{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Split oxygen-vacancy defects in Co\n",
    "We want to work out the symmetry analysis for our split oxygen-vacancy (V-O-V) defects $\\alpha$-Co (HCP) and $\\beta$-Co (FCC).\n",
    "\n",
    "The split defects can be represented simply as crowdion interstitial sites, for the purposes of symmetry analysis. We're interested in extracting the tensor expansions around those sites, and (eventually) computing the damping coefficients from the DFT data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.extend(['../'])\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "%matplotlib inline\n",
    "import onsager.crystal as crystal\n",
    "import onsager.OnsagerCalc as onsager\n",
    "from scipy.constants import physical_constants\n",
    "kB = physical_constants['Boltzmann constant in eV/K'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Lattice:\n",
      "  a1 = [ 0.   0.5  0.5]\n",
      "  a2 = [ 0.5  0.   0.5]\n",
      "  a3 = [ 0.5  0.5  0. ]\n",
      "#Basis:\n",
      "  (Co) 0.0 = [ 0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "betaCo = crystal.Crystal.FCC(1.0, 'Co')\n",
    "print(betaCo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0. ,  0. ,  0.5]),\n",
       " array([ 0.5,  0. ,  0.5]),\n",
       " array([ 0.5,  0. ,  0. ]),\n",
       " array([ 0. ,  0.5,  0. ]),\n",
       " array([ 0.5,  0.5,  0. ]),\n",
       " array([ 0. ,  0.5,  0.5])]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "betaCo.Wyckoffpos(np.array([0.5,0.,0.]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Lattice:\n",
      "  a1 = [ 0.   0.5  0.5]\n",
      "  a2 = [ 0.5  0.   0.5]\n",
      "  a3 = [ 0.5  0.5  0. ]\n",
      "#Basis:\n",
      "  (Co) 0.0 = [ 0.  0.  0.]\n",
      "  (O) 1.0 = [ 0.   0.   0.5]\n",
      "  (O) 1.1 = [ 0.5  0.   0.5]\n",
      "  (O) 1.2 = [ 0.5  0.   0. ]\n",
      "  (O) 1.3 = [ 0.   0.5  0. ]\n",
      "  (O) 1.4 = [ 0.5  0.5  0. ]\n",
      "  (O) 1.5 = [ 0.   0.5  0.5]\n"
     ]
    }
   ],
   "source": [
    "betaCoO = betaCo.addbasis(betaCo.Wyckoffpos(np.array([0.5,0.,0.])), ['O'])\n",
    "print(betaCoO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Ojumpnetwork = betaCoO.jumpnetwork(1,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Odiffuser = onsager.Interstitial(betaCoO, 1, betaCoO.sitelist(1), Ojumpnetwork)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We need to analyze the geometry of our representative site; we get the position, then find the zero entry in the position vector, and work from there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.7 ,  0.13,  0.  ],\n",
       "       [ 0.13, -2.7 ,  0.  ],\n",
       "       [ 0.  ,  0.  , -4.3 ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ppara, Pperp, Pshear = -2.70, -4.30, 0.13\n",
    "reppos = betaCoO.pos2cart(np.zeros(3), (1, Odiffuser.sitelist[0][0]))\n",
    "perpindex = [n for n in range(3) if np.isclose(reppos[n], 0)][0]\n",
    "paraindex = [n for n in range(3) if n != perpindex]\n",
    "shearsign = 1 if reppos[paraindex[0]]*reppos[paraindex[1]] > 0 else -1\n",
    "Pdipole = np.diag([Pperp if n == perpindex else Ppara for n in range(3)])\n",
    "Pdipole[paraindex[0], paraindex[1]] = shearsign*Pshear\n",
    "Pdipole[paraindex[1], paraindex[0]] = shearsign*Pshear\n",
    "Pdipole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nu0, Emig = 1e13, 0.91\n",
    "nsites, njumps = len(Odiffuser.sitelist), len(Odiffuser.jumpnetwork)\n",
    "betaCoOthermodict = {'pre': np.ones(nsites), 'ene': np.zeros(nsites), \n",
    "                     'preT': nu0*np.ones(nsites), 'eneT': Emig*np.ones(nsites)}\n",
    "beta = 1./(kB*300)  # 300K\n",
    "Llamb = Odiffuser.losstensors(betaCoOthermodict['pre'], beta*betaCoOthermodict['ene'], \n",
    "                              [Pdipole], \n",
    "                              betaCoOthermodict['preT'], beta*betaCoOthermodict['eneT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0619225494951 (0.0, 0.17066666666666686)\n",
      "0.0412816996634 (2.4132664014743868e-32, 0.0033799999999999629)\n"
     ]
    }
   ],
   "source": [
    "for (lamb, Ltens) in Llamb:\n",
    "    print(lamb, crystal.FourthRankIsotropic(Ltens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0198046875\n"
     ]
    }
   ],
   "source": [
    "sh1 = crystal.FourthRankIsotropic(Llamb[0][1])[1]\n",
    "sh2 = crystal.FourthRankIsotropic(Llamb[1][1])[1]\n",
    "print(sh2/sh1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Internal friction resonance. We do loading at a frequency of 1 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nuIF = 1.\n",
    "Trange = np.linspace(250,400,151)\n",
    "shlist = []\n",
    "for T in Trange:\n",
    "    beta = 1./(kB*T)\n",
    "    Llamb = Odiffuser.losstensors(betaCoOthermodict['pre'], beta*betaCoOthermodict['ene'], \n",
    "                                  [Pdipole], \n",
    "                                  betaCoOthermodict['preT'], beta*betaCoOthermodict['eneT'])\n",
    "    f1,L1,f2,L2 = Llamb[0][0], Llamb[0][1], Llamb[1][0], Llamb[1][1]\n",
    "    sh = crystal.FourthRankIsotropic(L1*nuIF*f1/(nuIF**2+f1**2) + \n",
    "                                     L2*nuIF*f2/(nuIF**2+f2**2))[1]\n",
    "    shlist.append(sh*kB*T)\n",
    "shear = np.array(shlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f7mVCBI3D4WD+/PlcffXVxMTEmI5TJdhsNp544gmysrJYsGCB6TgiyDwpCG1x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11351dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(Trange, shear/np.max(shear), 'k')\n",
    "ax1.set_ylabel('loss $Q$ [unitless]', fontsize='x-large')\n",
    "ax1.set_xlabel('$T$ [K]', fontsize='x-large')\n",
    "plt.show()\n",
    "# plt.savefig('FCC-Co-O-loss.pdf', transparent=True, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Temperature where peak maximum is found?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trange[np.argmax(shear)]"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}