{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General isotherm info\n",
    "\n",
    "Before we start the characterisation, let's have a cursory look at the isotherms. First, make sure the data is imported by running the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 5 isotherms with nitrogen at 77K\n",
      "Selected 2 room temperature calorimetry isotherms\n",
      "Selected 2 isotherms for IAST calculation\n",
      "Selected 3 isotherms for isosteric enthalpy calculation\n"
     ]
    }
   ],
   "source": [
    "# import isotherms\n",
    "%run import.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that some of the isotherms are measured with nitrogen at 77 kelvin, but\n",
    "don't know what the samples are. If we evaluate an isotherm, its key details are\n",
    "automatically displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "<span style=\"font-weight: bold\">[</span>\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">PointIsotherm</span><span style=\"color: #000000; text-decoration-color: #000000\"> 5b36aabf643c8557893f1a82aeedffaa</span><span style=\"font-weight: bold\">&gt;</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'nitrogen'</span> on <span style=\"color: #008000; text-decoration-color: #008000\">'MCM-41'</span> at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">77.355</span> K,\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">PointIsotherm</span><span style=\"color: #000000; text-decoration-color: #000000\"> e0590c7a3dae0ea88d7328c2c1a31fed</span><span style=\"font-weight: bold\">&gt;</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'nitrogen'</span> on <span style=\"color: #008000; text-decoration-color: #008000\">'NaY'</span> at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">77.355</span> K,\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">PointIsotherm</span><span style=\"color: #000000; text-decoration-color: #000000\"> 25abc19b921164e130f13e0126763853</span><span style=\"font-weight: bold\">&gt;</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'nitrogen'</span> on <span style=\"color: #008000; text-decoration-color: #008000\">'SiO2'</span> at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">77.355</span> K,\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">PointIsotherm</span><span style=\"color: #000000; text-decoration-color: #000000\"> b72b8b4b525dc901d2977ad577637462</span><span style=\"font-weight: bold\">&gt;</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'nitrogen'</span> on <span style=\"color: #008000; text-decoration-color: #008000\">'Takeda 5A'</span> at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">77.355</span> K,\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">PointIsotherm</span><span style=\"color: #000000; text-decoration-color: #000000\"> 5133355996d3f32458515fd1cf492813</span><span style=\"font-weight: bold\">&gt;</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'nitrogen'</span> on <span style=\"color: #008000; text-decoration-color: #008000\">'UiO-66(Zr)'</span> at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">77.355</span> K\n",
       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mPointIsotherm\u001b[0m\u001b[39m 5b36aabf643c8557893f1a82aeedffaa\u001b[0m\u001b[1m>\u001b[0m: \u001b[32m'nitrogen'\u001b[0m on \u001b[32m'MCM-41'\u001b[0m at \u001b[1;36m77.355\u001b[0m K,\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mPointIsotherm\u001b[0m\u001b[39m e0590c7a3dae0ea88d7328c2c1a31fed\u001b[0m\u001b[1m>\u001b[0m: \u001b[32m'nitrogen'\u001b[0m on \u001b[32m'NaY'\u001b[0m at \u001b[1;36m77.355\u001b[0m K,\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mPointIsotherm\u001b[0m\u001b[39m 25abc19b921164e130f13e0126763853\u001b[0m\u001b[1m>\u001b[0m: \u001b[32m'nitrogen'\u001b[0m on \u001b[32m'SiO2'\u001b[0m at \u001b[1;36m77.355\u001b[0m K,\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mPointIsotherm\u001b[0m\u001b[39m b72b8b4b525dc901d2977ad577637462\u001b[0m\u001b[1m>\u001b[0m: \u001b[32m'nitrogen'\u001b[0m on \u001b[32m'Takeda 5A'\u001b[0m at \u001b[1;36m77.355\u001b[0m K,\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mPointIsotherm\u001b[0m\u001b[39m 5133355996d3f32458515fd1cf492813\u001b[0m\u001b[1m>\u001b[0m: \u001b[32m'nitrogen'\u001b[0m on \u001b[32m'UiO-66\u001b[0m\u001b[32m(\u001b[0m\u001b[32mZr\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m at \u001b[1;36m77.355\u001b[0m K\n",
       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "isotherms_n2_77k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have a mesoporous templated silica, a zeolite, some amorphous silica, a\n",
    "microporous carbon and a common MOF.\n",
    "\n",
    "What about the isotherms which we'll use for isosteric calculations? Let's see\n",
    "what is the sample and what temperatures they are recorded at. We can use the\n",
    "standard `print` method on an isotherm for some detailed info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Material: TEST\n",
      "Adsorbate: n-butane\n",
      "Temperature: 298.15K\n",
      "Units: \n",
      "\tUptake in: mmol/g\n",
      "\tPressure in: bar\n",
      "Other properties: \n",
      "\tiso_type: isotherm\n",
      "\tmaterial_batch: TB\n",
      "\n",
      "[298.15, 323.15, 348.15]\n"
     ]
    }
   ],
   "source": [
    "print(isotherms_isosteric[0])\n",
    "print([isotherm.temperature for isotherm in isotherms_isosteric])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the isotherms that were measured in a combination with\n",
    "microcalorimetry. Besides the loading and pressure points, these isotherms also\n",
    "have a differential enthalpy of adsorption measured for each point. We can use\n",
    "the `isotherm.print_info` function, which also outputs a graph of the isotherm besides\n",
    "its properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Material: Takeda 5A\n",
      "Adsorbate: carbon dioxide\n",
      "Temperature: 303.0K\n",
      "Units: \n",
      "\tUptake in: mmol/g\n",
      "\tPressure in: bar\n",
      "Other properties: \n",
      "\tiso_type: Calorimetrie\n",
      "\tlab: MADIREL\n",
      "\tinstrument: CV\n",
      "\tmaterial_batch: Test\n",
      "\tactivation_temperature: 150.0\n",
      "\tuser: ADW\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "<span style=\"font-weight: bold\">(</span>\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">AxesSubplot:</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">xlabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Pressure [$bar$]'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">ylabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Loading [$mmol\\\\/g^{-1}$]'</span><span style=\"font-weight: bold\">&gt;</span>,\n",
       "    <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">AxesSubplot:</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">ylabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'$\\\\Delta_{ads}h$ $(-kJ\\\\/mol^{-1})$'</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "<span style=\"font-weight: bold\">)</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m(\u001b[0m\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Pressure \u001b[0m\u001b[32m[\u001b[0m\u001b[32m$bar$\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Loading \u001b[0m\u001b[32m[\u001b[0m\u001b[32m$mmol\\\\/g^\u001b[0m\u001b[32m{\u001b[0m\u001b[32m-1\u001b[0m\u001b[32m}\u001b[0m\u001b[32m$\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[1m>\u001b[0m,\n",
       "    \u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'$\\\\Delta_\u001b[0m\u001b[32m{\u001b[0m\u001b[32mads\u001b[0m\u001b[32m}\u001b[0m\u001b[32mh$ $\u001b[0m\u001b[32m(\u001b[0m\u001b[32m-kJ\\\\/mol^\u001b[0m\u001b[32m{\u001b[0m\u001b[32m-1\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m$'\u001b[0m\u001b[1m>\u001b[0m\n",
       "\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The second axis range limits are given to the print function\n",
    "isotherms_calorimetry[1].print_info(y2_range=(0,60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the isotherms which are to be used for IAST calculations, we'd like to plot\n",
    "them on the same graph, with the name of the adsorbate in the legend. For this\n",
    "we can use the more general `pygaps.graphing.plot_iso` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">AxesSubplot:</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">xlabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Pressure [$bar$]'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">ylabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Loading [$mmol\\\\/g^{-1}$]'</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Pressure \u001b[0m\u001b[32m[\u001b[0m\u001b[32m$bar$\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Loading \u001b[0m\u001b[32m[\u001b[0m\u001b[32m$mmol\\\\/g^\u001b[0m\u001b[32m{\u001b[0m\u001b[32m-1\u001b[0m\u001b[32m}\u001b[0m\u001b[32m$\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xmoc+iPM0UFeqFQlLvtdVQUEBP//5z3UPSrDWsqPgEEsWbeCLeas5VeSufICB62+axMzrz3GmQKmWBupKUFq3bs2vf/1rtm3bVu8l36dMmdJg4QT+pk+FU9O2d/dRlizawJKFG9mz+whxcYY+fTuxc8dhSj0VuoNrfruoooCSGmnJd4lkhw6eYOmijSxZtJGCrQcwBoYM80++OmHiQFq2SFZ38CingBKRqHHsWBGZSzaxZOFGNm3YA0D/gV257a6pnDNp4BlrJqk7eHSLmIAyxjwPXAH0AgqBj4FHrbVHKhxzO/Ak0A1YA9xnrV3lQLki0kgKC12sWLaZJQs3sm7tTqzP0qtPR759y7lMnDyYLl3b1vhadQePbhETUIAXuBVYC7QF/gK8AVwFYIyZDPwOuAaYD/wn8IkxZqC19oQTBYtIeFQdrzRkWHeyV21jycKN5OYU4C310aVrG66+dgITJw+mV++OTpcsjSBiAspa+3iFpweNMS8Df6+w7XvALGvtZwDGmBeA7+MPrLcarVCJaF6vj7mZuWTnb2fMgD5MzxhNvJp0IlrF8Upul4e4OIO1FmuhfYeWXHr5GCZOHky//l00TqmJiZiAqsYFwOoKz0cDb5Y9sdZaY0x2YHslxpi7gbsBevfu3bBVSsTwen1c8vDzZOZtochVQouUJDKG9mfeC48qpCLY/K/Xs2H9brxeH+CfOTw+Po4bbp7EFVePJy5OodRURWRAGWOuxX/FVHH0aSvgeJVDjwGtq77eWvsa8Br4x0E1TJUSaeZm5rJs/RaKXP6xL4XFbjLztjA3M5crJo1xuDqpyFrLxrw9zJ2TxfJlm6HKT6nP56PU61M4NXERF1DGmOuBPwAzrLVZFXadBKoO+24LbGmk0iRCbdt7gI+WZPO//55XHk5lilwl5ORvV0BFCI+nlKWLNvHpnCy2bT1Ai5bJTMgYQG52AW736bkbkzReSYiwgDLG3An8GrjSWru4yu5cIL3CsQZIA7TuQhPj8/lYuXEbHy7O4sMl2azZuhOAXp3bk5gQj6fCOj0tUpJIG9DHqVIl4NixIr6Yt5ovPs3l+LFTdO/Rnrv+3wWcN3UYSYnxGq8k1YqYgDLGPIi/C/kl1toV1RzyOvCpMeYtYCHwIJACvN94VYpTit0lfLlqHR8uyeKjJdnsO3Kc+Lg4Jo8cxG/uv5krJ6bTt1unau9BTc844zalNJJtWw/w6ZwslizcSGmpl7T0VC69Ip2Ro/tUar7TeCWpTsTMxWeMsUApUKmNxlrbssIxtwNPcXoc1L21jYPSXHzR68DR48xZmsOHi7P4bOVait0ltGqewqUTRjFjUjrTM0bRoU3lgZllvfhy8reTpl58jvB5faxasYVPPspiw/rdJCcnMGXacC65fAw9erav/Q2kyalpLr6ICaiGooCKTNV1B4+LM2zYsYcPF2fz4ZIslq7Lx1pLr84dmDFpDDPOTef80UNITkp0unypRlGRi2++WMe8T7I5eOAEHTu15pLL0ph64QhatkxxujyJYJosViJG1e7gKYkJdGzbmqSEeLbsOQDA2EF9eeqOa5gxKZ3RA3pr/EuEqG4BwP37j/Ppx9nM/2odbpeHIcN6cOsd5zN2Qn9dvUq9KKCk0f3rm0wWr9mMy+MBoLjEw84Dhxk7KJXf/eAOrpg4hp6d1RQUaaouAJiQGE9yciKFJ10kJMQzcfJgpl8xhr79uzhdqsQIBZQ0is279jFnaTYfLclmfs4GfFWalo2BqyeP5Z6rLnCoQqlNTlZB+WwPAJ4SL54SL5POG8xtd06lbbsWzhYoMUcBJQ2itNTLknWb+WiJP5Q27twLwIi+PZk5ZTwfL8um2O0pP75FSrK6g0ew4uIS5n2SXR5OZYyBHr06KJykQSigJGyOnixi3vLVfLQ0m7mZqzl6sojEhHimjRnG/ddcyBUT0+jbrXONUxKpO3jkOXGimHlzspg3N4eiQjcmzmB9p69+NaBWGpICSs6qtslXN+/aV36VtHD1Rrw+Hx3btGLGpDFcOSmdi8ePoFXzykvGx8fHMe+FR9UdPIIdPnSSOR+s5OvP1+B2lzIuoz9XXj2Of/59iQbUSqNRN3OpUXVXOuOH9ON/bruKT5bl8tHSbDbt3Af4m+6unDSGKyeNYcIQ9d6KVnt2H+HDWStYtCAPLEw6bwgzZo6nZ68OQIVefBpQK2GkcVAStDlLsrnpZ69QWOw+Y19SYgJT04Zy5cQxXDExjdRunRyoUMJl65b9fPDeclYs20xCYjzTLhzJFVeNo1PnM+ZiFgk7jYOSoGXnb6eomnC66Vvn8Icf3XVG051EF2st69fu5IP3VrAmdzvNmydz1cwJXHpFOm3aNne6PBEFlFTv4LETzM/Jq7oKAi2bJXPzhZMUTlHM57NkrdjCB7OWk79pH23aNuem2yZz4aWjad482enyRMopoKQSr9fHHz/+hsde/ycnTxXTp0sHDh0v5JRbve2iXWmpl6WLNvLhrBXs2nmYzl3acNf/u4Dzpw0jKVnTR0nkUUBJuVUbt3Hvi2+yYsNWpo0ZyisPfYdBPbupt10UqjglUc+e7TlypIiPP1zFoYMn6NW7A/c/NJ2Jkwfr31IimgJKOHayiP/507959YMv6dKuNX/7n3u56YKJ5fPfXTFpjBb8iyLlUxJt2oe7wmDoAYO6csf3pjFmbD+tVCtRQQHVhFlr+evni/nR7/7BoeMneWDmRfz0zmtp01I3yKNZ1qptbNywh1LP6YUbE5Piufq6DMaO7+9gZSLBUUA1Ueu27eK+/32TBbkbOWfYAD795cOMGZjqdFlST9sLDvLm619VCieAUo+X7QUHFVASVRRQTUzhKRc//cv7vPivebRu0YzXf/Rd7rpsCnFxuhcRzYqLS/j3O0v5dE4WSckJJCTGVwopTUkk0UgB1URYa5m1YCUPvfxXdh08wn9cPpVffO8GOrZtVfuLJWJZa1m2ZBNv//kbjh4p4oKLR3L9TZP47W8+0ZREEvUUUE1A/q79PPB/f+HT5asZ3b83/3zq+0wcPtDpsqSe9u4+yhuvf8Wa3O2k9uvMDx65koGDuwPw+JPXakoiiXoKqBjmcpfw3N/n8Nzf55CUGM9LD9zKfVddSEJCvNOlST2UuD188N5yPnx/JYlJ8XznP6Zx0aWVu//HxceRPr4f6eP7OVipSP0ooGJIxZnHvT4fb89bxNa9B7npgon8+r6b6dahrdMlSj1lr9rKm69/zYH9xzl3yhBu+c4U2rVv6XRZIg1CARUjymYeX7o+n1OuEgCaJyfx2a8e4aJxIx2uTurr0MET/OVP37AiM5/uPdrz46evY8So3k6XJdKgFFAxYm5mLkvWbqa45PTATGMM7pJSB6uS+ir1ePlkThaz3l2KtXDjrZO5fMZYEhLVTCuxTwEVI/75TWalcAI45S4hJ3+7ZoGIUuvX7uTPr33F7p2HGTehP7d/dyqdOrdxuiyRRqOAigFzlmTzjy+XEmcMvgrre7VISSJtQB8HK5NQHDtWxN/fWsDCb/Lo1Lk1P3r8Kg2wlSZJARXlZi9cyQ1Pv8zo/r1pkZJM1uaC8tVvNfN45Ks4qWvvPh05dOgk//r7EtwlHq6+LoOrr5tAsmYalyZKARXF/v3Ncm762auMHZTKp798mFbNm2nm8ShSPqnr5n24XR6MAWth+Mhe3PX/LqB7j/ZOlyjiKAVUlPrHl0u57Znfc86wAXzy/I9o3cK/gKBmHo8eOVkF5eEE/nBKTIxn+pXpCicRQL9eR6G/zFvErc/8jskjB/HpLx8uDyeJLtmrtpaHU5nSUv+kriKiK6io86ePv+F7v/oz3xozjA+f/QHNU7REd7TxeX18OHslX32+5ox9mtRV5DQFVISrODvEvsPHePWDL7lk/Eje//lDNEtOcro8CdKB/cd59aVP2Zi3m4xJAzl+7BTbth7QpK4i1VBARbCy2SEy87ZQWOwGoEPrlrz39IMKpyhjrWXB1+t5649fg4H7/vNSJp8/FOuzmtRVpAYKqAg2NzO3UjgBuEo8fJ2Tp44QUeTkiWL++LvPWb4sn6HDe3Lvg5fSqXNrAEy80aSuIjVQQEWw7PztFFUIJ9DsENEmN7uA3/92HidPFnPz7edx+YyxukISqSMFVAQbkdqD8sExAZodIjq43R7+/peFfPZJDj17deDRn1xDal91fhAJhgIqgq3dthtrLSlJibg9pZodIkps27Kfl1+cy57dR5h+ZTo33jqZpCT9qIkESz81EWrrngM8+7cPmXneOO6cPkWzQ0SBsu7j//7HEtq0bc7jT13LyNG62hUJlQIqAllreeD//kJCfDwvPXAbPTu31z2nCFex+/g55w7iu//vAlq20gBqkfpQQEWg2YtW8cmyXH5938307KwpbyJZTd3HjTFOlyYS9RRQEabwlIsH/+9tRvXvxYMzL3a6HDmLkyeK+ePvv2D50s1ndB8XkfpTQEWYp996n10Hj/Duk/eTkKBVUyNFxWUxUvt2xsQZXnvlM3UfF2lACqgIsmbrTl7816f8x+VTmTRikNPlSEDFZTFK3B7i4uLwen306Nme//7JTPr07eR0iSIxSQEVIXw+H/f+5k3atWrBc3ff4HQ5UkHVZTG8Xh/x8XHccPO5CieRBqQ2iQjx5qcLWbx2E7+850Y6tGnldDlSQcG2A2csi+Hz+di167BDFYk0DQqoCHD4+Eke+f07TB45iO9cMtnpcqSKU1WmmwItiyHSGBRQEeDRP7zL8aJifveDO4iL0z9JJPn4g1V8PHsVzVskk5yciDGQnKJlMUQag+5BOWzxmk386ZP5PHzjZYzo18vpciTA57P8/S8L+PiDVWRMGsQ9D1zM+jW7tCyGSCNSQDmotNTLvS++Sa/OHXji9mucLkcCSj1efv/yPBYv2MAll6dx+13TiIvTshgijU0B5YCyVXJfmf05a7bu5F9PP0DL5ilOlyXAqVNuXvzlR6zN3cFNt03mymvGa1YIEYcooBpZ2Sq5S9fnc8pVQnxcHL/74EuumTxOk8A67NjRIp7/+fvs3H6Iex+8lCnThjldkkiTpk/ERla2Su4pVwkAXp+P5XlbmJuZ63BlTdvePUd58rF32LvnKD96/CqFk0gEUEA1suz87RS5KndbLnL5V8kVZ+Rv2suTj72Dq7iEn/z0etLS+zpdkoigJr5GN2ZAHxLjEygpLS3fplVynZOzahv/+8JHtGnbgseenEnXbu2cLklEAhRQjWx6xmhSkhIp9XqxoFVyHTT/q3W89spn9EntxCM/uYa2bVs4XZKIVKCAamT7jx7nxKlibrv4XAb17KpVch1greWDWSt496+LGDm6Dz949EqaNUtyuiwRqUIB1chmL1oFwGO3XMnQPj0crqbp8Xl9vPWnr/lsbi7nThnCPd+/hIRELWsiEokUUI1s1oKVDOndXeHkgJKSUl7537ksX7qZK64ex023nUdcnMY4iUQqBVQjOnz8JN/k5PHozVc4XUqTU1Tk4lfPfsCG9bu57a6pXHZlutMliUgtFFCNaM7SHLw+H9dMHut0KU3KkcMnee6ns9iz5ygP/NdlTDpviNMliUgdKKAa0ayFK+nVuQNjB2ucTWPZtfMwz/10FqeK3Pz3T2YyYlRvp0sSkTpSQDWSwlMu5i1fwz0zvqW53RqQz+sjJ6uAgm0HSEiM54P3MklKSuSJZ24gta/WbxKJJnUOKGPMP0M8xyPW2oIQXxsz5i7Pxe3xcM15at5rKD6vj2effq/S8uxJSQk8+6sb6NJVA3BFok0wV1DXAdnAiToeb4DzgOeAguDKij3vL1xFp7atmDxysNOlxKycrIJK4QRgDOzeeVQBJRKFgm3iu9dau7wuBxpjEoCS4EuKPe4SD3OWZvPtaedoQG4DKth2oFI4gb9reUHBAa3jJBKFgvm0fBrYFcTx3sBr9gRVUQz6MmsdJ0+51LzXwNp3aHnGtqTkRFJTde9JJBrV+QrKWvt0MG9srbX4A6rJe3/hKlo1T+GC9OFOlxKzXMUlzP0oi7g4Q0JCPB5PKUnJiQwY2JW09FSnyxOREKgXXwPzen3MXrSKKyaOITkp0elyYpLPZ3nlpbns3HmYHz12FVgoKDhAampn0tJTiVOzqkhUqldAGWNes9beHa5iYtGiNRs5dPykmvca0D//vpiVmVu4/a6ppI/z32vSPSeR6FffXy0vDUsVgDHmRmPMQmPMCWNMaZV9dxhjfMaYwgqPf4Tr3A1p1sKVJCcmMn2CltNoCAu/Wc8H7y3nWxeN5NIrxjhdjoiEUa1XUMYYb027ABvGWo4CrwLNgNeq2b/VWjsgjOdrUF6vj08yc3jz04WMHtCbZslaziHcNm/cw+uvfs7Q4T2583saAC0Sa+pyBbUX6Gqtja/yiCOMPfSstfOstf8AtobrPZ3i9fq45OHn+fbTr3CiqJjc/O1c8vDzeL0+p0uLGYcOnuDXz31Iu/Yt+cEjV2rJDJEYVJeA+hCoaXTpp2GspTa9jDH7jDE7jTHvGGNqnNDOGHO3MWalMWblwYMHG7FEv7mZuWTmbaHY7R8G5vaUkpm3hbmZuY1eSyxyuTz86hcfUFJSysM/vppWrZs5XZKINIBaA8pae5+1dlEN+74X/pKqtQAYCXQHxgMu4HNjTLVrdFtrX7PWjrPWjuvUqVMjlXhadv52ilyVxygXuUrIyd/e6LXEGp/P8ur/zmXH9kM8+F+X07NXB6dLEpEGEhX9b621W621m6y1PmvtPuB7+MPqHIdLq9aYAX1okVL5nlOLlCTSBvRxqKLY8a9/LGFFZj633nE+aWM1K7xILAs6oIwxrzdEIUGygUdE3hWfnjGacYNPd3Nu2SyZjKH9mZ6hnnz1sXhBHrP/ncm0C0cwXT32RGJeKFdQF4S9CsAYE2+MSQGSAs9TAg9jjLncGNMz8Of2wCvAIWBZQ9RSX/Hxcfzm/psBuGFqBv/4yf3Me+FRzcNXD/mb9vKHlz9j6PCe3HX3BeqxJ9IERNJMErcBb1R4Xhz42heYCrwOtME/m/pi4CJrbWFjFhiMzbv2A/D4rVcyWk179XL40MnyHnsPqceeSJMRMQFlrX0TeLOG3Q8HHlFjw449GGMY2LOr06VEtbIee26Xh8efupbW6rEn0mSozamBbNixlz5dOtA8JdnpUqKWz2f53f99yvaCgzzww8vo1buj0yWJSCNSQDWQDTv2MKR3d6fLiGrvvbOE5Us3c8vtUxgzVnPriTQ1oQTU7rBXEWN8Ph8bd+5lSO9uTpcStZYs3MCsf2Uy9YLhXDYj3elyRMQBQQeUtfa8higkluw6eIRTrhJdQYUof9Nefv/yPIYM68F3/9+F6rEn0kSpia8BbNy5F0BXUCE4ctjfY69t2xaaY0+kiQupF58xJg/IAXIDj9XWWjX9BWzYURZQuoIKhtvt4Ve/+BBXcQmPP3kTrds0d7okEXFQqN3M3wb+A/h24Lk1xhzFH1Y5wELgE2ttSfUvj20bduyhbcvmdG7X2ulSooa/x948Crbu50ePXU2vPuqxJ9LUhdrE58M/YHYK0AuYCDyPf9bzO4E/AjuMMdeGo8hos2HHXob07q57J0GY9c+lZC7ZxM23T9FquCIChH4F9V/AHRVmOd8NLDfGvAZ8DvwM/wwQfzPGHLPWfln/UqPHhh17uWjscKfLiGg+r4+crAIKth3A5Srho/dXcv63hnP5VWOdLk1EIkSoAZWAf9qhSqy1x40xzwI/sdZOMMYMBv4HaDIBdaKomD2Hjur+01n4vD6effo98jfvw+3yANC8eRJ33j1NV50iUi7UJr73gSeMMdUtxlMKlF0+fAKMC/EcUUk9+GqXk1VQKZzAvwrxutW7HKxKRCJNqAH1I+AUsMUY84QxJj0w2/hU4FlgQ+C4knqcIypt2LEHUA++synYdoASt6fStpKSUgoKDjhUkYhEopCa+Ky1R40xE4HHgO8DTwZ2GWAncF3g+ThgW32LjCYbduwlIT6e/j06O11KxErt25mEhHg8Hm/5tqTkRFJT9T0TkdNCns080IX8aWPMT4FRQDf8azTlWmvLfj1egr/beZOxYcce+nfvTGJCxEwUH3GGDPNfXZbdbkpKTmTAwK6kpac6V5SIRJx6f4paay2nB+xW3Te/vu8fbfxdzHX/6Wxm/SsTj8fLTbefR2mpl9TUzqSlpxKnBR1FpAL9mh9GpaVe8nfv58qJWo68Jtu3HWTuR1l866KRzLhmvNPliEgEq/OvrMYYTYpWi4J9hyjxlOoKqgY+r48//v5zWrZqxk23TXa6HBGJcMFcQZ0yxqzj9HRGufjvNx1tiMKikXrwnd0Xn60mf9M+7n9oOi1baWVcETm7YALqO/g7Q6QBj+DvFGGNMbs4fQ8qB39o5Ye3zOhQFlCDdQV1hqNHCnnnr4sYMbo3504Z4nQ5IhIF6hxQ1tp3gHfKnhtjOgJj8IfWaOAq4FEgwRhTZK1tFeZaI5rX6+OzFWtpkZLM4jWbmJ4xmnjd9C/39hvzKfV4uevuCzRbhIjUSX26mR/CP+/e52XbjDGJwAj8odVkeL0+Lnn4eb7OzsNnLTf97BUyhvZn3guPKqSAnKxtLF20ketvmkS37u2cLkdEokTIn57GmDbGmBuNMT80xtxsjOltrfVYa7OttW+Fs8hINzczl8y8LfisBaCw2E1m3hbmZp7R877Jcbs9vPHaV3Tv0Y4rr2lSs16JSD2FFFDGmFHARuBv+CeD/SuwzRgzxxjTI4z1RYXs/O0UuSovfVXkKiEnf7tDFUWOWf9cxoH9x/nuPReSmKhRDSJSd6FeQf0WyAY6WWvbAS2BGUAnYJkxpkn1EhgzoA/Nk5MqbWuRkkTagD4OVRQZdm4/xMcfrOL8bw1n2IheTpcjIlEm1IBKB35trT0CYK09Za39GJiE/8rquTDVFxWmZ4xmZL+egH8ywpbNkskY2p/pGaOdLcxBPp/lT7//gmbNk7j5O1OcLkdEolCoAXUEOGOpDWutF/hfYHo9aoo68fFx/PKeGwG47eLJ/OMn9zf5DhJff7GGjRv2cOsd59O6tcY8iUjwQv0EfRf/elDVdckyNMEplE4UuQC4/5oLuWLSmCYdTsePneIfby9k6PCeTJk2zOlyRCRKhfop+iRwElhrjHnMGDPeGNPLGHM+/uXeF4atwihx9GQRAO1atXC4Euf99c35uFwevnvPhRrzJCIhCymgrLXFwFT8vfgeAZYBBcDX+FfU/X54yoseRwsDAdWyaQfUmtztLJqfx1XXjKdHz/ZOlyMiUaw+A3VdwCPGmB/jn0miI7DTWrsuXMVFkyMnCgFo27K5w5U4p6SklD//4Uu6dmvLVddlOF2OiES5cKwH5QFWhqGWqHb05ClaNU8hIaHpTvr+wXvL2bf3GI8/dS1JSU3uNqSIhFlInyLGmGTgTmAw/h59a4HV1totYawtqhwtLGrS95927zrCB7OWc+6UIYwc3bTHf4lIeIT6a+7fgavxB1MLIBUwxpgiYB3+Gc3vCUeB0eLoyaIme//JWv+Yp5TkRG6983ynyxGRGBFqQF0MPGCtfRXAGNMMGIl/ktiyR5Ny9GTTvYJa8PV68tbt4j/uvZC2bZvm90BEwi/UgNoBbCt7EujVtzzwaJKOnixiUK8mNcMTACdOFPPXN+czeEh3pl040ulyRCSGhDoO6jngvnAWEu2OFp6iXaum14Pv728toPhUCd+950Li4jTmSUTCJ9RxUG8DBcaYz40x3wqsA9WkNcV7UOvX7mT+V+u4/Kqx9OrT0elyRCTGhNqL74fA/YGnFwAeY8wGTi/9vtpa+3lNr4817hIPxe4S2rdu6XQpjcbjKeVPv/+Szl3aMPOGc5wuR0RiUKhNfD/GvwZUKjAcuB34GGgP/CfwaTiKixanpzlqOk18H81eyZ7dR7jze98iObnJX0CLSAMItZOEB3jTWrsj8DwP/wSyABhj2tazrqjS1KY52rvnKLP/lck55w4ibWxfp8sRkRgV6hXUX/E37VXLWnssxPeNSk1polhrLX9+7UsSEuO5/a6pTpcjIjEs1IDaDtxkjLnfGNN05/YJOHKi6QTU4gUbWJu7gxtvnUy79k3nnpuINL5Qm/ieBZrjX/r9p8aYRUAOgU4STW3Ko/ImvhgPqMJCF2+/MZ/+A7ty4cVNbiy2iDSyUAOqFdAP/4wRIwOPG/F3nogzxhRZa1uFp8TIV97EF4P3oHxeHzlZBRRsO8CmDbs5eeIUjz05k7gmvCCjiDSOkALKWmuBLYHH+2XbjTEpwIjAo8koC6hYW2rD5/Xx7NPvkb95H26XB4AOHVvRu7fGPIlIwwvXbOZr8I992op/6Y0mtfxGrC61kZNVUCmcAApPusjJKiB9fD8HKxORpkCzmYdBrC61UbDtACVuT6VtJSUeCgoOKKBEpMFpNvMwiNVpjlL7diYpObHSFVRSciKpqZ0drEpEmgrNZh4GR08W0b517AVUWnoqnbu0Yef2QwAkpyQyYGBX0tJTnS1MRJqEUAOqbDbzuWGsJWodPVnE4N6xt9SGiTMkJyXQpm1zLrp0NH37dSEtPVU9+ESkUWg28zA4WngqJpv4NubtIX/zPmZefw7Xfnsi6eP7KZxEpNFoNvMwiNXVdD+avYKWrVI4/4LhTpciIk2QZjOvJ5e7hGJ3ScwF1K6dh8lasZVLLh+j2cpFxBGazbyejhaeAmJvqY05s1eSlJTAxdPTnC5FRJoozWZeT7E4zdGRI4UsWpDH1AuG07p1M6fLEZEmSrOZ11MsLrXx6ZwsfD7LZTPGOl2KiDRhms28nmItoE4Vufli3mrOmTSILl3bOl2OiDRhms28nmJtqY0vP1tN8akSrrh6nNOliEgTp9nM66nsCqp9DASUx1PK3DlZjBjVm379uzhdjog0cWEddWmtdVlrV1pr3wzn+0ay00ttRH9ALV6wgaNHirjyGl09iYjz6hxQxpgHjTFBzRIaeE1MLx509OQpWrdoRnyUz7Dg81nmzF5Jn9ROjBzdx+lyRESCuoJ6EajzJ1egd9+LQO9gi4omRwtjYybz7FVb2b3rCFdeMw5jjNPliIgEdQ/KAL8wxhwJ4viYFyvTHH30/ko6dmpNxqRBTpciIgIEF1ALgHigU5CvORlURVHGH1DRPYvEpg172Ji3m+98d2rMrQosItGrzgFlrZ3agHVErSMnixgS5UttfDR7BS1bpjD1wpFOlyIiUi667+xHgGhfTXfP7iOsWr6Fi6ePJiVFk8KKSOSImIAyxtxojFlojDlhjCmtZv/txpgtxphTxphMY0xEzMMT7fegPv5gFQmJ8Vx82RinSxERqSRiAgo4CrwKPFR1hzFmMvA74F6gHfAe8IkxpnVjFliVy12Cq8QTtQF17GgRC75ez/nfGkGbttF9H01EYk/EBJS1dp619h/A1mp2fw+YZa39zFrrBl4A3MA1jVljVdG+1ManH2fj9fm4fEa606WIiJwhYgKqFqOBVWVPAlMtZQe2n8EYc7cxZqUxZuXBgwcbrKjT0xy1bLBzNJTi4hI+n5vLhIwBdO3WzulyRETOEC0B1Qo4XmXbMaDaJj5r7WvW2nHW2nGdOgXTKz440TyT+VefreHUKTdXXjPe6VJERKoV0mSxxpgpZ9ntA04AGwPNceFwEmhTZVtb/JPVOiZaA6rU4+WTj1YxbERP+g/s6nQ5IiLVCnW5jW8AW+G5qfIcwGWM+SPwX9Zab4jnKZMLlN8oMf65eNKAWfV833opX2ojyrqZL1m0kSOHC/nevRc5XYqISI1CbeK7ENgB/B64DBgX+PoHYCdwA/AL/J0bflqXNzTGxAeW60gKPE8JPAzwOjDTGHOBMSYJ+CGQQoWlPpxw+goqejpJWGv56P0V9OrdgdHpqU6XIyJSo1CvoL4PvGWtfarK9nnGmKeAO6y1VxpjEoA78C9kWJvbgDcqPC8OfO1rrV1kjLkPf1B1A9YAl1lrT4RYf1hE41IbOVnb2LXzMPf956WaFFZEIlqoV1AXA4tq2LcYmBb48wL8gVIra+2b1lpTzaMgsP8v1tp+1tpm1toJ1tpVtbxlg4vGpTbmzF5Jh46tmDh5sNOliIicVaifrEeAGTXsmxHYD9CcM3vfxYwjJwuj6v5T/qa9rF+7i8uuTNeksCIS8UJt4vsl8H/GmFTgI+Ag/lnOr8J/L+qBwHHTgBX1rDFiRds0R3Nmr6RFi2SmXaRJYUUk8oUUUNbal40xu4HHgFcC71MK5AAzrbWzA4f+Aiipf5mR6ejJU1HTQWLf3qMsX7aZq2ZOoFmzJKfLERGpVahXUFhr3wfeN8bE4b96Omit9VU55lA964toRwujZ6mNjz9YRUJCPJdcrklhRSQ6hBxQAMaYQUBP/F2+K/UKs9Z+Uq/KosDRk0VRMc3RsWNFzP9qHVOmDaNtu+hpkhSRpi3UmSSGAe8Cw6h+aXeLf/XdmBYtq+l+9kkOpaVeLp8xzulSRETqLNQrqD/gH1A7E1hPDN9nqkm0LLXhKi7hs7k5jM8YQLcemhRWRKJHqAE1BrjRWjsnnMVEk/KlNiK8m/nXX6ylqFCTwopI9Al1HNQWAvedmqpomCi2tNQ/KeyQYT0YMCg6OnOIiJQJNaB+CDxujOkXzmKihdfrY87SbAA27tyL1+ur5RXOWLZ4E4cOnuTKq3X1JCLRJ9Qmvl8APYANxpgC/GszVWKtnRB6WZHL6/VxycPPs3jtZgCe+/scFuRuYN4Lj0bUlEfWWubMXkmPXh1IG9vX6XJERIIWakCtDTyanLmZuWTmbcFV4gGg2F1CZt4W5mbmcsWkyBljtCZ3O9sLDnLPA5cQF6dJYUUk+oQ6k8Sd4S4kWmTnb6fIVbnTYpGrhJz87REVUB+9v5J27Vtw7nlDnC5FRCQkkdMmFSXGDOhDi5TKUwW1SEkibUAfhyo609Yt+1m7egeXXTmWhMSYH44mIjGqzldQxpjl+Nd5Wm+MWcGZK+hWEqv3oKZnjCZjaH8Wrt5ISamXFinJZAztz/SM0U6XVm7O7JU0a57Ety7WpLAiEr2CaeJbx+lFBNdRS0DFqvj4OOa98Cg3PPV/fLgkm3eeuJ/pGaMjooOEz+vjm6/WsnTxRiZkDCQlOdHpkkREQlbngKp438lae0eDVBMl4uPj6NqhLW1aNI+Y+04+r49nn36PDet3g4Xc7G08+/R7PP7ktcRFQHiKiARLn1whKip206JZstNllMvJKiB/0+kxWW53Kfmb95GTVeBsYSIiIQrmHtSUYN7YWrsg+HKiR5HLTYuUyAmogm0HcLtLK20rcXsoKDhA+vgmOZ5aRKJcMPegvsF/36lsUE3Fe1CGM+9JxXT3sSKXm5bNIme2p9S+nTHGYO3pf4ak5ERSUzs7WJWISOiCCaiKXcK6AX8GPgVmAQeAzsC1wCXAXeEqMFIVFkfWFVS37m2x1pKQEIfX6yMpOZEBA7uSlp7qdGkiIiEJppPEurI/G2OeBf5irf2fKod9aoz5OfAQ8EVYKoxQRS433dq3dbqMcksWbQTgu/dcyJEjhaSmdiYtPVUdJEQkaoU61dEFwMs17JuPP6BiWiR1krDWsmh+HsNH9mLqBSOcLkdEJCxC/fX6CHBVDfuuCeyPaZHUSWLL5n3s23uMyecPdboUEZGwCfUK6jngZWNMKvAhp+9BXQVMB74fluoiWCQF1ML5eSQmxTNh4kCnSxERCZtQJ4t91RizG3gcf1NfAlAK5AAzrbWzw1VgpIqUgCot9bJ00UbGTRhA8+bO1yMiEi6hXkFhrf0A+MAYEwd0Ag5aayNz5b4wKy31UuIpjYh7UKtztnPyRDGTp6h5T0RiS8gBVSYQSvvDUEvUKHK5ASLiCmrR/Dxatkph1JjImU1dRCQcQg4oY8y3ge8Bg4AzRqxaa2N2hGikBNSpU25WLs9n2gUjSEiI6XHRItIEhdSLzxhzM/AWkA/0xN9RYk7g/U5Qcxf0mBApAbViWT6eEi/nTR3maB0iIg0h1G7mDwM/A+4PPH/VWnsX0Bc4BJwKQ20Rq7DYBUBLh+9BLfxmPV27taX/wK6O1iEi0hBCDaiBwGJrrRfwAq0BrLUngeeJ8W7mRcWBKygHA+rwoZOsX7uTc88fijGm9heIiESZUAPqOFD26bwbqNiFzAAd6lNUpIuEJr4lCzdgLUyeMsSxGkREGlKonSRWAqOAefjvPz1hjCkFSoAngMzwlBeZIiGgFi3IY+DgbnTt1s6xGkREGlKoV1C/AHYE/vwEsBx4FXgD/z2ou+tfWuQ6HVDOLLexveAgOwoOaWojEYlpoc4ksQxYFvjzMeAqY0wykGytPRG+8iKT0/egFs3PIz4+jnPOHezI+UVEGkO9BuoaY7oDE4H2wGFgKf5u5jHNySY+n9fHkoUbGJ2eSuvWzRr9/CIijSWkgDLGxAO/xT9Qt+IIUa8x5jXggVie9sjJgFq/bhdHDhdy251TG/3cIiKNKdR7UE/jXzX3cSAVaBb4+nhg+1P1Ly1yFbncJCcmEu/AYoCL5ufRrHkS6eP6Nvq5RUQaU6hNfLcD/2Ot/VWFbTuAF4wxFngQf+eJmOTUYoVut4flSzeTMWkgScmJjX5+EZHGFOolQGdgdQ37Vgf2x6zCYpcjzXtZK7ZSXFyi3nsi0iSEGlCbgBtr2HcjsDHE940KRS63I9McLZqfR/sOLRk6vFejn1tEpLGF2sT3c+AdY0xv4N/4l9voDFwPTKPm8IoJTixWeOL4KXKzC7h8xlji4jS1kYjEvlDHQf3TGHMMf2eJl4BEwAOsAi611n4etgojkBMBtXTxRrxen5r3RKTJCLkbmrX2M2vtRPw9+LoCzay1k4BsY8yUcBUYiYqKSxq9k8Si+Xn0Se1Erz4dG/W8IiJOqXc/aWutz1p7oMK4p/OBr+v7vpGssa+g9u4+Sv6mfUyeqqsnEWk6Gn8gTwxo7IBatCAPY2DSeZq5XESaDgVUCBozoKy1LJqfx/BRvWnfvmWjnFNEJBIooELQmAN1N2/cy4H9x5k8Rc17ItK0KKCC5PX6cHs8jXYFtXB+HklJCUyYOLBRziciEinq3M3cGHMQsHU41LlV/BpBY04UW+rxsmzRRsZl9KdZs6QGP5+ISCQJZhzUK9QtoGJaYbELaJyAyskuoLDQpbFPItIk1TmgrLVPNWAdUaPsCqpls4ZfTXfR/Dxat27GyNF9GvxcIiKRRveggtRYq+kWFbnIWrGFSecNISEhvvYXiIjEGAVUkBrrHtTypZvxeLxq3hORJksBFaTGCqhF8/Po1r0d/QZ0adDziIhEKgVUkBojoA4dPMH6tbuYfP5QjNHM5SLSNCmggtQY96AWL9gAwLlTNLWRiDRdCqggNfQVlLWWhfPzGDykO126tm2Qc4iIRAMFVJAaOqC2Fxxk987DnKvOESLSxCmggtTQAbXwmzziE+I4Z9KgBnl/EZFooYAKUmGxi6TEhAYZm+Tz+liycANjxvalVetmYX9/EZFoooAKUlFxwy21sXbNTo4dLdLYJxERFFBBa8i1oBbNz6N582TGjO3XIO8vIhJNFFBBKnK5G2QePpfLw/Jlm8mYNJCkpGDm8BURiU0KqCA11BXUquX5uF0ezps6LOzvLSISjaImoIwxbxpjPMaYwgqP+xq7Dv9quuFfm2nR/Dw6dmrF4KE9wv7eIiLRKGoCKuAta23LCo9XG7uAhriCOnasiNU52zl3ylDi4jS1kYgIRF9AOa4hAmrpwo34fFa990REKoi2gLrWGHPEGLPJGPOCMaZldQcZY+42xqw0xqw8ePBgWAtoiIBatCCPvv0607NXh7C+r4hINIumgPotMAToCFwDnA+8Xt2B1trXrLXjrLXjOnXqFNYi/PegwhdQu3cdYWv+fl09iYhUETUBZa1dZa3db631WWvXAT8ArjPGNOzCTFUUhnmg7uIFeZg4w8TJg8P2niIisSBqAqoavsDXRutV4PX6cHs8YQson8+yaH4eI0f1pl37alsrRUSarKgJKGPMjcaYtoE/DwR+DXxorXU1Vg3hnih204Y9HDxwQs17IiLViJqAAu4BthpjioDPgGXAnY1ZQHlAheke1KL5eSQnJzA+Y0BY3k9EJJZEzZw61tqpTtdQtppuOKY68nhKWbZkI+MyBpDSAAN/RUSiXTRdQTmuyOVvTaxvE5/P62PWu8soKnTTtVtbfF5f7S8SEWliFFBBCMc9KJ/Xx7NPv8eH768AYM4Hq3j26fcUUiIiVSigghCOgMrJKiB/0z58PguA2+Uhf/M+crIKwlGiiEjMUEAFoeweVH06SRRsO4Db7am0rcTtoaDgQL1qExGJNQqoIITjCiq1b+czJoRNSk4kNbVzvWoTEYk1CqggFBbXP6AGDe2GtZb4+DiMgeSURAYM7EpaemqYqhQRiQ1R0808EoTjCio3ezvWwg23nEtpqZfU1M6kpacSF6/fFUREKlJABSEcA3UzF2+ibbsWXHHVOK39JCJyFvq1PQhFxW4SE+JJTAgt113FJeRkb2PCxIEKJxGRWiigglDftaCys7bhKfFyzqRBYaxKRCQ2KaCCUORy12uao8wlm2nTtjmDh3QPY1UiIrFJARWE+lxBud0eclZtZfw5A9UhQkSkDvRJGYT6rKabk7UNt7uUjIkDw1yViEhsUkAFoT5XUJlLNtOqdTOGDu8Z5qpERGKTAioIoQZUidtD9sqtTDhnAPFq3hMRqRN9WgahsNgVUkCtztmOy+VhwkT13hMRqSsFVBCKXKHdg8pcupmWLVMYNkLNeyIidaWACkJRcfBNfB5PKatWbGFcRn8SEuIbqDIRkdijgApCKPeg1uTsoPhUCRlq3hMRCYoCqo68Xh+uEk/QAZW5dBMtWiQzYlTvBqpMRCQ2KaDq6JQ7+IliSz1eVi3fwtgJ/UlIVPOeiEgwFFB1VLaabssgAmrtmh0UFbnVvCciEgIFVB2dXguq7nPxZS7ZRLNmSYxMU/OeiEiwFFB1FOxihaWlXlZmbiF9fD8SE7XslohIsBRQdRRsQK1fu4vCQpeW1hARCZECqo4Ki11A3TtJLF+6iZSUREal9WnIskREYpYCqo7KOknU5QrK6/WxYlk+Y8b1Iyk5saFLExGJSQqoOgqmiS9v3S5OnCgmY5KW1hARCZUCqo6CCajMpZtJTk4gLb1vQ5clIhKzFFB1VB5QtdyD8nl9rFi2mbSx/UhW856ISMgUUHVU13tQGzfs4fixU2reExGpJwVUHRW53CTEx5NUy5imzKWbSEyKZ4ya90RE6kUBVUdFLnet0xz5fJblSzeTNqYvKc2SGqkyEZHYpICqo9qW2vB5fcz5YCVHjxTRuWsbfF5fI1YnIhJ7FFB14PX62LxzH64SD3OWZOOtEj4+r49nn36Pf/5tMQBffLqaZ59+TyElIlIPCqhaeL0+Lnn4eZasy+fwiUJu+tkrXPLw85VCKiergPzN+8q3ud0e8jfvIyerwKGqRUSinwKqFnMzc8nM24LX5w+fwmI3mXlbmJuZW35MwbYDuF2eSq8rcXsoKDjQqLWKiMQSBVQtsvO3U+QqqbStyFVCTv728uepfTsTH1/5W5mUnEhqaudGqVFEJBYpoGoxZkAfWqRU7pHXIiWJtAGnJ4EdPaYPcXGGuDiDMZCcksiAgV1JS09t5GpFRGKHFiqqxfSM0WQM7c+XWesB/4q6GUP7Mz1jdPkxO3cexuPxcvH00bRp14LU1M6kpacSF6/8FxEJlQKqFvHxccx74VFSLr6TicMG8shNlzM9Y3SlJr3lSzdj4gzXfnsirds0d7BaEZHYoYCqA09pKaVeH9PPGcUVk8acsX/50s0MHdZD4SQiEkZqg6qD40XFALRu3uyMfbt3Hmb3riNMmKi590REwkkBVQuv18cHi1cBsHXPwTMG6WYu2wzA+IwBjV6biEgsUxPfWVQcpAvw6gdfkLtlO/NeeLT8HtTypZsZOLgb7Tu0crJUEZGYoyuosygbpFvs9o+DcpV4Kg3S3b/vGNu3HVTznohIA1BAnUVtg3SXL/U37004RwElIhJuCqizqG2Q7vJlm+nbvwudu7RxojwRkZimgDqLskG6ZYsUtkg5PUj38KGT5G/ap+Y9EZEGooA6i7JButdMHosx8Pef3FfeQWJFoPdehpr3REQahHrx1SI+Po5ObVvTtmULZpybXr49c+lmevXuQLce7RysTkQkdukKqg6OFZ6ibcvTs0QcO1bExrzdat4TEWlACqhaeL0+Nu7cU2k13ZWZW7AWBZSISANSE99ZlA3UXbWpAJ/PctPPXiFjaH+mtRpI125t6dW7o9MliojELAXUWZQN1PX5LOBfTTcrbxudm6dw5dXjMMY4XKGISOxSE99ZVDdQt62vOdZn1bwnItLAFFBnMapfb+KqXCT1SmxD89bJ9OvfxZmiRESaCAXUWVmsPf0sgTi6JLSi18COat4TEWlgCqizyMnfga9CQnVPbEO8iaOkhT3Lq0REJBwUUDXwen28v3BFpW29ktrgsh4mThjkUFUiIk2HAqoGczNzyd+9HwAD9ExsQ/fENngSfVw6fpSzxYmINAEKqBpk52/nlNuDAc5v2Z+JLVKJN3G09iXz/M/fx1dlZV0REQkvBVQNynrwdUtsTceElsQb/7fK+iB/8z5ysgqcLVBEJMYpoGrk7wgxplkP4qncY6/E7aGg4IATRYmINBkKqBqs3rqTLvGtaBGXfEaX8oSEeFJTOztUmYhI06CAqsGofr3pk9SO6kY7NW+eTFp6amOXJCLSpERVQBlj4o0xLxhjDhpjThpj3jPGNMiMraWlXnomtK12X/ce7YiLj6pvnYhI1Im2T9n/Bq4CMoCegW1vN8SJvvpkNXHGVDtjRLuOLRvilCIiUkG0zWZ+N/BTa+1WAGPMI0C+MSbVWlsQzhOd3FtU476Jk4aE81QiIlKNqLmCMsa0AXoDq8q2WWu3ACeAsI+cLS5yn7HNWosPS/q4vuE+nYiIVBE1AQW0Dnw9XmX7sQr7ADDG3G2MWWmMWXnw4MGQTmZrmG7Pi0/3n0REGkE0fdKeDHxtU2V7W/xXUeWsta9Za8dZa8d16tQppJO16doiqO0iIhJeURNQ1tpjwA4gvWybMaYf/qun1eE+30sv3IHFYm2FB5aXXrgj3KcSEZFqRE1ABbwGPGqM6WuMaQ08D8wLdwcJgJYtm/Onv95Hy+7N8SZaWnYPPG/ZPNynEhGRakRbL77ngHbACiAZ+By4taFO1rJlc/746r0N9fYiInIWURVQ1lov8KPAQ0REYli0NfGJiEgToYASEZGIpIASEZGIpIASEZGIpIASEZGIpIASEZGIpIASEZGIpIASEZGIpIASEZGIpIASEZGIZGxNCx/FCGPMQWB7Pd+mI3AoDOU0lmiqN5pqBdXb0FRvw4nkWvtYa89YGynmAyocjDErrbXjnK6jrqKp3miqFVRvQ1O9DSeaai2jJj4REYlICigREYlICqi6ec3pAoIUTfVGU62gehua6m040VQroHtQIiISoXQFJSIiEUkBJSIiEUkBJSIiEUkBdRbGmHhjzAvGmIPGmJPGmPeMMR0joK7njTHrjDEnjDF7jDGvG2PaVznmdmPMFmPMKWNMpjFmrFP1VmSMiTPGLDHGWGNMzwrbI65eY8yFxphlxphCY8whY8yrFfZFVL3GmK7GmHcD/1ePGmO+MsaMjoR6jTE3GmMWBv6/llaz/6y1GWPGGWOWB/ZvMcbc6lS9gVqXBL7Hh4wxc40xI52qt7bvbYXjng/8zN1aZXujfm+DZq3Vo4YH8GNgE9APaAO8B8yNgLqeBcYAiUAnYC7wQYX9k4Ei4GIgGXgE2A+0joDafwh8AVigZ6TWC0wFjgHXBWpKAdIjuN5ZwOdAeyAJ+CWwEzBO1wtcAtwE3AWUVtl31toCP3cHgUcD+y8CCoGJDtV7f6CGFoF6ngH2As2cqPdstVY4ZgKwGtgD3Fphe6N/b4P++zldQCQ/8E+R9N0Kz/sHPlhTna6tSp2XA8crPH8LeLvCcwPsAL7jcJ2DgC1AWpWAirh6gaXAczXsi8R6VwN3V3g+OPA97hgp9eIP/aof+GetDbgz8NxUOOZt4A0n6q3mmBaB7/MYJ+utqdZA8KwBJgIFVQLKse9tXR9q4quBMaYN0BtYVbbNWrsFOAGMcqquGlyA/wOqzGgq122B7MB2Rxhj4oA/Aw/jvzKpKKLqNca0wP9bp8sYkxVoyvnGGFM2TUxE1RvwAnCtMaajMSYFuBtYZK09RGTWW6a22kYDWYHtZbKIjNrB/7N3CsgPPI+0ep8CvrLWLq1mX6TVeoYEpwuIYK0DX49X2X6swj7HGWOuBb4HnF9hcysir+7/BPZZa2cZY1Kr7Iu0etvhvz/7PWA6sAH4EfCJMWYQkVcvwGLgO/ibbLz4m/emB/ZFYr1laqstYmsP/F/4I/BDa+3JwOaIqTfwC9X1+FssqhMxtdZEV1A1K/sP16bK9rb4r6IcZ4y5HngdmGGtzaqw6yQRVLcxZgD+e0/fr+GQiKqX0//2b1hrV1trS4Bf4L/nN4kIqzdwdfoF/vulbYDm+O+NLDTGdCHC6q2ittoisnZjzDDga+BX1trfV9gVEfUaY5KAN4D7rbWFNRwWEbWejQKqBtbaY/jbZ9PLthlj+uH/7WJ1DS9rNMaYO4E/AFdaa7+usjuXynUb/L9F5TZagZVNxt+ZY60x5hD+ZgSA1caY+4iweq21x/G311c3zYolwurF3zGiL/Bba+0Ja22JtfaP+H++zyHy6q2ottpy8XcIqmgMDtZujEkHvsF/j/KXVXZHSr3dgeHA3wJN1IeAXsDvjDF/CxwTKbXWzOmbYJH8wN+LbyP+H/7WwL+ATyOgrgeBw8D4GvZPxt8b5wL8Pbp+hIO9zPD/Rt+zwuMc/B/044CWkVZvoOaHgV3AMPxN4Y/g763VJkLr3Qj8Fv9N+wT8vbpK8PdAdbReIB5/L8iLgdLAn1M43cOwxtrw/0Z/MPDvkRQ4rqF78Z2t3nOBo1TokFLltY1a71lqja/yM9cTf7PvA0AHp763Qf/9nC4gkh+Bf+Rf4V/k6yT+rrwdI6AuC3gC/5nKH1WOuR3YChQDy4GxTtddobZUKvTii8R6Ax9GPwX24W+X/xpIi+B6hwJzAv9Xj+PveHBVJNQL3BH49676SK1LbcD4wPbiwHG3OlVv4P+Br+rPHnCeE/XW9r2tcmxB1Voa+3sb7EOTxYqISETSPSgREYlICigREYlICigREYlICigREYlICigREYlICigREYlICigREYlICigREYlICiiJecaYpwKriZY99gRWR+7vdG1OCSwfUvb9eKjKvuuMMR5jTHIDnbviv8e/G+IcEhsUUNJUHMe/aNtE/PO9pQFfBtZ+aqq+xv/9eKfK9tHAemutu4HO+8fAebMb6P0lRmg9KGkqSq21ywJ/XmaM2QEsBC7DPwlwJcaYeCDe+pfaiDhhqu9Ihe9JRWk0UHgYY5KstbuAXcaYiFnWQSKTrqCkqSpbxTUVwBjzpjFmpTHmamPMOsAFZAT2TTbGzDfGnDLGHDbGvG6MaVX2RsaY4caYT40xR4wxRcaYPGPM/UHs/6ZqU5cxZmqgCWxEfesLwWhggzHmV8aYA8aY/caYXwTWnapY4y3GmAWB5RwKjTFLjTETK+yPC/x9HzLGvGSMOQjk1KMuaWJ0BSVNVWrg674q236Jfxbz/cA2Y8y5wJfAbOA6oAPwHP5Vd68LvO5D/Kvu3gq4gcFUXpW0tv3B1BxKfXVmjGmHf92g+4C/ATcCFwH/jX9Z8z9VOHw48BawDUjGvwLxbGNMr8CVXT/8S608EqjvZvxLgIjUiQJKmgxjTNn/937Aq/iXUPmiwiEdgAuttTkVXvMPYIm19tsVtu3Gf/9qBP6A6wdcba1dEzjkywrHdjzb/iAFXZ+1dm2Q50gLfH3dWvuzwJ+/MsZMAa6mQkBZax+vcM4E/Ms1bAD6A3nAqMDuP1tr/yfIOkTUxCdNRgf8a2h58C/u1w/4trV2b4Vjdlf58G+O/2b+P40xCWUPYFHgfcYCR/AvBPd7Y8y3jTGdq5y3tv3BCKW+YI3Gv+T3S1W2b8C/KnLZuZsZYx4xxqwxxhQFzrchsPtU4OvIwJ+fCaEOEQWUNBnH8S/ONg7/6qKp1tq5VY7ZX+V5O/yLVr7K6XDz4G+mSwR6WWt9+Fcz3Qf8GdhnjFlojBkDUNv+IAVdXwjnGA0stdZW7cDQC9gN5cuyf4J/Zec38F9ZjQd+hz+QdgZeMxL4xlpbHEIdImrikyaj1Fq7spZjqq7eeSyw7Sn8H8hV7QGw1m4ArjXGJALnAc8DHxtjelprfbXtx9/hIanKe7cPV31BSsO/wmo5Y0wb/B0yHgtsmgJMBc6x1mZWOO4FYE3g7wT+Jr53Q6hBBFBAidTIWltkjFkGDLbW/rQOx3vw36/5DfB3oC3+Jr7a9u/C/6Ff0UXhrq82gebBYcDhKrsewn9l9nbgec/A100VXnsp/tB6LfC8Gf57Ubn1rUuaLgWUyNk9gr/DgQ/4N/6OFb2By4EfAynAr/BfKWzF3+z2KJBrrT1ijBl1tv2Bc7wPfNcY8yLwMTANuCQc9VlrN53txVUMxX8l198Y8xQwP1DHfwHXW2tPBo7LAnzAS8aYt/A3730H/9VcWSANx38LQQElIdM9KJGzsNYuwn910wn/FcRH+ENhJ/57QvsCX38MzMV/PygPmBF4i9r2Y639GHgcf7fw94E++K9awlFfMEbjv4d0KTA9UO/F+HsgflDhnHnA/fiv8mYBY/B3MTecDqSyDhJbgqxBpJyxtmqztojEOmPMN/ib8r4NeG0jfhAEBvzG4e9uf9BaG/R4LWkadAUl0nTNxH9v6T8b+bxPBM5b9b6bSCW6ghJpgowxg4Gy6ZB2WGsPNOK5uwPdA0+PWGu3Nta5JboooEREJCKpiU9ERCKSAkpERCKSAkpERCKSAkpERCKSAkpERCKSAkpERCLS/wdY+PxrlMyJngAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the characterisation module\n",
    "import pygaps.graphing as pgg\n",
    "\n",
    "pgg.plot_iso(\n",
    "    isotherms_iast,                         # the isotherms\n",
    "    branch='ads',                           # only the adsorption branch\n",
    "    lgd_keys=['material','adsorbate'],      # the isotherm properties making up the legend\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": 2
}