examples/misc/introduction_lammps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LAMMPS Integration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import openpathsampling as paths\n",
    "import openpathsampling.engines.lammps as eng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will run a test file and load all lines contained in _Lammps_ script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "script = open('simple-lj.lammps','r').read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output the script. In our case a simple 256 atom LJ liquid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 3d Lennard-Jones melt\n",
      "\n",
      "units\t\tlj\n",
      "atom_style\tatomic\n",
      "atom_modify\tmap array\n",
      "\n",
      "lattice\t\tfcc 0.8442\n",
      "region\t\tbox block 0 4 0 4 0 4\n",
      "create_box\t1 box\n",
      "create_atoms\t1 box\n",
      "mass\t\t1 1.0\n",
      "\n",
      "velocity\tall create 1.44 87287 loop geom\n",
      "\n",
      "pair_style\tlj/cut 2.5\n",
      "pair_coeff\t1 1 1.0 1.0 2.5\n",
      "\n",
      "neighbor\t0.3 bin\n",
      "neigh_modify\tdelay 0 every 20 check no\n",
      "\n",
      "fix\t\t1 all nve\n",
      "\n",
      "variable        fx atom fx\n",
      "\n",
      "run 1\n"
     ]
    }
   ],
   "source": [
    "print script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the engine as usual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "engine = eng.Engine(\n",
    "    inputs=script,\n",
    "    options={\n",
    "        'n_steps_per_frame': 10,\n",
    "        'n_frames_max': 5000\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "snap = engine.current_snapshot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  6.71591568e+00   6.71204076e+00   6.69920380e+00]\n",
      " [  8.26140999e-01   8.54909084e-01   1.70677164e-02]\n",
      " [  8.26080058e-01   6.71212319e+00   8.52072533e-01]\n",
      " [  1.60511914e-02   8.59451911e-01   8.34119868e-01]\n",
      " [  1.66978388e+00   6.71273899e+00   6.71721162e+00]\n",
      " [  2.51082874e+00   8.34554020e-01   6.70154349e+00]\n",
      " [  2.52286983e+00   6.70264024e+00   8.20375765e-01]\n",
      " [  1.66309512e+00   8.55269718e-01   8.52390006e-01]\n",
      " [  3.34631859e+00   1.72249075e-02   6.70319084e+00]\n",
      " [  4.20855665e+00   8.60395722e-01   3.46487244e-03]] nm\n"
     ]
    }
   ],
   "source": [
    "print snap.coordinates[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An then just run it, as you are used to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.83 s, sys: 120 ms, total: 2.95 s\n",
      "Wall time: 2.96 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "traj = engine.generate(engine.current_snapshot, [paths.LengthEnsemble(1000).can_append])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some simple plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    " 999 x      1 step(s) per frame    1.36 s\n",
    " 999 x     10 step(s) per frame    2.68 s\n",
    " 999 x    100 step(s) per frame   18.30 s\n",
    " 999 x   1000 step(s) per frame  166.00 s \n",
    "   1 x 100000 step(s) per frame   16.40 s \n",
    "   1 x  10000 step(s) per frame    1.73 s\n",
    "  99 x    100 step(s) per frame    1.79 s\n",
    "  99 x   1000 step(s) per frame   16.50 s\n",
    "   9 x    100 step(s) per frame    0.17 s\n",
    "```\n",
    "\n",
    "Assume time it takes is $c_1 \\cdot a + c_2 \\cdot a \\cdot b  = time$ would lead to\n",
    "```\n",
    "c2 =  163 us  # time per lammps step\n",
    "c1 = 1345 us  # time per ops step\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the time development of the potential energy of a trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trajectory[1000]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([sn.coordinates[0][1]._value for sn in traj])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17ef6ecd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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VvE1LgiAIsUmED1RDt6OE80DV6puDUY3VkBcXBEGIAaIcxGSe88XRx2B8uMNDbRbHDuSq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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ef6e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it. Go from there... Whohoo..."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}