lammps/python/examples/pylammps/simple.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using LAMMPS with iPython and Jupyter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n",
"2. Compile LAMMPS as a shared library and enable exceptions and PNG support\n",
" ```bash\n",
" cd $LAMMPS_DIR/src\n",
" python Make.py -m mpi -png -s exceptions -a file\n",
" make mode=shlib auto\n",
" ```\n",
"\n",
"3. Create a python virtualenv\n",
" ```bash\n",
" virtualenv testing\n",
" source testing/bin/activate\n",
" ```\n",
"\n",
"4. Inside the virtualenv install the lammps package\n",
" ```\n",
" (testing) cd $LAMMPS_DIR/python\n",
" (testing) python install.py\n",
" (testing) cd # move to your working directory\n",
" ```\n",
"\n",
"5. Install jupyter and ipython in the virtualenv\n",
" ```bash\n",
" (testing) pip install ipython jupyter\n",
" ```\n",
"\n",
"6. Run jupyter notebook\n",
" ```bash\n",
" (testing) jupyter notebook\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from lammps import IPyLammps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L = IPyLammps()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"# 3d Lennard-Jones melt\n",
"\n",
"L.units(\"lj\")\n",
"L.atom_style(\"atomic\")\n",
"L.atom_modify(\"map array\")\n",
"\n",
"L.lattice(\"fcc\", 0.8442)\n",
"L.region(\"box\", \"block\", 0, 4, 0, 4, 0, 4)\n",
"L.create_box(1, \"box\")\n",
"L.create_atoms(1, \"box\")\n",
"L.mass(1, 1.0)\n",
"\n",
"L.velocity(\"all\", \"create\", 1.44, 87287, \"loop geom\")\n",
"\n",
"L.pair_style(\"lj/cut\", 2.5)\n",
"L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
"\n",
"L.neighbor(0.3, \"bin\")\n",
"L.neigh_modify(\"delay\", 0, \"every\", 20, \"check no\")\n",
"\n",
"L.fix(\"1 all nve\")\n",
"\n",
"L.variable(\"fx atom fx\")\n",
"\n",
"L.info(\"all\")\n",
"\n",
"L.run(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"L.image(zoom=1.0)"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 0
}