{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stretched LDA H_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from CADMium import Psgrid\n",
    "from CADMium import Kohnsham\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/c/Users/victo/Dropbox/PHD/cadmium/CADMium/CADMium/kohnsham/scf.py:99: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dif_n = np.max(  self.grid.integrate(np.abs(self.n - old_n)) / self.grid.integrate(np.abs(self.n))  )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Distance: 1.0 -> Energy: -0.4145683783488223\n",
      " Distance: 1.1379310344827587 -> Energy: -0.47568922209238373\n",
      " Distance: 1.2758620689655173 -> Energy: -0.5161588699357786\n",
      " Distance: 1.4137931034482758 -> Energy: -0.5430750738553992\n",
      " Distance: 1.5517241379310345 -> Energy: -0.5608352742997781\n",
      " Distance: 1.6896551724137931 -> Energy: -0.572286799708926\n",
      " Distance: 1.8275862068965516 -> Energy: -0.5793186586685862\n",
      " Distance: 1.9655172413793103 -> Energy: -0.5832483470435494\n",
      " Distance: 2.103448275862069 -> Energy: -0.5849681825610669\n",
      " Distance: 2.2413793103448274 -> Energy: -0.5851247032649087\n",
      " Distance: 2.3793103448275863 -> Energy: -0.584190041325282\n",
      " Distance: 2.5172413793103448 -> Energy: -0.582499765526698\n",
      " Distance: 2.655172413793103 -> Energy: -0.5803082359628977\n",
      " Distance: 2.793103448275862 -> Energy: -0.5778025962083684\n",
      " Distance: 2.9310344827586206 -> Energy: -0.5751256071726312\n",
      " Distance: 3.0689655172413794 -> Energy: -0.5723756794966852\n",
      " Distance: 3.206896551724138 -> Energy: -0.56963327320139\n",
      " Distance: 3.3448275862068964 -> Energy: -0.5669558355896511\n",
      " Distance: 3.4827586206896552 -> Energy: -0.564385253940888\n",
      " Distance: 3.6206896551724137 -> Energy: -0.561951355511639\n",
      " Distance: 3.7586206896551726 -> Energy: -0.5596745547274161\n",
      " Distance: 3.896551724137931 -> Energy: -0.5575678812131128\n",
      " Distance: 4.0344827586206895 -> Energy: -0.5556385544351473\n",
      " Distance: 4.172413793103448 -> Energy: -0.5538922456588337\n",
      " Distance: 4.310344827586206 -> Energy: -0.5523220681349172\n",
      " Distance: 4.448275862068965 -> Energy: -0.5509272101741882\n",
      " Distance: 4.586206896551724 -> Energy: -0.54970180598681\n",
      " Distance: 4.724137931034482 -> Energy: -0.548638383586604\n",
      " Distance: 4.862068965517241 -> Energy: -0.5477283065816296\n",
      " Distance: 5.0 -> Energy: -0.5469621411394103\n",
      " Distance: 5.1 -> Energy: -0.5464910343707778\n",
      " Distance: 5.644444444444444 -> Energy: -0.5449932136635087\n",
      " Distance: 6.188888888888888 -> Energy: -0.5448648572321146\n",
      " Distance: 6.733333333333333 -> Energy: -0.5455947634006488\n",
      " Distance: 7.277777777777778 -> Energy: -0.5468031699309361\n",
      " Distance: 7.822222222222222 -> Energy: -0.5482334038344566\n",
      " Distance: 8.366666666666667 -> Energy: -0.5497240028198508\n",
      " Distance: 8.911111111111111 -> Energy: -0.551178506522132\n",
      " Distance: 9.455555555555556 -> Energy: -0.5525400886938037\n",
      " Distance: 10.0 -> Energy: -0.5537721681557687\n"
     ]
    }
   ],
   "source": [
    "dis_eq      = np.linspace(1.0,5,30)\n",
    "dis_st      = np.linspace(5.1,10,10)\n",
    "distances   = np.concatenate((dis_eq, dis_st))\n",
    "lda_energy  = []\n",
    "\n",
    "Za, Zb = 1,1\n",
    "Nmo = [[1,0]]\n",
    "N   = [[1,0]]\n",
    "pol = 2\n",
    "NP, NM = 7, [4,4] \n",
    "optKS = {\"xc_family\"  : \"lda\",\n",
    "         \"xfunc_id\"   : 1,   \n",
    "         \"cfunc_id\"   : 12,\n",
    "         \"sym\"        : True,\n",
    "         \"fractional\" : False}\n",
    "\n",
    "\n",
    "for d in distances:\n",
    "    #Initialize Grid and Separation\n",
    "    a =  d/2\n",
    "    L = np.arccosh(10./a) \n",
    "    loc = np.array(range(-4,5))\n",
    "    grid = Psgrid(NP, NM, a, L, loc)\n",
    "    grid.initialize()\n",
    "\n",
    "    #Kohn Sham Object. SCF Calcualtion\n",
    "    KS = Kohnsham(grid, Za, Zb, pol, Nmo, N, optKS)\n",
    "    KS.scf({\"disp\" : False})\n",
    "    lda_energy.append(KS.E.E)\n",
    "    print(f\" Distance: {d} -> Energy: {KS.E.E}\")\n",
    "    \n",
    "energy    = np.array(lda_energy)\n",
    "np.save('h2plus_distance.npy', distances)\n",
    "np.save('h2plus_lda.npy', lda_energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f74fa437e20>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_energy  = -0.25 \n",
    "energy    = np.array(lda_energy)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, dpi=75)\n",
    "\n",
    "ax.axhline(y=0, alpha=0.5, c=\"grey\", ls=\":\")\n",
    "ax.plot(distances, energy - 2*h_energy)\n",
    "# ax.set_ylim(-1.2,-0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}