lammps/python/examples/pylammps/interface_usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2: Using the PyLammps interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "\n",
    "Before running this example, make sure your Python environment can find the LAMMPS shared library (`liblammps.so`) and the LAMMPS Python package is installed. If you followed the [README](README.md) in this folder, this should already be the case. You can also find more information about how to compile LAMMPS and install the LAMMPS Python package in the [LAMMPS manual](https://lammps.sandia.gov/doc/Python_install.html). There is also a dedicated [PyLammps HowTo](https://lammps.sandia.gov/doc/Howto_pylammps.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup system"
   ]
  },
  {
   "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": [
    "# 3d Lennard-Jones melt\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.run(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the initial state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.image(zoom=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Queries about LAMMPS simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.system.natoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.communication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.fixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.computes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.dumps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with LAMMPS Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variable(\"a index 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variable(\"t equal temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "if sys.version_info < (3, 0):\n",
    "    # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n",
    "    x = float(L.lmp_print('\"${a}\"'))\n",
    "else:\n",
    "    # In Python 3 the print function can be redefined.\n",
    "    # x = float(L.print('\"${a}\"')\")\n",
    "    \n",
    "    # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n",
    "    x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables['t'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.eval(\"v_t/2.0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variable(\"b index a b c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables['b'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.eval(\"v_b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables['b'].definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.lmp.command('variable i loop 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variable(\"i loop 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables['i'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.next(\"i\")\n",
    "L.variables['i'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.eval(\"ke\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Atom data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.atoms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir(L.atoms[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.atoms[0].position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.atoms[0].id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.atoms[0].velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.atoms[0].force"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.atoms[0].type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.variables['fx'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing thermo data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.runs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.runs[0].thermo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.runs[0].thermo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir(L.runs[0].thermo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving session to as LAMMPS input file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyLammps can keep track of all LAMMPS commands that are executed. This allows you to prototype a script and then later on save it as a regular input script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = IPyLammps()\n",
    "\n",
    "# enable command history\n",
    "L.enable_cmd_history = True\n",
    "\n",
    "# 3d Lennard-Jones melt\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.run(10)\n",
    "\n",
    "# write LAMMPS input script with all commands executed so far (including implicit ones)\n",
    "L.write_script(\"in.output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat in.output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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