{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling in CNID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, let us verify the computed CNID. \n",
    "\n",
    "We do this by sampling the total energy of a system of Si(111)/SiC(0001) interface with \n",
    "displacing one crystal in the CNID."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import & prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting git+https://github.com/nmdl-mizo/interface_master.git@develop\n",
      "  Cloning https://github.com/nmdl-mizo/interface_master.git (to revision develop) to /tmp/pip-req-build-lq_mn61j\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/nmdl-mizo/interface_master.git /tmp/pip-req-build-lq_mn61j\n",
      "  Resolved https://github.com/nmdl-mizo/interface_master.git to commit 2bdf96ed4835b036557ac18de82b4fa2afdabf24\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  1059  100  1059    0     0   1312      0 --:--:-- --:--:-- --:--:--  1310\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
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      "100  1685  100  1685    0     0   2252      0 --:--:-- --:--:-- --:--:--  2249\n"
     ]
    }
   ],
   "source": [
    "!pip install git+https://github.com/nmdl-mizo/interface_master.git@develop\n",
    "!mkdir -p cif_files\n",
    "!curl https://raw.githubusercontent.com/nmdl-mizo/interface_master/develop/test_files/cif_files/Si_mp-149_conventional_standard.cif -o 'cif_files/Si_mp-149_conventional_standard.cif'\n",
    "!curl https://raw.githubusercontent.com/nmdl-mizo/interface_master/develop/test_files/cif_files/SiC_mp-568656_conventional_standard.cif -o 'cif_files/SiC_mp-568656_conventional_standard.cif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Again, making an two-D CSL interface following the previous tutorials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from interfacemaster.cellcalc import get_primitive_hkl, get_pri_vec_inplane, get_normal_index\n",
    "from interfacemaster.interface_generator import core, convert_vector_index, write_trans_file\n",
    "from numpy import array, dot, round, cross\n",
    "from numpy.linalg import inv, det"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning!, this programme will rewrite the POSCAR file in this dir!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonxie/.local/lib/python3.8/site-packages/pymatgen/io/cif.py:1121: UserWarning: Issues encountered while parsing CIF: Some fractional co-ordinates rounded to ideal values to avoid issues with finite precision.\n",
      "  warnings.warn(\"Issues encountered while parsing CIF: %s\" % \"\\n\".join(self.warnings))\n"
     ]
    }
   ],
   "source": [
    "my_interface = core('cif_files/Si_mp-149_conventional_standard.cif',\\\n",
    "                    'cif_files/SiC_mp-568656_conventional_standard.cif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hkl_1 = get_primitive_hkl([1,1,1], my_interface.conv_lattice_1, my_interface.lattice_1)\n",
    "hkl_2 = get_primitive_hkl([0,0,1], my_interface.conv_lattice_2, my_interface.lattice_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congrates, we found an appx CSL!\n",
      "\n",
      "U1 = \n",
      "[[ 1 -6]\n",
      " [-6  5]\n",
      " [ 5  1]]; sigma_1 = 54\n",
      "\n",
      "U2 = \n",
      "[[-3 -5]\n",
      " [-8  3]\n",
      " [ 0  0]]; sigma_2 = 49\n",
      "\n",
      "D = \n",
      "[[ 0.99590432  0.00730638 -0.0032107 ]\n",
      " [-0.0032107   0.99590432  0.00730638]\n",
      " [ 0.00730638 -0.0032107   0.99590432]]\n",
      "\n",
      "axis = [-7.47674648 -7.47674648 -7.47674648] ; theta = 15.21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "my_interface.parse_limit(du = 2e-2, S  = 2e-2, sgm1=50, sgm2=50, dd = 2e-2)\n",
    "\n",
    "#Do searching!\n",
    "my_interface.search_one_position_2D(hkl_1, hkl_2, theta_range = 180, \\\n",
    "                                    dtheta = 0.01, start = 15.21, integer_tol=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.06122449,  0.10204082],\n",
       "       [-0.16326531,  0.06122449],\n",
       "       [ 0.10204082, -0.16326531]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot(inv(my_interface.lattice_1), my_interface.CNID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell 1:\n",
      "[[-1  1 -6]\n",
      " [-1 -6  5]\n",
      " [-1  5  1]]\n",
      "cell 2:\n",
      "[[ 0 -3 -5]\n",
      " [ 0 -8  3]\n",
      " [-1  0  0]]\n"
     ]
    }
   ],
   "source": [
    "my_interface.compute_bicrystal_two_D(hkl_1 = hkl_1, hkl_2=hkl_2, \\\n",
    "                                     normal_ortho = True,\n",
    "                                     lim = 50, tol_ortho = 1e-2)\n",
    "my_interface.get_bicrystal(two_D = True, xyz_1 = [1,1,1], xyz_2 = [1,1,1], filetype = 'LAMMPS', filename = 'atominfile', dp1 = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate LAMMPS commands to sampling system energy in the CNID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interface structure can be complex, especially when we can have a 'triclinic' cell.\n",
    "\n",
    "Therefore, we provide some functions here to help you write some necessary input commands for LAMMPS.\n",
    "\n",
    "Note that it is far more effective to do looping by LAMMPS's looping variables than by generating many different atom files!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Define regions & groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_interface.define_lammps_regions(['right'],[11],['EDGE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that here we generated a 'blockfile' file defining a\n",
    "'right' group, which will be displaced during the simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Define translation variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify the periodicity of CNID, we translate in a 2x2 expansion of CNID divided into a fine 50x50 grids \n",
    "(dont worry, this won't take a long time :D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1, v2 = 2*dot(my_interface.orient, my_interface.CNID).T\n",
    "write_trans_file(v1,v2,50,50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we generated a 'paras' file, including the looping variables and \n",
    "the translation variables which will be applied in the 'displace_atoms' command.\n",
    "\n",
    "Now, put these two files in the 'CNID_check' folder and run LAMMPS in this folder!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running LAMMPS, a 'results' file is generated with the energy map by RBT. Now we just show the results here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import loadtxt\n",
    "dx, dy, dz, energy = loadtxt('LAMMPS_input_of_CNID_cheking/results', unpack = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb991429d60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dy, dz, c = energy, cmap = 'hot')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 2*2 periodicity is confirmed"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}