDKCECEavgANDObPt1sq608AO++O+nRAABQGQGIUARgRlub2T77mP3hD0mPBACAyghAhCIAI4491mzQoKRHAQBAZQQgQhGAEb/+tdlZZyU9CgAAKiMAEYoAjDjuOLOBA5MeBQAAlRGACEUARhx/vNmZZyY9CgAAKiMAEYoAjDjhBLMzzkh6FAAAVEYAIhQBGNHUZHbaaUmPAgCAyghAhCIAI0480ezUU5MeBQAAlRGACEUARpx0ktkppyQ9CgAAKiMAEYoAjPjd78yam/pcEicAABbhSURBVJMeBQAAlRGACEUARpx8sk8AAKQZAYhQBGDE//2fHwUEACDNCECEIgAjTjnFvwcIAECaEYAIRQBGnHqqnwkMAECaEYAIRQBGnH66XwsQAIA0IwARigCMOOMMvxsIAABpRgAiFAEYceaZfj9gAADSjABEKAIw4qyzzI47LulRAABQGQGIUARgxO9/b/brXyc9CgAAKiMAEYoAjBg0yGzTTc223DLpkQAAUB4BiFAEYMTZZ5tJPgEAkFYEIEIRgBGDBxOAAID0IwARigCMOPdcAhAAkH4EIEIRgBF/+AMBCABIPwIQoQjAiCFDigNw6dLkxgMAQCkEIEIRgBF//GNxAEpmY8YkNyYAAAoRgAhFAEacf35+ALa3+/NHH012XAAARBGACEUARlx4YX4ALltGAAIA0ocARCgCMGLo0FwALlzoEwEIAEgbAhChCMCIiy7KBeB555nNnUsAAgDShwBEKAIw4pJLcgH4+9+bzZzpzx980D8OBgAgDQhAhCIAIy69NBeAgwebTZ+ee33EEUmPDgAARwAiFAEYcdll+cH3t7/lXme/F1joww/NPvig+mMFADQuAhChCMCIxx7LD77CadNNzQ45xGzbbXM/06MHdw4BAFQXAYhQBGAJc+aY9epVOQZHjzZbtIhbxwEAqo8ARCgCsIJXXjE7+ujSAbj99vmvAQCoFgIQoQjAFTR9utmqqxYH4fJMm2Y2dmznjw8AUP8IQIQiAFdCe7vZ8OH5ARi9TMzo0WYjRuT/zCabcKQQABAPAhChCMBA0QhcsMDnZWNv6dLcemuvTQACAOJBACIUARjorLPyI/A3vzHbeGN/ftppufUIQABAXAhAVPIHSVMlzZP0pKTNS6xDAMZk4UKzfv088rKXhtl6a7OPP/blBCAAIC4EIMo5TdIkSZtJ6i7pAkkfSOpRsB4BGKNPPy0+OWT99X3ZV77SeQH4s5+Zvfxy52wbAJA+BCDKmSDpxMjrVSR9LOnIgvUIwJiNG2f2r3/lR+Chh+aev/de/H+mZPbLX8a/XQBAOhGAKKWnpHZJ2xfMf1TSpSXWJQA7wdixpa8f2KtX/H8WAQgAjYUARCnryQNw44L5d0q6vmAeAVgFL72UC8AePeLfPgEIAI2FAEQpK3wEsKmpyZqbm625udlaWlqSfl/XnVmzct8B/Na34t8+AQgA9a+lpeWzv6ubmpoIQJRU6juA08V3ABN1440ea/fdV3m91tYV2y4BCACNhSOAKOdUSe/LL/2ymqTzJU0RZwEn6qGHSt85JGruXF8+fXrHt9vRAPza18xGjer4dgEA6UQAopJzJU2TNF9cBzAV2tpy9xEeMsRjb9Gi/HWmT1/xs4VLBaBUfDs6yeySS1Zu7ACA9CAAEYoArLInn8w/K/jgg/OXT53q8999t/w2zjnH4zGrXACeeWbxvEsvDRs/ACB5BCBCEYAJmDgxPwLnzfOgGzcud4TwrbfK/3zh5WQks2OOKV6HAASA+kQAIhQBmJDBg83WWqv0tQIls1dfNfvRj/yI4S9+YXb//f5zCxbk1skiAAGgsRCACEUAJmzJkvIRWDi98kr+69mzc0FYKgAHDiyeRwACQO0jABGKAEyR6683W2edjgehZLbTTgQgADQaAhChCMAa8dFH/rHw44/nB2D37gQgADQaAhChCMAaFA3Abt3KB+BZZxXPIwABoPYRgAhFANag9dcv/ig4G4C//KXZLrsUB2B7OwEIAPWCAEQoArAGtbaaHXBAfgAeeaRfaDo6L/oR8LJlPm+jjTwGAQC1iwBEKAKwhs2aZbbZZpVPEtl5Z7+ryOLFuXkffOA/P2OGn1kMAKgtBCBCEYB1YMwYs5tu8o98t93WrHdvs333zQXf+uubffvb+WE4ZUru+fz5K3brOQBAsghAhCIA61hHLyXz0EP+WHhfYgBAOhGACEUA1rHx4/2ewv/4h9nNN5t973tmO+xQPgS7dPELS1cyb151xg4AKI8ARCgCsEEtXVo6As87r/zPjByZfws6AEAyCECEIgAb3H//a3bPPWaPPOJHCI8/3uzBB80uucSsa1ezIUPMpk71M4dvuskD8NZbza69NumRA0DjIgARigDEZ371qxW7Dd3ll5ttumn57U2ZUr2xA0AjIQARigDEZ6ZNM7vgAg+7hx/27w2OGOGXmtlkk/IheNhhftTwvffMbrjBjyTedpsvu/9+vz4hACA+BCBCEYBYIe+/b/a3v3koHn10x44UfulLZr/4hdkqq3T8z1mwwD96BgAUIwARigBEsMWL/eLSjzzi3x288EKzDTdcfhjuuqvZ737nQfn007ntjR6dWwcAUIwARCgCEJ1m1iyzF180u+sus7PPNjvooMpB+OqrHpHReXPnJv1fAQDpQwAiFAGIqmtt9aN8kyebnXba8o8U3nST2WuvmTU3m02Y4Nc2BIBGRgAiFAGIVHjhhRU7A3nvvXMXtT74YLNhw8xOOcXvanL//f584ECzH/3I7Kmn/M94/XWz3Xf354sX+6VtyrnqKl8HANKIAEQoAhCplD1zePZss//8x2zAALNttjHbaKPl39FkZaa5c80mTjSbOdNs4UKf9+ijSe6B0lpb/dqMABobAYhQBCBqUnu7fyz80EN+h5JrrvGzhk85xWzddXNh99WvhoXhKaeYrbee2U9+Yta7t1nfvmZ//7ufpdzaavbGG2bTp5s9/7xH6+LFZjNmFI938mTf3pIl+f8NUdOnm730UuX/7pYW387SpeH7EEDtIgARigBEw/r4Yz/x5IorzPbZJ94jiqutZrbbbmbduhUv23dfsxNOyL1+7jmzY47JvX75ZbNJk/zkmWefNbv7brOLLvIjoRtt5OtssIHZoYfmrrX45pv+32OWH5Zdu/pZ1jNn+tHU2bPNli0rv09efz33kfncub5dAOlDACIUAQiUsGyZH61btMiP6j39tNlHH5k98YRfA3GHHfyj6PXXjzcc45pWXbXy8j339Lu4ZF8fdlj+8p12yj3ffXeP0Keeyh2BPOEEv4SP5AHdr5/ZfvuZDR9uNn9+LkIXLiz/XcoZM3xfroxnnvHrUAKNigBEKAIQqJLZs/0j48mT/ePrN9/0s6GfeMLsn//0M5w/+cQ/Tn7qKY+cO+7w+HziCb/LytVX+91ZbrrJ7Prrc+v88IfJR2elaeBAsz59/BqRDzzg95zu0sWXXXWVH/UcNMhff/nLZltv7Sf6XHONr79ggZ/oc9ppZn/6k9kPfuDrzpqV/5H7kiV+NDX6UXtHrczPAEkhABGKAAQaWHt78XcRW1s9uMw8sBYt8nmTJ/vt/t54w2z8eP94+p13/Kjff//r34087TT/nuTWW5sdckj1Q3PNNf2xRw8P6lGjzJ580uzaa81uucU/Lh8xwsd+000epo8/7pPkFycfMsTs9NN9+bBhxfvsuef8+6YLF1b+OL0jli3jVolYOQQgQhGAAKpm/nx/XLIkd8LMvHkelQ8/nDsy+tJLfsT0ued8/vXXmw0d6kf/jjjCP6I+4ACzL3wh+aObhx/uH6kPG2b2l7+YnXii3/4wu3zrrf1xxx3NmprMrrzSbO21/eNvyYO5d2+P5wce8Hl9+vg9uc8+OxfX//63B+tdd/l9ug86yC+0fvPNZvfd56F7770e5EuXepwfcYSPr60td5LS4sV+wpGZr3fxxR74Tz1l9vOf+/az6y9aZPbYY/746af5sdrW5t9LXbQoN6/UyUnjx/s/HkaO9P+2xx/3+e3tHsDt7bnxmPktIydM8OczZqxcIH/4oX+/Nwlvvpn/39NZCECEIgAB1JWlS30aM8aPWL71lp/c8tprfhHxceP8upMvvODRc/fdHiUPP+xx9qc/mf3mN6XvXLPeemabbGK2/fZmPXuabbaZfxdU8hN0ttvO7JvfTD5Kyx0ZjU6rrbZy21p7bbO11sqfl/04X/ITj7baqvI2ot8/LTf16lU8r0cPs732yoX//vv7CVHZ5dtsk7/+T37ijwce6Ed0Jf/+6j//6e+DoUM9TF9+2Y/6PvaYnzC1ZIl//WDyZLONN/b3xNNP575uMGOGh+tHH/njnXf6UfPp03NRH/XII758zhy/baaZB/q11/qfN3Wqb2fhwvwTr954w2O2FAIQoQhAAKiC7Eft2cfsUbZp03Ifu7e25pa3tvrR0dbW3NG4adP8KGr2CFNbm0fIp5965H76qX/s/cgjZrfe6vfmvvNOsxtv9MAdNMjsl7/0o5TnnutHIR96yI8SHnCAn8zz3e+anXSSWf/+ZscdZ3byyT5v++09vrbc0j/eP+IIP/J50klmu+xits46Hpp77OFHEtdZx0Oxb18/kUjymOrf3+yMM/wEquhlmnr2LH3W/Fe+4sH45S/78t69c8vWWCP5uK40fetbyz8hq9T0uc+Zde+ee73ddmbHHpu/znHHEYAIQwACAOpC4fdZs69nzfILvY8c6UfbZs82e/tt/y7oE0/k7ll+9dV+VHjECI/oq6/2I3L//rcfJf7rXz2mb77ZP5YfOtRPbPr97z3QHnjAT2q6/Xaz3/7Wz5C/4w6zyy7za4qeeabZT3/q0XzPPWbXXeevzzjD4/rww4tjcK21PLqLQ5IARBgCEACAGjNzJgGIMAQgAAA1hu8AIhQBCABAjSEAEYoABACgxhCACEUAAgBQYwhAhCIAAQCoMQQgQhGAAADUGAIQoQhAAABqDAGIUAQgAAA1hgBEKAIQAIAaQwAiFAEIAECNIQARigAEAKDGEIAIRQACAFBjCECEIgABAKgxBCBCEYAAANQYAhChCEAAAGoMAYhQBCAAADWGAEQoAhAAgBpDACIUAQgAQI0hABGKAAQAoMYQgAhFAAIAUGMIQIQiAAEAqDEEIEIRgAAA1BgCEIV2ldQuaW5mmidpcoX1CUAAAGoMAYhCu0pqk9Slg+sTgDFqaWlJegh1gf0YH/ZlfNiX8WA/xoMARKFsAK7SwfUJwBg1NzcnPYS6wH6MD/syPuzLeLAf40EAolA2ACdJ+kjSvyXtUmF9AjBG/GKLB/sxPuzL+LAv48F+jAcB2DhukX+3ry3zWDiNzKy3jqRvS+oqaXVJp0haKGnLMtvtKcmmTJlic+bMYQqcmpqaEh9DPUzsR/ZlGif2JfsxTdOUKVMIwAbRQ9KXKkxfqPCzT0j6Y5llveVvICYmJiYmJqbam3oLKONxSeeXWdZF/ubpycTExMTExFRTU291/KRP1Lm9JX1T/oZYTdLv5B8Bb5PgmAAAANCJBslPAJkv6WP50b9dEx0RAAAAAAAAgOT8QdJU+R1DnpS0eaKjSZ/BkpYpd1eVuZLuiCzfUtJT8iOuH2TWL9So+/gISU9LmiM/e71rwfI49l1HtlHrlrcf2yW1Kv89Wrif2I/uQklvyPflVEnDJa1XsE4fSQ/K9+PHkv4iadWCdZokTZTvr5cl7bwS26hlHdmP78u/fhR9X+5bsE6j70dJOkfSe5Jmy/8bH5G0VcE6/K5E7E6Tf1y8maTuki6QvzF6JDmolBks/8u3lDUkfSg/w7qbpC0kTZF/7zKrkffxXvJ4OVrF4RLHvuvINupBpf0oeQDuXuHn2Y8550vqK4+InvJ/zL0WWd5FHja3yC+j1UfS65KuiKxzmKRZkvpltnOC/C/d3iuwjVq3vP0oedgdXWEb7Ee3kaQ1M89XlfR/8mv4Zk/s4HclOsUESSdGXq8i/xfIkckMJ5UqBeAA+f9Ro38hnyTp3chr9nHu4uTR/RTHvuvINupJqf0oeQD2r/Bz7MfytpLv0+xfwLtKWixprcg6+8vD5HOZ1yMlXVawnVcl/X4FtlFvCvej5AF4TIWfYT8W6y7pZPm+/HJmHr8rEbue8r84ti+Y/6ikS6s/nNQaLP+FM13+C+0O+VnWknS5/HB91Pfk/+ddQ+zjrFLhEse+W9426k2lAPxQ0ifyj9F+FVnGfqzsdPlfnlknSXqrYJ115ftwi8zrmfIjslHXSRqxAtuoN4X7UfLflx9J+lR+JO805X98y37M2Vd+NLRd/pWjSyLL+F2J2K0nf9NsXDD/TknXV384qbWZ/KMHyX/53C7/V1MPSTdK+nvB+pvI/0/1NbGPs0qFSxz7bnnbqDflAnB3+ZGDVSXtI/+L9bjMMvZjeXvK/3G3V2TeIEnPF6z3efk+3DHzepmk7xesM1TSYyuwjXpSaj9K/n2+1eXv1x3lQXhhZDn7sdgX5R/LHhKZx+9KxI6jUyunm/yLzXuKI4AdxRHAeJQLwEKDJT2Tec5+LG0/+RGX/QvmVzrqlP1SPUeucsrtx1IGyL+XlsV+LK2L/ISQb2de87sSnaLU9wamq7G+n7aiusnPuNxL0lFaue9mNNo+LhUuIfvupyuwjXrS0QA8R9Kzkdfsx3xHyqNlzxLLdpG0SKW/d9Yt83qkiv8B94py313ryDbqQaX9WMpR8rNUs9iPpa0qaYGkgzOv+V2JTnGq/FT9zeV3DTlffmZQI5yh2lGHKfdl3HUk3Sb/P9vq8n85TZU0RP7RxBbyM7GiZ1Y18j7uKv9ocm95uPTIvO6iePZdR7ZRDyrtx77yu/x8Tv5Lf29JM+SX18hiP+acKI+Wncos7yJplKSb5fvl6/KzW6Nnnh4qP3rVT77fj5dfpqT3Cmyj1i1vP24o3z/Z9+kOksYr/7tt7Ed3kqRemedfkX9sO1P+943E70p0onMlTZNfG+hJNc416jrqfvm/pObL/w91h6QNIsu3kJ8lvED+RfyzS2zjXDXmPh4g/2iiLTNln++SWR7HvuvINmpdpf24n6Sx8qMiM+V/QR5bYhvniv0o+b5bLA+N6PXpoiHTR9JDmWWfSPqzis86PUH+F+4C+Yk3/QqWd2QbtWx5+/G78nibI/84c4ykM+T/SIlq9P0o+XUOp8n/G6dKuk/Ft27ldyUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJX8S7l7niatj/wuDt9MeBwAAAANo11S/yr9WYMl/bdKfxYAAADKiCMAO3p/1MHy+4ECAACgyp6QNETSaHkALpB/DPtwZnlXSadIGitptqSXlB+JAyRNkXSCpImZn1Xm9ZuS5shv+H6bpC9llv1U0mJJS+U3np8raSdJ38iMYYPI9n+R2c5sSW9IOiqyLLv+UZJGZbbznKRNVngvAAAANJAnJJ2Xed4uafeC5edKelXShpnXB0iaL2n9zOsB8pC7VlIPSZ/PzD8o8jN9JD0v6Y7IdksdAfyGpDblAvAQeUDuJqmLpD3kwbh/ZP12SY9J6iWpm6R7Mv9NAAAAKKMwAAs/Ap4tac+CeY9JOivzfICkJZK6L+fPOVDSJ5HX5QIwegSwRdJlBev8SX7iSnT9nSLL95UHKgAAAMqoFIC9MvNmS5qZmWbJj8JdmVlngKQPSmz3YEnPSpqe+fl58qN7XTLLOxKAY+QfJUedKP+4Ort+9IihJO2amde1xJgAAACg/ABsU34AdpN/J7BfhZ8fIGlywbzekpZJOly5k0IOVH6Yna3lfwTckSOABCAAAMAKigbgVEnHFyy/TPknVqwmaWflvt9XKgA3lkfYHpnXG0l6Qflhdqyk95X/0XHhEcBD5Uccd838XH/5dwIPKLO+RAACAAAs10jlAvAoSZPkH/U+kJnXVdJv5WfizpI0TX6G8KaZ5aUCUJJOl5/9O0fSM/KPbqNh1lPSo5JmZP68HVX6iN4x8o+C52TGMCCyjCOAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA1fP/AW2tZQPkGdPwAAAAAElFTkSuQmCC\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f26a40123c8>]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plt.xlabel('iteration')\n",
+ "plt.ylabel('potential energy')\n",
+ "plt.plot(energies)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-2.7617330706844485"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.eval(\"pe\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-2.9871561644794546"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "emin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "23.597375873270526"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "estart"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "534"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "naccept"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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y3aObHJDfmncdFg8pv9o2erODccWLKmszpVLBE1faFbJvmWu3uHYxbjFwMxnx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I46PJJFkssL/f+N/lsgEA2RviBFcrgdssNDpvw20IuW6M+9/Ogpa5noS5vn0bN2/i8BDzOaKo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iT7DGXPGKzXOD5utMcVXUR7Ymtl3P3op8HoT0c8z8aHAWZxfJBlmbChLHxYrZpg3FteXngA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\ntWGcGckAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.image(zoom=1.6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# close dump file to access it\n",
+ "L.undump(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<video controls><source src=\"movie.mp4\"></video>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L.video(\"movie.mp4\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/python/examples/pylammps/mpi4py/hello.py b/python/examples/pylammps/mpi4py/hello.py
new file mode 100644
index 000000000..887a13f48
--- /dev/null
+++ b/python/examples/pylammps/mpi4py/hello.py
@@ -0,0 +1,4 @@
+from mpi4py import MPI
+
+comm=MPI.COMM_WORLD
+print("Hello from rank %d of %d" % (comm.rank, comm.size))
diff --git a/python/examples/pylammps/mpi4py/in.melt b/python/examples/pylammps/mpi4py/in.melt
new file mode 100644
index 000000000..8431a4344
--- /dev/null
+++ b/python/examples/pylammps/mpi4py/in.melt
@@ -0,0 +1,33 @@
+# 3d Lennard-Jones melt
+
+units lj
+atom_style atomic
+
+lattice fcc 0.8442
+region box block 0 10 0 10 0 10
+create_box 1 box
+create_atoms 1 box
+mass 1 1.0
+
+velocity all create 3.0 87287
+
+pair_style lj/cut 2.5
+pair_coeff 1 1 1.0 1.0 2.5
+
+neighbor 0.3 bin
+neigh_modify every 20 delay 0 check no
+
+fix 1 all nve
+
+#dump id all atom 50 dump.melt
+
+#dump 2 all image 25 image.*.jpg type type &
+# axes yes 0.8 0.02 view 60 -30
+#dump_modify 2 pad 3
+
+#dump 3 all movie 25 movie.mpg type type &
+# axes yes 0.8 0.02 view 60 -30
+#dump_modify 3 pad 3
+
+thermo 50
+run 250
diff --git a/python/examples/pylammps/mpi4py/melt.py b/python/examples/pylammps/mpi4py/melt.py
new file mode 100644
index 000000000..ad9c54c0b
--- /dev/null
+++ b/python/examples/pylammps/mpi4py/melt.py
@@ -0,0 +1,10 @@
+from mpi4py import MPI
+from lammps import PyLammps
+
+L = PyLammps()
+L.file('in.melt')
+
+
+if MPI.COMM_WORLD.rank == 0:
+ pe = L.eval("pe")
+ print("Potential Energy:", pe)
diff --git a/python/examples/ipython/simple.ipynb b/python/examples/pylammps/simple.ipynb
similarity index 100%
rename from python/examples/ipython/simple.ipynb
rename to python/examples/pylammps/simple.ipynb