bruker2csv_JN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f33116f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required packages\n",
    "import os\n",
    "import nmrglue as ng\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d41cfef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the path to the file you want to export below (ex: C/Bruker/TopSpin4.1.3/data/sample/pdata/1)\n",
    "data_dir = os.path.normpath('C:/Bruker/TopSpin4.1.3/data/400/Zeolite/22/pdata/1')\n",
    "dic, data = ng.bruker.read_pdata(data_dir)\n",
    "udic = ng.bruker.guess_udic(dic, data)\n",
    "uc = ng.fileiobase.uc_from_udic(udic)\n",
    "ppm_scale = uc.ppm_scale()\n",
    "norm_data = data/data.max()\n",
    "df = pd.DataFrame()\n",
    "df['ppm'] = ppm_scale\n",
    "df['int'] = data\n",
    "df['norm int'] = norm_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5604206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$^1$H (ppm)')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot unormalized data\n",
    "plt.plot(ppm_scale, data)\n",
    "plt.xlim(20,-10)\n",
    "plt.xlabel('$^1$H (ppm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5425ccc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$^1$H (ppm)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f2Abc6+5bzOwOM7shXe3TwFzg62b2mJmtH+PuRBquVIkppsFfzBnlOMab6Ly7faUqrcWTe/wD2rgrGZlIqwd33wBsGLHs9rrL12Q8LpEpUx/8hVyEe3LEznzOGjyyZBwDlVg7cMmU0p67EpxStS7405/N0u7p6a8A0NFaGFym6ZySNQW/BGegPLzHD0Nz+xvtWH8ZgI7W+j130x6/Nu5KRhT8EpwTpQrt6SERarNnTpQqjRzSoONpxT+vLvhrFb+mc0pWFPwSnBMDFea0JGE6tyU/uKwZHB+s+IdaPar4JWsKfglOb6nKnLTirwV/T9MEf63HP1Tx53MR+cjU45fMKPglOCdKFeakgT9nsOJvjlA9PnByxQ9Ju0ezeiQrCn4JzomBoT1jay2fZq74IZnSqYpfsqLgl+CcGKgMtng6WpLKutmDvyWv8+5KdhT8EpRSJWagEg/O6qlV/M2ycfdIb4mWfDS4t25NSyHSrB7JjIJfgnK4twTAorlFYKjH3ywV/4GeEp1zW05a3qqKXzKk4JegdB9PDhzbmQZ/Sz6ZMdMsFf+BngE6O04OflX8kiUFvwTl4Imk4q9V1WZGR2t+sLfeaN3HB+hKP5TqJRW/gl+yoeCXoBzsSSr+RXXtlMUdrew71t+oIQ0zVqtnbhN9OMnMp+CXoBzsGd7jB1gyv5UXmyD4q7Fz6MTAqMG/sL0wuH1CZLIU/BKUfcf6aclHdLQMTZdcOr+VPUcaH/x7j/YRO5y1oO2k2xbOKXK4t9xU5w2QmUvBL0F54VAvZ5/RjtnQsfeXzG/lQM9Aw4/Q+cLBXgBWLmo/6baF7UVKlZg+9fklAwp+CUot+Ostnd8K0PA+//OHkuA/p3POSbctbE92NDt0Qu0emTwFvwTD3dl5qJezR1TUS+YnrZU9R/oaMaxBO7p7KOYjlsxrPem2Be3JNokjveXpHpbMQgp+CcYLh3o5Uapy/uK5w5afm1bY27t7GjGsQU/sPsqFS+eRi04+BeQZc5LgV8UvWVDwSzA2PncYgMvOWThs+bIFbcwp5nj6xeONGBYAcexs3n2Mi5fNH/X22reAvUcb+61EZgcFvwRj4/OH6WjNc8HijmHLo8hYfWYHT+1rXPBv3XuMnoEKl56zYNTbl8xvJTLYfVjBL5On4Jcg9JWqfG/Li7z2vE6iUVopL1/Swba9x6nG2U6XvG/TLjY9f2jc9X70dDcArz2/c9TbC7mIM+e1sqvB2yFkdlDwSxDufnAHB0+UeN+Vq0a9/TXnd3K0r8wvXzic2WPuPtLHn379V7z9rp+dcv59HDv3bdrFZecsZHHHyRt2a5YtaFPFL5lQ8Must+twL5/70bNc/8olXL7qjFHXufplXRRyxnc3v5jZ465/bM/g5S17jo253kPPHuDXB07w7ivOOeX9reqcw7MN3gAts4OCX2a1/cf7effnf4EZ3HbdhWOuN6+1wO+uWcK9j+zkaAZTJqux841Hd7Gqcw65yPjOE3tHXa+/XOUz33+azrlFrnvlklPe54VL53Ggp8T+443fy1hmNgW/zFq/2nmEt975U/Yd6+dL77ucFWecvEdsvVvecD4nShU+9d1tk3rc/cf6+fNvbuaZ/T189JrVXHVBF/f84oXBQ0LXlKsxt3z1l/zyhSN8/M2vOOnkKyNduHQeAJt3H53U+ETy468iMrP0l6vc9cNnueuHz9LV0cI9N1/BxcsXjPt7Fy6dxwevOo+7fvgsizta+Og1F4y6IXg0O7p7+P7Wffxk+wEe2n6A2OEDr1vFDa86i9WLO3jbXQ9x3V//mJsuX0F7Mc/uI7385JkDPHewl0+uewVvftVZ4z7GJWcvoK2Q44Enu3nDy8+c0LhERmONOujT2rVrfePGjQ15bJmd3J0fbNvPX3x7CzsP9fF7Fy/lk+suYuGck49vP5Zq7Pyn+x7nHx7dxUXL5vHR37mA11/QRTE/9OU4jp3nD/Wybe8xtuw5yj9v3T84FfTcrjlcf9FS3nLJWZxfN2108+6j/NX/e5KfbD+AOyxoL7B68Vw++PrzuGbNxEP8w1/ZxM+ePchDt75h8PSREhYz2+Tuayd1Hwp+melKlZh/eXIff/vAdjbvPsb5i+dyxw2v4DVjTI0cj7vzrcf28JnvP80Lh3ppK+S4cGkH7cU81dh5Zn8PB9Lj+uci49KzF3D9K5fyxjVnsnzhqdtJtVM8zm05vdDe9Pxh3n7XT/ng68/ltuvH3mYhs1cWwa+SQWak/nKVTc8f5kdPd/ONR3dxoKfEykXt/OVbL+Idl60YVqG/VGbGWy5Zxr+6eCkPPLmfnz57kCdfPEZfuYq7c+X5i7ji3EW84qz5nL94Lm3FU/fm651u4Ndcds5C3vWbZ3P3j3dw1cu6eM15Qx9ux/rLzGstTOr+JQwTqvjN7Frgr4Ec8H/c/b+OuL0F+BJwGXAQeKe7P3eq+1TFLxNVrsZs39/D0/uO88y+HjbvOcpPnz1IqRKTj4yrX7aYmy5fwetWd00q8GeKEwMVfu9vfsLuw338u6vP482vOouHdxzkv3xrM79xzhmsPnMularT1dHC71+2nJWjHO1TZq5pafWYWQ54GngjsAt4BLjJ3bfWrfNh4GJ3/5CZ3Qi81d3fear7VfCHqVyNOd5f4VhfmWP9ZQ73ltl1uJcDx0sc6Stx6ERpcJ2egQr7jvaz91g/tbdpLjLOWdTO1Rcs5srVi7h81aJJV9Ez0YGeAf7in7byT7/aM2z5K5fNZ3e6d++R3hKxJwehu3zVGZzXNZcVZ7TRObeFJfNb6ZzbQmth4t9WpDlMV/D/FvAJd39Tev02AHf/VN0696fr/MzM8sCLQJef4s5DDH53T9sFUHXH4+RnZNBfjqm6U4iMQi6iv1Klr1QlSk8YUk3/KytVp7dUIRcZsUM1jnGH2n90PjLyUUTdeUYGL8cxxO7JY7unv+/E7smYRrkc161XqiRj7C9XyUcR5WpMZNAzUKW/XKWvXKVUianEMcf6KhzvLych31/mWF/ys7c09olE5rbkOWNOkXzOmN9WoL2Y48yOVpYtbOP8xXM5f/FcVi/uCKKqn6jt+3t49IXDHOkt8bZLlw87beOLR/v59uN7+PEzB3hi99FRj+zZ0ZKnq6OFeW0FOue2ML+twJnzkg+E1kJEayFHIRel/4xi7XJ++PX2Yo7WQm5wFpQBxXxEzozIjFzOyKe31d7ThZwNOyGOTMx09fiXATvrru8CfnOsddy9YmZHgUXAgbHudEf3CW66+2HMoP7jwdMIiz15Y5Sr6fXYB98w/ZUqhVyUHL7Wk9+phZ+7pz9r95f8LiQH48pHRiV2WnIRjlOJk2Ar5pLlpUo87HgtQ/fjw+6z9plWe6z+cpW2Qg4sqWorVadcjdMgTC7XnstslY+MtmISFPNa88xtzdPRUmBV5xzmtxWY11pgXluBea359GeB+e0FzlrQxpkdLeRzCvSXqvaBOJol81v5o9edyx+97lwAuo8P0H18gP3H+3nxaD97jvRx8ESJI31ljvWV2Xmoly39ZfYfH8j8mEWjMUs+ICKz9HKywEi+2RlJDtSvhw2tD8Ozo7ZectnqLkPtluHrpI85yu8Ou8/0qmF1l4fWq7v7k5bX1o/MGKjEFHMRxXySPeWKYwalakwhSvJsuj4Hp/U7spndDNwMMGfpeVTioVPdWd3/nCU/6CsllSWWVA+1N+PC9iKVOAlXi8CIBl+gYS9OeiFnQwFdiWMis7Q/HNFaSNYpV2OK+YgFbQXyuWjsN0fdm6D+xW4r5OgtVzEYrI7qK6VCLiKfiyjmjFwUDVY7OYNK7LQX8+Qjo1SNKVdj2gpJBRWn7+zaMdpzkdFezBHHEEWQi6Jhb7BK7FSqPuyDifS55yIjF5E+blKJRZZ8II68nEv/uCKz9NuFk4uMtrSqiwyKuYjYoaM1TyH9P5vovHeZfl0dLXR1tLCGeadcr/ZtsK9cZaBcpVwrYtL3ZrniQ5fT4qa3lHzrc2fwPVuqxsRpYVWuxoPfWt2Txxio1L6tJuvULuPJ+xig9naq3V6779rPWjFYK/gG75+6x6pbTv3yMYu6oeUMW+6jrDN8OaPdZ/oNv5iLKFVjBtJtU4WcUY2dQi5KvolPw4dtzUSCfzewou768nTZaOvsSls980k28g7j7ncDd0PS6vn6h15zOmMWkSlUK0jmtuSD3H7S7D7/3snfx0S+Wz8CrDazVWZWBG4E1o9YZz3wnvTy7wP/cqr+voiINM64H+dpz/4W4H6S6ZxfcPctZnYHsNHd1wOfB75sZtuBQyQfDiIi0oQm9D3O3TcAG0Ysu73ucj/wjmyHJiIiU0HTKEREAqPgFxEJjIJfRCQwCn4RkcAo+EVEAtOw4/Gb2XHgqYY8+PTo5BSHrJgFZvPzm83PDfT8ZrqXuXvH+KuNrZG75T012QMNNTMz26jnNzPN5ucGen4znZlN+uiWavWIiARGwS8iEphGBv/dDXzs6aDnN3PN5ucGen4z3aSfX8M27oqISGOo1SMiEhgFv4hIYKYl+M1shZk9YGZbzWyLmf1xuvwMM/u+mT2T/lw4HePJmpl9wcz2m9nmumWfMLPdZvZY+u/6Ro7xdI3x3GbF6zYaM3vOzJ5IX7NZd1JoM7vWzJ4ys+1mdmujxzNVZsvfX42ZvSPNztjM1o647bb09XzKzN40kfubroq/AnzM3dcAVwAfMbM1wK3AD9x9NfCD9PpM9EXg2lGW/w93f3X6b8Mot88EX+Tk5zZbXrex/Hb6ms2queBmlgPuBK4D1gA3pX+Hs9Vs+Pur2Qy8DXiwfmH6+t0IvILk7/Sz6et8StMS/O6+190fTS8fB7aRnKB9HfD36Wp/D7xlOsaTNXd/kOQENLPOGM9tVrxuAboc2O7uO9y9BNxD8lpKk3P3be4+2pEO1gH3uPuAu/8a2E7yOp/StPf4zWwlcAnwc+BMd9+b3vQicOZ0j2eK3WJmj6ftklnTDmF2v24OfM/MNpnZzY0eTMaWATvrru9Kl81Ws/Xvr95pvabTGvxmNhf4B+Cj7n6s/rb0HL2zaW7pXcB5wKuBvcB/b+hopsgsfN2udPdLSdohHzGz1zd6QDI6M/tnM9s8yr91zMC/v3GeT6am7Vg9ZlYgCf2vuPs30sX7zGypu+81s6XA/ukaz1Rz9321y2b2d8C3GzicrM3m1213+nO/mf0jydfmB0/9WzPGbmBF3fXl6bIZyd2vmch6M+Xvb6LPZ4TTek2na1aPkZyQfZu7f6bupvXAe9LL7wG+NR3jmQ5pINa8lWTjzGwxK183M5tjZh21y8DvMrtet0eA1Wa2ysyKJBsF1zd4TFNilv/91VsP3GhmLWa2ClgN/GK8X5qWPXfN7Ergx8ATQJwu/s8kff57gbOB54E/cPcZt5HUzL4GXE1yONh9wMfT668maYM8B3ywri8+Y4zx3L7JLHjdRjKzc4F/TK/mga+6+182cEiZS6c1/k8gB3xhtj2/GjP7MrPg76/GzN4K/A3QBRwBHnP3N6W3/RnwPpLZkx919++Oe386ZIOISFi0566ISGAU/CIigVHwi4gERsEvIhIYBb+ISGAU/CIigVHwy4xlZuea2efN7L5Gj0VkJlHwy4yVHmXy/fXLzKxnxPX3mtnfjvb7ZtZmZj+ayGFsT5eZFc3sQTObtsOjiIxHwS8hex/wDXevTtUDpIc//gHwzql6DJGXSsEvIfvXpMcZMrOVZvakmX3FzLaZ2X1m1j7W8hG/80Uzezpd5xozeyg9O1ntuOjfTB9LpCko+GXGMrNFZvY54BIzuy1d3FZ3ur3HgDvG+N0icK67P1e3+GXAZ939QuAY8OFxlgOcT3LI35en/94FXAn8KcnxqCA5QNhvTOa5imRJwS8zlrsfdPcPuft57v6pdHFf3en2Xg3cPsavd5Ic7KreTnd/KL38f0kC/FTLAX7t7k+4ewxsITklpZMckHBlOs4qUKod+VOk0RT8Eqo+oHXEspFHLPRxlgMM1F2O667HDD/fRQvQ/9KHKZI9Bb8Eyd0PAzkzqw//s83st9LL7wJ+Ms7yCTGzRcABdy9PZswiWVHwS8i+x/C2zVMkp1vcBiwkOX3fqZZP1G8D35nkWEUyo+PxS7DM7FLgP7j7u81sJfBtd79oxDqjLn+Jj/MN4FZ3f3oy4xXJiip+CZa7Pwo8MNU7cAHfVOhLM1HFLyISGFX8IiKBUfCLiARGwS8iEhgFv4hIYBT8IiKBUfCLiARGwS8iEhgFv4hIYBT8IiKB+f94FUrZlmyMRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ormalized data\n",
    "plt.plot(ppm_scale, norm_data)\n",
    "plt.xlim(20,-10)\n",
    "plt.xlabel('$^1$H (ppm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abbc6acb",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}