LoaderDemo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from src.DumpFileLoader import DumpFileLoader\n",
    "from src.com import convert_to_com\n",
    "from src.diffuse import compute_MSD, compute_diffusion_coefficient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ionic Liquids simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with output files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data into the memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an instance of the loader class and pass to the constructor the filepath of the LAMMPS output file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_filepath(output_dirname, filename):\n",
    "    current_directory = os.getcwd()\n",
    "    return os.path.join(current_directory, output_dirname, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading data...\n",
      "[==================================================] 100.0% Complete\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "output_dirname_il = 'dump_files'\n",
    "filename_il = 'dump.msd.data.lammpstrj'\n",
    "path_il = create_filepath(output_dirname_il,filename_il)\n",
    "dump = DumpFileLoader(path_il)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** For demonstration purposes the output file has been reduced to only a small excerpt from the original output file (about $\\frac{1}{16}$ of original size)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the output file and simulation properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check, what properties are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id', 'mol', 'type', 'xu', 'yu', 'zu']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump.keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a property stored in the output file, for instance x-coordinates of the atoms.\n",
    "\n",
    "The method returns an array, where rows represent consecutive timesteps and columns correspond to particular atoms. \n",
    "\n",
    "Shape of every matrix is equal to (n_timesteps x n_atoms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.34494,  4.64709,  5.00862, ..., 10.7796 , 10.4966 , 12.119  ],\n",
       "       [ 3.72086,  4.94313,  5.00453, ..., 11.6035 , 11.0183 , 12.9521 ],\n",
       "       [ 3.75051,  5.01989,  5.6879 , ..., 11.646  , 11.7183 , 13.5293 ],\n",
       "       ...,\n",
       "       [ 9.26553,  9.53225,  8.4591 , ...,  6.95133,  9.04607,  7.51784],\n",
       "       [ 8.57136,  8.85552,  7.79865, ...,  6.57726,  7.12234,  5.14131],\n",
       "       [ 7.55631,  8.27237,  7.85658, ...,  6.97524,  7.55401,  5.58372]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump.get_property(name='xu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can request a few properties at once, but then they will be assembled into arrays grouped by the timestep.\n",
    "\n",
    "The method returns a dictionary with a timestep-array key-value pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000e+00, 1.0000e+00],\n",
       "       [2.0000e+00, 2.0000e+00],\n",
       "       [3.0000e+00, 1.0000e+00],\n",
       "       ...,\n",
       "       [1.1998e+04, 1.4000e+01],\n",
       "       [1.1999e+04, 1.4000e+01],\n",
       "       [1.2000e+04, 1.4000e+01]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the array with selected properties at 0th timestep. Rows of each array correspond to particular atoms and columns represent specific properties.\n",
    "dump.get_custom_array(property_names=['id','type']).get(0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a convenience method, which returns three spatial coordinates of the atoms at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.34494 , 51.6558  , 14.1168  ],\n",
       "       [ 4.64709 , 51.5362  , 13.8074  ],\n",
       "       [ 5.00862 , 50.3206  , 13.6322  ],\n",
       "       ...,\n",
       "       [10.7796  ,  1.34339 , 22.7141  ],\n",
       "       [10.4966  , -0.585713, 21.9233  ],\n",
       "       [12.119   ,  0.581047, 21.1432  ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns of the array correspond to x,y,z atomic coordinates\n",
    "dump.get_coordinates_array().get(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recognize the molecules populating the studied system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DumpFileLoader class provides a method to recognize molecule types in the system based on the types of atoms, which constitute them. \n",
    "\n",
    "The method will work only if the each molecule type consists of disjoint sets of atomic types, i.e. it won't work if type A and type B molecules both contain a type C atom.\n",
    "\n",
    "The method requires the user to indicate, which types of the atoms correspond to which molecules and (optionally) provide names for these molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bmim/Ntf2 are the (shortened) names of ions present in the system\n",
    "# If the names of the molecules are not provided, the method will automatically name them mol1, mol2, ...\n",
    "dump.recognize_molecules(mols_by_atom_types=[[1,2,3,4,5,6,7,8,9,10,11,12],[13,14,15,16,17]],molnames=['bmim', 'ntf2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method will internally create two objects of a separate class (MoleculeType), representing a molecule. \n",
    "\n",
    "Instances of this class and thus collective properties of these molecules can be accessed with another method from the DumpFileLoader class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get objects representing collections of molecules of particular types\n",
    "bmim_mol = dump.get_molecule_type('bmim')\n",
    "ntf2_mol = dump.get_molecule_type('ntf2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Types of the atoms in \"bmim\" type molecule: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
      "Number of \"ntf2\" molecules in the system: 300\n"
     ]
    }
   ],
   "source": [
    "# Exemplary properties\n",
    "print(f'Types of the atoms in \"bmim\" type molecule: {bmim_mol.atom_types}')\n",
    "print(f'Number of \"ntf2\" molecules in the system: {ntf2_mol.nummols}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Molecular Center-of-Mass (COM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COM of a collection of molecules can be computed with a function called convert_to_com() from \"com\" package.\n",
    "\n",
    "The function takes an instance of DumpFileLoader class as an argument and modifies it in-place, so the new properties becomes available, namely, spatial coordinates of the molecules.\n",
    "\n",
    "To determine the COM of the collection of point particles, the knowledge of the mass of each particle is necessary. The function doesn't require to specify the exact mapping beteween the atom type and the mass, but only if LAMMPS output files contained the \"element\" property. If this is not the case, then the dictionary with types and masses must be provided. In this case, only the second approach is feasible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing COM for molecules of type: bmim\n",
      "[==================================================] 100.0% Complete\n",
      "Computing COM for molecules of type: ntf2\n",
      "[==================================================] 100.0% Complete\n"
     ]
    }
   ],
   "source": [
    "# Conversion with explicit type-to-mass mapping\n",
    "# key: atom type, value: mass\n",
    "TYPE_MASS_MAP = {\n",
    "    1 : 14.007,\n",
    "    2 : 12.011, \n",
    "    3 : 12.011, \n",
    "    4 : 12.011,\n",
    "    5 : 1.008, \n",
    "    6 : 12.011, \n",
    "    7 : 1.008, \n",
    "    8 : 1.008, \n",
    "    9 : 12.011, \n",
    "    10 : 12.011, \n",
    "    11 : 1.008, \n",
    "    12 : 12.001,\n",
    "    13 : 12.001, \n",
    "    14 : 18.998, \n",
    "    15 : 32.066, \n",
    "    16 : 14.000,\n",
    "    17 : 15.999\n",
    "}\n",
    "\n",
    "convert_to_com(dump_obj=dump, type_to_mass_map=TYPE_MASS_MAP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New properties are now available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.73627316,  9.51567366, 49.74607202, ...,  4.51353512,\n",
       "         3.15239237, 34.37975716],\n",
       "       [ 4.794112  ,  9.4775941 , 50.27156235, ...,  5.21096495,\n",
       "         2.82895607, 34.40709327],\n",
       "       [ 5.43377311,  9.39680091, 49.70253523, ...,  5.95625701,\n",
       "         2.54295986, 35.15429271],\n",
       "       ...,\n",
       "       [ 9.12005669, 11.17132652, 49.81019071, ...,  4.44348585,\n",
       "         4.75230141, 32.36748976],\n",
       "       [ 8.5478359 , 10.85251582, 50.04375468, ...,  4.0803734 ,\n",
       "         3.9954628 , 31.98672228],\n",
       "       [ 8.23961716, 10.90107774, 49.19767655, ...,  3.48880359,\n",
       "         3.60053005, 31.9045971 ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x-coordinate of center-of-mass of \"bmim\" type molecules at different timesteps\n",
    "bmim_mol.get_property('COM_x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18.60247009, 35.55398619, 30.67612932, ..., 15.17217033,\n",
       "        48.0819595 , -1.34222687],\n",
       "       [18.06583964, 35.78571356, 30.31571683, ..., 14.61555347,\n",
       "        47.64379476, -1.61553866],\n",
       "       [18.89926567, 35.72912265, 29.79019837, ..., 15.63878333,\n",
       "        47.79759638, -0.8722287 ],\n",
       "       ...,\n",
       "       [16.61636395, 30.36172599, 30.30868843, ..., 17.23400848,\n",
       "        44.39131352,  1.34704694],\n",
       "       [15.21338696, 30.84712675, 31.06287901, ..., 16.31415774,\n",
       "        44.28523817,  1.1949276 ],\n",
       "       [16.00823733, 31.20191614, 30.80599118, ..., 16.80992105,\n",
       "        44.18859033,  1.26480308]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y-coordinate of center-of-mass of \"bmim\" type molecules at different timesteps\n",
    "ntf2_mol.get_property('COM_y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean-Square-Displacement (MSD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean-square-displacement of a collection of molecules can be computed with a function called compute_MSD() from \"diffuse\" package.\n",
    "\n",
    "The function takes an instance of MoleculeType class as an argument.\n",
    "\n",
    "For more effective use of the simulation data, the MSD is computed using half of available simulation time. This allows to use different timesteps as reference points (origins), relative to which the MSD is computed. The separation of succesive time origins can be controlled with origin_step_interval parameter. For example, if  origin_step_interval=10, the MSD will be computed with respect to the molecular coordinates at 0th, 10th, 20th, ... timestep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as_key=True indicates, that the result will be stored as an attribute of the \"bmim\" object\n",
    "compute_MSD(moltype_obj=bmim_mol, origin_step_interval=10, as_key=True)\n",
    "compute_MSD(moltype_obj=ntf2_mol, origin_step_interval=10, as_key=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MSD computed at different timesteps (rows) relative to successive time origins (columns) is now available as a separate property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.62113262, 1.75816815, 1.72128133, 1.68288179],\n",
       "       [2.60381004, 2.37221966, 2.207438  , 2.26664253],\n",
       "       [3.17929162, 2.81605136, 3.10109807, 3.00220839],\n",
       "       [3.35303378, 3.19171788, 3.8366876 , 3.26772588],\n",
       "       [4.14804025, 3.73398913, 3.94287522, 3.69262895]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmim_mol.get_property('MSD')[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MSDs computed with respect to different time origins can be plotted as a function of simulation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_MSD_vs_time(moltype: DumpFileLoader.MoleculeType):\n",
    "    fig, ax = plt.subplots(figsize=(8,4), ncols=2, sharey=True)\n",
    "    fig.suptitle(f'Plot of MSD vs simulation time for {moltype.name} molecule type')\n",
    "\n",
    "    timestep_length = moltype.timesteps[1] / 1000\n",
    "    MSD = moltype.get_property('MSD')\n",
    "    n_origins = MSD.shape[1]\n",
    "    time_interval = np.arange(1, MSD.shape[0] + 1) * timestep_length\n",
    "\n",
    "    for reference_timestep in range(n_origins):\n",
    "        ax[0].plot(time_interval, MSD[:,reference_timestep], label=f'{reference_timestep + 1}')\n",
    "\n",
    "    for axes in ax:\n",
    "        axes.set_xlabel('t (ps)', labelpad=10)\n",
    "        axes.set_ylim([0,14])\n",
    "\n",
    "    ax[0].set_ylabel('MSD $\\mathregular{(\\u00c5^2)}$', labelpad=10)\n",
    "    ax[0].set_title('')\n",
    "    ax[0].legend(title='Time origin')\n",
    "    \n",
    "    ax[1].plot(time_interval, MSD.mean(axis=1), color='tab:purple', label='mean')\n",
    "    ax[1].tick_params(axis='y',length=0)\n",
    "    ax[1].legend(loc='upper left')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.subplots_adjust(wspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_MSD_vs_time(bmim_mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_MSD_vs_time(ntf2_mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Because this data is a fragment of a larger file, the simulation time is quite short (~120 ps). Actual computations of dynamical properties of these compounds (ionic liquids) require much longer timespan (a few nanoseconds)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diffusion coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diffusion coefficient of molecules of particular type can be computed using a function called compute_diffusion_coefficient() from \"diffuse\" package.\n",
    "\n",
    "This function uses the following relation:\n",
    "\n",
    "$D = \\frac{1}{6t} \\lim_{t\\to\\infty} MSD(t)$\n",
    "\n",
    "And utilizes the estimate the slope of the MSD(t) curve to compute the diffusion coefficient. The MSD used for computations is the average with respect to different time origins.\n",
    "\n",
    "The function takes MoleculeType object as an argument and requires that the MSD property is available for given type of a molecule. The output includes the diffusion coefficient, the estimate of the slope of MSD(t) curve and the standard error of the slope. \n",
    "\n",
    "The function allows to specify two additional parameters - start_time and end_time - which in turn allow to determine the part of MSD vs time curve used for computations. necessary as a significant amount of time is needed for the molecules to reach the \"diffusive regime\", where the slope of the curve becomes approximately constant [Alavi,2020].\n",
    "\n",
    "\\\n",
    "Alavi, S. Molecular Simulations, Fundamentals and Practice, **2020**, p. 248-252, Wiley-VCH Verlag GmbH & Co. KGaA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bmim diffusion coefficient: 1.42e-10 m^2/s, slope of MSD(t) curve: 8.50e-05 Angstrom^2/s and std of the slope 1.63e-06 Angstrom^2/s\n"
     ]
    }
   ],
   "source": [
    "# Start_time and end_time are the indices of particular timesteps\n",
    "# Index = (real simulation time / timestep) - 1 -> (40000 fs / 4000) fs = 9\n",
    "D_bmim, D_bmim_slope, D_bmim_std = compute_diffusion_coefficient(bmim_mol)\n",
    "print(\"Bmim diffusion coefficient: {:0.2e} m^2/s, slope of MSD(t) curve: {:0.2e} Angstrom^2/s and std of the slope {:0.2e} Angstrom^2/s\".format(D_bmim,D_bmim_slope,D_bmim_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save your work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DumpFileLoader class comes with two methods, which allow to serialize and deserialize the instances of the class to binary files in order to preserve the their state and prevent the necessity to load large output files again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The object has been saved to: output_saved.pkl\n"
     ]
    }
   ],
   "source": [
    "# Save the state\n",
    "dump.save_dump_state(filename='output_saved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Restore the object\n",
    "dump_restored = DumpFileLoader.load_saved_state('./output_saved.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.62113262, 1.75816815, 1.72128133, 1.68288179],\n",
       "       [2.60381004, 2.37221966, 2.207438  , 2.26664253],\n",
       "       [3.17929162, 2.81605136, 3.10109807, 3.00220839],\n",
       "       [3.35303378, 3.19171788, 3.8366876 , 3.26772588],\n",
       "       [4.14804025, 3.73398913, 3.94287522, 3.69262895]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All previously computed properties are still there\n",
    "bmim_restored = dump_restored.get_molecule_type('bmim')\n",
    "bmim_restored.get_property('MSD')[:5]"
   ]
  }
 ],
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