{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trajectory tests\n",
    "\n",
    "Compare speed of processing LAMMPS trajectories using different modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyscal.core as pc\n",
    "from pyscal.core import Trajectory\n",
    "import pyscal.traj_process as ptp\n",
    "import dask.bag as db\n",
    "from ase.io import read\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Approach 1 - `pyscal.traj_process`\n",
    "\n",
    "Use pyscal `traj_process` to split the file. Reading is done in a second step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.37 ms ± 397 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "files = ptp.split_trajectory(\"traj.light\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advantages\n",
    "- Quite fast\n",
    "- Does not read the complete file into memory\n",
    "\n",
    "### Disadvantages\n",
    "- Creates a number of small files into which the larger file is written\n",
    "- Affects the hard disk space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 2 - `dask.bag`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the file in dask is a two step approach. First a lazy iterator is created, then the data is computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag = db.read_text(\"traj.light\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "474 ms ± 44 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "x = bag.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advantages\n",
    "- None\n",
    "\n",
    "### Disadvantages\n",
    "- Super slow\n",
    "- Reads the whole file at the same time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 3 - `ase.io.read`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39 ms ± 953 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "d = read(\"traj.light\", index=\":\", format=\"lammps-dump-text\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advantages\n",
    "- No temporary files created\n",
    "- Reasonably fast\n",
    "\n",
    "### Disadvantages\n",
    "- Reads the whole file at the same time\n",
    "- Cannot write dump files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 4 - `Trajectory` class from pyscal\n",
    "\n",
    "The `Trajectory` class creates a object which gets basic information about the file without reading in the file at all. The reading is only done as when required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj = Trajectory(\"traj.light\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trajectory module supports indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sl = traj[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.62 ms ± 45.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sl.to_file(\"dump\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advantages\n",
    "- Fastest approach\n",
    "- At most only one snapshot is kept in memory\n",
    "- No temporary files\n",
    "\n",
    "### Disadvantages\n",
    "- Only supports lammps files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time (ms)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = [3.37, 474, 39, 2.62]\n",
    "names = [\"traj_process\", \"dask\", \"ase\", \"Trajectory\"]\n",
    "plt.bar(range(len(times)), times, log=True, tick_label=names)\n",
    "plt.ylabel(\"Time (ms)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test for a larger file. We will exclude `dask` as it is pretty slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "345 ms ± 9.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "files = ptp.split_trajectory(\"traj.3.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.3 s ± 59.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "d = read(\"traj.3.dat\", index=\":\", format=\"lammps-dump-text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj = Trajectory(\"traj.3.dat\")\n",
    "sl = traj[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "374 ms ± 2.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sl.to_file(\"dump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time (ms)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bkpPpFsoA66rqz1r7b9FtMNsLuL2qjm3bMd7WXjPA1qp6TpLXAacA9wLfplv4nwj8DfCT1v7yqrq2X++ZNCpJng+sqKrfH/J8zwGur6r3DHO+c2EgDEH7NX878ItVde8Mzy+lW29/6JBLk+a1JCvoeg4vrarPDnG+G+lW9R5bVfcMa75z5Ubl4bgBePdMYSBpcKpqDd1q0WHP9wnDnmc/2EMYoiT70+2Tvr1n9LhrqiQNjIEgSQLcy0iS1BgIkiTAQJAkNQaCJAmA/wM0zlmXyNUfRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = [345, 5300, 374]\n",
    "names = [\"traj_process\", \"ase\", \"Trajectory\"]\n",
    "plt.bar(range(len(times)), times, log=True, tick_label=names)\n",
    "plt.ylabel(\"Time (ms)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trajectory performs quite well even for the larger file. Main advantage - it does not write until absolutely necessary! "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lammps",
   "language": "python",
   "name": "lammps"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}