{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48cb61ff",
   "metadata": {},
   "source": [
    "## Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c004d5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.base import ChemicalSystem\n",
    "from scm.plams import AMSJob, Settings, view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3513432c",
   "metadata": {},
   "source": [
    "## System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f5b7c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = ChemicalSystem(\n",
    "    \"\"\"\n",
    "System\n",
    "    Atoms\n",
    "        O 1.48603879 -1.49561627 0.0 \n",
    "        C 1.29751002 -0.30552432 0.0 \n",
    "        O 0.07403584000000001 0.25228979 0.0 \n",
    "        C -1.02449892 -0.67494471 0.0 \n",
    "        C -2.30056502 0.12358769 0.0 \n",
    "        C 2.36905363 0.74347075 0.0 \n",
    "        H -0.9418758699999999 -1.31519741 0.88039373 \n",
    "        H -0.9418758699999999 -1.31519741 -0.88039373 \n",
    "        H -2.36617127 0.7582087199999999 0.88525259 \n",
    "        H -3.15628689 -0.55419212 0.0 \n",
    "        H -2.36617127 0.7582087199999999 -0.88525259 \n",
    "        H 3.34355252 0.26293272 0.0 \n",
    "        H 2.26362714 1.38098693 0.87932777 \n",
    "        H 2.26362714 1.38098693 -0.87932777 \n",
    "    End\n",
    "End\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e45b5230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view(cs, guess_bonds=True, height=200, width=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59832642",
   "metadata": {},
   "source": [
    "## Job settings: DFT and NMR\n",
    "\n",
    "There are three calculations:\n",
    "\n",
    "1. A DFT calculation with a main results file ``adf.rkf``. We must set the ``Save TAPE10`` input option. The ``TAPE10`` file is later used by the ``$AMSBIN/nmr`` program.\n",
    "\n",
    "2. An ``$AMSBIN/cpl`` calculation. This program *modifies* ``adf.rkf``\n",
    "\n",
    "3. An ``$AMSBIN/nmr`` calculation. This program reads ``TAPE10`` and *modifies* ``adf.rkf``.\n",
    "\n",
    "In the end, all results are stored on ``adf.rkf``.\n",
    "\n",
    "There is no PLAMS job for the ``$AMSBIN/cpl`` and ``$AMSBIN/nmr`` programs. Instead, we set up the calculation explicitly in the ``settings.runscript.postamble_lines`` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16e5bac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DFT Settings\n",
    "s = Settings()\n",
    "s.input.ams.Task = \"SinglePoint\"\n",
    "s.input.adf.Basis.Type = \"TZ2P-J\"\n",
    "s.input.adf.Basis.Core = \"None\"\n",
    "s.input.adf.Save = \"TAPE10\"\n",
    "s.input.adf.xc.GGA = \"OPBE\"\n",
    "s.input.adf.Symmetry = \"NOSYM\"\n",
    "s.input.adf.NumericalQuality = \"Good\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b62f92d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Mapping, List\n",
    "\n",
    "\n",
    "def get_cpl_script(s: Mapping) -> List[str]:\n",
    "    ret = (\n",
    "        ['\"$AMSBIN/cpl\" <<eor']\n",
    "        + [\"ADFFile adf.rkf\"]\n",
    "        + [\"NMRCOUPLING\"]\n",
    "        + [f\"  {k} {v}\" for k, v in s.items()]\n",
    "        + [\"END\"]\n",
    "        + [\"eor\"]\n",
    "        + [\"\"]\n",
    "    )\n",
    "    return ret\n",
    "\n",
    "\n",
    "def get_nmr_script(s: Mapping) -> List[str]:\n",
    "    ret = (\n",
    "        ['\"$AMSBIN/nmr\" <<eor']\n",
    "        + [\"ADFFile adf.rkf\", \"TAPE10File TAPE10\"]\n",
    "        + [\"NMR\"]\n",
    "        + [f\"  {k} {v}\" for k, v in s.items()]\n",
    "        + [\"End\"]\n",
    "        + [\"eor\"]\n",
    "        + [\"\"]\n",
    "    )\n",
    "    return ret\n",
    "\n",
    "\n",
    "cpl_settings = dict(RespAllAtomsOfType=\"H\", PertAllAtomsOfType=\"H\", fc=\"\", ADFGUI=\"\")\n",
    "nmr_settings = dict(out=\"\", adfgui=\"\", AllAtomsOfType=\"H\", u1k=\"best\", calc=\"all\")\n",
    "\n",
    "s.runscript.postamble_lines = get_cpl_script(cpl_settings) + get_nmr_script(nmr_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec423e2",
   "metadata": {},
   "source": [
    "## Inspect the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af6cedf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task SinglePoint\n",
      "\n",
      "System\n",
      "   Atoms\n",
      "      O 1.48603879 -1.4956162699999997 0\n",
      "      C 1.29751002 -0.30552432 0\n",
      "      O 0.07403583999999999 0.25228979 0\n",
      "      C -1.02449892 -0.67494471 0\n",
      "      C -2.30056502 0.12358769 0\n",
      "      C 2.36905363 0.74347075 0\n",
      "      H -0.94187587 -1.31519741 0.88039373\n",
      "      H -0.94187587 -1.31519741 -0.88039373\n",
      "      H -2.36617127 0.75820872 0.88525259\n",
      "      H -3.15628689 -0.55419212 0\n",
      "      H -2.36617127 0.75820872 -0.88525259\n",
      "      H 3.34355252 0.26293272 0\n",
      "      H 2.26362714 1.38098693 0.87932777\n",
      "      H 2.26362714 1.38098693 -0.87932777\n",
      "   End\n",
      "End\n",
      "\n",
      "Engine adf\n",
      "  Basis\n",
      "    Core None\n",
      "    Type TZ2P-J\n",
      "  End\n",
      "  NumericalQuality Good\n",
      "  Save TAPE10\n",
      "  Symmetry NOSYM\n",
      "  xc\n",
      "    GGA OPBE\n",
      "  End\n",
      "EndEngine\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "job = AMSJob(settings=s, molecule=cs, name=\"ethyl_acetate_nmr\")\n",
    "print(job.get_input())  # DFT settings in the normal input (ethyl_acetate_nmr.in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8f501fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unset AMS_SWITCH_LOGFILE_AND_STDOUT\n",
      "unset SCM_LOGFILE\n",
      "AMS_JOBNAME=\"ethyl_acetate_nmr\" AMS_RESULTSDIR=. $AMSBIN/ams --input=\"ethyl_acetate_nmr.in\" < /dev/null\n",
      "\n",
      "\"$AMSBIN/cpl\" <<eor\n",
      "ADFFile adf.rkf\n",
      "NMRCOUPLING\n",
      "  RespAllAtomsOfType H\n",
      "  PertAllAtomsOfType H\n",
      "  fc \n",
      "  ADFGUI \n",
      "END\n",
      "eor\n",
      "\n",
      "\"$AMSBIN/nmr\" <<eor\n",
      "ADFFile adf.rkf\n",
      "TAPE10File TAPE10\n",
      "NMR\n",
      "  out \n",
      "  adfgui \n",
      "  AllAtomsOfType H\n",
      "  u1k best\n",
      "  calc all\n",
      "End\n",
      "eor\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(job.get_runscript())  # post-processing utilities in the runscript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e36cb8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19.03|15:50:08] JOB ethyl_acetate_nmr STARTED\n",
      "[19.03|15:50:08] JOB ethyl_acetate_nmr RUNNING\n",
      "[19.03|15:58:01] JOB ethyl_acetate_nmr FINISHED\n",
      "[19.03|15:58:01] JOB ethyl_acetate_nmr SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "job.run();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434d659f",
   "metadata": {},
   "source": [
    "## Results (basic)\n",
    "\n",
    "Let's see which NMR results are available on adf.rkf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b227549",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NMR Coupling J const InputOrder', 'NMR Shielding Tensor InputOrder', 'NMR Coupling K tens InputOrder', 'NMR Shieldings InputOrder', 'NMR Coupling K const InputOrder', 'NMR Coupling J tens InputOrder']\n"
     ]
    }
   ],
   "source": [
    "# job = AMSJob.load_external(\"/path/to/adf.rkf\")   # to load a previous job\n",
    "props = job.results.get_engine_properties()\n",
    "nmr_keys = [x for x in props.keys() if \"NMR\" in x]\n",
    "print(nmr_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6406990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scm.plams.tools.postprocess_results import broaden_results\n",
    "\n",
    "sigma_ref_h = 31.7\n",
    "shieldings = np.array(props[\"NMR Shieldings InputOrder\"])\n",
    "\n",
    "chemical_shifts = sigma_ref_h - np.array(shieldings)\n",
    "\n",
    "x, y = broaden_results(\n",
    "    chemical_shifts,\n",
    "    areas=np.ones(chemical_shifts.shape),  # weigh all peaks equally\n",
    "    broadening_width=0.01,\n",
    "    broadening_type=\"lorentzian\",\n",
    "    x_data=(0, 5, 0.01),  # x range and resolution\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"1H-NMR (no coupling)\")\n",
    "ax.set_xlabel(\"Chemical shift (ppm)\")\n",
    "ax.set_ylabel(\"Intensity (arbitrary)\")\n",
    "ax.plot(x, y)\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3830ceea",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this Python example you learned how to set up and run the NMR calculation.\n",
    "\n",
    "For more detailed analysis, including multiplet splitting (coupling), see the GUI tutorial.\n",
    "\n",
    "**Recommendation**: Use AMSspectra for further analysis."
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
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