{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### He2 PDFT Inversion - WuYang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from CADMium import Pssolver, Psgrid, Partition, Inverter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform PDFT Calculation. \n",
    "\n",
    "Code should run as it is but for idential calculations increase to grid size to: [7,12,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: If len(KS) > 1 Has not been migrated from matlab\n",
      "                Total Energy ( a.u.)                               Inversion                \n",
      "\n",
      "                __________________                ____________________________________     \n",
      "\n",
      "Iteration         A              B                  iters      optimality        res       \n",
      "\n",
      "___________________________________________________________________________________________ \n",
      "\n",
      "    1           -3.10770     -3.10770       1.000e+00 \n",
      "    2           -2.78065     -2.78065       1.311e-01 \n",
      "    3           -2.84459     -2.84459       2.499e-02 \n",
      "    4           -2.83289     -2.83289       4.594e-03 \n",
      "    5           -2.83476     -2.83476       7.331e-04 \n",
      "    6           -2.83441     -2.83441       1.386e-04 \n",
      "    7           -2.83446     -2.83446       2.161e-05 \n",
      "    8           -2.83445     -2.83445       4.111e-06 \n",
      "    9           -2.83446     -2.83446       6.506e-07 \n",
      "   10           -2.83445     -2.83445       1.232e-07 \n",
      "   11           -2.83445     -2.83445       1.957e-08 \n",
      "                Total Energy ( a.u.)                               Inversion                \n",
      "\n",
      "                __________________                ____________________________________     \n",
      "\n",
      "Iteration         A              B                  iters      optimality        res       \n",
      "\n",
      "___________________________________________________________________________________________ \n",
      "\n",
      "    1            -2.83452        -2.83452             4       +1.328e-07      +1.000e+00\n",
      "    2            -2.83449        -2.83449             4       +1.644e-07      +1.416e-05\n",
      "    3            -2.83444        -2.83444             4       +4.033e-08      +1.785e-05\n",
      "    4            -2.83443        -2.83443             4       +2.953e-07      +9.766e-06\n",
      "    5            -2.83445        -2.83445             4       +3.990e-08      +6.625e-06\n",
      "    6            -2.83446        -2.83446             4       +2.135e-07      +4.754e-06\n",
      "    7            -2.83446        -2.83446             4       +8.459e-08      +3.026e-06\n",
      "    8            -2.83446        -2.83446             3       +2.504e-07      +2.291e-06\n",
      "    9            -2.83445        -2.83445             3       +3.527e-07      +1.274e-06\n",
      "   10            -2.83445        -2.83445             3       +4.116e-07      +8.811e-07\n",
      "   11            -2.83445        -2.83445             3       +1.610e-07      +6.841e-07\n",
      "   12            -2.83446        -2.83446             3       +2.572e-07      +3.592e-07\n",
      "   13            -2.83446        -2.83446             3       +1.889e-07      +2.320e-07\n",
      "   14            -2.83445        -2.83445             3       +1.841e-07      +1.834e-07\n",
      "   15            -2.83445        -2.83445             3       +2.167e-07      +9.048e-08\n",
      "   16            -2.83445        -2.83445             3       +1.406e-07      +5.431e-08\n",
      "   17            -2.83445        -2.83445             3       +3.729e-07      +4.470e-08\n",
      "   18            -2.83445        -2.83445             3       +1.618e-07      +2.132e-08\n",
      "   19            -2.83445        -2.83445             2       +8.412e-08      +1.088e-08\n",
      "   20            -2.83445        -2.83445             3       +1.979e-07      +9.887e-09\n",
      "   21            -2.83445        -2.83445             3       +1.988e-07      +4.731e-09\n",
      "   22            -2.83445        -2.83445             2       +3.542e-07      +2.527e-09\n",
      "   23            -2.83445        -2.83445             2       +3.373e-07      +1.951e-09\n",
      "   24            -2.83445        -2.83445             2       +2.187e-07      +9.715e-10\n"
     ]
    }
   ],
   "source": [
    "a = 3.5\n",
    "#Nuclear charge for fragments A and B\n",
    "Za, Zb = 2,2\n",
    "#Set polarization 1-Unpolarized, 2-Polarized\n",
    "pol = 1\n",
    "#Fragment a electrons [alpha, beta]\n",
    "Nmo_a = [[1]] #Number of molecular orbitals to calculate\n",
    "N_a   = [[2]]\n",
    "#Ensemble mix\n",
    "nu_a = 1\n",
    "#Fragment b electrons\n",
    "Nmo_b = [[1]]\n",
    "N_b   = [[2]]\n",
    "#Ensemble mix\n",
    "nu_b = 1\n",
    "\n",
    "#Molecular elctron configuration\n",
    "Nmo_m = [[2]]\n",
    "N_m   = [[4]]\n",
    "\n",
    "#Set up grid\n",
    "NP = 7\n",
    "NM = [12,12]\n",
    "L = np.arccosh(15/a)\n",
    "loc = np.array(range(-4,5)) #Stencil outline\n",
    "\n",
    "grid = Psgrid(NP, NM, a, L, loc)\n",
    "grid.initialize()\n",
    "\n",
    "part = Partition(grid, Za, Zb, pol, Nmo_a, N_a, nu_a, Nmo_b, N_b, nu_b, {\n",
    "                                                                      \"kinetic_part_type\" : \"inversion\",\n",
    "                                                                      \"ab_sym\" : True,\n",
    "                                                                      \"ens_spin_sym\" : False,\n",
    "                                                                        })\n",
    "\n",
    "#Setup inverter object\n",
    "mol_solver = Pssolver(grid, Nmo_m, N_m)\n",
    "part.inverter = Inverter(grid, mol_solver, {\"disp\"         : True,\n",
    "                                            \"invert_type\"  : \"wuyang\", \n",
    "                                            \"ab_sym\"       : True,\n",
    "                                            \"ens_spin_sym\" : False})\n",
    "\n",
    "#Make isolated calculation\n",
    "part.optPartition.isolated = True\n",
    "part.scf({\"disp\" : True})\n",
    "\n",
    "#Save isolated densities\n",
    "D0_frag_a = part.KSa.n.copy()\n",
    "D0_frag_b = part.KSa.n.copy()\n",
    "\n",
    "#Make pdft calculation\n",
    "part.optPartition.isolated = False\n",
    "part.scf({\"disp\"       : True,\n",
    "          \"alpha\"      : [0.6],\n",
    "          \"max_iter\"   : 200,\n",
    "          \"e_tol\"      : 1e-9,\n",
    "          \"continuing\" : True})\n",
    "\n",
    "\n",
    "#Store full densities under the presence of vp.\n",
    "Dvp_frag_a = part.KSa.n.copy()\n",
    "Dvp_frag_b = part.KSb.n.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Generate Figure 9. Parititon Potential. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc7bce962e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full, x,y = grid.plotter(part.V.vp[:,0])\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "\n",
    "plot = ax.contourf(x,y,full, levels=100, cmap=\"jet\", vmin=-0.05, vmax=0.05)\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xlim([-5,5])\n",
    "ax.set_ylim([-5,5])\n",
    "\n",
    "fig.colorbar(plot) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Generate Figure 9. Difference between Fragment Density and Isolated Atomic Density. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc7a4081e50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D_grid, x, y = grid.plotter(D0_frag_a[:,0])\n",
    "D_vp_grid, _, _ = grid.plotter(Dvp_frag_a[:,0])\n",
    "\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "\n",
    "plot = ax.contourf(x,y, D_vp_grid - D_grid, levels=100, cmap=\"jet\", vmin=-0.05, vmax=0.05)\n",
    "\n",
    "ax.set_xlim([-2,2])\n",
    "ax.set_ylim([-2,2])\n",
    "\n",
    "fig.colorbar(plot)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Generate Figure 11. Components of the Partition Potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc791e28d60>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis, vp      = grid.axis_plot(part.V.vp[:,0])\n",
    "x_axis, vp_kin  = grid.axis_plot(part.V.vp_kin[:,0])\n",
    "x_axis, vp_xc   = grid.axis_plot(part.V.vp_x[:,0] + part.V.vp_c[:,0] )\n",
    "x_axis, vp_hext = grid.axis_plot( part.V.vp_h[:,0] + part.V.vp_pot[:,0])\n",
    "\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "\n",
    "ax.plot(x_axis, vp, label='Total')\n",
    "ax.plot(x_axis, vp_kin, label='Kinetic')\n",
    "ax.plot(x_axis, vp_xc, label='XC')\n",
    "ax.plot(x_axis, vp_hext, label=\"H + Vext\")\n",
    "\n",
    "ax.set_xlim(0,7)\n",
    "ax.set_ylim(-0.04, 0.01)\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Generate Table 9. Energies and Components of Ep, in atomic Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ea': -2.834454719259801,\n",
       " 'Eb': -2.834454719259801,\n",
       " 'Ef': -5.668909438519602,\n",
       " 'Tsf': 5.534566261725247,\n",
       " 'Eksf': array([[-2.27933258]]),\n",
       " 'Enucf': -13.249455410000271,\n",
       " 'Exf': -1.723416970279257,\n",
       " 'Ecf': -0.22215558251414871,\n",
       " 'Ehf': 3.9915522625488284,\n",
       " 'Vhxcf': 5.4337173462492805,\n",
       " 'Ep': -0.5714524443701554,\n",
       " 'Ep_pot': -1.1428814150973046,\n",
       " 'Ep_kin': 3.941987072764164e-06,\n",
       " 'Ep_hxc': 0.5714250287400764,\n",
       " 'Et': -6.240361882889758,\n",
       " 'Vnn': 0.5714285714285714,\n",
       " 'E': -5.668933311461187,\n",
       " 'evals_a': array([], dtype=float64),\n",
       " 'evals_b': array([], dtype=float64),\n",
       " 'Ep_h': 0.571452361315929,\n",
       " 'Ep_x': -2.088050478343817e-05,\n",
       " 'Ep_c': -6.452071069140697e-06}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = {}\n",
    "for i in part.E.__dict__:\n",
    "    if i.startswith(\"__\") is False:\n",
    "        values.update({i : getattr(part.E, i)})\n",
    "values"
   ]
  }
 ],
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