{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03527051-56fb-4913-b3e5-7f888c66cf8b",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e27e35b5-a435-46e0-92e4-ea20d1ffb665",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.plams import Settings, Molecule, view, AMSJob\n",
    "from scm.plams.recipes.adfnbo import ADFNBOJob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e10d95-f2d1-4bfc-90f9-d99f56d8c618",
   "metadata": {},
   "source": [
    "## Define molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5f2cd23-3358-4c50-b37c-f2cd93e6667d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mol = Molecule(\"methane.xyz\")\n",
    "def get_molecule(input_string):\n",
    "    job = AMSJob.from_input(input_string)\n",
    "    return job.molecule[\"\"]\n",
    "\n",
    "\n",
    "mol = get_molecule(\n",
    "    \"\"\"\n",
    "System\n",
    "    Atoms\n",
    "         C      0.000000      0.000000      0.000000\n",
    "         H      0.631600      0.631600      0.631600\n",
    "         H      0.631600     -0.631600     -0.631600\n",
    "         H     -0.631600      0.631600     -0.631600\n",
    "         H     -0.631600     -0.631600      0.631600\n",
    "    End\n",
    "End\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "view(mol, guess_bonds=True, width=200, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d316d3-4892-4c1a-a938-af8ffae5c2a7",
   "metadata": {},
   "source": [
    "## Create and run job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "694502ee-6430-42d0-b8ae-91b4d4bc6819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.03|17:31:51] JOB plamsjob STARTED\n",
      "[23.03|17:31:51] JOB plamsjob RUNNING\n",
      "[23.03|17:31:53] JOB plamsjob FINISHED\n",
      "[23.03|17:31:53] JOB plamsjob SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "s = Settings()\n",
    "s.input.AMS.Task = \"SinglePoint\"\n",
    "s.input.ADF.basis.type = \"DZP\"\n",
    "s.input.ADF.xc.lda = \"SCF VWN\"\n",
    "s.input.ADF.relativity.level = \"scalar\"\n",
    "s.adfnbo = [\"write\", \"spherical\", \"fock\"]\n",
    "\n",
    "j = ADFNBOJob(molecule=mol, settings=s)\n",
    "r = j.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9373fb7d-16bc-491d-afbd-516e2ea9774d",
   "metadata": {},
   "source": [
    "## Print results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d85acae-8903-48bd-9560-c81392636569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " NATURAL BOND ORBITALS (Summary):\n",
      "\n",
      "                                                     Principal Delocalizations\n",
      "           NBO                 Occupancy    Energy   (geminal,vicinal,remote)\n",
      " ===============================================================================\n",
      " Molecular unit  1  (CH4)\n",
      " ------ Lewis --------------------------------------\n",
      "    1. CR ( 1) C  1             2.00000    -9.77904\n",
      "    2. BD ( 1) C  1- H  2       1.99927    -0.42240\n",
      "    3. BD ( 1) C  1- H  3       1.99927    -0.42240\n",
      "    4. BD ( 1) C  1- H  4       1.99927    -0.42240\n",
      "    5. BD ( 1) C  1- H  5       1.99927    -0.42240\n",
      " ------ non-Lewis ----------------------------------\n",
      "    6. BD*( 1) C  1- H  2       0.00023     0.46676\n",
      "    7. BD*( 1) C  1- H  3       0.00023     0.46676\n",
      "    8. BD*( 1) C  1- H  4       0.00023     0.46676\n",
      "    9. BD*( 1) C  1- H  5       0.00023     0.46676\n",
      "   10. RY ( 1) C  1             0.00000     4.16091\n",
      "   11. RY ( 2) C  1             0.00000    20.97822\n",
      "   12. RY ( 3) C  1             0.00000     0.62722\n",
      "   13. RY ( 4) C  1             0.00000     1.51869\n",
      "   14. RY ( 5) C  1             0.00000     0.78050\n",
      "   15. RY ( 6) C  1             0.00000     2.01737\n",
      "   16. RY ( 7) C  1             0.00000     1.83189\n",
      "   17. RY ( 8) C  1             0.00000     2.03239\n",
      "   18. RY ( 9) C  1             0.00000     1.79930\n",
      "   19. RY (10) C  1             0.00000     1.79930\n",
      "   20. RY ( 1) H  2             0.00049     0.37840\n",
      "   21. RY ( 2) H  2             0.00000     0.95619\n",
      "   22. RY ( 3) H  2             0.00000     0.95619\n",
      "   23. RY ( 4) H  2             0.00000     1.52007\n",
      "   24. RY ( 1) H  3             0.00049     0.37840\n",
      "   25. RY ( 2) H  3             0.00000     0.95619\n",
      "   26. RY ( 3) H  3             0.00000     0.95619\n",
      "   27. RY ( 4) H  3             0.00000     1.52007\n",
      "   28. RY ( 1) H  4             0.00049     0.37840\n",
      "   29. RY ( 2) H  4             0.00000     0.95619\n",
      "   30. RY ( 3) H  4             0.00000     0.95619\n",
      "   31. RY ( 4) H  4             0.00000     1.52007\n",
      "   32. RY ( 1) H  5             0.00049     0.37840\n",
      "   33. RY ( 2) H  5             0.00000     0.95619\n",
      "   34. RY ( 3) H  5             0.00000     0.95619\n",
      "   35. RY ( 4) H  5             0.00000     1.52007\n",
      "          -------------------------------\n",
      "                 Total Lewis    9.99709  ( 99.9709%)\n",
      "           Valence non-Lewis    0.00093  (  0.0093%)\n",
      "           Rydberg non-Lewis    0.00199  (  0.0199%)\n",
      "          -------------------------------\n",
      "               Total unit  1   10.00000  (100.0000%)\n",
      "              Charge unit  1    0.00000\n"
     ]
    }
   ],
   "source": [
    "lines = r.get_output_chunk(\n",
    "    begin=\"NATURAL BOND ORBITALS (Summary):\", end=\"Charge unit\", inc_begin=True, inc_end=True\n",
    ")\n",
    "for line in lines:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae619ea6-0f0e-4037-945e-c8b5d4062203",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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