{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d2cfbecb-cde9-442f-91b2-ba7e340f349d",
   "metadata": {},
   "source": [
    "## Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9770bc1-3519-4b98-8d40-ecc213229b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.libbase import ChemicalSystem\n",
    "from scm.plams import AMSJob, Settings, view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8530601-0ed9-4032-b44a-8e20f2e9c02d",
   "metadata": {},
   "source": [
    "## System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28a7828d-5f8c-4e46-a8de-fec9e6a25327",
   "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": "f78fdf45-e82a-4017-842e-632d6e7e5afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=800x400>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize system with bonds\n",
    "cs_with_bonds = cs.copy()\n",
    "cs_with_bonds.guess_bonds()\n",
    "view(cs_with_bonds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70d8596-bf80-4d8a-baf7-ea6146b9610a",
   "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 ``$AMSBIN/nmr`` programs. Instead, we set up the calculation explicitly in the ``settings.runscript.postamble_lines`` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55a93962-8238-4aa5-bb80-2a3d457130e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DFT Settings\n",
    "s = Settings()\n",
    "s.input.ams.Task = \"SinglePoint\"\n",
    "s.input.adf.Basis.Type = \"TZ2P-J\"\n",
    "s.input.adf.Basis.Core = \"None\"\n",
    "s.input.adf.Save = \"TAPE10\"\n",
    "s.input.adf.xc.GGA = \"OPBE\"\n",
    "s.input.adf.Symmetry = \"NOSYM\"\n",
    "s.input.adf.NumericalQuality = \"Good\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "459d3f2e-106c-40d0-a888-7ee8fd4db46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Mapping, List\n",
    "\n",
    "\n",
    "def get_cpl_script(s: Mapping) -> List[str]:\n",
    "    ret = (\n",
    "        ['\"$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": "1cd10240-3be5-4c7e-9ed9-75a857150e55",
   "metadata": {},
   "source": [
    "## Inspect the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e07f9428-5e27-4cb8-bc25-bcaa9281efee",
   "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": "934db0f9-7e71-4315-8362-2dcd2f6179ce",
   "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": "e1fbe3bb-a6dc-4b07-b0ca-3ee9c3456751",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.12|15:44:51] JOB ethyl_acetate_nmr STARTED\n",
      "[16.12|15:44:51] JOB ethyl_acetate_nmr RUNNING\n",
      "[16.12|15:51:33] JOB ethyl_acetate_nmr FINISHED\n",
      "[16.12|15:51:33] JOB ethyl_acetate_nmr SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "job.run();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2703902f-98ed-4da8-855e-648296fa7cf7",
   "metadata": {},
   "source": [
    "## Results (basic)\n",
    "\n",
    "Let's see which NMR results are available on adf.rkf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0a13b00-37ba-44af-9f70-ddee3f0ac5a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NMR Coupling J tens InputOrder', 'NMR Coupling K tens InputOrder', 'NMR Coupling K const InputOrder', 'NMR Coupling J const InputOrder', 'NMR Shielding Tensor InputOrder', 'NMR Shieldings 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": "fb07a276-3779-44cd-8870-7858cb2dd843",
   "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);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e2e6ee4-504c-4675-ad65-c7c17db9bf63",
   "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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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "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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 },
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