{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36ccd9a2-835c-4d42-9ea9-5db1d559c882",
   "metadata": {},
   "source": [
    "# Load and Save File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d200bdf7-9ef7-423e-82b2-0e405f9cbe1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mdapy as mp\n",
    "import numpy as np\n",
    "from time import time\n",
    "mp.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3d6240b-a917-4cec-aa40-6c7f9b74579f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 14, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ovito\n",
    "ovito.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abc2c16a-9a9a-4b71-8172-98b533112dc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atom number is 4000000.\n",
      "Saving to test.data.\n",
      "Saving to test.dump\n",
      "Saving to test.xyz\n",
      "Saving to test.mp\n"
     ]
    }
   ],
   "source": [
    "def init():\n",
    "    system = mp.build_crystal('Cu', 'FCC', 3.615, nx=100, ny=100, nz=100)\n",
    "    N = system.N\n",
    "    print(f'Atom number is {N}.')\n",
    "    print('Saving to test.data.')\n",
    "    system.write_data('test.data', element_list=['Cu'])\n",
    "    print('Saving to test.dump')\n",
    "    system.write_dump('test.dump')\n",
    "    print('Saving to test.xyz')\n",
    "    system.write_xyz('test.xyz')\n",
    "    print('Saving to test.mp')\n",
    "    system.write_mp('test.mp')\n",
    "init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "23455935-e385-4238-b6a8-66d632314aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_load_time(fmt:str):\n",
    "    start = time()\n",
    "    for _ in range(3):\n",
    "        _ = mp.System(f'test.{fmt}')\n",
    "    end = time()\n",
    "    mdapy_time = (end - start)/3\n",
    "    if fmt == 'mp':\n",
    "        ovito_time = -1\n",
    "    else:\n",
    "        start = time()\n",
    "        for _ in range(3):\n",
    "            pipeline = ovito.io.import_file(f'test.{fmt}')\n",
    "            pipeline.compute()\n",
    "            del pipeline \n",
    "        end = time()\n",
    "        ovito_time = (end - start)/3\n",
    "    return mdapy_time, ovito_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5d8d528-d6cf-4c09-8236-ed38b98350d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_save_time(fmt:str):\n",
    "    system = mp.System(f'test.{fmt}')\n",
    "    start = time()\n",
    "    for _ in range(1):\n",
    "        if fmt == 'dump':\n",
    "            system.write_dump(f'mdapy.{fmt}')\n",
    "        elif fmt == 'data':\n",
    "            system.write_data(f'mdapy.{fmt}')\n",
    "        elif fmt == 'xyz':\n",
    "            system.write_xyz(f'mdapy.{fmt}')\n",
    "        elif fmt == 'mp':\n",
    "            system.write_mp(f'mdapy.{fmt}')\n",
    "    end = time()\n",
    "    mdapy_time = (end - start)/3\n",
    "    if fmt == 'mp':\n",
    "        ovito_time = -1\n",
    "    else:\n",
    "        pipeline = ovito.io.import_file(f'test.{fmt}')\n",
    "        start = time()\n",
    "        for _ in range(1):\n",
    "            if fmt == 'dump':\n",
    "                ovito.io.export_file(pipeline, f'ovito.{fmt}', format='lammps/dump', columns=[\"Particle Identifier\", \"Particle Type\", \"Position.X\", \"Position.Y\", \"Position.Z\"])\n",
    "            elif fmt == 'data':\n",
    "                ovito.io.export_file(pipeline, f'ovito.{fmt}', format='lammps/data', atom_style='atomic')\n",
    "            elif fmt == 'xyz':\n",
    "                ovito.io.export_file(pipeline, f'ovito.{fmt}', format='xyz', columns=[\"Particle Identifier\", \"Particle Type\", \"Position.X\", \"Position.Y\", \"Position.Z\"])\n",
    "        end = time()\n",
    "        ovito_time = (end - start)/3\n",
    "    return mdapy_time, ovito_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad48bef7-0257-4860-8df3-7dca81f009d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_time = []\n",
    "for fmt in ['dump', 'data', 'xyz', 'mp']:\n",
    "    load_time.append([*get_load_time(fmt)])\n",
    "load_time = np.array(load_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "429ff710-d3ee-4bf7-b88c-79fe2514ef97",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_time = []\n",
    "for fmt in ['dump', 'data', 'xyz', 'mp']:\n",
    "    save_time.append([*get_save_time(fmt)])\n",
    "save_time = np.array(save_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "254f0ead-5d92-4c44-9a86-d0a7809d314d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Load data')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 501.969x413.386 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mp.set_figure(figdpi=150)\n",
    "x = np.arange(4)*2\n",
    "ax.bar(x, load_time[:, 0], label = 'mdapy')\n",
    "ax.bar(x[:-1]+1, load_time[:-1, 1], label='ovito')\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xticks(x+0.5)\n",
    "ax.set_xticklabels(['dump', 'data', 'xyz', 'mp'])\n",
    "ax.set_ylabel('Time (s)')\n",
    "ax.set_title('Load data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2220f078-08f1-45d9-b8d1-b65fe4813cbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Save data')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 501.969x413.386 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mp.set_figure(figdpi=150)\n",
    "x = np.arange(4)*2\n",
    "ax.bar(x, save_time[:, 0], label = 'mdapy')\n",
    "ax.bar(x[:-1]+1, save_time[:-1, 1], label='ovito')\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xticks(x+0.5)\n",
    "ax.set_xticklabels(['dump', 'data', 'xyz', 'mp'])\n",
    "ax.set_ylabel('Time (s)')\n",
    "ax.set_title('Save data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e86ea3b-9dd6-4a90-b12f-e395b80b76a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "from glob import glob \n",
    "\n",
    "for i in glob('mdapy*')+glob('ovito*')+glob('test*'):\n",
    "    os.remove(i)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ovi",
   "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}