{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Storing the output data\n",
    "\n",
    "**Running the code in this notebook (under Mott-Schottky conditions with real data) takes approximately 2 minutes (iMac with 4 Ghz i7 processor).**\n",
    "\n",
    "The suggested way to store the calculation output is to use a ```pd.DataFrame```. Example storage is shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyscses.defect_species import DefectSpecies\n",
    "from pyscses.set_of_sites import SetOfSites\n",
    "from pyscses.constants import boltzmann_eV\n",
    "from pyscses.calculation import Calculation, calculate_activation_energies\n",
    "from pyscses.set_up_calculation import calculate_grid_offsets\n",
    "from pyscses.grid import Grid\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boundary_conditions = 'periodic'\n",
    "site_charge = False\n",
    "system = 'mott-schottky'\n",
    "core_model = False\n",
    "site_model = 'site_explicit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.0005\n",
    "conv = 1e-8\n",
    "grid_x_min = -6.094e-9\n",
    "grid_x_max = 5.16e-9\n",
    "bulk_x_min = -5.783e-9\n",
    "bulk_x_max = -2.502e-9\n",
    "\n",
    "dielectric = 55\n",
    "\n",
    "index = 111\n",
    "b = 7.65327e-10\n",
    "c = 7.65327e-10\n",
    "\n",
    "temp = [673, 773, 873, 973, 1000, 1173, 1273, 1373]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "valence = [ +2.0, -1.0 ]\n",
    "site_labels = [ 'O', 'Ce' ]\n",
    "defect_labels = ['Vo', 'Gd']\n",
    "mole_fractions = np.array([ [ 0.05, 0.2 ] ])\n",
    "initial_guess = [ [ 0.05, 0.2 ] ]\n",
    "mobilities = [ 1.0, 0.0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = '../input_data/Gd_CeO2_111_data.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empty DataFrames are created and appended to at the end of each calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = { 'core_model':str, \n",
    "          'site_model':str, \n",
    "          'MS_GC':str, \n",
    "          'site_charge':bool, \n",
    "          'resistivity_scaling':bool, \n",
    "          'temperature':str, \n",
    "          'input_mole_fraction':str,\n",
    "          'x':np.float64,\n",
    "          'phi':np.float64,\n",
    "          'rho':np.float64,\n",
    "          'Vo_mole_fraction':np.float64,\n",
    "          'Gd_mole_fraction':np.float64}\n",
    "\n",
    "labels=[ 'core_model', 'site_model', 'MS_GC', 'site_charge', 'temperature', 'input_mole_fractions', 'x', 'phi', 'rho', 'Vo_mole_fraction', 'Gd_mole_fraction']\n",
    "all_data = pd.DataFrame( columns=labels )\n",
    "filename = 'theory_paper_data.csv'\n",
    "all_data.to_csv(filename, index=False)\n",
    "\n",
    "labels_1 = ['core_model', 'site_model', 'MS_GC', 'site_charge', 'temperature', 'mole_fraction', 'space_charge_potential', 'parallel_resistivity_ratio', 'perpendicular_resistivity_ratio', 'MS_space_charge_potential', 'activation_energy' ]\n",
    "key_data = pd.DataFrame( columns=labels_1 )\n",
    "filename_1 = 'space_charge_properties_{}.csv'.format(index)\n",
    "key_data.to_csv(filename_1, index = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "limits, laplacian_limits = calculate_grid_offsets( data, grid_x_min, grid_x_max, 'single' )\n",
    "\n",
    "for m in mole_fractions:\n",
    "    \n",
    "    parallel_resistivity_ratios = []\n",
    "    perpendicular_resisitivity_ratios = []\n",
    "    temperatures = []\n",
    "    mole_fraction_list = []\n",
    "    space_charge_potentials =[]\n",
    "    ms_space_charge_potentials = []\n",
    "    c_m = []\n",
    "    s_m = []\n",
    "    t_m = []\n",
    "    s_c = []\n",
    "\n",
    "    key_data_read_in = pd.read_csv(filename_1)\n",
    "    for t in temp:\n",
    "    \n",
    "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
    "\n",
    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charge, core_model, t )\n",
    "        if site_model == 'continuum':\n",
    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
    "        if system == 'mott-schottky':\n",
    "            for site in all_sites.subset( 'Ce' ):\n",
    "                site.defect_with_label('Gd').fixed = True\n",
    "        if system == 'gouy-chapman':\n",
    "            for site in all_sites.subset( 'Ce' ):\n",
    "                site.defect_with_label('Gd').fixed = False\n",
    "        grid = Grid.grid_from_set_of_sites( all_sites, limits, laplacian_limits, b, c )\n",
    "        \n",
    "        all_data = pd.DataFrame( columns=labels )\n",
    "        all_data_read_in = pd.read_csv(filename, dtype=dtype)\n",
    "        \n",
    "        c_o = Calculation( grid, bulk_x_min, bulk_x_max, alpha, conv, dielectric, t, boundary_conditions )\n",
    "        c_o.form_subgrids( site_labels )\n",
    "        if system == 'gouy-chapman':\n",
    "            c_o.mole_fraction_correction( m, system, initial_guess )\n",
    "        c_o.solve(system)\n",
    "        c_o.mole_fractions()\n",
    "        c_o.calculate_resistivity_ratio( 'positive', 2e-2 )\n",
    "        c_o.solve_MS_approx_for_phi( valence[0] )\n",
    "        \n",
    "        all_data['x'] = grid.x\n",
    "        all_data['phi'] = c_o.phi\n",
    "        all_data['rho'] = c_o.rho\n",
    "        all_data['Vo_mole_fraction'] = c_o.mf[site_labels[0]]\n",
    "        all_data['Gd_mole_fraction'] = c_o.mf[site_labels[1]]\n",
    "        all_data['core_model'] = str(core_model)\n",
    "        all_data['site_model'] = str(site_model)\n",
    "        all_data['MS_GC'] = str(system)\n",
    "        all_data['site_charge'] = str(site_charge)\n",
    "        all_data['temperature'] = t\n",
    "        all_data['input_mole_fractions'] = str(m)\n",
    "        all_data_appended = all_data_read_in.append(all_data, ignore_index=True)\n",
    "        all_data_appended.to_csv(filename, index=False)\n",
    "        \n",
    "        temperatures.append(t)\n",
    "        mole_fraction_list.append(str(m))\n",
    "        if site_charge == True:\n",
    "            space_charge_potentials.append( (max(c_o.phi) - c_o.phi[0] ) )\n",
    "        else:\n",
    "            space_charge_potentials.append(max(c_o.phi))\n",
    "        parallel_resistivity_ratios.append(c_o.parallel_resistivity_ratio)\n",
    "        perpendicular_resisitivity_ratios.append(c_o.perpendicular_resistivity_ratio)\n",
    "        ms_space_charge_potentials.append(c_o.ms_phi)\n",
    "        c_m.append(str(core_model))\n",
    "        s_m.append(str(site_model))\n",
    "        t_m.append(str(system))\n",
    "        s_c.append(str(site_charge))\n",
    "        \n",
    "    activation_energies = calculate_activation_energies( perpendicular_resisitivity_ratios, temp)\n",
    "    key_data_array = np.column_stack(( temperatures[1:-1], mole_fraction_list[1:-1], space_charge_potentials[1:-1], parallel_resistivity_ratios[1:-1], perpendicular_resisitivity_ratios[1:-1], ms_space_charge_potentials[1:-1], activation_energies[1:-1], c_m[1:-1], s_m[1:-1], t_m[1:-1], s_c[1:-1] ))\n",
    "                \n",
    "    key_data =  pd.DataFrame( key_data_array, columns=labels_1 )\n",
    "    \n",
    "    key_data_appended = key_data_read_in.append( key_data, ignore_index=True )\n",
    "    key_data_appended.to_csv( filename_1, index=False)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1165a6f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116673cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168ebc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_data\n",
      "\n",
      "  core_model   site_model     MS_GC site_charge temperature  \\\n",
      "0        773  [0.05 0.2 ]   0.33517  0.00963335      221569   \n",
      "1        873  [0.05 0.2 ]   0.32801  0.00966261     61133.1   \n",
      "2        973  [0.05 0.2 ]  0.321155  0.00968779     22115.6   \n",
      "3       1000  [0.05 0.2 ]  0.319353  0.00969412     17424.4   \n",
      "4       1173  [0.05 0.2 ]  0.308277  0.00837419     5550.74   \n",
      "5       1273  [0.05 0.2 ]  0.302223  0.00837756     3187.59   \n",
      "\n",
      "       mole_fraction space_charge_potential parallel_resistivity_ratio  \\\n",
      "0  0.524870661512625                0.75023                      False   \n",
      "1  0.540890287355384               0.746274                      False   \n",
      "2  0.556897814968529               0.741121                      False   \n",
      "3  0.561233609511401               0.728981                      False   \n",
      "4  0.595437038124449               0.699566                      False   \n",
      "5  0.612871542996122               0.704616                      False   \n",
      "\n",
      "  perpendicular_resistivity_ratio MS_space_charge_potential activation_energy  \n",
      "0                   site_explicit             mott-schottky             False  \n",
      "1                   site_explicit             mott-schottky             False  \n",
      "2                   site_explicit             mott-schottky             False  \n",
      "3                   site_explicit             mott-schottky             False  \n",
      "4                   site_explicit             mott-schottky             False  \n",
      "5                   site_explicit             mott-schottky             False  \n",
      "all_data\n",
      "\n",
      "    core_model     site_model          MS_GC site_charge temperature  \\\n",
      "0        False  site_explicit  mott-schottky       False         673   \n",
      "1        False  site_explicit  mott-schottky       False         673   \n",
      "2        False  site_explicit  mott-schottky       False         673   \n",
      "3        False  site_explicit  mott-schottky       False         673   \n",
      "4        False  site_explicit  mott-schottky       False         673   \n",
      "5        False  site_explicit  mott-schottky       False         673   \n",
      "6        False  site_explicit  mott-schottky       False         673   \n",
      "7        False  site_explicit  mott-schottky       False         673   \n",
      "8        False  site_explicit  mott-schottky       False         673   \n",
      "9        False  site_explicit  mott-schottky       False         673   \n",
      "10       False  site_explicit  mott-schottky       False         673   \n",
      "11       False  site_explicit  mott-schottky       False         673   \n",
      "12       False  site_explicit  mott-schottky       False         673   \n",
      "13       False  site_explicit  mott-schottky       False         673   \n",
      "14       False  site_explicit  mott-schottky       False         673   \n",
      "15       False  site_explicit  mott-schottky       False         673   \n",
      "16       False  site_explicit  mott-schottky       False         673   \n",
      "17       False  site_explicit  mott-schottky       False         673   \n",
      "18       False  site_explicit  mott-schottky       False         673   \n",
      "19       False  site_explicit  mott-schottky       False         673   \n",
      "20       False  site_explicit  mott-schottky       False         673   \n",
      "21       False  site_explicit  mott-schottky       False         673   \n",
      "22       False  site_explicit  mott-schottky       False         673   \n",
      "23       False  site_explicit  mott-schottky       False         673   \n",
      "24       False  site_explicit  mott-schottky       False         673   \n",
      "25       False  site_explicit  mott-schottky       False         673   \n",
      "26       False  site_explicit  mott-schottky       False         673   \n",
      "27       False  site_explicit  mott-schottky       False         673   \n",
      "28       False  site_explicit  mott-schottky       False         673   \n",
      "29       False  site_explicit  mott-schottky       False         673   \n",
      "..         ...            ...            ...         ...         ...   \n",
      "834      False  site_explicit  mott-schottky       False        1373   \n",
      "835      False  site_explicit  mott-schottky       False        1373   \n",
      "836      False  site_explicit  mott-schottky       False        1373   \n",
      "837      False  site_explicit  mott-schottky       False        1373   \n",
      "838      False  site_explicit  mott-schottky       False        1373   \n",
      "839      False  site_explicit  mott-schottky       False        1373   \n",
      "840      False  site_explicit  mott-schottky       False        1373   \n",
      "841      False  site_explicit  mott-schottky       False        1373   \n",
      "842      False  site_explicit  mott-schottky       False        1373   \n",
      "843      False  site_explicit  mott-schottky       False        1373   \n",
      "844      False  site_explicit  mott-schottky       False        1373   \n",
      "845      False  site_explicit  mott-schottky       False        1373   \n",
      "846      False  site_explicit  mott-schottky       False        1373   \n",
      "847      False  site_explicit  mott-schottky       False        1373   \n",
      "848      False  site_explicit  mott-schottky       False        1373   \n",
      "849      False  site_explicit  mott-schottky       False        1373   \n",
      "850      False  site_explicit  mott-schottky       False        1373   \n",
      "851      False  site_explicit  mott-schottky       False        1373   \n",
      "852      False  site_explicit  mott-schottky       False        1373   \n",
      "853      False  site_explicit  mott-schottky       False        1373   \n",
      "854      False  site_explicit  mott-schottky       False        1373   \n",
      "855      False  site_explicit  mott-schottky       False        1373   \n",
      "856      False  site_explicit  mott-schottky       False        1373   \n",
      "857      False  site_explicit  mott-schottky       False        1373   \n",
      "858      False  site_explicit  mott-schottky       False        1373   \n",
      "859      False  site_explicit  mott-schottky       False        1373   \n",
      "860      False  site_explicit  mott-schottky       False        1373   \n",
      "861      False  site_explicit  mott-schottky       False        1373   \n",
      "862      False  site_explicit  mott-schottky       False        1373   \n",
      "863      False  site_explicit  mott-schottky       False        1373   \n",
      "\n",
      "    input_mole_fractions             x           phi           rho  \\\n",
      "0            [0.05 0.2 ] -5.938769e-09  7.533900e-04  8.766165e+08   \n",
      "1            [0.05 0.2 ] -5.860658e-09 -1.667642e-02 -2.801518e+09   \n",
      "2            [0.05 0.2 ] -5.782546e-09  3.251067e-04  8.887471e+08   \n",
      "3            [0.05 0.2 ] -5.626324e-09  1.610345e-04  8.931088e+08   \n",
      "4            [0.05 0.2 ] -5.548213e-09 -1.708674e-02 -2.801518e+09   \n",
      "5            [0.05 0.2 ] -5.470102e-09  9.683374e-05  8.948466e+08   \n",
      "6            [0.05 0.2 ] -5.313880e-09  6.705316e-05  8.954279e+08   \n",
      "7            [0.05 0.2 ] -5.235769e-09 -1.715722e-02 -2.801518e+09   \n",
      "8            [0.05 0.2 ] -5.157658e-09  4.985988e-05  8.956394e+08   \n",
      "9            [0.05 0.2 ] -5.001436e-09  3.733608e-05  8.957137e+08   \n",
      "10           [0.05 0.2 ] -4.923325e-09 -1.718365e-02 -2.801518e+09   \n",
      "11           [0.05 0.2 ] -4.845214e-09  2.670102e-05  8.957323e+08   \n",
      "12           [0.05 0.2 ] -4.688992e-09  1.725049e-05  8.957162e+08   \n",
      "13           [0.05 0.2 ] -4.610881e-09 -1.720226e-02 -2.801518e+09   \n",
      "14           [0.05 0.2 ] -4.532770e-09  9.584396e-06  8.957946e+08   \n",
      "15           [0.05 0.2 ] -4.376548e-09  7.598021e-07  8.957600e+08   \n",
      "16           [0.05 0.2 ] -4.298436e-09 -1.721926e-02 -2.801518e+09   \n",
      "17           [0.05 0.2 ] -4.220325e-09 -7.926324e-06  8.957214e+08   \n",
      "18           [0.05 0.2 ] -4.064103e-09 -1.502395e-05  8.957831e+08   \n",
      "19           [0.05 0.2 ] -3.985992e-09 -1.723461e-02 -2.801518e+09   \n",
      "20           [0.05 0.2 ] -3.907881e-09 -2.284490e-05  8.957190e+08   \n",
      "21           [0.05 0.2 ] -3.751659e-09 -2.898746e-05  8.957526e+08   \n",
      "22           [0.05 0.2 ] -3.673548e-09 -1.724752e-02 -2.801518e+09   \n",
      "23           [0.05 0.2 ] -3.595437e-09 -3.471356e-05  8.957739e+08   \n",
      "24           [0.05 0.2 ] -3.439215e-09 -4.082882e-05  8.958067e+08   \n",
      "25           [0.05 0.2 ] -3.361104e-09 -1.726037e-02 -2.801518e+09   \n",
      "26           [0.05 0.2 ] -3.282993e-09 -4.856371e-05  8.957401e+08   \n",
      "27           [0.05 0.2 ] -3.126771e-09 -5.541646e-05  8.957946e+08   \n",
      "28           [0.05 0.2 ] -3.048660e-09 -1.727509e-02 -2.801518e+09   \n",
      "29           [0.05 0.2 ] -2.970549e-09 -6.341818e-05  8.957358e+08   \n",
      "..                   ...           ...           ...           ...   \n",
      "834          [0.05 0.2 ]  2.187091e-09  2.432909e-03  9.144405e+08   \n",
      "835          [0.05 0.2 ]  2.265203e-09 -1.487427e-02 -2.801518e+09   \n",
      "836          [0.05 0.2 ]  2.343314e-09  2.417243e-03  9.148179e+08   \n",
      "837          [0.05 0.2 ]  2.499536e-09  2.414706e-03  9.150023e+08   \n",
      "838          [0.05 0.2 ]  2.577647e-09 -1.488284e-02 -2.801518e+09   \n",
      "839          [0.05 0.2 ]  2.655758e-09  2.418292e-03  9.150966e+08   \n",
      "840          [0.05 0.2 ]  2.811980e-09  2.424434e-03  9.151534e+08   \n",
      "841          [0.05 0.2 ]  2.890091e-09 -1.487162e-02 -2.801518e+09   \n",
      "842          [0.05 0.2 ]  2.968202e-09  2.431012e-03  9.151302e+08   \n",
      "843          [0.05 0.2 ]  3.124424e-09  2.438915e-03  9.151610e+08   \n",
      "844          [0.05 0.2 ]  3.202535e-09 -1.485638e-02 -2.801518e+09   \n",
      "845          [0.05 0.2 ]  3.280646e-09  2.447009e-03  9.151890e+08   \n",
      "846          [0.05 0.2 ]  3.436868e-09  2.454253e-03  9.151560e+08   \n",
      "847          [0.05 0.2 ]  3.514979e-09 -1.484127e-02 -2.801518e+09   \n",
      "848          [0.05 0.2 ]  3.593091e-09  2.461887e-03  9.151908e+08   \n",
      "849          [0.05 0.2 ]  3.749313e-09  2.468605e-03  9.151656e+08   \n",
      "850          [0.05 0.2 ]  3.827424e-09 -1.482737e-02 -2.801518e+09   \n",
      "851          [0.05 0.2 ]  3.905535e-09  2.475348e-03  9.152135e+08   \n",
      "852          [0.05 0.2 ]  4.061757e-09  2.480319e-03  9.152139e+08   \n",
      "853          [0.05 0.2 ]  4.139868e-09 -1.481743e-02 -2.801518e+09   \n",
      "854          [0.05 0.2 ]  4.217979e-09  2.483499e-03  9.153143e+08   \n",
      "855          [0.05 0.2 ]  4.374201e-09  2.481133e-03  9.154962e+08   \n",
      "856          [0.05 0.2 ]  4.452312e-09 -1.482558e-02 -2.801518e+09   \n",
      "857          [0.05 0.2 ]  4.530423e-09  2.466400e-03  9.157867e+08   \n",
      "858          [0.05 0.2 ]  4.686645e-09  2.428367e-03  9.164940e+08   \n",
      "859          [0.05 0.2 ]  4.764756e-09 -1.491498e-02 -2.801518e+09   \n",
      "860          [0.05 0.2 ]  4.842867e-09  2.340365e-03  9.179389e+08   \n",
      "861          [0.05 0.2 ]  4.999089e-09  2.147905e-03  9.209306e+08   \n",
      "862          [0.05 0.2 ]  5.077200e-09 -1.535611e-02 -2.801518e+09   \n",
      "863          [0.05 0.2 ]  5.155312e-09  1.738558e-03  9.271567e+08   \n",
      "\n",
      "     Vo_mole_fraction  Gd_mole_fraction  \n",
      "0            0.046936               0.0  \n",
      "1            0.000000               0.2  \n",
      "2            0.047586               0.0  \n",
      "3            0.047819               0.0  \n",
      "4            0.000000               0.2  \n",
      "5            0.047912               0.0  \n",
      "6            0.047943               0.0  \n",
      "7            0.000000               0.2  \n",
      "8            0.047955               0.0  \n",
      "9            0.047959               0.0  \n",
      "10           0.000000               0.2  \n",
      "11           0.047960               0.0  \n",
      "12           0.047959               0.0  \n",
      "13           0.000000               0.2  \n",
      "14           0.047963               0.0  \n",
      "15           0.047961               0.0  \n",
      "16           0.000000               0.2  \n",
      "17           0.047959               0.0  \n",
      "18           0.047962               0.0  \n",
      "19           0.000000               0.2  \n",
      "20           0.047959               0.0  \n",
      "21           0.047961               0.0  \n",
      "22           0.000000               0.2  \n",
      "23           0.047962               0.0  \n",
      "24           0.047964               0.0  \n",
      "25           0.000000               0.2  \n",
      "26           0.047960               0.0  \n",
      "27           0.047963               0.0  \n",
      "28           0.000000               0.2  \n",
      "29           0.047960               0.0  \n",
      "..                ...               ...  \n",
      "834          0.048961               0.0  \n",
      "835          0.000000               0.2  \n",
      "836          0.048981               0.0  \n",
      "837          0.048991               0.0  \n",
      "838          0.000000               0.2  \n",
      "839          0.048996               0.0  \n",
      "840          0.048999               0.0  \n",
      "841          0.000000               0.2  \n",
      "842          0.048998               0.0  \n",
      "843          0.049000               0.0  \n",
      "844          0.000000               0.2  \n",
      "845          0.049001               0.0  \n",
      "846          0.049000               0.0  \n",
      "847          0.000000               0.2  \n",
      "848          0.049001               0.0  \n",
      "849          0.049000               0.0  \n",
      "850          0.000000               0.2  \n",
      "851          0.049003               0.0  \n",
      "852          0.049003               0.0  \n",
      "853          0.000000               0.2  \n",
      "854          0.049008               0.0  \n",
      "855          0.049018               0.0  \n",
      "856          0.000000               0.2  \n",
      "857          0.049033               0.0  \n",
      "858          0.049071               0.0  \n",
      "859          0.000000               0.2  \n",
      "860          0.049149               0.0  \n",
      "861          0.049309               0.0  \n",
      "862          0.000000               0.2  \n",
      "863          0.049642               0.0  \n",
      "\n",
      "[864 rows x 11 columns]\n",
      "\n",
      "perpendicular grain boundary resistivity =  2006.9356535584202\n",
      "parallel grain boundary resistivity =  0.008381304778692202\n",
      "space charge potential =  0.29640540295079343\n",
      "Mott-Schottky approximated space charge potential =  0.630885754688719\n"
     ]
    }
   ],
   "source": [
    "plt.plot(grid.x, c_o.phi)\n",
    "plt.xlabel( '$x$ $\\mathrm{coordinate}$' )\n",
    "plt.ylabel('$\\Phi$ $\\mathrm{( eV )}$')\n",
    "plt.savefig('phi.pdf')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(grid.x, c_o.rho)\n",
    "plt.xlabel( '$x$ $\\mathrm{coordinate}$'  )\n",
    "plt.ylabel(' $\\mathrm{charge density}$ $(\\mathrm{C m}^{-1})$')\n",
    "plt.savefig('rho.pdf')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(grid.x, c_o.mf[site_labels[0]], label = '$\\mathrm{Vo}$')\n",
    "plt.plot(grid.x, c_o.mf[site_labels[1]], label = '$\\mathrm{Gd}$')\n",
    "plt.xlabel( '$x$ $\\mathrm{coordinate}$'  )\n",
    "plt.ylabel('$x_{i}$')\n",
    "plt.legend()\n",
    "plt.savefig('mf.pdf')\n",
    "plt.show()\n",
    "\n",
    "print('key_data')\n",
    "print()\n",
    "print(key_data_appended)\n",
    "print('all_data')\n",
    "print()\n",
    "print(all_data_appended)\n",
    "print()\n",
    "print('perpendicular grain boundary resistivity = ', c_o.perpendicular_resistivity_ratio)\n",
    "print('parallel grain boundary resistivity = ', c_o.parallel_resistivity_ratio)\n",
    "print('space charge potential = ', max(c_o.phi))\n",
    "print('Mott-Schottky approximated space charge potential = ', c_o.ms_phi)"
   ]
  }
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